The concept of physical layer security builds on the pivotal idea of turning the channel’s imperfections, such as noise and fading, into a source of security. This is established through appropriately designed coding techniques and signal processing strategies. In this vein, it has been shown that fading channels can enhance the transmission of confidential information and that a secure communication can be achieved even when the channel to the eavesdropper is better than the main channel. However, to fully benefit from what fading has to offer, the knowledge of the channel state information at the transmitter (CSIT) is of primordial importance. In practical wireless communication systems, CSIT is usually obtained, prior to data transmission, through CSI feedback sent by the receivers. The channel links over which this feedback information is sent can be either noisy, rate-limited, or delayed, leading to CSIT uncertainty. In this paper, we present a comprehensive review of recent and ongoing research works on physical layer security with CSIT uncertainty. We focus on both information theoretic and signal processing approaches to the topic when the uncertainty concerns the channel to the wiretapper or the channel to the legitimate receiver. Moreover, we present a classification of the research works based on the considered channel uncertainty. Mainly, we distinguish between the cases when the uncertainty comes from an estimation error of the CSIT, from a CSI feedback link with limited capacity, or from an outdated CSI.

An Overview of Physical Layer Security in Wireless Communication Systems With CSIT Uncertainty

In this paper, we propose an opportunistic downlink interference alignment (ODIA) for interference-limited cellular downlink, which intelligently combines user scheduling and downlink IA techniques. The proposed ODIA not only efficiently reduces the effect of inter-cell interference from other-cell base stations (BSs) but also eliminates intra-cell interference among spatial streams in the same cell. We show that the minimum number of users required to achieve a target degrees-of-freedom can be fundamentally reduced, i.e., the fundamental user scaling law can be improved by using the ODIA, compared with the existing downlink IA schemes. In addition, we adopt a limited feedback strategy in the ODIA framework, and then analyze the number of feedback bits required for the system with limited feedback to achieve the same user scaling law of the ODIA as the system with perfect channel state information. We also modify the original ODIA in order to further improve the sum-rate, which achieves the optimal multiuser diversity gain, i.e., $\log \log N$ , per spatial stream even in the presence of downlink inter-cell interference, where $N$ denotes the number of users in a cell. Simulation results show that the ODIA significantly outperforms existing interference management techniques in terms of sum rate in realistic cellular environments. Note that the ODIA operates in a non-collaborative and decoupled manner, i.e., it requires no information exchange among BSs and no iterative beamformer optimization between BSs and users, thus leading to an easier implementation.

Opportunistic Downlink Interference Alignment for Multi-Cell MIMO Networks

In this paper, we study hop-by-hop beamforming and combining in a decode-and-forward (DF) multiple-input multiple-output (MIMO) relay network, with two antennas at each node, over Nakagami-m fading channels. We derive a closed-form expression of the approximate average bit error rate of the considered cooperative scheme with M-ary phase shift keying constellation. In addition, analytical outage probability and diversity order of the considered DF MIMO relay system are also obtained.

Performance Analysis of Hop-by-Hop Beamforming and Combining in DF MIMO Relay System over Nakagami-m Fading Channels

In this paper, we propose an opportunistic downlink interference alignment (ODIA) for interference-limited cellular downlink, which intelligently combines user scheduling and downlink IA techniques. The proposed ODIA not only efficiently reduces the effect of inter-cell interference from other-cell base stations (BSs) but also eliminates intra-cell interference among spatial streams in the same cell. We show that the minimum number of users required to achieve a target degrees-of-freedom (DoF) can be fundamentally reduced, i.e., the fundamental user scaling law can be improved by using the ODIA, compared with the existing downlink IA schemes. In addition, we adopt a limited feedback strategy in the ODIA framework, and then analyze the number of feedback bits required for the system with limited feedback to achieve the same user scaling law of the ODIA as the system with perfect CSI. We also modify the original ODIA in order to further improve sum-rate, which achieves the optimal multiuser diversity gain, i.e., $\log\log N$, per spatial stream even in the presence of downlink inter-cell interference, where $N$ denotes the number of users in a cell. Simulation results show that the ODIA significantly outperforms existing interference management techniques in terms of sum-rate in realistic cellular environments. Note that the ODIA operates in a non-collaborative and decoupled manner, i.e., it requires no information exchange among BSs and no iterative beamformer optimization between BSs and users, thus leading to an easier implementation.

Conventional optimization-driven secure relay selection relies on maximization algorithm and accurate channel state information (CSI) of both legitimate and eavesdropper channels. Particularly, estimating and collecting accurate eavesdropper CSI is a difficult task. In this paper, we exploit the benefits of machine learning in solving secure relay selection problem from a data-driven perspective. We convert secure relay selection to a multiclass-classification problem and solve it by a decision-tree-based scheme, which is composed of three phases - preparing training data, building decision tree and predicting relay selection. To meet decision tree’s requirement that input features must take discrete values, a feature extraction method is proposed to generate discrete input by quantizing the accurate CSI of legitimate and eavesdropper channels. By this means, the decision-tree-based relay selection only requires quantized CSI feedback which takes substantially fewer bits in predicting phase. For the purpose of optimizing quantization parameters and enhancing decision tree prediction, we further derive three splitting criteria, i.e. information gain, information gain ratio and Gini index. Simulation results show that if the quantization parameters are set properly, the proposed decision-tree-based scheme can achieve satisfactory performance in terms of average secrecy rate while reducing computational complexity and feedback amount.

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Data-Driven Relay Selection for Physical-Layer Security: A Decision Tree Approach

This paper investigates a multiuser MIMO relay downlink system with imperfect channel estimation and limited feedback. We analyze the achievable rate loss due to channel state information (CSI) mismatch arising from both channel estimation and quantization feedback. We first derive an upper bound to characterize the effect of imperfect CSI, and then present a limited feedback strategy for both the two-hop channels in the relay system. This newly presented feedback strategy reveals the relationship among the CSI quantization levels, the transmit power, and the pilots for channel estimation. An optimized pilot design at the base station and the relay is also presented by applying the derived bounds for the two-hop system.

/pdf/achievable-rate-analysis-and-feedback-design-for-multiuser-qcevcz0r8d.pdf

Achievable Rate Analysis and Feedback Design for Multiuser MIMO Relay with Imperfect CSI

This paper investigates a multiuser MIMO relay downlink system with imperfect channel estimation and limited feedback. Compared with the system using perfect channel state information (CSI), we analyze the achievable rate loss due to CSI mismatch arising from both channel estimation and CSI quantization feedback. An upper bound to the rate loss is firstly derived to characterize the effect of imperfect CSI. Given the rate loss bound, we then present a limited feedback strategy for both two-hop channels in the relay system to guarantee a bounded rate loss for growing SNRs. This newly presented strategy reveals the relationship between CSI quantization level and other system parameters, including the transmit power and the pilots for channel estimation, on the entire two-hop system. Finally, computer simulations are carried out for verifying the correctness of our derived results.

/pdf/achievable-rate-analysis-and-feedback-design-for-multiuser-2o2in14uvs.pdf

Achievable rate analysis and feedback design for multiuser relay with imperfect CSI

Unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) systems offer significant advantages in coverage and deployment flexibility, but suffer from endurance limitations due to the limited onboard energy. This article proposes to improve the energy efficiency of UAV-enabled WPT systems with multiple ground sensors by utilizing reconfigurable intelligent surface (RIS). Specifically, the total energy consumption of the UAV is minimized, while meeting the energy requirement of each sensor. First, we consider a fly-hover-broadcast (FHB) protocol, in which the UAV radiates radio-frequency (RF) signals only at several hovering locations. The energy minimization problem is formulated to jointly optimize the UAV’s trajectory, hovering time, and the RIS’s reflection coefficients. To solve this complex nonconvex problem, we propose an efficient algorithm. Specifically, the successive convex approximation (SCA) framework is adopted to jointly optimize the UAV’s trajectory and hovering time, in which a minorization–maximization (MM) algorithm that maximizes the minimum charged energy of all sensors is provided to update the reflection coefficients. Then, we investigate the general scenario in which the RF signals are radiated during the flight, aiming to minimize the total energy consumption of the UAV by jointly optimizing the UAV’s trajectory, flight time, and the RIS’s reflection coefficients. By applying the path discretization (PD) protocol, the optimization problem is formulated with a finite number of variables. A high-quality solution for this more challenging problem is obtained. Finally, our simulation results demonstrate the effectiveness of the proposed algorithm and the benefits of RIS in energy saving.

Energy Minimization in RIS-Assisted UAV-Enabled Wireless Power Transfer Systems

In this article, we investigate a reconfigurable intelligent surface (RIS)-aided multiuser full-duplex secure communication system with hardware impairments at transceivers and RIS, where multiple eavesdroppers overhear the two-way transmitted signals simultaneously, and a RIS is applied to enhance the secrecy performance. Aiming at maximizing the sum secrecy rate (SSR), a joint optimization problem of the transmit beamforming at the base station (BS) and the reflecting beamforming at the RIS is formulated under the transmit power constraint of the BS and the unit modulus constraint of the phase shifters. As the environment is time varying and the system is high dimensional, this nonconvex optimization problem is mathematically intractable. A deep reinforcement learning (DRL)-based algorithm is explored to obtain the satisfactory solution by repeatedly interacting with and learning from the dynamic environment. Extensive simulation results illustrate that the DRL-based secure beamforming algorithm is proved to be significantly effective in improving the SSR. It is also found that the performance of the DRL-based method can be greatly improved and the convergence speed of the neural network can be accelerated with appropriate neural network parameters.

Deep Reinforcement Learning for RIS-Aided Multiuser Full-Duplex Secure Communications With Hardware Impairments

In this paper, we investigate a multiuser MIMO downlink with imperfect channel state information (CSI) from the physical-layer security provision. In a classical transmitter (Alice)–legitimate receiver (Bob)–eavesdropper (Eve) model using artificial noise to disturb Eve’s reception, we consider a general scenario where all nodes are equipped with multiple antennas, and Bob’s CSI is acquired by pilot-assisted channel estimation. For designing the secure transmit beamforming, we utilize random matrix quantization (RMQ) to quantize Bob’s channel estimate, and then Bob feeds it back to Alice. Due to the effects of imperfect CSI at Alice, the secrecy performance is upper bounded in the high signal-to-noise ratio (SNR) regions. In order to avoid the interference-limited phenomenon, we present a scaled strategy for the secrecy system utilizing derived upper bound on the secrecy rate loss. Moreover, it is shown that our derived results can be easily extended to a special case where both legitimate and eavesdropping receivers equip a single antenna each, where random vector quantization (RVQ) is utilized instead. By employing our proposed feedback strategy, the secrecy rate increases with transmit power, and the certain secrecy requirement can be guaranteed.

Zhangjie Peng

Papers

Achievable Rate Analysis and Feedback Design for Multiuser MIMO Relay with Imperfect CSI

Achievable rate analysis and feedback design for multiuser relay with imperfect CSI

Energy Minimization in RIS-Assisted UAV-Enabled Wireless Power Transfer Systems

Deep Reinforcement Learning for RIS-Aided Multiuser Full-Duplex Secure Communications With Hardware Impairments

On Performance and Feedback Strategy of Secure Multiuser Communications With MMSE Channel Estimate