Cognitive Radio Systems via Game Theory
Radio regulatory bodies are recently recognizing that rigid spectrum
assignment granting exclusive use to licensed services is highly
inefficient. A more efficient way to utilize the scarce spectrum
resources is with a dynamic spectrum access, depending on the real
spectrum usage and traffic demands. The concept of cognitive radio has
recently received great attention from the research community as a
promising paradigm to achieve efficient use of the frequency resource by
allowing the coexistence of licensed (primary) and unlicensed
(secondary) users in the same bandwidth.
We consider underlay/interweave multi-antenna networks, where primary users establish proper null and/or soft shaping constraints on the transmit covariance matrix of secondary users, so that the interference generated by secondary users is conﬁned within the interference-temperature limits. The secondary users compete then for the resource allocation, which can be formally modeled with game theory to obtain a completely decentralized approach.
- Yang Yang, Gesualdo Scutari, Peiran Song, and Daniel P. Palomar, “Robust MIMO Cognitive Radio Systems Under Interference Temperature Constraints,” IEEE Journal on Selected Areas in Communications, vol. 31, no. 11, pp. 2465-2482, Nov. 2013.
- Jiaheng Wang, Gesualdo Scutari, and Daniel P. Palomar, “Robust MIMO Cognitive Radio via Game Theory,” IEEE Trans. on Signal Processing, vol. 59, no. 3, pp. 1183-1201, March 2011.
- Gesualdo Scutari and Daniel P. Palomar, “MIMO Cognitive Radio: A Game Theoretical Approach,” IEEE Trans. on Signal Processing, vol. 58, no. 2, pp. 761-780, Feb. 2010.
- Gesualdo Scutari, Daniel P. Palomar, and Sergio Barbarossa, “Competitive Optimization of Cognitive Radio MIMO Systems via Game Theory,” in Convex Optimization in Signal Processing and Communications, Cambridge Univ. Press, 2009. [book]
- Gesualdo Scutari, Daniel P. Palomar, and Sergio Barbarossa, “Cognitive MIMO Radio: Competitive Optimality Design Based on Subspace Projections,” IEEE Signal Processing Magazine, vol. 25, no. 6, pp. 46-59, Nov. 2008.