Distributed estimation for Radio Environment Map in cognitive radio networks

Radio Environment Map (REM) is a map that indicates the radio signal strength (RSS) over a finite geographical region. It enables cognitive radio (CR) users to opportunistically access to the spectrum unoccupied by the primary users (PUs) who are licensed to use specific spectrum. In this paper, a basis expansion based scheme is presented to estimate REM. We take the sparsity of preselected basis functions and the inherent time-varying spectrum usage of primary users into account such that the RSS estimation is formulated as a recursive least squares (RLS) problem with an l1 norm constraint. A distributed algorithm is presented to solve the RLS problem. Each SU only need to exchange information with its one-hop neighbours to sketch the REM with radio signal strength. Finally, simulation studies are presented to validate the performance of the proposed method.

[1]  T. Aaron Gulliver,et al.  On the construction of Radio Environment Maps for Cognitive Radio Networks , 2013, 2013 IEEE Wireless Communications and Networking Conference (WCNC).

[2]  Krishnendu Chakrabarty,et al.  Smart diagnosis: Efficient board-level diagnosis and repair using artificial neural networks , 2011, 2011 IEEE International Test Conference.

[3]  Georgios B. Giannakis,et al.  Distributed Spectrum Sensing for Cognitive Radio Networks by Exploiting Sparsity , 2010, IEEE Transactions on Signal Processing.

[4]  Guan Xinping,et al.  Consensus protocol for heterogeneous multi-agent systems: A Markov chain approach , 2013 .

[5]  Shuai Li,et al.  An Adaptive Deviation-tolerant Secure Scheme for distributed cooperative spectrum sensing , 2012, 2012 IEEE Global Communications Conference (GLOBECOM).

[6]  Emilio Calvanese Strinati,et al.  Interference-aware dynamic spectrum access in cognitive radio network , 2011, 2011 IEEE 22nd International Symposium on Personal, Indoor and Mobile Radio Communications.

[7]  Xin-Ping Guan,et al.  Distributed optimal consensus filter for target tracking in heterogeneous sensor networks , 2011, 2011 8th Asian Control Conference (ASCC).

[8]  Sergio Barbarossa,et al.  Distributed RLS estimation for cooperative sensing in small cell networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[9]  Georgios B. Giannakis,et al.  Advances in Spectrum Sensing and Cross-Layer Design for Cognitive Radio Networks , 2014 .

[10]  Ian F. Akyildiz,et al.  Cooperative spectrum sensing in cognitive radio networks: A survey , 2011, Phys. Commun..

[11]  Gengfa Fang,et al.  Improved performance of spectrum cartography based on compressive sensing in cognitive radio networks , 2013, 2013 IEEE International Conference on Communications (ICC).

[12]  Gonzalo Mateos,et al.  Group-Lasso on Splines for Spectrum Cartography , 2010, IEEE Transactions on Signal Processing.

[13]  Zhiqiang Li,et al.  A Distributed Consensus-Based Cooperative Spectrum-Sensing Scheme in Cognitive Radios , 2010, IEEE Transactions on Vehicular Technology.

[14]  Valentin Rakovic,et al.  Integration of heterogeneous spectrum sensing devices towards accurate REM construction , 2012, 2012 IEEE Wireless Communications and Networking Conference (WCNC).