MIMO Detection for High-Order QAM Based on a Gaussian Tree Approximation

This paper proposes a new detection algorithm for MIMO communication systems employing high order QAM constellations. The factor graph that corresponds to this problem is very loopy; in fact, it is a complete graph. Hence, a straightforward application of the Belief Propagation (BP) algorithm yields very poor results. Our algorithm is based on an optimal tree approximation of the Gaussian density of the unconstrained linear system. The finite-set constraint is then applied to obtain a loop-free discrete distribution. It is shown that even though the approximation is not directly applied to the exact discrete distribution, applying the BP algorithm to the loop-free factor graph outperforms current methods in terms of both performance and complexity. The improved performance of the proposed algorithm is demonstrated on the problem of MIMO detection

Adaptive Dijkstra’s Search Algorithm for MIMO detection

Employing Maximum Likelihood (ML) algorithm for signal detection in a large-scale Multiple-Input- Multiple-Output (MIMO) system with high modulation order is a computationally expensive approach. In this paper an adaptive best first search detection algorithm is proposed. The proposed Adaptive Dijkstra’s Search (ADS) algorithm exploits the resources available in the search procedure to reduce the required number of nodes to be visited in the tree. A tunable parameter is used to control the number of the best possible candidate nodes required. Unlike the conventional DS, the ADS algorithm results in signal detection with low computation complexity and quasi-optimal performance for systems under low and medium SNR regimes. Simulation results demonstrate a 25% computational complexity reduction, compared to the conventional DS

Probabilistically Bounded Soft Sphere Detection for MIMO-OFDM Receivers: Algorithm and System Architecture

Iterative soft detection and channel decoding for MIMO OFDM downlink receivers is studied in this work. Proposed inner soft sphere detection employs a variable upper bound for number of candidates per transmit antenna and utilizes the breath-first candidate-search algorithm. Upper bounds are based on probability distribution of the number of candidates found inside the spherical region formed around the received symbol-vector. Detection accuracy of unbounded breadth-first candidate search is preserved while significant reduction of the search latency and area cost is achieved. This probabilistically bounded candidate-search algorithm improves error-rate performance of non-probabilistically bounded soft sphere detection algorithms, while providing smaller detection latency with same hardware resources. Prototype architecture of soft sphere detector is synthesized on Xilinx FPGA and for an ASIC design. Using area-cost of a single soft sphere detector, a level of processing parallelism required to achieve targeted high data rates for future wireless systems (for example, 1 Gbps data rate) is determined.NokiaNational Science Foundatio

Soft Sphere Detection with Bounded Search for High-Throughput MIMO Receivers

We propose a soft sphere detection algorithm where search-bounds are determined based on the distribution of candidates found inside the sphere for different search levels. Detection accuracy of unbounded search is preserved while significant saving of memory space and reduction of latency is achieved. This probabilistic search algorithm provides significantly better frame-error rate performance than the soft K-best solution and has comparable performance and smaller computational complexity than the bounded depth-first search method. Techniques for efficient and flexible architecture design of soft sphere detectors are also presented. The estimated hardware cost is lower than the hardware cost of other soft sphere detectors from the literature, while high detection throughput per channel use is achieved

Reduced Receivers for Faster-than-Nyquist Signaling and General Linear Channels

Fast and reliable data transmission together with high bandwidth efficiency are important design aspects in a modern digital communication system. Many different approaches exist but in this thesis bandwidth efficiency is obtained by increasing the data transmission rate with the faster-than-Nyquist (FTN) framework while keeping a fixed power spectral density (PSD). In FTN consecutive information carrying symbols can overlap in time and in that way introduce a controlled amount of intentional intersymbol interference (ISI). This technique was introduced already in 1975 by Mazo and has since then been extended in many directions. Since the ISI stemming from practical FTN signaling can be of significant duration, optimum detection with traditional methods is often prohibitively complex, and alternative equalization methods with acceptable complexity-performance tradeoffs are needed. The key objective of this thesis is therefore to design reduced-complexity receivers for FTN and general linear channels that achieve optimal or near-optimal performance. Although the performance of a detector can be measured by several means, this thesis is restricted to bit error rate (BER) and mutual information results. FTN signaling is applied in two ways: As a separate uncoded narrowband communication system or in a coded scenario consisting of a convolutional encoder, interleaver and the inner ISI mechanism in serial concatenation. Turbo equalization where soft information in the form of log likelihood ratios (LLRs) is exchanged between the equalizer and the decoder is a commonly used decoding technique for coded FTN signals. The first part of the thesis considers receivers and arising stability problems when working within the white noise constraint. New M-BCJR algorithms for turbo equalization are proposed and compared to reduced-trellis VA and BCJR benchmarks based on an offset label idea. By adding a third low-complexity M-BCJR recursion, LLR quality is improved for practical values of M. M here measures the reduced number of BCJR computations for each data symbol. An improvement of the minimum phase conversion that sharpens the focus of the ISI model energy is proposed. When combined with a delayed and slightly mismatched receiver, the decoding allows a smaller M without significant loss in BER. The second part analyzes the effect of the internal metric calculations on the performance of Forney- and Ungerboeck-based reduced-complexity equalizers of the M-algorithm type for both ISI and multiple-input multiple-output (MIMO) channels. Even though the final output of a full-complexity equalizer is identical for both models, the internal metric calculations are in general different. Hence, suboptimum methods need not produce the same final output. Additionally, new models working in between the two extremes are proposed and evaluated. Note that the choice of observation model does not impact the detection complexity as the underlying algorithm is unaltered. The last part of the thesis is devoted to a different complexity reducing approach. Optimal channel shortening detectors for linear channels are optimized from an information theoretical perspective. The achievable information rates of the shortened models as well as closed form expressions for all components of the optimal detector of the class are derived. The framework used in this thesis is more general than what has been previously used within the area

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms