AMP-based Joint Activity Detection and Channel Estimation for Massive Grant-Free Access in OFDM-based Wideband Systems
Zhiyan Li, Ying Cui, Danny H.K. Tsang

TL;DR
This paper introduces AMP-based algorithms for joint device activity detection and channel estimation in OFDM wideband systems, significantly improving accuracy and reducing computation time under frequency-selective fading.
Contribution
It develops a new factor graph model and two AMP algorithms for the first time to address joint detection and estimation in this context.
Findings
AMP algorithms outperform existing methods in accuracy
AMP-A-AC has lower computational complexity
Algorithms effectively handle small pilot lengths
Abstract
To realize orthogonal frequency division multiplexing (OFDM)-based grant-free access for wideband systems under frequency-selective fading, existing device activity detection and channel estimation methods need substantial accuracy improvement or computation time reduction. In this paper, we aim to resolve this issue. First, we present an exact time-domain signal model for OFDM-based grant-free access under frequency-selective fading. Then, we present a maximum a posteriori (MAP)-based device activity detection problem and two minimum mean square error (MMSE)-based channel estimation problems. The MAP-based device activity detection problem and one of the MMSE-based channel estimation problems are formulated for the first time. Next, we build a new factor graph that captures the exact statistics of time-domain channels and device activities. Based on it, we propose two approximate…
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Taxonomy
TopicsIndoor and Outdoor Localization Technologies · Wireless Networks and Protocols · Advanced Wireless Communication Technologies
