Sparse Blind Demixing for Low-latency Signal Recovery in Massive Iot Connectivity
Jialin Dong, Yuanming Shi, Zhi Ding
Abstract
Internet-of-Things (IoT) networks are envisioned to typically include a massive number of devices with sporadic and low-latency uplink service needs. This paper presents a blind demixing approach to support the data recovery of multiple simultaneous and unscheduled device transmissions without a priori channel state information (CSI). The proposed joint receiver leverages the group sparse bilinear characteristics of the underlying problem that involves active device detection and data recovery. We exploit the manifold geometry of rank-one matrices in the lifted bilinear equation and apply smoothed ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> /ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm to induce the group sparsity for active device detection. We further develop a smoothed Riemannian algorithm to solve the sparse blind demixing optimization problem. Numerical results demonstrate the algorithmic advantage and desirable performance of the proposed algorithm.
BibTeX
@inproceedings{icassp2019_sparseblinddemix,
title = {Sparse Blind Demixing for Low-latency Signal Recovery in Massive Iot Connectivity},
author = {Jialin Dong and Yuanming Shi and Zhi Ding},
booktitle = {ICASSP 2019},
year = {2019}
}