1,427 research outputs found
Sparse Signal Processing Concepts for Efficient 5G System Design
As it becomes increasingly apparent that 4G will not be able to meet the
emerging demands of future mobile communication systems, the question what
could make up a 5G system, what are the crucial challenges and what are the key
drivers is part of intensive, ongoing discussions. Partly due to the advent of
compressive sensing, methods that can optimally exploit sparsity in signals
have received tremendous attention in recent years. In this paper we will
describe a variety of scenarios in which signal sparsity arises naturally in 5G
wireless systems. Signal sparsity and the associated rich collection of tools
and algorithms will thus be a viable source for innovation in 5G wireless
system design. We will discribe applications of this sparse signal processing
paradigm in MIMO random access, cloud radio access networks, compressive
channel-source network coding, and embedded security. We will also emphasize
important open problem that may arise in 5G system design, for which sparsity
will potentially play a key role in their solution.Comment: 18 pages, 5 figures, accepted for publication in IEEE Acces
Fixed-complexity quantum-assisted multi-user detection for CDMA and SDMA
In a system supporting numerous users the complexity of the optimal Maximum Likelihood Multi-User Detector (ML MUD) becomes excessive. Based on the superimposed constellations of K users, the ML MUD outputs the specific multilevel K-user symbol that minimizes the Euclidean distance with respect to the faded and noise-contaminated received multi-level symbol. Explicitly, the Euclidean distance is considered as the Cost Function (CF). In a system supporting K users employing M-ary modulation, the ML MUD uses MK CF evaluations (CFE) per time slot. In this contribution we propose an Early Stopping-aided Durr-Høyer algorithm-based Quantum-assisted MUD (ES-DHA QMUD) based on two techniques for achieving optimal ML detection at a low complexity. Our solution is also capable of flexibly adjusting the QMUD's performance and complexity trade-off, depending on the computing power available at the base station. We conclude by proposing a general design methodology for the ES-DHA QMUD in the context of both CDMA and SDMA systems
MIMO Systems: Principles, Iterative Techniques, and advanced Polarization
International audienceThis chapter considers the principles of multiple-input multiple-output (MIMO) wireless communication systems as well as some recent accomplishments concerning their implementation. By employing multiple antennas at both transmitter and receiver, very high data rates can be achieved under the condition of deployment in a rich-scattering propagation medium. This interesting property of MIMO systems suggests their use in the future high-rate and high-quality wireless communication systems. Several concepts in MIMO systems are reviewed in this chapter. We first consider MIMO channel models and recall the basic principles of MIMO structures and channel modeling. We next study the MIMO channel capacity and present the early developments in these systems concerning the information theory aspect. Iterative signal detection is considered next; it considers iterative techniques for space-time decoding. As the capacity is inversely proportional to the spatial channel correlation, MIMO antennas should be sufficiently separated, usually by several wavelengths. In order to minimize antennas' deployment, we present advanced polarization diversity techniques for MIMO systems and explain how they can help to reduce the spatial correlation in order to achieve high transmission rates. We end the chapter by considering the application of MIMO systems in local area networks, as well as their potential in enhancing range, localization, and power efficiency of sensor networks
Anonymous Networking amidst Eavesdroppers
The problem of security against timing based traffic analysis in wireless
networks is considered in this work. An analytical measure of anonymity in
eavesdropped networks is proposed using the information theoretic concept of
equivocation. For a physical layer with orthogonal transmitter directed
signaling, scheduling and relaying techniques are designed to maximize
achievable network performance for any given level of anonymity. The network
performance is measured by the achievable relay rates from the sources to
destinations under latency and medium access constraints. In particular,
analytical results are presented for two scenarios:
For a two-hop network with maximum anonymity, achievable rate regions for a
general m x 1 relay are characterized when nodes generate independent Poisson
transmission schedules. The rate regions are presented for both strict and
average delay constraints on traffic flow through the relay.
For a multihop network with an arbitrary anonymity requirement, the problem
of maximizing the sum-rate of flows (network throughput) is considered. A
selective independent scheduling strategy is designed for this purpose, and
using the analytical results for the two-hop network, the achievable throughput
is characterized as a function of the anonymity level. The throughput-anonymity
relation for the proposed strategy is shown to be equivalent to an information
theoretic rate-distortion function
Gossip Algorithms for Distributed Signal Processing
Gossip algorithms are attractive for in-network processing in sensor networks
because they do not require any specialized routing, there is no bottleneck or
single point of failure, and they are robust to unreliable wireless network
conditions. Recently, there has been a surge of activity in the computer
science, control, signal processing, and information theory communities,
developing faster and more robust gossip algorithms and deriving theoretical
performance guarantees. This article presents an overview of recent work in the
area. We describe convergence rate results, which are related to the number of
transmitted messages and thus the amount of energy consumed in the network for
gossiping. We discuss issues related to gossiping over wireless links,
including the effects of quantization and noise, and we illustrate the use of
gossip algorithms for canonical signal processing tasks including distributed
estimation, source localization, and compression.Comment: Submitted to Proceedings of the IEEE, 29 page
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
Compressive Sensing for Spread Spectrum Receivers
With the advent of ubiquitous computing there are two design parameters of
wireless communication devices that become very important power: efficiency and
production cost. Compressive sensing enables the receiver in such devices to
sample below the Shannon-Nyquist sampling rate, which may lead to a decrease in
the two design parameters. This paper investigates the use of Compressive
Sensing (CS) in a general Code Division Multiple Access (CDMA) receiver. We
show that when using spread spectrum codes in the signal domain, the CS
measurement matrix may be simplified. This measurement scheme, named
Compressive Spread Spectrum (CSS), allows for a simple, effective receiver
design. Furthermore, we numerically evaluate the proposed receiver in terms of
bit error rate under different signal to noise ratio conditions and compare it
with other receiver structures. These numerical experiments show that though
the bit error rate performance is degraded by the subsampling in the CS-enabled
receivers, this may be remedied by including quantization in the receiver
model. We also study the computational complexity of the proposed receiver
design under different sparsity and measurement ratios. Our work shows that it
is possible to subsample a CDMA signal using CSS and that in one example the
CSS receiver outperforms the classical receiver.Comment: 11 pages, 11 figures, 1 table, accepted for publication in IEEE
Transactions on Wireless Communication
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