3,285 research outputs found
Asynchronous Code-Division Random Access Using Convex Optimization
Many applications in cellular systems and sensor networks involve a random
subset of a large number of users asynchronously reporting activity to a base
station. This paper examines the problem of multiuser detection (MUD) in random
access channels for such applications. Traditional orthogonal signaling ignores
the random nature of user activity in this problem and limits the total number
of users to be on the order of the number of signal space dimensions.
Contention-based schemes, on the other hand, suffer from delays caused by
colliding transmissions and the hidden node problem. In contrast, this paper
presents a novel pairing of an asynchronous non-orthogonal code-division random
access scheme with a convex optimization-based MUD algorithm that overcomes the
issues associated with orthogonal signaling and contention-based methods. Two
key distinguishing features of the proposed MUD algorithm are that it does not
require knowledge of the delay or channel state information of every user and
it has polynomial-time computational complexity. The main analytical
contribution of this paper is the relationship between the performance of the
proposed MUD algorithm in the presence of arbitrary or random delays and two
simple metrics of the set of user codewords. The study of these metrics is then
focused on two specific sets of codewords, random binary codewords and
specially constructed algebraic codewords, for asynchronous random access. The
ensuing analysis confirms that the proposed scheme together with either of
these two codeword sets significantly outperforms the orthogonal
signaling-based random access in terms of the total number of users in the
system.Comment: Journal version of work presented at 2010 Allerton Conference on
Communication, Control and Computing. Version 2 includes additional analysis
of randomly distributed user delays as well as a comparison with a matched
filter receive
A stochastic approximation algorithm for stochastic semidefinite programming
Motivated by applications to multi-antenna wireless networks, we propose a
distributed and asynchronous algorithm for stochastic semidefinite programming.
This algorithm is a stochastic approximation of a continous- time matrix
exponential scheme regularized by the addition of an entropy-like term to the
problem's objective function. We show that the resulting algorithm converges
almost surely to an -approximation of the optimal solution
requiring only an unbiased estimate of the gradient of the problem's stochastic
objective. When applied to throughput maximization in wireless multiple-input
and multiple-output (MIMO) systems, the proposed algorithm retains its
convergence properties under a wide array of mobility impediments such as user
update asynchronicities, random delays and/or ergodically changing channels.
Our theoretical analysis is complemented by extensive numerical simulations
which illustrate the robustness and scalability of the proposed method in
realistic network conditions.Comment: 25 pages, 4 figure
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
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