6,199 research outputs found
MIMO Networks: the Effects of Interference
Multiple-input/multiple-output (MIMO) systems promise enormous capacity
increase and are being considered as one of the key technologies for future
wireless networks. However, the decrease in capacity due to the presence of
interferers in MIMO networks is not well understood. In this paper, we develop
an analytical framework to characterize the capacity of MIMO communication
systems in the presence of multiple MIMO co-channel interferers and noise. We
consider the situation in which transmitters have no information about the
channel and all links undergo Rayleigh fading. We first generalize the known
determinant representation of hypergeometric functions with matrix arguments to
the case when the argument matrices have eigenvalues of arbitrary multiplicity.
This enables the derivation of the distribution of the eigenvalues of Gaussian
quadratic forms and Wishart matrices with arbitrary correlation, with
application to both single user and multiuser MIMO systems. In particular, we
derive the ergodic mutual information for MIMO systems in the presence of
multiple MIMO interferers. Our analysis is valid for any number of interferers,
each with arbitrary number of antennas having possibly unequal power levels.
This framework, therefore, accommodates the study of distributed MIMO systems
and accounts for different positions of the MIMO interferers.Comment: Submitted to IEEE Trans. on Info. 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
Group Importance Sampling for Particle Filtering and MCMC
Bayesian methods and their implementations by means of sophisticated Monte
Carlo techniques have become very popular in signal processing over the last
years. Importance Sampling (IS) is a well-known Monte Carlo technique that
approximates integrals involving a posterior distribution by means of weighted
samples. In this work, we study the assignation of a single weighted sample
which compresses the information contained in a population of weighted samples.
Part of the theory that we present as Group Importance Sampling (GIS) has been
employed implicitly in different works in the literature. The provided analysis
yields several theoretical and practical consequences. For instance, we discuss
the application of GIS into the Sequential Importance Resampling framework and
show that Independent Multiple Try Metropolis schemes can be interpreted as a
standard Metropolis-Hastings algorithm, following the GIS approach. We also
introduce two novel Markov Chain Monte Carlo (MCMC) techniques based on GIS.
The first one, named Group Metropolis Sampling method, produces a Markov chain
of sets of weighted samples. All these sets are then employed for obtaining a
unique global estimator. The second one is the Distributed Particle
Metropolis-Hastings technique, where different parallel particle filters are
jointly used to drive an MCMC algorithm. Different resampled trajectories are
compared and then tested with a proper acceptance probability. The novel
schemes are tested in different numerical experiments such as learning the
hyperparameters of Gaussian Processes, two localization problems in a wireless
sensor network (with synthetic and real data) and the tracking of vegetation
parameters given satellite observations, where they are compared with several
benchmark Monte Carlo techniques. Three illustrative Matlab demos are also
provided.Comment: To appear in Digital Signal Processing. Related Matlab demos are
provided at https://github.com/lukafree/GIS.gi
Analysis and Optimization of Cooperative Wireless Networks
Recently, cooperative communication between users in wireless networks has attracted a considerable amount of attention. A significant amount of research has been conducted to optimize the performance of different cooperative communication schemes, subject to some resource constraints such as power, bandwidth, and time. However, in previous research, each optimization problem has been investigated separately, and the optimal solution for one problem is usually not optimal for the other problems.
This dissertation focuses on joint optimization or cross-layer optimization in wireless cooperative networks. One important obstacle is the non-convexity of the joint optimization problem, which makes the problem difficult to solve efficiently. The first contribution of this dissertation is the proposal of a method to efficiently solve a joint optimization problem of power allocation, time scheduling and relay selection strategy in Decode-and-Forward cooperative networks. To overcome the non-convexity obstacle, the dual optimization method for non-convex problems \cite{Yu:2006}, is applied by exploiting the time-sharing properties of wireless OFDM systems when the number of subcarriers approaches infinity.
The second contribution of this dissertation is the design of practical algorithms to implement the aforementioned method for optimizing the cooperative network. The difficulty of this work is caused by the randomness of the data, specifically, the randomness of the channel condition, and the real-time requirements of computing. The proposed algorithms were analyzed rigorously and the convergence of the algorithms is shown.\\
Furthermore, a joint optimization problem of power allocation and computational functions for the advanced cooperation scheme, Compute-and-Forward, is also analyzed, and an iterative algorithm to solve this problem is also introduced
- …