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Nian Xia

Publications and source records attributed to Nian Xia.

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Measured-Pattern-Aware Pinching-Antenna Systems With Coupling-Efficiency Optimization

Pinching-antenna (PA) systems have been widely investigated as a flexible architecture for waveguide-enabled wireless transmission. Existing analytical models, however, often rely on isotropic radiation assumptions and simplified couplingefficiency settings, which may overlook two practical design factors: the geometry-dependent radiation pattern of each PA and the sequential extraction of guided power along the waveguide. In this paper, we propose a measured-radiation-pattern-aware PA framework that incorporates an externally obtained radiation pattern, waveguide attenuation, and coupling-dependent power extraction. For a single PA, the resulting placement rule balances directional gain, waveguide loss, and free-space path loss, leading to a coupling-efficiency threshold for outperforming a fixed isotropic antenna. For multiple PAs, we study phase-matched placement and coupling-efficiency design under both uniform and independently controllable coupling. The uniform-coupling case yields a one-dimensional optimality condition and reveals that the preferred coupling efficiency decreases as more phasematched PAs participate in coherent combining. The independently controllable case admits a closed-form power-allocation structure, where stronger effective directional channels receive larger radiated power fractions. Numerical results based on a representative measured PA radiation pattern demonstrate the importance of jointly accounting for measured-radiation-patternaware placement and coupling-efficiency optimization.

cs.IT

Joint Power Allocation and Antenna Placement for Pinching-Antenna Systems under User Location Uncertainty

Pinching antenna systems have attracted much attention recently owing to its capability to maintain reliable line-of-sight (LoS) communication in high-frequency bands. By guiding signals through a waveguide and emitting them via a movable pinching antenna, these systems enable dynamic control of signal propagation and spatial adaptability. However, their performance heavily depends on effective resource allocation-encompassing power, bandwidth, and antenna positioning-which becomes challenging under imperfect channel state information (CSI) and user localization uncertainty. Existing studies largely assume perfect CSI or ideal user positioning, while our prior work considered uniform localization errors, an oversimplified assumption. In this paper, we develop a robust resource allocation framework for multiuser downlink pinching antenna systems under Gaussian-distributed localization uncertainty, which more accurately models real-world positioning errors. An energy efficiency (EE) maximization problem is formulated subject to probabilistic outage constraints, and an analytical power allocation strategy is derived under given antenna positions. On this basis, the heuristic particle swarm optimization (PSO) algorithm is employed to identify the antenna position that achieves the global EE configuration. Simulation results illustrate that the proposed scheme greatly enhances both EE and system reliability compared with fixed-antenna benchmark, validating its effectiveness for practical high-frequency wireless deployments.

cs.IT

Mode Switching-based STAR-RIS with Discrete Phase Shifters

The increasing demand for cost-effective, high-speed Internet of Things (IoT) applications in the coming sixth-generation (6G) networks has driven research toward maximizing spectral efficiency and simplifying hardware designs. In this context, we investigate the sum rate maximization problem for a mode-switching discrete-phase shifters simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided multi-antenna access point network, emphasizing hardware efficiency and reduced cost. A mixed-integer nonlinear optimization framework is formulated for joint optimization of the active beamforming matrix, user power allocation, and STAR-RIS phase shift vectors, including binary transmission/reflection amplitudes and discrete phase shifters. To solve the formulated problem, we employ a block coordinate descent method, dividing it into three subproblems tackled using difference-of-concave programming and combinatorial optimization techniques. Numerical results validate the effectiveness of the proposed joint optimization approach, consistently achieving superior sum rate performance compared to partial optimization methods, thereby underscoring its potential for efficient and scalable 6G IoT systems.

cs.IT