Technology Category
Machine Learning: Hardware-aware MLPlanning, Routing, and Scheduling: Activity and Plan RecognitionConstraint Satisfaction and Optimization: Distributed CSP/Optimization
Robust Learning-Based Sparse Recovery for Device Activity Detection in Grant-Free Random Access Cell-Free Massive MIMO: Enhancing Resilience to Impairments
🤖 AI Summary
This work addresses device activity detection in cell-free massive MIMO mMTC systems with grant-free random access, where the goal is to recover the sparse activation pattern of a massive number of devices from non-orthogonal pilot sequences under distributed AP cooperation. We propose a lightweight, data-driven sparse recovery algorithm that integrates distributed signal acquisition with centralized learning-based detection. To our knowledge, this is the first work to systematically model and evaluate the robustness of such algorithms against practical input perturbations—including channel estimation errors—and fixed-point quantization noise. By jointly designing sparse priors, signal fusion mechanisms, and quantization-aware training, the algorithm achieves significantly improved detection accuracy in typical mMTC scenarios. It simultaneously ensures high detection precision and hardware deployment resilience, enabling low-overhead, real-time implementation on resource-constrained edge infrastructure.
Technology Category
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Device activity detection in grant-free random access for mMTC
Robust sparse recovery in cell-free massive MIMO systems
Enhancing resilience to impairments and input perturbations
Innovation
Methods, ideas, or system contributions that make the work stand out.
Data-driven algorithm for device activity detection
Collaborative detection using distributed antenna arrays
Robustness to input perturbations and fixed-point effects
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