Chandramani Kishore Singh

Pavamana K J, Chandramani Kishore Singh

Mobile networks are experiencing prodigious increase in data volume and user density , which exerts a great burden on mobile core networks and backhaul links. An efficient technique to lessen this problem is to use caching i.e. to bring the data closer to the users by making use of the caches of edge network nodes, such as fixed or mobile access points and even user devices. The performance of a caching depends on contents that are cached. In this paper, we examine the problem of content caching at the wireless edge(i.e. base stations) to minimize the discounted cost incurred over infinite horizon. We formulate this problem as a restless bandit problem, which is hard to solve. We begin by showing an optimal policy is of threshold type. Using these structural results, we prove the indexability of the problem, and use Whittle index policy to minimize the discounted cost.

Pavamana K J, Chandramani Kishore Singh

We study content caching with recommendations in a wireless network where the users are connected through a base station equipped with a finite-capacity cache. We assume a fixed set of contents with unknown user preferences and content popularities. The base station can cache a subset of the contents and can also recommend subsets of the contents to different users in order to encourage them to request the recommended contents. Recommendations, depending on their acceptability, can thus be used to increase cache hits. We first assume that the users' recommendation acceptabilities are known and formulate the cache hit optimization problem as a combinatorial multi-armed bandit (CMAB). We propose a UCB-based algorithm to decide which contents to cache and recommend and provide an upper bound on the regret of this algorithm. Subsequently, we consider a more general scenario where the users' recommendation acceptabilities are also unknown and propose another UCB-based algorithm that learns these as well. We numerically demonstrate the performance of our algorithms and compare these to state-of-the-art algorithms.