Low Power Adaptive Equaliser Architectures for Wireless LMMSE Receivers

Power consumption requires critical consideration during system design for portable wireless communication devices as it has a direct influence on the battery weight and volume required for operation. Wideband Code Division Multiple Access (W-CDMA) techniques are favoured for use in future generation mobile communication systems. This thesis investigates novel low power techniques for use in system blocks within a W-CDMA adaptive linear minimum mean squared error (LMMSE) receiver architecture. Two low power techniques are presented for reducing power dissipation in the LMS adaptive filter, this being the main power consuming block within this receiver. These low power techniques are namely the decorrelating transform, this is a differential coefficient technique, and the variable length update algorithm which is a dynamic tap-length optimisation technique. The decorrelating transform is based on the principle of reducing the wordlength of filter coefficients by using the computed difference between adjacent coefficients in calculation of the filter output. The effect of reducing the wordlength of filter coefficients being presented to multipliers in the filter is a reduction in switching activity within the multiplier thus reducing power consumed. In the case of the LMS adaptive filter, with coefficients being continuously updated, the decorrelating transform is applied to these calculated coefficients with minimal hardware or computational overhead. The correlation between filter coefficients is exploited to achieve a wordlength reduction from 16 bits down to 10 bits in the FIR filter block. The variable length update algorithm is based on the principle of optimising the number of operational filter taps in the LMS adaptive filter according to operating conditions. The number of taps in operation can be increased or decreased dynamically according to the mean squared error at the output of the filter. This algorithm is used to exploit the fact that when the SNR in the channel is low the minimum mean squared error of the short equaliser is almost the same as that of the longer equaliser. Therefore, minimising the length of the equaliser will not result in poorer MSE performance and there is no disadvantage in having fewer taps in operation. If fewer taps are in operation then switching will not only be reduced in the arithmetic blocks but also in the memory blocks required by the LMS algorithm and FIR filter process. This reduces the power consumed by both these computation intensive functional blocks. Power results are obtained for equaliser lengths from 73 to 16 taps and for operation with varying input SNR. This thesis then proposes that the variable length LMS adaptive filter is applied in the adaptive LMMSE receiver to create a low power implementation. Power consumption in the receiver is reduced by the dynamic optimisation of the LMS receiver coefficient calculation. A considerable power saving is seen to be achieved when moving from a fixed length LMS implementation to the variable length design. All design architectures are coded in Verilog hardware description language at register transfer level (RTL). Once functional specification of the design is verified, synthesis is carried out using either Synopsys DesignCompiler or Cadence BuildGates to create a gate level netlist. Power consumption results are determined at the gate level and estimated using the Synopsys DesignPower tool

Capacity, coding and interference cancellation in multiuser multicarrier wireless communications systems

Multicarrier modulation and multiuser systems have generated a great deal of research during the last decade. Orthogonal Frequency Division Multiplexing (OFDM) is a multicarrier modulation generated with the inverse Discrete Fourier Transform, which has been adopted for standards in wireless and wire-line communications. Multiuser wireless systems using multicarrier modulation suffer from the effects of dispersive fading channels, which create multi-access, inter-symbol, and inter-carrier interference (MAI, ISI, ICI). Nevertheless, channel dispersion also provides diversity, which can be exploited and has the potential to increase robustness against fading. Multiuser multi-carrier systems can be implemented using Orthogonal Frequency Division Multiple Access (OFDMA), a flexible orthogonal multiplexing scheme that can implement time and frequency division multiplexing, and using multicarrier code division multiple access (MC-CDMA). Coding, interference cancellation, and resource sharing schemes to improve the performance of multiuser multicarrier systems on wireless channels were addressed in this dissertation. Performance of multiple access schemes applied to a downlink multiuser wireless system was studied from an information theory perspective and from a more practical perspective. For time, frequency, and code division, implemented using OFDMA and MC-CDMA, the system outage capacity region was calculated for a correlated fading channel. It was found that receiver complexity determines which scheme offers larger capacity regions, and that OFDMA results in a better compromise between complexity and performance than MC-CDMA. From the more practical perspective of bit error rate, the effects of channel coding and interleaving were investigated. Results in terms of coding bounds as well as simulation were obtained, showing that OFDMAbased orthogonal multiple access schemes are more sensitive to the effectiveness of the code to provide diversity than non-orthogonal, MC-CDMA-based schemes. While cellular multiuser schemes suffer mainly from MAI, OFDM-based broadcasting systems suffer from ICI, in particular when operating as a single frequency network (SFN). It was found that for SFN the performance of a conventional OFDM receiver rapidly degrades when transmitters have frequency synchronization errors. Several methods based on linear and decision-feedback ICI cancellation were proposed and evaluated, showing improved robustness against ICI. System function characterization of time-variant dispersive channels is important for understanding their effects on single carrier and multicarrier modulation. Using time-frequency duality it was shown that MC-CDMA and DS-CDMA are strictly dual on dispersive channels. This property was used to derive optimal matched filter structures, and to determine a criterion for the selection of spreading sequences for both DS and MC CDMA. The analysis of multiple antenna systems provided a unified framework for the study of DS-CDMA and MC-CDMA on time and frequency dispersive channels, which can also be used to compare their performance

Adaptive DS-CDMA multiuser detection for time variant frequency selective Rayleigh fading channel

The current digital wireless mobile system such as IS-95, which is based on direct sequence Code Division Multiple Access (DS-CDMA) technology, will not be able to meet the growing demands for multimedia service due to low information exchanging rate. Its capacity is also limited by multiple accessed interference (MAI) signals. This work focuses on the development of adaptive algorithms for multiuser detection (MUD) and interference suppression for wideband direct sequence code division multiple access (DS-CDMA) systems over time-variant frequency selective fading channels. In addition, channel acquisition and delay estimation techniques are developed to combat the uncertainty introduced by the wireless propagation channel. This work emphasizes fast and simple techniques that can meet practical needs for high data rate signal detection. Most existing literature is not suitable for the large delay spread in wideband systems due to high computational/ hardware complexity. A de-biasing decorrelator is developed whose computational complexity is greatly reduced without sacrificing performance. An adaptive bootstrap symbolbased signal separator is also proposed for a time-variant channel. These detectors achieve MUD for asynchronous, large delay spread, fading channels without training sequences. To achieve high data rate communication, a finite impulse response (FIR) filter based detector is presented for M-ary QAM modulated signals in a multipath Rayleigh fading channel. It is shown that the proposed detector provides a stable performance for QAM signal detection with unknown fading and phase shift. It is also shown that this detector can be easily extended to the reception of any M-ary quadrature modulated signal. A minimum variance decorrelating (MVD) receiver with adaptive channel estimator is presented in this dissertation. It provides comparable performance to a linear MMSE receiver even in a deep fading environment and can be implemented blindly. Using the MVD receiver as a building-block, an adaptive multistage parallel interference cancellation (PIC) scheme and a successive interference cancellation (SIC) scheme were developed. The total number of stages is kept at a minimum as a result of the accurate estimating of the interfering users at the earliest stages, which reduces the implementation complexity, as well as the processing delay. Jointly with the MVD receiver, a new transmit diversity (TD) scheme, called TD-MVD, is proposed. This scheme improves the performance without increasing the bandwidth. Unlike other TD techniques, this TDMVD scheme has the inherent advantage to overcome asynchronous multipath transmission. It brings flexibility in the design of TD antenna systems without restrict signal coordination among those multiple transmissions, and applicable for both existing and next generation of CDMA systems. A maximum likelihood based delay and channel estimation algorithm with reduced computational complexity is proposed. This algorithm uses a diagonal simplicity technique as well as the asymptotically uncorrelated property of the received signal in the frequency domain. In combination with oversampling, this scheme does not suffer from a singularity problem and the performance quickly approaches the Cramer-Rao lower bound (CRLB) while maintaining a computational complexity that is as low as the order of the signal dimension

The Space and Earth Science Data Compression Workshop

This document is the proceedings from a Space and Earth Science Data Compression Workshop, which was held on March 27, 1992, at the Snowbird Conference Center in Snowbird, Utah. This workshop was held in conjunction with the 1992 Data Compression Conference (DCC '92), which was held at the same location, March 24-26, 1992. The workshop explored opportunities for data compression to enhance the collection and analysis of space and Earth science data. The workshop consisted of eleven papers presented in four sessions. These papers describe research that is integrated into, or has the potential of being integrated into, a particular space and/or Earth science data information system. Presenters were encouraged to take into account the scientists's data requirements, and the constraints imposed by the data collection, transmission, distribution, and archival system

Information Measures For Statistical Orbit Determination

The current Situational Space Awareness (SSA) is faced with a huge task of tracking the increasing number of space objects. The tracking of space objects requires frequent and accurate monitoring for orbit maintenance and collision avoidance using methods for statistical orbit determination. Statistical orbit determination enables us to obtain estimates of the state and the statistical information of its region of uncertainty given by the probability density function (PDF). As even collision events with very low probability are important, accurate prediction of collisions require the representation of the full PDF of the random orbit state. Through representing the full PDF of the orbit state for orbit maintenance and collision avoidance, we can take advantage of the statistical information present in the heavy tailed distributions, more accurately representing the orbit states with low probability. The classical methods of orbit determination (i.e. Kalman Filter and its derivatives) provide state estimates based on only the second moments of the state and measurement errors that are captured by assuming a Gaussian distribution. Although the measurement errors can be accurately assumed to have a Gaussian distribution, errors with a non-Gaussian distribution could arise during propagation between observations. In order to obtain an accurate representation of the PDF that incorporates higher order statistical information, we propose the use of nonlinear estimation methods such as the Particle Filter. A Particle Filter (PF) is proposed as a nonlinear filtering technique that is capable of propagating and estimating a more complete representation of the state distribution as an accurate approximation of a full PDF. The PF uses Monte Carlo runs to generate particles that approximate the full PDF representation. Moreover, during longer state propagations, we propose to represent the final state vector as a compressed probability mass function (PMF). Multivariate PDF compressions are computationally costly and could potentially be numerically intractable. We tackle this issue by decorrelating the nonlinear multivariate state PMFs using an improved nonlinear factor analysis (NFA) that uses a multilayer perceptron (MLP) network to model the state nonlinearities and obtain the sources that also incorporates the Fast Independent Component Analysis (FastICA [a faster computational method for ICA]) to obtain the independent and decorrelated states. Methods such as the Principal Component Analysis (PCA) are based on utilizing moments that only incorporate the second order statistics, hence will not suffice in maintaining maximum information content. On the other hand, the Independent Component Analysis (ICA) is a non-Gaussian decorrelator that is based on a linear mapping scheme, that does not incorporate the non-linear information. The PDF compressions are achieved by implementing the fast-Fourier Transform (FFT) and the wavelet transform (WT) to construct a smaller subset of data for data allocation and transmission cost reduction. The accuracy of tracking the space objects as well as reduced costs will help increase the capability of tracking the increased number of space objects. We use statistical information measures such as the Kolmogorov-Smirnov (K-S) test and the Kullback-Leibler Divergence (KLD) metric to quantify the accuracy of the reconstructed state vector and the cost reduction is measured by the number of terms required to represent the states. A performance plot illuminates the performances of the transforms over a range of compression rates. Simulations are performed on real and simulated data to demonstrate the approach for this work

Polynomial matrix eigenvalue decomposition techniques for multichannel signal processing

Polynomial eigenvalue decomposition (PEVD) is an extension of the eigenvalue decomposition (EVD) for para-Hermitian polynomial matrices, and it has been shown to be a powerful tool for broadband extensions of narrowband signal processing problems. In the context of broadband sensor arrays, the PEVD allows the para-Hermitian matrix that results from the calculation of a space-time covariance matrix of the convolutively mixed signals to be diagonalised. Once the matrix is diagonalised, not only can the correlation between different sensor signals be removed but the signal and noise subspaces can also be identified. This process is referred to as broadband subspace decomposition, and it plays a very important role in many areas that require signal separation techniques for multichannel convolutive mixtures, such as speech recognition, radar clutter suppression, underwater acoustics, etc. The multiple shift second order sequential best rotation (MS-SBR2) algorithm, built on the most established SBR2 algorithm, is proposed to compute the PEVD of para-Hermitian matrices. By annihilating multiple off-diagonal elements per iteration, the MS-SBR2 algorithm shows a potential advantage over its predecessor (SBR2) in terms of the computational speed. Furthermore, the MS-SBR2 algorithm permits us to minimise the order growth of polynomial matrices by shifting rows (or columns) in the same direction across iterations, which can potentially reduce the computational load of the algorithm. The effectiveness of the proposed MS-SBR2 algorithm is demonstrated by various para-Hermitian matrix examples, including randomly generated matrices with different sizes and matrices generated from source models with different dynamic ranges and relations between the sources’ power spectral densities. A worked example is presented to demonstrate how the MS-SBR2 algorithm can be used to strongly decorrelate a set of convolutively mixed signals. Furthermore, the performance metrics and computational complexity of MS-SBR2 are analysed and compared to other existing PEVD algorithms by means of numerical examples. Finally, two potential applications of theMS-SBR2 algorithm, includingmultichannel spectral factorisation and decoupling of broadband multiple-input multiple-output (MIMO) systems, are demonstrated in this dissertation