Cognitive Radio: An Information-Theoretic Perspective

In this paper, we consider a communication scenario in which the primary and the cognitive radios wish to communicate to different receivers, subject to mutual interference. In the model that we use, the cognitive radio has noncausal knowledge of the primary radio's codeword. We characterize the largest rate at which the cognitive radio can reliably communicate under the constraint that 1) no rate degradation is created for the primary user, and 2) the primary receiver uses a single-user decoder just as it would in the absence of the cognitive radio. The result holds in a ldquolow-interferencerdquo regime in which the cognitive radio is closer to its receiver than to the primary receiver. In this regime, our results are subsumed by the results derived in a concurrent and independent work (Wu , 2007). We also demonstrate that, in a ldquohigh-interferencerdquo regime, multiuser decoding at the primary receiver is optimal from the standpoint of maximal jointly achievable rates for the primary and cognitive users.

[1]  Max H. M. Costa,et al.  Writing on dirty paper , 1983, IEEE Trans. Inf. Theory.

[2]  Patrick Mitran,et al.  Space-time diversity enhancements using collaborative communications , 2005, IEEE Transactions on Information Theory.

[3]  Mung Chiang,et al.  Channel capacity and state estimation for state-dependent Gaussian channels , 2005, IEEE Transactions on Information Theory.

[4]  Shlomo Shamai,et al.  The Capacity Region of the Gaussian Multiple-Input Multiple-Output Broadcast Channel , 2006, IEEE Transactions on Information Theory.

[5]  Hiroshi Sato,et al.  The capacity of the Gaussian interference channel under strong interference , 1981, IEEE Trans. Inf. Theory.

[6]  I. Marić.  The Strong Interference Channel with Unidirectional Cooperation , 2006 .

[7]  Max H. M. Costa,et al.  On the Gaussian interference channel , 1985, IEEE Trans. Inf. Theory.

[8]  Wei Wu,et al.  Capacity of a Class of Cognitive Radio Channels: Interference Channels With Degraded Message Sets , 2007, IEEE Transactions on Information Theory.

[9]  David Tse,et al.  Fundamentals of Wireless Communication , 2005 .

[10]  Amos Lapidoth,et al.  Nearest neighbor decoding for additive non-Gaussian noise channels , 1996, IEEE Trans. Inf. Theory.

[11]  I. Maric,et al.  The Capacity Region of the Strong Interference Channel with Common Information , 2005, Conference Record of the Thirty-Ninth Asilomar Conference onSignals, Systems and Computers, 2005..

[12]  Patrick Mitran,et al.  Achievable rates in cognitive radio channels , 2006, IEEE Transactions on Information Theory.

[13]  Wei Wu,et al.  On the Capacity of Gaussian Weak Interference Channels with Degraded Message sets , 2006, 2006 40th Annual Conference on Information Sciences and Systems.

[14]  Jon M. Peha,et al.  Real-Time Secondary Markets for Spectrum , 2003 .

[15]  Joy A. Thomas,et al.  Feedback can at most double Gaussian multiple access channel capacity , 1987, IEEE Trans. Inf. Theory.

[16]  Amos Lapidoth,et al.  The Gaussian watermarking game , 2000, IEEE Trans. Inf. Theory.

[17]  Aydano B. Carleial,et al.  Interference channels , 1978, IEEE Trans. Inf. Theory.

[18]  Amir Dembo,et al.  Information theoretic inequalities , 1991, IEEE Trans. Inf. Theory.

[19]  Shlomo Shamai,et al.  The capacity region of the Gaussian MIMO broadcast channel , 2004, International Symposium onInformation Theory, 2004. ISIT 2004. Proceedings..