Physical-Layer Security for Intelligent-Reflecting-Surface-Aided Wireless-Powered Communication Systems

In view of the scarce spectrum resources and inconvenient deployment in wireless communications, this work focuses on an unmanned aerial vehicle (UAV) enabled non-orthogonal multiple access (NOMA) system where a UAV deployed as a base station serves two users by NOMA. A joint power allocation and aerial jamming (PAAJ) scheme is proposed to achieve reliable and secure communications for the system in the presence of a malicious eavesdropper. To be specific, dynamic power allocation is adopted to ensure the reliability of the system and another friendly UAV jammer is introduced to improve the security of the system. The closed-form expressions of connection outage probability (COP), secrecy outage probability (SOP), and effective secrecy throughput (EST) are derived to evaluate the performance of the security-required user in the practical condition of probabilistic line-of-sight (LoS) and non-line-of-sight (NLoS) air-to-ground (A2G) channels. Moreover, the asymptotic EST is analyzed to obtain further insights. Numerical results show the validity of theoretical derivations and the superiority of the proposed PAAJ scheme over benchmark schemes. The EST of the UAV-enabled NOMA communication system outperforms the conventional terrestrial NOMA system at the low transmitting power from the UAV base station. There exists an optimal height of the UAV base station to maximize the EST, which provides a useful framework for designing UAV-enabled communication systems with heterogeneous service requirements.

This letter studies the joint active and passive beamforming in intelligent reflecting surface (IRS)-enhanced wireless powered mobile edge computing (MEC) networks, in which a hybrid access point (HAP) is deployed to charge multiple wireless devices (WDs) and assist in computation of the offloaded tasks from the WDs. An IRS is deployed to enhance the downlink wireless power transfer (WPT) and the uplink tasks offloading (TO). To maximize the systems’ sum computation rate, an optimization problem is formulated by jointly optimizing the time assignment of WPT and TO, the active beamforming at the HAP, the transmit power and computing frequency of the WDs, and the passive beamforming at the IRS. To tackle the non-convex problem, we present an efficient algorithm by utilizing the alternating optimization (AO) and semidefinite relaxation (SDR) techniques. Simulation results show that by deploying IRS in the system, a significant computation rate gain can be achieved. Especially, when the IRS is closer to the WDs or the HAP, the much higher computation rate can be reached.

In this paper, we studied the secure transmission of a hybrid automatic repeat request with chase combining (HARQ-CC) system, under the existence of multiple eavesdroppers and limited latency. First, we analyzed some critical performance metrics, including connection outage probability (COP), secrecy outage probability (SOP) and effective secrecy throughput (EST). Then, to maximize the EST, three optimization problems of rate adaption were discussed: (i) optimizing the code rate with a given secrecy redundancy rate by a parameterized closed-form solution; (ii) optimizing the secrecy redundancy rate with a given code rate by a fixed-point method; (iii) optimizing both code rate and secrecy redundancy rate by an iterative optimization algorithm. We also considered COP and SOP constraints among the problems while corresponding solutions were deduced. Finally, numerical and simulated results verified our conclusions that the approximated SOP matches well with Monte–Carlo simulation for a strict reliable constraint, and that the optimized transmitting rate enhances EST efficiently with multiple eavesdroppers and retransmissions. Moreover, the influence of the number of eavesdroppers on secrecy performance was analyzed. Briefly, secrecy performance inevitably deteriorates with increasing number of eavesdroppers due to raised information leakage.

Secure communication of intelligent reflecting surface-aided NOMA in massive MIMO networks

In this paper, we consider deploying an intelligent reflecting surface (IRS) to enhance the downlink (DL) energy transfer and uplink (UL) information transmission efficiency for secure multiple-input multiple-output (MIMO) wireless powered communication networks (WPCNs). We aim to maximize the secrecy throughput of all users by jointly optimizing the DL/UL time allocation, the energy transmit covariance matrix of hybrid access point (AP), the information transmit beamforming matrix of users and the phase shifts of IRS in DL/UL, subject to constraints of energy/information transmit power at the hybrid AP/users and that of unit-modulus IRS phase shifts for DL/UL. To tackle the non-convex problem, we first transform the original problem into an equivalent form based on the mean-square error (MSE) method given time allocation, and then apply the alternating algorithm to update the optimization variables iteratively. Specifically, the energy covariance matrix and the information beamforming matrix are obtained based on the dual subgradient method. For the IRS phase shifts, we investigate two IRS beamforming reflection setups, namely different DL/UL IRS beamforming and identical DL/UL IRS beamforming. For the former case, the second-order cone programming technique and the Majorization-Minimization algorithm/element by element iterative algorithm are applied to obtain the DL and UL IRS phase shifts, respectively. For the latter case, the IRS phase shifts are obtained by the successive convex approximation technique. To further reduce the computational complexity of the single-user system, we derive the closed-form solutions of IRS phase shifts in each iteration for the two different reflection setups. Simulation results show that all the proposed algorithms can greatly improve the secrecy throughput compared to the conventional system without IRS.

In this paper, we investigate the fundamental multiple access (MA) scheme in an active intelligent reflecting surface (IRS) aided energy-constrained Internet-of-Things (IoT) system, where an active IRS is deployed to assist the uplink transmission from multiple IoT devices to an access point (AP). Our goal is to maximize the sum throughput by optimizing the IRS beamforming vectors across time and resource allocation. To this end, we first study two typical active IRS aided MA schemes, namely time division multiple access (TDMA) and non-orthogonal multiple access (NOMA), by analytically comparing their achievable sum throughput and proposing corresponding algorithms. Interestingly, we prove that given only one available IRS beamforming vector, the NOMA-based scheme generally achieves a larger throughput than the TDMA-based scheme, whereas the latter can potentially outperform the former if multiple IRS beamforming vectors are available to harness the favorable time selectivity of the IRS. To strike a flexible balance between the system performance and the associated signaling overhead incurred by more IRS beamforming vectors, we then propose a general hybrid TDMA-NOMA scheme with device grouping, where the devices in the same group transmit simultaneously via NOMA while devices in different groups occupy orthogonal time slots. By controlling the number of groups, the hybrid TDMA-NOMA scheme is applicable for any given number of IRS beamforming vectors available. Despite of the non-convexity of the considered optimization problem, we propose an efficient algorithm based on alternating optimization, where each subproblem is solved optimally. Simulation results illustrate the practical superiorities of the active IRS over the passive IRS in terms of the coverage extension and supporting multiple energy-limited devices, and demonstrate the effectiveness of our proposed hybrid MA scheme for flexibly balancing the performance-cost tradeoff.

This work considers covert communications with the aid of an intelligent reflecting surface (IRS). We jointly design the transmit power and IRS's reflection phase shifts and amplitudes to enhance covert communication performance. To this end, we first derive the total detection error rate at a warden. We then determine the IRS's optimal phase shifts and develop an effective one-dimensional search method to identify the IRS's optimal amplitudes. We show that the IRS can significantly improve the achievable covertness. In addition, optimizing the IRS's amplitudes on top of its phase shifts significantly outperforms fixing them as unit in covert communications.

This paper investigates the secure transmission in hybrid automatic repeat request (HARQ)-assisted non-orthogonal multiple access (NOMA) networks, where a security-required (SR) user is paired with an opportunity-served (OS) user to perform NOMA. Different from the previous works where NOMA and HARQ are separately considered for secure transmissions, in this paper, both NOMA and HARQ are utilized to enhance the security of the SR user. We derive the closed-form expressions for connection outage probability (COP), secrecy outage probability (SOP), reliable and secure transmission probability (RSP) and effective secrecy throughput (EST) of the SR user in both maximum ratio combining (MRC) and selection combining (SC) schemes. The security-reliability trade-off (SRT) results of the SR user in both MRC and SC schemes are also provided. Analysis and simulation results show that HARQ improves secrecy performance in MRC and SC schemes in terms of RSP, especially in low transmit power region. Besides, since every retransmission is utilized in the MRC scheme, it strictly outperforms the SC scheme in terms of the SRT metric. However, the SC scheme has the potential to achieve higher EST than the MRC scheme by optimizing the transmit power or the power allocation factor. Significantly, the quality of service (QoS) of the OS user can be guaranteed by pairing an SR user who is far away from the transmitter and the secrecy performance of the SR user can be enhanced by pairing an OS user.

This paper tackles the complex problem of maximizing the ergodic sum-rate in intelligent reflecting surface (IRS)-assisted full-duplex (FD) cluster non-orthogonal multiple access wireless-powered communication networks. The primary challenge lies in the joint optimization of hybrid access point (HAP) beamforming vectors, IRS phase shifts, and uplink power allocation for sensors, while considering practical constraints— including channel uncertainties, a nonlinear energy harvesting model, self-interference at both the HAP and sensors due to FD transmission, the rank-one constraint of the IRS, and the limited buffer capacity of the sensors. These factors result in an NP-hard optimization problem that conventional approaches cannot solve efficiently. To address this, we propose utilizing a deep reinforcement learning framework based on the twin delayed deep deterministic policy gradient (TD3) algorithm. Our results show that TD3 improves the system’s average sum-rate by up to 12% and 48% over the deep deterministic policy gradient (DDPG) and random allocation benchmarks, respectively. The results also show that both TD3 and DDPG have similar computational complexity. Thus, TD3 achieves a favorable balance between performance and computational complexity, making it a scalable and practical solution for next-generation networks where spectrum efficiency and throughput maximization are critical in hyper-dense environments.

Simultaneous wireless information and power transmission (SWIPT)-enabled vehicle-to-vehicle (V2V) systems use radio frequency (RF) signals for both information transmission and power. It reduces energy consumption, increases spectrum efficiency, and optimizes resource allocation to improve overall system performance. In this article, we investigate the physical-layer security (PLS) for a SWIPT enabled V2V cooperative communication system in which a source vehicle (S) transmits information to a destination vehicle (D) through an intermediate vehicle treated as relay node (R). During analysis, it is assumed that two independent vehicular nodes in the same road, i.e., eavesdroppers (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$E_{1}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$E_{2}$ </tex-math></inline-formula>) are trying to intercept the information from different legitimate channels. The communication links are assumed to follow the double Nakagami-m (DNm) fading distribution. The study examines the security performance of the networks by calculating the secrecy outage probability (SOP), strictly positive secrecy capacity (SPSC), intercept probability (IP), and connection outage probability (COP). Next, we extend to the analysis of tradeoff between security and reliability as well as tradeoff between secrecy rate and secrecy outage which are termed as security and reliability tradeoff (SRT) and effective secrecy throughput (EST). For a comprehensive analysis, the results are produced for different values of the fading parameter <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(m)$ </tex-math></inline-formula>, power splitting factor <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(\rho)$ </tex-math></inline-formula>, and secrecy thresholds. The results clearly indicate that an increase in m leads to an enhancement in the secrecy performance. Additionally, the results show that secrecy performance improves as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula> decreases. Finally, the Monte-Carlo simulation results are presented to verify our proposed analytical models.

This paper presents a comparative analysis of intelligent reflecting surface (IRS) technology versus conventional amplify-and-forward (AF) and decode-and-forward (DF) relay schemes in wireless communication. By focusing on transmit power, energy efficiency, and the minimum IRS elements required to outperform traditional relaying, we explore IRS’s potential as an energy efficient alternative. Our analysis shows that IRS configurations, especially with optimal phase shifts, achieve substantial power savings and superior energy efficiency over to AF and DF relays. Moreover, IRS requires fewer elements to meet or exceed relaying performance under higher data rate demands, making it an ideal choice for energy conscious, high performance network designs. These results highlight IRS technology as a promising solution for sustainable, next-generation communication networks.

Intelligent reflecting surface (IRS) has recently been emerged as an effective way for improving the performance of wireless networks by reconfiguring the propagation environment through a large number of passive reflecting elements. This game-changing technology is especially important for stepping into the Internet of Everything (IoE) era, where high performance is demanded with very limited available resources. In this paper, we study a backscatter-assisted wireless powered communication network (BS-WPCN), in which a number of energy-constrained users, powered by a power station (PS), transmit information to an access point (AP) via backscatter and active wireless information transfer, with their communication being aided by an IRS. Using a practical energy harvesting (EH) model which is able to capture the characteristics of realistic energy harvesters, we investigate the maximization of total network throughput. Specifically, IRS reflection coefficients, PS transmit and AP receive beamforming vectors, power and time allocation are designed through a two-stage algorithm, assuming minimum mean square error (MMSE) receiver at the AP. The effectiveness of the proposed algorithm is confirmed via extensive numerical simulations. We also show that our proposed scheme is readily applicable to practical IRS-aided networks with discrete phase shift values.

Combining intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) offers a new degree of freedom to improve the coverage performance. However, it is more challenging to secure the air-ground transmission, due to the line-of-sight (LoS) links established by UAV. Since IRS is a promising solution for wireless environment reconfiguration, in this paper, we propose an aerial IRS-assisted secure transmission design in wireless networks. In particular, an access point (AP) equipped with a uniform planar array serves several single-antenna legitimate users in the presence of multiple single-antenna eavesdroppers, whose precise positions are unknown. An IRS is mounted on the UAV to help establish desired virtual LoS links between the AP and legitimate users, while ensuring their security. We aim to maximize the worst-case sum secrecy rate by jointly optimizing the hovering position of UAV, the transmit beamforming of AP and the phase shifts of IRS, subject to the requirement of minimum rate for legitimate users. To tackle this non-convex problem, we first decompose it into three subproblems, which are transformed into convex ones via successive convex approximation. An alternating algorithm is then proposed to solve them iteratively. Simulation results show the effectiveness of the proposed scheme and the security improvement by the joint optimization.

Intelligent Reflecting Surfaces (IRS) is a groundbreaking innovative technique to boost energy and spectral efficiency in upcoming wireless systems, enabling simultaneous wireless information and power transfer (SWIPT). This paper optimizes Energy Harvesting Efficiency (EHE) in a multi-input multi-output (MIMO) SWIPT system, where an access point (AP)/base station (BS) uses an IRS to serve multi energy receivers (ERs) and information receivers (IRs). We formulate an EHE optimization problem, designing active and passive beamforming while satisfying the AP/BS power constraints, IRS unit-modulus constraints and IR rate demands in a linear EH model. The Linear Matrix Inequalities (LMIs) are approximated for efficient solutions. And second-order cone programming (SOCP) is solved via convex optimization (CVX), while the penalty convex-concave procedure (CCP) guarantees convexity to non-convex problems. Next, an alternating optimization (AO)-reliant iterative algorithm derived sub-optimal solutions with slack variables. According to simulation results, for all ERs within a linear EH design/model, the proposed algorithm improves total EHE by 40% to 45% compared to randomize, No-IRS, and decode-and-forward (DF) relay strategies. Ultimately, highlighting IRS’s benefit in improving quality of service (QoS) by tweaking its elements and AP/BS antennas.

This article introduces a novel strategy for wireless communication security utilizing intelligent reflecting surfaces (IRS). The IRS is strategically deployed to mitigate jamming attacks and eavesdropper threats while improving signal reception for legitimate users (LUs) by redirecting jamming signals toward desired communication signals leveraging physical layer security (PLS). By integrating the IRS into the backscatter communication system, we enhance the overall secrecy rate of LU, by dynamically adjusting IRS reflection coefficients and active beamforming at the base station (BS). A design problem is formulated to jointly optimize IRS reflecting beamforming and BS active beamforming, considering time-varying channel conditions and desired secrecy rate requirements. We propose a novel approach based on deep reinforcement learning (DRL) named Deep-PLS. This approach aims to determine an optimal beamforming policy capable of thwarting eavesdroppers in evolving environmental conditions. Extensive simulation studies validate the efficacy of our proposed strategy, demonstrating superior performance compared to traditional IRS approaches, IRS backscattering-based anti-eavesdropping methods, and other benchmark strategies in terms of secrecy performance.