Secrecy Energy Efficiency Research Articles

Joint resource allocation and beamforming design for RIS-aided symbiotic radio networks: A DRL approach

In this paper, we investigate a Reconfigurable Intelligent Surface (RIS)-assisted secure Symbiosis Radio (SR) network to address the information leakage of the primary transmitter (PTx) to potential eavesdroppers. Specifically, the RIS serves as a secondary transmitter in the SR network to ensure the security of the communication between the PTx and the Primary Receiver (PRx), and simultaneously transmits its information to the PTx concurrently by configuring the phase shifts. Considering the presence of multiple eavesdroppers and uncertain channels in practical scenarios, we jointly optimize the active beamforming of PTx and the phase shifts of RIS to maximize the secrecy energy efficiency of RIS-supported SR networks while satisfying the quality of service requirement and the secure communication rate. To solve this complicated non-convex stochastic optimization problem, we propose a secure beamforming method based on Proximal Policy Optimization (PPO), which is an efficient deep reinforcement learning algorithm, to find the optimal beamforming strategy against eavesdroppers. Simulation results show that the proposed PPO-based method is able to achieve fast convergence and realize the secrecy energy efficiency gain by up to 22% when compared to the considered benchmarks.

Optimizing Secrecy Energy Efficiency in RIS-assisted MISO systems using Deep Reinforcement Learning

This article investigates the maximization of secrecy energy efficiency (SEE) in B5G mobile systems where a suite of reconfigurable intelligent surface (RIS) modules is incorporated. Taking into account the location information of legitimate users and eavesdroppers, we formulate the problem as a joint optimization of the phase shifts, physical orientations, and locations of the RIS modules, as well as resource allocation at the base station (BS). The problem is then solved by leveraging a deep reinforcement learning (DRL) approach proposed in this paper. The case study results demonstrate the effectiveness of the proposed scheme in improving the secrecy energy efficiency of communication systems using RIS.

Novel Noise Injection Scheme to Guarantee Zero Secrecy Outage under Imperfect CSI.

The paper proposes a novel artificial noise (AN) injection strategy in multiple-input single-output multiple-antenna-eavesdropper (MISOME) systems under imperfect channel estimation at the legitimate channel to achieve zero secrecy outage probability under any circumstance. The zero secrecy outage is proved to always be achievable regardless of the eavesdropper's number of antennas or location when the pair secrecy and codeword rates are chosen properly. The results show that when there is perfect channel state information, the zero-outage secrecy throughput increases with the transmit power, which is important for secrecy design. Additionally, an analysis of the secrecy throughput and secrecy energy efficiency gives further insight into the effectiveness of the proposed scheme.

Secrecy Energy Efficiency Maximization for Secure Unmanned-Aerial-Vehicle-Assisted Simultaneous Wireless Information and Power Transfer Systems

Unmanned aerial vehicle (UAV)-assisted simultaneous wireless information and power transfer (SWIPT) systems have recently gained significant attraction in internet-of-things (IoT) applications that have limited or no infrastructure. Specifically, the free mobility of UAVs in three-dimensional (3D) space allows us good-quality channel links, thereby enhancing the communication environment and improving performance in terms of achievable rates, latency, and energy efficiency. Meanwhile, IoT devices can extend their battery life by harvesting the energy following the SWIPT protocol, which leads to an increase in the overall system lifespan. In this paper, we propose a secure UAV-assisted SWIPT system designed to optimize the secrecy energy efficiency (SEE) of a ground network, wherein a base station (BS) transmits confidential messages to an energy-constrained device in the presence of a passive eavesdropper. Here, we employ a UAV acting as a helper node to improve the SEE of the system and to aid in the energy harvesting (EH) of the battery-limited ground device following the SWIPT protocol. To this end, we formulate the SEE maximization problem by jointly optimizing the transmit powers of the BS and UAV, the power-splitting ratio for EH operations, and the UAV’s flight path. The solution is obtained via a proposed algorithm that leverages successive convex approximation (SCA) and Dinkelbach’s method. Through simulations, we corroborate the feasibility and effectiveness of the proposed algorithm compared to conventional partial optimization approaches.

Secrecy Energy Efficiency for Distributed-IRSs Assisted Uplink Networks

In this correspondence, we aim at achieving the energy-efficient secrecy transmission for an uplink network assisted by distributed intelligent reflecting surfaces (D-IRSs). The secrecy energy efficiency (SEE) maximization problem is investigated through jointly optimizing the discrete reflecting phase shifts, the transmit power, and the IRS switching status. To handle this non-convex problem, we decompose it into three subproblems, and then the majorization-minimization based algorithm, the Dinkelbach method and the Lagrangian dual method are adopted to obtain the solutions of reflecting phase shifts, transmit power and IRS switching status, respectively. The subproblems are alternatively optimized based on the iterative algorithm. Simulation results show that the proposed scheme can significantly improve the SEE compared to the centralized IRS, especially when the eavesdropping channel is stronger.

Physical Layer Security for Wireless-Powered Ambient Backscatter Cooperative Communication Networks

Low power consumption and high spectrum efficiency as the great challenges for multi-device access to Internet-of-Things (IoT) have put forward stringent requirements on the future intelligent network. Ambient backscatter communication (ABcom) is regarded as a promising technology to cope with the two challenges, where backscatter device (BD) can reflect ambient radio frequency (RF) signals without additional bandwidth. However, minimalist structural design of BD makes ABcom security vulnerable in wireless propagation environments. By virtue of this fact, this paper considers the physical layer security (PLS) of a wireless-powered ambient backscatter cooperative communication network threatened by an eavesdropper, where the BD with nonlinear energy harvesting model cooperates with decode-and-forward (DF) relay for secure communication. The PLS performance is investigated by deriving the secrecy outage probability (SOP) and secrecy energy efficiency (SEE). Specifically, the closed-form and asymptotic expressions of SOP are derived as well as the secrecy diversity order for the first time. As an energy-constrained device, balancing power consumption and security is major concern for BD, thus the SEE of the proposed network is studied. The results from numerical analysis show that the performance improvement of SOP and SEE is impacted by system parameters, including transmit power, secrecy rate threshold, reflection efficiency and distance between the source and BD, which provide guidance on balancing security and energy efficiency in ambient backscatter cooperative relay networks.

Mode Adaptive Secure UAV Relay Transmissions

Unmanned aerial vehicles (UAV) can enhance wireless transmission security by improving the secrecy rate through relaying the legitimate transmission and injecting artificial noise to the eavesdroppers. However, due to the inherent mechanical energy consumption and the long-range signal propagation pathloss, the traditional cruising and hovering UAV relay modes are deficient in energy utilization and secrecy rate, respectively. Modeling of the secrecy energy efficiency with respect to UAV mode adaptation is limited in the literature and deserves further investigation. Therefore, in this paper, we propose a secrecy rate-energy efficient UAV relay mode adaptation scheme for secure wireless communications scenarios where the eavesdropping locations are known, i.e., the eavesdropper is a hijacked internal legitimate node. In this secure system, the UAV cooperatively assists the legitimate transmissions and injects artificial interference to the eavesdropper. We investigate optimum UAV placement and relay period planning for the hovering and the cruising relay modes, respectively. By modeling the UAV modes transfer and maximizing the secrecy energy efficiency, the optimum mode adaptation ratios are obtained under both the secrecy rate-prioritized and the energy-saving criteria. In addition, the proposed UAV relay mode adaptation mechanism is further studied for each UAV task division, to adapt to the dynamic channel variations and enhance the security. Our theoretical analysis shows that the mode adaptation ratio is determined by the secrecy rate-energy gain between the cruising and the hovering modes, and by the energy consumption threshold of the UAV relays. Our numerical results demonstrate that the proposed scheme achieves higher secrecy energy efficiency than the traditional schemes.

Ensemble Learning Aided QPSO-Based Framework for Secrecy Energy Efficiency in FD CR-NOMA Systems

Cognitive radio (CR), non-orthogonal multiple access (NOMA), and full-duplex (FD) communications have been considered key technologies for providing spectrum utilization improvement and higher energy efficiency on the internet of things (IoT) networks and next-generation communication. However, security concerns are still an issue to be addressed because confidential information is exposed in wireless systems. To solve this problem, we design a novel artificial intelligence (AI)-based framework for maximizing the secrecy energy efficiency (SEE) in FD cooperative relay underlay CR-NOMA systems that are exposed to multiple eavesdroppers. First, we formulate the non-convex SEE optimization problem as bi-level optimization, subject to constraints that satisfy the quality-of-service requirements of secondary users. In particular, the outer problem is solved with ensemble learning (EL) to select the optimal relay. Regarding the inner problem, we propose a quantum particle swarm optimization (QPSO)-based technique to optimize power allocation. In addition, for comparison purposes, we describe a cooperative relay CR network with orthogonal multiple access (OMA), rate-splitting multiple access (RSMA), and half-duplex technologies. Moreover, we evaluate comparative schemes based on machine learning algorithms and swarm intelligence baseline schemes. Furthermore, the proposed EL-aided QPSO-based framework achieves performance close to the optimal solutions, with a meaningful reduction in computation complexity.

Secrecy Performance Analysis of Mixed-ADC/DAC Cell-Free Massive MIMO in the Presence of Multiple Eavesdroppers

This paper studies the secrecy performance of the cell-free massive multi-input multi-output (MIMO) system, where access points (APs) are equipped with a mixed analog-to-digital converter (ADC) and digital-to-analog converter (DAC) architecture. The distributed antenna structure is prone to multiple eavesdropping network threats. Specifically, legitimate transmissions can be affected by non-colluding and colluding eavesdroppers. Based on the above considerations, we determine the expressions of the sum achievable secrecy rate and secrecy energy efficiency (SEE) with applications of the additive quantization noise model and the conjugate performing precoding method. These provide the composite measures to analyze the effects of the mixed-ADC/DAC architecture on secrecy performance. We also propose a SEE-maximization power control (SEEMPC) scheme, which can be solved by the sequential convex approximation algorithm. In addition to verifying the effectiveness of the proposed SEEMPC algorithm, numerical simulations are carried out to evaluate the impact of some key design parameters, including the number of APs, quantization bit of coarse ADCs/DACs, ratio of ideal ADCs/DACs, and power control strategy.

Secrecy energy efficiency optimization for multiuser massive MIMO wiretap systems with transmit antenna selection

Massive multiple-input-multiple-output (Massive MIMO) significantly improves the capacity of wireless communication systems. However, large-scale antennas bring high hardware costs, and security is a vital issue in Massive MIMO networks. To deal with the above problems, antenna selection (AS) and artificial noise (AN) are introduced to reduce energy consumption and improve system security performance, respectively. In this paper, we optimize secrecy energy efficiency (SEE) in a downlink multi-user multi-antenna scenario, where a multi-antenna eavesdropper attempts to eavesdrop the information from the base station (BS) to the multi-antenna legitimate receivers. An optimization problem is formulated to maximize the SEE by jointly optimizing the transmit beamforming vectors, the artificial noise vector and the antenna selection matrix at the BS. The formulated problem is a nonconvex mixed integer fractional programming problem. To solve the problem, a successive convex approximation (SCA)-based joint antenna selection and artificial noise (JASAN) algorithm is proposed. After a series of relaxation and equivalent transformations, the nonconvex problem is approximated to a convex problem, and the solution is obtained after several iterations. Simulation results show that the proposed algorithm has good convergence behavior, and the joint optimization of antenna selection and artificial noise can effectively improve the SEE while ensuring the achievable secrecy rate.

Secrecy Energy Efficiency Maximization for Multi-User Multi-Eavesdropper Cell-Free Massive MIMO Networks

In this paper, the secrecy energy efficiency (SEE) optimization problem is studied in multi-user multi-eavesdropper cell-free massive multiple-input multiple-output (CF-mMIMO) networks, where a large number of distributed access points (APs) equipped with multiple antennas connect to a central processing unit via backhaul links to communicate with multiple users in the presence of multiple active non-colluding eavesdroppers. In order to theoretically analyze the SEE performance of CF-mMIMO systems, we first provide a lower bound on the ergodic secrecy rate. Meanwhile, a realistic model is used to measure the power consumption of CF-mMIMO networks, which takes into account the radio transmit power of all APs, circuit power consumption, and the power consumed by the backhaul link transmissions. Then, we develop a confidential and energy-efficient design for transmit power allocation subject to individual quality-of-service requirements for the users and total transmit power constraint of all APs. For the reason that the proposed SEE maximization (SEEM) problem is non-convex, we give a two-tier iterative power allocation algorithm, capitalizing on Dinkelbach's method and successive convex approximation approach. Simulation results show that the proposed SEEM algorithm converges fast and outperforms the conventional secrecy rate maximization and energy efficiency maximization strategies in terms of SEE performance.

Energy-Efficient Secure Computation Offloading in Wireless Powered Mobile Edge Computing Systems

This paper investigates secure offloading for a wireless powered mobile edge computing (MEC) system. Specifically, an energy-constrained user is first charged by a power beacon (PB) via wireless power transfer (WPT) and then leverages the harvested energy to offload its computation tasks to a MEC server in the presence of multiple eavesdroppers. To enhance the security of computation offloading, we propose a novel physical layer security-assisted scheme, in which the PB can also play the role of a cooperative jammer by transmitting jamming signals to interfere with the eavesdroppers. The objective is to maximize secrecy energy efficiency (SEE) of the MEC system by jointly optimizing the transmit power for WPT, offloading, and jamming, the time allocated for WPT and offloading, and the computation task partition while satisfying the secrecy offloading rate constraint and the energy causality constraint. To deal with the highly non-convex problem, we develop an efficient two-layer algorithm by resorting to variable substitutions, successive convex approximation, and the Dinkelbach method. Simulation results demonstrate that our designed scheme yields a SEE performance enhancement as compared to benchmarks.

Secrecy Energy Efficiency Maximization for Distributed Intelligent-Reflecting-Surface-Assisted MISO Secure Communications

This article investigates energy-efficient secure communication design with the help of multiple phase-adjustable intelligent reflecting surfaces (IRSs). By creating desirable radiation patterns of wireless environment, the IRSs are used to significantly improve the number of bits securely delivered to the destination per energy consumption in Joule, also known as the secrecy energy efficiency (SEE). By manipulating the discrete reflecting coefficients of multiple IRSs and taking advantage of the active beamforming, this article develops an efficient alternating optimization algorithm based on successive convex approximation and penalty-based techniques to effectively fight against multiple eavesdroppers. Simulation results characterize the graceful tradeoff between conflicting performance metrics, i.e., total power consumption and secrecy rate in the multi-IRS-aided secure communication system, and corroborate that incorporating multiple IRSs is beneficial to both the SEE and secrecy rate enhancement compared to existing related benchmarks.

Secrecy Energy Efficiency Enhancement in UAV-Assisted MEC System

A secrecy energy efficiency optimization scheme for a multifunctional unmanned aerial vehicle (UAV) assisted mobile edge computing system is proposed to solve the computing power and security issues in the Internet-of-Things scenario. The UAV can switch roles between a computing UAV and jamming UAV based on the channel conditions. To ensure the security of the content and the system energy efficiency in the process of offloading computing tasks, the UAV trajectory, uplink transmit power, user scheduling, and offload task are jointly optimized, and an updated-rate assisted block coordinate descent (BCD) algorithm is used. Simulation results show that this scheme efficiently improves the secrecy performance and energy efficiency of the system. Compared with the benchmark scheme, the secrecy energy efficiency of the scheme is improved by 38.5%.

SecBoost: Secrecy-Aware Deep Reinforcement Learning Based Energy-Efficient Scheme for 5G HetNets

In this paper, we propose a secrecy-aware energy-efficient scheme for a two-tier heterogeneous network (HetNet), consisting of a sub-6 GHz macrocell and multiple millimeter wave (mmWave) picocells. Each picocell is assumed to have several users and an eavesdropper (Eve) which intercepts the signal of the picocell users. In the proposed scheme, firstly, to maximize the secrecy energy-efficiency (SEE) of picocell users, a joint optimization problem of power control, channel allocation, and beamforming is formulated by considering the minimum secrecy rate and signal-to-interference-plus-noise ratio (SINR) constraints. Due to the non-convex nature of the aforementioned optimization problem in a highly dynamic HetNet environment, we transform it into a reinforcement learning (RL) problem using the Markov decision process (MDP). Then, a multi-agent reinforcement learning (MARL) technique is used to obtain the maximum long-term reward. Moreover, we propose a multi-agent cooperative deep reinforcement learning (DRL) scheme known as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SecBoost</i> to solve the MDP with large number of action and state spaces. It uses the dueling and double-Q architecture of dueling double deep Q-network (D3QN) to optimize power control, channel allocation, and beamforming vectors to maximize the SEE of picocells. Also, prioritized experience replay is used to increase the sampling efficiency of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SecBoost</i> . The SEE performance of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SecBoost</i> is compared with MARL, multi-agent deep Q-network (MA-DQN), state-of-the-art joint beamforming based secrecy energy efficiency maximization (JBF-SEEM) scheme, and one-time pad based encrypted data transmission (O-EDT). Simulation results demonstrated that the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SecBoost</i> scheme achieves 14.7%, 8.33%, 30%, and 69% better average SEE in comparison to MARL, MA-DQN, JBF-SEEM, and O-EDT schemes, respectively, which reveals its effectiveness in improving SEE of picocells.

Secure Satellite Transmission With Active Reconfigurable Intelligent Surface

The long distance characteristic of satellite transmission makes it difficult to achieve secure communication due to the highly correlated channels. This letter investigates secure satellite multicast transmission in the presence of an eavesdropper (Eve), where the direct links from the satellite to its users and Eve are blocked by obstacles, and an active reconfigurable intelligent surface (RIS) is adopted to rebuild the satellite downlinks and eliminate the “double fading” effect. By assuming imperfect wiretap channel state information is available, our objective is to maximize the minimal secrecy energy efficiency while guaranteeing the transmit power budgets at the satellite and RIS. In order to solve the above intractable optimization problem, an alternating optimization scheme is proposed, where the successive convex approximation method and Nemirovski Lemma are utilized to obtain the beamforming weight vectors with a fixed reflective factor while the optimization of the reflective factor is solved using the S-Lemma with fixed beamforming weight vectors, iteratively. Finally, our simulation results demonstrate that the proposed scheme outperforms the existing benchmark schemes.

Maximizing the Secrecy Energy Efficiency of the Cooperative Rate-Splitting Aided Downlink in Multi-Carrier UAV Networks

Although Unmanned Aerial Vehicles (UAVs) are capable of significantly improving the information security by detecting the eavesdropper's location, their limited energy motivates our research to propose a secure and energy efficient scheme. Thanks to the common-message philosophy introduced by Rate-Splitting (RS), we no longer have to allocate a portion of the transmit power to radiate Artificial Noise (AN), and yet both the Energy Efficiency (EE) and secrecy can be improved. Hence we define and study the Secrecy Energy Efficiency (SEE) of a multi-carrier multi-UAV network, in which Cooperative Rate-Splitting (CRS) is employed by each multi-antenna UAV Base-Station (UAV-BS) for protecting their downlink transmissions against an external eavesdropper ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Eve$</tex-math></inline-formula> ). Furthermore, we consider the challenging scenario in which CRS is employed by each multi-antenna UAV-BS to protect their corresponding downlink transmissions against an external <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Eve$</tex-math></inline-formula> . We further consider a difficult scenario in terms of security in which only imperfect channel state information of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Eve$</tex-math></inline-formula> is available at the Tx. Accordingly, we conceive a robust secure resource allocation algorithm, which maximizes the SEE by jointly optimizing both the user association matrix and the network parameter allocation problem, including the RS precoders, time slot sharing and power allocation. Due to the non-convexity of the problem, it is decoupled into a pair of convex sub-problems. Firstly, new two-tier intra-cell optimization problems are formulated for achieving <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\xi$</tex-math></inline-formula> -optimal solutions by iterative block coordinate decent programming. Then, the power of each sub-channel is optimized by formulating the associated power control problem.

On the secrecy rate balancing problem of two‐way multiple‐input multiple‐output relaying networks using rank‐1 beamforming strategy

AbstractThe physical layer security of a two‐way relay network (TWRN) in the presence of an eavesdropper is considered, where two multi‐antenna nodes try to exchange messages via a multi‐antenna two‐way relay node. It is assumed that the transmission occurs in two hops. Throughout the first hop, the messages are concurrently transmitted at the same time from two transmitting nodes to the relay, while an eavesdropper tries to overhear the signals. Throughout the second hop, to prevent eavesdropping, the relay broadcasts a combination of the received signals in the null‐space of the eavesdropper's channel gain vector. The objective is to explore the proper power allocation policy at transmitting ends as well as an effective beamforming strategy at the relay to address the secrecy rate balancing (SRB) problem. The radio frequency multiple‐input multiple‐output (RF‐MIMO) architecture design is proposed in which the relay can merely process one data stream; hence, the relay beamforming matrix is of rank‐one. The underlying problem is non‐convex. To tackle this issue, the original problem is divided into two sub‐problems, where using semi‐definite relaxation (SDR) and sequential parametric cone approximation (SPCA) techniques, are converted into convex forms, where suboptimal solutions are derived. Numerical results demonstrate effectiveness of the proposed method compared to the existing works addressed in the literature and also show that our proposed solution has better performance even for the secrecy energy efficiency (SEE) metric.

Secrecy Energy Efficiency Optimization for Reconfigurable Intelligent Surface-Aided Multiuser MISO Systems

Currently, the reconfigurable intelligent surface (RIS) has been applied to improve the physical layer security in wireless networks. In this paper, we focus on the secure transmission in RIS-aided multiple-input single-output (MISO) systems. Specifically, by assuming that only imperfect channel state information (CSI) of the eavesdropper can be obtained, we investigated the robust secrecy energy efficiency (SEE) optimization via jointly designing the active beamforming (BF), artificial noise (AN) at Alice, and the passive phase shifter at the RIS. The formulated problem is hard to handle due to the complicated secrecy rate expression as well as the infinite constraints introduced by the CSI uncertainties. By utilizing the Taylor expansion, we transformed the fractional programming into a convex problem, while all the constraints are approximated via the successive convex approximation and constrained concave-convex procedure. Then, by using the extended S-Lemma, we transform the infinite constraints into linear matrix inequality, which is convex. Finally, an alternate optimization (AO) algorithm was proposed to solve the reformulated problem. Simulation results demonstrated the performance of the proposed design.

NOMA-Based Cognitive Satellite Terrestrial Relay Network: Secrecy Performance Under Channel Estimation Errors and Hardware Impairments

Nonorthogonal multiple access (NOMA) and cognitive integrated satellite terrestrial relay networks are the promising and key part for the next-generation wireless networks. This article researches the joint effects of channel estimation errors (CEEs) and hardware impairments on the secrecy performance of cognitive integrated satellite terrestrial relay networks. The noncolluding eavesdropping scheme is applied in the multiple eavesdroppers, where the eavesdropper with the highest eavesdropping capacity is selected to overhear the legitimate transmission signal. Moreover, the detailed analysis for the secrecy outage probability (SOP) is obtained based on the utilized partial terrestrial relay selection strategy. To obtain the insightful conclusions, the asymptotic analysis along with the secrecy coding gain and secrecy diversity order for the SOP are further derived, which gives the effective methods to valuate the impacts of CEEs and hardware impairments on the considered system with the NOMA scheme in high signal-to-noise ratio regime. Moreover, simulations are derived for the secrecy energy efficiency. Finally, Monte Carlo simulations are given to prove the correctness of the theoretical SOP analysis.