40 research outputs found
Uniquely Decodable Ternary Codes for Synchronous CDMA Systems
In this paper, we consider the problem of recursively designing uniquely
decodable ternary code sets for highly overloaded synchronous code-division
multiple-access (CDMA) systems. The proposed code set achieves larger number of
users than any other known state-of-the-art ternary codes that
offer low-complexity decoders in the noisy transmission. Moreover, we propose a
simple decoder that uses only a few comparisons and can allow the user to
uniquely recover the information bits. Compared to maximum likelihood (ML)
decoder, which has a high computational complexity for even moderate code
length, the proposed decoder has much lower computational complexity. We also
derived the computational complexity of the proposed recursive decoder
analytically. Simulation results show that the performance of the proposed
decoder is almost as good as the ML decoder.Comment: arXiv admin note: text overlap with arXiv:1806.0395
Zero-delay source-channel coding
In this thesis, we investigate the zero-delay transmission of source samples over three
different types of communication channel models. First, we consider the zero-delay
transmission of a Gaussian source sample over an additive white Gaussian noise (AWGN)
channel in the presence of an additive white Gaussian (AWG) interference, which is
fully known by the transmitter. We propose three parameterized linear and non-linear
transmission schemes for this scenario, and compare the corresponding mean square
error (MSE) performances with that of a numerically optimized encoder, obtained using
the necessary optimality conditions. Next, we consider the zero-delay transmission of a
Gaussian source sample over an AWGN channel with a one-bit analog-to-digital (ADC)
front end. We study this problem under two different performance criteria, namely the
MSE distortion and the distortion outage probability (DOP), and obtain the optimal
encoder and the decoder for both criteria. As generalizations of this scenario, we consider
the performance with a K-level ADC front end as well as with multiple one-bit ADC
front ends. We derive necessary conditions for the optimal encoder and decoder, which
are then used to obtain numerically optimized encoder and decoder mappings. Finally,
we consider the transmission of a Gaussian source sample over an AWGN channel with
a one-bit ADC front end in the presence of correlated side information at the receiver.
Again, we derive the necessary optimality conditions, and using these conditions obtain
numerically optimized encoder and decoder mappings. We also consider the scenario
in which the side information is available also at the encoder, and obtain the optimal
encoder and decoder mappings. The performance of the latter scenario serves as a lower
bound on the performance of the case in which the side information is available only at
the decoder.Open Acces
Computer program for fast Karhunen Loeve transform algorithm
The fast KL transform algorithm was applied for data compression of a set of four ERTS multispectral images and its performance was compared with other techniques previously studied on the same image data. The performance criteria used here are mean square error and signal to noise ratio. The results obtained show a superior performance of the fast KL transform coding algorithm on the data set used with respect to the above stated perfomance criteria. A summary of the results is given in Chapter I and details of comparisons and discussion on conclusions are given in Chapter IV
On distributed coding, quantization of channel measurements and faster-than-Nyquist signaling
This dissertation considers three different aspects of modern digital communication
systems and is therefore divided in three parts.
The first part is distributed coding. This part deals with source and source-
channel code design issues for digital communication systems with many transmitters
and one receiver or with one transmitter and one receiver but with side information at
the receiver, which is not available at the transmitter. Such problems are attracting
attention lately, as they constitute a way of extending the classical point-to-point
communication theory to networks. In this first part of this dissertation, novel source
and source-channel codes are designed by converting each of the considered distributed
coding problems into an equivalent classical channel coding or classical source-channel
coding problem. The proposed schemes come very close to the theoretical limits and
thus, are able to exhibit some of the gains predicted by network information theory.
In the other two parts of this dissertation classical point-to-point digital com-
munication systems are considered. The second part is quantization of coded chan-
nel measurements at the receiver. Quantization is a way to limit the accuracy of
continuous-valued measurements so that they can be processed in the digital domain.
Depending on the desired type of processing of the quantized data, different quantizer
design criteria should be used. In this second part of this dissertation, the quantized
received values from the channel are processed by the receiver, which tries to recover
the transmitted information. An exhaustive comparison of several quantization cri-
teria for this case are studied providing illuminating insight for this quantizer design
problem.
The third part of this dissertation is faster-than-Nyquist signaling. The Nyquist
rate in classical point-to-point bandwidth-limited digital communication systems is
considered as the maximum transmission rate or signaling rate and is equal to twice
the bandwidth of the channel. In this last part of the dissertation, we question this
Nyquist rate limitation by transmitting at higher signaling rates through the same
bandwidth. By mitigating the incurred interference due to the faster-than-Nyquist
rates, gains over Nyquist rate systems are obtained
Decentralized Narrowband and Wideband Spectrum Sensing with Correlated Observations
This dissertation evaluates the utility of several approaches to the design of good distributed sensing systems for both narrowband and wideband spectrum sensing problems with correlated sensor observations
Optimal Cooperative Spectrum Sensing for Cognitive Radio
The rapid increasing interest in wireless communication has led to the continuous development of wireless devices and technologies. The modern convergence and interoperability of wireless technologies has further increased the amount of services that can be provided, leading to the substantial demand for access to the radio frequency spectrum in an efficient manner. Cognitive radio (CR) an innovative concept of reusing licensed spectrum in an opportunistic manner promises to overcome the evident spectrum underutilization caused by the inflexible spectrum allocation. Spectrum sensing in an unswerving and proficient manner is essential to CR. Cooperation amongst spectrum sensing devices are vital when CR systems are experiencing deep shadowing and in a fading environment. In this thesis, cooperative spectrum sensing (CSS) schemes have been designed to optimize detection performance in an efficient and implementable manner taking into consideration: diversity performance, detection accuracy, low complexity, and reporting channel bandwidth reduction. The thesis first investigates state of the art spectrums sensing algorithms in CR. Comparative analysis and simulation results highlights the different pros, cons and performance criteria of a practical CSS scheme leading to the problem formulation of the thesis. Motivated by the problem of diversity performance in a CR network, the thesis then focuses on designing a novel relay based CSS architecture for CR. A major cooperative transmission protocol with low complexity and overhead - Amplify and Forward (AF) cooperative protocol and an improved double energy detection scheme in a single relay and multiple cognitive relay networks are designed. Simulation results demonstrated that the developed algorithm is capable of reducing the error of missed detection and improving detection probability of a primary user (PU).
To improve spectrum sensing reliability while increasing agility, a CSS scheme based on evidence theory is next considered in this thesis. This focuses on a data fusion combination rule. The combination of conflicting evidences from secondary users (SUs) with the classical Dempster Shafter (DS) theory rule may produce counter-intuitive results when combining SUs sensing data leading to poor CSS performance. In order to overcome and minimise the effect of the counter-intuitive results, and to enhance performance of the CSS system, a novel state of the art evidence based decision fusion scheme is developed. The proposed approach is based on the credibility of evidence and a dissociability degree measure of the SUs sensing data evidence. Simulation results illustrate the proposed scheme improves detection performance and reduces error probability when compared to other related evidence based schemes under robust practcial scenarios.
Finally, motivated by the need for a low complexity and minmum bandwidth reporting channels which can be significant in high data rate applications, novel CSS quantization schemes are proposed. Quantization methods are considered for a maximum likelihood estimation (MLE) and an evidence based CSS scheme. For the MLE based CSS, a novel uniform and optimal output entropy quantization scheme is proposed to provide fewer overhead complexities and improved throughput. While for the Evidence based CSS scheme, a scheme that quantizes the basic probability Assignment (BPA) data at each SU before being sent to the FC is designed. The proposed scheme takes into consideration the characteristics of the hypothesis distribution under diverse signal-to-noise ratio (SNR) of the PU signal based on the optimal output entropy. Simulation results demonstrate that the proposed quantization CSS scheme improves sensing performance with minimum number of quantized bits when compared to other related approaches
Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications
Communication systems to date primarily aim at reliably communicating bit sequences. Such an approach provides efficient engineering designs that are agnostic to the meanings of the messages or to the goal that the message exchange aims to achieve. Next generation systems, however, can be potentially enriched by folding message semantics and goals of communication into their design. Further, these systems can be made cognizant of the context in which communication exchange takes place, thereby providing avenues for novel design insights. This tutorial summarizes the efforts to date, starting from its early adaptations, semantic-aware and task-oriented communications, covering the foundations, algorithms and potential implementations. The focus is on approaches that utilize information theory to provide the foundations, as well as the significant role of learning in semantics and task-aware communications