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SPARSE BLIND DEMIXING FOR LOW-LATENCY SIGNAL RECOVERY IN MASSIVE CONNECTIVITY

Citation Author(s):
Yuanming Shi, Zhi Ding
Submitted by:
Jialin Dong
Last updated:
16 February 2019 - 9:46pm
Document Type:
Poster
Document Year:
2019
Event:
Presenters:
Yuanming Shi
Paper Code:
2840

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 `1=`2-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.

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