Cake-cutting approach for privacy-enhanced base station sharing in a linear model of user assignment

We consider a base station sharing scenario of cellular wireless networks. In the proposed framework, service providers, according to the channel parameters of their users, evaluate each base station. Based on these evaluations, using fair division methods, the capacity of base stations is shared among the service providers. The benefit of the proposed approach is that sensitive data of individual users is not used for the central algorithm determining the resulting sharing of resources, so service providers must not forward these data to a third party. We analyze the efficiency of the proposed method using a Monte Carlo simulation, in which the user-base station assignment and bandwidth allocation are carried out via a linear framework.

[1]  Mérouane Debbah,et al.  On the base station selection and base station sharing in self-configuring networks , 2009, VALUETOOLS.

[2]  Erel Segal-Halevi,et al.  Monotonicity and competitive equilibrium in cake-cutting , 2015 .

[3]  Olav Tirkkonen,et al.  Resource Sharing Optimization for Device-to-Device Communication Underlaying Cellular Networks , 2011, IEEE Transactions on Wireless Communications.

[4]  Robert E. Kahn,et al.  Resource-sharing computer communications networks , 1972 .

[5]  Erel Segal-Halevi,et al.  Resource-monotonicity and Population-monotonicity in Connected Cake-cutting , 2017, Math. Soc. Sci..

[6]  Mehdi Bennis,et al.  Inter Base Station Resource Sharing and Improving the Overall Efficiency of B3G Systems , 2007, 2007 IEEE 66th Vehicular Technology Conference.

[7]  J. Lilleberg,et al.  Spectrum sharing in a cellular system , 2004, Eighth IEEE International Symposium on Spread Spectrum Techniques and Applications - Programme and Book of Abstracts (IEEE Cat. No.04TH8738).

[8]  Bhaskar Krishnamachari,et al.  Microeconomic analysis of base-station sharing in green cellular networks , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[9]  Carlo Fischione,et al.  Optimizing Client Association in 60 GHz Wireless Access Networks , 2013, ArXiv.

[10]  Ariel Orda,et al.  Atomic Resource Sharing in Noncooperative Networks , 2001, Telecommun. Syst..

[11]  Carlo Fischione,et al.  Optimizing Client Association for Load Balancing and Fairness in Millimeter-Wave Wireless Networks , 2015, IEEE/ACM Transactions on Networking.

[12]  Seung-Jae Han,et al.  Fairness and Load Balancing in Wireless LANs Using Association Control , 2004, IEEE/ACM Transactions on Networking.

[13]  Antonio Capone,et al.  Dynamic Resource Trading in Sliced Mobile Networks , 2019, IEEE Transactions on Network and Service Management.

[14]  Zhu Han,et al.  Coalitional Games for Resource Allocation in the Device-to-Device Uplink Underlaying Cellular Networks , 2014, IEEE Transactions on Wireless Communications.

[15]  Carlo Fischione,et al.  Auction-Based Resource Allocation in MillimeterWave Wireless Access Networks , 2013, IEEE Communications Letters.

[16]  Randeep Bhatia,et al.  MiFi: A framework for fairness and QoS assurance in current IEEE 802.11 networks with multiple access points , 2004, IEEE INFOCOM 2004.

[17]  Ariel D. Procaccia.  Cake Cutting Algorithms , 2016, Handbook of Computational Social Choice.

[18]  Eitan Altman,et al.  On optimal call admission control in resource-sharing system , 2001, IEEE Trans. Commun..

[19]  Vincenzo Sciancalepore,et al.  From network sharing to multi-tenancy: The 5G network slice broker , 2016, IEEE Communications Magazine.