On the use of stochastic estimator learning automata for dynamic channel allocation in broadcast networks

Due to its fixed assignment nature, the well-known TDMA protocol suffers from poor performance when the offered traffic is bursty. In this paper, a new time division multiple access protocol which is capable of operating efficiently under bursty traffic conditions is introduced. According to the proposed protocol, the station which grants permission to transmit at each time slot is selected by means of stochastic estimator learning automata. The system which consists of the automata and the network is analyzed and it is proved that the probability of selecting an idle station asymptotically tends to be minimized. Therefore, the number of idle slots is drastically reduced and consequently, the network throughput is improved. Furthermore, due the use of a stochastic estimator, the automata are capable of being rapidly adapted to the sharp changes of the dynamic bursty traffic environment. Extensive simulation results are presented which indicate that the proposed protocol achieves a significantly higher performance than other well-known time division multiple access protocols when operating under bursty traffic conditions.