A learning automata based power management for ad-hoc networks

Power management is a very important aspect of ad-hoc networks. It directly impacts the network throughput among other network metrics. On the other hand, transmission power management may result in disconnected networks and increased level of collisions. In this paper, we introduce a transmission power control based on stochastic learning automata (SLA) to modify the transmission power. Based on the level of successful transmissions and the level of packet retransmissions, the SLA will modify the transmission power level either by increasing it or decreasing it. The probabilistic nature of SLA makes it a useful choice for ad-hoc networks. Using the network simulator NS, we show that using SLA for transmission power will result in an increased system bandwidth and a decrease in the collision levels.