arXiv subjects

Zhaoming Hu

Publications and source records attributed to Zhaoming Hu.

5 recordsLinked to original sources

arXiv2026

Security-Aware Pinching-Antenna Systems (PASS): Physical-Layer Security Transmission

This paper investigates heterogeneous secure multi-user transmission in pinching-antenna systems (PASS), where dynamically adjustable pinching antennas reshape both guided-wave and free-space propagation to improve communication and confidentiality performance. Unlike conventional physical-layer security designs that represent different security requirements merely through weights or thresholds, heterogeneous services may change the logical role of each receiver for each information stream. To address this issue, we establish a unified role-dependent PASS transmission framework comprising low-, medium-, and high-security modes. These modes respectively maximize the minimum legitimate-user rate, protect confidential streams against external eavesdroppers, and further prevent non-target legitimate users from intercepting unauthorized information. The resulting joint optimization of information beamforming, artificial noise, and pinching-antenna positions is formulated as a long-horizon continuous-control problem. Two learning-based controllers are then developed to provide complementary complexity-performance tradeoffs. First, heterogeneous security-aware proximal policy optimization (HSPPO) directly transforms mode-specific rate and secrecy violations into normalized smooth feedback embedded in the proximal-policy-optimization advantage, enabling lightweight and violation-sensitive control. Second, multi-relational hierarchy-aware diffusion policy optimization (MRHA-DPO) combines a PASS-aware multi-relational graph encoder, a graph-conditioned hierarchical velocity network, and exact-inversion DPO training to achieve topology-aware and expressive control. The proposed framework enables a common PASS platform to flexibly support service-dependent confidentiality requirements while balancing online efficiency and control capability.

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arXiv2026

Beamforming Design for Pinching Antenna Systems Enabled Cognitive Radio Systems

A pinching antenna system (PASS) assisted cognitive radio (CR) system is proposed. A secondary system sum rate maximization problem is formulated by jointly considering the base station (BS) power budget, the pinching antenna (PA) deployment constraints, and the interference tolerance requirements of primary users. To address the resulting non-convex problem, a tractable reformulation based on the weighted minimum mean-square error (WMMSE) approach is adopted, followed by the development of an alternating optimization (AO) algorithm. Within this framework, the auxiliary variables are updated in closed form, enabling an efficient transformation of the digital beamforming subproblem to a convex form, while the PA deployment is refined through a tailored element-wise optimization strategy. Numerical results validate the effectiveness of the proposed design and show consistent performance gains compared with conventional benchmark schemes.

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arXiv2025

PASS-Enhanced MEC: Joint Optimization of Task Offloading and Uplink PASS Beamforming

A pinching-antenna system (PASS)-enhanced mobile edge computing (MEC) architecture is investigated to improve the task offloading efficiency and latency performance in dynamic wireless environments. By leveraging dielectric waveguides and flexibly adjustable pinching antennas, PASS establishes short-distance line-of-sight (LoS) links while effectively mitigating the significant path loss and potential signal blockage, making it a promising solution for high-frequency MEC systems. We formulate a network latency minimization problem to joint optimize uplink PASS beamforming and task offloading. The resulting problem is modeled as a Markov decision process (MDP) and solved via the deep reinforcement learning (DRL) method. To address the instability introduced by the $\max$ operator in the objective function, we propose a load balancing-aware proximal policy optimization (LBPPO) algorithm. LBPPO incorporates both node-level and waveguide-level load balancing information into the policy design, maintaining computational and transmission delay equilibrium, respectively. Simulation results demonstrate that the proposed PASS-enhanced MEC with adaptive uplink PASS beamforming exhibit stronger convergence capability than fixed-PA baselines and conventional MIMO-assisted MEC, especially in scenarios with a large number of UEs or high transmit power.

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arXiv2025

Pinching Antenna Systems (PASS) for Cell-Free Communications

A pinching antenna system (PASS) assisted cell-free communication system is proposed. A sum rate maximization problem under the BS power budget constraint and PA deployment constraint is formulated. To tackle the proposed non-convex optimization problem, an alternating optimization (AO) algorithm is developed. In particular, the digital beamforming sub-problem is solved using the weighted minimum mean square error (WMMSE) method, whereas the pinching beamforming sub-problem is handled via a penalty based approach combined with element-wise optimization. Simulation results demonstrate that: 1) the PASS assisted cell-free systems achieve superior performance over benchmark schemes; 2) increasing the number of PAs per waveguides can improve the advantage of PASS assisted cell-free systems; and 3) the cell-free architecture mitigates the average user rate degradation as the number of users increases.

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arXiv2023

Caching-at-STARS: the Next Generation Edge Caching

A simultaneously transmitting and reflecting surface (STARS) enabled edge caching system is proposed for reducing backhaul traffic and ensuring the quality of service. A novel Caching-at-STARS structure, where a dedicated smart controller and cache memory are installed at the STARS, is proposed to satisfy user demands with fewer hops and desired channel conditions. Then, a joint caching replacement and information-centric hybrid beamforming optimization problem is formulated for minimizing the network power consumption. As long-term decision processes, the optimization problems based on independent and coupled phase-shift models of Caching-at-STARS contain both continuous and discrete decision variables, and are suitable for solving with deep reinforcement learning (DRL) algorithm. For the independent phase-shift Caching-at-STARS model, we develop a frequency-aware based twin delayed deep deterministic policy gradient (FA-TD3) algorithm that leverages user historical request information to serialize high-dimensional caching replacement decision variables. For the coupled phase-shift Caching-at-STARS model, we conceive a cooperative TD3 \& deep-Q network (TD3-DQN) algorithm comprised of FA-TD3 and DQN agents to decide on continuous and discrete variables respectively by observing the network external and internal environment. The numerical results demonstrate that: 1) The Caching-at-STARS-enabled edge caching system has advantages over traditional edge caching, especially in scenarios where Zipf skewness factors or cache capacity is large; 2) Caching-at-STARS outperforms the RIS-assisted edge caching systems; 3) The proposed FA-TD3 and cooperative TD3-DQN algorithms are superior in reducing network power consumption than conventional TD3.

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