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Joint Activity Detection and Channel Estimation in Massive Machine-Type Communications with Low-Resolution ADC by Xue, Ye; Liu, An; Li, Yang; Shi, Qingjiang; Lau, Vincent is a scholarly article available to read on EtoBox.

What is Joint Activity Detection and Channel Estimation in Massive Machine-Type Communications with Low-Resolution ADC about?

In massive machine-type communications, data transmission is usually considered sporadic, and thus inherently has a sparse structure. This paper focuses on the joint activity detection (AD) and channel estimation (CE) problems in massive-connected communication systems with low-resolution analog-to-digital converters. To further exploit the sparse structure in transmission, we propose a maximum posterior probability (MAP) estimation problem based on both sporadic activity and sparse channels for joint AD and CE. Moreover, a majorization-minimization-based method is proposed for solving the MAP problem. Finally, various numerical experiments verify that the proposed scheme outperforms state-of-the-art methods.

Author

Xue, Ye; Liu, An; Li, Yang; Shi, Qingjiang; Lau, Vincent

Published

2023

Language

EN