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IEEE Open Journal of the Communications Society

Improving Secrecy Capacity in the Face of Eavesdropping in SWIPT CIoT Networks With Actor-Critic DRL

Nada Abdel Khalek, Walaa Hamouda

Published Jan 1, 2025• 1 citation
Publisher

Abstract

One of the key enablers of 6th-generation (6G) wireless networks is cognitive radio, offering optimized spectrum utilization, enhanced device intelligence, and improved security. This article investigates secure communication in an energy-harvesting (EH) cognitive Internet of Things (CIoT) network operating over cascaded fading channels. Here, a CIoT transmitter employs an intelligent strategy to allocate time for simultaneous wireless information and power transfer (SWIPT) and transmission power to maximize the secrecy rate. The CIoT receiver operates in full-duplex (FD) mode, receiving confidential messages while simultaneously emitting cooperative jamming signals to disrupt an eavesdropper. We formulate the challenging non-convex optimization problem to maximize secrecy capacity while ensuring spectrum sharing and energy constraints and model the CIoT agent’s decision-making as a model-free Markov decision process (MDP). We then propose a deep reinforcement learning (DRL) approach to determine the optimal strategy for secure transmission and resource allocation. Specifically, we derive the instantaneous secrecy rate and employ a deep deterministic policy gradient (DDPG) algorithm with a lightweight actor-critic architecture to efficiently address dynamic channel occupancy, EH opportunities, and fading conditions. The proposed DDPG algorithm allows the CIoT agent to adapt to dynamic environments, enhance transmission security, and extend network lifetime without prior knowledge. Extensive simulations confirm its convergence and effectiveness in improving secrecy capacity, throughput, and energy efficiency. Moreover, the results attest to its superior performance compared to existing benchmarks.

DOI
10.1109/ojcoms.2025.3578149

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One of the key enablers of 6th-generation (6G) wireless networks is cognitive radio, offering optimized spectrum utilization, enhanced device intelligence, and improved security. This article investigates secure communication in an energy-harvesting (EH) cognitive Internet of Things (CIoT) network operating over cascaded fading channels. Here, a CIoT transmitter employs an intelligent strategy to allocate time for simultaneous wireless information and power transfer (SWIPT) and transmission power to maximize the secrecy rate. The CIoT receiver operates in full-duplex (FD) mode, receiving confidential messages while simultaneously emitting cooperative jamming signals to disrupt an eavesdropper. We formulate the challenging non-convex optimization problem to maximize secrecy capacity while ensuring spectrum sharing and energy constraints and model the CIoT agent’s decision-making as a model-free Markov decision process (MDP). We then propose a deep reinforcement learning (DRL) approach to determine the optimal strategy for secure transmission and resource allocation. Specifically, we derive the instantaneous secrecy rate and employ a deep deterministic policy gradient (DDPG) algorithm with a lightweight actor-critic architecture to efficiently address dynamic channel occupancy, EH opportunities, and fading conditions. The proposed DDPG algorithm allows the CIoT agent to adapt to dynamic environments, enhance transmission security, and extend network lifetime without prior knowledge. Extensive simulations confirm its convergence and effectiveness in improving secrecy capacity, throughput, and energy efficiency. Moreover, the results attest to its superior performance compared to existing benchmarks.
Physical-layer security (PLS) for an underlay cognitive radio network (CRN)-based simultaneous wireless information and power transfer (SWIPT) over cascaded κ-µ fading channels is investigated. The network is composed of a pair of secondary users (SUs), a primary user (PU) receiver, and an eavesdropper attempting to intercept the data shared by the SUs. To improve the SUs’ data transmission security, we assume a full-duplex (FD) SU destination, which employs energy harvesting (EH) to extract the power required for generating jamming signals to be emitted to confound the eavesdropper. Two scenarios are presented and compared; harvesting and non-harvesting eavesdropper. Moreover, a trade-off between the system’s secrecy and reliability is explored. PLS is studied in terms of the probability of non-zero secrecy capacity and the intercept probability, whereas the reliability is studied in terms of the outage probability. Results reveal the great impact of jamming over the improvement of the SUs’ secrecy. Additionally, our work indicates that studying the system’s secrecy over cascaded channels has an influence on the system’s PLS that cannot be neglected.
Simultaneous wireless information and power transfer (SWIPT) has recently drawn significant interests for its dual use of radio signals to provide wireless data and energy access at the same time. However, a challenging secrecy communication problem arises in the SWIPT system since the messages sent to information receivers (IRs) may be eavesdropped by energy receivers (ERs), which are presumed to receive energy from the same signals broadcast by the transmitter. To tackle this problem, we propose in this paper an artificial noise (AN) aided transmission scheme to facilitate the secrecy information transmission to IRs and yet meet the energy harvesting requirement for ERs, under the assumption that the AN can be cancelled at IRs but not at ERs. Specifically, the proposed scheme splits the power at the transmitter into two parts, to send the confidential message to the IR and an AN to interfere with the ER against eavesdropping, respectively. Under a simplified three-node wiretap channel setup, the transmit power allocations and power splitting ratios over fading channels are jointly optimized to maximize the average secrecy information rate for the IR subject to a combination of average and peak power constraints at the transmitter as well as an average energy harvesting constraint at the ER. The formulated problem is shown to be non-convex, for which we propose an efficient algorithm by iteratively optimizing the transmit power allocations and power splitting ratios over fading channels. Finally, the performance of the proposed scheme is evaluated by simulations and compared against other heuristic schemes in terms of achievable (secrecy) rate-energy trade-off.
Limited power and computational resources make the employment of complex classical encryption schemes unrealistic in resource-limited networks, e.g., the Internet of Things (IoT). To this end, physical layer security (PLS) has shown great potential in securing such resource-limited networks. To further combat the power scarcity in IoT nodes, radio frequency (RF) based energy harvesting (EH) is an attractive energy source while relaying can enhance the energy efficiency and extend the range of data transmission. Additionally, due to deploying low-cost hardware, imperfections in the RF chain of IoT transceivers are common. Against this background, in this paper, we investigate an untrusted EH relay-aided secure communication with RF impairments. Specifically, the relay simultaneously receives the desired signal from the source and the jamming from the destination in the first phase. Hence the relay is unable to extract the confidential desired signal. The resultant composite signal is then amplified by the relay in the second phase by using the energy harvested from the composite signal followed by its transmission to the destination. Since the destination is the original source of the jamming, its effect can be readily subtracted from the composite signal to recover the original desired signal of the source. Moreover, in the face of hardware impairments (HWIs) in all nodes, maintaining optimal power management both at the source and destination may impose excessive computations on an IoT node. We solve this problem by deep learning (DL) based optimal power management maximizing the secrecy rate based on the instantaneous channel coefficients. We show that our learning-based scheme can reach the accuracy of the exhaustive search method despite its considerably lower computational complexity. Moreover, we developed an optimization framework for judiciously sharing HWIs across the nodes, so that we attain the maximum secrecy rate. To derive an efficient solution, we utilize a majorization-minimization (MM)algorithm, which is a particular instance in the family of successive convex approximation (SCA) methods. The simulation results show that the proposed HWI aware design considerably improves the secrecy rate.
Recently, simultaneous wireless information and power transfer (SWIPT) has emerged as a potential solution for improving energy efficiency. However, it is also unavoidable to be eavesdropped on by security threats. In this paper, we investigate the secure transmission of the SWIPT system where a user is equipped with power-splitting (PS) scheme while an eavesdropper (Eve) tries to listen to the private information of the transmitter-user communication link. Our objective is to optimize the system secrecy rate while satisfying the requirements on energy harvesting (EH) at the user and transmit power at the transmitter. For solving the optimization problem, we utilize the feasible point pursuit (FPP) technique and the successive convex approximation (SCA) iterative-based algorithm. Although this algorithm can effectively solve our non-convex problem, it is often quite complex and time-consuming due to the many mathematical transformations and the need for the number of iterations to be converged to the optimal solutions. Therefore, we also consider the deep learning (DL)-based approach which helps the optimization problem to achieve an efficient solution in terms of the computation time. The simulation results validate the proposed scheme significantly improve the system secrecy rate. Moreover, the DL scheme achieved similar performance to the optimization algorithm, but with low computation time.
In this paper, we study a robust beamforming design for multi-user multiple-input multiple-output secrecy networks with simultaneous wireless information and power transfer (SWIPT). In this system, an access point, multiple Internet of Things devices under the non-Linear energy harvesting model with a help of one cooperative jammer (CJ). We employ artificial noise (AN) generation to facilitate efficient wireless energy transfer and secure transmission. To achieve energy harvesting fairness, we aim to maximize the minimum harvested energy among users subject to secrecy rate constraint and total transmit power constraint in the presence of channel estimation errors. By incorporating a norm-bounded channel uncertainty model, we propose an algorithm based on sequential parametric convex approximation (SPCA). Finally, simulation results show that the proposed SPCA method outperforms the traditional AN-aided method and CJ-aided method.
Simultaneous wireless information and power transfer (SWIPT) has recently gathered much research interest from both academia and industry as a key enabler of energy harvesting Internet-of-things (IoT) networks. Due to a number of growing use cases of such networks, it is important to study their performance limits from the perspective of physical layer security (PLS). With this intent, this work aims to provide a novel analysis of the ergodic secrecy capacity of a SWIPT system is provided for Rician and Nakagami- m faded communication links. For a realistic evaluation of the system, the imperfections of channel estimations for different receiver designs of the SWIPT-based IoT systems have been taken into account. Subsequently, the closed-form expressions of the ergodic secrecy capacities for the considered scenario are provided and, then, validated through extensive simulations. The results indicate that an error ceiling appears due to imperfect channel estimation at high values of signal-to-noise ratio (SNR). More importantly, the secrecy capacity under different channel conditions stops increasing beyond a certain limit, despite an increase of the main link SNR. The in-depth analysis of secrecy-energy trade-off has also been performed and a comparison has been provided for imperfect and perfect channel estimation cases. As part of the continuous evolution of IoT networks, the results provided in this work can help in identifying the secrecy limits of IoT networks in the presence of multiple eavesdroppers.