Robust Learning-Based Sparse Recovery for Device Activity Detection in Grant-Free Random Access Cell-Free Massive MIMO: Enhancing Resilience to Impairments

• URL: http://arxiv.org/abs/2503.10280v1
• Date: Thu, 13 Mar 2025 11:46:35 GMT
• Title: Robust Learning-Based Sparse Recovery for Device Activity Detection in Grant-Free Random Access Cell-Free Massive MIMO: Enhancing Resilience to Impairments
• Authors: Ali Elkeshawy, Haifa Fares, Amor Nafkha,
• Abstract summary: This paper explores activity detection in grant-free random access for machine-type communication (mMTC)<n>It proposes a simple and efficient data-driven algorithm tailored for device activity detection, implemented centrally at the CPU.
• Score: 0.19662978733004596
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Lightweight Learning for Grant-Free Activity Detection in Cell-Free Massive MIMO Networks [0.19662978733004596]
  Grant-free random access (GF-RA) is a promising access technique for massive machine-type communications (mMTC) in future wireless networks. This study investigates the efficiency of employing supervised machine learning techniques to tackle the challenges on the device activity detection (AD) We propose a novel lightweight data-driven algorithmic framework specifically designed for activity detection in GF-RA for mMTC.
  arXiv  Detail & Related papers  (2025-03-14T11:18:47Z)
• Deep Learning-Based Approach for User Activity Detection with Grant-Free Random Access in Cell-Free Massive MIMO [0.8520624117635328]
  This paper explores the application of supervised machine learning models to tackle activity detection issues. We introduce a data-driven algorithm specifically designed for user activity detection in Cell-Free Massive Multiple-Input Multiple-Output (CF-mMIMO) networks. The results are compelling: the algorithm achieves an exceptional 99% accuracy rate, confirming its efficacy in real-world applications.
  arXiv  Detail & Related papers  (2024-06-11T11:08:33Z)
• Joint User and Data Detection in Grant-Free NOMA with Attention-based BiLSTM Network [14.569960447904327]
  We propose an attention-based bidirectional long short-term memory (BiLSTM) network to solve the multi-user detection problem. The proposed framework does not require prior knowledge of device sparsity levels and channels for performing MUD. The results show that the proposed network achieves better performance compared to existing benchmark schemes.
  arXiv  Detail & Related papers  (2022-09-14T03:18:23Z)
• Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI [108.08079323459822]
  This paper studies a new multi-intelligent edge artificial-latency (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC) We measure the inference accuracy by adopting an approximate but tractable metric, namely discriminant gain.
  arXiv  Detail & Related papers  (2022-07-03T06:57:07Z)
• Asynchronous Parallel Incremental Block-Coordinate Descent for Decentralized Machine Learning [55.198301429316125]
  Machine learning (ML) is a key technique for big-data-driven modelling and analysis of massive Internet of Things (IoT) based intelligent and ubiquitous computing. For fast-increasing applications and data amounts, distributed learning is a promising emerging paradigm since it is often impractical or inefficient to share/aggregate data. This paper studies the problem of training an ML model over decentralized systems, where data are distributed over many user devices.
  arXiv  Detail & Related papers  (2022-02-07T15:04:15Z)
• Deep Learning-Based Active User Detection for Grant-free SCMA Systems [12.565459084483045]
  We propose two novel group-based deep neural network active user detection schemes. Schemes learn the nonlinear mapping, i.e., multi-dimensional codebook structure and the channel characteristic. offline pre-trained model is able to detect the active devices without any channel state information.
  arXiv  Detail & Related papers  (2021-06-21T15:34:14Z)
• Moving Object Classification with a Sub-6 GHz Massive MIMO Array using Real Data [64.48836187884325]
  Classification between different activities in an indoor environment using wireless signals is an emerging technology for various applications. In this paper, we analyze classification of moving objects by employing machine learning on real data from a massive multi-input-multi-output (MIMO) system in an indoor environment.
  arXiv  Detail & Related papers  (2021-02-09T15:48:35Z)
• End-to-End Object Detection with Fully Convolutional Network [71.56728221604158]
  We introduce a Prediction-aware One-To-One (POTO) label assignment for classification to enable end-to-end detection. A simple 3D Max Filtering (3DMF) is proposed to utilize the multi-scale features and improve the discriminability of convolutions in the local region. Our end-to-end framework achieves competitive performance against many state-of-the-art detectors with NMS on COCO and CrowdHuman datasets.
  arXiv  Detail & Related papers  (2020-12-07T09:14:55Z)
• Coded Stochastic ADMM for Decentralized Consensus Optimization with Edge Computing [113.52575069030192]
  Big data, including applications with high security requirements, are often collected and stored on multiple heterogeneous devices, such as mobile devices, drones and vehicles. Due to the limitations of communication costs and security requirements, it is of paramount importance to extract information in a decentralized manner instead of aggregating data to a fusion center. We consider the problem of learning model parameters in a multi-agent system with data locally processed via distributed edge nodes. A class of mini-batch alternating direction method of multipliers (ADMM) algorithms is explored to develop the distributed learning model.
  arXiv  Detail & Related papers  (2020-10-02T10:41:59Z)
• Identity-Aware Attribute Recognition via Real-Time Distributed Inference in Mobile Edge Clouds [53.07042574352251]
  We design novel models for pedestrian attribute recognition with re-ID in an MEC-enabled camera monitoring system. We propose a novel inference framework with a set of distributed modules, by jointly considering the attribute recognition and person re-ID. We then devise a learning-based algorithm for the distributions of the modules of the proposed distributed inference framework.
  arXiv  Detail & Related papers  (2020-08-12T12:03:27Z)
• Jointly Sparse Signal Recovery and Support Recovery via Deep Learning with Applications in MIMO-based Grant-Free Random Access [9.709229853995991]
  We propose two model-driven approaches, based on the standard auto-encoder structure for real numbers. One is to jointly design the common measurement matrix and jointly sparse signal recovery method. The other aims to jointly design the common measurement matrix and jointly sparse support recovery method.
  arXiv  Detail & Related papers  (2020-08-05T08:26:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.