Enhanced Secrecy Performance of Multihop IoT Networks with Cooperative Hybrid-Duplex Jamming

© 2005-2012 IEEE. As the number of connected devices is exponentially increasing, security in Internet of Things (IoT) networks presents a major challenge. Accordingly, in this work we investigate the secrecy performance of multihop IoT networks assuming that each node is equipped with only two antennas, and can operate in both Half-Duplex (HD) and Full-Duplex (FD) modes. Moreover, we propose an FD Cooperative Jamming (CJ) scheme to provide higher security against randomly located eavesdroppers, where each information symbol is protected with two jamming signals by its two neighbouring nodes, one of which is the FD receiver. We demonstrate that under a total power constraint, the proposed FD-CJ scheme significantly outperforms the conventional FD Single Jamming (FD-SJ) approach, where only the receiving node acts as a jammer, especially when the number of hops is larger than two. Moreover, when the Channel State Information (CSI) is available at the transmitter, and transmit beamforming is applied, our results demonstrate that at low Signal-to-Noise Ratio (SNR), higher secrecy performance is obtained if the receiving node operates in HD and allocates both antennas for data reception, leaving only a single jammer active; while at high SNR, a significant secrecy enhancement can be achieved with FD jamming. Our proposed FD-CJ scheme is found to demonstrate a great resilience over multihop networks, as only a marginal performance loss is experienced as the number of hops increases. For each case, an integral closed-form expression is derived for the secrecy outage probability, and verified by Monte Carlo simulations

A hybrid relay and intelligent reflecting surface network and its ergodic performance analysis

This letter proposes a novel hybrid relay and Intelligent Reflecting Surface (IRS) assisted system for future wireless networks. We demonstrate that for practical scenarios where the amount of radiated power and/or the number of reflecting elements are/is limited, the performance of an IRS-supported system can be significantly enhanced by utilizing a simple Decode-and-Forward (DF) relay. Tight upper bounds for the ergodic capacity are derived for the proposed scheme under different channel environments, and shown to closely match Monte-Carlo simulations

Sum Rate Maximization in IRS-assisted Wireless Power Communication Networks

Wireless powered communication networks (WPCNs) are a promising technology supporting resource-intensive devices in the Internet of Things (IoT). However, their transmission efficiency is very limited over long distances. The newly emerged intelligent reflecting surface (IRS) can effectively mitigate the propagation-induced impairment by controlling the phase shifts of passive reflection elements. In this paper, we integrate IRS into WPCNs to assist both the energy and information transmission. We aim to maximize the uplink (UL) sum rate of all IoT devices (IoTDs) by jointly optimizing the time allocation variable, energy beam matrix at the power transmitting base station (PTBS), receive beamforming matrix at the information receiving base station (IRBS), and the phase shifts of the IRS both in the UL and downlink (DL) subject to time allocation constraint, together with transmit power constraint for the PTBS and unit modulus constraints. This problem is very difficult to solve directly due to the highly coupled variables, which results in the optimization problem taking neither linear nor convex form. Hence, we decouple this problem into three subproblems by using the block coordinate descent (BCD) method. The UL receive beamforing matrix and phase shift are alternatively optimized in the UL optimization subproblem with fixed time allocation and the DL variables. The DL optimization subproblem is solved by the proposed successive convex approximation (SCA) algorithm. Simulation results demonstrate that the performance of integrating IRS and WPCNs outperforms traditional WPCNs. Besides, the results show that IRS is an effective method to preserve the tradeoff of energy efficiency and transmission efficiency in the IoT

Efficient Low-Complexity Antenna Selection Algorithms in Multi-User Massive MIMO Systems with Matched Filter Precoding

In this work, two efficient low complexity Antenna Selection (AS) algorithms are proposed for downlink Multi-User (MU) Massive Multiple-Input Multiple-Output (M-MIMO) systems with Matched Filter (MF) precoding. Both algorithms avoid vector multiplications during the iterative selection procedure to reduce complexity. Considering a system with N antennas at the Base Station (BS) serving K single-antenna users in the same time-frequency resources, the first algorithm divides the available antennas into K groups, with the kth group containing the N/K antennas that have the maximum channel norms for the kth user. Therefore, the Signal-to-Interference plus Noise Ratio (SINR) for the kth user can be maximized by selecting a subset of the antennas from only the kth group, thereby resulting in a search space reduction by a factor of K. The second algorithm is a semiblind interference rejection method that relies only on the signs of the interference terms, and at each iteration the antenna that rejects the maximum number of interference terms will be selected. The performance of our proposed methods is evaluated under perfect and imperfect Channel State Information (CSI) and compared with other low complexity AS schemes in terms of the achievable sum rate as well as the energy efficiency. In particular, when the Signal-to-Noise Ratio (SNR) is 10 dB, and for a system with 20 MHz of bandwidth, the proposed methods outperform the case where all the antennas are employed by 108.8 and 49.2 Mbps for the first and second proposed algorithms, respectively, given that the BS has perfect CSI knowledge and is equipped with 256 antennas, out of which 64 are selected to serve 8 single-antenna users

Buffer-aided relay selection for cooperative NOMA in the internet of things

The nonorthogonal multiple access (NOMA) well improves the spectrum efficiency which is particularly essential in the Internet of Things (IoT) system involving massive number of connections. It has been shown that applying buffers at relays can further increase the throughput in the NOMA relay network. This is however valid only when the channel signal-to-noise ratios (SNRs) are large enough to support the NOMA transmission. While it would be straightforward for the cooperative network to switch between the NOMA and the traditional orthogonal multiple access (OMA) transmission modes based on the channel SNR-s, the best potential throughput would not be achieved. In this paper, we propose a novel prioritization-based buffer-aided relay selection scheme which is able to seamlessly combine the NOMA and OMA transmission in the relay network. The analytical expression of average throughput of the proposed scheme is successfully derived. The proposed scheme significantly improves the data throughput at both low and high SNR ranges, making it an attractive scheme for cooperative NOMA in the IoT

Performance Analysis for Multi-Hop Full-Duplex IoT Networks Subject to Poisson Distributed Interferers

Multi-hop relaying is a fundamental technology that will enable connectivity in large-scale networks such as those encounted in IoT applications. However, the end-to-end transmission rate decreases dramatically as the number of hops increases when half-duplex (HD) relaying is employed. In this paper, we investigate the outage probability and symbol-error rate for both HD and full-duplex (FD) transmission schemes in multi-hop networks subject to interference from randomly distributed third-party devices. We model the locations of the interfering devices as a Poisson point process. We derive a closed-form expression for the outage probability and approximations for the symbol-error rate for HD and FD transmissions employing BPSK and QPSK. The symbol-error rate results are obtained by using a Markov chain model for the multi-hop decode-and-forward links. This model accurately accounts for the nonlinear dynamical nature of the network, whereby erroneous symbol decoding can be “corrected” by a second erroneous decoding operation later in the network. We verify the analytical results through simulations and show the HD and FD schemes can be utilized to reduce the error-rate and outage probability of the system according to different residual self-interference levels and interferer densities. The results provide clear guidelines for implementing HD and FD in multi-hop networks

Energy Efficiency Optimization for Mutual-Coupling-Aware Wireless Communication System based on RIS-enhanced SWIPT

The widespread deployment of the Internet of Things (IoT) is promoting interest in simultaneous wireless information and power transfer (SWIPT), the performance of which can be further improved by employing a reconfigurable intelligent surface (RIS). In this paper, we propose a novel RIS-enhanced SWIPT system built on an electromagnetic-compliant framework. The mutual-coupling effects in the whole system are presented explicitly. Moreover, the reconfigurability of RIS is no longer expressed by the reflection-coefficient matrix but by the impedances of the tunable circuit. For comparison, both the no-coupling and the coupling-awareness cases are discussed. In particular, the energy efficiency (EE) is maximized by cooperatively optimizing the impedance parameters of the RIS elements as well as the active beamforming vectors at the base station (BS). For the coupling-awareness case, the considered problem is split into several sub-problems and solved alternatively due to its nonconvexity. Firstly, it is transformed into a more solvable form by applying the Neuman series approximation, which can be resolved iteratively. Then an alternative optimization (AO) framework and semi-definite relaxation (SDR), successive convex approximation (SCA), and Dinkelbach’s algorithm are applied to solve each sub-problem decomposed from it. Owning to the similarity between the two cases, the no-coupling one can be viewed as a reduced form of the coupling case and thus solved through a similar approach. Numerical results reveal the influence of mutual-coupling effects on the EE, especially in the RIS with closely spaced elements. In addition, physical beam designs are presented to demonstrate how the RIS assists SWIPT through various reflecting states in different conditions

Deep Hybrid Neural Network-Based Channel Equalization in Visible Light Communication

In this letter, the channel impairments compensation of visible light communication is formulated as a time sequence with memory prediction. Then we propose efficient nonlinear post equalization, using a combined long-short term memory (LSTM) and deep neural network (DNN), to learn the complicated channel characteristics and recover the original transmitted signal. We leverage the long-term memory parameters of LSTM to represent the sequence causality within the memory channel and refine the results by DNN to improve the reconstruction accuracy. Results demonstrate that the proposed scheme can robustly address the overall channel impairments and accurately recover the original transmitted signal with fairly fast convergence speed. Besides, it can achieve better balance between performance and complexity than that of the conventional competitive approaches, which demonstrates the potential and validity of the proposed methodology for channel equalization.</p

Deep Reinforcement Learning Based Relay Selection in Intelligent Reflecting Surface Assisted Cooperative Networks

This paper proposes a deep reinforcement learning (DRL) based relay selection scheme for cooperative networks with the intelligent reflecting surface (IRS). We consider a practical phase-dependent amplitude model in which the IRS reflection amplitudes vary with the discrete phase-shifts. Furthermore, we apply the relay selection to reduce the signal loss over distance in IRS-assisted networks. To solve the complicated problem of joint relay selection and IRS reflection coefficient optimization, we introduce DRL to learn from the environment to obtain the solution and reduce the computational complexity. Simulation results show that the throughput is significantly improved with the proposed DRL-based algorithm compared to random relay selection and random reflection coefficients methods

Max-Ratio Relay Selection in Secure Buffer-Aided Cooperative Wireless Networks

This paper considers the security of transmission in buffer-aided decode-and-forward cooperative wireless networks. An eavesdropper which can intercept the data transmission from both the source and relay nodes is considered to threaten the security of transmission. Finite size data buffers are assumed to be available at every relay in order to avoid having to select concurrently the best source-to-relay and relay-to-destination links. A new max-ratio relay selection policy is proposed to optimize the secrecy transmission by considering all the possible source-to-relay and relay-to-destination links and selecting the relay having the link which maximizes the signal to eavesdropper channel gain ratio. Two cases are considered in terms of knowledge of the eavesdropper channel strengths: exact and average gains, respectively. Closed-form expressions for the secrecy outage probability for both cases are obtained, which are verified by simulations. The proposed max-ratio relay selection scheme is shown to outperform one based on a max-min-ratio relay scheme