International Telemetering Conference Proceedings / October 25-28, 1993 / Riviera Hotel and Convention Center, Las Vegas, Nevada / With the advancement in speed and complexity of Application Specific Integrated Circuits (ASICs), Digital Signal Processing (DSP) algorithms can now be used to achieve fully programmable, multiple channel demodulation of Frequency Modulation (FM) multiplexes. This paper describes the DSP algorithms and ASIC implementation used in the design of a digital FM demodulator system. Each digital demodulator has programmable subcarrier frequency demodulation to 4 MHz, programmable digital output filtering, and tape speed compensation (TSC). The demodulator output is available in both digital form for direct computer interface and in analog form for conventional analysis.

Abstract Subsampling can be used in a radio receiver to perform signal downconversion and sample-and-hold operations in order to relieve the operation frequency and bandwidth requirements of the subsequent discrete-time circuitry. However, due to the inherent aliasing behaviour of wideband noise and interference in subsampling, and the difficulty of implementing appropriate bandpass anti-aliasing filtering at high frequencies, straightforward use of a low subsampling rate can result in significant degradation of the receiver dynamic range. The aim of this thesis is to investigate and implement methods for integrating filtering into high-frequency signal sampling and downconversion by subsampling to alleviate the requirements for additional front-end filters and to mitigate the effects of noise and out-of-band signal aliasing, thereby facilitating use in integrated high-quality radio receivers. The charge-domain sampling technique studied here allows simple integration of both continuous-and discrete-time filtering functions into high-frequency signal sampling. Gated current integration results in a lowpass sin(x)/x(sinc(x)) response capable of performing built-in anti-aliasing filtering in baseband signal sampling. Weighted integration of several successive current samples can be further used to obtain an embedded discrete-time finite-impulse-response (FIR) filtering response, which can be used for internal anti-aliasing and image-rejection filtering in the downconversion of bandpass signals by subsampling. The detailed analysis of elementary charge-domain sampling circuits presented here shows that the use of integrated FIR filtering with subsampling allows acceptable noise figures to be achieved and can provide effective internal anti-aliasing rejection. The new methods for increasing the selectivity of elementary charge-domain sampling circuits presented here enable the integration of advanced, digitally programmable FIR filtering functions into high-frequency signal sampling, thereby markedly relieving the requirements for additional anti-aliasing, image rejection and possibly even channel selection filters in a radio receiver. BiCMOS and CMOS IF sampler implementations are presented in order to demonstrate the feasibility of the charge-domain sampling technique for integrated anti-aliasing and image-rejection filtering in IF signal quadrature downconversion by subsampling. Circuit measurements show that this sampling technique for built-in filtering results in an accurate frequency response and allows the use of high subsampling ratios while still achieving a competitive dynamic range.

Digital filters play a key role in many DSP applications and FIR filters are usually selected because of their simplicity and stability against IIR filters.In this thesis eight architectures for multi-stream FIR filtering are studied. Primarily, three kinds of architectures are implemented and evaluated: one-toone mapping, time-multiplexed and pipeline interleaving. During implementation, practical considerations are taken into account such as implementation approach and number representation. Of interest is to see the performance comparison of different architectures, including area and power. The trade-off between area and power is an attractive topic for this work. Furthermore, the impact of the filter order and pipeline interleaving are studied.The result shows that the performance of different architectures differ a lot even with the same sample rate for each stream. It also shows that the performance of different architectures are affected by the filter order differently. Pipeline interleaving improves area utilization at the cost of rapid increment of power. Moreover, it has negative impact on the maximum working frequency.All the FIR filter architectures are synthesized in a 65nm technology.

In system identification scenario, the linear approximation of the system modelled by its impulse response, is estimated in real time by gradient type Least Mean Square (LMS) or Recursive Least Squares (RLS) algorithms. In recent applications like acoustic echo cancellation, the order of the impulse response to be estimated is very high, and these traditional approaches are inefficient and real time implementation becomes difficult. Alternatively, the system is modelled by a set of shorter adaptive filters operating in parallel on subsampled signals. This approach, referred to as subband adaptive filtering, is expected to reduce not only the computational complexity but also to improve the convergence rate of the adaptive algorithm. But in practice, different subband adaptive algorithms have to be used to enhance the performance with respect to complexity, convergence rate and processing delay. A single subband adaptive filtering algorithm which outperforms the full band scheme in all applications is yet to be realized. This thesis is intended to study the subband adaptive filtering techniques and explore the possibilities of better algorithms for performance improvement. Three different subband adaptive algorithms have been proposed and their performance have been verified through simulations. These algorithms have been applied to acoustic echo cancellation and EEG artefact minimization problems. Details of the work To start with, the fast FIR filtering scheme introduced by Mou and Duhamel has been generalized. The Perfect Reconstruction Filter Bank (PRFB) is used to model the linear FIR system. The structure offers efficient implementation with reduced arithmetic complexity. By using a PRFB with non adjacent filters non overlapping, many channel filters can be eliminated from the structure. This helps in reducing the complexity of the structure further, but introduces approximation in the model. The modelling error depends on the stop band attenuation of the filters of the PRFB. The error introduced due to approximation is tolerable for applications like acoustic echo cancellation. The filtered output of the modified generalized fast filtering structure is given by (formula) where, Pk(z) is the main channel output, Pk,, k+1 (z) is the output of auxiliary channel filters at the reduced rate, Gk (z) is the kth synthesis filter and M the number of channels in the PRFB. An adaptation scheme is developed for adapting the main channel filters. Auxiliary channel filters are derived from main channel filters. Secondly, the aliasing problem of the classical structure is reduced without using the cross filters. Aliasing components in the estimated signal results in very poor steady state performance in the classical structure. Attempts to eliminate the aliasing have reduced the computation gain margin and the convergence rate. Any attempt to estimate the subband reference signals from the aliased subband input signals results in aliasing. The analysis filter Hk(z) having the following antialiasing property (formula) can avoid aliasing in the input subband signal. The asymmetry of the frequency response prevents the use of real analysis filters. In the investigation presented in this thesis, complex analysis filters and real'synthesis filters are used in the classical structure, to reduce the aliasing errors and to achieve superior convergence rate. PRFB is traditionally used in implementing Interpolated FIR (IFIR) structure. These filters may not be ideal for processing an input signal for an adaptive algorithm. As third contribution, the IFIR structure is modified using discrete finite frames. The model of an FIR filter s is given by Fc, with c = Hs. The columns of the matrix F forms a frame with rows of H as its dual frame. The matrix elements can be arbitrary except that the transformation should be implementable as a filter bank. This freedom is used to optimize the filter bank, with the knowledge of the input statistics, for initial convergence rate enhancement . Next, the proposed subband adaptive algorithms are applied to acoustic echo cancellation problem with realistic parameters. Speech input and sufficiently long Room Impulse Response (RIR) are used in the simulations. The Echo Return Loss Enhancement (ERLE)and the steady state error spectrum are used as performance measures to compare these algorithms with the full band scheme and other representative subband implementations. Finally, Subband adaptive algorithm is used in minimization of EOG (Electrooculogram) artefacts from measured EEG (Electroencephalogram) signal. An IIR filterbank providing sufficient isolation between the frequency bands is used in the modified IFIR structure and this structure has been employed in the artefact minimization scheme. The estimation error in the high frequency range has been reduced and the output signal to noise ratio has been increased by a couple of dB over that of the fullband scheme. Conclusions Efforts to find elegant Subband adaptive filtering algorithms will continue in the future. However, in this thesis, the generalized filtering algorithm could offer gain in filtering complexity of the order of M/2 and reduced misadjustment . The complex classical scheme offered improved convergence rate, reduced misadjustment and computational gains of the order of M/4 . The modifications of the IFIR structure using discrete finite frames made it possible to eliminate the processing delay and enhance the convergence rate. Typical performance of the complex classical case for speech input in a realistic scenario (8 channel case), offers ERLE of more than 45dB. The subband approach to EOG artefact minimization in EEG signal was found to be superior to their fullband counterpart. (Refer PDF file for Formulas)