Secrecy Energy Efficiency Maximization Research Articles

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.

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.

Dual-UAV Enabled Secure Data Collection With Propulsion Limitation

Unmanned aerial vehicles (UAVs) have been widely utilized to improve the end-to-end performance of wireless communications. However, its line-of-sight makes UAV communication vulnerable to malicious eavesdroppers. In this paper, we propose two cooperative dual-UAV enabled secure data collection schemes to ensure security, with the practical propulsion energy consumption considered. We first maximize the worst-case average secrecy rate with the average propulsion power limitation, where the scheduling, the transmit power, the trajectory and the velocity of the two UAVs are jointly optimized. To solve the non-convex multivariable problem, we propose an iterative algorithm based on block coordinate descent and successive convex approximation. To further save the on-board energy and prolong the flight time, we then maximize the secrecy energy efficiency of UAV data collection, which is a fractional and mixed integer nonlinear programming problem. Based on the Dinkelbach method, we transform the objective function into an integral expression and propose an iterative algorithm to obtain a suboptimal solution to secrecy energy efficiency maximization. Numerical results show that the average secrecy rate is maximized in the first scheme with propulsion limitation, while in the second scheme, the secrecy energy efficiency is maximized with the optimal velocity to save propulsion power and improve secrecy rate simultaneously.

Secrecy Energy-Efficient UAV Communication via Trajectory Design and Power Control

This paper investigates the problem of maximizing the secrecy energy efficiency (SEE) for unmanned aerial vehicle- (UAV-) to-ground wireless communication system, in which a fixed-wing UAV tries to transmit covert information to a terrestrial legitimate destination receiver with multiple terrestrial eavesdroppers. In particular, we intend to maximize the worst-case SEE of UAV by jointly optimizing UAV’s flight trajectory and transmit power over a finite flight period. However, the formulated problem is challenging to solve because of its large-scale nonconvexity. For efficiently solving this problem, we first decouple the above optimization problem into two subproblems and then propose an alternating iterative algorithm by adopting block coordinate descent method and Dinkelbach’s algorithm as well as successive convex approximation technique to seek a suboptimal solution. For the sake of performance comparison, two benchmark schemes, the secrecy rate maximization (SRM) scheme and constrained energy minimization (CEM) scheme are considered to obtain more useful insights. Finally, simulation results are executed to verify that our proposed SEE maximization (SEEM) algorithm is superior to two benchmark schemes for the UAV-ground communication system.

Beamforming Aided Secrecy Energy Efficiency Maximization in Heterogeneous Cellular Networks

This paper investigates the secrecy energy efficiency (SEE) for a two-tier heterogeneous cellular network having a macro cell and multiple small cells, where the macro cell consists of a macro base station (MBS), a macro user and an eavesdropper and meanwhile each small cell comprises of a small base station (SBS), a small user and an eavesdropper. We consider the use of artificial noise (AN) signals emitted at MBS and SBSs at the expense of transmit power to defend against eavesdroppers. For the sake of obtaining a better balance between the secrecy rate (SR) and energy efficiency of heterogeneous cellular networks, we propose an SEE maximization (SEEM) scheme in small cells through jointly optimizing the beamformers of confidential signals and AN at MBS and SBSs, called joint beamforming based SEEM (JBF-SEEM) while guaranteeing the SR requirements and the power constraints for MBS and each SBS. However, the proposed JBF-SEEM problem is a complicated optimization problem because of the non-convex fractional form. Thus, we firstly transform the original objective function in fractional form into a subtractive one through applying the Dinkelbach's method, and then the non-convex subtractive form is approximated into a convex optimization problem by combining the penalty function and the difference of two-convex functions approach. Furthermore, we propose a two-tier iterative JBF-SEEM algorithm to obtain the near-optimal beamforming solution. Numerical results show that the JBF-SEEM algorithm is convergent while achieving a better SEE performance than conventional approaches.

Secure and Energy-Efficient Precoding for MIMO Two-Way Untrusted Relay Systems

This paper focuses on the multiple-input-multiple-output (MIMO) two-way relay system with an untrusted relay and investigates its secure and energy-efficient precoding design issue based on the physical layer security technology. We first provide theoretical modeling for the index of secrecy energy efficiency (SEE) and formulate the optimal precoding design for SEE maximization (SEEM) as a high-dimensional non-convex programming problem. By exploring the techniques like fractional programming, alternate optimization and semi-definite programming, we then develop a hierarchical theoretical framework to solve the SEEM problem and thus to identify the optimal precoding designs for the source and relay. Furthermore, we demonstrate the proposed theoretical framework is also applicable to the problem of precoding design for secrecy sum rate maximization. Finally, with the help of generalized singular value decomposition, we propose a sub-optimal relay precoding design scheme with significantly lower computational complexity. Extensive numerical results provided in the paper indicate that the proposed schemes can remarkably improve the SEE performance in MIMO two-way untrusted relay systems.

Secrecy energy efficiency optimization for DF relaying IoT systems with passive eavesdropping terminal

In this paper, we investigate a cooperative Internet of Things (IoT) communication network with decode-and-forward relay nodes that can be eavesdropped, where the relay node harvests energy from the received signal power to charge its battery, which is used to retransmit the received signal to the destination. The secrecy communication of the energy harvesting Gaussian eavesdropping channel has been studied. The expression of secrecy energy efficiency for the cooperative IoT system with a battery-enabled relay has been derived, which depends on energy absorption rate and energy harvesting time. Then we perform analytical and numerical analysis on the performance on secrecy energy efficiency. Our analytical results reveal that the secrecy energy efficiency has a maximum under constraints. Finally, we demonstrate that our proposed model and methods are feasible from simulation results based on proposed secrecy energy efficiency maximization algorithm.

Secrecy Energy Efficiency Maximization in an Underlying Cognitive Radio–NOMA System with a Cooperative Relay and an Energy-Harvesting User

Security is considered a critical issue in the deployment of 5G networks because of the vulnerability of information that can be intercepted by eavesdroppers in wireless transmission environments. Thus, physical layer security has emerged as an alternative for the secure enabling of 5G technologies and for tackling this security issue. In this paper, we study the secrecy energy efficiency (SEE) in a downlink underlying cognitive radio (CR)—non-orthogonal multiple access (NOMA) system with a cooperative relay. The system has an energy-harvesting (EH) user and an eavesdropper, where the transmitter provides direct communication with a close secondary user and a distant secondary user via the relay. Our objective is to maximize the SEE of the CR-NOMA system under the constraints of a minimum information rate for the secondary users, a minimum amount of energy harvested by the EH user, and maximum power availability at the transmitter and the relay that still prevents them from causing unacceptable interference with the primary user. The proposed solution to maximize the SEE is based on the low-computational—complexity particle swarm optimization (PSO) algorithm. For validation purposes, we compare the optimization outcomes obtained by the PSO algorithm with the optimal exhaustive search method. Furthermore, we compare the performance of our proposed CR-NOMA scheme with the conventional orthogonal multiple access (OMA) scheme.

Artificial-Noise-Aided Energy-Efficient Secure Beamforming for Multi-Eavesdroppers in Cognitive Radio Networks

In this article, we investigate optimal beamforming at a multiantenna primary base station (PBS) and a multiantenna cognitive base station (CBS) for energy-efficient (EE) secure downlink communication in cognitive radio networks with one single-antenna primary user (PU), one single-antenna cognitive user (CU), and multiple single-antenna eavesdropping nodes. An artificial noise transmission scheme is used by CBS to protect the data against the eavesdropping security attacks at the cost of extra power consumption. To improve the secrecy energy efficiency (SEE), we propose a SEE maximization (SEEM) scheme by exploiting the instantaneous channel state information (CSI) of the eavesdroppers under the secrecy rate (SR) constraints of the PBS-PU and CBS-CU channels, the quality-of-service requirement of the PU, and the transmit power constraint of the CBS. When the eavesdropping links’ instantaneous CSI are unknown at the legitimate transmitters (i.e., PBS and CBS), we propose another SEEM scheme based on the statistical CSI of the eavesdropping links. Since the formulated optimization problems with fractional objective functions are nonconvex and mathematically intractable, we first transform them into equivalent subtractive problems, and then, employ the difference of two-convex functions approximation method to arrive at approximate convex problems. In addition, new two-tier optimal BF algorithms are proposed. Finally, simulation results are presented to illustrate the effectiveness and performance gains of our proposed SEEM schemes over conventional SR-only maximization and EE-only maximization schemes.

Secrecy Energy Efficiency Optimization for Downlink Two-User OFDMA Networks With SWIPT

Simultaneous wireless information and power transfer (SWIPT), as a promising technology by which mobile devices are designed by both information decoding and energy harvesting at the same time, has recently drawn significant interests. However, in multiuser scenario, a challenging physical layer security issue arises as the confident messages sent to the intended information receivers may be eavesdropped by other receivers. To address this problem, in this paper, secrecy energy efficiency maximization (SEEM) problem is investigated for two-user orthogonal frequency-division multiple access networks with SWIPT. Specifically, a power splitting scheme is adopted for each user, which would split received radio frequency signals into two independent parts for information decoding and energy harvesting, respectively. Taking both the individual secrecy quality of service requirement and the individual harvested energy constraint into consideration, the SEEM problem is formulated as a nonlinear fractional programming problem. Moreover, according to Dinkelbach's method, a power allocation and power splitting (PAPS) algorithm is proposed by solving the equivalent convex optimization problem. Finally, numerical results verify the impact of static circuit power consumption, maximum transmit power, power split ratio, subcarrier bandwidth, and path loss on secrecy energy efficiency of our PAPS algorithm.

Security-Aware Cross-Layer Resource Allocation for Heterogeneous Wireless Networks

In this paper, a security-aware energy-efficient resource allocation is modeled as a fractional programming problem for heterogeneous multi-homing networks. The security-aware resource allocation is formulated as a secrecy energy efficiency maximization problem subject to the average packet delay, the average packet dropping probability, and the total available power consumption. In order to guarantee the packet-level quality of service (QoS), first, the average packet delay and the average packet dropping probability requirement for each mobile terminal at the link layer are transformed into a minimum secrecy rate constraint at the physical layer. Then, the non-convex secrecy energy efficiency maximization problem is approximated by a convex problem through epigraph representation. A security-aware energy-efficient resource allocation algorithm is then proposed leveraging dual-decomposition method and bi-section search method. Finally, a heuristic security-aware resource allocation algorithm is proposed to serve as a benchmark. Simulation results demonstrate that the proposed security-aware energy-efficient resource allocation algorithm not only improves the secrecy energy efficiency and throughput, but also guarantees the packet-level QoS.

Secrecy Energy Efficiency Optimization for Artificial Noise Aided Physical-Layer Security in OFDM-Based Cognitive Radio Networks

In this paper, we investigate the power allocation of primary base station (PBS) and cognitive base station (CBS) across different orthogonal frequency division multiplexing (OFDM) subcarriers for energy-efficient secure downlink communication in OFDM-based cognitive radio networks (CRNs) with the existence of an eavesdropper having multiple antennas. We propose a secrecy energy efficiency maximization (SEEM) scheme by exploiting the instantaneous channel state information (ICSI) of the eavesdropper, called ICSI based SEEM (ICSI-SEEM) scheme with a given total transmit power budget for different OFDM subcarriers of both PBS and CBS. As for the case when the eavesdropper's ICSI is unknown, we also propose an SEEM scheme through using the statistical CSI (SCSI) of the eavesdropper, namely SCSI based SEEM (SCSI-SEEM) scheme. Since the ICSI-SEEM and SCSI-SEEM problems are fractional and non-convex, we first transform them into equivalent subtractive problems, and then achieve approximate convex problems through employing the difference of two-convex functions approximation method. Finally, new two-tier power allocation algorithms are proposed to achieve $\varepsilon$-optimal solutions of our formulated ICSI-SEEM and SCSI-SEEM problems. Simulation results illustrate that the ICSI-SEEM has a better secrecy energy efficiency (SEE) performance than SCSI-SEEM, and moreover, the proposed ICSI-SEEM and SCSI-SEEM schemes outperform conventional SR maximization and EE maximization approaches in terms of their SEE performance.

Secure Communications in NOMA System: Subcarrier Assignment and Power Allocation

Secure communication is a promising technology for wireless networks because it ensures secure transmission of information. In this paper, we investigate the joint subcarrier (SC) assignment and power allocation problem for non-orthogonal multiple access (NOMA) amplify-and-forward two-way relay wireless networks, in the presence of eavesdroppers. By exploiting cooperative jamming (CJ) to enhance the security of the communication link, we aim to maximize the achievable secrecy energy efficiency by jointly designing the SC assignment, user pair scheduling and power allocation. Assuming the perfect knowledge of the channel state information (CSI) at the relay station, we propose a low-complexity subcarrier assignment scheme (SCAS-1), which is equivalent to many-to-many matching games, and then SCAS-2 is formulated as a secrecy energy efficiency maximization problem. The secure power allocation problem is modeled as a convex geometric programming problem, and then solved by interior point methods. Simulation results demonstrate that the effectiveness of the proposed SSPA algorithms under scenarios of using and not using CJ, respectively.

Energy-efficient trajectory planning for UAV-aided secure communication

Wireless communication with unmanned aerial vehicles (UAVs) has aroused great research interest recently. This paper is concerned with the UAV's trajectory planning problem for secrecy energy efficiency maximization (SEEM) in the UAV communication system. Specifically, we jointly consider the secrecy throughput and UAV's energy consumption in a three-node (fixed-wing UAV-aided source, destination, and eavesdropper) wiretap channel. By ignoring the energy consumption on radiation and signal processing, the system's secrecy energy efficiency is defined as the total secrecy rate normalized by the UAV's propulsion energy consumption within a given time horizon. Nonetheless, the SEEM problem is nonconvex and thus is intractable to solve. As a compromise, we propose an iterative algorithm based on sequential convex programming (SCP) and Dinkel-bach's method to seek a suboptimal solution for SEEM. The algorithm only needs to solve convex problems, and thus is computationally efficient to implement. Additionally, we prove that the proposed algorithm has Karush-Kuhn-Tucker (KKT) point convergence guarantee. Lastly, simulation results demonstrate the efficacy of our proposed algorithm in improving the secrecy energy efficiency for the UAV communication system.

Secrecy Energy Efficiency Optimization in AN-Aided Distributed Antenna Systems With Energy Harvesting

In this paper, the artificial-noise (AN)-aided downlink distributed antenna system with simultaneous wireless information and power transfer is considered, in which the legitimate user uses the power-splitting (PS) scheme to simultaneously decode information and harvest energy. To prevent eavesdroppers from wiretapping the legitimate user, AN is transmitted with the confidential signal at the transmitter. The secrecy energy efficiency maximization (SEEM) problem is studied under the constraints of the minimum secrecy rate requirement, minimum required harvested energy, total radio frequency transmission power, and range of the PS ratio. Because of the coupling of variables, the formulated SEEM problem is non-convex and intractable. Then, a double-layer iterative optimization algorithm is proposed. The outer layer is a 1-D search algorithm for the introduced tight relaxation variable, whereas the inner one is based on the Dinkelbach method to address the fractional optimization problem. The closed-form expressions for the power of the transmitted confidential signal and AN power are further derived. The simulation results reveal the efficiency of the proposed algorithm and the advantage of AN in improving the system secrecy energy efficiency.

Optimal Energy-Efficient Design of Confidential Multiple-Antenna Systems

Energy-efficient resource allocation in multiple-antenna wiretap channels is investigated, subject to maximum power and minimum secrecy capacity/rate constraints. Two energy-efficient metrics are optimized, namely the secrecy energy efficiency, defined as the ratio between the system secrecy capacity and the consumed power, and the secret-key energy efficiency, defined as the ratio between the system secret-key capacity and the consumed power. If the legitimate receiver and the eavesdropper have a single antenna, and the transmitter has multiple antennas, the global solution can be expressed by a simple formula that requires negligible complexity to be computed. Instead, if all nodes have multiple-antennas, provably convergent and computationally-friendly iterative algorithms are provided, which are able to determine the global maximum of the secret-key energy efficiency and candidate solutions of the secrecy energy efficiency maximization problem. Numerical results assess the performance of the proposed methods.