Implementation of the Trigonometric LMS Algorithm using Original Cordic Rotation

The LMS algorithm is one of the most successful adaptive filtering algorithms. It uses the instantaneous value of the square of the error signal as an estimate of the mean-square error (MSE). The LMS algorithm changes (adapts) the filter tap weights so that the error signal is minimized in the mean square sense. In Trigonometric LMS (TLMS) and Hyperbolic LMS (HLMS), two new versions of LMS algorithms, same formulations are performed as in the LMS algorithm with the exception that filter tap weights are now expressed using trigonometric and hyperbolic formulations, in cases for TLMS and HLMS respectively. Hence appears the CORDIC algorithm as it can efficiently perform trigonometric, hyperbolic, linear and logarithmic functions. While hardware-efficient algorithms often exist, the dominance of the software systems has kept those algorithms out of the spotlight. Among these hardware- efficient algorithms, CORDIC is an iterative solution for trigonometric and other transcendental functions. Former researches worked on CORDIC algorithm to observe the convergence behavior of Trigonometric LMS (TLMS) algorithm and obtained a satisfactory result in the context of convergence performance of TLMS algorithm. But revious researches directly used the CORDIC block output in their simulation ignoring the internal step-by-step rotations of the CORDIC processor. This gives rise to a need for verification of the convergence performance of the TLMS algorithm to investigate if it actually performs satisfactorily if implemented with step-by-step CORDIC rotation. This research work has done this job. It focuses on the internal operations of the CORDIC hardware, implements the Trigonometric LMS (TLMS) and Hyperbolic LMS (HLMS) algorithms using actual CORDIC rotations. The obtained simulation results are highly satisfactory and also it shows that convergence behavior of HLMS is much better than TLMS.Comment: 12 pages, 5 figures, 1 table. Published in IJCNC; http://airccse.org/journal/cnc/0710ijcnc08.pdf, http://airccse.org/journal/ijc2010.htm

Joint workload scheduling and BBU allocation in cloud-RAN for 5G networks

Copyright 2017 ACM. Cloud-radio access network (C-RAN) emerges as a solution to satisfy the demand for a diverse range of applications, massive connectivity, and network heterogeneity. C-RAN uses central cloud network for processing user requests. Efficient management of cloud resources (e.g., computation and transmission resources) is one of the important challenges in C-RAN. In this paper, we investigate a joint workload scheduling and baseband unit (BBU) allocation in Cloud-RAN for 5G networks. First, we establish a queueing model in C-RAN. We then formulate an optimization problem for joint workload scheduling and BBU allocation with the aim to minimize mean response time and aggregate power. Queueing stability and workload conservation constraints are considered in the optimization problem. To solve this problem, we propose an energy efficient joint workload scheduling and BBU allocation (EE-JWSBA) algorithm using the concept of queueing theory. The EE-JWSBA algorithm is evaluated via simulations by considering three different scheduling weights (e.g., random, normalized, and upper limit). Simulation results demonstrate the effectiveness of proposed scheme using different scheduling weights

Performance of integrated workload scheduling and pre-fetching in multimedia mobile cloud computing

Abstract This paper focuses on an integrated workload scheduling and pre-fetching model in a multimedia mobile cloud computing environment to enhance the performance of response time and reduce the cost to process multimedia data. The response time and cost optimization problems are presented along with the computation resources such as virtual machines (VMs) allocation, workload conservation, queueing stability constraints, and to overcome the total response time and cost, a heuristic approach of workload scheduling method is proposed. The integrated workload scheduling at pre-fetcher and cloud are considered to study the effects of various parameters such as VM’s processing speed, pre-fetcher’s utilization, the user requests arrival rate. The performance analysis results reveal that the cost and transmission speed are directly relevant factors, meaning that, once the rate of data transmission is increasing, the cost is also increasing and vice versa. Hence, the time and cost efficient workload scheduling is essential to satisfy both delay and cost in pre-fetcher enabled multimedia cloud systems

A PERFORMANCE ANALYSIS OF LMS, RLS AND LATTICE BASED ALGORITHMS AS APPLIED TO THE AREA OF LINEAR PREDICTION

Abstract: This paper presents a performance analysis of three categories of adaptive filtering algorithms in the application of linear prediction. The classes of algorithms considered are Least-Mean-Square (LMS) based, Recursive Least-Squares (RLS) based and Lattice based adaptive filtering algorithms. The performances of the algorithms in each class are compared in terms of convergence behavior, execution time and filter length. The analysis determines the best converging algorithm from each class. Finally the best performing algorithm for adaptive linear prediction is selected