Green design for smart antenna system using iterative beamforming algorithms

In wireless sensor networks operating over short inter-node distances, both computation power and radio power influence the battery life. In such a scenario, to evaluate the utility of Smart Antennas (SA) from a power perspective, one has to consider the power consumed in the beamforming (BF) unit (computation power) and the power consumed in the radio unit (radio power). Both computation power and radio power in turn depend on the number of iterations of the BF algorithms. In this paper, two iterative adaptive BF algorithms, Least Mean Square (LMS) algorithm and Recursive Least Square (RLS) algorithm are considered. Computation power measurements have been carried out for a StrongARM SA-1100 processor platform. A closed form expression for optimal number of iterations has been derived for a given bit error rate (BER) that minimizes the total power consumption. It is found that optimal number of iterations increases linearly with path loss exponent and decreases logarithmic with BER. We have analyzed the effect of different BERs and path loss exponents on the optimal number of iterations. Simulation results suggest that RLS algorithm becomes more effective compared to the LMS algorithm in terms of number of iterations at higher path loss exponents. This study yields a new, power optimal stopping criterion, thereby providing a green design for SA systems.