This paper presents a novel heuristic deep reinforcement learning (HDRL) framework designed to optimize reconfigurable intelligent surface (RIS) phase shifts in secure satellite communication systems utilizing rate splitting multiple access (RSMA). The proposed HDRL approach addresses the challenges of large action spaces inherent in deep reinforcement learning by integrating heuristic algorithms, thus improving exploration efficiency and leading to faster convergence toward optimal solutions. We validate the effectiveness of HDRL through comprehensive simulations, demonstrating its superiority over traditional algorithms, including random phase shift, greedy algorithm, exhaustive search, and Deep Q-Network (DQN), in terms of secure sum rate and computational efficiency. Additionally, we compare the performance of RSMA with non-orthogonal multiple access (NOMA), highlighting that RSMA, particularly when implemented with an increased number of RIS elements, significantly enhances secure communication performance. The results indicate that HDRL is a powerful tool for improving the security and reliability of RSMA satellite communication systems, offering a practical balance between performance and computational demands.

This paper proposes a novel semi-self sensing hybrid reconfigurable intelligent surface (SS-HRIS) in terahertz (THz) bands, where the RIS is equipped with reflecting elements divided between passive and active elements in addition to sensing elements. SS-HRIS along with integrated sensing and communications (ISAC) can help to mitigate the multipath attenuation that is abundant in THz bands. In our proposed scheme, sensors are configured at the SS-HRIS to receive the radar echo signal from a target. A joint base station (BS) beamforming and HRIS precoding matrix optimization problem is proposed to maximize the sum rate of communication users while maintaining satisfactory sensing performance measured by the Cramér- Rao bound (CRB) for estimating the direction of angles of arrival (AoA) of the echo signal and thermal noise at the target. The CRB expression is first derived and the sum rate maximization problem is formulated subject to communication and sensing performance constraints. To solve the complex non-convex optimization problem, deep deterministic policy gradient (DDPG)- based deep reinforcement learning (DRL) algorithm is proposed, where the reward function, the action space and the state space are modeled. Simulation results show that the proposed DDPG- based DRL algorithm converges well and achieves better performance than several baselines, such as the soft actor-critic (SAC), proximal policy optimization (PPO), greedy algorithm and random BS beamforming and HRIS precoding matrix schemes. Moreover, it demonstrates that adopting HRIS significantly enhances the achievable sum rate compared to passive RIS and random BS beamforming and HRIS precoding matrix schemes.

This paper investigates the problem of Cramér-Rao Bound (CRB) minimization in a semi-self sensing (SS) active reconfigurable intelligent surface (RIS)-enabled integrated sensing and communication (ISAC) system in millimeter-wave (mmWave). Unlike conventional RIS, the proposed active SS-RIS architecture incorporates both reflecting and sensing elements, enabling direct radar echo reception while enhancing communication performance. A joint optimization problem is formulated to design the base station (BS) beamforming and active RIS precoding matrix with the objective of minimizing the CRB of the target's angle-of-arrival (AoA) estimation, subject to communication quality-of-service (QoS) constraints and power limitations at both the BS and RIS. Given the inherent non-convexity and computational complexity of the optimization problem, a twin delayed DDPG (TD3)-based deep reinforcement learning (DRL) framework is proposed to efficiently optimize both BS beamforming and active SS-RIS precoding matrix. Simulation results demonstrate that the proposed TD3-based approach significantly outperforms other machine learning (ML) benchmark schemes, such as deep deterministic policy gradient (DDPG) and proximal policy optimization (PPO), in terms of sensing accuracy (CRB minimization) given the present constraints. Moreover, we have shown that increasing the number of sensing elements in the SS-RIS offers substantial gains, confirming the effectiveness of active SS-RIS in enhancing both sensing and communication performance in ISAC systems.

The integration of unmanned aerial vehicles (UAVs) with mobile edge computing (MEC) and Internet of Things (IoT) technology is crucial for efficient resource management and sustainable agricultural productivity in smart frames. This paper addresses the critical need for optimizing task offloading in secure UAV-assisted smart farm networks, aiming to reduce total delay and energy consumption while maintaining robust security in data communications. We propose a multi-agent deep reinforcement learning (DRL)-based approach using a deep double Q-network (DDQN) with an action mask (AM), designed to manage task offloading dynamically and efficiently. Simulation results demonstrate the superior performance of our method in managing task offloading, highlighting significant improvements in operational efficiency, such as reduced delay and energy consumption. This aligns with the goal of developing sustainable and energy-efficient solutions for next-generation network infrastructures, making our approach an advanced solution for performance and sustainability in smart farming applications.

In precision farming, timely information is vital for effective decision-making in irrigation and pest control processes. Unmanned Aerial Vehicles (UAVs) and Multi-access Edge Computing (MEC) servers optimize data collection from ground sensors. However, integrating sensors, controllers, and actuators complicates data freshness in closed-loop communication. Computational resource optimization is also crucial, especially in energy-constrained equipment and competitive resource scenarios. This work optimizes data freshness and task turnaround time (TAT) with a UAV trajectory and MEC offloading strategy. Analysis includes assessing delays in uplink, downlink, and queues. This gains significance under dynamic network conditions and variable resources. A hierarchical deep reinforcement approach is proposed. Numerical results indicate the proposed solution outperforms baselines, improving data freshness by up to $31 \%$ and simultaneously decreasing TAT by $34 \%$.

In this work, we study the performance of reconfigurable intelligent surface (RIS)-aided communication systems over the sum of cascaded Rician fading channels. To facilitate the performance analysis of the practical scenario, we consider the case that imperfect channel state information (CSI) is available at the RIS. We derive the closed-form expressions of several performance metrics in terms of the exact outage probability, ergodic capacity and average bit error rate (BER). Through analytical and numerical results, we examine the effect of the number of reflecting elements at the RIS and different system parameters on the overall system performance.

In this letter, we study the interacting effects of the exclusion zone and eavesdropper colluding strategy on the secrecy performance of multiple-antenna transmission system. In particular, a base station (BS) applies random beamforming and user scheduling to enhance the legitimate user channel while multiple eavesdroppers try, either individually or jointly, to overhear the transmitted signal. We derive the closed-form expressions of secrecy outage probability (SOP) of the system with and without exclusion zone under different colluding strategies. Through selected numerical examples, we develop the design guideline on the size of the exclusion zone for a target security level subject to eavesdroppers' density and colluding strategy.

Random unitary beamforming (RUB) can reduce the implementation complexity of massive multiple- input singleoutput (MIMO) downlink transmission by only requiring partial channel state information (CSI) at the transmitter. In this paper, we investigate the secrecy performance of a single- cell massive MIMO system with RUB in the presence of multiple noncolluding eavesdroppers. Specifically, we derive the closed-form expressions of the secrecy outage probability (SOP) for a massive MIMO transmission and its single legitimate user case. Numerical results are presented to illustrate the performance-complexity tradeoff among RUB based, zero-forcing (ZF) beamforming based, and maximum ratio transmission (MRT) based massive MIMO transmission schemes. We show that RUB based scheme can enhance secrecy performance of massive MIMO transmission with lower implementation complexity.

Conventional beamforming transmission techniques can enhance physical layer secrecy performance while requiring the full channel state information (CSI) of legitimate users and even that of eavesdroppers at the transmitter. However, providing full CSI of legitimate users at the transmitter can be challenging in practice. Thus, it is of considerable interest to enhance secrecy performance with partial CSI of legitimate users at the transmitter. Random unitary beamforming (RUB) is a low‐complexity multiple antennas transmission scheme requiring limited CSI. In this study, the authors investigate the secrecy performance of RUB transmission over multiple‐input single‐output single‐eavesdropper and multiuser multiple‐input multiple‐output single‐eavesdropper channels. They also propose a novel RUB‐based artificial noise (AN) method for multiple antennas communication system. They derive the closed‐form expressions of the exact and the asymptotic ergodic secrecy rate and the secrecy outage probability for these transmission scenarios. Numerical results are presented to illustrate the trade‐off between performance and complexity of the resulting physical layer security design. They show that the deployment of RUB and RUB‐based AN offers an attractive solution for enhancing the security of wireless transmission systems.

Random unitary beamforming (RUB) is a multiple antenna transmission scheme requiring limited channel state information (CSI) with low computational complexity. In this paper, we investigate the security performance of RUB over a multiple-input single-output single-eavesdropper (MISOSE) broadcast channel. We derive the closed-form expressions of ergodic secrecy rate and the secrecy outage probability (SOP) of the resulting design. Meanwhile, the effect of artificial noise (AN) on physical layer (PHY) security performance of RUB-based MISOSE transmission is also investigated. Numerical results are presented to illustrate the tradeoff between performance and complexity of the proposed PHY security design. We show that the deployment of RUB offers an attractive solution for enhancing the security of wireless transmission systems.

The surge of mobile data traffic has spurred academia and industries to begin developing 5G networks. 5G is meant to overcome limitations of 4G cellular technology relying on the dominant trend of mobile network densification with the deployment of small cell base stations. To accelerate this process, low complexity and inexpensive remote radio heads (RRHs) are deployed massively and connected to a centralized pool of resources. In this work, we study the problem of inter-cell interference (ICI) which arises in frequency reuse one multi-tier 5G networks. We entrust the management of RRHs to a software-defined network controller and we take advantage of network functions virtualization. Our contributions consist of proposing Dynamic Strict Fractional Frequency Reuse (DSFFR), a method to relieve ICI which dynamically divides the small cell area in a different number of sectors. Furthermore, we formulate a joint scheduling problem composed of two schedulers which operate at different time granularity to transmit downlink packets. Modeling the coverage area with the tool of stochastic geometry and solving with simulations the joint scheduling problem, we are able to show that DSFFR outperforms the static scheme. Performances are addressed in terms of spectral efficiency and packet blocking probability.