Salil Kumar Sanyal

Abstract: Recently, efficient spectrum estimation has become a fundamental requirement for any wireless communication technology. Accurate detection of spectrum in a populated and noisy environment reduces spectrum scarcity problem. Even though theoretical formulation is sufficient, physical realization and its real-time performance analysis are rare to find out. So, in this work we have investigated thoroughly the autoregressive (AR) modelling of a received signal using Yule-Walker (YW) method with the concept of confidence interval (CI) and Levinson–Durbin algorithm (LDA). The received signal strength has been observed extensively with variations of different radio gain parameters, data length and lag order along with the statistical analysis. Nonlinear curve fitting technique has been used to characterize the correlation between them. The physical study and real-time application of these algorithms have been implemented and verified on an FPGA-based platform by Rice University’s Wireless Open-Access Research Platform test bed in association with WARPLab.

Abstract: Modern wireless communication systems have witnessed increasing use of channel coding techniques to enhance the throughput and to reduce latency. Interleavers are playing an important role to make the communication systems more robust and resilient in such channel coding approaches. The Long-Term Evolution (LTE)/LTE-Advanced of the 3rd Generation Partnership Project (3GPP) uses Quadrature Permutation Polynomial (QPP) interleaver in its Turbo coding scheme. The address generator of the interleaver contains a quadratic expression having square and modulus function whose direct digital hardware is not yet available in the literature. A novel algorithm has now been proposed which can provide low complexity hardware solution to implement the interleaver address generator. This paper describes VHDL model and timing simulation of the proposed address generator using ModelSim XE-III software. Due to absence of implementation results in the literature, comparison of this work is made by implementing conventional LUT-based technique on the same FPGA. Such comparison shows better FPGA resource utilization by 71.16% and improved operating speed by 82.26% in favour of the novel proposed technique.

Abstract: Complex binary number system (CBNS) finds extensive applications in the faster computation of various digital signal processing (DSP) algorithms. In this paper, an attempt has been undertaken to develop various computational circuits based on CBNS for implementation in Spartan XC3S700A FPGA platform. The circuits have been designed following a modular approach. The designed modules involve simple logic gates leading ultimately to efficient implementation on FPGA. The codes for the modules have been developed using verilog hardware description language (HDL). Structural-level designs of nibble size CBNS adder, multiplier, and subtractor have been exclusively accomplished involving these modules. In the design of multiplier and subtractor, a new concept of sub-block has been introduced to efficiently utilize the limited input capability of the designed modules. The proposed design involves less hardware complexity, silicon area, and path delay compared to existing works. Simulation results and performance metrics for all the three CBNS circuits have been included.

Abstract: Cognitive radio (CR) has become an emerging field to rescue wireless communication applications from the spectrum scarcity problem. Spectrum estimation (SE) has been a key ingredient for faster and efficient network implementations using the concept of CR. In this work, we have performed a comparative study of SE technique for the CR systems by employing the null hypothesis approach. Autoregressive (AR), autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) modelling based on optimal data length and goodness of fit (GoF) has been utilized for optimal spectrum modelling. The optimization of the modelling has been achieved through the Akaike information criteria (AIC) and Bayesian information criteria (BIC). Validation and optimization of the time-series data samples have been accomplished using Fit (%) along with \(\chi^{2}\) test GoF. The entire process of SE along with the validation of data samples has been verified on the RICE University’s FPGA-based WARP radio testbed in association with MATLAB. A thorough statistical analysis of variance and Standard Error (SER) of the received samples has been carried out for the optimization of sample time-series data length for optimal performance of the receiver or users. It is noteworthy that, we could achieve a much accurate and frugal SE with a data length of 250 only using ARIMA (3,1,2) model of the data samples with a significant improvement in power spectral density (PSD) compared to other conventional approaches. Extensive experimental work has been incorporated to establish the work.

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Abstract: Cognitive radio (CR) has become an emerging field to rescue wireless communication applications from the spectrum scarcity problem. Spectrum estimation (SE) has been a key ingredient for faster and efficient network implementations using the concept of CR. In this work, we have performed a comparative study of SE technique for the CR systems by employing the null hypothesis approach. Autoregressive (AR), autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) modelling based on optimal data length and goodness of fit (GoF) has been utilized for optimal spectrum modelling. The optimization of the modelling has been achieved through the Akaike information criteria (AIC) and Bayesian information criteria (BIC). Validation and optimization of the time-series data samples have been accomplished using Fit (%) along with \(\chi^{2}\) test GoF. The entire process of SE along with the validation of data samples has been verified on the RICE University’s FPGA-based WARP radio testbed in association with MATLAB. A thorough statistical analysis of variance and Standard Error (SER) of the received samples has been carried out for the optimization of sample time-series data length for optimal performance of the receiver or users. It is noteworthy that, we could achieve a much accurate and frugal SE with a data length of 250 only using ARIMA (3,1,2) model of the data samples with a significant improvement in power spectral density (PSD) compared to other conventional approaches. Extensive experimental work has been incorporated to establish the work.