Nasirud Din Gohar

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—An efficient hardware implementation of Gaussian Random Number (GRN) generator based on Central Limit Theorem (CLT) is presented. CLT, although very simple to implement, is never used to generate high quality Gaussian numbers. This is due to the fact that direct implementation of CLT provides very poor accuracy in tail regions of the probability density(More)
Box Muller (BM) algorithm is extensively used for generation of high quality Gaussian Random Numbers (GRNs) in hardware. Most efficient published implementation of BM method utilizes transformation of 32-bit data path to 16 bits and use of first degree piece-wise polynomial approximation to compute logarithmic and square root functions. In this work, we(More)
Random Number (GRN) generator based upon Box-Muller (BM) and CORDIC algorithms is presented. We will illustrate a novel hardware architecture with flexible design space that unifies the two algorithms. A major advantage of this work is that unlike any of the previously reported architectures, it is possible to eliminate hardware multipliers and memory(More)
Filter-based fading channel simulators universally use White Gaussian Noise (WGN) to generate complex tap coefficients. In this work, we will show that replacing WGN source by Uniform Random Number Generator (URNG) results in improved simulation speed in case of software simulator; and reduced area/power in case of hardware simulator. We will verify, both(More)
— Gaussian random numbers (GRNs) generated by central limit theorem (CLT) suffer from errors due to deviation from ideal Gaussian behavior for any finite number of additions. In this paper, we will show that it is possible to compensate the error in CLT, thereby correcting the resultant probability density function, particularly in the tail regions. We will(More)
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