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Search Results (405)

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Keywords = orthogonal frequency-division multiplexing (OFDM)

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19 pages, 1472 KiB  
Article
Generalized Filter Bank Orthogonal Frequency Division Multiplexing: Low-Complexity Waveform for Ultra-Wide Bandwidth and Flexible Services
by Yu Xin, Jian Hua, Tong Bao, Yaxing Hao, Ziheng Xiao, Xin Nie and Fanggang Wang
Entropy 2024, 26(11), 994; https://doi.org/10.3390/e26110994 (registering DOI) - 18 Nov 2024
Viewed by 232
Abstract
Terahertz (THz) communication is a crucial technique in sixth generation (6G) mobile networks, which allow for ultra-wide bandwidths to enable ultra-high data rate wireless communication. However, the current subcarrier spacing and the size of fast Fourier transform (FFT) of the orthogonal frequency division [...] Read more.
Terahertz (THz) communication is a crucial technique in sixth generation (6G) mobile networks, which allow for ultra-wide bandwidths to enable ultra-high data rate wireless communication. However, the current subcarrier spacing and the size of fast Fourier transform (FFT) of the orthogonal frequency division multiplexing (OFDM) in 5G NR are insufficient regarding the bandwidth requirements of terahertz scenarios. In this paper, a novel waveform is proposed to address the ultra-wideband issue, namely the generalized filter bank orthogonal frequency division multiplexing (GFB-OFDM) waveform. The main advantages are summarized as follows: (1) The K-point IFFT is implemented by two levels of IFFTs in smaller sizes, i.e, performing M-point IFFT in N times and performing N-point IFFT in M times, where K=N×M. (2) The proposed waveform can accommodate flexible subcarrier spacings and different numbers of the subbands to provide various services in a single GFB-OFDM symbol. (3) Different bandwidths can be supported using a fixed filter since the filtering is performed on each subband. In contrast, the cyclic prefix orthogonal frequency division multiplexing (CP-OFDM) in 4G/5G requires various filters. (4) The existing detection for CP-OFDM can be directly employed as the detector of the proposed waveform. Lastly, the comprehensive simulation results demonstrate that GFB-OFDM outperforms CP-OFDM in terms of the out-of-band leakage, complexity and error performance. Full article
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<p>GFB-OFDM transmitter diagram. The encoded and modulated data are first divided into <span class="html-italic">N</span> subbands, with each subband undergoing a subcarrier-level IFFT. The resulting data is then processed through a subband-level IFFT along the subband dimension, followed by a polyphase filter. The output is transmitted after passing through the digital-to-analog converter and radio frequency module.</p>
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<p>GFB-OFDM data-processing procedure with the number of modulated symbols <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>8192</mn> </mrow> </semantics></math>, the number of subbands <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>8</mn> </mrow> </semantics></math> and the number of subcarriers in each subband <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>1024</mn> </mrow> </semantics></math>.</p>
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<p>Processing at the receiver. The received time-domain signal is first converted into a digital signal via an ADC, followed by the removal of the cyclic prefix. After undergoing an FFT, the signal is converted to the frequency domain. In the frequency domain, it passes through the symbol detection module, as well as the demodulation and decoding module, resulting in the output information bits.</p>
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<p>The locations of the <span class="html-italic">N</span> subbands in the frequency domain.</p>
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<p>The out-of-band leakage of CP-OFDM and GFB-OFDM is evaluated. Specifically, the number of the subbands are 8 and 1 in the corresponding (<b>a</b>,<b>b</b>). The results demonstrate that the out-of-band leakage of GFB-OFDM is lower than the OFDM because of the polyphase filter operation.</p>
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<p>The The BLER of CP-OFDM and GFB-OFDM is evaluated for the same subcarrier spacing scenario. The rate-<math display="inline"><semantics> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mn>3</mn> <mn>4</mn> </mfrac> </mstyle> </semantics></math> LDPC channel coding, the 64QAM modulation and the AWGN channel are employed. The result demonstrates that GFB-OFDM maintains a nearly identical BLER performance compared to CP-OFDM.</p>
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<p>The locations of the eight subbands in the frequency domain under two cases. (<b>a</b>) represents the mixture scenarios of different subcarrier spacings, which were 480 kHz and 960 kHz, respectively. (<b>b</b>) represents single-carrier and multi-carrier mixture scenarios.</p>
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<p>Subband data-processing procedure for different subcarrier spacing with the number of modulated symbols <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>8192</mn> </mrow> </semantics></math>, the number of subbands <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>8</mn> </mrow> </semantics></math>, and the number of subcarriers in each subband <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>1024</mn> </mrow> </semantics></math> or <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>512</mn> </mrow> </semantics></math>.</p>
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<p>The BLER of CP-OFDM and GFB-OFDM is evaluated in different subcarrier spacing scenarios. The rate-<math display="inline"><semantics> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mn>3</mn> <mn>4</mn> </mfrac> </mstyle> </semantics></math> LDPC channel coding, the 64 QAM modulation, and the AWGN channel are employed. Specifically, subbands with subcarrier spacings of 480 kHz and 960 kHz coexist. The result demonstrates that the BLER performance of GFB-OFDM is significantly superior to that of CP-OFDM.</p>
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<p>Subband data-processing procedure for a single-carrier and multi-carrier mixture scenario with the number of modulated symbols <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>8192</mn> </mrow> </semantics></math>, the number of subbands <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>8</mn> </mrow> </semantics></math> and the number of subcarriers in each subband <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>1024</mn> </mrow> </semantics></math> or <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>512</mn> </mrow> </semantics></math>.</p>
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<p>The BLER of CP-OFDM and GFB-OFDM is evaluated in single-carrier and multi-carrier mixture scenarios. The rate-<math display="inline"><semantics> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mn>3</mn> <mn>4</mn> </mfrac> </mstyle> </semantics></math> LDPC channel coding, the 64 QAM modulation, and the AWGN channel are employed. The result demonstrates that the BLER performance of GFB-OFDM is superior to that of CP-OFDM.</p>
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<p>CP-OFDM data processing with filtering. The number of modulated symbols, subbands, and subcarriers are set as <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>8192</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>8</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>1024</mn> </mrow> </semantics></math>.</p>
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<p>The complexity of CP-OFDM and GFB-OFDM is evaluated under a varying number of subbands. Under different numbers of subbands, the number of multiplications required by GFB-OFDM is less than that of CP-OFDM. In addition, as the number of subbands increases, the number of multiplications required by CP-OFDM increases rapidly. In contrast, the number of multiplications required by GFB-OFDM increases slowly.</p>
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12 pages, 400 KiB  
Article
Properties and Analysis of the Guard Interval in Infinite Impulse Response–Orthogonal Frequency Division Multiplexing Systems
by Mengwan Jiang, Jiehao Luo and Dejin Kong
Electronics 2024, 13(22), 4519; https://doi.org/10.3390/electronics13224519 - 18 Nov 2024
Viewed by 206
Abstract
Recently, an orthogonal frequency division multiplexing (OFDM) technique for the infinite impulse response (IIR) channel (IIR-OFDM) was proposed, which carries the dedicated guard interval to maintain the circular convolution of the received signal and channel coefficients. Therefore, the loop of an IIR channel [...] Read more.
Recently, an orthogonal frequency division multiplexing (OFDM) technique for the infinite impulse response (IIR) channel (IIR-OFDM) was proposed, which carries the dedicated guard interval to maintain the circular convolution of the received signal and channel coefficients. Therefore, the loop of an IIR channel can be converted to the frequency domain, and single-tap equalization can still be used to equalize loop interference, like classical OFDM. In this paper, we describe how to build the IIR system based on the channel with loops and derive the properties of the dedicated guard interval for a general multi-order IIR channel, which is different from the classical cyclic prefix (CP) obtained by replicating the samples at the tail end of the signal. In particular, we address two special models for first-order and delay IIR channels. It is demonstrated that the guard interval composition and power characteristics of the two special models are similar. Moreover, the complexity of the guard interval depends not only on the maximum delay of the loop, but also on the number of loops. Finally, we simulate the IIR-OFDM performance under different IIR channels. Full article
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<p>The amplitude of the IIR-OFDM signal at different <math display="inline"><semantics> <mi>β</mi> </semantics></math> values.</p>
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<p>BER comparison under the IIR channel of the multipath loop.</p>
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<p>BER comparison under the 1st-order IIR channel.</p>
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<p>BER comparison under the special delay IIR channel.</p>
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19 pages, 6931 KiB  
Article
A Hybrid Deep Learning Framework for OFDM with Index Modulation Under Uncertain Channel Conditions
by Md Abdul Aziz, Md Habibur Rahman, Rana Tabassum, Mohammad Abrar Shakil Sejan, Myung-Sun Baek and Hyoung-Kyu Song
Mathematics 2024, 12(22), 3583; https://doi.org/10.3390/math12223583 - 15 Nov 2024
Viewed by 332
Abstract
Index modulation (IM) is considered a promising approach for fifth-generation wireless systems due to its spectral efficiency and reduced complexity compared to conventional modulation techniques. However, IM faces difficulties in environments with unpredictable channel conditions, particularly in accurately detecting index values and dynamically [...] Read more.
Index modulation (IM) is considered a promising approach for fifth-generation wireless systems due to its spectral efficiency and reduced complexity compared to conventional modulation techniques. However, IM faces difficulties in environments with unpredictable channel conditions, particularly in accurately detecting index values and dynamically adjusting index assignments. Deep learning (DL) offers a potential solution by improving detection performance and resilience through the learning of intricate patterns in varying channel conditions. In this paper, we introduce a robust detection method based on a hybrid DL (HDL) model designed specifically for orthogonal frequency-division multiplexing with IM (OFDM-IM) in challenging channel environments. Our proposed HDL detector leverages a one-dimensional convolutional neural network (1D-CNN) for feature extraction, followed by a bidirectional long short-term memory (Bi-LSTM) network to capture temporal dependencies. Before feeding data into the network, the channel matrix and received signals are preprocessed using domain-specific knowledge. We evaluate the bit error rate (BER) performance of the proposed model using different optimizers and equalizers, then compare it with other models. Moreover, we evaluate the throughput and spectral efficiency across varying SNR levels. Simulation results demonstrate that the proposed hybrid detector surpasses traditional and other DL-based detectors in terms of performance, underscoring its effectiveness for OFDM-IM under uncertain channel conditions. Full article
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<p>Generalized data transmission process for an OFDM-IM system.</p>
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<p>Structure of the proposed HDL detector for OFDM-IM systems.</p>
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<p>The internal configuration of an LSTM cell.</p>
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<p>Training loss of the proposed HDL model for different equalizers with data setup <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>N</mi> <mo>,</mo> <mi>A</mi> <mo>,</mo> <mi>M</mi> <mo>)</mo> <mo>=</mo> <mo>(</mo> <mn>4</mn> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>4</mn> <mo>)</mo> </mrow> </semantics></math>: (<b>a</b>) training loss for the ZF equalizer, (<b>b</b>) training loss for the MMSE equalizer, and (<b>c</b>) training loss for the DFE equalizer.</p>
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<p>Training loss of the proposed HDL model for different modulation orders and data combinations with the ZF equalizer: (<b>a</b>) training loss for the <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>N</mi> <mo>,</mo> <mi>A</mi> <mo>,</mo> <mi>M</mi> <mo>)</mo> <mo>=</mo> <mo>(</mo> <mn>4</mn> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>4</mn> <mo>)</mo> </mrow> </semantics></math> setup, (<b>b</b>) training loss for the <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>N</mi> <mo>,</mo> <mi>A</mi> <mo>,</mo> <mi>M</mi> <mo>)</mo> <mo>=</mo> <mo>(</mo> <mn>8</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>8</mn> <mo>)</mo> </mrow> </semantics></math> setup, and (<b>c</b>) training loss for the <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>N</mi> <mo>,</mo> <mi>A</mi> <mo>,</mo> <mi>M</mi> <mo>)</mo> <mo>=</mo> <mo>(</mo> <mn>8</mn> <mo>,</mo> <mn>4</mn> <mo>,</mo> <mn>16</mn> <mo>)</mo> </mrow> </semantics></math> setup.</p>
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<p>The confusion matrix of the proposed HDL-based model.</p>
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<p>Performance of the HDL-based detector with (<b>a</b>) different learning rates and (<b>b</b>) different batch sizes in for the <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>4</mn> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>4</mn> <mo>)</mo> </mrow> </semantics></math> data combination.</p>
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<p>Performance of the HDL-based detector at various training SNRs for the <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>4</mn> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>4</mn> <mo>)</mo> </mrow> </semantics></math> data configuration.</p>
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<p>Performance of the proposed HDL-based detector with various equalizers for the <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>4</mn> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>4</mn> <mo>)</mo> </mrow> </semantics></math> data configuration.</p>
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<p>BER performance of the proposed HDL-based detector utilizing different optimizers for the <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>4</mn> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>4</mn> <mo>)</mo> </mrow> </semantics></math> data configuration.</p>
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<p>BER performance of the proposed HDL-based detector for various modulation orders and data setup.</p>
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<p>BER performance comparison of the proposed HDL-based detector with other detectors under imperfect CSI conditions for the <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>4</mn> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>4</mn> <mo>)</mo> </mrow> </semantics></math> data combinations.</p>
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<p>Throughput and SE of the proposed HDL-based OFDM-IM system: (<b>a</b>) throughput performance and (<b>b</b>) SE performance.</p>
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15 pages, 4386 KiB  
Article
A Novel Embedded Side Information Transmission Scheme Based on Polar Code for Peak-to-Average Power Ratio Reduction in Underwater Acoustic OFDM Communication System
by Siyu Xing, Bo Wei, Yanting Yu and Xiaodong Gong
Sensors 2024, 24(22), 7200; https://doi.org/10.3390/s24227200 - 10 Nov 2024
Viewed by 521
Abstract
In this paper, we proposed an embedded side information (SI) transmission scheme based on polar code construction for PAPR reduction using the PTS scheme in the underwater Acoustic (UWA) Orthogonal Frequency Division Multiplexing (OFDM) communication system. We use polar codes due to the [...] Read more.
In this paper, we proposed an embedded side information (SI) transmission scheme based on polar code construction for PAPR reduction using the PTS scheme in the underwater Acoustic (UWA) Orthogonal Frequency Division Multiplexing (OFDM) communication system. We use polar codes due to the ability of the arbitrarily designed code rate. Additionally, polar codes can be employed to establish a nested code structure consisting of multiple subsets. The SI bits can be embedded in a polar codeword by exploiting these features. Thus, the approach does not occupy existing data rates or cause additional loss in data transmission rates. At the same time, it embeds m-sequence into the polar code as an indicator vector for the blind SI detector, which makes the blind SI detector able to autonomously discriminate SI at the receiver. Simulation and tank experiment results indicate that the proposed embedded SI transmission scheme has the potential to significantly decrease the likelihood of whole-symbol error caused by SI errors. Meanwhile, the proposed PTS scheme eliminates the need to wait for the entire packet to be received before obtaining the SI, thereby preventing waste of data storage devices and ensuring real-time performance of the Underwater Acoustic Communication (UAC) OFDM system. This achieves symbol-level real-time calculation for the system. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies for Marine Intelligent Systems)
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<p>The framework of the C-PTS technique.</p>
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<p>The polar code construction diagram designed for the proposed embedded SI scheme.</p>
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<p>The framework of the embedded SI transmission based on polar code construction for PAPR reduction using the PTS scheme in UWA OFDM communication system. The pseudo-optimum phase rotation candidate selection scheme in this Figure is proposed in Ref. [<a href="#B14-sensors-24-07200" class="html-bibr">14</a>].</p>
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<p>The channel impulse response of the UWA simulation channel.</p>
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<p>The PAPR reduction performance comparison of the original OFDM signal, the C-PTS scheme and the pseudo-optimum PTS scheme.</p>
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<p>The SI error rate comparison among the different <math display="inline"><semantics> <mrow> <msubsup> <mi mathvariant="script">F</mi> <mrow> <mi>S</mi> <mi>I</mi> </mrow> <mi>C</mi> </msubsup> </mrow> </semantics></math> positions.</p>
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<p>The BER performance comparison among the different <math display="inline"><semantics> <mrow> <msubsup> <mi mathvariant="script">F</mi> <mrow> <mi>d</mi> <mi>a</mi> <mi>t</mi> <mi>a</mi> </mrow> <mi>C</mi> </msubsup> </mrow> </semantics></math>.</p>
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<p>The overall BER performance comparison among different combinations of <math display="inline"><semantics> <mrow> <msubsup> <mi mathvariant="script">F</mi> <mrow> <mi>S</mi> <mi>I</mi> </mrow> <mi>C</mi> </msubsup> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msubsup> <mi mathvariant="script">F</mi> <mrow> <mi>d</mi> <mi>a</mi> <mi>t</mi> <mi>a</mi> </mrow> <mi>C</mi> </msubsup> </mrow> </semantics></math>.</p>
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<p>The real CIR of the tank experiment channel.</p>
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<p>Tank experimental results comparison of the SI transmission between the C-PTS and the embedded SI transmission based on the polar code construction. (<b>a</b>) SI error rate of each symbol in C-PTS scheme; (<b>b</b>) SI error rate of each symbol in the proposed scheme; (<b>c</b>) BER of each symbol in C-PTS scheme; (<b>d</b>) BER of each symbol in the proposed scheme.</p>
Full article ">Figure 10 Cont.
<p>Tank experimental results comparison of the SI transmission between the C-PTS and the embedded SI transmission based on the polar code construction. (<b>a</b>) SI error rate of each symbol in C-PTS scheme; (<b>b</b>) SI error rate of each symbol in the proposed scheme; (<b>c</b>) BER of each symbol in C-PTS scheme; (<b>d</b>) BER of each symbol in the proposed scheme.</p>
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<p>Comparison of transmitted figure results from the tank experiment. (<b>a</b>) The transmitted figure result of the C-PTS scheme; (<b>b</b>) the transmitted figure result of the proposed scheme.</p>
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18 pages, 16158 KiB  
Article
Orthogonal Frequency Division Diversity and Multiplexing for 6G OWC: Principle and Underwater Use Case
by Jiamin Chen, Chen Chen, Zhihong Zeng, Min Liu, Jia Ye, Cuiwei He, Shenjie Huang, H. Y. Fu and Harald Haas
Photonics 2024, 11(11), 1051; https://doi.org/10.3390/photonics11111051 - 8 Nov 2024
Viewed by 385
Abstract
In this paper, we, for the first time, propose and demonstrate an orthogonal frequency division diversity and multiplexing (OFDDM) scheme for the sixth-generation (6G) underwater optical wireless communication (UOWC) systems. In OFDDM, the subcarriers are grouped into subblocks; the subcarriers within each subblock [...] Read more.
In this paper, we, for the first time, propose and demonstrate an orthogonal frequency division diversity and multiplexing (OFDDM) scheme for the sixth-generation (6G) underwater optical wireless communication (UOWC) systems. In OFDDM, the subcarriers are grouped into subblocks; the subcarriers within each subblock transmit the same constellation symbol through diversity transmission, while different subblocks transmit different constellation symbols via multiplexing transmission. As a result, OFDDM can support hybrid diversity and multiplexing transmission simultaneously. Moreover, the combination of subblock interleaving and low-complexity diversity is further proposed to efficiently mitigate the adverse low-pass effect and substantially reduce the computational complexity, respectively. The feasibility of OFDDM adapting to the various transmission conditions in UOWC systems has been verified via both simulations and experiments. Experimental results demonstrate that a striking 106.1% effective bandwidth extension can be obtained using OFDDM in comparison to conventional orthogonal frequency division multiplexing (OFDM) for a fixed spectral efficiency of 1 bit/s/Hz. Furthermore, OFDDM with adaptive bit loading can also gain a remarkable 13.3% capacity improvement compared with conventional OFDM with adaptive bit loading. Full article
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<p>Block diagram of OFDDM: (<b>a</b>) transmitter and (<b>b</b>) receiver.</p>
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<p>Illustration of OFDDM spectrum with different diversity factors: (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>, (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math>, and (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>8</mn> </mrow> </semantics></math>.</p>
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<p>Illustration of OFDDM spectrum: (<b>a</b>) Tx, without interleaving, (<b>b</b>) Tx, with interleaving, (<b>c</b>) Rx, without interleaving, and (<b>d</b>) Rx, with interleaving; w/o: without, w/: with, interl.: interleaving.</p>
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<p>Measured frequency response from the experimental UOWC system.</p>
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<p>Achievable spectral efficiency vs. constellation order <span class="html-italic">M</span> for OFDDM with different diversity factors.</p>
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<p>Simulation BER vs. SNR for OFDDM using channel-based diversity combining with <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>2</mn> <mo>,</mo> <mn>4</mn> </mrow> </semantics></math> over (<b>a</b>) the AWGN channel, (<b>b</b>) the low-pass channel without interleaving, and (<b>c</b>) the low-pass channel with interleaving.</p>
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<p>Theoretical and simulation BER vs. SNR for OFDDM using channel-based diversity, combining with <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>2</mn> <mo>,</mo> <mn>4</mn> </mrow> </semantics></math>: (<b>a</b>) the low-pass channel without interleaving and (<b>b</b>) the low-pass channel with interleaving.</p>
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<p>Simulation BER vs. SNR for OFDDM with <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math> and different diversity factors over (<b>a</b>) the AWGN channel, (<b>b</b>) the low-pass channel without interleaving, and (<b>c</b>) the low-pass channel with interleaving.</p>
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<p>Available SNR margin vs. normalized spectral efficiency for OFDDM in comparison to conventional OFDM.</p>
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<p>Simulation BER vs. transmission distance using OFDDM over a low-pass channel.</p>
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<p>Simulation BER vs. SNR for different schemes with a target spectral efficiency of (<b>a</b>) 1 bit/s/Hz, (<b>b</b>) 1.5 bits/s/Hz, (<b>c</b>) 2 bits/s/Hz, and (<b>d</b>) 2.5 bits/s/Hz.</p>
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<p>Experimental setup of the VCSEL-based UOWC system.</p>
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<p>Experimental BER vs. effective bandwidth for OFDDM using SNR-based and channel-based diversity-combining approaches with (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math> and (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>4</mn> </mrow> </semantics></math>.</p>
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<p>Experimental BER vs. diversity factor <span class="html-italic">d</span> for OFDDM without and with interleaving at different effective bandwidths at a target spectral efficiency of 1 bit/s/Hz.</p>
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<p>Experimental BER vs. effective bandwidth for conventional OFDM and OFDDM with optimal <span class="html-italic">d</span> at a target spectral efficiency of 1 bit/s/Hz.</p>
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<p>Bit loading vs. subcarrier/subblock for OFDM/OFDDM with <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>. Insets show the corresponding received constellation diagrams for OFDDM with <math display="inline"><semantics> <mrow> <mi>d</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>.</p>
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<p>Achievable rate vs. diversity factor <span class="html-italic">d</span> for OFDDM with adaptive bit loading at different effective bandwidths.</p>
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<p>Achievable rate vs. effective bandwidth for conventional OFDM with adaptive bit loading and OFDDM with adaptive bit loading with optimal <span class="html-italic">d</span>.</p>
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9 pages, 2494 KiB  
Article
Utilization of Optical OFDM Modulation on Blue LED VLC Datacom Without Equalization for 4 m Wireless Link
by Yuan-Zeng Lin, Chien-Hung Yeh, Wen-Piao Lin and Chi-Wai Chow
Micromachines 2024, 15(11), 1322; https://doi.org/10.3390/mi15111322 - 30 Oct 2024
Viewed by 441
Abstract
To achieve higher visible light communication (VLC) traffic capacity, using the wide bandwidth light-emitting diode (LED) and spectral efficiency modulation signal, is currently the most commonly used method. In this demonstration, we apply the orthogonal frequency division multiplexing quadrature amplitude modulation (OFDM-QAM) with [...] Read more.
To achieve higher visible light communication (VLC) traffic capacity, using the wide bandwidth light-emitting diode (LED) and spectral efficiency modulation signal, is currently the most commonly used method. In this demonstration, we apply the orthogonal frequency division multiplexing quadrature amplitude modulation (OFDM-QAM) with bit- and power-loading algorithm on single blue LED to achieve >1 Gbit/s VLC capacity, when a 400 MHz bandwidth avalanche photodiode (APD)-based receiver (Rx) is exploited for decoding. Here, the higher sensitivity APD can be applied to compensate for the wireless VLC link length in the proposed LED VLC system, and due to the lower LED illumination (255 to 40 lux), is used for the indoor access network after passing the wireless link length of 1 to 4 m. As a result, using single blue LED can achieve 0.962 to 1.057 Gbit/s OFDM rate with available 400 MHz bandwidth APD in poorly illuminated condition indoors without applying analogy equalization. Full article
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<p>Experimental setup of the presented blue LED VLC transmission system.</p>
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<p>(<b>a</b>) Measured output spectrum of blue LED. (<b>b</b>) Detected illumination of LED after passing through 1 to 4 m free space link length, respectively. Insets are the photos of corresponding light spots through different VLC link lengths.</p>
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<p>(<b>a</b>) Corresponding SNR and (<b>b</b>) bit/symbol of each OFDM subcarrier measured over a 400 MHz bandwidth after 1 to 4 m free space VLC transmission lengths, respectively.</p>
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<p>The measured constellation performance of OFDM-QAM VLC signal after passing through (<b>a</b>) 1, (<b>b</b>) 2, (<b>c</b>) 3 and (<b>d</b>) 4 m free space link length, respectively.</p>
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<p>Measured VLC traffic rate and BER under the VLC link length of 1 to 4 m, respectively.</p>
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17 pages, 6781 KiB  
Communication
An Iterative Orthogonal Frequency Division Multiplexing Receiver with Sequential Inter-Carrier Interference Canceling Modified Delay and Doppler Profiler for an Underwater Multipath Channel
by Suguru Kuniyoshi, Shiho Oshiro, Rie Saotome and Tomohisa Wada
J. Mar. Sci. Eng. 2024, 12(10), 1712; https://doi.org/10.3390/jmse12101712 - 27 Sep 2024
Viewed by 544
Abstract
In 2023, we proposed the modified delay and Doppler profiler (mDDP) as an inter-carrier interference (ICI) countermeasure for underwater acoustic orthogonal frequency division multiplexing (OFDM) mobile communications in a multipath environment. However, the performance improvement in the computer simulation and pool experiments was [...] Read more.
In 2023, we proposed the modified delay and Doppler profiler (mDDP) as an inter-carrier interference (ICI) countermeasure for underwater acoustic orthogonal frequency division multiplexing (OFDM) mobile communications in a multipath environment. However, the performance improvement in the computer simulation and pool experiments was not significant. In a subsequent study, the accuracy of the channel transfer function (CTF), which is the input for the mDDP channel parameter estimation, was considered insufficient. Then a sequential ICI canceling mDDP was devised. This paper presents simulations of underwater OFDM communications using an iterative one- to three-step mDDP. The non-reflective pool experiment conditions are a two-wave multipath environment where the receiving transducer moves at a speed of 0.25 m/s and is subjected to a Doppler shift in the opposite direction. As NumCOL, the number of taps in the multitap equalizer which removes ICI, was increased, the bit error rate (BER) of 0.0526661 at NumCOL = 1 was significantly reduced by a factor of approximately 45 to a BER of 0.0011655 at NumCOL = 51 for the sequential ICI canceling mDDP. Full article
(This article belongs to the Section Ocean Engineering)
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<p>UWA communication under multipath Doppler condition.</p>
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<p>OFDM signal after the shrink and expansion processing.</p>
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<p>Block diagram of previous receiver.</p>
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<p>Time–frequency representation of OFDM signal of transmitter and receiver.</p>
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<p>Block diagram of proposed iterative multistep mDDP receiver.</p>
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<p>Flowchart of iterative multistep modified delay and Doppler profiler.</p>
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<p>Computer simulation model assuming reverse two-path multipath condition.</p>
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<p>Delay time and DUR in simulation.</p>
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<p>Result of computer simulation assuming time dependencies of measured BER and constellation (real part of complex).</p>
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<p>Result of computer simulation assuming constellations in moving period.</p>
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<p>Comparison of simulated BER.</p>
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<p>BER vs. CNR of conventional and iterative mDDP on NumCOL = 21.</p>
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<p>Non-reflective pool experiment environment.</p>
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<p>The received spectrum on RX transducer.</p>
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<p>Results of pool experiment environment time dependencies of measured BER and constellation (real part of complex).</p>
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<p>BER comparison of non-reflective pool experiment.</p>
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18 pages, 21647 KiB  
Article
Modified Hybrid Integration Algorithm for Moving Weak Target in Dual-Function Radar and Communication System
by Wenshuai Ji, Tao Liu, Yuxiao Song, Haoran Yin, Biao Tian and Nannan Zhu
Remote Sens. 2024, 16(19), 3601; https://doi.org/10.3390/rs16193601 - 27 Sep 2024
Viewed by 634
Abstract
To detect moving weak targets in the dual function radar communication (DFRC) system of an orthogonal frequency division multiplexing (OFDM) waveform, a modified hybrid integration method is addressed in this paper. A high-speed aircraft can cause range walk (RW) and Doppler walk (DW), [...] Read more.
To detect moving weak targets in the dual function radar communication (DFRC) system of an orthogonal frequency division multiplexing (OFDM) waveform, a modified hybrid integration method is addressed in this paper. A high-speed aircraft can cause range walk (RW) and Doppler walk (DW), rendering traditional detection methods ineffective. To overcome RW and DW, this paper proposes an integration approach combining DFRC and OFDM. The proposed approach consists of two primary components: intra-frame coherent integration and hybrid multi-inter-frame integration. After the echo signal is re-fragmented into multiple subfragments, the first step involves integrating energy across fixed situations within intra-frames for each subcarrier. Subsequently, coherent integration is performed across the subfragments, followed by the application of a Radon transform (RT) to generate frames based on the properties derived from the coherent integration output. This paper provides detailed expressions and analyses for various performance metrics of our proposed method, including the communication bit error ratio (BER), responses of coherent and non-coherent outputs, and probability of detection. Simulation results demonstrate the effectiveness of our strategy. Full article
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<p>Detailed processing flowchart of the MHI.</p>
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<p>Sketch map of the flowchart for intra-frame integration.</p>
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<p>Sketch map of the flowchart for inter-frame integration.</p>
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<p>Modified GRT integration path.</p>
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<p>Intra-frame integration results. (<b>a</b>) Range across echo, (<b>b</b>) intra-frame integration, (<b>c</b>) one frame integration result of a single target, (<b>d</b>) the distance slice of (<b>c</b>), (<b>e</b>) the Doppler slice of (<b>d</b>).</p>
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<p>Single target results for <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>S</mi> <mi>N</mi> <mi>R</mi> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math> dB). (<b>a</b>) MTD results, (<b>b</b>) HI integration results, (<b>c</b>) proposed method’s result.</p>
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<p>Single target results for <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>S</mi> <mi>N</mi> <mi>R</mi> <mo>=</mo> <mo>−</mo> <mn>20</mn> </mrow> </semantics></math> dB). (<b>a</b>) MTD result, (<b>b</b>) HI integration result, (<b>c</b>) proposed method’s result.</p>
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<p>Simulation results for multiple targets (scenario 1). (<b>a</b>) Multiple-target echo distribution, (<b>b</b>) integration result on velocity–range plane, (<b>c</b>) integration result on range–acceleration plane, (<b>d</b>) integration result on velocity–acceleration plane.</p>
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<p>Simulation results for multiple targets (scenario 2). (<b>a</b>) Multiple-target echo distribution, (<b>b</b>) integration result on velocity–range plane, (<b>c</b>) integration result on range–acceleration plane, (<b>d</b>) integration result on velocity–acceleration plane.</p>
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<p>Simulation results for multiple targets (scenario 3). (<b>a</b>) Multiple-target echo distribution, (<b>b</b>) integration result on velocity–range plane, (<b>c</b>) integration result on range–acceleration plane, (<b>d</b>) integration result on velocity–acceleration plane.</p>
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<p>Communication BER results. (<b>a</b>) OFDM IRCS BER with AWGN, (<b>b</b>) BER with different modulation methods, (<b>c</b>) BER with different demodulation methods.</p>
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<p>Detection probabilities of different methods.</p>
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<p>Computational complexity of different methods.</p>
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19 pages, 12255 KiB  
Article
Zadoff–Chu Sequence Pilot for Time and Frequency Synchronization in UWA OFDM System
by Seunghwan Seol, Yongcheol Kim, Minho Kim and Jaehak Chung
Electronics 2024, 13(18), 3679; https://doi.org/10.3390/electronics13183679 - 16 Sep 2024
Viewed by 828
Abstract
In underwater communications for 6G, Doppler effects cause the coherent time to become similar to or shorter than the orthogonal frequency division multiplexing (OFDM) symbol length. Conventional time and frequency synchronization methods require additional training symbols for synchronization, which reduces the traffic data [...] Read more.
In underwater communications for 6G, Doppler effects cause the coherent time to become similar to or shorter than the orthogonal frequency division multiplexing (OFDM) symbol length. Conventional time and frequency synchronization methods require additional training symbols for synchronization, which reduces the traffic data rate. This paper proposes the Zadoff–Chu sequence (ZCS) pilot-based OFDM for time and frequency synchronization. The proposed method transmits ZCS as a pilot for OFDM symbols and simultaneously transmits traffic data to increase the traffic data rate while estimating the CFO at each coherence time. For time–frequency synchronization, the correlation of the ZCS pilot is used to perform coarse and fine time and frequency synchronization in two stages. Since the traffic data cause interference with the correlation of ZCS pilots, we theoretically analyzed the relationship between the amount of traffic data and interference and verified it through computer simulations. The synchronization and BER performance of the proposed ZCS pilot-based OFDM were evaluated by conduction computer simulations and a practical ocean experiment. Compared to the methods of Ren, Yang, and Avrashi, the proposed method demonstrated a 6.3% to 14.3% increase in traffic data rate with similar BER performance and a 2 dB to 3.8 dB SNR gain for a 14.3% to 23.8% decrease in traffic data rate. Full article
(This article belongs to the Special Issue 5G/B5G/6G Wireless Communication and Its Applications)
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<p>Structure of the UWA OFDM system.</p>
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<p>The underwater channel for the simulation: (<b>a</b>) channel delay profile, (<b>b</b>) sound speed profile.</p>
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<p>The structure of the data frame: (<b>a</b>) Ren’s method [<a href="#B15-electronics-13-03679" class="html-bibr">15</a>] and Yang’s method [<a href="#B12-electronics-13-03679" class="html-bibr">12</a>], (<b>b</b>) Avrashi’s method [<a href="#B29-electronics-13-03679" class="html-bibr">29</a>], and (<b>c</b>) proposed method.</p>
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<p>The OFDM symbol structure (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>N</mi> </mrow> <mrow> <mi>p</mi> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mn>2</mn> <mo>,</mo> <mo> </mo> <mo> </mo> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>p</mi> </mrow> </msub> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>): (<b>a</b>) Ren’s method [<a href="#B15-electronics-13-03679" class="html-bibr">15</a>] and Yang’s method [<a href="#B12-electronics-13-03679" class="html-bibr">12</a>], (<b>b</b>) Avrashi’s method [<a href="#B29-electronics-13-03679" class="html-bibr">29</a>], (<b>c</b>) proposed method (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>N</mi> </mrow> <mrow> <mi>c</mi> </mrow> </msub> <mo>=</mo> <msub> <mrow> <mi>N</mi> </mrow> <mrow> <mi>d</mi> </mrow> </msub> </mrow> </semantics></math>), (<b>d</b>) proposed method (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>N</mi> </mrow> <mrow> <mi>c</mi> </mrow> </msub> <mo>=</mo> <mn>1</mn> <mo>/</mo> <mn>2</mn> <msub> <mrow> <mi>N</mi> </mrow> <mrow> <mi>d</mi> </mrow> </msub> </mrow> </semantics></math>), and (<b>e</b>) proposed method (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>N</mi> </mrow> <mrow> <mi>c</mi> </mrow> </msub> <mo>=</mo> <mn>1</mn> <mo>/</mo> <mn>3</mn> <msub> <mrow> <mi>N</mi> </mrow> <mrow> <mi>d</mi> </mrow> </msub> </mrow> </semantics></math>).</p>
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<p>Normalized correlation ratio at SNR 0 dB. (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>N</mi> </mrow> <mrow> <mi>p</mi> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>N</mi> </mrow> <mrow> <mi>p</mi> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>.</p>
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<p>The timing synchronization performance. (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>N</mi> </mrow> <mrow> <mi>p</mi> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>N</mi> </mrow> <mrow> <mi>p</mi> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>.</p>
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<p>The MSE of CFO estimation according to the traffic data rate. (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>N</mi> </mrow> <mrow> <mi>p</mi> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>N</mi> </mrow> <mrow> <mi>p</mi> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>.</p>
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<p>The MSE of CFO estimation according to <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>p</mi> </mrow> </msub> </mrow> </semantics></math>. (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>N</mi> </mrow> <mrow> <mi>p</mi> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>N</mi> </mrow> <mrow> <mi>p</mi> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>.</p>
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<p>The BER result according to the traffic data rate. (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>N</mi> </mrow> <mrow> <mi>p</mi> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>N</mi> </mrow> <mrow> <mi>p</mi> <mi>s</mi> </mrow> </msub> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>.</p>
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<p>Location of the ocean experiment.</p>
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<p>Ocean experiment: (<b>a</b>) configuration, (<b>b</b>) measured SSP.</p>
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<p>Spectrogram of received signals. (<b>a</b>) Conventional method, (<b>b</b>) proposed method.</p>
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<p>Ocean experiment parameter. (<b>a</b>) Estimated SNR, (<b>b</b>) estimated maximum Doppler frequency.</p>
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19 pages, 5157 KiB  
Article
Underwater Acoustic Orthogonal Frequency-Division Multiplexing Communication Using Deep Neural Network-Based Receiver: River Trial Results
by Sabna Thenginthody Hassan, Peng Chen, Yue Rong and Kit Yan Chan
Sensors 2024, 24(18), 5995; https://doi.org/10.3390/s24185995 - 15 Sep 2024
Viewed by 727
Abstract
In this article, a deep neural network (DNN)-based underwater acoustic (UA) communication receiver is proposed. Conventional orthogonal frequency-division multiplexing (OFDM) receivers perform channel estimation using linear interpolation. However, due to the significant delay spread in multipath UA channels, the frequency response often exhibits [...] Read more.
In this article, a deep neural network (DNN)-based underwater acoustic (UA) communication receiver is proposed. Conventional orthogonal frequency-division multiplexing (OFDM) receivers perform channel estimation using linear interpolation. However, due to the significant delay spread in multipath UA channels, the frequency response often exhibits strong non-linearity between pilot subcarriers. Since the channel delay profile is generally unknown, this non-linearity cannot be modeled precisely. A neural network (NN)-based receiver effectively tackles this challenge by learning and compensating for the non-linearity through NN training. The performance of the DNN-based UA communication receiver was tested recently in river trials in Western Australia. The results obtained from the trials prove that the DNN-based receiver performs better than the conventional least-squares (LS) estimator-based receiver. This paper suggests that UA communication using DNN receivers holds great potential for revolutionizing underwater communication systems, enabling higher data rates, improved reliability, and enhanced adaptability to changing underwater conditions. Full article
(This article belongs to the Special Issue Advanced Acoustic Sensing Technology)
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<p>Structure of a neuron.</p>
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<p>ReLU and leaky ReLU [<a href="#B26-sensors-24-05995" class="html-bibr">26</a>].</p>
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<p>NN training process.</p>
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<p>Architecture of an LSTM layer.</p>
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<p>Architecture of the CNN-based receiver.</p>
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<p>Shelly Jetty, Western Australia.</p>
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<p>Frame structure.</p>
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<p>Block diagram of the transmitter.</p>
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<p>Block diagram of the receiver.</p>
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<p>Architecture of the LSTM-based receiver.</p>
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<p>River trial setup.</p>
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<p>BER performance of the proposed NN-based receiver.</p>
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<p>Tank setup.</p>
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<p>Indoor tank channel profile.</p>
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<p>River trial 1 channel profile.</p>
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<p>BER performance of the NN-based receivers in river trial 1.</p>
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<p>River trial 2 channel profile.</p>
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<p>BER performance of the NN-based receivers in river trial 2 with 1000 packets for testing, 4 layers and 200 epochs.</p>
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<p>BER performance of the NN-based receivers with 2000 packets for training, 2000 packets for testing in river trial 2.</p>
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21 pages, 4992 KiB  
Article
Enhancing Security of Telemedicine Data: A Multi-Scroll Chaotic System for ECG Signal Encryption and RF Transmission
by José Ricardo Cárdenas-Valdez, Ramón Ramírez-Villalobos, Catherine Ramirez-Ubieta and Everardo Inzunza-Gonzalez
Entropy 2024, 26(9), 787; https://doi.org/10.3390/e26090787 - 14 Sep 2024
Viewed by 945
Abstract
Protecting sensitive patient data, such as electrocardiogram (ECG) signals, during RF wireless transmission is essential due to the increasing demand for secure telemedicine communications. This paper presents an innovative chaotic-based encryption system designed to enhance the security and integrity of telemedicine data transmission. [...] Read more.
Protecting sensitive patient data, such as electrocardiogram (ECG) signals, during RF wireless transmission is essential due to the increasing demand for secure telemedicine communications. This paper presents an innovative chaotic-based encryption system designed to enhance the security and integrity of telemedicine data transmission. The proposed system utilizes a multi-scroll chaotic system for ECG signal encryption based on master–slave synchronization. The ECG signal is encrypted by a master system and securely transmitted to a remote location, where it is decrypted by a slave system using an extended state observer. Synchronization between the master and slave is achieved through the Lyapunov criteria, which ensures system stability. The system also supports Orthogonal Frequency Division Multiplexing (OFDM) and adaptive n-quadrature amplitude modulation (n-QAM) schemes to optimize signal discretization. Experimental validations with a custom transceiver scheme confirmed the system’s effectiveness in preventing channel overlap during 2.5 GHz transmissions. Additionally, a commercial RF Power Amplifier (RF-PA) for LTE applications and a development board were integrated to monitor transmission quality. The proposed encryption system ensures robust and efficient RF transmission of ECG data, addressing critical challenges in the wireless communication of sensitive medical information. This approach demonstrates the potential for broader applications in modern telemedicine environments, providing a reliable and efficient solution for the secure transmission of healthcare data. Full article
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<p>Chaotic attractor.</p>
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<p>Error state responses.</p>
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<p>Architecture of n-QAM scheme.</p>
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<p>Overall diagram scheme.</p>
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<p>Block diagram of the transmission testbed proposed. Part A: Signal transmission and control. Part B: Signal path and measurement.</p>
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<p>Photo of the experimental testbed. Equipment pertinent to the setup: (<b>A</b>) Altera Cyclone V FPGA SoC-Kit. (<b>B</b>) AD9361 RF Agile Transceiver operating at a center frequency of 2.45 GHz. (<b>C</b>) Mini-circuits ZFBP-2400-S+ bandpass filter. (<b>D</b>) Mini-circuits for power amplifiers ZX60-V63+. (<b>E</b>) Coupler mini-circuits ZHDC-16-63-S+. (<b>F</b>) SIGLENT SSA 3032X Spectrum Analyzer. (<b>G</b>) GW INSTEK GPS-3303 Power Supply. (<b>H</b>) Display HOST PC-MATLAB R2024a.</p>
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<p>A 128-QAM with a power amplifier using a scale factor of 0.05.</p>
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<p>An ECG signal decrypted under a 128-QAM scheme.</p>
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<p>ECG signal encrypted under the 128-QAM modulation scheme.</p>
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<p>ECG signal with tachycardia encrypted under the 128-QAM modulation scheme.</p>
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<p>128-QAM constellation of an encrypted ECG signal.</p>
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<p>Cross-correlation of ideal received signal and transmitted–received ECG signal.</p>
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<p>Discrete Fourier transform of transmitted and received ECG signal.</p>
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<p>Histogram of transmitted and received ECG signal.</p>
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35 pages, 28009 KiB  
Article
Optoelectronics Interfaces for a VLC System for UHD Audio-Visual Content Transmission in a Passenger Van: HW Design
by Carlos Iván del Valle Morales, Juan Sebastián Betancourt Perlaza, Juan Carlos Torres Zafra, Iñaki Martinez-Sarriegui and José Manuel Sánchez-Pena
Sensors 2024, 24(17), 5829; https://doi.org/10.3390/s24175829 - 8 Sep 2024
Viewed by 1151
Abstract
This work aims to provide the hardware (HW) design of the optoelectronics interfaces for a visible-light communication (VLC) system that can be employed for several use cases. Potential applications include the transmission of ultra-high-definition (UHD) streaming video through existing reading lamps installed in [...] Read more.
This work aims to provide the hardware (HW) design of the optoelectronics interfaces for a visible-light communication (VLC) system that can be employed for several use cases. Potential applications include the transmission of ultra-high-definition (UHD) streaming video through existing reading lamps installed in passenger vans. In this use case, visible light is employed for the downlink, while infrared light is used for the uplink channel, acting as a remote controller. Two primary components -a Light Fidelity (LiFi) router and a USB dongle—were designed and implemented. The ‘LiFi Router’, handling the downlink channel, comprises components such as a visible Light-Emitting Diode (LED) and an infrared receiver. Operating at a supply voltage of 12 V and consuming current at 920 mA, it is compatible with standard voltage buses found in transport vehicles. The ‘USB dongle’, responsible for the uplink, incorporates an infrared LED and a receiver optimized for visible light. The USB dongle works at a supply voltage of 5 V and shows a current consumption of 1.12 A, making it well suited for direct connection to a universal serial bus (USB) port. The bandwidth achieved for the downlink is 11.66 MHz, while the uplink’s bandwidth is 12.27 MHz. A system competent at streaming UHD video with the feature of being single-input multiple-output (SIMO) was successfully implemented via the custom hardware design of the optical transceivers and optoelectronics interfaces. To ensure the system’s correct performance at a distance of 110 cm, the minimum signal-to-noise ratio (SNRmin) for both optical links was maintained at 10.74 dB. We conducted a proof-of-concept test of the VLC system in a passenger van and verified its optimal operation, effectively illustrating its performance in a real operating environment. Exemplifying potential implementations possible with the hardware system designed in this work, a bit rate of 15.2 Mbps was reached with On–Off Keying (OOK), and 11.25 Mbps was obtained with Quadrature Phase Shift Keying (QPSK) using Orthogonal Frequency-Division Multiplexing (OFDM) obtaining a bit-error rate (BER) of 3.3259 × 10−5 in a passenger van at a distance of 72.5 cm between the LiFi router and the USB dongle. As a final addition, a solar panel was installed on the passenger van’s roof to power the user’s laptop and the USB dongle via a power bank battery. It took 13.4 h to charge the battery, yielding a battery life of 22.3 h. This characteristic renders the user’s side of the system entirely self-powered. Full article
(This article belongs to the Special Issue Sensing Technologies and Optical Communication)
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<p>The LiFi router, located on the vehicle’s interior roof, connects to the content server via a wireless Internet connection. The USB dongle, connected to the user’s portable device, receives and plays the audio-visual content as described.</p>
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<p>Block diagram of the VLC proposed system.</p>
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<p>Equivalent simplified circuit of the LUW-CN7N-KYLX-EMKM OSRAM LED for AC.</p>
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<p>LED driver based on a division voltage for n-type MOSFET.</p>
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<p>Phase-advance equalizer.</p>
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<p>Phase-advance equalizer implemented for (<b>a</b>) the first stage for a pole located at 1.8 MHz, and (<b>b</b>) the second stage for a pole located at 9 MHz.</p>
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<p>LED driver implemented with 2 equalization and 2 amplification stages.</p>
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<p>Simulated frequency response of the proposed LED driver based on two amplification and two equalization stages.</p>
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<p>(<b>a</b>) Set-up implemented to take the frequency response measurement of the LUW-CN7N-KYLX-EMKM OSRAM LED and its LED driver based on 2 equalization and 2 amplification stages; (<b>b</b>) frequency response measurement of the LUW-CN7N-KYLX-EMKM OSRAM LED and its LED driver based on 2 equalization and 2 amplification stages.</p>
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<p>Frequency response measurement of the IR HSDL-4250 LED and its LED driver based on 2 equalization and 2 amplification stages.</p>
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<p>PD driver implemented with three amplifier stages.</p>
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<p>Simulated frequency response of the PD driver implemented with three amplifier stages.</p>
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<p>(<b>a</b>) Set-up of the frequency response of the PD driver implemented with three amplifier stages using the LUW-CN7N-KYLX-EMKM OSRAM LED; (<b>b</b>) frequency response of the PD driver implemented with three amplifier stages using the LUW-CN7N-KYLX-EMKM OSRAM LED.</p>
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<p>Frequency response of the PD driver implemented with three amplifier stages using the IR HSDL-4250 LED.</p>
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<p>Block diagram of the FPGA/LED driver interface.</p>
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<p>Block diagram of the PD driver/FPGA interface.</p>
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<p>Schematic design of the FPGA/LED driver connection.</p>
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<p>Schematic design of the PD driver/FPGA connection.</p>
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<p>TE0720 SoC includes the FPGA Xilinx XA7z020-1CLG484Q.</p>
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<p>Diagram of the interaction between external interface, TE0720 SoC, and internal interface.</p>
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<p>Internal interface PCB: (<b>a</b>) top view; (<b>b</b>) bottom view.</p>
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<p>(<b>a</b>) LiFi router external interface PCB; (<b>b</b>) USB dongle external interface PCB.</p>
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<p>Block diagram of all PCBs that are part of the system.</p>
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<p>Schematic connection of the components of the system for LiFi router and USB dongle.</p>
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<p>(<b>a</b>) PCBs stack of TE0720 SoC, internal and external interfaces; (<b>b</b>) LiFi router.</p>
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<p>(<b>a</b>) PCBs stack of TE0720 SoC, internal and external interfaces; (<b>b</b>) USB dongle.</p>
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<p>Set-up carried out to test every module of the system.</p>
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<p>Scheme of the testing procedure for the transmitter block.</p>
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<p>Tests carried out in the transmitter block utilizing a (<b>a</b>) visible LED and (<b>b</b>) IR LED.</p>
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<p>Implemented test to validate the receiver block.</p>
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<p>ILA captured at the ADC’s outputs.</p>
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<p>Test carried out to validate the Tx block and Rx block working in closed loop.</p>
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<p>LiFi router and USB dongle packed in boxes.</p>
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<p>The VLC system developed was deployed using a Ford Transit model van.</p>
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<p>The user’s laptop playing a UHD video streaming due to the implemented USB dongle.</p>
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15 pages, 8532 KiB  
Article
Data-Aided Maximum Likelihood Joint Angle and Delay Estimator Over Orthogonal Frequency Division Multiplex Single-Input Multiple-Output Channels Based on New Gray Wolf Optimization Embedding Importance Sampling
by Maha Abdelkhalek, Souheib Ben Amor and Sofiène Affes
Sensors 2024, 24(17), 5821; https://doi.org/10.3390/s24175821 - 7 Sep 2024
Viewed by 713
Abstract
In this paper, we propose a new data-aided (DA) joint angle and delay (JADE) maximum likelihood (ML) estimator. The latter consists of a substantially modified and, hence, significantly improved gray wolf optimization (GWO) technique by fully integrating and embedding within it the powerful [...] Read more.
In this paper, we propose a new data-aided (DA) joint angle and delay (JADE) maximum likelihood (ML) estimator. The latter consists of a substantially modified and, hence, significantly improved gray wolf optimization (GWO) technique by fully integrating and embedding within it the powerful importance sampling (IS) concept. This new approach, referred to hereafter as GWOEIS (for “GWO embedding IS”), guarantees global optimality, and offers higher resolution capabilities over orthogonal frequency division multiplex (OFDM) (i.e., multi-carrier and multi-path) single-input multiple-output (SIMO) channels. The traditional GWO randomly initializes the wolfs’ positions (angles and delays) and, hence, requires larger packs and longer hunting (iterations) to catch the prey, i.e., find the correct angles of arrival (AoAs) and time delays (TDs), thereby affecting its search efficiency, whereas GWOEIS ensures faster convergence by providing reliable initial estimates based on a simplified importance function. More importantly, and beyond simple initialization of GWO with IS (coined as IS-GWO hereafter), we modify and dynamically update the conventional simple expression for the convergence factor of the GWO algorithm that entirely drives its hunting and tracking mechanisms by accounting for new cumulative distribution functions (CDFs) derived from the IS technique. Simulations unequivocally confirm these significant benefits in terms of increased accuracy and speed Moreover, GWOEIS reaches the Cramér–Rao lower bound (CRLB), even at low SNR levels. Full article
(This article belongs to the Special Issue Feature Papers in the 'Sensor Networks' Section 2024)
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<p>Position updating in GWOEIS.</p>
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<p>Flow chart of GWOEIS algorithm.</p>
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<p>MSE vs. the SNR in dB for <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math> (<math display="inline"><semantics> <mrow> <mi mathvariant="bold-italic">θ</mi> <mo>=</mo> <mo>[</mo> <msup> <mn>20</mn> <mo>∘</mo> </msup> <mo>,</mo> <mspace width="0.277778em"/> <msup> <mn>45</mn> <mo>∘</mo> </msup> <mo>]</mo> </mrow> </semantics></math>; <math display="inline"><semantics> <mi mathvariant="bold-italic">τ</mi> </semantics></math> = [25 ns, 62.5 ns]), <math display="inline"><semantics> <mrow> <mi>R</mi> <mo>=</mo> <mn>30</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mi>H</mi> </msub> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math> of: (<b>a</b>) the <span class="html-italic">Q</span> TDs, (<b>b</b>) the <span class="html-italic">Q</span> AoAs, and (<b>c</b>) the <math display="inline"><semantics> <mrow> <mi>P</mi> <mo>×</mo> <mi>K</mi> </mrow> </semantics></math> channel coefficients (on average, per element, for all three parameter types).</p>
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<p>MSE vs. the SNR in dB for <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math> (<math display="inline"><semantics> <mrow> <mi mathvariant="bold-italic">θ</mi> <mo>=</mo> <mo>[</mo> <msup> <mn>20</mn> <mo>∘</mo> </msup> <mo>,</mo> <mspace width="0.277778em"/> <msup> <mn>45</mn> <mo>∘</mo> </msup> <mo>]</mo> </mrow> </semantics></math>; <math display="inline"><semantics> <mi mathvariant="bold-italic">τ</mi> </semantics></math> = [25 ns, 62.5 ns]), <math display="inline"><semantics> <mrow> <mi>R</mi> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mi>H</mi> </msub> <mo>=</mo> <mn>30</mn> </mrow> </semantics></math> of: (<b>a</b>) the <span class="html-italic">Q</span> TDs, (<b>b</b>) the <span class="html-italic">Q</span> AoAs, and (<b>c</b>) the <math display="inline"><semantics> <mrow> <mi>P</mi> <mo>×</mo> <mi>K</mi> </mrow> </semantics></math> channel coefficients (on average, per element, for all three parameter types).</p>
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<p>MSE vs. the SNR in dB and the samples size <span class="html-italic">R</span> for <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math> (<math display="inline"><semantics> <mrow> <mi mathvariant="bold-italic">θ</mi> <mo>=</mo> <mo>[</mo> <msup> <mn>20</mn> <mo>∘</mo> </msup> <mo>,</mo> <mspace width="0.277778em"/> <msup> <mn>45</mn> <mo>∘</mo> </msup> <mo>]</mo> </mrow> </semantics></math>; <math display="inline"><semantics> <mi mathvariant="bold-italic">τ</mi> </semantics></math> = [25 ns, 62.5 ns]) and <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mi>H</mi> </msub> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>.</p>
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<p>MSE vs. the SNR in dB and the iterations number (e.g., <math display="inline"><semantics> <msub> <mi>T</mi> <mi>H</mi> </msub> </semantics></math>) for <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math> (<math display="inline"><semantics> <mrow> <mi mathvariant="bold-italic">θ</mi> <mo>=</mo> <mo>[</mo> <msup> <mn>20</mn> <mo>∘</mo> </msup> <mo>,</mo> <mspace width="0.277778em"/> <msup> <mn>45</mn> <mo>∘</mo> </msup> <mo>]</mo> </mrow> </semantics></math>; <math display="inline"><semantics> <mi mathvariant="bold-italic">τ</mi> </semantics></math> = [25 ns, 62.5 ns]) and <math display="inline"><semantics> <mrow> <mi>R</mi> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>.</p>
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<p>MSE vs. the SNR in dB and the number of paths <span class="html-italic">Q</span> for <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mi>H</mi> </msub> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>R</mi> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>.</p>
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<p>RMSE vs. the SNR in dB and the temporal separation <math display="inline"><semantics> <msub> <mo>Δ</mo> <mi>τ</mi> </msub> </semantics></math> in ns with <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>R</mi> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mi>H</mi> </msub> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>.</p>
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<p>RMSE vs. the SNR in dB and the angular separation <math display="inline"><semantics> <msub> <mo>Δ</mo> <mi>θ</mi> </msub> </semantics></math> in degrees with <math display="inline"><semantics> <mrow> <mi>Q</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>R</mi> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mi>H</mi> </msub> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>.</p>
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17 pages, 4225 KiB  
Article
Z-OFDM: A New High-Performance Solution for Underwater Acoustic Communication
by Haitao Su, Jiaxuan Chen, Angdi Li, Hongzhi Hu and Cuifeng Xu
Electronics 2024, 13(17), 3543; https://doi.org/10.3390/electronics13173543 - 6 Sep 2024
Viewed by 638
Abstract
This paper presents Z-OFDM, a high-performance solution for underwater acoustic communication. Traditional underwater orthogonal frequency division multiplexing (OFDM) systems suffer from spectrum leakage and distortion due to the narrowband nature of underwater acoustic signals and the picket fence effect of the fast Fourier [...] Read more.
This paper presents Z-OFDM, a high-performance solution for underwater acoustic communication. Traditional underwater orthogonal frequency division multiplexing (OFDM) systems suffer from spectrum leakage and distortion due to the narrowband nature of underwater acoustic signals and the picket fence effect of the fast Fourier transform (FFT). Z-OFDM addresses these issues by integrating zoom-fast Fourier transform (ZoomFFT) with OFDM and redesigning the modulator and demodulator to replace the conventional FFT. This integration enhances spectral resolution, resulting in higher channel capacity, improved Signal to Interference plus Noise Ratio (SINR), and reduced Bit Error Rate (BER). Computer simulations using underwater acoustic channels from Fuxian Lake and Wuyuan Bay demonstrate that the Z-OFDM system achieves a 6 dB gain compared to conventional OFDM systems at a BER of 103. These results demonstrate the effectiveness of Z-OFDM in overcoming the limitations of traditional FFT-based OFDM systems in underwater environments. Full article
(This article belongs to the Special Issue New Advances in Underwater Communication Systems)
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<p>Block diagram of ZoomFFT.</p>
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<p>Composition of the UWA OFDM system based on ZoomFFT.</p>
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<p>Average capacity of Z-OFDM system over AWGN channel.</p>
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<p>SINR of Z-OFDM system over AWGN channel.</p>
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<p>BER of Z-OFDM system over AWGN channel.</p>
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<p>The map of the outfield in the experiment.</p>
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<p>Time evolution of the magnitude impulse response at two different distances.</p>
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<p>BER of the communication system in this experiment.</p>
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<p>BER over time varies underwater acoustic channel of unit time in Fuxain Lake.</p>
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<p>BER over time varies underwater acoustic channel of unit time in Wuyuan Bay.</p>
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19 pages, 805 KiB  
Article
Channel Estimation and Iterative Decoding for Underwater Acoustic OTFS Communication Systems
by Lei Liu, Chao Ma, Yong Duan, Xinyu Liu and Xin Qing
J. Mar. Sci. Eng. 2024, 12(9), 1559; https://doi.org/10.3390/jmse12091559 - 5 Sep 2024
Viewed by 573
Abstract
Orthogonal Time–Frequency Space (OTFS) is an innovative modulation method that ensures efficient and secure communication over a time-varying channel. This characteristic inspired us to integrate OTFS technology with underwater acoustic (UWA) communications to counteract the time-varying and overspread characteristics of UWA channels. However, [...] Read more.
Orthogonal Time–Frequency Space (OTFS) is an innovative modulation method that ensures efficient and secure communication over a time-varying channel. This characteristic inspired us to integrate OTFS technology with underwater acoustic (UWA) communications to counteract the time-varying and overspread characteristics of UWA channels. However, implementing OTFS in UWA communications presents challenges related to overspread channels. To handle these challenges, we introduce a specialized OTFS system and offer frame design recommendations for UWA communications in this article. We propose a Doppler compensation method and a dual-domain joint channel estimation method to address the issues caused by severe Doppler effects in UWA communication. Additionally, we propose an OTFS system detection approach. This approach incorporates an iterative detection process which facilitates soft information exchange between a message passing (MP) detector and a low-density parity check (LDPC) decoder. By conducting simulations, we demonstrate that the proposed UWA OTFS system significantly outperforms Orthogonal Frequency-Division Multiplexing (OFDM), Initial Estimate Iterative Decoding Feedback (IE-IDF-MRC), and two-dimensional Passive Time Reversal Decision Feedback Equalization (2D-PTR-DFE) in UWA channels. Full article
(This article belongs to the Special Issue Applications of Underwater Acoustics in Ocean Engineering)
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<p>Zero-padded OTFS system.</p>
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<p>The UWA channel power response.</p>
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<p>The dual-domain joint UWA OTFS channel estimation method.</p>
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<p>The structure of IDF-MP.</p>
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<p>Sound speed profile.</p>
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<p>The same UWA Channel in (<b>a</b>) DT domain, (<b>b</b>) DD domain, and (<b>c</b>) DD domain after Doppler compensation.</p>
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<p>BER comparison of OTFS frames with different <span class="html-italic">M</span> and <span class="html-italic">N</span> under different relative speeds.</p>
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<p>BER comparison of OTFS frames with different ZP lengths.</p>
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<p>BER comparison of the IDF-MP method under different iterations.</p>
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<p>BER comparison of the IDF-MP, OFDM, IE-IDF-MRC, and 2D-PTR-DEF methods.</p>
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