This work presents a novel, low-complexity hardware implementation of the Two-Choice Hard Thresholding Pursuit (TCHTP) algorithm for sparse channel estimation (CE) in the delay-Doppler (DD) domain, specifically designed for Orthogonal Time Frequency Space (OTFS) modulation. Unlike prior compressed sensing methods, the proposed approach does not require prior knowledge of channel sparsity or statistics, making it highly suitable for real-world, high-mobility scenarios. We formulate the CE problem in a sparse framework and develop a hardware-friendly variant of TCHTP that jointly estimates channel coefficients and their DD positions. To reduce complexity in the coefficient estimation stage involving the inverse operation, we explore and implement three matrix decomposition strategies—singular value decomposition (SVD), QR decomposition, and a novel hybrid QR+SVD approach—on a system-on-chip platform using hardware-software co-design. The proposed hybrid architecture achieves up to 70.6% memory savings, 42.4% reduction in DSP usage, and a$132\times $speedup over the conventional design, while maintaining BER performance. Additionally, it achieves 10% lower power consumption and delivers an$84.2\times $increase in end-to-end physical layer throughput.

Integrated Sensing and Communication (ISAC) is a key enabler of high-speed, ultra-low-latency vehicular communication in 6G. ISAC leverages radar signal processing (RSP) to localize multiple unknown targets amid static clutter by jointly estimating range, azimuth, and Doppler velocity (3D), thereby enabling highly directional beamforming toward intended mobile users. However, the speed and accuracy of RSP significantly impact communication throughput. This work proposes a novel 3D reconfigurable RSP accelerator, implemented on a Zynq Multi-processor System-on-Chip (MPSoC) using a hardware–software co-design approach and fixed-point optimization. We propose two RSP frameworks: (1) high-accuracy and high-complexity, and (2) low-complexity and low-accuracy, along with their respective architectures. Then, we develop an adaptive architecture that dynamically switches between these two frameworks based on the signal-to-clutter-plus-noise ratio. This adaptive reconfiguration achieves up to$5.6\times $faster RSP compared to state-of-the-art designs. At the system level, the proposed RSP-based ISAC delivers a 24% improvement in communication throughput without increasing hardware complexity.

Multiarmed bandits (MABs) are online machine learning algorithms that aim to identify the optimal arm without prior statistical knowledge via the exploration-exploitation tradeoff. The performance metric, regret, and computational complexity of the MAB algorithms degrade with the increase in the number of arms,K. In applications such as wireless communication, radar systems, and sensor networks,K, i.e., the number of antennas, beams, bands, etc., is expected to be large. In this work, we consider focused exploration-based MAB, which outperforms conventional MAB for largeK, and its mapping on various edge processors and multiprocessor system on a chip (MPSoC) via hardware-software co-design (HSCD) and fixed point (FP) analysis. The proposed architecture offers 67% reduction in average cumulative regret, 84% reduction in execution time on edge processor, 97% reduction in execution time using FPGA-based accelerator, and 10% savings in resources over state-of-the-art MABs for large$K=100$.

In this work, we present an intelligent and reconfigurable wireless physical layer (PHY) that dynamically adjusts the transmission parameters for a given radio frequency (RF) environment. The proposed PHY is based on orthogonal frequency division multiplexing (OFDM) and can dynamically augment OFDM with a finite impulse response (FIR) low-pass filter to improve the out-of-band emissions (OOBE). To make these adaptations intelligently, we employ multiarmed bandit (MAB)-based online learning algorithms, specifically upper confidence bound with control variate (UCB-CV). UCB-CV enhances traditional UCB by incorporating additional information such as interference level and transmit power, allowing it to manage interference more effectively. These algorithms are integrated into the PHY of an FPGA-based OFDM transceiver on the Zynq system-on-chip (SoC), facilitating real-time decision-making based on side-channel interference and other parameters. Our comparative analysis highlights the enhanced performance of the UCB-CV algorithm over the traditional UCB in terms of reducing the bit-error rate (BER) and managing interference more effectively. Unlike the traditional UCB, UCB-CV leverages side information through a control variate approach, incorporating the coefficient of variation (CV) into reward estimation to better handle interference. Additionally, we underline the advantages of filtered-OFDM (FOFDM) compared to standard OFDM. Notably, FOFDM significantly reduces OOBE by 20–75 dBW/Hz and improves BER. In environments with high interference, UCB-CV achieves a throughput improvement of 29.54% compared to UCB.

The channel estimation (CE) in ultra-reliable vehicular communication is challenging due to dynamically varying channel conditions. In this direction, various data pilot-aided (DPA) and deep neural network (DNN) approaches have been proposed to improve the CE obtained using the known preamble transmitted at the beginning of the data frame. In this work, we propose an efficient architecture for the time domain reliable test frequency domain interpolation (TRFI) CE scheme by exploiting the tri-diagonal matrix algorithm (TDMA) approach and demonstrate 99.4% reduction in latency on Zynq system on chip (ZSoC). Using TRFI-TDMA, we develop two efficient architectures of TRFI-DNN to achieve the desired trade-off between complexity and latency. We demonstrate the superiority of the proposed architectures in terms of bit error rate for different channels, signal-to-noise ratios, and word length.

Channel estimation (CE) is one of the critical signal-processing tasks of the wireless physical layer (PHY). Recent deep learning (DL) based CE have outperformed statistical approaches such as least-square-based CE (LS) and linear minimum mean square error-based CE (LMMSE). However, existing CE approaches have not yet been realized on system-on-chip (SoC). The first contribution of this paper is to efficiently implement the existing state-of-the-art CE algorithms on Zynq SoC (ZSoC), comprising of ARM processor and field programmable gate array (FPGA), via hardware-software co-design and fixed point analysis. We validate the superiority of DL-based CE and LMMSE over LS for various signal-to-noise ratios (SNR) and wireless channels in terms of mean square error (MSE) and bit error rate (BER). We also highlight the high complexity, execution time, and power consumption of DL-based CE and LMMSE approaches. To address this, we propose a novel compute-efficient LS-augmented interpolated deep neural network (LSiDNN) based CE algorithm and realize it on ZSoC. The proposed LSiDNN offers 88-90% lower execution time and 38-85% lower resource utilization than state-of-the-art DL-based CE for identical MSE and BER. LSiDNN offers significantly lower MSE and BER than LMMSE, and the gain improves with increased mobility between transceivers. It offers 75% lower execution time and 90-94% lower resource utilization than LMMSE.

Multiarmed Bandit (MAB) algorithms identify the best arm among multiple arms via exploration-exploitation trade-off without prior knowledge of arm statistics. Their usefulness in wireless radio, Internet of Things (IoT), and robotics demand deployment on edge devices, and hence, a mapping on system-on-chip (SoC) is desired. Theoretically, the Bayesian-approach-based Thompson sampling (TS) algorithm offers better performance than the frequentist-approach-based upper confidence bound (UCB) algorithm. However, TS is not synthesizable due to Beta function. We address this problem by approximating it via a pseudorandom number generator (PRNG)-based architecture and efficiently realize the TS algorithm on Zynq SoC. In practice, the type of arms distribution (e.g., Bernoulli, Gaussian) is unknown, and hence, a single algorithm may not be optimal. We propose a reconfigurable and intelligent MAB (RI-MAB) framework. Here, intelligence enables the identification of appropriate MAB algorithms in an unknown environment, and reconfigurability allows on-the-fly switching between algorithms on the SoC. This eliminates the need for parallel implementation of algorithms resulting in huge savings in resources and power consumption. We analyze the functional correctness, area, power, and execution time of the proposed and existing architectures for various arm distributions, word length, and hardware-software codesign approaches. We demonstrate the superiority of the RI-MAB algorithm and its architecture over the TS and UCB algorithms.

In wireless networks, the transmitter adapts its parameters based on the receiver’s feedback to achieve a high throughput. The throughput also depends on factors like interference level and channel gain, which can be measured at the transmitter. They provide useful information about the instantaneous throughput as well. For example, higher interference implies a lower throughput. This work treats any measurable quality with a non-zero correlation with the throughput as side information (SI). We also study how it can be exploited to quickly learn the channel that offers higher throughput (reward). When the mean value of the SI is known, using control variate theory, we develop online learning algorithms that require fewer samples to learn and can improve the learning rate compared to cases where SI is ignored. Specifically, we incorporated SI in the Upper Confidence Bound (UCB) algorithm and proposed the UCBwSI algorithm. We quantify the gain achieved in terms of the regret and show that the improvement in regret over state-of-the-art UCB is proportional to the correlation between the reward and SI. Simulations demonstrate a 5-10% improvement in the bit-error rate. Even when the mean of the SI is unknown, we demonstrate the superiority of the UCBwSI over UCB.

Reliable and fast channel estimation is crucial for next-generation wireless networks supporting a wide range of vehicular and low-latency services. Recently, deep learning (DL)-based channel estimation has been explored as an efficient alternative to conventional least-square (LS) and linear minimum mean square error (LMMSE) approaches. Most of these DL approaches have not been realized on system on chip (SoC), and preliminary study shows that their complexity exceeds the complexity of the entire physical layer (PHY). The high latency of DL is another concern. This article considers the design and implementation of deep neural network (DNN) augmented LS (LSDNN)-based channel estimation for preamble-based orthogonal frequency-division multiplexing (OFDM) PHY on SoC. We demonstrate the gain in performance compared with the conventional LS and LMMSE approaches. Via software–hardware codesign, word-length optimization, and reconfigurable architectures, we demonstrate the superiority of the LSDNN over LS and LMMSE for a wide range of signal-to-noise ratio (SNR), number of pilots, preamble types, and wireless channels. Furthermore, we evaluate the performance, power, and area (PPA) of the LS and LSDNN application-specific integrated circuit (ASIC) implementations in 45-nm technology. We demonstrate that word-length optimization can substantially improve PPA for the proposed architecture in ASIC implementations.

With the introduction of spectrum sharing and heterogeneous services in next-generation networks, the base stations need to sense the wideband spectrum and identify the spectrum resources to meet the quality-of-service, bandwidth, and latency constraints. Sub-Nyquist sampling (SNS) enables digitization for sparse wideband spectrum without needing Nyquist speed analog-to-digital converters (ADCs). However, SNS demands additional signal processing algorithms for spectrum reconstruction, such as the well-known orthogonal matching pursuit (OMP) algorithm. OMP is also widely used in other compressed sensing applications. The first contribution of this work is efficiently mapping the OMP algorithm on the Zynq system-on-chip (ZSoC) consisting of an ARM processor and field-programmable gate array (FPGA). Experimental analysis shows a significant degradation in OMP performance for sparse spectrum. Also, OMP needs prior knowledge of spectrum sparsity. We address these challenges via deep-learning (DL)-based architectures and efficiently map them on the ZSoC platform as a second contribution. Via hardware–software codesign (HSCD), different versions of the proposed architecture obtained by partitioning between software (SW) (ARM processor) and hardware (HW) (FPGA) are considered. The resource, power, and execution time comparisons for given memory constraints and a wide range of word lengths (WLs) are presented for these architectures.

To bring large-scale artificial intelligence of things (AIoT) to reality, wireless networks need intelligence to identify resources in a limited shared noncontiguous spectrum. In this article, we address this challenge via a sub-Nyquist sampling-based wideband spectrum analyzer deployed in the AIoT gateway. The noncontiguous nature demands learning the channel occupancy. However, the identification of channel status can fail when the number of busy channels in a selected subset is higher than the number of analog-to-digital converters,$K$. We model this subset selection problem as multiplay multiarmed bandit. First, we demonstrate the learnability of such a problem via a learning algorithm with a subset size of$K$(no sensing failure). For wideband sparse spectrum, we extend this algorithm using a novel subset size estimation approach to identify the optimal subset that gives the best possible throughput and could have a size potentially larger than$K$. These algorithms are mapped on the system-on-chip, and in-depth performance analysis demonstrates their superiority over state-of-the-art approaches.

The opportunistic spectrum access (OSA) algorithms allow secondary users (SUs) to exploit vacant channels with an aim to maximize overall spectrum utilization/throughput. The design of OSA algorithm is challenging for ad hoc networks due to lack of coordination among SUs and unknown channel statistics. It becomes even more challenging for the dynamic networks where the SUs can enter or leave the network any time without prior agreement. Most of the existing algorithms assume either prior knowledge of the number of SUs or need wideband sensing to sense all channels simultaneously to guarantee optimal channel allocation among SUs. Our goal in this paper is to develop distributed OSA algorithm for dynamic ad hoc networks that offers higher throughput without compromising on the number of SUs collisions. The proposed distributed algorithm is based on multi-player multi-arm bandit framework and they allow SUs to independently estimate the number of other SUs and channel statistics. We derive the upper bounds on the throughput loss (regret) and number of collisions. Exhaustive synthetic results and experimental results on universal software radio peripherals (USRP) based testbed validate our claims and superiority of the proposed algorithm.

In this work, an intelligent and reconfigurable ultra-wideband angular sensing (UWAS) framework is proposed which is independent of the maximum number of active transmissions in a wideband spectrum unlike the existing UWAS methods. To perform the above task, we propose a sub-Nyquist sampling and sparse ruler based multi-antenna array receiver architecture. By characterizing and selecting a set of frequency bands via a learning algorithm, the proposed receiver allows sensing of more active transmissions than the number of antenna over an unlimited bandwidth. The simulation results show that due to the learning based approach, the proposed UWAS outperforms when compared to non-learning based UWAS method.

GNU Radio (GNUR) allows rapid prototyping of various communication and signal processing (CSP) systems and enables easy integration with software-defined radios (SDRs) for over-the-air testing. Recently, RFNoC (Radio Frequency Network on Chip) tool allows hardware acceleration in GNUR but it is compatible only with high-end SDRs which are expensive and bulky. Such SDR and hence, RFNoC supports only homogeneous hardware and may not be useful in SDR independent and remotely deployed light-weight systems. In this paper, we develop a generalized framework for enabling the acceleration of GNUR operations on any System on Chip (SoC). First, we develop a novel framework that allows high-speed two-way communication between GNUR and SoC memory. Then, for efficient data processing in the SoC, we propose a direct memory access (DMA) based framework to read, process (in the SoC) and write the data in memory for subsequent transfer to GNUR. We demonstrate the functional accuracy of the proposed framework and significant acceleration factor over GNUR based approach.

Multi-armed bandit (MAB) algorithms are designed to identify the best arm among several arms in an unknown environment. They guarantee optimal balance between exploration (select all arms sufficient number of times) and exploitation (select best arm as many times as possible). They are widely used in applications such as website advertisement, robotics, healthcare, finance, and wireless radios. Robotics and radio applications need integration of MAB algorithms with the PHY on the hardware to meet the stringent area, power and latency constraints. Moreover, a single MAB algorithm may not be suitable for various scenarios and hence, the application needs to switch between MAB algorithms on-the-fly. In this paper, we efficiently map the MAB algorithms on Zynq System on Chip (ZSoC) and make it reconfigurable such that the number of arms, as well as type of algorithm, can be changed on-the-fly. We validate the functional correctness and usefulness of the proposed architectures via realistic wireless application and detailed complexity analysis demonstrates the feasibility of the proposed solution in realizing intelligent radios/robots.

Efficient utilization of licensed spectrum in the cognitive radio network is challenging due to lack of coordination among the Secondary Users (SUs). Distributed algorithms proposed in the literature aim to maximize the network throughput by ensuring orthogonal channel allocation for the SUs. However, these algorithms work under the assumption that all the SUs faithfully follow the algorithms which may not always hold due to the decentralized nature of the network. In this paper, we study distributed algorithms that are robust against malicious behavior (jamming attack). We consider both the cases of jammers launching coordinated and uncoordinated attacks. In the coordinated attack, the jammers select non-overlapping channels to attack in each time slot and can significantly increase the number of collisions for SUs. We setup the problem in each scenario as a multi-player bandit and develop algorithms. The analysis shows that when the SUs faithfully implement proposed algorithms, the regret is constant with high probability. We validate our claims through exhaustive synthetic experiments and also through a realistic USRP based experiment.

We consider an ad hoc network where multiple users access the same set of channels. The channel characteristics are unknown and could be different for each user (heterogeneous). No controller is available to coordinate channel selections by the users, and if multiple users select the same channel, they collide and none of them receive any rate (or reward). For such a completely decentralized network we develop algorithms that aim to achieve optimal network throughput. Due to lack of any direct communication between the users, we allow each user to exchange information by transmitting in a specific pattern and sense such transmissions from others. However, such transmissions and sensing for information exchange do not add to network throughput. For the wideband sensing and narrowband sensing scenarios, we first develop explore-and-commit algorithms that converge to near-optimal allocation with high probability in a small number of rounds. Building on this, we develop an algorithm that gives logarithmic regret. We validate our claims through extensive experiments and show that our algorithms perform significantly better than the state-of-the-art CSM-MAB, dE3and dE3-TS algorithms.

Next generation networks are expected to be ultra-dense and aim to explore spectrum sharing paradigm that allows users to communicate in licensed, shared as well as unlicensed spectrum. Such ultra-dense networks will incur significant signaling load at base stations leading to a negative effect on spectrum and energy efficiency. To minimize signaling overhead, an ad-hoc approach is being considered for users communicating in the unlicensed and shared spectrums. For such users, decisions need to be completely decentralized as: 1) No communication between users and signaling from the base station is possible which necessitates independent channel selection at each user. A collision occurs when multiple users transmit simultaneously on the same channel, 2) Channel qualities may be heterogeneous, i.e., they are not same across all users, and moreover, are unknown, and 3) The network could be dynamic where users can enter or leave anytime. We develop a multi-armed bandit based distributed algorithm for static networks and extend it for the dynamic networks. The algorithms aim to achieve stable orthogonal allocation (SOC) in finite time and meet the above three constraints with two novel characteristics: 1) Low complexity narrowband radio compared to wideband radio in existing works, and 2) Epoch-less approach for dynamic networks. We establish convergence of our algorithms to SOC and validate via extensive simulation experiments.

Spectral parameter approximation based tunable digital filters (SPA-TDFs) are in demand for applications such as software defined radio, cognitive radio in wireless networks as well as radar, sonar and speech signal processing. SPA-TDFs offer variable frequency as well as group delay responses and have advantages such as linear phase, lower group delay, and fewer tunable parameters over other TDFs. This paper deals with the design of an efficient pipelined architecture of SPA-TDF using Farrow structure and modified coefficient decimation method (MCDM) for an implementation on Zynq System on Chip (ZSoC) testbed. The proposed work considers fully parallel, serial-parallel and fully serial configurations of SPA-TDF as opposed to only parallel configuration considered in the literature. To the best of our knowledge, the proposed work is the first attempt to analyze the serialization effect on the throughput and power consumption of the SPA-TDFs. Such analysis is useful in the selection of appropriate serialization level to meet the desired throughput for a given power constraints and vice-versa.

Recently, the circuits capable of RF energy harvesting (RFEH) simultaneously from multiple dedicated RF sources have been developed. To enable RFEH from any ambient and unknown RF sources (operating in distinct frequency bands), such circuits need the capability to characterize RF sources. In this paper, a new characterization and selection policy (CSP) using parallel and aggregated multi-armed bandit (PA-MAB) framework has been proposed to aid optimum RF source selection in multi-antenna RFEH circuits. The novel contributions of this paper are, 1) Parallel framework which allows simultaneous selection of multiple optimal arms (i.e. RF sources), 2) Bayesian approach based Thompson Sampling algorithm to accomplish the characterization and selection tasks, 3) Bayesian inference to estimate the RF potential of individual band from the aggregated energy harvested from multiple chosen bands, and 4) Tunable RFEH duration to minimize the number of frequency band switching and increase RFEH duration. Derived theoretical performance bounds, as well as simulation results, validate the superiority of the proposed CSP over existing CSPs in stationary as well as quasi-stationary spectrum environments.

The dynamic spectrum access (DSA) algorithms aim to maximize network throughput by ensuring orthogonal channel allocation among secondary users (SUs) in cognitive radio network (CRN). The DSA is challenging in the infrastructure-less CRN (ICRN) due to lack of coordination among SUs. Most of the existing algorithms assume prior knowledge of the number of active SUs to guarantee orthogonalization. The musical chair (MC) algorithm is the state-of-the-art algorithm where the number of SUs are unknown and estimated independently at each SU. However, MC algorithm incurs a significant number of SU collisions and hence, the aggregate throughput is poor compared to centralized algorithms. In this paper, we setup DSA in ICRN as a multi-player multi-armed Bandit problem and develop distributed algorithm which allows SUs to orthogonalize in optimal channels with a negligible number of collisions. We also develop realistic USRP based testbed to validate the performance of the DSA algorithms in the real radio environment. Experimental results validate the superiority of the proposed algorithm over existing algorithms in terms of network throughput and fairness in channel access. Furthermore, a fewer number of collisions and hence, fewer retransmissions lead to efficient use of battery power, spectrum and time.

An opportunistic spectrum access (OSA) in the infrastructure-less network has received significant attention in last few years due to their ability to improve spectrum utilization as well as usefulness in the infrastructure-less networks established for disaster relief and military applications. The main research problem for feasible implementation of such network is to achieve coordination among secondary users (SUs) (i.e. unlicensed users). Existing algorithms incur a significant number of collisions which in turn require retransmissions and hence, lead to inefficient use of battery power, spectrum and time. In this paper, we set-up the problem as a multi-player Bandit and develop a new distributed algorithm which allows SUs to select one of the top channels with a significantly fewer number of collisions. We show that the proposed algorithm has constant regret with high confidence. We validate our claims and the superiority of the proposed algorithm over existing state-of-the-art algorithms through the exhaustive simulated experiments as well as a realistic USRP based experiments in the real radio environment.

Efficient utilization of licensed spectrum in the cognitive radio network is challenging due to lack of coordination among the Secondary Users (SUs). Distributed algorithms proposed in the literature aim to maximize the network throughput by ensuring orthogonal channel allocation for the SUs. However, these algorithms work under the assumption that all the SUs faithfully follow the algorithms which may not always hold due to the decentralized nature of the network. Moreover, they are vulnerable to Denial of Service attacks. In this paper, we study distributed algorithms that are robust against malicious behavior (jamming attack). We consider jammers launching coordinated attack where they select non-overlapping channels in each time slot and can lead to significantly higher number of collisions for SUs than uncoordinated attack. We setup the problem as a multiplayer bandit and develop distributed learning algorithms. The analysis shows that when the SUs faithfully implement proposed algorithms, the regret is constant with high probability. We validate our claims through exhaustive synthetic experiments and also through a realistic USRP based experiments.

This paper presents a design of linear-phase, low-complexity, reconfigurable digital filter bank that offers independent and complete control over the bandwidth as well as the center frequency of all subbands. The proposed filter bank is designed by integrating spectral parameter approximation (SPA) technique with the modified coefficient decimation method (MCDM), referred to as SPA-MCDM-FB. The architectural details, design examples and complexity comparisons show that the SPA-MCDM-FB is easy to design and offers substantial savings in gate count, number of variable multipliers and group delay over other filter banks. Moreover, these savings increase further with the increase in the filter-bank resolution (i.e., number of subbands). The SPA-MCDM-FB is then combined with the upper confidence bound (UCB)-based decision-making algorithm to search the vacant band(s) of any desired bandwidth for spectrum-sensing application in cognitive radio (CR). The simulations results verify that the proposed scheme offers superior performance [i.e., improved utilization of vacant subband(s)] and needs fewer gate counts compared to uniform filter bank and UCB-algorithm-based schemes. Furthermore, the functionality and advantages of the SPA-MCDM-FB are also verified for the channelization operation in CR supporting multiple communication standards.

This brief presents a new low-complexity reconfigurable fast filter bank (RFFB) for wireless communication applications such as spectrum sensing and channelization. In RFFB, the bandwidth and center frequency of sub-bands can be varied with high frequency resolution without hardware reimplementation. This is achieved with an improved modified frequency transformation-based variable digital filter (MFT-VDF) at the first stage of the proposed multistage implementation. Existing second-order frequency transformation-based low-pass VDFs have limited cutoff frequency range which is approximately 12.5% of the sampling frequency. The proposed low-pass MFT-VDF offers unabridged control over the cutoff frequency on a wide frequency range thereby, improving the cutoff frequency range of existing VDFs. The design example shows that the RFFB is easy to design and offers substantial savings in gate counts over other filter banks.

In this brief, an efficient implementation of reconfigurable warped digital filter with variable low-pass, high-pass, bandpass, and bandstop responses is presented. The warped filters, obtained by replacing each unit delay of a digital filter with an all-pass filter, are widely used for various audio processing applications. However, warped filters require first-order all-pass transformation to obtain variable low-pass or high-pass responses, and second-order all-pass transformation to obtain variable bandpass or bandstop responses. To overcome this drawback, the proposed method combines the warped filters with the coefficient decimation technique. The proposed architecture provides variable low-pass or high-pass responses with fine control over cut-off frequency and variable bandwidth bandpass or bandstop responses at an arbitrary center frequency without updating the filter coefficients or filter structure. The design example shows that the proposed variable digital filter is simple to design and offers substantial savings in gate counts and power consumption over other approaches.

This paper presents the design of a variable linear phase finite impulse response filter based on second order frequency transformations and coefficient decimation. The design of variable digital filters (VDFs) using first and second order frequency transformations have been proposed in literature. The VDF using second order transformation has better cut-off slope characteristics compared to the VDF using first order transformation. However, the former has the drawback of limited range (approximately 25% of the half of the sampling frequency) over which the cut-off frequency, fc, can be varied. It also fails to provide variable lowpass, highpass, bandpass or bandstop responses from a fixed-coefficient lowpass filter using the same architecture. The architecture proposed here overcomes the above mentioned disadvantages using coefficient decimation technique. The design example shows that the range over which fccan be varied is 2.65 times wider in the proposed VDF than the VDF in [7] and for a given frequency range, the proposed VDF offers a total gate count saving of 33% and 41% over the VDF in [11] and [7] respectively. Also, the proposed architecture provides variable lowpass, highpass, bandpass or bandstop responses from a fixed coefficient lowpass filter.

This paper presents a new method for the design of finite impulse response (FIR) filter that provides variable frequency responses. The proposed idea is to replace each unit delay operator in a fixed-coefficient FIR filter with the 2ndorder FIR fractional delay (FD) structure and the cutoff frequency, fcof the filter is changed by changing the FD value. The change in FD results in change in amplitude and length of an impulse response. This in turn changes fcand transition bandwidth (TBW) of an FIR filter. The mathematical relation between cut-off frequency, TBW and FD value D is derived. The design example shows that the proposed method provides very fine control over fc.