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Game theoretic approach for bandwidth sharing in future generation networks

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Abstract

With the advent of 4G LTE networks, the demand for bandwidth has rapidly increased among users of mobile devices, especially handheld smartphones. Consequently, the 4G data plans offered by mobile network operators have become expensive in many countries around the world. Moreover, due to the discrete tiers of the data plans, users often experience shortage or surplus. In order to facilitate sharing of bandwidth among users, we propose a game theoretic framework where users share bandwidth with each other. The sharing mechanism is based on the distribution of surplus bandwidth of some users to overcome the shortage of others. All users sometimes serve as providers (i.e., offering bandwidth to others) and sometimes as receivers (i.e., receiving bandwidth from the providers), thus constituting a user-provided network (UPN). The sharing game is designed in such a way that those receiving bandwidth are obligated to offer the same to others at a later point of time. Unused bandwidth of a user is shared to other users who require bandwidth at that instance, who must return the bandwidth that they use to the provider of the bandwidth at a later time based on requirement, by a coordination-based model of user interaction in the UPN that consists of these entities. Further, we have developed a coordination-based game model to better simulate user interaction in this context. In order to validate the proposed framework, we conducted simulations experiments as well as conducted experiments with handheld devices where bandwidth from a 4G LTE commercial network provider was shared among the devices. Results reveal that a Nash Equilibrium is reached for the overall set of interactions between users sharing and receiving bandwidth, provided that a Nash Equilibrium is reached in the interactions between each pair of users.

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Correspondence to Judhajit Sanyal.

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Sanyal, J., Samanta, T. & Chatterjee, M. Game theoretic approach for bandwidth sharing in future generation networks. Computing 103, 613–640 (2021). https://doi.org/10.1007/s00607-020-00872-z

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