Resolving the Double Near-Far Problem via Wireless Powered Pinching-Antenna Networks

This letter introduces a novel wireless powered communication system, referred to as a wireless powered pinching-antenna network (WPPAN), utilizing a single waveguide with pinching antennas to address the double near-far problem inherent in wireless powered networks. In the proposed WPPAN, users harvest energy from spatially distributed pinching antennas in the downlink and use the collected power to transmit messages in the uplink. Furthermore, to manage the combinatorial complexity associated with activating the pinching antennas, we propose three approaches of varying complexity to simplify the original resource allocation problem and then solve it efficiently using convex optimization methods. Simulation results confirm that the proposed WPPAN system effectively mitigates the double near-far problem by providing antenna resources closer to the users, thereby enhancing both downlink energy harvesting and uplink data transmission.

Pinching Antenna-enabled ISAC Systems: Exploiting Look-Angle Dependence of RCS for Target Diversity

We investigate a novel integrated sensing and communication (ISAC) system supported by pinching antennas (PAs), which can be dynamically activated along a dielectric waveguide to collect spatially diverse observations. This capability allows different PAs to view the same target from different angles across time, thereby introducing target diversity, which is a key advantage over conventional fixed antenna arrays. To quantify the sensing reliability, we adopt the outage probability as a performance metric, capturing the likelihood that the accumulated radar echo signal power falls below a detection threshold. In contrast to traditional ISAC models that assume deterministic sensing channels, we explicitly account for the look-angle dependence of radar cross-section (RCS) by modeling it as a random variable. We ensure the long-term quality-of-service (QoS) for communication users by enforcing an accumulated data rate constraint over time. We derive an exact closed-form expression for the sensing outage probability based on the distribution of weighted sums of exponentially distributed random variables. Since the resulting expression is highly non-convex and intractable for optimization, we use a tractable upper bound based on the Chernoff inequality and formulate a PA activation optimization problem. A successive convex approximation (SCA) framework is proposed to efficiently solve the formulated problem. Numerical results show that dynamically activating different PAs across time slots significantly enhances sensing reliability compared to repeatedly activating the same PA at a fixed position and conventional antenna selection schemes, respectively. These findings highlight the benefits of integrating outage-based reliability metrics and target diversity into ISAC systems using PAs.

Approximate Partially Decentralized Linear EZF Precoding for Massive MU-MIMO Systems

Massive multi-user multiple-input multiple-output (MU-MIMO) systems enable high spatial resolution, high spectral efficiency, and improved link reliability compared to traditional MIMO systems due to the large number of antenna elements deployed at the base station (BS). Nevertheless, conventional massive MU-MIMO BS transceiver designs rely on centralized linear precoding algorithms, which entail high interconnect data rates and a prohibitive complexity at the centralized baseband processing unit. In this paper, we consider an MU-MIMO system, where each user device is served with multiple independent data streams in the downlink. To address the aforementioned challenges, we propose a novel decentralized BS architecture, and develop a novel decentralized precoding algorithm based on eigen-zero-forcing (EZF). Our proposed approach relies on parallelizing the baseband processing tasks across multiple antenna clusters at the BS, while minimizing the interconnection requirements between the clusters, and is shown to closely approach the performance of centralized EZF.