Power line communications (PLC) surfers performance degradation due mainly to impulsive noise interference generated by electrical appliances. This thesis studies coded modulation techniques to improve the spectral efficiency and error performance of PLC. Considered in the first part is the application of bit-interleaved coded modulation with iterative decoding (BICM-ID) in class-A impulsive noise environment. In particular, the optimal soft-output demodulator and its suboptimal version are presented for an additive class-A noise (AWAN) channel so that iterative demodulation and decoding can be performed at the receiver. The effect of signal mapping on the error performance of BICM-ID systems in impulsive noise is then investigated, with both computer simulations and a tight error bound on the asymptotic performance. Extrinsic information transfer (EXIT) chart analysis is performed to illustrate the convergence properties of different mappings. The superior performance of BICMID compared to orthogonal frequency-division multiplexing (OFDM) is also clearly demonstrated.<p>Motivated by the successes of both BICM-ID and OFDM in improving the error performance of communications systems in impulsive noise environment, the second part of this thesis introduces a novel scheme of bit-interleaved coded OFDM with iterative decoding (BI-COFDM-ID) over the class-A impulsive noise channel. Here, an iterative receiver composed of outer and inner iteration loops is first described in detail. Error performance improvements of the proposed iterative receiver with different iteration strategies are presented and discussed. Performance comparisons of BI-COFDM-ID, BICM-ID and iteratively decoded OFDM are made to illustrate the superiority of BI-COFDM-ID. The effect of signal mapping on the error performance of BI-COFDM-ID is also studied.

Brachytherapy is radiation therapy performed by placing a radiation source near or inside a tumor. Difference between the current water-based brachytherapy dose formalism (TG-43) and new model based dose calculation algorithms (MBSCAs) can differ by more than a factor of 10 in the calculated doses. There is a need for voxel-by-voxel cross-section assignment, ideally, both the tissue composition and mass density of every voxel should be known for individual patients. A method for determining tissue composition via three material decomposition (3MD) from dual energy CT scans was developed at Linköping university. The method (named DIRA) is a model based iterative reconstruction algorithm that utilizes two photon energies for image reconstruction and 3MD for quantitative tissue classification of the reconstructed volumetric dataset. This thesis has investigated the accuracy of the 3MD method applied on prostate tissue in an anthropomorphic phantom when using two different approximations of soft tissues in DIRA. Also the distributions of CT-numbers for soft tissues in a contemporary dual energy CT scanner have been determined. An investigation whether these distributions can be used for tissue classification of soft tissues via thresholding has been conducted. It was found that the relative errors of mass energy absorption coefficient (MEAC) and linear attenuation coefficient (LAC) of the approximated mixture as functions of photon energy were less than 6 \% in the energy region from 1 keV to 1 MeV. This showed that DIRA performed well for the selected anthropomorphic phantom and that it was relatively insensitive to choice of base materials for the approximation of soft tissues. The distributions of CT-numbers of liver, muscle and kidney tissues overlapped. For example a voxel containing muscle could be misclassified as liver in 42 cases of 100. This suggests that pure thresholding is insufficient as a method for tissue classification of soft tissues and that more advanced methods should be used.

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Power line communications (PLC) surfers performance degradation due mainly to impulsive noise interference generated by electrical appliances. This thesis studies coded modulation techniques to improve the spectral efficiency and error performance of PLC. Considered in the first part is the application of bit-interleaved coded modulation with iterative decoding (BICM-ID) in class-A impulsive noise environment. In particular, the optimal soft-output demodulator and its suboptimal version are presented for an additive class-A noise (AWAN) channel so that iterative demodulation and decoding can be performed at the receiver. The effect of signal mapping on the error performance of BICM-ID systems in impulsive noise is then investigated, with both computer simulations and a tight error bound on the asymptotic performance. Extrinsic information transfer (EXIT) chart analysis is performed to illustrate the convergence properties of different mappings. The superior performance of BICMID compared to orthogonal frequency-division multiplexing (OFDM) is also clearly demonstrated.<p>Motivated by the successes of both BICM-ID and OFDM in improving the error performance of communications systems in impulsive noise environment, the second part of this thesis introduces a novel scheme of bit-interleaved coded OFDM with iterative decoding (BI-COFDM-ID) over the class-A impulsive noise channel. Here, an iterative receiver composed of outer and inner iteration loops is first described in detail. Error performance improvements of the proposed iterative receiver with different iteration strategies are presented and discussed. Performance comparisons of BI-COFDM-ID, BICM-ID and iteratively decoded OFDM are made to illustrate the superiority of BI-COFDM-ID. The effect of signal mapping on the error performance of BI-COFDM-ID is also studied.

Iterative processing has been shown to be very effective in multiuser space-time block coding (STBC) systems. The complexity and efficiency of an iterative receiver depend heavily on how the log-likelihood ratios (LLRs) of the coded bits are computed and exchanged at the receiver among its three major components, namely the multiuser detector, the maximum a posterior probability (MAP) demodulators and the MAP channel decoders. This thesis first presents a method to quantitatively measure the system complexities with floating-point operations (FLOPS) and a technique to evaluate the iterative receiver's convergence property based on mutual information and extrinsic information transfer (EXIT) charts.<p>Then, an integrated iterative receiver is developed by applying the sigma mappings for M-ary quadrature amplitude modulation (M-QAM) constellations. Due to the linear relationship between the coded bits and the transmitted channel symbol, the multiuser detector can work on the bit-level and hence improves the convergence property of the iterative receiver. It is shown that the integrated iterative receiver is an attractive candidate to replace the conventional receiver when a few receive antennas and a high-order M-QAM constellation are employed.<p> Finally, a more general two-loop iterative receiver is proposed by introducing an inner iteration loop between the MAP demodulators and the MAP convolutional decoders besides the outer iteration loop that involves the multiuser detection (MUD) as in the conventional iterative receiver. The proposed two-loop iterative receiver greatly improves the iteration efficiency. It is demonstrated that the proposed two-loop iterative receiver can achieve the same asymptotic performance as that of the conventional iterative receiver, but with much less outer-loop iterations.

The main objective of this dissertation is to present the structural design, performance evaluation, and complexity reduction of iterative joint channel estimation and data detection receivers. One of the main technical challenges in advanced wireless communications stems from the characteristics of a wireless channel, e.g., time selectivity of a channel, mobility of users, and multipath propagation. Channel estimation is essential for achieving reliable information transmission for practical wireless communication applications. Numerous channel estimation structures have been developed for different underlying channels using pilot-symbol assisted modulation (PSAM) approaches. However, since pilot symbols carry no data information, the time and the power spent on pilot symbols degrades the efficiency and the throughput of the system. Therefore, it is necessary to minimize the pilot insertion ratio without degrading the error performance. This motivates our research on iterative joint channel estimation and data detection receivers with full- and reduced- or low-complexity. In this thesis, we first propose an iterative channel estimator (ICE), based on a maximum a posteriori (MAP) algorithm, for single-carrier systems with PSAM structures. In contrast to existing MAP channel estimators, the proposed channel estimator has a lower computational complexity, which increases linearly with the modulation alphabet size. The computational complexity is reduced by exploiting a survivor in an efficient manner, while achieving comparable error performance to a full complexity receiver. For orthogonal frequency division multiplexing (OFDM) systems, we also propose novel signal constellations to facilitate channel estimation without pilot symbol transmission, and analyze the bit error rate for the proposed constellations. We also develop a suitable joint channel estimation and data detector with full- and low-complexity for the proposed constellations. This low-complexity ICE achieves an error performance comparable to the ICE with full-complexity. Finally, for vertical Bell Laboratories layered space-time OFDM systems, we propose an ICE based on a PSAM structure for time-varying multipath fading channels. By exploiting the statistical properties of a wireless channel, we also develop a method to suppress intercarrier interference due to the channel time selectivity, and propose a low-complexity ICE that exploits a priori information in an efficient manner.

A relatively new preconditioning technique called support graph preconditioning has many merits over the traditional incomplete factorization based methods. A major limitation of this technique is that it is applicable to symmetric diagonally dominant matrices only. This work presents a technique that can be used to transform the symmetric positive definite matrices arising from elliptic finite element problems into symmetric diagonally dominant M-matrices. The basic idea is to approximate the element gradient matrix by taking the gradients along chosen edges, whose unit vectors form a new coordinate system. For Lagrangian elements, the rows of the element gradient matrix in this new coordinate system are scaled edge vectors, thus a diagonally dominant symmetric semidefinite M-matrix can be generated to approximate the element stiffness matrix. Depending on the element type, one or more such coordinate systems are required to obtain a global nonsingular M-matrix. Since such approximation takes place at the element level, the degradation in the quality of the preconditioner is only a small constant factor independent of the size of the problem. This technique of element coordinate transformations applies to a variety of first order Lagrangian elements. Combination of this technique and other techniques enables us to construct an M-matrix preconditioner for a wide range of second order elliptic problems even with higher order elements. Another contribution of this work is the proposal of a new variant of Vaidya’s support graph preconditioning technique called modified domain partitioned support graph preconditioners. Numerical experiments are conducted for various second order elliptic finite element problems, along with performance comparison to the incomplete factorization based preconditioners. Results show that these support graph preconditioners are superior when solving ill-conditioned problems. In addition, the domain partition feature provides inherent parallelism, and initial experiments show a good potential of parallelization and scalability of these preconditioners.

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.

Recent advancements in iterative processing have allowed communication systems to perform close to capacity limits withmanageable complexity.For manychannels such as the AWGN and &#64258;at fading channels, codes that perform only a fraction of a dB from the capacity have been designed in the literature. In this dissertation, we will focus on the design and analysis of near-capacity achieving codes for another important class of channels, namely inter-symbol interference (ISI)channels. We propose various coding schemes such as low-density parity-check (LDPC) codes, parallel and serial concatenations for ISI channels when there is no spectral shaping used at the transmitter. The design and analysis techniques use the idea of extrinsic information transfer (EXIT) function matching and provide insights into the performance of di&#64256;erent codes and receiver structures. We then present a coding scheme which is the concatenation of an LDPC code with a spectral shaping block code designed to be matched to the channel??s spectrum. We will discuss how to design the shaping code and the outer LDPC code. We will show that spectral shaping matched codes can be used for the parallel concatenation to achieve near capacity performance. We will also discuss the capacity of multiple antenna ISI channels. We study the effects of transmitter and receiver diversities and noisy channel state information on channel capacity.

Fast and accurate solvers for capacitance extraction are needed by the VLSI industry in order to achieve good design quality in feasible time. With the development of technology, this demand is increasing dramatically. Three-dimensional capacitance extraction algorithms are desired due to their high accuracy. However, the present 3D algorithms are slow and thus their application is limited. In this dissertation, we present several novel techniques to significantly speed up capacitance extraction algorithms based on boundary element methods (BEM) and to compute the capacitance extraction in the presence of floating dummy conductors. We propose the PHiCap algorithm, which is based on a hierarchical refinement algorithm and the wavelet transform. Unlike traditional algorithms which result in dense linear systems, PHiCap converts the coefficient matrix in capacitance extraction problems to a sparse linear system. PHiCap solves the sparse linear system iteratively, with much faster convergence, using an efficient preconditioning technique. We also propose a variant of PHiCap in which the capacitances are solved for directly from a very small linear system. This small system is derived from the original large linear system by reordering the wavelet basis functions and computing an approximate LU factorization. We named the algorithm RedCap. To our knowledge, RedCap is the first capacitance extraction algorithm based on BEM that uses a direct method to solve a reduced linear system. In the presence of floating dummy conductors, the equivalent capacitances among regular conductors are required. For floating dummy conductors, the potential is unknown and the total charge is zero. We embed these requirements into the extraction linear system. Thus, the equivalent capacitance matrix is solved directly. The number of system solves needed is equal to the number of regular conductors. Based on a sensitivity analysis, we propose the selective coefficient enhancement method for increasing the accuracy of selected coupling or self-capacitances with only a small increase in the overall computation time. This method is desirable for applications, such as crosstalk and signal integrity analysis, where the coupling capacitances between some conductors needs high accuracy. We also propose the variable order multipole method which enhances the overall accuracy without raising the overall multipole expansion order. Finally, we apply the multigrid method to capacitance extraction to solve the linear system faster. We present experimental results to show that the techniques are significantly more efficient in comparison to existing techniques.