Efficient Solvers for Coupling-Aware Beamforming in Continuous Aperture Arrays

In continuous aperture arrays (CAPAs), careful consideration of the underlying physics is essential, among which electromagnetic (EM) mutual coupling plays a critical role in beamforming performance. Building on a physically consistent mutual coupling model, the beamforming design is formulated as a functional optimization whose optimality condition leads to a Fredholm integral equation. The incorporation of the coupling model, however, substantially increases computational complexity, necessitating efficient and accurate integral equation solvers. In this letter, we propose two efficient solvers: 1) a coordinate-transformation-based kernel approximation that preserves the operator structure while alleviating discretization demands, and 2) a direct lower-upper (LU)-based solver that stably handles the Nyström-discretized system. Numerical results demonstrate improved accuracy and reduced computational overhead compared to conventional methods, with the LU-based solver emerging as an efficient and scalable solution for large-scale CAPA optimization via offline factorization.

Evolution of UE in Massive MIMO Systems for 6G: From Passive to Active

As wireless networks continue to evolve, stringent latency and reliability requirements and highly dynamic channels expose fundamental limitations of gNB-centric massive multiple-input multiple-output (mMIMO) architectures, motivating a rethinking of the user equipment (UE) role. In response, the UE is transitioning from a passive transceiver into an active entity that directly contributes to system-level performance. In this context, this article examines the evolving role of the UE in mMIMO systems during the transition from fifth-generation (5G) to sixth-generation (6G), bridging third generation partnership project (3GPP) standardization, device implementation, and architectural innovation. Through a chronological review of 3GPP Releases 15 to 19, we highlight the progression of UE functionalities from basic channel state information (CSI) reporting to artificial intelligence (AI) and machine learning (ML)-based CSI enhancement and UE-initiated beam management. We further examine key implementation challenges, including multi-panel UE (MPUE) architectures, on-device intelligent processing, and energy-efficient operation, and then discuss corresponding architectural innovations under practical constraints. Using digital-twin-based evaluations, we validate the impact of emerging UE-centric functionalities, illustrating that UE-initiated beam reporting improves throughput in realistic mobility scenarios, while a multi-panel architecture enhances link robustness compared with a single-panel UE.