1,798 research outputs found
Large-System Analysis of Multiuser Detection with an Unknown Number of Users: A High-SNR Approach
We analyze multiuser detection under the assumption that the number of users
accessing the channel is unknown by the receiver. In this environment, users'
activity must be estimated along with any other parameters such as data, power,
and location. Our main goal is to determine the performance loss caused by the
need for estimating the identities of active users, which are not known a
priori. To prevent a loss of optimality, we assume that identities and data are
estimated jointly, rather than in two separate steps. We examine the
performance of multiuser detectors when the number of potential users is large.
Statistical-physics methodologies are used to determine the macroscopic
performance of the detector in terms of its multiuser efficiency. Special
attention is paid to the fixed-point equation whose solution yields the
multiuser efficiency of the optimal (maximum a posteriori) detector in the
large signal-to-noise ratio regime. Our analysis yields closed-form approximate
bounds to the minimum mean-squared error in this regime. These illustrate the
set of solutions of the fixed-point equation, and their relationship with the
maximum system load. Next, we study the maximum load that the detector can
support for a given quality of service (specified by error probability).Comment: to appear in IEEE Transactions on Information Theor
Infinite Factorial Finite State Machine for Blind Multiuser Channel Estimation
New communication standards need to deal with machine-to-machine
communications, in which users may start or stop transmitting at any time in an
asynchronous manner. Thus, the number of users is an unknown and time-varying
parameter that needs to be accurately estimated in order to properly recover
the symbols transmitted by all users in the system. In this paper, we address
the problem of joint channel parameter and data estimation in a multiuser
communication channel in which the number of transmitters is not known. For
that purpose, we develop the infinite factorial finite state machine model, a
Bayesian nonparametric model based on the Markov Indian buffet that allows for
an unbounded number of transmitters with arbitrary channel length. We propose
an inference algorithm that makes use of slice sampling and particle Gibbs with
ancestor sampling. Our approach is fully blind as it does not require a prior
channel estimation step, prior knowledge of the number of transmitters, or any
signaling information. Our experimental results, loosely based on the LTE
random access channel, show that the proposed approach can effectively recover
the data-generating process for a wide range of scenarios, with varying number
of transmitters, number of receivers, constellation order, channel length, and
signal-to-noise ratio.Comment: 15 pages, 15 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
Multiuser detection in a dynamic environment Part I: User identification and data detection
In random-access communication systems, the number of active users varies
with time, and has considerable bearing on receiver's performance. Thus,
techniques aimed at identifying not only the information transmitted, but also
that number, play a central role in those systems. An example of application of
these techniques can be found in multiuser detection (MUD). In typical MUD
analyses, receivers are based on the assumption that the number of active users
is constant and known at the receiver, and coincides with the maximum number of
users entitled to access the system. This assumption is often overly
pessimistic, since many users might be inactive at any given time, and
detection under the assumption of a number of users larger than the real one
may impair performance.
The main goal of this paper is to introduce a general approach to the problem
of identifying active users and estimating their parameters and data in a
random-access system where users are continuously entering and leaving the
system. The tool whose use we advocate is Random-Set Theory: applying this, we
derive optimum receivers in an environment where the set of transmitters
comprises an unknown number of elements. In addition, we can derive
Bayesian-filter equations which describe the evolution with time of the a
posteriori probability density of the unknown user parameters, and use this
density to derive optimum detectors. In this paper we restrict ourselves to
interferer identification and data detection, while in a companion paper we
shall examine the more complex problem of estimating users' parameters.Comment: To be published on IEEE Transactions on Information Theor
Compressive Random Access Using A Common Overloaded Control Channel
We introduce a "one shot" random access procedure where users can send a
message without a priori synchronizing with the network. In this procedure a
common overloaded control channel is used to jointly detect sparse user
activity and sparse channel profiles. The detected information is subsequently
used to demodulate the data in dedicated frequency slots. We analyze the system
theoretically and provide a link between achievable rates and standard
compressing sensing estimates in terms of explicit expressions and scaling
laws. Finally, we support our findings with simulations in an LTE-A-like
setting allowing "one shot" sparse random access of 100 users in 1ms.Comment: 6 pages, 3 figures, published at Globecom 201
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