A New Low Complexity Uniform Filter Bank Based on the Improved Coefficient Decimation Method

In this paper, we propose a new uniform filter bank (FB) based on the improved coefficient decimation method (ICDM). In the proposed FB’s design, the ICDM is used to obtain different multi-band frequency responses using a single lowpass prototype filter. The desired subbands are individually obtained from these multi-band frequency responses by using low order frequency response masking filters and their corresponding ICDM output frequency responses. We show that the proposed FB is a very low complexity alternative to the other FBs in literature, especially the widely used discrete Fourier transform based FB (DFTFB) and the CDM based FB (CDFB). The proposed FB can have a higher number of subbands with twice the center frequency resolution when compared with the CDFB and DFTFB. Design example and implementation results show that our FB achieves 86.59% and 58.84% reductions in resource utilizations and 76.95% and 47.09% reductions in power consumptions when compared with the DFTFB and CDFB respectively

On board Processor and Processing Strategies for Next Generation Reconfigurable Satellite Payloads

Today, the increasing demand in higher data rates necessitates new methods as well as higher flexibility for satellite telecommunication payloads in order to address a variety of applications and customers. This paper presents one of these processing strategies that is applicable to today’s processing satellite payloads aiming to meet those demands. For this purpose, a two-tier filter bank is designed as part of a digital onboard processor, which first divides the spectrum at the output of the ADC into a number of sub-bands extracting all the stacked channels in the digital domain. Following the analysis section of the first tier of operations, the extracted channels go under a secondary channelisation process to obtain much finer granularity of 31.25 kHz or 50 kHz depending on the communication standard used for data transmission. The implementation of the channeliser was delivered on a bit-true simulation model and the input and the output of the channelisers were compared and evaluated both in the time and frequency domains

Low-complexity filter for software-defined radio by modulated interpolated coefficient decimated filter in a hybrid farrow

Realising a low-complexity Farrow channelisation algorithm for multi-standard receivers in software-defined radio is a challenging task. A Farrow filter operates best at low frequencies while its performance degrades towards the Nyquist region. This makes wideband channelisation in software-defined radio a challenging task with high computational complexity. In this paper, a hybrid Farrow algorithm that combines a modulated Farrow filter with a frequency response interpolated coefficient decimated masking filter is proposed for the design of a novel filter with low computational complexity. A design example shows that the HFarrow filter bank achieved multiplier reduction of 50%, 70% and 64%, respectively, in comparison with non-uniform modulated discrete Fourier transform (NU MDFT FB), coefficient decimated filter bank (CD FB) and interpolated coefficient decimated (ICDM) filter algorithms. The HFarrow filter bank is able to provide the same number of sub-band channels as other algorithms such as non-uniform modulated discrete Fourier transform (NU MDFT FB), coefficient decimated filter bank (CD FB) and interpolated coefficient decimated (ICDM) filter algorithms, but with less computational complexity.https://www.mdpi.com/journal/sensorsam2023Electrical, Electronic and Computer Engineerin

Design and Implementation of an RF Front-End for Software Defined Radios

Software Defined Radios have brought a major reformation in the design standards for radios, in which a large portion of the functionality is implemented through pro­ grammable signal processing devices, giving the radio the ability to change its op­ erating parameters to accommodate new features and capabilities. A software radio approach reduces the content of radio frequency and other analog components of the traditional radios and emphasizes digital signal processing to enhance overall receiver flexibility. Field Programmable Gate Arrays (FPGA) are a suitable technology for the hardware platform as they offer the potential of hardware-like performance coupled with software-like programmability. Software defined radio is a very broad field, encompassing the design of various technologies all the way from the antenna to RF, IF, and baseband digital design. The RF section primarily consists of analog hardware modules. The IF and baseband sections are primarily digital. It is the general process of the radio to convert the incoming signal from RF to IF and then IF to baseband for better signal processing system. In this thesis, some of major building blocks of a Software defined radio are de­ signed and implemented using FPGAs. The design of a Digital front end, which provides the bridge between the baseband and analog RF portions of a wireless receiver, is synthesized. The Digital front end receiver consists of a digital down converter(DDC) which in turn comprises of a direct digital frequency synthesizer (DDFS), a phase accumulator and a low pass filter. The signal processing block of the DDFS is executed using Co-ordinate Rotation Digital Computer (CORDIC) iii Abstract algorithm. Cascaded-Integrator-Comb filters (CIC) are implemented for changing the sample rate of the incoming data. Application of a DDC includes software ra­ dios, multicarrier, multimode digital receivers, micro and pico cell systems,broadband data applications, instrumentation and test equipment and in-building wireless tele­ phony. Also, in this thesis, interfaces for connecting Texas Instruments high speed and high resolution Analog-to-Digital converters (ADC) and Digital-to-Analog converters (DAC) with Xilinx Virtex-5 FPGAs are also implemented and demonstrated

Design and Implementation of Complexity Reduced Digital Signal Processors for Low Power Biomedical Applications

Wearable health monitoring systems can provide remote care with supervised, inde-pendent living which are capable of signal sensing, acquisition, local processing and transmission. A generic biopotential signal (such as Electrocardiogram (ECG), and Electroencephalogram (EEG)) processing platform consists of four main functional components. The signals acquired by the electrodes are amplified and preconditioned by the (1) Analog-Front-End (AFE) which are then digitized via the (2) Analog-to-Digital Converter (ADC) for further processing. The local digital signal processing is usually handled by a custom designed (3) Digital Signal Processor (DSP) which is responsible for either anyone or combination of signal processing algorithms such as noise detection, noise/artefact removal, feature extraction, classification and compres-sion. The digitally processed data is then transmitted via the (4) transmitter which is renown as the most power hungry block in the complete platform. All the afore-mentioned components of the wearable systems are required to be designed and fitted into an integrated system where the area and the power requirements are stringent. Therefore, hardware complexity and power dissipation of each functional component are crucial aspects while designing and implementing a wearable monitoring platform. The work undertaken focuses on reducing the hardware complexity of a biosignal DSP and presents low hardware complexity solutions that can be employed in the aforemen-tioned wearable platforms. A typical state-of-the-art system utilizes Sigma Delta (Σ∆) ADCs incorporating a Σ∆ modulator and a decimation filter whereas the state-of-the-art decimation filters employ linear phase Finite-Impulse-Response (FIR) filters with high orders that in-crease the hardware complexity [1–5]. In this thesis, the novel use of minimum phase Infinite-Impulse-Response (IIR) decimators is proposed where the hardware complexity is massively reduced compared to the conventional FIR decimators. In addition, the non-linear phase effects of these filters are also investigated since phase non-linearity may distort the time domain representation of the signal being filtered which is un-desirable effect for biopotential signals especially when the fiducial characteristics carry diagnostic importance. In the case of ECG monitoring systems the effect of the IIR filter phase non-linearity is minimal which does not affect the diagnostic accuracy of the signals. The work undertaken also proposes two methods for reducing the hardware complexity of the popular biosignal processing tool, Discrete Wavelet Transform (DWT). General purpose multipliers are known to be hardware and power hungry in terms of the number of addition operations or their underlying building blocks like full adders or half adders required. Higher number of adders leads to an increase in the power consumption which is directly proportional to the clock frequency, supply voltage, switching activity and the resources utilized. A typical Field-Programmable-Gate-Array’s (FPGA) resources are Look-up Tables (LUTs) whereas a custom Digital Signal Processor’s (DSP) are gate-level cells of standard cell libraries that are used to build adders [6]. One of the proposed methods is the replacement of the hardware and power hungry general pur-pose multipliers and the coefficient memories with reconfigurable multiplier blocks that are composed of simple shift-add networks and multiplexers. This method substantially reduces the resource utilization as well as the power consumption of the system. The second proposed method is the design and implementation of the DWT filter banks using IIR filters which employ less number of arithmetic operations compared to the state-of-the-art FIR wavelets. This reduces the hardware complexity of the analysis filter bank of the DWT and can be employed in applications where the reconstruction is not required. However, the synthesis filter bank for the IIR wavelet transform has a higher computational complexity compared to the conventional FIR wavelet synthesis filter banks since re-indexing of the filtered data sequence is required that can only be achieved via the use of extra registers. Therefore, this led to the proposal of a novel design which replaces the complex IIR based synthesis filter banks with FIR fil-ters which are the approximations of the associated IIR filters. Finally, a comparative study is presented where the hybrid IIR/FIR and FIR/FIR wavelet filter banks are de-ployed in a typical noise reduction scenario using the wavelet thresholding techniques. It is concluded that the proposed hybrid IIR/FIR wavelet filter banks provide better denoising performance, reduced computational complexity and power consumption in comparison to their IIR/IIR and FIR/FIR counterparts

Channelization for Multi-Standard Software-Defined Radio Base Stations

As the number of radio standards increase and spectrum resources come under more pressure, it becomes ever less efficient to reserve bands of spectrum for exclusive use by a single radio standard. Therefore, this work focuses on channelization structures compatible with spectrum sharing among multiple wireless standards and dynamic spectrum allocation in particular. A channelizer extracts independent communication channels from a wideband signal, and is one of the most computationally expensive components in a communications receiver. This work specifically focuses on non-uniform channelizers suitable for multi-standard Software-Defined Radio (SDR) base stations in general and public mobile radio base stations in particular. A comprehensive evaluation of non-uniform channelizers (existing and developed during the course of this work) shows that parallel and recombined variants of the Generalised Discrete Fourier Transform Modulated Filter Bank (GDFT-FB) represent the best trade-off between computational load and flexibility for dynamic spectrum allocation. Nevertheless, for base station applications (with many channels) very high filter orders may be required, making the channelizers difficult to physically implement. To mitigate this problem, multi-stage filtering techniques are applied to the GDFT-FB. It is shown that these multi-stage designs can significantly reduce the filter orders and number of operations required by the GDFT-FB. An alternative approach, applying frequency response masking techniques to the GDFT-FB prototype filter design, leads to even bigger reductions in the number of coefficients, but computational load is only reduced for oversampled configurations and then not as much as for the multi-stage designs. Both techniques render the implementation of GDFT-FB based non-uniform channelizers more practical. Finally, channelization solutions for some real-world spectrum sharing use cases are developed before some final physical implementation issues are considered