ABSTRACT We develop a Markov decision process (MDP) framework to autonomously make guidance decisions for satellite collision avoidance maneuver (CAM) and a reinforcement learning policy gradient (RL-PG) algorithm to enable direct optimization of guidance policy using historic CAM data. In addition to maintaining acceptable collision risks, this approach seeks to minimize the average propellant consumption of CAMs by making early maneuver decisions. We model CAM as a continuous state, discrete action and finite horizon MDP, where the critical decision is determining when to initiate the maneuver. By deciding to maneuver earlier than conventional methods, the Markov policy effectively favors CAMs that achieve comparable rates of collision risk reduction while consuming less propellant. Using historical data of tracked conjunction events, we verify this framework and conduct an extensive parameter-sensitivity study. When evaluated on synthetic conjunction events, the trained policy consumes significantly less propellant overall and per maneuver in comparison to a conventional cut-off policy that initiates maneuvers 24 hours before the time of closest approach (TCA). On historical conjunction events, the trained policy consumes more propellant overall but consumes less propellant per maneuver. For both historical and synthetic conjunction events, the trained policy is slightly more conservative in identifying conjunctions events that warrant CAMs in comparison to cutoff policies.

--Networks built on the IEEE 802.11 standard have experienced rapid growth in the last decade. Their field of application is vast, including smart home applications, Internet of Things (IoT), and short-range high throughput static and dynamic inter-vehicular communication networks. Within such networks, Channel State Information (CSI) provides a detailed view of the state of the communication channel and represents the combined effects of multipath propagation, scattering, phase shift, fading, and power decay. In this work, we investigate the problem of jamming attack detection in static and dynamic vehicular networks. We utilize ESP32-S3 modules to set up a communication network between an Unmanned Aerial V ehicle (UA V) and a Ground Control Station (GCS), to experimentally test the combined effects of a constant jammer on recorded CSI parameters, and the feasibility of jamming detection through CSI analysis in static and dynamic communication scenarios. The rapid expansion of IEEE 802.11 networks over the past decade has revolutionized wireless communications, particularly in such applications as smart homes [1], Internet of Things (IoT) [2], industrial automation, and short-range high-throughput vehicular networks [3]. This can be contributed to their high throughput capabilities, ease of deployment, and increasingly growing demand for internet connectivity. However, the widespread usage and extensive deployment of these networks make them an attractive target for malicious actors, and thus, more exposed and susceptible to jamming attacks.

With the rapid advances in programmable materials, reconfigurable intelligent surfaces (RIS) have become a pivotal technology for future wireless communications. The simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) can both transmit and reflect signals, enabling comprehensive signal control and expanding application scenarios. This paper introduces an unmanned aerial vehicle (UAV) to further enhance system flexibility and proposes an optimization design for the spectrum efficiency of the STAR-RIS-UAV-assisted wireless communication system. We present a deep reinforcement learning (DRL) algorithm capable of iteratively optimizing beamforming, phase shifts, and UAV positioning to maximize the system's sum rate through continuous interactions with the environment. To improve exploration in deterministic policies, we introduce a stochastic perturbation factor, which enhances exploration capabilities. As exploration is strengthened, the algorithm's ability to accurately evaluate the state-action value function becomes critical. Thus, based on the deep deterministic policy gradient (DDPG) algorithm, we propose a convolution-augmented deep deterministic policy gradient (CA-DDPG) algorithm that balances exploration and evaluation to improve the system's sum rate. The simulation results demonstrate that the CA-DDPG algorithm effectively interacts with the environment, optimizing the beamforming matrix, phase shift matrix, and UAV location, thereby improving system capacity and achieving better performance than other algorithms.

Abstract--Low-altitude wireless networks (LA WNs) represent a promising architecture that integrates unmanned aerial vehicles (UA Vs) as aerial nodes to provide enhanced coverage, reliability, and throughput for diverse applications. However, these networks face significant security vulnerabilities from both known and potential unknown eavesdroppers, which may threaten data confidentiality and system integrity. T o solve this critical issue, we propose a novel secure communication framework for LA WNs where the selected UA Vs within a swarm function as a virtual antenna array (V AA), complemented by intelligent reflecting surface (IRS) to create a robust defense against eavesdropping attacks. Specifically, we formulate a multi-objective optimization problem that simultaneously maximizes the secrecy rate while minimizing the maximum sidelobe level and total energy consumption, requiring joint optimization of UA V excitation current weights, flight trajectories, and IRS phase shifts. This problem presents significant difficulties due to the dynamic nature of the system and heterogeneous components. Thus, we first transform the problem into a heterogeneous Markov decision process (MDP). Then, we propose a heterogeneous multi-agent control approach (HMCA) that integrates a dedicated IRS control policy with a multi-agent soft actor-critic framework for UA V control, which enables coordinated operation across heterogeneous network elements. Simulation results show that the proposed HMCA achieves superior performance compared to baseline approaches in terms of secrecy rate improvement, sidelobe suppression, and energy efficiency. Furthermore, we find that the collaborative and passive beamforming synergy between V AA and IRS creates robust security guarantees when the number of UA Vs increases. Jiahui Li, Xinyue Liang, and Hui Kang are with the College of Computer Science and Technology, Jilin University, Changchun 130012, China (E-mails: lijiahui@jlu.edu.cn; Geng Sun is with the College of Computer Science and Technology, Jilin University, Changchun 130012, China, and also with the Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China. He is also with the College of Computing and Data Science, Nanyang Technological University, Singapore 639798 (E-mail: sungeng@jlu.edu.cn).

In this paper, the precoding design is investigated for maximizing the throughput of millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems with obstructed direct communication paths. In particular, a reconfigurable intelligent surface (RIS) is employed to enhance MIMO transmissions, considering mmWave characteristics related to line-of-sight (LoS) and multipath effects. The traditional exhaustive search (ES) for optimal codewords in the continuous phase shift is computationally intensive and time-consuming. To reduce computational complexity, permuted discrete Fourier transform (DFT) vectors are used for finding codebook design, incorporating amplitude responses for practical or ideal RIS systems. However, even if the discrete phase shift is adopted in the ES, it results in significant computation and is time-consuming. Instead, the trained deep neural network (DNN) is developed to facilitate faster codeword selection. Simulation results show that the DNN maintains sub-optimal spectral efficiency even as the distance between the end-user and the RIS has variations in the testing phase. These results highlight the potential of DNN in advancing RIS-aided systems.

In this paper, we propose Capacity-Net, a novel unsupervised learning approach aimed at maximizing the achievable rate in reflecting intelligent surface (RIS)-aided millimeter-wave (mmWave) multiple input multiple output (MIMO) systems. To combat severe channel fading of the mmWave spectrum, we optimize the phase-shifting factors of the reflective elements in the RIS to enhance the achievable rate. However, most optimization algorithms rely heavily on complete and accurate channel state information (CSI), which is often challenging to acquire since the RIS is mostly composed of passive components. To circumvent this challenge, we leverage unsupervised learning techniques with implicit CSI provided by the received pilot signals. Specifically, it usually requires perfect CSI to evaluate the achievable rate as a performance metric of the current optimization result of the unsupervised learning method. Instead of channel estimation, the Capacity-Net is proposed to establish a mapping among the received pilot signals, optimized RIS phase shifts, and the resultant achievable rates. Simulation results demonstrate the superiority of the proposed Capacity-Net-based unsupervised learning approach over learning methods based on traditional channel estimation.

This study considers multiple reconfigurable intelligent surfaces (RISs)-aided multiuser downlink systems with the goal of jointly optimizing the transmitter precoding and RIS phase shift matrix to maximize spectrum efficiency. Unlike prior work that assumed ideal RIS reflectivity, a practical coupling effect is considered between reflecting amplitude and phase shift for the RIS elements. This makes the optimization problem non-convex. To address this challenge, we propose a deep deterministic policy gradient (DDPG)-based deep reinforcement learning (DRL) framework. The proposed model is evaluated under both fixed and random numbers of users in practical mmWave channel settings. Simulation results demonstrate that, despite its complexity, the proposed DDPG approach significantly outperforms optimization-based algorithms and double deep Q-learning, particularly in scenarios with random user distributions.

In meta-learning and its downstream tasks, many methods rely on implicit adaptation to task variations, where multiple factors are mixed together in a single entangled representation. This makes it difficult to interpret which factors drive performance and can hinder generalization. In this work, we introduce a disentangled multi-context meta-learning framework that explicitly assigns each task factor to a distinct context vector. By decoupling these variations, our approach improves robustness through deeper task understanding and enhances generalization by enabling context vector sharing across tasks with shared factors. We evaluate our approach in two domains. First, on a sinusoidal regression task, our model outperforms baselines on out-of-distribution tasks and generalizes to unseen sine functions by sharing context vectors associated with shared amplitudes or phase shifts. Second, in a quadruped robot locomotion task, we disentangle the robot-specific properties and the characteristics of the terrain in the robot dynamics model. By transferring disentangled context vectors acquired from the dynamics model into reinforcement learning, the resulting policy achieves improved robustness under out-of-distribution conditions, surpassing the baselines that rely on a single unified context. Furthermore, by effectively sharing context, our model enables successful sim-to-real policy transfer to challenging terrains with out-of-distribution robot-specific properties, using just 20 seconds of real data from flat terrain, a result not achievable with single-task adaptation.

Energy efficiency is a critical factor in the performance and autonomy of quadrupedal robots. While previous research has focused on mechanical design and actuation improvements, the impact of gait parameters on energetics has been less explored. In this paper, we hypothesize that gait parameters, specifically duty factor, phase shift, and stride duration, are key determinants of energy consumption in quadrupedal locomotion. To test this hypothesis, we modeled the Unitree A1 quadrupedal robot and developed a locomotion controller capable of independently adjusting these gait parameters. Simulations of bounding gaits were conducted in Gazebo across a range of gait parameters at three different speeds: low, medium, and high. Experimental tests were also performed to validate the simulation results. The findings demonstrate that optimizing gait parameters can lead to significant reductions in energy consumption, enhancing the overall efficiency of quadrupedal locomotion. This work contributes to the advancement of energy-efficient control strategies for legged robots, offering insights directly applicable to commercially available platforms.

This paper investigates a joint beamforming and resource allocation problem in downlink reconfigurable intelligent surface (RIS)-assisted orthogonal frequency division multiplexing (OFDM) systems to minimize the average delay, where data packets for each user arrive at the base station (BS) stochastically. The sequential optimization problem is inherently a Markov decision process (MDP), thus falling within the remit of reinforcement learning. To effectively handle the mixed action space and reduce the state space dimensionality, a hybrid deep reinforcement learning (DRL) approach is proposed. Specifically, proximal policy optimization (PPO)-Theta is employed to optimize the RIS phase shift design, while PPO-N is responsible for subcarrier allocation decisions. The active beamforming at the BS is then derived from the jointly optimized RIS phase shifts and subcarrier allocation decisions. To further mitigate the curse of dimensionality associated with subcarrier allocation, a multi-agent strategy is introduced to optimize the subcarrier allocation indicators more efficiently. Moreover, to achieve more adaptive resource allocation and accurately capture the network dynamics, key factors closely related to average delay, such as the number of backlogged packets in buffers and current packet arrivals, are incorporated into the state space. Furthermore, a transfer learning framework is introduced to enhance the training efficiency and accelerate convergence. Simulation results demonstrate that the proposed algorithm significantly reduces the average delay, enhances resource allocation efficiency, and achieves superior system robustness and fairness compared to baseline methods.