On the use of stochastic estimator learning automata in time division multiple access systems: A methodology

Due to its "xed assignment nature, the well-known TDMA protocol su!ers from poor performance when the o!ered tra$c is bursty. In this paper, a new time division multiple access protocol which is capable of operating e$ciently under bursty tra$c conditions is introduced. According to the proposed protocol, the station which grants permission to transmit at each time slot is selected by means of stochastic estimator learning automata. The system which consists of the automata and the network is analyzed and it is proved that the probability of selecting an idle station asymptotically tends to be minimized. Therefore, the number of idle slots is drastically reduced and consequently, the network throughput is improved. Furthermore, due to the use of a stochastic estimator, the automata are capable of being rapidly adapted to the sharp changes of the dynamic bursty tra$c environment. Extensive simulation results are presented which indicate that the proposed protocol achieves a signi"cantly higher performance than other well-known time division multiple access protocols when operating under bursty tra$c conditions. ( 2000 Published by Elsevier Science B.V. All rights