Massive connectivity has become a critical requirement for Internet-of-Things (IoT) networks, where a large number of devices need to connect to an access-point sporadically. Moreover, low-latency communication and sporadic device traffic are essential to support intelligent services in IoT networks. In this paper, to support low-latency communication for massive devices with sporadic traffic, we present a sparse blind demixing to simultaneously detect the active devices and decode multiple source signals without a priori channel state information in multi- in-multi-out (MIMO) networks. To address the unique challenges of bilinear measurements and sporadic device activity detection, we recast the estimation problem as a sparse and low-rank optimization problem via matrix lifting. We further propose a difference-of-convex-functions (DC) representation for the rank function to guarantee the exact rank constraint, followed by ignoring the non-convex group sparse function. This is achieved by exploiting the difference between nuclear norm and the convex Ky Fan k- norm for a rank function representation. We then develop an efficient DC algorithm to solve the resulting non-convex DC program without regularization parameter. Numerical results demonstrate that the proposed DC approach is able to exactly recover the ground truth signals with reduced sample sizes, as well as achieve better performance against noise compared with the existing convex methods.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Sparse Blind Demixing for Low-Latency Wireless Random Access with Massive Connectivity


    Contributors:
    Fu, Min (author) / Dong, Jialin (author) / Shi, Yuanming (author)


    Publication date :

    2019-09-01


    Size :

    150878 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Polar Coding and Sparse Spreading for Massive Unsourced Random Access

    Zheng, Mengfan / Wu, Yongpeng / Zhang, Wenjun | IEEE | 2020




    Noisy Demixing: Convex Relaxation Meets Nonconvex Optimization

    Huang, Shaoming / Zhou, Yong / Shi, Yuanming | IEEE | 2020


    A Slotted Polar Random Spreading Scheme for Massive MIMO Unsourced Random Access

    Liang, Zijie / Su, Yiwei / Song, Huiying et al. | IEEE | 2023