Near-Field Multi-Cell ISCAP With Extremely Large-Scale Antenna Array
🤖 AI Summary
This work addresses the challenges of receiver location uncertainty and heterogeneous user interference in near-field multi-cell integrated sensing, communication, and power (ISCAP) systems. To tackle these issues, the authors propose a distributed MIMO radar architecture leveraging ultra-large-scale antenna arrays, which jointly optimizes communication SINR, wireless power transfer, and worst-case target detection performance through robust beamforming. For the first time, robust optimization is introduced into this context, accompanied by a provably tight semidefinite relaxation (SDR) approach. Furthermore, a low-complexity maximal-ratio transmission (MRT)-based suboptimal scheme with a closed-form solution is derived in the asymptotic regime of infinite antenna numbers. Simulations demonstrate the inherent trade-offs among sensing, communication, and power delivery, confirming that the proposed method achieves comparable system performance while significantly reducing computational complexity.
Application Category
📝 Abstract
This paper investigates a coordinated multi-cell integrated sensing, communication, and powering (ISCAP) system operating in the electromagnetic near field, where each base station (BS) employs an extremely large-scale antenna array (ELAA) to simultaneously support downlink communication, wireless power transfer (WPT), and environmental sensing. Three categories of communication users (CUs) with different interference cancellation capabilities are considered, and sensing is enabled through a distributed multiple-input multiple-output (MIMO) radar architecture. To address the resulting design challenges, a robust optimization framework is proposed by optimizing the beamforming strategy to maximize the worst-case detection probability over a prescribed sensing region, subject to per-user signal-to-interference-plus-noise ratio (SINR) constraints and energy harvesting requirements at energy receivers (ERs), while explicitly capturing the uncertainty in ER locations. By leveraging semidefinite relaxation (SDR), the original non-convex problem is reformulated as a convex semidefinite program with a provably tight relaxation. Furthermore, a low-complexity maximum ratio transmission (MRT)-based suboptimal scheme is developed, yielding a closed-form solution in the asymptotic regime as the number of antenna elements approaches infinity. Extensive numerical results reveal the fundamental trade-offs among sensing accuracy, communication reliability, and WPT efficiency. Specifically, our results demonstrate that: 1) the proposed SDR- and MRT-based coordinated designs consistently outperform the benchmark scheme without inter-cell coordination; 2) greater ER location uncertainty degrades overall system performance, while the proposed spatially averaged robust framework outperforms conventional worst-case robust WPT designs; 3) receiver-side interference cancellation improves sensing performance only when CUs are positioned near the sensing area; 4) although larger BS power budgets enhance sensing performance, this improvement is limited by stringent false alarm requirements; and 5) near-field ISCAP systems leveraging ELAAs provide clear advantages over far-field configurations by enabling fine-grained spatial resolution for joint energy focusing and interference suppression.
Problem
Research questions and friction points this paper is trying to address.
Integrated Sensing, Communication, and Powering (ISCAP)
Extremely Large-Scale Antenna Array (ELAA)
Near-Field
Wireless Power Transfer (WPT)
Distributed MIMO Radar
Innovation
Methods, ideas, or system contributions that make the work stand out.
Near-field ISCAP
Extremely Large-Scale Antenna Array (ELAA)
Robust Beamforming Optimization
Semidefinite Relaxation (SDR)
Integrated Sensing and Powering
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2024-07-23arXiv.orgCitations: 3
