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With the commercialization of the fifth generation mobile networks, researchers are now focusing on discovering the potential key techniques of the next generation mobile networks, which are expected to provide more accurate perception, lower latency, and higher network capacity. As the communication equipments increase exponentially, wireless transmission environment becomes more complex, leveraging wireless security challenges more and more severe, such as computationally powerful interception, intelligent malicious jamming, communication behavior monitoring. The physical layer security (PLS) techniques have been widely explored as a complement to traditional encryption schemes by exploiting the randomness characteristics of wireless channels to achieve the security from the physical layer. Additionally, reconfigurable intelligent surface (RIS) is considered as a key enabling technology for the six generation (6G) mobile networks, due to its ability to achieve the reconstruction of wireless channel, which is also regarded as a good match with PLS techniques for improving communication security. This paper presents a comprehensive review of the latest research on the integration of RIS and PLS. First, we introduce the principles of PLS from the development of secure communications and the basics of RIS based on the generalized Snell’s law. Then, we categorize RIS according to different hardware architectures, of which the corresponding scenarios are also presented. Subsequently, we review recent works on RIS-assisted PLS in different communication networks, and classify the security scenarios in which RIS is integrated with various advanced communication technologies. Finally, we discuss the potential future research directions and challenges of RIS-aided PLS communications.

Localization schemes based on one-way Channel Frequency Response (CFR) are limited in terms of the available coherent bandwidth due to random timing and phase offsets. Two-way CFR-based localization schemes utilize higher coherent bandwidth but suffer from Angle-of-Arrival (AoA) estimation ambiguity. In this paper, we investigate efficient localization schemes for ubiquitous WiFi networks in the 5GHz unlicensed frequency band by utilizing both the one-way and two-way WiFi Channel State Information (CSI) to overcome the AoA estimation ambiguity arising from the two-way-channel-based localization algorithm. We use the two-way CFR to increase the total available coherent bandwidth through stitching and the one-way CFR to resolve the AoA estimation ambiguity. We also propose complexity reduction methods for our localization scheme. Our proposed reduced-complexity localization scheme achieves 58cm median localization accuracy for our collected WiFi data and simulation environment in multipath scenarios with a 98% reduction in computational complexity compared to the state-of-the-art.

Indoor Localization is gaining increased importance due to numerous location-based services in healthcare, logistics, and security, to name few, that are expected to be provided by next-generation wireless networks. Such services are characterized by stringent accuracy requirements, short response time, and lower cost which makes the localization problem more challenging and deserving of attention. A key element of the localization process is distance estimation (also known as ranging). In this paper, we design and analyze an efficient decimeter-level two-way ranging scheme for ubiquitous WiFi networks in the 5 GHz frequency band whose accuracy approaches ideal one-way ranging with no phase mismatches. We investigate the idea of channel frequency response (CFR) stitching across non-contiguous WiFi channels and how two-way CFR measurements help in achieving the CFR coherency necessary for accurate ranging. In addition, we quantify the decrease in ranging accuracy of two-way compared to one-way ranging due to SNR degradation, Line-of-Sight (LoS) component shrinkage, and doubling the multipath delay spread. Furthermore, We design a novel scheme to bridge the performance gap between two-way ranging and ideal one-way ranging which operates in three main steps: square-root of the two-way CFR, followed by phase unwrapping, and finally deep fade detection and phase errors correction. Our proposed scheme achieves significant performance gains over two-way ranging with only a slight performance gap from ideal one-way ranging. Moreover, our proposed scheme enjoys robustness as it preserves the ranging accuracy gains in various WiFi communication scenarios when operating at different SNR levels, different multipath channel models, and different CFR bandwidths, as well as operating under system impairments such as Sample Timing Offset (STO). The accuracy gains achieved by the proposed schemes are demonstrated using both simulations and an in-house WiFi testbed. Finally, we quantify the added complexity of our proposed scheme and show it to be insignificant compared to that of the MUSIC super-resolution ranging steps which confirms the practical viability of our proposed scheme.

We consider the problem of minimizing the total energy consumption due to the computation and communication tasks of federated learning (FL) under bandwidth and latency constraints. To avoid channel state information (CSI) feedback to the transmitter, we adopt outage probability as an additional constraint in the energy minimization problem. First, we define a feasibility metric based on the system design parameters to exclude slow clients (stragglers). Then, we propose a novel client selection algorithm, after excluding stragglers, based on dividing the remaining clients into clusters, where clients within the same cluster collaborate in a communication round to train their local models. For each communication round, one client cluster is selected in a round-robin fashion. Furthermore, we formulate and solve a resource allocation problem to optimize the transmit power, clock frequency, allocated bandwidth, and communication latency of clients within each cluster to minimize the total energy subject to total bandwidth, latency, and outage constraints. Moreover, we extend our FL design framework to the case of no CSI at both the client and server ends using differential transmission to eliminate CSI estimation pilot overhead and complexity at comparable total energy consumption and learning accuracy to coherent transmission. We test our proposed algorithms over MNIST and Fashion-MNIST datasets in iid and non-iid settings. Our proposed client selection algorithm reduces the number of participating clients per communication round by 41% compared to the baseline while maintaining the same learning accuracy. Moreover, our results demonstrate that increasing the number of receive antennas at the server from one to four can reduce the number of communication rounds required to reach a predetermined testing accuracy level by up to 53% for the Fashion-MNIST dataset.

In autonomous, as well as manually operated vehicles, monitoring the driver visual attention provides useful information about the behavior, intent and vigilance level of the driver. The gaze of the driver can be formulated in terms of a probabilistic visual map representing the region around which the driver’s attention is focused. The area of the estimated region changes based on the level of confidence of the estimation. This paper proposes a framework based on convolutional neural networks (CNNs) that takes the head pose and the eye appearance of the driver as inputs, and creates a fusion model that estimates the driver’s gaze on a 2D grid. The model contains upsampling layers to create estimations at multiple resolutions. The model is trained using data collected from 59 subjects with continuous recordings where the subject looks at a moving target in a parked car, and glances at a set of markers inside the car while driving the vehicle and while the car is parked. Our fusion framework provides superior performance than unimodal systems trained exclusively with head pose or eye appearance information. It estimates the gaze region with the target location lying within the 75% confidence region with an accuracy of 92.54%.

In recent years, radar emitter signal recognition has enjoyed a wide range of applications in electronic support measure systems and communication security. More and more deep learning algorithms have been used to improve the recognition accuracy of radar emitter signals. However, complex deep learning algorithms and data preprocessing operations have a huge demand for computing power, which cannot meet the requirements of low power consumption and high real-time processing scenarios. Therefore, many research works have remained in the experimental stage and cannot be actually implemented. To tackle this problem, this paper proposes a resource reuse computing acceleration platform based on field programmable gate arrays (FPGA), and implements a one-dimensional (1D) convolutional neural network (CNN) and long short-term memory (LSTM) neural network (NN) model for radar emitter signal recognition, directly targeting the intermediate frequency (IF) data of radar emitter signal for classification and recognition. The implementation of the 1D-CNN-LSTM neural network on FPGA is realized by multiplexing the same systolic array to accomplish the parallel acceleration of 1D convolution and matrix vector multiplication operations. We implemented our network on Xilinx XCKU040 to evaluate the effectiveness of our proposed solution. Our experiments show that the system can achieve 7.34 giga operations per second (GOPS) data throughput with only 5.022 W power consumption when the radar emitter signal recognition rate is 96.53%, which greatly improves the energy efficiency ratio and real-time performance of the radar emitter recognition system.

In this paper, we investigate enhanced super-resolution range estimators with decimeter-level accuracy for multi-antenna and multipath Bluetooth systems. To enhance the traditional MUSIC range estimator for a two-way frequency-hopping Bluetooth channel model, we apply forward-backward averaging and bandwidth extrapolation using Burg’s algorithm to improve ranging accuracy, which is limited by the used Bluetooth bandwidth and the quality of the estimated sample covariance matrix. For the multi-antenna case, we compare the Summed Antenna Processing and Individual Antenna Processing methods to process multiple-antenna Bluetooth channel measurements and enhance the range estimation accuracy compared to the single-antenna case. In addition, we investigate a sparsity-aware range estimator which exploits the sparsity of Bluetooth channel impulse response and achieves comparable ranging accuracy to the enhanced MUSIC estimator but at a much lower computational complexity. We apply the greedy Orthogonal Matching Pursuit algorithm to heuristically solve the sparsity-constrained optimization problem for Bluetooth ranging. Furthermore, we evaluate the computational complexity of our investigated Bluetooth range estimators with two complexity-reduction techniques to further reduce the complexity of MUSIC range estimator. Moreover, we analyze the Cramer-Rao Lower Bound (CRLB) on unbiased range estimation using the frequency-hopping Bluetooth channel model and derive a new insightful CRLB expression for a two-path channel model. Finally, we evaluate the Root-Mean-Square Error and Empirical Cumulative Distribution Function performance of our investigated range estimators both on simulated and real-world Bluetooth data that we collected in line-of-sight (LOS) and non-line-of-sight (NLOS) multipath scenarios. Our proposed enhancements on the range estimators improved the ranging accuracy by 58% for our collected Bluetooth data.

We consider a power constrained downlink communication scenario where energy efficiency, reliability, and latency take precedence over rate, as in some Internet of Things (IoT) applications. To reduce its receiver power consumption and complexity, we assume that the IoT device has a single RF chain and investigate the finite-resolution analog-to-digital converter (ADC) operation with differential Phase Shift Keying (PSK) modulation. A lower ADC resolution leads to an exponential decrease in power consumption, while adopting differential PSK enables the use of a low-cost detector with no channel state information (CSI) or carrier phase recovery circuitry. Our main goal in this article is to compare, both analytically and numerically using representative IoT system design parameters, the receiver energy efficiency of the proposed differential PSK system with a coherent PSK system that uses estimated CSI under reliability and latency constraints in Rayleigh slow-fading and ADC quantization distortion conditions. To mitigate the ADC finite-resolution effects without requiring CSI at the receiver or transmitter, we propose and analyze a PSK differentially-modulated Alamouti space-time block transmission scheme with only two RF chains at the transmitter while still restricting the IoT device to have a single RF chain. Our results demonstrate that in the considered low-rate, and outage and latency-constrained scenario with stringent power consumption requirements, differentially-modulated Alamouti transmissions to a single RF-chain IoT device with a low-resolution ADC is an attractive choice in terms of receiver energy efficiency, reliability, and transmission latency.

The Proliferation of connected and embedded devices has played a critical role in the development of next generation Internet of Things (IoT), and consequently, the diffusion of low power networks. The sustainability of such networks entails energy aware devices that are capable of self-sustainable operations by harvesting and recycling energy from various sources. Accordingly, integration of wireless-powered networks (WPN) with ambient backscatter communications (AmBC), in which a backscatter transmitter conveys data to a destination by backscattering ambient signals, has recently emerged as a promising key technology for low power wireless networks. However, these technologies are not optimized and/or designed for large-scale networks and a massive number of IoT devices.

Different from conventional wireless sensor networks (WSNs), ultra-reliable and low-latency WSNs (uRLLWSNs), being an important application of 5G networks, must meet more stringent performance requirements. In this paper, we propose a novel algorithm to improve uRLLWSNs’ performance by applying machine learning techniques and genetic algorithms. Using the $K$ -means clustering algorithm to construct a 2-tier network topology, the proposed algorithm designs the fetal dataset, denoted by the population, and develops a clustering method of energy conversion to prevent overloaded cluster heads. A multi-objective optimization model is formulated to simultaneously satisfy multiple optimization objectives including the longest network lifetime and the highest network connectivity and reliability. Under this model, the principal component analysis algorithm is adopted to eliminate the various optimization objectives’ dependencies and rank their importance levels. Considering the NP-hardness of wireless network scheduling, the genetic algorithm is used to identify the optimal chromosome for designing a near-optimal clustering network topology. Moreover, we prove the convergence of the proposed algorithm both locally and globally. Simulation results are presented to demonstrate the viability of the proposed algorithm compared to state-of-the-art algorithms at an acceptable computational complexity.

We investigate the physical-layer security of an uplink wireless time-division multiple-access channel with energy-harvesting source nodes. We consider a set of source nodes equipped with rechargeable batteries and information buffers communicating confidentially with a base station, Bob, in the presence of a passive eavesdropper, Eve. An energy-harvesting rechargeable-battery cooperative jammer is assumed to assist the source nodes to confidentially send their information messages. We propose a two-level optimization formulation to improve the system's security performance. At the first optimization level, we propose a jamming scheme under energy constraints at different nodes to reduce the secrecy outage probabilities without relying on the eavesdropper's instantaneous channel state information. At the second optimization level, we optimize the number of energy packets used at the source nodes and the cooperative jammer as well as the time-slot allocation probabilities to maximize the secure throughput under the network's queues stability constraints and an application-specific secure throughput for each legitimate source node. The numerical results show the significant performance gains of our proposed optimization relative to two important benchmarks. We verify our theoretical findings through simulations and quantify the impact of key system design parameters on the security performance.

We investigate the physical-layer security of uplink single-carrier frequency-division multiple-access (SC-FDMA) systems. Multiple users, Alices, send confidential messages to a common legitimate base-station, Bob, in the presence of an eavesdropper, Eve. To secure the legitimate transmissions, each user superimposes an artificial noise (AN) signal on the time-domain SC-FDMA data symbol. We reduce the computational and storage requirements at Bob's receiver by assuming simple per-sub-channel detectors. We assume that Eve has global channel knowledge of all links in addition to high computational capabilities, where she adopts high-complexity detectors such as single-user maximum likelihood (ML), multi-user minimum-mean-square-error, and multi-user ML. We analyze the correlation properties of the time-domain AN signal and illustrate how Eve can exploit them to reduce the AN effects. We prove that the number of useful AN streams that can degrade Eve's signal-to-noise ratio is dependent on the channel memories of Alices-Bob and Alices-Eve links. Furthermore, we enhance the system security for the case of partial Alices-Bob channel knowledge at Eve, where Eve only knows the precoding matrices of the data and AN signals instead of knowing the entire Alices-Bob channel matrices, and propose a hybrid security scheme that integrates temporal AN with channel-based secret key extraction.

Cooperative communication has attracted significant attention in the last decade due to its ability to increase the spatial diversity order with only single-antenna nodes. However, most of the techniques in the literature are not suitable for large cooperative networks such as device-to-device and wireless sensor networks that are composed of a massive number of active devices, which significantly increases the relay selection complexity. Therefore, to solve this problem and enhance the spatial and frequency diversity orders of large amplify and forward cooperative communication networks, in this paper, we develop three multiple relay selection and distributed beamforming techniques that exploit sparse signal recovery theory to process the subcarriers using the low complexity orthogonal matching pursuit algorithm (OMP). In particular, by separating all the subcarriers or some subcarrier groups from each other and by optimizing the selection and beamforming vector(s) using OMP algorithm, a higher level of frequency diversity can be achieved. This increased diversity order allows the proposed techniques to outperform existing techniques in terms of bit error rate at a lower computation complexity. A detailed performance-complexity tradeoff, as well as Monte Carlo simulations, are presented to quantify the performance and efficiency of the proposed techniques.

Power line communication (PLC) is a growing technology which utilizes the existing pre-installed power delivery infrastructure for data transmission. While it is true that the history of PLC technology goes back to the beginning of the last century, when the first data transmission over power lines took place for low data rate control and monitoring purposes, PLC has recently regained a considerable amount of research attention due to the dawn of the internet and the increasing need for fast connectivity. PLC is also expected to serve as a reliable communication medium for many emerging applications of Internet of Things (IoT) and Smart Grids (SGs).

Correlation-based techniques used for frame synchronization can suffer significant performance degradation over multi-path frequency-selective channels. In this paper, we propose a joint frame synchronization and channel estimation (JFSCE) framework as a remedy to this problem. This framework, however, increases the size of the resulting combined channel vector which should capture both the channel impulse response vector and the frame boundary offset and, therefore, its estimation becomes more challenging. On the other hand, because the combined channel vector is sparse, sparse channel estimation methods can be applied. We propose several JFSCE methods using popular sparse signal recovery algorithms which exploit the sparsity of the combined channel vector. Subsequently, the sparse channel vector estimate is used to design a sparse equalizer. Our simulation results and experimental measurements using software defined radios show that in some scenarios our proposed method improves the overall system performance significantly, in terms of the mean square error between the transmitted and the equalized symbols compared to the conventional method.

The cyclostationary noise in low-voltage narrowband powerline communications (NB-PLC) severely degrades the communication reliability. In this paper, we adopt single-input multi-output (SIMO) transmission to enhance the reliability of NB-PLC. Considering the SIMO receiver structure, we exploit the NB-PLC noise cyclostationarity and the high spatial correlations across multiple receive phases to design practical and efficient noise mitigation techniques. In particular, we propose two time-domain frequency-shift (FRESH) filtering-based cyclostationary signal recovery techniques with different performance and complexity levels. The proposed time-domain-based FRESH filtering techniques minimize the meansquared error in estimating the orthogonal frequency division multiplexing (OFDM) information signal in the time-domain. The FRESH filtering exploits the cyclic auto-correlation of both the NB-PLC noise and the OFDM information signal in addition to their cyclic cross-correlation across the receive phases. Moreover, we propose a frequency-domain-based cyclostationary noise mitigation technique that minimizes the meansquared error in estimating the OFDM information signal in the frequency-domain. The proposed frequencydomain-based technique exploits the cyclostationarity of the noise to estimate its power spectral density as well as the cross-correlation, per frequency subchannel, over multiple stationary noise temporal regions. Our proposed SIMO NB-PLC noise mitigation techniques are shown via simulation results conducted using noise field measurements to achieve considerable performance gains over single-input single-output techniques. In addition, we show that our proposed techniques achieve considerable performance gains over the conventional SIMO maximal-ratio-combiner designed assuming stationary noise.

The cyclic prefix (CP) is appended in orthogonal frequency division multiplexing (OFDM) signals to combat inter-symbol interference (ISI) and inter-carrier interference (ICI) induced by the communication channel, which limits its spectral efficiency. Therefore, inserting an insufficient CP and equalizing the resulting ICI and ISI is a method that has been circulating the literature for a while, aiming at increasing the efficiency of OFDM systems. In this paper, we propose a reduced-complexity sparse linear equalizer and a decision-feedback equalizer for OFDM signals with insufficient CP. A performance-complexity trade-off is highlighted, where we show that it is possible to equalize the received signal with a reduced complexity equalizer while having a limited performance loss. Our proposed equalizer designs are not only less complex to realize, but are shown to provide a higher data rate. The proposed equalizers are further evaluated in terms of the worst-case coherence, a metric determining the effectiveness of our used approach. Numerical results show that we can significantly and reliably reduce the order of the design complexity while performing very close to the conventional complex optimal equalizers.

We propose a new sparsity-aware framework to model and mitigate the joint effects of narrow-band interference (NBI) and impulsive noise (IN) in hybrid powerline and unlicensed wireless communication systems. The proposed mitigation techniques, based on the principles of compressive sensing, exploit the inherent (non-contiguous or contiguous) sparse structures of NBI and IN in the frequency and time domains, respectively. For the non-contiguous NBI and IN, we develop a multi-level orthogonal matching pursuit recovery algorithm that exploits prior knowledge about the sparsity level at each receive antenna and powerline to further reduce computational complexity without performance loss. In addition, for the non-contiguous asynchronous NBI scenario, we investigate the application of time-domain windowing to enhance the NBI's sparsity and, hence, improve the NBI mitigation performance. For the contiguous NBI and IN scenario, we estimate the NBI and IN signals by modeling their burstiness as block-sparse vectors with and without prior knowledge of the bursts' boundaries. Moreover, we show how to exploit the spatial correlations of the NBI and IN across the receive antennas and powerlines to convert a non-contiguous NBI and IN problem to a block-sparse estimation problem with much lower complexity. Furthermore, we investigate a Bayesian linear minimum mean square error-based approach for estimating both non-contiguous and contiguous NBI and IN based on their second-order statistics to further improve the estimation performance. Finally, our numerical results illustrate the superiority of the joint processing of our proposed NBI and IN sparsity-based mitigation techniques compared to separate processing of the wireless and powerline received signals.

Non-orthogonal multiple access (NOMA) has been recently proposed as a viable technology that can potentially improve the spectral efficiency of fifth generation wireless networks and beyond. However, in practical communication scenarios, transceiver architectures inevitably suffer from radiofrequency (RF) front-end related impairments that can lead to degradation of the overall system performance with in-phase/quadrature-phase imbalance (IQI) constituting a major impairment in direct-conversion transceivers. In this paper, we quantify the effects of IQI on the performance of NOMA-based single-carrier (SC) and multi-carrier (MC) systems under multipath fading conditions. This is realized by first deriving analytic expressions for the signal-to-interference-plus-noise ratio and the outage probability of both SC and MC NOMA systems subject to IQI at the transmitter and/or receiver (RX) side. Furthermore, we derive asymptotic diversity orders for all considered impairment scenarios. Capitalizing on these results, we demonstrate that the effects of IQI differ considerably among NOMA users and depend on the underlying systems' parameters. For example, for a target data rate and power allocation ratio satisfying a given condition, IQI does not affect the asymptotic diversity of SC NOMA systems, whereas the asymptotic diversity of MC NOMA systems, suffering from RX IQI, is always zero. Moreover, it is shown that for both SC and MC NOMA systems, the first sorted user appears more robust to IQI, which indicates that higher order users are more sensitive to the considered impairment.

Non-orthogonal multiple access (NOMA) has been recently recognized as a promising multiple access technique to significantly improve the spectral efficiency of mobile communication networks. The key idea of NOMA is to encourage spectrum sharing, which is similar to the concept of cognitive radio networks. Take power domain NOMA as an example, where the power domain is used for multiple access. This is fundamentally different from the previous generations of mobile networks which have been relying on the time/frequency/code domain. It is worth mentioning that NOMA has been demonstrated to be compatible with many 5G techniques. Thus, the purpose of this Special Section in IEEE Access was to provide a forum for the latest research and innovations in NOMA technologies as well as their applications, and to bridge the gap between theory and practice in the design of 5G multiple access. We received a total of 29 submissions, and after a rigorous review process, 13 articles have been selected for publication, which are briefly discussed next.

We develop an analytical framework to characterize the effect of impulsive noise on the performance of relay-assisted simultaneous wireless information and power transfer (SWIPT) systems. We derive novel closed-form expressions for the pairwise error probability considering two variants based on the availability of channel state information (CSI), namely, blind relaying and CSI-assisted relaying. We further consider two energy harvesting (EH) techniques, i.e., instantaneous EH (IEH) and average EH (AEH). Capitalizing on the derived analytical results, we present a detailed numerical investigation of the diversity order for the underlying scenarios under the impulsive noise assumption. For the case when two relays and the availability of a direct link, it is demonstrated that the considered SWIPT system with blind AEH-relaying is able to achieve an asymptotic diversity order of less than 3, which is equal to the diversity order achieved by CSI-assisted IEH-relaying. This result suggests that, by employing the blind AEH relaying, the power consumption of the network can be reduced, due to eliminating the need of CSI estimation. This can be achieved without any performance loss. Our results further show that placing the relays close to the source can significantly mitigate the detrimental effects of impulsive noise. Extensive Monte Carlo simulation results are presented to validate the accuracy of the proposed analytical framework.

We derive closed-form expressions for the achievable rates of a buffer-aided full-duplex (FD) multiple-input multiple-output Gaussian relay channel. The FD relay still suffers from residual self-interference (RSI) after the application of self-interference mitigation techniques. We investigate both cases of a slow-RSI channel where the RSI is fixed over the entire codeword, and a fast-RSI channel where the RSI changes from one symbol duration to another within the codeword. We show that the RSI can be completely eliminated in the slow-RSI case when the FD relay is equipped with a buffer while the fast RSI cannot be eliminated. For the fixed-rate data transmission scenario, we derive the optimal transmission strategy that should be adopted by the source node and relay node to maximize the system throughput. We verify our analytical findings through simulations.

We propose a new scheme to secure a wireless-powered untrusted cooperative-communication network, where a legitimate source node (Alice) transmits her information messages to a legitimate destination node (Bob) through the multiple amplify-and-forward untrusted relays. The relay nodes are assumed to be honest but curious nodes; hence, they are trusted at the service level but are untrusted at the information level. To reduce the energy consumption of the network, only one relay node is selected in each time slot to forward Alice's information signal. We assume a power-splitting-based energy-harvesting scheme, where each relay node splits its received signal into information and energy streams. Since the relay nodes are assumed to be untrusted at the information level, they attempt to decode the information intended to Bob while harvesting energy at the same time. When the relaying mode is selected, the scheme is realized over two non-overlapping time phases. To prevent any information leakage to the untrusted relay nodes, Bob and a cooperative jammer (John) inject jamming (artificial noise) signals during the first phase. During the second phase, the untrusted relay nodes that will not be forwarding the information signal must harvest energy to accumulate more energy to help Alice in future time slots. Moreover, the cooperative jammer will jam the untrusted relays to further power their batteries and prevent them from decoding the information-forwarding relay signal in case they decided to cheat and decode it. We model the battery state transitions at each relay as a finite-state Markov chain and analyze it. Our numerical results show the security gains of our proposed scheme relative to two benchmark schemes.

We investigate the impact of the wireless network's physical layer (PHY) security and reliability on demand-side management operation in the smart grid. We assume that consumers communicate their energy demands with the utility, and we investigate the tradeoff between the wireless network's PHY security and reliability. We consider passive and active attacks on the smart grid, and show their impacts on both the wireless communications system's reliability and security. To improve the wireless communications system's security, we propose an artificial-noise (AN)-aided scheme for orthogonal-division multiplexing-based wireless systems. We show that increasing the PHY security of the legitimate transmissions decreases the communication reliability between the smart meters and the meter data-management system since to secure the transmissions, a portion of the transmit powers will be assigned to AN signal transmissions. To further secure the smart meters' transmissions and enhance their reliability, we propose a new encoding scheme where the data are encoded over the available smart meters' communication (i.e., non-idle) times in a given hour.