Tan Le Thanh

Human action recognition is an integral part of smart health monitoring, where intelligence behind the services is obtained and improves through sensor information. It poses tremendous challenges due to huge diversities of human actions and also a large variation in how a particular action can be performed. This problem has been intensified more with the emergence of Internet of Things (IoT), which has resulted in larger datasets acquired by a massive number of sensors. The big data based machine learning is the best candidate to deal with this grand challenge. However, one of the biggest challenges in using large datasets in machine learning is to label sufficient data to train a model accurately .Instead of using expensive supervised learning, we propose a semi-supervised classifier for time-series data. The proposed framework is the joint design of variational auto-encoder (VAE) and convolutional neural network (CNN). In particular, the VAE intends to extract the salient characteristics of human activity data and to provide the useful criteria for the compressed sensing reconstruction, while the CNN aims for extracting the discriminative features and for producing the low-dimension latent codes. Given a combination of labeled and raw time-series data, our architecture utilizes compressed samples from the latent vector in a deconvolutional decoder to reconstruct the input time-series. We intend to train the classifier to detect human actions for smart health systems.

—This paper studies the joint optimal resource allocation and probabilistic caching design for Device-to-Device (D2D) communications in a wireless heterogeneous network (HetNet) with full-duplex (FD) relays. In particular, popular contents can be cached at user devices as well as at relays that are located close to users. A user can request contents from another user via D2D communications and also from a nearby relay equipped with FD radios. In the case that there is a caching miss (i.e., the requested contents are not found at the other users/relays within the coverage range), users can connect to the base station (BS) via a relay by using the FD communication technology. Subsequently, we develop mathematical models to analyze the throughput performance with edge caching where both co-channel system level interference and FD self-interference are considered. Due to the high complexity of stochastic optimization, we develop low-complexity optimization formulation by decomposing the original problem into three simple sub-problems that can be efficiently solved. Finally, numerical results are presented to illustrate developed theoretical findings in the paper and significant performance gains of the throughput performance.

—This paper studies the joint communication, caching and computing design problem for achieving the operational excellence and the cost efficiency of the vehicle networks. Moreover , the resource allocation policy is designed by considering the vehicle mobility and the hard service deadline constraint. These critical challenges have often been either neglected completely or addressed inadequately in the existing work on the vehicular networks because of their high complexity. We develop the deep reinforcement learning with the multi-timescale framework to tackle these grand challenges in this paper. Furthermore, we propose the mobility-aware reward estimation for the large timescale model to mitigate the complexity due to the large action space. Finally, numerical results are presented to illustrate the theoretical findings developed in the paper and to quantify the performance gains attained.

In this paper, we consider the Medium Access Control (MAC) protocol design for full-duplex cognitive radio networks (FDCRNs). Our design exploits the fact that full-duplex (FD) secondary users (SUs) can perform spectrum sensing and access simultaneously, which enable them to detect the primary users’ (PUs) activity during transmission. The developed FD MAC protocol employs the standard backoff mechanism as in the 802.11 MAC protocol. However, we propose to adopt the frame fragmentation during the data transmission phase for timely detection of active PUs where each data packet is divided into multiple fragments and the active SU makes sensing detection at the end of each data fragment. Then, we develop a mathematical model to analyze the throughput performance of the proposed FD MAC protocol. Furthermore, we propose an algorithm to configure the MAC protocol so that efficient self-interference management and sensing overhead control can be achieved. Finally, numerical results are presented to evaluate the performance of our design and demonstrate the throughput enhancement compared to the existing half-duplex (HD) cognitive MAC protocol.

In this paper, we consider the joint design of data compression and 802.15.4-based medium access control (MAC) protocol for smartgrids with renewable energy. We study the setting where a number of nodes, each of which comprises electricity load and/or renewable sources, report periodically their injected powers to a data concentrator. Our design exploits the correlation of the reported data in both time and space to perform efficient data compression using the compressed sensing (CS) technique and efficiently engineer the MAC protocol so that the reported data can be recovered reliably within minimum reporting time. Specifically, we perform the following design tasks: i) we employ the two-dimensional (2D) CS technique to compress the reported data in the distributed manner; ii) we propose to adapt the 802.15.4 MAC protocol frame structure to enable efficient data transmission and reliable data reconstruction; and iii) we develop an analytical model based on which we can obtain the optimal parameter configuration to minimize the reporting delay. Finally, numerical results are presented to demonstrate the effectiveness of our design.

In this paper, we propose a semi-distributed cooperative spectrum sensing (SDCSS) and channel access framework for multi-channel cognitive radio networks (CRNs). In particular, we consider a SDCSS scheme where secondary users (SUs) perform sensing and exchange sensing outcomes with each other to locate spectrum holes. In addition, we devise the p-persistent CSMA-based cognitive medium access control (MAC) protocol integrating the SDCSS to enable efficient spectrum sharing among SUs. We then perform throughput analysis and develop an algorithm to determine the spectrum sensing and access parameters to maximize the throughput for a given allocation of channel sensing sets. Moreover, we consider the spectrum sensing set optimization problem for SUs to maximize the overall system throughput. We present both exhaustive search and low-complexity greedy algorithms to determine the sensing sets for SUs and analyze their complexity. We also show how our design and analysis can be extended to consider reporting errors. Finally, extensive numerical results are presented to demonstrate the significant performance gain of our optimized design framework with respect to non-optimized designs as well as the impacts of different protocol parameters on the throughput performance.

In this paper, we consider the channel allocation problem for throughput maximization in cognitive radio networks with hardware-constrained secondary users. Specifically, we assume that secondary users exploit spectrum holes on a set of channels where each secondary user can use at most one available channel for communication. We develop two channel assignment algorithms that can efficiently utilize spectrum opportunities on these channels. In the first algorithm, secondary users are assigned distinct sets of channels. We show that this algorithm achieves the maximum throughput limit if the number of channels is sufficiently large. In addition, we propose an overlapping channel assignment algorithm, that can improve the throughput performance compared to the non-overlapping channel assignment algorithm. In addition, we design a distributed MAC protocol for access contention resolution and integrate the derived MAC protocol overhead into the second channel assignment algorithm. Finally, numerical results are presented to validate the theoretical results and illustrate the performance gain due to the overlapping channel assignment algorithm.

In this paper, we consider the channel assignment problem for cognitive radio networks with hardware-constrained secondary users (SUs). In particular, we assume that SUs exploit spectrum holes on a set of channels where each SU can use at most one available channel for communication. We present the optimal brute-force search algorithm to solve the corresponding nonlinear integer optimization problem and analyze its complexity. Because the optimal solution has exponential complexity with the numbers of channels and SUs, we develop two low-complexity channel assignment algorithms that can efficiently utilize the spectrum holes. In the first algorithm, SUs are assigned distinct sets of channels. We show that this algorithm achieves the maximum throughput limit if the number of channels is sufficiently large. In addition, we propose an overlapping channel assignment algorithm that can improve the throughput performance compared with its nonoverlapping channel assignment counterpart. Moreover, we design a distributed medium access control (MAC) protocol for access contention resolution and integrate it into the overlapping channel assignment algorithm. We then analyze the saturation throughput and the complexity of the proposed channel assignment algorithms. We also present several potential extensions, including the development of greedy channel assignment algorithms under the max-min fairness criterion and throughput analysis, considering sensing errors. Finally, numerical results are presented to validate the developed theoretical results and illustrate the performance gains due to the proposed channel assignment algorithms.

In this paper, we investigate the joint cooperative spectrum sensing and access design problem for multi-channel cognitive radio networks. A general heterogeneous setting is considered where the probabilities that different channels are available, SNRs of the signals received at secondary users (SUs) due to transmissions from primary users (PUs) for different users and channels can be different. We assume a cooperative sensing strategy with a general a-out-of-b aggregation rule and design a synchronized MAC protocol so that SUs can exploit available channels. We analyze the sensing performance and the throughput achieved by the joint sensing and access design. Based on this analysis, we develop algorithms to find optimal parameters for the sensing and access protocols and to determine channel assignment for SUs to maximize the system throughput. Finally, numerical results are presented to verify the effectiveness of our design and demonstrate the relative performance of our proposed algorithms and the optimal ones.

We investigate the fair channel assignment and
access design problem for cognitive radio ad hoc network in
this paper. In particular, we consider a scenario where ad hoc
network nodes have hardware constraints which allow them
to access at most one channel at any time. We investigate a
fair channel allocation problem where each node is allocated a
subset of channels which are sensed and accessed periodically
by their owners by using a MAC protocol. Toward this end,
we analyze the complexity of the optimal brute-force search
algorithm which finds the optimal solution for this NP-hard
problem. We then develop low-complexity algorithms that can
work efficiently with a MAC protocol algorithm, which resolves
the access contention from neighboring secondary nodes. Also,
we develop a throughput analytical model, which is used in
the proposed channel allocation algorithm and for performance
evaluation of its performance. Finally, we present extensive
numerical results to demonstrate the efficacy of the proposed
algorithms in achieving fair spectrum sharing among traffic flows
in the network

In this paper, we consider the channel allocationproblem for throughput maximization in cognitive radio net-works with hardware-constrained secondary users. Specifically,we assume that secondary users exploit spectrum holes on aset of channels where each secondary user can use at most oneavailable channel for communication. We develop two channelassignment algorithms that can efficiently utilize spectrum op-portunities on these channels. In the first algorithm, secondaryusers are assigned distinct sets of channels. We show that thisalgorithm achieves the maximum throughput limit if the numberof channels is sufficiently large. In addition, we propose anoverlapping channel assignment algorithm, that can improve thethroughput performance compared to the non-overlapping chan-nel assignment algorithm. In addition, we design a distributedMAC protocol for access contention resolution and integratethe derived MAC protocol overhead into the second channelassignment algorithm. Finally, numerical results are presentedto validate the theoretical results and illustrate the performancegain due to the overlapping channel assignment algorithm.

In this paper, we consider the channel assignment
problem for cognitive radio networks with hardware-constrained
secondary users (SUs). In particular, we assume that SUs exploit
spectrum holes on a set of channels where each SU can use at most
one available channel for communication. We present the optimal
brute-force search algorithm to solve the corresponding nonlinear
integer optimization problem and analyze its complexity. Because
the optimal solution has exponential complexity with the numbers
of channels and SUs, we develop two low-complexity channel assignment
algorithms that can efficiently utilize the spectrum holes.
In the first algorithm, SUs are assigned distinct sets of channels.
We show that this algorithm achieves the maximum throughput
limit if the number of channels is sufficiently large. In addition,
we propose an overlapping channel assignment algorithm that can
improve the throughput performance compared with its nonoverlapping
channel assignment counterpart. Moreover, we design
a distributed medium access control (MAC) protocol for access
contention resolution and integrate it into the overlapping channel
assignment algorithm.We then analyze the saturation throughput
and the complexity of the proposed channel assignment algorithms.
We also present several potential extensions, including the
development of greedy channel assignment algorithms under the
max–min fairness criterion and throughput analysis, considering
sensing errors. Finally, numerical results are presented to validate
the developed theoretical results and illustrate the performance
gains due to the proposed channel assignment algorithms.

In this paper, we apply the maximal ratio combining (MRC) technique to achieve higher detection probability in cognitive radio networks over correlated Rayleigh fading channels. We present a simple approach to derive the probability of detection in closed-form expression. The numerical results reveal that the detection performance is a monotonically increasing function with respect to the number of antennas. Moreover, we provide sets of complementary receiver operating characteristic (ROC) curves to illustrate the effect of antenna correlation on the sensing performance of cognitive radio networks employing MRC schemes in some respective scenarios.

Cognitive radio allows unlicensed (cognitive) users to use licensed frequency bands by exploiting spectrum sensing techniques to detect whether or not the licensed (primary) users are present. In this paper, we present a compressed sensing applied to spectrum-occupancy detection in wide-band applications. The collected analog signals from each cognitive radio (CR) receiver at a fusion center are transformed to discrete-time signals by using analog-to-information converter (AIC) and then employed to calculate the autocorrelation. For signal reconstruction, we exploit a novel approach to solve the optimization problem consisting of minimizing both a quadratic (l2) error term and an l1-regularization term. In specific, we propose the Basic gradient projection (GP) and projected Barzilai-Borwein (PBB) algorithm to offer a better performance in terms of the mean squared error of the power spectrum density estimate and the detection probability of licensed signal occupancy.