论文简报
cs.IT 2605.28318v1 值得读

Fluid Antenna System Meets Low-Resolution ADCs in Energy-Efficient Cell-Free Massive MIMO

Jun Qian, Ross Murch, Khaled B. Letaief

发布日期：2026-05-27 11:19 相关性：1.0000 价值：0.7600 分类：cs.IT
arXiv 摘要 PDF 原文 网页正文

摘要

This paper proposes a novel fluid antenna system (FAS)-enabled architecture to improve energy efficiency (EE) without sacrificing capacity. Specifically, we integrate FAS into cell-free massive MIMO systems to counteract low-resolution ADCs. We establish a comprehensive uplink transmission model and derive analytical expressions for SE and EE. These expressions explicitly capture the quantization error under slow fluid antenna multiple access and quantify the benefits of low-resolution ADCs on EE. Furthermore, we formulate a joint optimization problem to maximize EE performance. To solve this, we develop an efficient alternating optimization framework. This framework leverages the Dinkelbach algorithm-based fractional programming for power control, alongside novel accelerated projected gradient ascent (APGA) algorithms to optimize both continuous FAS positions and discrete ADC bit allocations. Numerical results reveal that low-resolution ADCs aggressively compress signals to save hardware power, which inevitably degrades SE but maintains EE. However, FASs can recover this SE loss thanks to their spatial flexibility and significantly boost EE by improving the received signal prior to destructive quantization. Furthermore, optimized power control can prevent quantization-induced multi-user interference, while efficient bit allocation can reduce exponential hardware power. Ultimately, our proposed FAS-enabled system, coupled with efficient power control and bit allocation, effectively improves system performance and outperforms traditional fixed-position antennas. It establishes a highly robust and energy-efficient paradigm for 6G networks.

相关性判断

high
相关方向
wireless_communications massive_mimo energy_efficiency quantization resource_allocation
判断依据

Directly on cell-free massive MIMO with low-resolution ADCs, spectral/energy efficiency analysis, and joint optimization for 6G communications; clearly within communications and adjacent information-theoretic review scope.

价值判断

High relevance to cs.IT wireless communications: combines cell-free massive MIMO, low-resolution ADC quantization, FAS, SE/EE analysis, and resource allocation. Structure analysis indicates substantial technical content, including analytical SE/EE derivations and a multi-block optimization framework with Dinkelbach fractional programming and APGA. The paper appears worth deeper reading because it addresses a concrete 6G energy-efficiency trade-off with explicit baselines and design variables.

核心问题与主要方法

核心问题

How to improve energy efficiency in uplink cell-free massive MIMO with low-resolution ADCs without overly sacrificing spectral efficiency

场景：Uplink TDD cell-free massive MIMO with distributed APs, user-side fluid antennas, low-resolution ADCs at APs, and slow fluid antenna multiple access

主要方法

Models low-resolution AP quantization with AQNM, including quantization distortion as a function of ADC bit depth and quantization-noise covariance in the received signal model. Uses user-side FAS position variables to alter the channel before quantization, aiming to improve received signal quality prior to ADC distortion. Evaluates uplink SE through SINR expressions under s-FAMA and local MMSE processing, with AP-level detection and CPU combining. Defines EE as sum SE divided by total power consumption, including ADC power that scales exponentially with resolution via the Walden figure-of-merit expression. Solves EE maximization through alternating optimization: Dinkelbach/fractional programming for power control and APGA with projections for FAS positions and ADC bit allocation.

关键贡献与后续阅读

关键贡献

Introduces an uplink channel and transmission model for cell-free massive MIMO with user-side 2D FASs and AP-side low-resolution ADCs, explicitly incorporating AQNM quantization effects. Derives SE and EE expressions under local MMSE processing and a detailed uplink power model to expose the trade-off between ADC hardware power savings and quantization-induced SE loss. Formulates a QoS-constrained EE maximization problem over power-control coefficients, continuous FAS positions, and ADC bit allocations. Develops an alternating optimization framework combining Dinkelbach-based fractional programming for power control with APGA-based projected updates for FAS position selection and bit allocation. Provides simulation evidence that low-to-moderate ADC precision, especially around 4-5 bits in the reported setup, can be EE-favorable and that FAS/joint optimization improves EE over fixed-position and unoptimized baselines.

研究启发

How sensitive are the reported EE gains to imperfect CSI and channel-estimation overhead for FAS positions under low-resolution ADCs? Are the APGA bit-allocation results robust when ADC bits are truly discrete rather than relaxed and projected? How do the gains compare against simpler user-centric AP selection or partial AP participation baselines with low-resolution ADCs but no FAS? Does the power model include realistic FAS movement/control overhead, or only RF/user/AP/backhaul/ADC terms?

限制与不确定性

Novelty may be partly incremental if FAS, low-resolution ADCs, and cell-free massive MIMO have each been studied separately and the main contribution is their integration. Perfect instantaneous CSI and ideal backhaul assumptions may limit practical significance. Evidence is based on abstract and structure analysis only, not full-paper verification of derivations or numerical rigor.

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