Efficient OFDMA distributed optimization algorithm exploiting multi-user diversity

Future generations of wireless systems require opportunistic spectrum access techniques to effectively detect and access temporarily unused spectrum bands. Cognitive radios, with their ability to learn and adapt to their environment, promise to possess such powerful capabilities. As a consequence, the spectrum allocation of a wireless system could quickly and appropriately auto-adapt to react to a sudden traffic variation. In this article, we propose an innovative and efficient distributed spectrum allocation algorithm, called DABVT-OFDMA, whose objective is to maximize the system throughput by exploiting multi-user diversity. The algorithm is capable of learning over time and adapt the spectrum allocation when changes occur in the radio environment. Such an algorithm finds its application in the scope of future WLAN systems (e.g.: 802.11x)