A blind implementation of multi-dimensional matched filtering in a Maximum-Likelihood receiver for SIMO channels

In order to establish the optimal receiver strategy, in terms of error rate for Single Input Multi Output (SIMO) wireless channels, the Maximum Likelihood (ML) detection should be performed following a multi-dimensional matched filter. However, the implementation of the matched filter and the ML detection both need the estimation of the channel impulse response in advance. In this work, we propose a novel method to establish the matched filters of the SIMO channel blindly alongside a three-step technique for the blind and adaptive ML detection of the symbol vector. With the use of the novel method, the system will benefit from the bandwidth efficiency point of view due to the use of blind schemes. The constant modulus algorithm is utilized to perform the blind matched filtering operation and later Least Mean Squared algorithm is introduced for further correction on the matched filter estimate. The blindly estimated matched filters are incorporated into the ML detector so that the transmitted symbols are found and therefore the channel is equalized. Simulations are provided to present the equalization performance and convergence speed of the novel technique

Multi-dimensional matched filter identification technique for channel equalization deployed in spatial diversity receivers

This paper proposes a multi-dimensional matched filtering technique for spatial diversity receivers. The coefficients of the multi-dimensional matched filter are identified by making use of an adaptive filter, the update of which doesn't require the transmission of any training symbols within the transmitted data stream. Therefore the use of the proposed technique improves the data rate efficiency. Furthermore, it is well known that implementing multi-dimensional matched filtering is essential for equalization purposes to obtain the optimum error rate performance from spatial diversity receivers. For that reason the technique is designed not only to identify the unknown matched filter but also to simultaneously lead to the equalization of the channel too. In order to update the adaptive filter, the Constant Modulus Algorithm (CMA) is utilized, which is an implementation convenient algorithm. Therefore the proposed technique is not computationally complex in comparison to those identification algorithms proposed for spatial diversity receivers. Simulations are provided to present the equalization performance of the novel technique

All-adaptive blind matched filtering for the equalization and identification of multipath channels: a practical approach

Blind matched filter receiver is advantageous over the state-of-the-art blind schemes due the simplicity in its implementation. To estimate the multipath communication channels, it uses neither any matrix decomposition methods nor statistics of the received data higher than the second order ones. On the other hand, the realization of the conventional blind matched filter receiver requires the noise variance to be estimated and the equalizer parameters to be calculated in state-space with relatively costly matrix operations. In this paper, a novel architecture is proposed to simplify a potential hardware implementation of the blind matched filter receiver. Our novel approach transforms the blind matched filter receiver into an all-adaptive format which replaces all the matrix operations. Furthermore, the novel design does not need for any extra step to estimate the noise variance. In this paper we also report on a comparative channel equalization and channel identification scenario, looking into the performances of the conventional and our novel all-adaptive blind matched filter receiver through simulations

Blind correlation-based DFE receiver for the equalization of single input multi output communication channels

In this paper, the correlation-based decision feedback equalizer (DFE), where the received data from multiple antennas are processed by a multi-dimensional matched filter and then combined prior to the equalization with a single input single output DFE, is discussed and its blind implementation is introduced. To perform the correlation-based DFE blindly, the multi-dimensional matched filter is replaced by an adaptive filter and the DFE filter weights are calculated via manipulating over the second order statistics of the received data. In the blind architecture, the adaptive filter converges to matched filter equivalents, therefore the matched filters of the corresponding communication channels are also blindly be estimated in addition to the blind equalization process. The mean-squared error of the estimation of matched filters and the equalization performance of the proposed blind architecture are also studied and simulated

Multi-Stage Complex Notch Filtering for Interference Detection and Mitigation to Improve the Acquisition Performance of GPS

Continuous Wave Interferences (CWIs) can degrade the accuracy of a Global Positioning System (GPS) receiver and moreover it can completely deteriorate receiver’s normal operation. In this paper a low-cost anti-jamming system design is presented for the mitigation and detection of CWIs for GPS receivers. The anti-jamming system comprises of parameterizable Complex Adaptive Notch Filter (CANF) module which is able to detect and excise single or multiple CWIs. The CANF module is composed of a first, second and third order infinite-impulse response filter with an Auto-Regressive Moving Averager structure. The proposed CANF detects the existence of the CWI and estimates JNR level of incoming signal by using the statistical value of the adaptive parameter b0. The impact of the CANF module on the acquisition is analyzed. Moreover, a simple and innovative system level model is proposed which can utilize each CANF efficiently with threshold setting of JNR estimation within the adaptation block. Threshold setting parameters provide trade-off between effective excision of CWI, order of the filter and power consumption. This results in a parameterizable CANF module and provide effective solution for the mitigation of interferences with a high-power profile for GPS based applications

Area and Power Efficient Implementation of db4 Wavelet Filter Banks for ECG Applications Using Reconfigurable Multiplier Blocks

There is an increasing demand for wavelet-based real-time on-node signal processing in portable medical devices which raises the need for reduced hardware size, cost and power consumption. This paper presents an improved Reconfigurable Multiplier Block (ReMB) architecture for an 8-tap Daubechies wavelet filter employed in a tree structured filter bank which targets the recent Field-Programmable-Gate-Array (FPGA) technologies. The ReMB is used to replace the expensive and power hungry multiplier blocks as well as the coefficient memories required in time-multiplexed finite impulse response filter architectures. The proposed architecture is implemented on a Kintex-7 FPGA and the resource utilization, maximum operating frequency and the estimated dynamic power consumption figures are reported and compared with the literature. The results demonstrated that the proposed architecture reduces the hard- ware utilization by 30% and improves the power consumption by 44% in comparison to architectures with general purpose multipliers. Thus, the proposed implementation can be deployed in low-cost low-power embedded platforms for portable medical devices

Multiplier Free Implementation of 8-tap Daubechies Wavelet Filters for Biomedical Applications

Due to an increasing demand for on-sensor biosignal processing in wireless ambulatory applications, it is crucial to reduce the power consumption and hardware cost of the signal processing units. Discrete Wavelet Transform (DWT) is very popular tool in artifact removal, detection and compression for time-frequency analysis of biosignals and can be implemented as two-branch filter bank. This work proposes a new, completely multiplier free filter architecture for implementing Daubechies wavelets which targets Field-Programmable-Gate-Array (FPGA) technologies by replacing multipliers with Reconfigurable Multiplier Blocks (ReMBs). The results have shown that the proposed technique reduces the hardware complexity by 40% in terms of Look-Up Table (LUT) count and can be used in low-cost embedded platforms for ambulatory physiological signal monitoring and analysis

IIR Wavelet Filter Banks for ECG Signal Denoising

ElectroCardioGram (ECG) signals are widely used for diagnostic purposes. However, it is well known that these recordings are usually corrupted with different type of noise/artifacts which might lead to misdiagnosis of the patient. This paper presents the design and novel use of Infinite Impulse Response (IIR) filter based Discrete Wavelet Transform (DWT) for ECG denoising that can be employed in ambulatory health monitoring applications. The proposed system is evaluated and compared in terms of denoising performance as well as the computational complexity with the conventional Finite Impulse Response (FIR) based DWT systems. For this purpose, raw ECG data from MIT-BIH arrhythmia database are contaminated with synthetic noise and denoised with the aforementioned filter banks. The results from 100 Monte Carlo simulations demonstrated that the proposed filter banks provide better denoising performance with fewer arithmetic operations than those reported in the open literature

Hybrid IIR/FIR Wavelet Filter Banks for ECG Signal Denoising

ElectroCardioGram (ECG) signals are usually corrupted with various types of artifacts which degrade the signal quality and might lead to misdiagnosis. The wavelet denoising technique is widely studied in the artifact removal literature which employs conventional Finite Impulse Response (FIR) wavelet filter banks for decomposing, thresholding and reconstructing the noisy signal to obtain high fidelity and clean ECG signal. However, the use of high order FIR wavelet filters increases the hardware complexity and cost of the system. This paper presents novel hybrid Infinite Impulse Response (IIR)/FIR Discrete Wavelet Transform (DWT) filter banks that can be employed in ambulatory health monitoring applications for denoising purposes. The proposed systems are evaluated and compared to the conventional FIR based DWT systems in terms of the computational complexity as well as the denoising performance. The results from 100 Monte Carlo simulations demonstrated that the proposed filter banks provide better denoising performance with fewer arithmetic operations than those reported in the open literature

Tracking and Mitigation of Chirp-Type Interference in GPS Receivers Using Adaptive Notch Filters

A Global Positioning System (GPS) receiver is extremely prone to intentional and unintentional interference due to weak signal power experienced on the surface of the earth, which severely affects the navigation functionality and occasionally avoids the receivers from acquiring the GPS signal. This work presents a comparative performance analysis of two different types of Adaptive Notch Filtering (ANF) algorithms for GPS specific applications that are (1) Direct form 2nd Order ANF and (2) Lattice-based ANF for tracking and mitigation of Chirp-type Interference. Three classes of chirp-type interference signals, studied in this paper, are linear chirp, quadratic chirp and cubic chirp. Performance of each ANF algorithm is evaluated at the output of the acquisition module in terms of search-grid SNR and Peak metric