Signal adaptive, multiple-clock-cycle hardware implementation (MCI) of an optimal (Wiener) filter for highly nonstationary two-dimensional (2D) FM signals estimation is developed here. It uses results of the space/spatial-frequency (S/SF) analysis in real-time processing of nonstationary 2D signals and is based on the correspondence of the filter's region of support to the local frequency (LF) of the filtered 2D signal and on the S/SF analysis-based LF estimation. The MCI approach helps the proposed design to minimize clock cycle time and to optimize critical design performances related to the hardware complexity, making it a suitable system for real-time and on-a-chip implementation. However, the major advantage of the proposed design is the ability to take variable (signal adaptive) number of clock cycles in different S/SF points within the execution. This property helps the design to optimize the execution time (the main drawback of the classical MCI approaches in comparison to the single-clock-cycle ones), but also to provide the highest quality LF estimation, high S/SF resolution, and a very efficient filtering of nonstationary 2D FM signals. In this way, it is qualified as an optimal solution for wide range of practical implementations. The implementation is verified by a field-programmable gate array (FPGA) circuit design.

Signal adaptive hardware implementation of a system for highly nonstationary two-dimensional FM signal estimation

This paper investigates the multiple relay codedcooperation scheme based on asymmetric turbo code (ATC) with multiple receive antennas over Rayleigh block fading channels. An encoding scheme based on ATC is proposed for coded-cooperation, i.e. distributed asymmetric turbo code (DATC). The code matched interleaver (CMI) is selected by a rigorous comparison with a uniform-random interleaver (URI). This optimum choice of interleaver at the relay nodes provides maximum benefit from DATC coded-cooperation scheme. Practically in any wireless communication system, the channel side information (CSI) is usually unknown at the receiver. Therefore, spatial normalized least mean square (NLMS) adaptive transversal filters are employed to estimate the CSI at the destination node. Moreover, in coded-cooperation scheme, the effectiveness and validation of spatial NLMS adaptive transversal filters is also verified by simulation results. Quadrature phase shift keying (QPSK) is used in codedcooperation scheme and corresponding soft-demodulators are employed along with joint iterative soft-input soft-output (SISO) decoder at the destination node. Monte Carlo simulations show that the proposed scheme incorporates coding gain, diversity gain and cooperation gain successfully, which eventually results in net gain of 2.7 to 3.5 dBs over non-cooperation ATC counterpart.

/pdf/asymmetric-turbo-code-for-coded-cooperative-wireless-1p4nelqzjx.pdf

Asymmetric Turbo Code for Coded-Cooperative Wireless Communication Based on Matched Interleaver with Channel Estimation and Multi-Receive Antennas at the Destination

Multiple-clock-cycle, signal adaptive, and fully pipelined hardware design of the optimal (Wiener) space/spatial-frequency (S/SF) filter is developed in this paper. All implementation and verification details, as well as the extensive comparative analysis, are provided. The developed solution optimizes critical design performances related to the hardware complexity, in line with multiple-clock-cycle nature. Variable (signal adaptive) number of clock cycles, taken within the execution in different S/SF points, provides this solution to retain the optimized time requirements, as well as high resolution, selectivity, and estimation quality of the corresponding recently proposed signal adaptive filtering solution. However, as the major contribution, the fully pipelined implementation enables the developed design to additionally improve the time required for execution. The achieved improvement corresponds to a clock cycle per each S/SF point performed within the estimation that results in the significant comparative improvement in execution time of up to 50% in terms of S/SF points lying outside the local frequency of the estimated 2D frequency-modulated signal. The implementation is tested on a highly nonstationary multicomponent signal and is verified by a field programmable gate array circuit design.

Superior Execution Time Design of a Space/Spatial-Frequency Optimal Filter for Highly Nonstationary 2D FM Signal Estimation

In our daily day, improvement of makhraj for Arabic alphabets is a topic that very useful in many applications and environments. The existing system cannot recognize the appropriate pronunciation of each alphabet with the existence of noise. As an example “ha”, with the disturbance from the noise, the system may recognize wrong alphabet like “kho”. This paper focus on noise removal in makhraj recognition using Least Mean Square (LMS) Algorithm based on Adaptive Filter to search for the optimal solution to adaptive filter, including system identification and noise cancellation. There are 30 Arabic alphabets from until. However, this project will only use 7 alphabets as samples that are from until. The speech processing will be used to obtain same waveform output from two different situations. The filtered data will be processed to match the standard pronunciations and it will be integrated with filter design process in MATLAB. As a result, the waveform of noise cancellation using LMS algorithm is quite similar with the waveform of reference signal. As a conclusion, it is proved that noise cancellation method remove noise from unknown system.

Speech processing for makhraj recognition

This paper introduces a low complexity adaptive channel estimation technique for OFDM based two-way relay systems. The adaptive filter used is known as Group Fast Array Multichannel 2D-Recursive Least Square (GFAM 2DRLS) filter. It has a computational complexity comparable to that of 2D-Normalized Least Mean Square (2D-NLMS) algorithm while maintaining the same convergence rate as the classic 2D Recursive Least Square (2D-RLS) algorithm. It considers the correlation of the channel frequency response in both time and frequency, while estimating the channel. In order to reduce the number of training data for time varying channel, the channel estimation is carried out based on the Decision Directed (DD) principle. It is assumed that the relay is capable of performing complex signal processing tasks. Hence the channel estimation is performed at the relay. Since the Channel State Information (CSI) is available at the relay, it could perform Multiple Input Multiple Output (MIMO) precoding of the transmitted data. Hence CSI is not required at the transmitting nodes. The convergence rate of GFAM 2D-RLS is compared with the existing 2D-NLMS algorithm and the computational complexity at each iteration is tabulated. Simulations are performed using MATLAB.

Low Complexity 2D Adaptive Channel Estimation for OFDM Based Two-Way Relay Systems

This paper presents a MIMO two-way relaying scheme where the transceiver filter at the Relay Station (RS) processes the data using Least Mean Square Algorithm (LMS) and Normalized Least Mean Square (NLMS) algorithm. The relaying technique used is Amplify and Forward scheme (AF). Channel State Information (CSI) is assumed to be available only at the RS. So the transmit and receive processing are both done at the RS. The data at the two nodes S 1 and S 2 is Quadrature Phase Shift Keying (QPSK) modulated. The metrics considered for performance comparison of the LMS/NLMS transceiver filter is Mean Square Error (MSE) and Bit Error Rate (BER). The computer simulations are performed using MATLAB.

Performance comparison of LMS/NLMS based transceiver filters for MIMO two-way relaying scheme

In this paper a Fast Array Multichannel Two-Dimensional Recursive Least Square (FAM 2D-RLS) adaptive filter is proposed for estimating an OFDM channel in frequency domain. This filter makes use of the shift structure of the input data vector. Thus the computational cost of the classical RLS filter which is O(M
 2) is reduced to O(M) for each iteration where M is the order of the filter. In order to ensure numerical stability in finite precision, we make use of array-based methods for implementing FAM 2D-RLS. The adaptive filters illustrated in the standard literature consist of a weight vector and desired data as a scalar. But in our scenario of OFDM channel estimation the weight is a matrix while the desired data are a vector. Hence the algorithm for the matrix form of FAM-2D RLS and its steady state equations are derived. Numerical stability, steady state and convergence performance are verified using MATLAB simulations.

Fast Array Multichannel 2D-RLS Based OFDM Channel Estimator

In this paper we introduce an adaptive channel estimation scheme for two-way relay with node capability. The adaptive filter used is called Block Fast Array Multichannel 2D-Recursive Least Square (BFAM 2D-RLS). The modulation used is Orthogonal Frequency Division Multiplexing (OFDM) and channel estimation is done in the frequency domain. The proposed channel estimator considers correlation of channel frequency response in both time and frequency domain. The computational complexity is calculated and convergence performance of the adaptive channel estimator is studied. This paper also introduces an AOFDM scheme for two-way relay systems. The effect of channel estimation error on performance of Adaptive OFDM (AOFDM) is analyzed for pedestrian A (pedA) channel. All computer simulations are performed using MATLAB ®.

http://ieeexplore.ieee.org/iel7/6992915/7002065/07002077.pdf

Performance study of BFAM 2D-RLS channel estimator for AOFDM based two-way relay

This paper presents a MIMO two-way relaying scheme where the transceiver filter at the Relay Station (RS) processes the data using Recursive Least Square (RLS) technique based on Inverse QR (IQR) algorithm. The relaying technique used is Amplify and Forward scheme (AF). The channel is assumed to be frequency selective and the Channel State Information (CSI) is available only at the RS. So the transmit and receive processing are both done at the RS. Orthogonal Frequency Division Multiplexing (OFDM) is used so as to combat the Inter-Symbol Interference (ISI) occurring due to the frequency selectivity of the channel. The performance of the IQR-RLS algorithm is verified by comparing its Mean-Square error performance (MSE) with that of a transceiver filter based on Normalized Least Mean Square (NLMS) algorithm.

Arun Joy

Papers

Performance comparison of LMS/NLMS based transceiver filters for MIMO two-way relaying scheme

Fast Array Multichannel 2D-RLS Based OFDM Channel Estimator

Low Complexity 2D Adaptive Channel Estimation for OFDM Based Two-Way Relay Systems

Performance study of BFAM 2D-RLS channel estimator for AOFDM based two-way relay

An IQR-RLS based transceiver filter For MIMO two-way relaying in frequency selective environment