Hybrid Driven Learning for Joint Activity Detection and Channel Estimation in IRS-Assisted Massive Connectivity

We consider the uplink connectivity for massive machine-type communications (mMTC) assisted by intelligent reconfigurable surfaces (IRSs), where device activity detection (DAD) and channel estimation (CE) are challenging due to limited pilot sequences. Moreover, differentiation among device types ca...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on wireless communications 2024-09, Vol.23 (9), p.10834-10849
Main Authors: Zheng, Shuntian, Wu, Sheng, Jia, Haoge, Jiang, Chunxiao, Kuang, Linling
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Data models
Deep learning
Hybrid driven
joint device activity detection and channel estimation
Machine learning
massive machine-type communication
Message passing
Noise levels
Performance degradation
Performance evaluation
Reconfigurable intelligent surfaces
Signal processing algorithms
Vectors
Wireless communication
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We consider the uplink connectivity for massive machine-type communications (mMTC) assisted by intelligent reconfigurable surfaces (IRSs), where device activity detection (DAD) and channel estimation (CE) are challenging due to limited pilot sequences. Moreover, differentiation among device types causes channels to deviate from the assumed characteristics, leading to performance degradation of conventional compressive sensing (CS) algorithms. To this end, two innovative networks driven by the hybrid of model and data are proposed exploiting the iterative frameworks and deep neural networks. We first present a hybrid driven iterative shrinkage thresholding algorithm, dubbed HISTA-Net, where a dual attention network (DAN) is embedded within the iterations to adaptively suppress iterative noise and enhance sparsity properties. Subsequently, we encapsulate data driven network and the intrinsic channel matrix knowledge, and derive a hybrid driven approximate message passing network (HAMP-Net) to further improve the sparse recovery performance. Our experiments demonstrate that the proposed networks outperform existing CS methodologies and deep learning strategies in accuracy, convergence, and generalization ability. Remarkably, the proposed HAMP-Net reduces pilot overhead by 30%, and achieves an NMSE gain of 3 dB for signal-to-noise ratios exceeding 15 dB.
ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2024.3376381