Learning-automata-based TDMA protocols for broadcast communication systems with bursty traffic

A learning automata-based time-division multiple-access protocol for broadcast networks, 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 learning automata. The learning automata update the choice probability of each station according to the network feedback information in such a way that it asymptotically tends to be proportional to the probability that this station is ready. In this manner, the number of idle slots is minimized and the network performance is significantly improved. Furthermore, the portion of the bandwidth assigned to each station is dynamically adapted to the station's needs.