Improved algorithm for online probabilistic transmission power control of base stations based on observed system throughput in heterogeneous networks

Abstract

In this paper, we propose an improvement to our previously reported online probabilistic transmission power control method for base stations (BSs) in heterogeneous networks where low transmission-power pico-BSs are overlaid onto a high transmission-power macro-BS. The previously reported method requires only a single metric to be exchanged among BSs, which is a measure of system throughput at each cell. The previous method adaptively controls the activation probability of each BS individually, depending on the time variation in the system throughput and the temporal transmission power states of each BS. As a result, the system throughput is enhanced due to appropriately reduced inter-cell interference. However, since the previous method updates the transmission power of each BS with a small step size at each iteration, there is room for further improvement in the convergence rate and ability to track the change in user distribution. The proposed new algorithm defines a limited set of candidate transmission power levels. The application probability of each candidate transmission power level is updated based on the time variation in the system throughput and the temporal transmission power states of each BS. As a result, the performance after convergence and the ability to track the change in user distribution is improved compared to the previous method. Computer simulation results show the effectiveness of the proposed method compared to conventional approaches.