Philip Orlik
Mitsubishi Electric Research Laboratories, Electronics and Communications, Department Member
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In this paper, a multiple cluster-based transmission diversity scheme is proposed for asynchronous joint transmissions (JT) in private networks. The use of multiple clusters or small cells is adopted to reduce the transmission distance to... more
In this paper, a multiple cluster-based transmission diversity scheme is proposed for asynchronous joint transmissions (JT) in private networks. The use of multiple clusters or small cells is adopted to reduce the transmission distance to users thereby increasing data-rates and reducing latency. To further increase the spectral efficiency and achieve flexible spatial degrees of freedom, we consider that a distributed remote radio unit system (dRRUS) is installed in each of the clusters. A key characteristic of deploying the dRRUS in private networks is the associated multipath-rich and asynchronous delay propagation environment. Therefore, we consider asynchronous multiple signal reception at the remote radio units and propose an intersymbol interference free distributed cyclic delay diversity (dCDD) scheme for JT to achieve the full transmit diversity gain without requiring full channel state information of the private network. The spectral efficiency of the proposed dCDD-based JT is analyzed by deriving a new closed-form expression, and then compared with link-level simulations for non-identically distributed frequency selective fading over the entire network. Due to its distributed structure, the dRRUS relies on backhaul communications between the private network server and cluster master (CM), which is the main backhaul connection, and between the CM to remote radio units, which are the secondary backhaul connections. Thus, it is important for us to investigate the impact of reliability of main and secondary backhaul connections on the system. Our results show that the resulting composite backhaul connections can be accurately modeled by our proposed product of independent Bernoulli processes.
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Research Interests: Computer Science and Iv
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Research Interests: Computer Science and Iv
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This paper presents a fault detection system for photovoltaic standalone applications based on Gaussian Process Regression (GPR). The installation is a communication repeater from the Confederación Hidrográfica del Ebro (CHE), public... more
This paper presents a fault detection system for photovoltaic standalone applications based on Gaussian Process Regression (GPR). The installation is a communication repeater from the Confederación Hidrográfica del Ebro (CHE), public institution which manages the hydrographic system of Aragón, Spain. Therefore, fault-tolerance is a mandatory requirement, complex to fulfill since it depends on the meteorology, the state of the batteries and the power demand. To solve it, we propose an online voltage prediction solution where GPR is applied in a real and large dataset of two years to predict the behavior of the installation up to 48 hour. The dataset captures electrical and thermal measures of the lead-acid batteries which sustain the installation. In particular, the crucial aspect to avoid failures is to determine the voltage at the end of the night, so different GPR methods are studied. Firstly, the photovoltaic standalone installation is described, along with the dataset. Then, the...
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IEEE 802.11ah and IEEE 802.15.4g are two wireless technologies designed for outdoor IoT applications. Both technologies have communication range up to 1000 meters. Therefore, 802.11ah network and 802.15.4g network are likely to coexist.... more
IEEE 802.11ah and IEEE 802.15.4g are two wireless technologies designed for outdoor IoT applications. Both technologies have communication range up to 1000 meters. Therefore, 802.11ah network and 802.15.4g network are likely to coexist. Our simulation results show that using standard defined coexistence mechanisms, 802.11ah network can severely interfere with 802.15.4g network and lead to significant packet loss in 802.15.4g network. As a result, additional coexistence control mechanisms are needed. Due to asymmetrical features such as modulation scheme and frame structure, 802.11ah devices and 802.15.4g devices cannot perform automatic cooperation. Thus, self-coexistence control techniques are preferred. This paper proposes learning based self-coexistence control techniques for 802.11ah devices to mitigate the interference impact of 802.11ah network on 802.15.4g network. We first present a α-Fairness based energy detection clear channel assessment (ED-CCA) method that enables 802.11ah devices to detect more ongoing 802.15.4g packet transmissions. We then introduce a Q-Learning based backoff mechanism for 802.11ah devices to avoid interfering with 802.15.4g packet transmission process. The proposed coexistence techniques can achieve fair spectrum sharing between 802.11ah network and 802.15.4g network.
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In various wireless applications, a receiver picks up data packets from multiple users where the packets share a common preamble, but otherwise carry different payloads, are not in temporal sync and are frequency shifted due to Doppler... more
In various wireless applications, a receiver picks up data packets from multiple users where the packets share a common preamble, but otherwise carry different payloads, are not in temporal sync and are frequency shifted due to Doppler effect and oscillator imperfections. We pose the problem of identifying the number of interfering packets and extracting the payloads as one of finding a sparse representation in a redundant dictionary. However, because of large size of the dictionary due to unknown packet payloads, direct application of conventional recovery methods does not lead to computationally tractable estimation schemes. To overcome this issue, we propose Orthogonal Matching Pursuit with Approximate Atoms (OMP-AA) algorithm aimed to facilitate identification of packet collisions and payload extraction. The simulation study shows that the proposed method performs well compared to an oracle estimator which has perfect knowledge of the packet parameters.
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Soft video delivery, i.e., analog video transmission, has been proposed to provide graceful video quality in unstable wireless channels. However, existing analog schemes need to transmit a significant amount of metadata to a receiver for... more
Soft video delivery, i.e., analog video transmission, has been proposed to provide graceful video quality in unstable wireless channels. However, existing analog schemes need to transmit a significant amount of metadata to a receiver for power allocation and decoding operations. It causes large overheads and quality degradation because of rate and power losses. To reduce the overheads while keeping high video quality, we propose a new analog transmission scheme. Our scheme exploits a Gaussian Markov random field for modeling video sequences to significantly reduce the required amount of metadata, which are obtained by fitting into the Lorentzian function. Our scheme achieves not only reduced overhead but also improved video quality, by using the fitting function and parameters for metadata. Evaluations using several test video sequences demonstrate that our proposed scheme reduces overheads by 97 % with 3.4 dB improvement of video quality compared to the existing analog video transm...
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Our previous studies introduced a mid-grained intermediate-level channel measurement – spatial beam signal-to-noise ratios (SNRs) that are inherently available and defined in the 60-GHz IEEE 802.11ad/ay standards – for the... more
Our previous studies introduced a mid-grained intermediate-level channel measurement – spatial beam signal-to-noise ratios (SNRs) that are inherently available and defined in the 60-GHz IEEE 802.11ad/ay standards – for the fingerprinting-based indoor localization. In this paper, we take one step further to use the mid-grained channel measurement for human monitoring applications including human pose and seat occupancy classifications. The effectiveness of the mid-grained channel measurement is validated by an in-house experimental dataset that includes 5 separate data collection sessions using classical classification methods and modern deep neural networks. Our preliminary result shows that mmWave beam SNRs are capable of delivering high classification accuracy above 90%.