Jihwan Moon

In this paper, we study the power allocation for an integrated sensing and communication (ISAC) system which tracks a mobile target. We first model the problem as a Markov decision process, and then tackle it with a soft actor-critic (SAC) based deep reinforcement learning (DRL) approach. We also combine a Dirichlet policy, which naturally produces normalized continuous actions under random target motion. To exploit different features of sensing and communication operations, we carefully design a reward function such that the system can dynamically control power allocation to conserve resources. The simulation results demonstrate that the proposed scheme enhances tracking performance compared to other baselines while sustaining communication performance.

In this paper, we investigate the achievable performance of covert communications using power-domain binary embedding as a side channel. A source node transmits a public message to a destination node using conventional constellation-domain modulation, while simultaneously embedding a binary covert message for a covert receiver such as a radiometer or an Internet of Things sensor operating under different wireless technologies. Assuming noise uncertainty, we study the joint probability of covert transmission and successful decoding, which captures both covert rate and reliability. Numerical results evaluate the performance under various system parameters.

Satellite communication (SatCom) networks recently require more robust authentication techniques than before, driven by the increasing demands of applications such as commercial communication services and military operations. A physical-layer authentication (PLA) technique that exploits inherent wireless communication features, such as channel fading characteristics, has emerged as a promising solution against potential threats to the networks. Recent studies have shown that machine learning-based PLA methods can effectively compensate for the unexpected threats to traditional security protocols. However, previous research has not sufficiently considered practical feasibility, which is crucial, especially in SatCom networks (e.g., operating time limit). This paper presents enhanced physical-layer authentication with super-resolution for satellites (ePASS), integrating convolutional neural network models and super-resolution techniques for rapid authentication in SatCom networks. We evaluate the practical feasibility and detection performance of the proposed ePASS against spoofing attacks through extensive simulations, compared with the existing schemes.

Multi-access edge computing (MEC) has been widely recognized as a promising solution for alleviating the computational burden on edge devices, particularly in supporting fast and real-time processing of resource-intensive applications. In this paper, we propose a decentralized offloading decision strategy based on multi-agent deep reinforcement learning (MADRL), aiming to minimize the overall task completion latency experienced by edge devices. Our proposed scheme adopts a grant-free access mechanism during the initialization of offloading in a fully decentralized manner, which serves as the key feature of our strategy. As a result, determining the optimal offloading factor becomes significantly more challenging due to the simultaneous access attempts from multiple edge devices. To resolve this problem, we consider a discrete action space-based deep reinforcement learning (DRL) approach, termed deep Q network (DQN), to enable each edge device to learn a decentralized computation offloading policy based solely on its local observation without requiring global network information. In our design, each edge device dynamically adjusts its offloading factor according to its observed channel state and the number of active users, thereby balancing local and remote computation loads adaptively. Furthermore, the proposed MADRL-based framework jointly accounts for user association and offloading decision optimization to mitigate access collisions and computation bottlenecks in a multi-user environment. We perform extensive computer simulations using MATLAB R2023b to evaluate the performance of the proposed strategy, focusing on the task completion latency under various system configurations. The numerical results demonstrate that our proposed strategy effectively reduces the overall task completion latency and achieves faster convergence of learning performance compared with conventional schemes, confirming the efficiency and scalability of the proposed decentralized approach.

In this paper, we study covert communication in a multi-antenna two-way relay system, where two source nodes exchange their messages through a relay. Then, the relay tries to disseminate covert messages without being detected by an unauthorized node, the warden. We first derive a closed-form expression of the detection error probability (DEP) and identify the best detection strategy from the perspective of the warden. Then, we aim to maximize the covert rate by optimizing the transmit power and the beamforming matrix at the relay with the worst-case DEP into consideration. We propose a joint optimization algorithm based on the semi-definite relaxation principle. Two additional algorithms with maximum ratio combining and zero-forcing techniques are also presented as suboptimal solutions. Numerical results verify the accuracy of our analytic derivations, and we confirm that the proposed scheme outperforms other methods.

In this paper, we study covert communications in a hybrid decode-and-forward (DF)/ amplify-and-forward (AF) relay system. The considered relay in normal operation forwards messages from a source node to a destination node in either DF or AF mode on request. Meanwhile, the source and destination nodes also attempt to secretly exchange covert messages such as confidential or sensitive information and avoid detection by the covert message detector embedded on the relay. We first establish an optimal DF/AF mode selection criterion to maximize the covert rate based on the analyses of delay-aware achievable covert rates of individual DF and AF modes. To further reduce the time complexity, we propose a low-complexity selection criterion as well for practical use. The numerical results demonstrate the covert rate gain as high as 50% and running time gain as high as 20% for particular system parameters, which verify the effectiveness of the proposed criteria.

This paper addresses the fair distribution of available energy (AE) in a massive multiple-input multiple-output (mMIMO) simultaneous wireless information and power transfer (SWIPT) sensor network, where sensor nodes are equipped with low-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs). We define the system model and estimate the channel using linear minimum mean square error (LMMSE) estimation. To analyze system performance, we derive closed-form expressions for the signal-to-interference-plus-noise ratio (SINR), and harvested energy. These expressions allow us to evaluate the impact of low-resolution ADCs/DACs on both the achievable rate and energy harvesting. Based on these expressions, we formulate an optimization problem fair AE distribution. To solve this non-convex problem, we propose an alternating optimization (AO) algorithm that optimizes the downlink/uplink (DL/UL) powers and the power splitting (PS) ratio. Monte Carlo simulations confirm that our closed-form analysis accurately represents the system performance and that the optimization algorithm distributes AE more fairly compared to the equal power and power splitting case.

In this paper, we investigate the performance of covert communications in different types of a relay system: decode-and-forward (DF), compress-and-forward (CF) and amplify-and-forward (AF). We consider a source node that attempts to send both public and covert messages to a destination node through a relay on which a covert message detector is embedded. By taking the minimum detection error probability (DEP) at the relay into account, we optimize the power distribution between the public and covert messages to achieve the maximum covert rate. We further make a delay-aware comparison among DF, CF and AF relay systems with the obtained closed-form covert rates and conduct an extensive examination on the asymptotic behaviors in different limits. Our analyses reveal that CF or AF tend to outperform DF for high source transmit power or low relay transmit power, while various system parameters such as the processing delay, minimum required quality of service for public messages and DEP threshold lead to different performance relationships among DF, CF and AF for high relay transmit power. Numerical results verify our investigation into the performance comparison in various channel models.

Covert communications have arisen as an effective communications security measure that overcomes some of the limitations of cryptography and physical layer security. The main objective is to completely conceal from external devices the very existence of the link for exchanging confidential messages. In this paper, we take a step further and consider a scenario in which a covert communications node disguises itself as another functional entity for even more covertness. To be specific, we study a system where a source node communicates with a seemingly receive-only destination node which, in fact, is full-duplex (FD) and covertly delivers critical messages to another hidden receiver while evading the surveillance. Our aim is to identify the achievable covert rate at the hidden receiver by optimizing the public data rate and the transmit power of the FD destination node subject to the worst-case detection error probability (DEP) of the warden. Closed-form solutions are provided, and we investigate the effects of various system parameters on the covert rate through numerical results, one of which reveals that applying more (less) destination transmit power achieves a higher covert rate when the source transmit power is low (high). Since our work provides a performance guideline from the information-theoretic point of view, we conclude this paper with a discussion on possible future research such as analyses with practical modulations and imperfect channel state information.