Decentralized Dynamic Spectrum Allocation Based on Adaptive Antenna Array Interference Mitigation Diversity

Abstract

A new class of decentralized dynamic spectrum allocation (DSA) algorithms that exploit adaptive antenna array interference mitigation (IM) diversity at the receiver, is proposed for interference-limited environments with high level of frequency reuse. The system model consists of base stations (BS) that can optimize uplink frequency allocation to their subscriber stations (SS) to achieve the least impact of IM on the useful signal, assuming no control over band allocation of other BSs sharing the same bands. It is demonstrated that when the number of SSs that share the same frequency approaches the number of antenna elements at a BS, the potential performance gain is most significant for the IM-based DSA compared to the random frequency allocation. A ?good neighbor? decentralized DSA strategy is introduced. The convergence and convergence rate of IM-based DSA are investigated by means of the theory of absorbing Markov chains and statistical simulations. It is shown that the ?good neighbor? strategy leads to much better convergence properties of the entire system compared with the conventional ?selfish? approach. This suggests that the ?good neighbor? IM-based DSA techniques may have a practical perspective even in the scenarios, where global convergence with probability one cannot be established.