Text

Data-Driven Compressed Sensing for Massive Wireless Access

The central challenge in massive machine-type communications (mMTC) is to connect a large number of uncoordinated devices through a limited spectrum. The typical mMTC communication pattern is sporadic, with short packets. This could be exploited in grant-free random access in which the activity detection, channel estimation, and data recovery are formulated as a sparse recovery problem and solved via compressed sensing algorithms. This approach results in new challenges in terms of high computational complexity and latency. We present how data-driven methods can be applied in grant-free random access and demonstrate the performance gains. Variations of neural networks for the problem are discussed, as well as future challenges and potential directions.

Ketersediaan

Informasi Detail

Judul Seri

IEEE COMMUNICATIONS MAGAZINE; Vol.60 No.11 November 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 28-34

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

NEURAL NETWORKS
WIRELESS NETWORKS
CHANNEL ESTIMATION
COMPRESSED SENSING
ARTIFICIAL NEURAL NETWORKS
SPARSE MATRICES
FUZZY CONTROL
DATA MODELS
MACHINE-TO-MACHINE COMMUNICATIONS
ANTENNA MEASUREMENTS

Info Detail Spesifik

DOI: 10.1109/MCOM.004.2200164

Pernyataan Tanggungjawab

Yanna Bai, Wei Chen, Feifei Sun, Bo Ai, Petar Popovski

Versi lain/terkait

Lampiran Berkas

Komentar