6 research outputs found
Random Access in C-RAN for User Activity Detection with Limited-Capacity Fronthaul
Cloud-Radio Access Network (C-RAN) is characterized by a hierarchical
structure in which the baseband processing functionalities of remote radio
heads (RRHs) are implemented by means of cloud computing at a Central Unit
(CU). A key limitation of C-RANs is given by the capacity constraints of the
fronthaul links connecting RRHs to the CU. In this letter, the impact of this
architectural constraint is investigated for the fundamental functions of
random access and active User Equipment (UE) identification in the presence of
a potentially massive number of UEs. In particular, the standard C-RAN approach
based on quantize-and-forward and centralized detection is compared to a scheme
based on an alternative CU-RRH functional split that enables local detection.
Both techniques leverage Bayesian sparse detection. Numerical results
illustrate the relative merits of the two schemes as a function of the system
parameters.Comment: 6 pages, 3 figures, under revision in IEEE Signal Processing Letter
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
Stable Throughput and Delay Analysis of a Random Access Network With Queue-Aware Transmission
In this work we consider a two-user and a three-user slotted ALOHA network
with multi-packet reception (MPR) capabilities. The nodes can adapt their
transmission probabilities and their transmission parameters based on the
status of the other nodes. Each user has external bursty arrivals that are
stored in their infinite capacity queues. For the two- and the three-user cases
we obtain the stability region of the system. For the two-user case we provide
the conditions where the stability region is a convex set. We perform a
detailed mathematical analysis in order to study the queueing delay by
formulating two boundary value problems (a Dirichlet and a Riemann-Hilbert
boundary value problem), the solution of which provides the generating function
of the joint stationary probability distribution of the queue size at user
nodes. Furthermore, for the two-user symmetric case with MPR we obtain a lower
and an upper bound for the average delay without explicitly computing the
generating function for the stationary joint queue length distribution. The
bounds as it is seen in the numerical results appear to be tight. Explicit
expressions for the average delay are obtained for the symmetrical model with
capture effect which is a subclass of MPR models. We also provide the optimal
transmission probability in closed form expression that minimizes the average
delay in the symmetric capture case. Finally, we evaluate numerically the
presented theoretical results.Comment: Submitted for journal publicatio