Energy Efficiency in Cache-Enabled Small Cell Networks With Adaptive User Clustering

Abstract

Using a network of cache enabled small cells, traffic during peak hours can be reduced considerably through proactively fetching the content that is most probable to be requested. In this paper, we aim at exploring the impact of proactive caching on an important metric for future generation networks, namely, energy efficiency (EE). We argue that, exploiting the correlation in user content popularity profiles in addition to the spatial repartitions of users with comparable request patterns, can result in considerably improving the achievable energy efficiency of the network. In this paper, the problem of optimizing EE is decoupled into two related subproblems. The first one addresses the issue of content popularity modeling. While most existing works assume similar popularity profiles for all users in the network, we consider an alternative caching framework in which, users are clustered according to their content popularity profiles. In order to showcase the utility of the proposed clustering scheme, we use a statistical model selection criterion, namely Akaike information criterion (AIC). Using stochastic geometry, we derive a closed-form expression of the achievable EE and we find the optimal active small cell density vector that maximizes it. The second subproblem investigates the impact of exploiting the spatial repartitions of users with comparable request patterns. After considering a snapshot of the network, we formulate a combinatorial optimization problem that enables to optimize content placement such that the used transmission power is minimized. Numerical results show that the clustering scheme enable to considerably improve the cache hit probability and consequently the EE compared with an unclustered approach. Simulations also show that the small base station allocation algorithm results in improving the energy efficiency and hit probability. arXiv:1703.07646v3 [cs.IT] 8 Nov 20172

A Review on Green Caching Strategies for Next Generation Communication Networks

“…Cluster-based selection of the caching nodes also reduces overall energy requirements [117], [118]. A framework for MEC clustering and cache space allotment was introduced in [2].…”

“…The collaborative approach partitioned the cache space into three logical groups and optimized storage allocation. For a HetNet, another clustering framework was developed by Hajri et al [117]. The proposed strategy optimally acti-This work is licensed under a Creative Commons Attribution 4.0 License.…”

“…[2], [51], [117]- [119] Optimized caching and selected networks nodes for content allocation based on clustering approach.…”

A Survey on Recent Trends and Open Issues in Energy Efficiency of 5G

“…In [7], the idea of proactive caching based on the content popularity on small cells has been proposed for improving the energy efficiency. Owing to the abundance of small cells, networks are getting constrained by the overall backhaul link capacity and much of the load is corresponding to transactions of the same requests repeatedly.…”

“…A few of the prominent examples include the introduction of a newer Radio Resource Control (RRC) state for context signalling and cutting down on the redundant state changes [6]. Utilization of advanced clustering and caching techniques on the RAN side have been highly appreciated for their benefits of improving the latency of getting the data requested by a group of users and possibly eliminating the factor of clogging the network by a huge number of requests for the same content [7,8]. A case study of commercial resource sharing among different operators bears fruitful results in terms of reduced deployment costs and good data rates with minimum interference among them [9].…”