A Non-Stationary Bandit-Learning Approach to Energy-Efficient Femto-Caching with Rateless-Coded Transmission

• URL: http://arxiv.org/abs/2005.04154v1
• Date: Mon, 13 Apr 2020 09:07:17 GMT
• Title: A Non-Stationary Bandit-Learning Approach to Energy-Efficient Femto-Caching with Rateless-Coded Transmission
• Authors: Setareh Maghsudi and Mihaela van der Schaar
• Abstract summary: We study a resource allocation problem for joint caching and transmission in small cell networks. We then formulate the problem as to select a file from the cache together with a transmission power level for every broadcast round. In contrast to the state-of-the-art research, the proposed approach is especially suitable for networks with time-variant statistical properties.
• Score: 98.47527781626161
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The ever-increasing demand for media streaming together with limited backhaul capacity renders developing efficient file-delivery methods imperative. One such method is femto-caching, which, despite its great potential, imposes several challenges such as efficient resource management. We study a resource allocation problem for joint caching and transmission in small cell networks, where the system operates in two consecutive phases: (i) cache placement, and (ii) joint file- and transmit power selection followed by broadcasting. We define the utility of every small base station in terms of the number of successful reconstructions per unit of transmission power. We then formulate the problem as to select a file from the cache together with a transmission power level for every broadcast round so that the accumulated utility over the horizon is maximized. The former problem boils down to a stochastic knapsack problem, and we cast the latter as a multi-armed bandit problem. We develop a solution to each problem and provide theoretical and numerical evaluations. In contrast to the state-of-the-art research, the proposed approach is especially suitable for networks with time-variant statistical properties. Moreover, it is applicable and operates well even when no initial information about the statistical characteristics of the random parameters such as file popularity and channel quality is available.

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