1,506 research outputs found

    Fundamental Limits on Latency in Transceiver Cache-Aided HetNets

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    Stringent mobile usage characteristics force wire- less networks to undergo a paradigm shift from conventional connection-centric to content-centric deployment. With respect to 5G, caching and heterogeneous networks (HetNet) are key technologies that will facilitate the evolution of highly content- centric networks by facilitating unified quality of service in terms of low-latency communication. In this paper, we study the impact of transceiver caching on the latency for a HetNet consisting of a single user, a receiver and one cache-assisted transceiver. We define an information-theoretic metric, the delivery time per bit (DTB), that captures the delivery latency. We establish coinciding lower and upper bounds on the DTB as a function of cache size and wireless channel parameters; thus, enabling a complete characterization of the DTB optimality of the network under study. As a result, we identify cache beneficial and non-beneficial channel regimes.Comment: 5 pages, ISIT 201

    Caching with Unknown Popularity Profiles in Small Cell Networks

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    A heterogenous network is considered where the base stations (BSs), small base stations (SBSs) and users are distributed according to independent Poisson point processes (PPPs). We let the SBS nodes to posses high storage capacity and are assumed to form a distributed caching network. Popular data files are stored in the local cache of SBS, so that users can download the desired files from one of the SBS in the vicinity subject to availability. The offloading-loss is captured via a cost function that depends on a random caching strategy proposed in this paper. The cost function depends on the popularity profile, which is, in general, unknown. In this work, the popularity profile is estimated at the BS using the available instantaneous demands from the users in a time interval [0,τ][0,\tau]. This is then used to find an estimate of the cost function from which the optimal random caching strategy is devised. The main results of this work are the following: First it is shown that the waiting time τ\tau to achieve an ϵ>0\epsilon>0 difference between the achieved and optimal costs is finite, provided the user density is greater than a predefined threshold. In this case, τ\tau is shown to scale as N2N^2, where NN is the support of the popularity profile. Secondly, a transfer learning-based approach is proposed to obtain an estimate of the popularity profile used to compute the empirical cost function. A condition is derived under which the proposed transfer learning-based approach performs better than the random caching strategy.Comment: 6 pages, Proceedings of IEEE Global Communications Conference, 201

    A Learning-Based Approach to Caching in Heterogenous Small Cell Networks

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    A heterogenous network with base stations (BSs), small base stations (SBSs) and users distributed according to independent Poisson point processes is considered. SBS nodes are assumed to possess high storage capacity and to form a distributed caching network. Popular files are stored in local caches of SBSs, so that a user can download the desired files from one of the SBSs in its vicinity. The offloading-loss is captured via a cost function that depends on the random caching strategy proposed here. The popularity profile of cached content is unknown and estimated using instantaneous demands from users within a specified time interval. An estimate of the cost function is obtained from which an optimal random caching strategy is devised. The training time to achieve an ϵ>0\epsilon>0 difference between the achieved and optimal costs is finite provided the user density is greater than a predefined threshold, and scales as N2N^2, where NN is the support of the popularity profile. A transfer learning-based approach to improve this estimate is proposed. The training time is reduced when the popularity profile is modeled using a parametric family of distributions; the delay is independent of NN and scales linearly with the dimension of the distribution parameter.Comment: 12 pages, 5 figures, published in IEEE Transactions on Communications, 2016. arXiv admin note: text overlap with arXiv:1504.0363
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