Matrix computations (3rd ed.)
On Limits of Wireless Communications in a Fading Environment when UsingMultiple Antennas
Wireless Personal Communications: An International Journal
Analysis and design of cognitive radio networks and distributed radio resource management algorithms
Analysis and design of cognitive radio networks and distributed radio resource management algorithms
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Performance of distributed dynamic frequency selection schemes for interference reducing networks
MILCOM'06 Proceedings of the 2006 IEEE conference on Military communications
Channel assignment schemes for cellular mobile telecommunication systems: A comprehensive survey
IEEE Communications Surveys & Tutorials
Using game theory to analyze wireless ad hoc networks
IEEE Communications Surveys & Tutorials
IEEE Transactions on Wireless Communications
IEEE Transactions on Wireless Communications
Cognitive radio: brain-empowered wireless communications
IEEE Journal on Selected Areas in Communications
Spectrum sharing for unlicensed bands
IEEE Journal on Selected Areas in Communications
Joint Beamforming and Power Allocation for Multiple Access Channels in Cognitive Radio Networks
IEEE Journal on Selected Areas in Communications
Distributed Rule-Regulated Spectrum Sharing
IEEE Journal on Selected Areas in Communications
Competitive Design of Multiuser MIMO Systems Based on Game Theory: A Unified View
IEEE Journal on Selected Areas in Communications
| Hi-index | 35.68 |
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.