VLSI Design and Implementation for Adaptive Filter using LMS Algorithm

Adaptive filters, as part of digital signal systems, have been widely used, as well as in applications such as adaptive noise cancellation, adaptive beam forming, channel equalization, and system identification. However, its implementation takes a great deal and becomes a very important field in digital system world. When FPGA (Field Programmable Logic Array) grows in area and provides a lot of facilities to the designers, it becomes an important competitor in the signal processing market. In general FIR structure has been used more successfully than IIR structure in adaptive filters. However, when the adaptive FIR filter was made this required appropriate algorithm to update the filter’s coefficients. The algorithm used to update the filter coefficient is the Least Mean Square (LMS) algorithm which is known for its simplification, low computational complexity, and better performance in different running environments. When compared to other algorithms used for implementing adaptive filters the LMS algorithm is seen to perform very well in terms of the number of iterations required for convergence. This phenomenon can be achieved by a sufficient choice of bit length to represent the filter’s coefficients. This paper presents a lowcost and high performance programmable digital finite impulse response (FIR) filter. It follows the adaptive algorithm used for the development of the system. The architecture employs the computation sharing algorithm to reduce the computation complexity

Multiplierless CSD techniques for high performance FPGA implementation of digital filters.

I leverage FastCSD to develop a new, high performance iterative multiplierless structure based on a novel real-time CSD recoding, so that more zero partial products are introduced. Up to 66.7% zero partial products occur compared to 50% in the traditional modified Booth's recoding. Also, this structure reduces the non-zero partial products to a minimum. As a result, the number of arithmetic operations in the carry-save structure is reduced. Thus, an overall speed-up, as well as low-power consumption can be achieved. Furthermore, because the proposed structure involves real time CSD recoding and does not require a fixed value for the multiplier input to be known a priori, the proposed multiplier can be applied to implement digital filters with non-fixed filter coefficients, such as adaptive filters.My work is based on a dramatic new technique for converting between 2's complement and CSD number systems, and results in high-performance structures that are particularly effective for implementing adaptive systems in reconfigurable logic.My research focus is on two key ideas for improving DSP performance: (1) Develop new high performance, efficient shift-add techniques ("multiplierless") to implement the multiply-add operations without the need for a traditional multiplier structure. (2) There is a growing trend toward design prototyping and even production in FPGAs as opposed to dedicated DSP processors or ASICs; leverage this trend synergistically with the new multiplierless structures to improve performance.Implementation of digital signal processing (DSP) algorithms in hardware, such as field programmable gate arrays (FPGAs), requires a large number of multipliers. Fast, low area multiply-adds have become critical in modern commercial and military DSP applications. In many contemporary real-time DSP and multimedia applications, system performance is severely impacted by the limitations of currently available speed, energy efficiency, and area requirement of an onboard silicon multiplier.I also introduce a new multi-input Canonical Signed Digit (CSD) multiplier unit, which requires fewer shift/add/subtract operations and reduced CSD number conversion overhead compared to existing techniques. This results in reduced power consumption and area requirements in the hardware implementation of DSP algorithms. Furthermore, because all the products are produced simultaneously, the multiplication speed and thus the throughput are improved. The multi-input multiplier unit is applied to implement digital filters with non-fixed filter coefficients, such as adaptive filters. The implementation cost of these digital filters can be further reduced by limiting the wordlength of the input signal with little or no sacrifice to the filter performance, which is confirmed by my simulation results. The proposed multiplier unit can also be applied to other DSP algorithms, such as digital filter banks or matrix and vector multiplications.Finally, the tradeoff between filter order and coefficient length in the design and implementation of high-performance filters in Field Programmable Gate Arrays (FPGAs) is discussed. Non-minimum order FIR filters are designed for implementation using Canonical Signed Digit (CSD) multiplierless implementation techniques. By increasing the filter order, the length of the coefficients can be decreased without reducing the filter performance. Thus, an overall hardware savings can be achieved.Adaptive system implementations require real-time conversion of coefficients to Canonical Signed Digit (CSD) or similar representations to benefit from multiplierless techniques for implementing filters. Multiplierless approaches are used to reduce the hardware and increase the throughput. This dissertation introduces the first non-iterative hardware algorithm to convert 2's complement numbers to their CSD representations (FastCSD) using a fixed number of shift and logic operations. As a result, the power consumption and area requirements required for hardware implementation of DSP algorithms in which the coefficients are not known a priori can be greatly reduced. Because all CSD digits are produced simultaneously, the conversion speed and thus the throughput are improved when compared to overlap-and-scan techniques such as Booth's recoding

A low-power quadrature digital modulator in 0.18um CMOS

Quadrature digital modulation techniques are widely used in modern communication systems because of their high performance and flexibility. However, these advantages come at the cost of high power consumption. As a result, power consumption has to be taken into account as a main design factor of the modulator.In this thesis, a low-power quadrature digital modulator in 0.18um CMOS is presented with the target system clock speed of 150 MHz. The quadrature digital modulator consists of several key blocks: quadrature direct digital synthesizer (QDDS), pulse shaping filter, interpolation filter and inverse sinc filter. The design strategy is to investigate different implementations for each block and compare the power consumption of these implementations. Based on the comparison results, the implementation that consumes the lowest power will be chosen for each block. First of all, a novel low-power QDDS is proposed in the thesis. Power consumption estimation shows that it can save up to 60% of the power consumption at 150 MHz system clock frequency compared with one conventional design. Power consumption estimation results also show that using two pulse shaping blocks to process I/Q data, cascaded integrator comb (CIC) interpolation structure, and inverse sinc filter with modified canonic signed digit (MCSD) multiplication consume less power than alternative design choices. These low-power blocks are integrated together to achieve a low-power modulator. The power consumption estimation after layout shows that it only consumes about 95 mW at 150 MHz system clock rate, which is much lower than similar commercial products. The designed modulator can provide a low-power solution for various quadrature modulators. It also has an output bandwidth from 0 to 75 MHz, configurable pulse shaping filters and interpolation filters, and an internal sin(x)/x correction filter

Optimisation of multiplier-less FIR filter design techniques

This thesis is concerned with the design of multiplier-less (ML) finite impulse response (FIR) digital filters. The use of multiplier-less digital filters results in simplified filtering structures, better throughput rates and higher speed. These characteristics are very desirable in many DSP systems. This thesis concentrates on the design of digital filters with power-of-two coefficients that result in simplified filtering structures. Two distinct classesof ML FIR filter design algorithms are developed and compared with traditional techniques. The first class is based on the sensitivity of filter coefficients to rounding to power-of-two. Novel elements include extending of the algorithm for multiple-bands filters and introducing mean square error as the sensitivity criterion. This improves the performance of the algorithm and reduces the complexity of resulting filtering structures. The second class of filter design algorithms is based on evolutionary techniques, primarily genetic algorithms. Three different algorithms based on genetic algorithm kernel are developed. They include simple genetic algorithm, knowledge-based genetic algorithm and hybrid of genetic algorithm and simulated annealing. Inclusion of the additional knowledge has been found very useful when re-designing filters or refining previous designs. Hybrid techniques are useful when exploring large, N-dimensional searching spaces. Here, the genetic algorithm is used to explore searching space rapidly, followed by fine search using simulated annealing. This approach has been found beneficial for design of high-order filters. Finally, a formula for estimation of the filter length from its specification and complementing both classes of design algorithms, has been evolved using techniques of symbolic regression and genetic programming. Although the evolved formula is very complex and not easily understandable, statistical analysis has shown that it produces more accurate results than traditional Kaiser's formula. In summary, several novel algorithms for the design of multiplier-less digital filters have been developed. They outperform traditional techniques that are used for the design of ML FIR filters and hence contributed to the knowledge in the field of ML FIR filter design

Adaptive multilevel quadrature amplitude radio implementation in programmable logic

Emerging broadband wireless packet data networks are increasingly employing spectrally efficient modulation methods like Quadrature Amplitude Modulation (QAM) to increase the channel efficiency and maximize data throughput. Unfortunately, the performance of high level QAM modulations in the wireless channel is sensitive to channel imperfections and throughput is degraded significantly at low signal-to-noise ratios due to bit errors and packet retransmission. To obtain a more “robust” physical layer, broadband systems are employing multilevel QAM (M-QAM) to mitigate this reduction in throughput by adapting the QAM modulation level to maintain acceptable packet error rate (PER) performance in changing channel conditions. This thesis presents an adaptive M-QAM modem hardware architecture, suitable for use as a modem core for programmable software defined radios (SDRs) and broadband wireless applications. The modem operates in “burst” mode, and can reliably synchronize to different QAM constellations “burst-by-burst”. Two main improvements exploit commonality in the M-QAM constellations to minimize the redundant hardware required. First, the burst synchronization functions (carrier, clock, amplitude, and modulation level) operate reliably without prior knowledge of the QAM modulation level used in the burst. Second, a unique bit stuffing and shifting technique is employed which supports variable bit rate operation, while reducing the core signal processing functions to common hardware for all constellations. These features make this architecture especially attractive for implementation with Field Programmable Gate Arrays (FPGAs) and Application-Specific Integrated Circuits (ASICs); both of which are becoming popular for highly integrated, cost-effective wireless transceivers

Design and analysis of short word length DSP systems for mobile communication

Recently, many general purpose DSP applications such as Least Mean Squares-Like single-bit adaptive filter algorithms have been developed using the Short Word Length (SWL) technique and have been shown to achieve similar performance as multi-bit systems. A key function in SWL systems is sigma delta modulation (ΣΔM) that operates at an over sampling ratio (OSR), in contrast to the Nyquist rate sampling typically used in conventional multi-bit systems. To date, the analysis of SWL (or single-bit) DSP systems has tended to be performed using high-level tools such as MATLAB, with little work reported relating to their hardware implementation, particularly in Field Programmable Gate Arrays (FPGAs). This thesis explores the hardware implementation of single-bit systems in FPGA using the design and implementation in VHDL of a single-bit ternary FIR-like filter as an illustrative example. The impact of varying OSR and bit-width of the SWL filter has been determined, and a comparison undertaken between the area-performance-power characteristics of the SWL FIR filter compared to its equivalent multi-bit filter. In these experiments, it was found that single-bit FIR-like filter consistently outperforms the multi-bit technique in terms of its area, performance and power except at the highest filter orders analysed in this work. At higher orders, the ΣΔ approach retains its power and performance advantages but exhibits slightly higher chip area. In the second stage of thesis, three encoding techniques called canonical signed digit (CSD), 2’s complement, and Redundant Binary Signed Digit (RBSD) were designed and investigated on the basis of area-performance in FPGA at varying OSR. Simulation results show that CSD encoding technique does not offer any significant improvement as compared to 2’s complement as in multi-bit domain. Whereas, RBSD occupies double the chip area than other two techniques and has poor performance. The stability of the single-bit FIR-like filter mainly depends upon IIR remodulator due to its recursive nature. Thus, we have investigated the stability IIR remodulator and propose a new model using linear analysis and root locus approach that takes into account the widely accepted second order sigma-delta modulator state variable upper bounds. Using proposed model we have found new feedback parameters limits that is a key parameter in single-bit IIR remodulator stability analysis. Further, an analysis of single-bit adaptive channel equalization in MATLAB has been performed, which is intended to support the design and development of efficient algorithm for single-bit channel equalization. A new mathematical model has been derived with all inputs, coefficients and outputs in single-bit domain. The model was simulated using narrowband signals in MATLAB and investigated on the basis of symbol error rate (SER), signal-to-noise ratio (SNR) and minimum mean squared error (MMSE). The results indicate that single-bit adaptive channel equalization is achievable with narrowband signals but that the harsh quantization noise has great impact in the convergence

Digital Filters

The new technology advances provide that a great number of system signals can be easily measured with a low cost. The main problem is that usually only a fraction of the signal is useful for different purposes, for example maintenance, DVD-recorders, computers, electric/electronic circuits, econometric, optimization, etc. Digital filters are the most versatile, practical and effective methods for extracting the information necessary from the signal. They can be dynamic, so they can be automatically or manually adjusted to the external and internal conditions. Presented in this book are the most advanced digital filters including different case studies and the most relevant literature

Application of Neural Networks with CSD Coefficients for Human Face Recognition

Face recognition is one of the most popular, reliable and widely used applications in real world. It is the main biometric used by humans in many security, law enforcement and commercial systems and high demand of this application attracts researchers from various fields such as image processing, pattern recognition, neural network and computer vision etc. In a Human Face Recognition Systems, we start with pre-processing of the data followed by feature extraction for dimensionality reduction and then classification. In this thesis, neural network classifier with CSD coefficients is used to make the area required for implementation of recognition system more efficient. The FPGA implementation of the proposed technique indicates almost 50% saving in the area required for face recognition application by using neural network classifier with CSD coefficients while the processing speed is improved in comparison to its binary counterpart. Extensive experimental results were conducted to show the utility of the proposed technique

Digital Filters and Signal Processing

Digital filters, together with signal processing, are being employed in the new technologies and information systems, and are implemented in different areas and applications. Digital filters and signal processing are used with no costs and they can be adapted to different cases with great flexibility and reliability. This book presents advanced developments in digital filters and signal process methods covering different cases studies. They present the main essence of the subject, with the principal approaches to the most recent mathematical models that are being employed worldwide