Joint Power Allocation and Radiation Optimization in NOMA-Assisted Pinching Antenna Systems

This paper explores a joint optimization of transmit power allocation and radiation coefficients in a downlink Pinching Antenna SyStem (PASS) employing Non-Orthogonal Multiple Access (NOMA). By leveraging the PASS-enabled flexible channel adjustment and NOMA's power allocation adaptability, a sum rate maximization problem is formulated with the objective of simultaneously optimizing base station (BS)'s transmit power coefficients and pinching antenna (PA)'s radiation powers. Due to its non-convexity and complexity, the formulated optimization problem is challenging to solve directly. Hence, we decompose the main problem into two sub-problems, namely transmit power allocation sub-problem and PA radiation power allocation sub-problem. In the first sub-problem, closed-form solutions are derived for the BS's power allocation among NOMA users. Meanwhile, in the second sub-problem, we optimize the PA's radiation power utilizing successive convex approximation (SCA). These two sub-problems are solved alternatively using Alternating Optimization (AO) until convergence. It should be noted that decoding order plays a significant role in NOMA-assisted PASS. Hence, two variations of decoding order are considered, namely: i) a high-complexity exhaustive search approach, and, ii) a low-complexity alternative that utilizes pre-determined channel information. Numerical results show that our proposed approach substantially improves the system's sum-rate compared to widely adopted equal power allocation PASS schemes.

Crosstalk-Resilient Beamforming for Movable Antenna Enabled Integrated Sensing and Communication

This paper investigates a movable antenna (MA) enabled integrated sensing and communication (ISAC) system under the influence of antenna crosstalk. First, it generalizes the antenna crosstalk model from the conventional fixed-position antenna (FPA) system to the MA scenario. Then, a Cramer-Rao bound (CRB) minimization problem driven by joint beamforming and antenna position design is presented. Specifically, to address this highly non-convex flexible beamforming problem, we deploy a deep reinforcement learning (DRL) approach to train a flexible beamforming agent. To ensure stability during training, a Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm is adopted to balance exploration with reward maximization for efficient and reliable learning. Numerical results demonstrate that the proposed crosstalk-resilient (CR) algorithm enhances the overall ISAC performance compared to other benchmark schemes.