1,043 research outputs found
A New Proposed PDF for the Sub-Optimum Receiver Architecture
The detection performance of communication systems in general is limited by the presence of undesirable energy in the received signal. And this undesirable energy at communication receiver is modeled as the sum of gaussian noise and impulsive interference for which closed form probability density function generally does not exist. Due to this implementation of optimum receivers becomes very difficult. In this paper an alternate PDF is proposed written in closed form which provides a much simple architecture
Underwater acoustic communications in warm shallow water channels
Ph.DDOCTOR OF PHILOSOPH
Analysis of low-density parity-check codes on impulsive noise channels
PhD ThesisCommunication channels can severely degrade a signal, not only due to
fading effects but also interference in the form of impulsive noise. In
conventional communication systems, the additive noise at the receiver
is usually assumed to be Gaussian distributed. However, this assumption
is not always valid and examples of non-Gaussian distributed noise
include power line channels, underwater acoustic channels and manmade
interference. When designing a communication system it is useful
to know the theoretical performance in terms of bit-error probability
(BEP) on these types of channels. However, the effect of impulses on
the BEP performance has not been well studied, particularly when error correcting
codes are employed. Today, advanced error-correcting codes
with very long block lengths and iterative decoding algorithms, such as
Low-Density Parity-Check (LDPC) codes and turbo codes, are popular
due to their capacity-approaching performance. However, very long
codes are not always desirable, particularly in communications systems
where latency is a serious issue, such as in voice and video communication
between multiple users. This thesis focuses on the analysis of short
LDPC codes. Finite length analyses of LDPC codes have already been
presented for the additive white Gaussian noise channel in the literature,
but the analysis of short LDPC codes for channels that exhibit impulsive
noise has not been investigated.
The novel contributions in this thesis are presented in three sections.
First, uncoded and LDPC-coded BEP performance on channels exhibiting
impulsive noise modelled by symmetric -stable (S S) distributions
are examined. Different sub-optimal receivers are compared and a new
low-complexity receiver is proposed that achieves near-optimal performance.
Density evolution is then used to derive the threshold signal-tonoise
ratio (SNR) of LDPC codes that employ these receivers. In order
to accurately predict the waterfall performance of short LDPC codes, a
nite length analysis is proposed with the aid of the threshold SNRs of
LDPC codes and the derived uncoded BEPs for impulsive noise channels.
Second, to investigate the e ect of impulsive noise on wireless channels,
the analytic BEP on generalized fading channels with S S noise is derived.
However, it requires the evaluation of a double integral to obtain
the analytic BEP, so to reduce the computational cost, the Cauchy-
Gaussian mixture model and the asymptotic property of S S process
are used to derive upper bounds of the exact BEP. Two closed-form expressions
are derived to approximate the exact BEP on a Rayleigh fading
channel with S S noise. Then density evolution of different receivers is
derived for these channels to nd the asymptotic performance of LDPC
codes. Finally, the waterfall performance of LDPC codes is again estimated
for generalized fading channels with S S noise by utilizing the
derived uncoded BEP and threshold SNRs.
Finally, the addition of spatial diversity at the receiver is investigated.
Spatial diversity is an effective method to mitigate the effects of fading
and when used in conjunction with LDPC codes and can achieve
excellent error-correcting performance. Hence, the performance of conventional
linear diversity combining techniques are derived. Then the
SNRs of these linear combiners are compared and the relationship of
the noise power between different linear combiners is obtained. Nonlinear
detectors have been shown to achieve better performance than
linear combiners hence, optimal and sub-optimal detectors are also presented
and compared. A non-linear detector based on the bi-parameter
Cauchy-Gaussian mixture model is used and shows near-optimal performance
with a significant reduction in complexity when compared with
the optimal detector. Furthermore, we show how to apply density evolution
of LDPC codes for different combining techniques on these channels
and an estimation of the waterfall performance of LDPC codes is derived
that reduces the gap between simulated and asymptotic performance.
In conclusion, the work presented in this thesis provides a framework
to evaluate the performance of communication systems in the presence
of additive impulsive noise, with and without spatial diversity at the
receiver. For the first time, bounds on the BEP performance of LDPC
codes on channels with impulsive noise have been derived for optimal
and sub-optimal receivers, allowing other researchers to predict the performance
of LDPC codes in these type of environments without needing
to run lengthy computer simulations
Novel SαS PDF approximations and their applications in wireless signal detection
Three new approximations to the probability density function (PDF) of the symmetric alpha stable (SαS) distribution are proposed. The first two approximations use rational functions while the third approximation uses power functions. Using these approximations, new detectors for signals in symmetric alpha stable noise are also derived. Numerical results show that all these new approximations have good accuracies. Numerical results also show that the new detectors based on these approximations outperform the existing detectors, especially when the characteristic exponent of the symmetric alpha stable distribution is small
Optimal Detection for Diffusion-Based Molecular Timing Channels
This work studies optimal detection for communication over diffusion-based
molecular timing (DBMT) channels. The transmitter simultaneously releases
multiple information particles, where the information is encoded in the time of
release. The receiver decodes the transmitted information based on the random
time of arrival of the information particles, which is modeled as an additive
noise channel. For a DBMT channel without flow, this noise follows the L\'evy
distribution. Under this channel model, the maximum-likelihood (ML) detector is
derived and shown to have high computational complexity. It is also shown that
under ML detection, releasing multiple particles improves performance, while
for any additive channel with -stable noise where (such as
the DBMT channel), under linear processing at the receiver, releasing multiple
particles degrades performance relative to releasing a single particle. Hence,
a new low-complexity detector, which is based on the first arrival (FA) among
all the transmitted particles, is proposed. It is shown that for a small number
of released particles, the performance of the FA detector is very close to that
of the ML detector. On the other hand, error exponent analysis shows that the
performance of the two detectors differ when the number of released particles
is large.Comment: 16 pages, 9 figures. Submitted for publicatio
Modeling and Mitigation of Wireless Communications Interference for Spectrum Sharing with Radar
Due to both economic incentives and policy mandates, researchers increasingly face the challenge of enabling spectrum sharing between radar and wireless communications systems. In the past eight years, researchers have begun to suggest a wide variety of approaches to radar-communications spectrum sharing, ranging from transmitter design to receiver design, from spatial to temporal to other-dimensional multiplexing, and from cooperative to non-cooperative sharing. Within this diverse field of innovation, this dissertation makes two primary contributions. First, a model for wireless communications interference and its effects on adaptive-threshold radar detection is proposed. Based on both theoretical and empirical study, we find evidence for both Gaussian and non-Gaussian communications interference models, depending on the modeling situation. Further, such interference can impact radar receivers via two mechanisms—model mismatch and boost to the underlying noise floor—and both mechanisms deserve attention. Second, an innovative signal processing algorithm is proposed for radar detection in the presence of cyclostationary, linearly-modulated, digital communications (LMDC) interference (such as OFDM or CDMA) and a stationary background component. The proposed detector consists of a novel whitening filter followed by the traditional matched filter. Performance results indicate that the proposed cyclostationary-based detector outperforms a standard equivalent detector based on a stationary interference model, particularly when the number of cyclostationary LMDC transmitters is small and their interference-to-noise ratio (INR) is large relative to the stationary background
- …