162 research outputs found
Estimation and detection techniques for doubly-selective channels in wireless communications
A fundamental problem in communications is the estimation of the channel.
The signal transmitted through a communications channel undergoes distortions
so that it is often received in an unrecognizable form at the receiver.
The receiver must expend significant signal processing effort in order to be
able to decode the transmit signal from this received signal. This signal processing
requires knowledge of how the channel distorts the transmit signal,
i.e. channel knowledge. To maintain a reliable link, the channel must be
estimated and tracked by the receiver.
The estimation of the channel at the receiver often proceeds by transmission
of a signal called the 'pilot' which is known a priori to the receiver.
The receiver forms its estimate of the transmitted signal based on how this
known signal is distorted by the channel, i.e. it estimates the channel from
the received signal and the pilot. This design of the pilot is a function of the
modulation, the type of training and the channel. [Continues.
Merging Belief Propagation and the Mean Field Approximation: A Free Energy Approach
We present a joint message passing approach that combines belief propagation
and the mean field approximation. Our analysis is based on the region-based
free energy approximation method proposed by Yedidia et al. We show that the
message passing fixed-point equations obtained with this combination correspond
to stationary points of a constrained region-based free energy approximation.
Moreover, we present a convergent implementation of these message passing
fixedpoint equations provided that the underlying factor graph fulfills certain
technical conditions. In addition, we show how to include hard constraints in
the part of the factor graph corresponding to belief propagation. Finally, we
demonstrate an application of our method to iterative channel estimation and
decoding in an orthogonal frequency division multiplexing (OFDM) system
A Reduced Complexity Ungerboeck Receiver for Quantized Wideband Massive SC-MIMO
Employing low resolution analog-to-digital converters in massive
multiple-input multiple-output (MIMO) has many advantages in terms of total
power consumption, cost and feasibility of such systems. However, such
advantages come together with significant challenges in channel estimation and
data detection due to the severe quantization noise present. In this study, we
propose a novel iterative receiver for quantized uplink single carrier MIMO
(SC-MIMO) utilizing an efficient message passing algorithm based on the
Bussgang decomposition and Ungerboeck factorization, which avoids the use of a
complex whitening filter. A reduced state sequence estimator with bidirectional
decision feedback is also derived, achieving remarkable complexity reduction
compared to the existing receivers for quantized SC-MIMO in the literature,
without any requirement on the sparsity of the transmission channel. Moreover,
the linear minimum mean-square-error (LMMSE) channel estimator for SC-MIMO
under frequency-selective channel, which do not require any cyclic-prefix
overhead, is also derived. We observe that the proposed receiver has
significant performance gains with respect to the existing receivers in the
literature under imperfect channel state information.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessibl
Iterative graphical algorithms for phase noise channels.
Doctoral Degree. University of KwaZulu-Natal, Durban.This thesis proposes algorithms based on graphical models to detect signals and charac-
terise the performance of communication systems in the presence of Wiener phase noise.
The algorithms exploit properties of phase noise and consequently use graphical models
to develop low complexity approaches of signal detection. The contributions are presented
in the form of papers.
The first paper investigates the effect of message scheduling on the performance of
graphical algorithms. A serial message scheduling is proposed for Orthogonal Frequency
Division Multiplexing (OFDM) systems in the presence of carrier frequency offset and
phase noise. The algorithm is shown to have better convergence compared to non-serial
scheduling algorithms.
The second paper introduces a concept referred to as circular random variables which
is based on exploiting the properties of phase noise. An iterative algorithm is proposed
to detect Low Density Parity Check (LDPC) codes in the presence of Wiener phase noise.
The proposed algorithm is shown to have similar performance as existing algorithms with
very low complexity.
The third paper extends the concept of circular variables to detect coherent optical
OFDM signals in the presence of residual carrier frequency offset and Wiener phase noise.
The proposed iterative algorithm shows a significant improvement in complexity compared
to existing algorithms.
The fourth paper proposes two methods based on minimising the free energy function
of graphical models. The first method combines the Belief Propagation (BP) and the
Uniformly Re-weighted BP (URWBP) algorithms. The second method combines the Mean
Field (MF) and the URWBP algorithms. The proposed methods are used to detect LDPC
codes in Wiener phase noise channels. The proposed methods show good balance between
complexity and performance compared to existing methods.
The last paper proposes parameter based computation of the information bounds of
the Wiener phase noise channel. The proposed methods compute the information lower
and upper bounds using parameters of the Gaussian probability density function. The
results show that these methods achieve similar performance as existing methods with low
complexity
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