4 research outputs found
Compressive Channel Estimation and Multi-user Detection in C-RAN
This paper considers the channel estimation (CE) and multi-user detection
(MUD) problems in cloud radio access network (C-RAN). Assuming that active
users are sparse in the network, we solve CE and MUD problems with compressed
sensing (CS) technology to greatly reduce the long identification pilot
overhead. A mixed L{2,1}-regularization functional for extended sparse
group-sparsity recovery is proposed to exploit the inherently sparse property
existing both in user activities and remote radio heads (RRHs) that active
users are attached to. Empirical and theoretical guidelines are provided to
help choosing tuning parameters which have critical effect on the performance
of the penalty functional. To speed up the processing procedure, based on
alternating direction method of multipliers and variable splitting strategy, an
efficient algorithm is formulated which is guaranteed to be convergent.
Numerical results are provided to illustrate the effectiveness of the proposed
functional and efficient algorithm.Comment: 6 pages, 3 figure
Compressive Massive Access for Internet of Things: Cloud Computing or Fog Computing?
This paper considers the support of grant-free massive access and solves the
challenge of active user detection and channel estimation in the case of a
massive number of users. By exploiting the sparsity of user activities, the
concerned problems are formulated as a compressive sensing problem, whose
solution is acquired by approximate message passing (AMP) algorithm.
Considering the cooperation of multiple access points, for the deployment of
AMP algorithm, we compare two processing paradigms, cloud computing and fog
computing, in terms of their effectiveness in guaranteeing ultra reliable
low-latency access. For cloud computing, the access points are connected in a
cloud radio access network (C-RAN) manner, and the signals received at all
access points are concentrated and jointly processed in the cloud baseband
unit. While for fog computing, based on fog radio access network (F-RAN), the
estimation of user activity and corresponding channels for the whole network is
split, and the related processing tasks are performed at the access points and
fog processing units in proximity to users. Compared to the cloud computing
paradigm based on traditional C-RAN, simulation results demonstrate the
superiority of the proposed fog computing deployment based on F-RAN.Comment: 7 pages, 7 figures, accepted by IEEE International Conference on
Communications (ICC) 2020, Dublin, Irelan