Experimental Evaluation of Optimal Rate Delay and Power Allocation Algorithm in Wireless Control Networks

Muhammad Tahir,S K Mazumder

doi:10.1155/2010/650756

Abstract

The network utility maximization (NUM) framework, widely used for wireless networks to achieve optimal resource allocation, has led to both centralized as well as distributed algorithms. We compare the convergence performance of centralized realization of the NUM framework with that of distributed realization by implementing the algorithms using a hardware test‐bed. Experimental results show a superior convergence performance for centralized implementation compared to the distributed implementation, which is attributed to the dominance of communication delay over processing delay. The convergence results for the distributed case also show a tradeoff between processing time and the associated communication overhead providing an optimal termination criterion for the convergence of different subproblems.

Highlights

Since the seminal work by Kelly et al [1] was published, the basic network utility maximization (NUM) problem has been extensively studied for rate allocation and congestion control in wired [2] as well as wireless networks [3,4,5]
We develop a performance evaluation model and validate its convergence performance using the experimental results
The model can be used for performance evaluation of both distributed as well as centralized realizations of different NUM frameworks

Summary

Introduction

Since the seminal work by Kelly et al [1] was published, the basic network utility maximization (NUM) problem has been extensively studied for rate allocation and congestion control in wired [2] as well as wireless networks [3,4,5]. To compare the convergence performance of distributed and centralized implementations, we have selected the following NUM problem of [5]: maximize log(rs). In (1), dl(q) and dl(t) are queuing and transmission delays, respectively, at link l ∈ L (L being the set of network links and |L| is the total number of links in the network), ds represents the end-to-end delay for transmission session s, μ is the packet length, cl(P) given by log(SINRl(P)), for high link SINR, is the capacity at link l, rs is the end-to-end session rate for transmission session s ∈ S with an associated shortest path consisting of a subset of links L(s) ⊆ L, Dmax(s) is the end-to-end delay threshold, and Rmin(s) is the minimum rate. Instead power control is used to maximize the link packet success rate, ρl With these hardware limitations, we compare the convergence performance, of the distributed and centralized implementations, using Texas Instrument’s TMS320C6713

Rate Delay and Power Allocation Algorithm

Performance Evaluation Model

Experimental Results

Conclusion