Wireless sensor networks have attracted the interest of the process industry. A process plant typically contains thousands of devices, monitoring or controlling the process. Today, all these devices are usually connected with wires. Using wireless technology simplifies deployment of new devices in a network and eliminates the need for extensive wiring. But wireless communication is also more sensitive than its wired counterpart. Therefore work is needed to make wireless sensor networks a viable option in many applications. Important issues are, for example, robustness, energy efficiency, and latency. One of the leading communication protocols for industrial wireless sensor networks is the WirelessHART protocol. This thesis investigates three ways of improving performance of the protocol, in terms of reliability and energy requirements. First, the structure of a WirelessHART packet is studied and the removal of certain fields is suggested to make the communication overhead smaller. Second, forward error correcting codes are evaluated using simulations in MATLAB. Third, measurement experiments in actual industrial environments are conducted where radio signals are transmitted and received. The variability of the received signal strength is measured and the effect that polarization diversity has on the signal variability is analyzed. The findings indicate that substantial improvements can be attained by employing polarization diversity, which can reduce channel variability and increase the expected signal strength significantly. The improvements in channel gain can be on the order of several tens of dB. The evaluations of forward error correcting codes show that the reliability is improved, with a channel gain of 3 dB. The study of the WirelessHART packet structure indicate that the packet sizes can be reduced by 15%. In turn, this also reduces energy requirements and packet error rates by 15%. This is equivalent to a gain in SNR on the order of a tenth of a dB.

In this dissertation, advanced channel decoding techniques based on bit-level soft information are studied. Two main approaches are proposed: bit-level probabilistic iterative decoding and bit-level algebraic soft-decision (list) decoding (ASD). In the first part of the dissertation, we first study iterative decoding for high density parity check (HDPC) codes. An iterative decoding algorithm, which uses the sum product algorithm (SPA) in conjunction with a binary parity check matrix adapted in each decoding iteration according to the bit-level reliabilities is proposed. In contrast to the common belief that iterative decoding is not suitable for HDPC codes, this bit-level reliability based adaptation procedure is critical to the conver-gence behavior of iterative decoding for HDPC codes and it significantly improves the iterative decoding performance of Reed-Solomon (RS) codes, whose parity check matrices are in general not sparse. We also present another iterative decoding scheme for cyclic codes by randomly shifting the bit-level reliability values in each iteration. The random shift based adaptation can also prevent iterative decoding from getting stuck with a significant complexity reduction compared with the reliability based parity check matrix adaptation and still provides reasonable good performance for short-length cyclic codes. In the second part of the dissertation, we investigate ASD for RS codes using bit-level soft information. In particular, we show that by carefully incorporating bit¬level soft information in the multiplicity assignment and the interpolation step, ASD can significantly outperform conventional hard decision decoding (HDD) for RS codes with a very small amount of complexity, even though the kernel of ASD is operating at the symbol-level. More importantly, the performance of the proposed bit-level ASD can be tightly upper bounded for practical high rate RS codes, which is in general not possible for other popular ASD schemes. Bit-level soft-decision decoding (SDD) serves as an efficient way to exploit the potential gain of many classical codes, and also facilitates the corresponding per-formance analysis. The proposed bit-level SDD schemes are potential and feasible alternatives to conventional symbol-level HDD schemes in many communication sys-tems.

This dissertation is divided into two main topics. In the first topic, we study the joint source-channel coding problem of transmitting an analog source over a Gaussian channel in two cases - (i) the presence of interference known only to the transmitter and (ii) in the presence of side information about the source known only to the receiver. We introduce hybrid digital analog forms of the Costa and Wyner-Ziv coding schemes. We present random coding based schemes in contrast to lattice based schemes proposed by Kochman and Zamir. We also discuss superimposed digital and analog schemes for the above problems which show that there are infinitely many schemes for achieving the optimal distortion for these problems. This provides an extension of the schemes proposed by Bross and others to the interference/source side information case. The result of this study shows that the proposed hybrid digital analog schemes are more robust to a mismatch in channel signal-to-noise ratio (SNR), than pure separate source coding followed by channel coding solutions. We then discuss applications of the hybrid digital analog schemes for transmitting under a channel SNR mismatch and for broadcasting a Gaussian source with bandwidth compression. We also study applications of joint source-channel coding schemes for a cognitive setup and also for the setup of transmitting an analog Gaussian source over a Gaussian channel, in the presence of an eavesdropper. In the next topic, we consider joint physical layer coding and network coding solutions for bi-directional relaying. We consider a communication system where two transmitters wish to exchange information through a central relay. The transmitter and relay nodes exchange data over synchronized, average power constrained additive white Gaussian noise channels. We propose structured coding schemes using lattices for this problem. We study two decoding approaches, namely lattice decoding and minimum angle decoding. Both the decoding schemes can be shown to achieve the upper bound at high SNRs. The proposed scheme can be thought of as a joint physical layer, network layer code which outperforms other recently proposed analog network coding schemes. We also study extensions of the bi-directional relay for the case with asymmetric channel links and also for the multi-hop case. The result of this study shows that structured coding schemes using lattices perform close to the upper bound for the above communication system models.

Turbo Code, due to its excellent error correction capability, has been widely used in many modern wireless digital communication systems as well as data storage systems in recent years. However, because the decoding of the Turbo Code involves finding all the state probability and transition sequence, its hardware implementation is not straightforward as it requires a lot of memory and memory operation. In this thesis, a design of Turbo Code decoder IP (Intellectual Property) is proposed which can be parameterized with different word-lengths and code rates. The design of the core SISO (Soft-In Soft-Out) unit used in Turbo Code decoder is based on the algorithm of SOVA (Soft-Output Viterbi Algorithm). Based on the hybrid trace-back scheme, the SISO proposed in this thesis can achieve fast path searching and path memory reduction which can be up to 70% compared with the traditional trace-back approach. In addition, every iterative of Turbo Code decoding performs two SISO operations on the block of data with normal and interleaving order. In our proposed architecture, these two SISO operations can be implemented on a single SISO unit with only slightly control overhead. In order to improve the bit error rate performance, the threshold and normalization techniques are applied to our design. In addition, the termination criteria circuit is also included in our design such that the iteration cycle of the decoding can be reduced. The proposed Turbo Code decoder has been thoroughly tested and verified, and can be qualified as a robust IP.

This thesis proposes a framework for combined source-channel coding under power and bandwidth constrained noisy channel. The framework is then applied to progressive image coding transmission using constant envelope M-ary Phase Shift Key (MPSK) signaling over an Additive White Gaussian Channel (AWGN) channel. First the framework for uncoded MPSK signaling is developed. Then, it’s extended to include coded modulation using Trellis Coded Modulation (TCM) for MPSK signaling. Simulation results show that coded MPSK signaling performs 3.1 to 5.2 dB better than uncoded MPSK signaling depending on the constellation size. Finally, an adaptive TCM system is presented for practical implementation of the proposed scheme, which outperforms uncoded MPSK system over all signal to noise ratio (Es/No) ranges for various MPSK modulation formats. In the second part of this thesis, the performance of the scheme is investigated from the channel capacity point of view. Using powerful channel codes like Turbo and Low Density Parity Check (LDPC) codes, the combined source-channel coding scheme is shown to be within 1 dB of the performance limit with MPSK channel signaling.

In this dissertation, we studied two seemingly uncorrelated, but conceptually related problems in terms of source-channel coding: 1) wireless image transmission and 2) Costa ("dirty-paper") code design. In the first part of the dissertation, we consider progressive image transmission over a wireless system employing space-time coded OFDM. The space-time coded OFDM system based on a newly built broadband MIMO fading model is theoretically evaluated by assuming perfect channel state information (CSI) at the receiver for coherent detection. Then an adaptive modulation scheme is proposed to pick the constellation size that offers the best reconstructed image quality for each average signal-to-noise ratio (SNR). A more practical scenario is also considered without the assumption of perfect CSI. We employ low-complexity decision-feedback decoding for differentially space- time coded OFDM systems to exploit transmitter diversity. For JSCC, we adopt a product channel code structure that is proven to provide powerful error protection and bursty error correction. To further improve the system performance, we also apply the powerful iterative (turbo) coding techniques and propose the iterative decoding of differentially space-time coded multiple descriptions of images. The second part of the dissertation deals with practical dirty-paper code designs. We first invoke an information-theoretical interpretation of algebraic binning and motivate the code design guidelines in terms of source-channel coding. Then two dirty-paper code designs are proposed. The first is a nested turbo construction based on soft-output trellis-coded quantization (SOTCQ) for source coding and turbo trellis- coded modulation (TTCM) for channel coding. A novel procedure is devised to balance the dimensionalities of the equivalent lattice codes corresponding to SOTCQ and TTCM. The second dirty-paper code design employs TCQ and IRA codes for near-capacity performance. This is done by synergistically combining TCQ with IRA codes so that they work together as well as they do individually. Our TCQ/IRA design approaches the dirty-paper capacity limit at the low rate regime (e.g., < 1:0 bit/sample), while our nested SOTCQ/TTCM scheme provides the best performs so far at medium-to-high rates (e.g., >= 1:0 bit/sample). Thus the two proposed practical code designs are complementary to each other.

We study the problem of communicating a discrete time analog source over a channel such that the resulting distortion is minimized. For ergodic channels, Shannon showed that separate source and channel coding is optimal. In this work we study this problem for non-ergodic channels. Although not much can be said about the general problem of transmitting any analog sources over any non-ergodic channels with any distortion metric, for many practical problems like video broadcast and voice transmission, we can gain insights by studying the transmission of a Gaussian source over a wireless channel with mean square error as the distortion measure. Motivated by different applications, we consider three different non-ergodic channel models - (1) Additive white Gaussian noise (AWGN) channel whose signal-to-noise ratio (SNR) is unknown at the transmitter; (2) Rayleigh fading multiple-input multiple-output MIMO channel whose SNR is known at the transmitter; and (3) Rayleigh fading MIMO channel whose SNR is unknown at the transmitter. The traditional approach to study these problems has been to fix certain SNRs of interest and study the corresponding achievable distortion regions. However, the problems formulated this way have not been solved even for simple setups like 2 SNRs for the AWGN channel. We are interested in performance over a wide range of SNR and hence we use the distortion SNR exponent metric to study this problem. Distortion SNR exponent is defined as the rate of decay of distortion with SNR in the high SNR limit. We study several layered transmissions schemes where the source is first compressed in layers and then the layers are transmitted using channel codes that provide variable error protection. Results show that in several cases such layered transmission schemes are optimal in terms of the distortion SNR exponent. Specifically, if the band- width expansion (number of channel uses per source sample) is b, we show that the optimal distortion SNR exponent for the AWGN channel is b and it is achievable using a superposition based layered scheme. For the L-block Rayleigh fading M x N MIMO channel the optimal exponent is characterized for b < (|N - M|+1)= min(M;N) and b > MNL2. This corresponds to the entire range of b when min(M;N) = 1 and L = 1. The results also show that the exponents obtained using layered schemes which are a small subclass of joint source channel coding (JSCC) schemes are, surprisingly, as good as and better in some cases than achievable exponent of all other JSCC schemes reported so far.

In this thesis, we consider the distributed classification problem in wireless sensor networks (WSNs). Sensor nodes in WSNs detect environmental variations and make their decisions individually, after which their decisions, possibly in the presence of faults, are transmitted to a fusion center. In literature, the distributed classification fusion using error correcting codes has been shown to have good sensor fault-tolerance capability. In this thesis, we extend the fault-tolerant classification system using error correcting code by using two-dimensional channel coding. We also extend the binary coding in literature to the M-ary code. This thesis then suggests a code construction method with low computational complexity. Based on the suggest code construction method, this thesis then conducts a series experiment to investigate the performance of the suggested method.

Rapid advances in sensor technologies have fueled massive torrents of data streaming across networks. Such large volume of information, indeed, restricts the operational performance of data processing, causing inefficiency in sensing, computation, communication and control. Hence, classical data processing techniques need to be re-analyzed and re-designed prior to be applied to modern networked data systems. This thesis aims to understand and characterize fundamental principles and interactions in and among sensing, compression, communication, computation and control, involved in networked data systems. In this regard, the thesis investigates four problems. The common theme is the design and analysis of optimized low-delay transmission strategies with affordable complexity for reliable communication of acquired data over networks with the objective of providing high quality of service for users. In the first three problems considered in the thesis, an emerging framework for data acquisition, namely, compressed sensing, is used which performs acquisition and compression simultaneously. The first problem considers the design of iterative encoding schemes, based on scalar quantization, for transmission of compressed sensing measurements over rate-limited links. Our approach is based on an analysis-by-synthesis principle, where the motivation is to reflect non-linearity in reconstruction, raised by compressed sensing, via synthesis, on choosing the best quantized value for encoding, via analysis. Our design shows significant reconstruction performance compared to schemes that only consider direct quantization of compressed sensing measurements. In the second problem, we investigate the design and analysis of encoding--decoding schemes, based on vector quantization, for transmission of compressed sensing measurements over rate-limited noisy links. In so realizing, we take an approach adapted from joint source-channel coding framework. We show that the performance of the studied system can approach the fundamental theoretical bound by optimizing the encoder-decoder pair. The price, however, is increased complexity at the encoder. To address the encoding complexity of the vector quantizer, we propose to use low-complexity multi-stage vector quantizer whose optimized design shows promising performance. In the third problem considered in the thesis, we take one step forward, and study joint source-channel coding schemes, based on vector quantization, for distributed transmission of compressed sensing measurements over noisy rate-limited links. We design optimized distributed coding schemes, and analyze theoretical bounds for such topology. Under certain conditions, our results reveal that the performance of the optimized schemes approaches the analytical bounds. In the last problem and in the context of control under communication constraints, we bring the notion of system dynamicity into the picture. Particularly, we study relations among stability in dynamical networked control systems, performance of real-time coding schemes and the coding complexity. For this purpose, we take approaches adapted from separate source-channel coding, and derive theoretical bounds on the performance of two types of coding schemes: dynamic repetition codes, and dynamic Fountain codes. We analytically and numerically show that the dynamic Fountain codes, over binary-input symmetric channels, with belief propagation decoding, are able to provide system stability in a networked control system. The results in the thesis evidently demonstrate that impressive performance gain is feasible by employing tools from communication and information theory to control and sensing. The insights offered through the design and analysis will also reveal fundamental pieces for understanding real-world networked data puzzle. / <p>QC 20140407</p>

Joint source-channel coding (JSCC) has been acknowledged to have superior performance over separate source-channel coding in terms of coding efficiency, delay and complexity. In the first part of this thesis, we study a hybrid digital-analog (HDA) JSCC system to transmit a memoryless Gaussian source over a memoryless Gaussian channel under bandwidth compression. Information-theoretic upper bounds on the asymptotically optimal mean squared error distortion of the system are obtained. An allocation scheme for distributing the channel input power between the analog and the digital signals is derived for the HDA system with mismatched channel conditions. A low-complexity and low-delay version of the system is next designed and implemented. We then propose an image communication application demonstrating the effectiveness of HDA coding. In the second part of this thesis, we consider problems in information hiding. We begin by considering a single-user joint compression and private watermarking (JCPW) problem. For memoryless Gaussian sources and memoryless Gaussian attacks, an exponential upper bound on the probability of error in decoding the watermark is derived. Numerical examples show that the error exponent is positive over a (large) subset of the entire achievable region derived by Karakos and Papamarcou (2003). We then extend the JCPW problem to a multi-user setting. Two encoders independently embed two secret information messages into two correlated host sources subject to a pair of tolerable distortion levels. The (compressed) outputs are subject to multiple access attacks. The tradeoff between the achievable watermarking rates and the compression rates is studied for discrete memoryless host sources and discrete memoryless multiple access channels. We derive an inner bound and an outer bound with single-letter characterization for the achievable compression and watermarking rate region. We next consider a problem where two correlated sources are separately embedded into a common host source. A single-letter sufficient condition is established under which the sources can be successfully embedded into the host source under multiple access attacks. Finally, we investigate a public two-user information hiding problem under multiple access attacks. Inner and outer bounds for the embedding capacity region are obtained with single-letter characterization. / Thesis (Ph.D, Mathematics & Statistics) -- Queen's University, 2007-09-28 23:11:21.398