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Abstract — In this paper, we present a belief propagation (BP) based algorithm for decoding non-orthogonal space-time block codes (STBC) from cyclic division algebras (CDA) having large dimensions. The proposed approach involves message passing on Markov random field (MRF) representation of the STBC MIMO system. Adoption of BP approach to decode non-orthogonal STBCs of large dimensions has not been reported so far. Our simulation results show that the proposed BP-based decoding achieves increasingly closer to SISO AWGN performance for increased number of dimensions. In addition, it also achieves near-capacity turbo coded BER performance; for e.g., with BP decoding of 24 × 24 STBC from CDA using BPSK (i.e., 576 real dimensions) and rate-1/2 turbo code (i.e., 12 bps/Hz spectral efficiency), coded BER performance close to within just about 2.5 dB from the theoretical MIMO capacity is achieved. Keywords – Non-orthogonal STBCs, large dimensions, low-complexity decoding, belief propagation, Markov random fields, high spectral efficiencies. I.

In this paper, we present a general approach to finite-memory detection. From a semi-tutorial perspective, a number of previous results are rederived and new insights are gained within a unified framework. A probabilistic derivation of the well-known Viterbi algorithm (VA), forward-backward (FB), and sum-product (SP) algorithms, shows that a basic metric emerges naturally under very general causal-ity and finite-memory conditions. This result implies that detection solutions based on one algorithm can be systematically extended to other algorithms. For stochastic channels described by a suitable parametric model, a conditional Markov property is shown to imply this finite-memory condition. Unfortunately, this property is seldom met in practice and optimality cannot be claimed. We show, however, that in the case of transmission over a linear channel characterized by a single time-invariant stochastic parameter, a finite-memory detection strategy is asymptotically optimal, regardless of the particular algorithm used (VA, FB, or SP). We consider, as examples, linear predictive and noncoherent detection schemes. The final conclusion is that while asymptotic optimality for increasing complexity can often be achieved, key issues in the design of detection algorithms are the computational efficiency and the performance for limited complexity. Index Terms MAP sequence/symbol detection, iterative detection, graph-based detection, adaptive detection, finite-memory detection, Viterbi algorithm, forward-backward algorithm, sum-product algorithm.

During the past five years, advances in the information-theoretic analysis of “one-dimensional (1D) ” recording channels have clarified the limits on linear densities that can be achieved by track-oriented magnetic and optical storage technologies. Channel architectures incorporating powerful codes, such as turbo codes and low-density parity-check codes, have been shown to achieve performance very close to the information-theoretic limits. As 1D track-oriented data storage technologies reach maturity, there is increasing interest in “two-dimensional (2D) ” recording technologies, such as two-dimensional optical storage (TwoDOS) and holographic storage. This paper provides an overview of some recently developed techniques for determining analytical bounds and simulation-based estimates for achievable densities of such 2D recording channels, as well as some recently proposed signal processing and coding methods that can move system performance closer to the information-theoretic limits.

We investigate potential turbo equalization (TE) schemes for high density advanced magnetic recording systems. Compared to conventional TE schemes based on partial response maximum likelihood (PRML) detection, filtering based TE schemes are attractive due to their low computational complexity. In this paper, we focus on soft feedback equalization (SFE) [1]. We modify the generic SFE to deal with jitter noise. Furthermore, novel bidirectional SFE (2D-SFE) and modified SFE (MSFE) schemes are proposed to improve the performance of the generic SFE. Simulation results suggest that, for longitudinal channels, SFE and its variants perform competitively with conventional PRML based schemes with a much lower complexity. For perpendicular channels, however, the performance deteriorates.

This thesis introduces a novel application of factor graphs to the domain of analog circuits. It proposes a technique of leveraging factor graphs for performing statistical yield analysis of analog circuits that is much faster than the standard Monte Carlo/Simulation Program With Integrated Circuit Emphasis (SPICE) simulation techniques. We have designed a tool chain to model an analog circuit and its corresponding factor graph and then use a Gaussian message passing approach along the edges of the graph for yield calculation. The tool is also capable of estimating unknown parameters of the circuit given known output statistics through backward message propagation in the factor graph. The tool builds upon the concept of domain-specific modeling leveraged for modeling and interpreting different kinds of analog circuits. Generic Modeling Environment (GME) is used to design modeling environment for analog circuits. It is a configurable tool set that supports creation of domain-specific design environments for different applications. This research has developed a generalized methodology that could be applied towards design automation of different kinds of analog circuits, both linear and nonlinear. The tool has been successfully used to model linear amplifier circuits and a nonlinear Metal Oxide Semiconductor Field Effect Transistor (MOSFET) circuit. The results obtained by Monte Carlo simulationsiv performed on these circuits are used as a reference in the project to compare against the tool’s results. The tool is tested for its efficiency in terms of time and accuracy against the standard results. (104 pages) To my loving family and friends.... v vi