Unlocking the Deployment of Spectrum Sharing With a Policy Enforcement Framework

Spectrum sharing has been proposed as a promising way to increase the efficiency of spectrum usage by allowing incumbent operators (IOs) to share their allocated radio resources with licensee operators (LOs), under a set of agreed rules. The goal is to maximize a common utility, such as the sum rate throughput, while maintaining the level of service required by the IOs. However, this is only guaranteed under the assumption that all “players” respect the agreed sharing rules. In this paper, we propose a comprehensive framework for licensed shared access (LSA) networks that discourages LO misbehavior. Our framework is built around three core functions: misbehavior detection via the employment of a dedicated sensing network; a penalization function; and, a behavior-driven resource allocation. To the best of our knowledge, this is the first time that these components are combined for the monitoring/policing of the spectrum under the LSA framework. Moreover, a novel simulator for LSA is provided as an open access tool, serving the purpose of testing and validating our proposed techniques via a set of extensive system-level simulations in the context of mobile network operators, where IOs and several competing LOs are considered. The results demonstrate that violation of the agreed sharing rules can lead to a great loss of resources for the misbehaving LOs, the amount of which is controlled by the system. Finally, we promote that including a policy enforcement function as part of the spectrum sharing system can be beneficial for the LSA system, since it can guarantee compliance with the spectrum sharing rules and limit the short-term benefits arising from misbehavior.

[1]  Sergios Theodoridis,et al.  Machine Learning: A Bayesian and Optimization Perspective , 2015 .

[2]  Joseph Mitola,et al.  Cognitive Radio An Integrated Agent Architecture for Software Defined Radio , 2000 .

[3]  Stephen P. Boyd,et al.  Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..

[4]  Nicola Marchetti,et al.  Fair and regulated spectrum allocation in licensed shared access networks , 2016, 2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[5]  Joseph Mitola,et al.  Cognitive radio: making software radios more personal , 1999, IEEE Wirel. Commun..

[6]  Yang Yang,et al.  A novel line search method for nonsmooth optimization problems , 2015, 2015 23rd European Signal Processing Conference (EUSIPCO).

[7]  Kaigui Bian,et al.  PHY-Layer Authentication Using Duobinary Signaling for Spectrum Enforcement , 2016, IEEE Transactions on Information Forensics and Security.

[8]  Leonard Kleinrock,et al.  Analysis of A time‐shared processor , 1964 .

[9]  Georgios B. Giannakis,et al.  Optimal resource allocation for MIMO ad hoc cognitive radio networks , 2008, 2008 46th Annual Allerton Conference on Communication, Control, and Computing.

[10]  R. Tibshirani Regression Shrinkage and Selection via the Lasso , 1996 .

[11]  Brent A. Johnson,et al.  Model selection and inference for censored lifetime medical expenditures , 2016, Biometrics.

[12]  Sudharman K. Jayaweera,et al.  A subspace-based policy enforcement method in dynamic spectrum leasing schemes , 2011, 2011 IEEE 13th International Conference on Communication Technology.

[13]  Martin B. H. Weiss,et al.  Enforcement in Dynamic Spectrum Access Systems , 2012 .

[14]  Mung Chiang,et al.  “See Something, Say Something” Crowdsourced Enforcement of Spectrum Policies , 2016, IEEE Transactions on Wireless Communications.

[15]  Yang Yang,et al.  An online parallel algorithm for spectrum sensing in cognitive radio networks , 2014, 2014 48th Asilomar Conference on Signals, Systems and Computers.

[16]  Michael Elad,et al.  From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images , 2009, SIAM Rev..