3 research outputs found
Throughput Maximization in Cloud Radio Access Networks using Network Coding
This paper is interested in maximizing the total throughput of cloud radio
access networks (CRANs) in which multiple radio remote heads (RRHs) are
connected to a central computing unit known as the cloud. The transmit frame of
each RRH consists of multiple radio resources blocks (RRBs), and the cloud is
responsible for synchronizing these RRBS and scheduling them to users. Unlike
previous works that consider allocating each RRB to only a single user at each
time instance, this paper proposes to mix the flows of multiple users in each
RRB using instantly decodable network coding (IDNC). The proposed scheme is
thus designed to jointly schedule the users to different RRBs, choose the
encoded file sent in each of them, and the rate at which each of them is
transmitted. Hence, the paper maximizes the throughput which is defined as the
number of correctly received bits. To jointly fulfill this objective, we design
a graph in which each vertex represents a possible user-RRB association,
encoded file, and transmission rate. By appropriately choosing the weights of
vertices, the scheduling problem is shown to be equivalent to a maximum weight
clique problem over the newly introduced graph. Simulation results illustrate
the significant gains of the proposed scheme compared to classical coding and
uncoded solutions.Comment: 7 pages, 7 figure
Hybrid Scheduling/Signal-Level Coordination in the Downlink of Multi-Cloud Radio-Access Networks
In the context of resource allocation in cloud-radio access networks, recent
studies assume either signal-level or scheduling-level coordination. This
paper, instead, considers a hybrid level of coordination for the scheduling
problem in the downlink of a multi-cloud radio-access network, as a means to
benefit from both scheduling policies. Consider a multi-cloud radio access
network, where each cloud is connected to several base-stations (BSs) via high
capacity links, and therefore allows joint signal processing between them.
Across the multiple clouds, however, only scheduling-level coordination is
permitted, as it requires a lower level of backhaul communication. The frame
structure of every BS is composed of various time/frequency blocks, called
power-zones (PZs), and kept at fixed power level. The paper addresses the
problem of maximizing a network-wide utility by associating users to clouds and
scheduling them to the PZs, under the practical constraints that each user is
scheduled, at most, to a single cloud, but possibly to many BSs within the
cloud, and can be served by one or more distinct PZs within the BSs' frame. The
paper solves the problem using graph theory techniques by constructing the
conflict graph. The scheduling problem is, then, shown to be equivalent to a
maximum-weight independent set problem in the constructed graph, in which each
vertex symbolizes an association of cloud, user, BS and PZ, with a weight
representing the utility of that association. Simulation results suggest that
the proposed hybrid scheduling strategy provides appreciable gain as compared
to the scheduling-level coordinated networks, with a negligible degradation to
signal-level coordination
A Tutorial on Clique Problems in Communications and Signal Processing
Since its first use by Euler on the problem of the seven bridges of
K\"onigsberg, graph theory has shown excellent abilities in solving and
unveiling the properties of multiple discrete optimization problems. The study
of the structure of some integer programs reveals equivalence with graph theory
problems making a large body of the literature readily available for solving
and characterizing the complexity of these problems. This tutorial presents a
framework for utilizing a particular graph theory problem, known as the clique
problem, for solving communications and signal processing problems. In
particular, the paper aims to illustrate the structural properties of integer
programs that can be formulated as clique problems through multiple examples in
communications and signal processing. To that end, the first part of the
tutorial provides various optimal and heuristic solutions for the maximum
clique, maximum weight clique, and -clique problems. The tutorial, further,
illustrates the use of the clique formulation through numerous contemporary
examples in communications and signal processing, mainly in maximum access for
non-orthogonal multiple access networks, throughput maximization using index
and instantly decodable network coding, collision-free radio frequency
identification networks, and resource allocation in cloud-radio access
networks. Finally, the tutorial sheds light on the recent advances of such
applications, and provides technical insights on ways of dealing with mixed
discrete-continuous optimization problems