Location Aware Opportunistic Bandwidth Sharing between Static and Mobile Users with Stochastic Learning in Cellular Networks

TL;DR

This paper introduces a stochastic learning approach for location-aware bandwidth sharing between static and mobile users in cellular networks, optimizing data rates based on user location and movement.

Contribution

It formulates a Markov decision process for dynamic bandwidth allocation and proposes novel single and multi-timescale stochastic approximation algorithms for learning optimal policies.

Findings

Performance improvements demonstrated through numerical simulations

Trade-offs between data rate optimization and fairness analyzed

Algorithms adapt to unknown transition dynamics in the network

Abstract

We consider location-dependent opportunistic bandwidth sharing between static and mobile downlink users in a cellular network. Each cell has some fixed number of static users. Mobile users enter the cell, move inside the cell for some time and then leave the cell. In order to provide higher data rate to mobile users, we propose to provide higher bandwidth to the mobile users at favourable times and locations, and provide higher bandwidth to the static users in other times. We formulate the problem as a long run average reward Markov decision process (MDP) where the per-step reward is a linear combination of instantaneous data volumes received by static and mobile users, and find the optimal policy. The transition structure of this MDP is not known in general. To alleviate this issue, we propose a learning algorithm based on single timescale stochastic approximation. Also, noting that the unconstrained MDP can be used to solve a constrained problem, we provide a learning algorithm based on multi-timescale stochastic approximation. The results are extended to address the issue of fair bandwidth sharing between the two classes of users. Numerical results demonstrate performance improvement by our scheme, and also the trade-off between performance gain and fairness.