提出高效数据驱动算法，提升分布式基站系统中设备活动检测的抗干扰能力。
Robust Learning-Based Sparse Recovery for Device Activity Detection in Grant-Free Random Access Cell-Free Massive MIMO: Enhancing Resilience to Impairments
- 基于中央处理器的端到端数据驱动方法，实现多基站协同设备活动检测。
- 在非正交导频下保持高精度检测，对信号扰动和定点表示具有强鲁棒性。
- 适用于大规模机器通信场景，尤其适合抗干扰要求高的工业物联网应用。
大规模MIMO被视为支持海量机器类通信（mMTC）的关键技术。尽管共置大规模MIMO阵列的大量接入方案已广泛研究，本文探讨了在无授权随机接入的分布式基站大规模MIMO系统中进行设备活动检测的问题。通过有限簇的接入点（APs）从大量地理分布的APs协作服务更多设备。活跃设备发送非正交导频序列至各AP，AP将接收信号转发至中央处理单元（CPU）进行协同活动检测。本文提出一种简单高效的基于数据驱动的算法，集中部署于CPU。同时，研究评估了该算法对输入扰动的鲁棒性，并分析了采用定点表示对其性能的影响。
原文摘要 · Abstract (English)
Massive MIMO is considered a key enabler to support massive machine-type communication (mMTC). While massive access schemes have been extensively analyzed for co-located massive MIMO arrays, this paper explores activity detection in grant-free random access for mMTC within the context of cell-free massive MIMO systems, employing distributed antenna arrays. This sparse support recovery of device activity status is performed by a finite cluster of access points (APs) from a large number of geographically distributed APs collaborating to serve a larger number of devices. Active devices transmit non-orthogonal pilot sequences to APs, which forward the received signals to a central processing unit (CPU) for collaborative activity detection. This paper proposes a simple and efficient data-driven algorithm tailored for device activity detection, implemented centrally at the CPU. Furthermore, the study assesses the algorithm's robustness to input perturbations and examines the effects of adopting fixed-point representation on its performance.
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