Bit-Error-Rate-Minimizing Channel Shortening Using Post-FEQ Diversity Combining and a Genetic Algorithm

In advanced wireline or wireless communication systems, i.e., DSL, IEEE 802.11a/g, HIPERLAN/2, etc., a cyclic prefix which is proportional to the channel impulse response is needed to append a multicarrier modulation (MCM) frame for operating the MCM accurately. This prefix is used to combat inter symbol interference (ISI). In some cases, the channel impulse response can be longer than the cyclic prefix (CP). One of the most useful techniques to mitigate this problem is reuse of a Channel Shortening Equalizer (CSE) as a linear preprocessor before the MCM receiver in order to shorten the effective channel length. Channel shortening filter design is a widely examined topic in the literature. Most channel shortening equalizer proposals depend on perfect channel state information (CSI). However, this information may not be available in all situations. In cases where channel state information is not needed, blind adaptive equalization techniques are appropriate. In wireline communication systems (such as DMT), the CSE design is based on maximizing the bit rate, but in wireless systems (OFDM), there is a fixed bit loading algorithm, and the performance metric is Bit Error Rate (BER) minimization. In this work, a CSE is developed for multicarrier and single-carrier cyclic prefixed (SCCP) systems which attempts to minimize the BER. To minimize the BER, a Genetic Algorithm (GA), which is an optimization method based on the principles of natural selection and genetics, is used. If the CSI is shorter than the CP, the equalization can be done by a frequency domain equalizer (FEQ), which is a bank of complex scalars. However, in the literature the adaptive FEQ design has not been well examined. The second phase of this thesis focuses on different types of algorithms for adapting the FEQ and modifying the FEQ architecture to obtain a lower BER. Simulation results show that this modified architecture yields a 20 dB improvement in BER

Blind joint maximum likelihood channel estimation and data detection for single-input multiple-output systems

A blind adaptive scheme is proposed for joint maximum likelihood (ML) channel estimation and data detection of single-input multiple-output (SIMO) systems. The joint ML optimization of the channel and data estimation is decomposed into an iterative optimization loop. An efficient global optimization algorithm termed as the repeated weighted boosting aided search is employed first to identify the unknown SIMO channel model, and then the Viterbi algorithm is used for the maximum likelihood sequence estimation of the unknown data sequence. A simulation example is used for demonstrating the efficiency of this joint ML optimization scheme designed for blind adaptive SIMO systems

Bacterial Foraging Based Channel Equalizers

A channel equalizer is one of the most important subsystems in any digital communication receiver. It is also the subsystem that consumes maximum computation time in the receiver. Traditionally maximum-likelihood sequence estimation (MLSE) was the most popular form of equalizer. Owing to non-stationary characteristics of the communication channel MLSE receivers perform poorly. Under these circumstances ‘Maximum A-posteriori Probability (MAP)’ receivers also called Bayesian receivers perform better. Natural selection tends to eliminate animals with poor “foraging strategies” and favor the propagation of genes of those animals that have successful foraging strategies since they are more likely to enjoy reproductive success. After many generations, poor foraging strategies are either eliminated or shaped into good ones (redesigned). Logically, such evolutionary principles have led scientists in the field of “foraging theory” to hypothesize that it is appropriate to model the activity of foraging as an optimization process. This thesis presents an investigation on design of bacterial foraging based channel equalizer for digital communication. Extensive simulation studies shows that the performance of the proposed receiver is close to optimal receiver for variety of channel conditions. The proposed receiver also provides near optimal performance when channel suffers from nonlinearities

Bit-error-rate Optimization for CDMA Ultra-wideband System Using Generalized Gaussian Approach

Ultra-wideband is a wireless technology arisen for future high speed multimedia applications. It can provide data rate in excess of Gigabits per second by transmitting impulse signal through the free space. However, the ultra-wideband indoor channel models proposed by the IEEE P802.15.3a suffer long multipath propagation. Due to this multipath effect, several studies have been done to improve the bit-error-rate performance of the ultra-wideband system in the existence of severe interference. Yet, most of the proposed algorithms were formulated based on the Gaussian distribution, which is not true in ultra-wideband. In this paper, we first analyze the statistical behavior of the CDMA-UWB signal by applying the Kullback-Leibler divergence index. Based on the analysis, a non-Gaussian equalizer is developed by deriving an enhanced bit-error-rate optimization algorithm using the Generalized Gaussian approach. The proposed equalizer has been shown to achieve a performance gain of at least 1.5dB to 2dB over the other equalizers simulated under IEEE P802.15.3a channel models

Joint transceiver design for MIMO channel shortening.

Channel shortening equalizers can be employed to shorten the effective impulse response of a long intersymbol interference (ISI) channel in order, for example, to decrease the computational complexity of a maximum-likelihood sequence estimator (MLSE) or to increase the throughput efficiency of an orthogonal frequency-division multiplexing (OFDM) transmission scheme. In this paper, the issue of joint transmitter–receiver filter design is addressed for shortening multiple-input multiple-output (MIMO) ISI channels. A frequency-domain approach is adopted for the transceiver design which is effectively equivalent to an infinite-length time-domain design. A practical space–frequency waterfilling algorithm is also provided. It is demonstrated that the channel shortening equalizer designed according to the time-domain approach suffers from an error-floor effect. However, the proposed techniques are shown to overcome this problem and outperform the time-domain channel shortening filter design. We also demonstrate that the proposed transceiver design can be considered as a MIMO broadband beamformer with constraints on the time-domain multipath length. Hence, a significant diversity gain could also be achieved by choosing strong eigenmodes of the MIMO channel. It is also found that the proposed frequency-domain methods have considerably low computational complexity as compared with their time-domain counterparts

Contributions to adaptive equalization and timing recovery for optical storage systems

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Efficient channel equalization algorithms for multicarrier communication systems

Blind adaptive algorithm that updates time-domain equalizer (TEQ) coefficients by Adjacent Lag Auto-correlation Minimization (ALAM) is proposed to shorten the channel for multicarrier modulation (MCM) systems. ALAM is an addition to the family of several existing correlation based algorithms that can achieve similar or better performance to existing algorithms with lower complexity. This is achieved by designing a cost function without the sum-square and utilizing symmetrical-TEQ property to reduce the complexity of adaptation of TEQ to half of the existing one. Furthermore, to avoid the limitations of lower unstable bit rate and high complexity, an adaptive TEQ using equal-taps constraints (ETC) is introduced to maximize the bit rate with the lowest complexity. An IP core is developed for the low-complexity ALAM (LALAM) algorithm to be implemented on an FPGA. This implementation is extended to include the implementation of the moving average (MA) estimate for the ALAM algorithm referred as ALAM-MA. Unit-tap constraint (UTC) is used instead of unit-norm constraint (UNC) while updating the adaptive algorithm to avoid all zero solution for the TEQ taps. The IP core is implemented on Xilinx Vertix II Pro XC2VP7-FF672-5 for ADSL receivers and the gate level simulation guaranteed successful operation at a maximum frequency of 27 MHz and 38 MHz for ALAM-MA and LALAM algorithm, respectively. FEQ equalizer is used, after channel shortening using TEQ, to recover distorted QAM signals due to channel effects. A new analytical learning based framework is proposed to jointly solve equalization and symbol detection problems in orthogonal frequency division multiplexing (OFDM) systems with QAM signals. The framework utilizes extreme learning machine (ELM) to achieve fast training, high performance, and low error rates. The proposed framework performs in real-domain by transforming a complex signal into a single 2–tuple real-valued vector. Such transformation offers equalization in real domain with minimum computational load and high accuracy. Simulation results show that the proposed framework outperforms other learning based equalizers in terms of symbol error rates and training speeds

An investigation into the performance of a power-of-two coefficient transversal equalizer in a 34Mbit/s QPSK digital radio during frequency-selective fading conditions

Bibliography: leaves 82-91.Under certain atmospheric conditions, multipath propagation can occur. The interaction of radio waves arriving at a receiver, having travelled via paths of differing length, results in the phenomenon of frequency-selective fading. This phenomenon manifests as a notch in the received spectrum and causes a severe degradation in the performance of a digital radio system. As the total power in the received bandwidth may be unaffected, the Automatic Gain Control is not able to correct for this distortion, and so other methods are required. The dissertation commences with a summary of the phenomenon of multipath as this provides the context for the investigations which follow. The adaptive equalizer was developed to combat the distortion introduced by frequency-selective fading. It achieves this by applying an estimate of the inverse of the distorting channel's transfer function. The theory on adaptive equalizers has been well established, and a summary of this theory is presented in the form of Wiener Filter theory and the Wiener-Hopf equations. An adaptive equalizer located in a 34MBit/s QPSK digital radio is required to operate at very high speed, and its digital hardware implementation is not a trivial task. In order to reduce the cost and complexity, a compromise was proposed. If the tap weights of the equalizer could be represented by power-of-two binary numbers, the equalizer circuitry can be dramatically simplified. The aim of the dissertation was to investigate the performance of this simplified equalizer structure and to determine whether a power-of-two equalizer was a viable consideration

Multiuser MIMO-OFDM for Next-Generation Wireless Systems

This overview portrays the 40-year evolution of orthogonal frequency division multiplexing (OFDM) research. The amelioration of powerful multicarrier OFDM arrangements with multiple-input multiple-output (MIMO) systems has numerous benefits, which are detailed in this treatise. We continue by highlighting the limitations of conventional detection and channel estimation techniques designed for multiuser MIMO OFDM systems in the so-called rank-deficient scenarios, where the number of users supported or the number of transmit antennas employed exceeds the number of receiver antennas. This is often encountered in practice, unless we limit the number of users granted access in the base station’s or radio port’s coverage area. Following a historical perspective on the associated design problems and their state-of-the-art solutions, the second half of this treatise details a range of classic multiuser detectors (MUDs) designed for MIMO-OFDM systems and characterizes their achievable performance. A further section aims for identifying novel cutting-edge genetic algorithm (GA)-aided detector solutions, which have found numerous applications in wireless communications in recent years. In an effort to stimulate the cross pollination of ideas across the machine learning, optimization, signal processing, and wireless communications research communities, we will review the broadly applicable principles of various GA-assisted optimization techniques, which were recently proposed also for employment inmultiuser MIMO OFDM. In order to stimulate new research, we demonstrate that the family of GA-aided MUDs is capable of achieving a near-optimum performance at the cost of a significantly lower computational complexity than that imposed by their optimum maximum-likelihood (ML) MUD aided counterparts. The paper is concluded by outlining a range of future research options that may find their way into next-generation wireless systems

Development and applications of adaptive IIR and subband filters

Adaptive infinite impulse response (IIR) filter is a challenging research area. Identifiers and Equalizers are among the most essential digital signal processing devices for digital communication systems. In this study, we consider IIR channel both for system identification and channel equalization purposes. We focus on four different approaches: Least Mean Square (LMS), Recursive Least Square (RLS), Genetic Algorithm (GA) and Subband Adaptive Filter (SAF). ). The performance of conventional LMS and RLS based IIR system identification and channel equalization are found with the help of computer simulations. And also the convergence speed and the ability to locate the global optimum solution using a population based algorithm named Genetic Algorithm is given