Optimal Content Placement for Offloading in Cache-Enabled Heterogeneous Wireless Networks

Abstract

Abstract-Caching at base stations (BSs) is a promising way to offload traffic and eliminate backhaul bottleneck in heterogeneous networks (HetNets). In this paper, we investigate the optimal content placement maximizing the successful offloading probability in a cache-enabled HetNet where a tier of multiantenna macro BSs (MBSs) is overlaid with a tier of helpers with caches. Based on probabilistic caching framework, we resort to stochastic geometry theory to derive the closed-form successful offloading probability and formulate the caching probability optimization problem, which is not concave in general. In two extreme cases with high and low user-to-helper density ratios, we obtain the optimal caching probability and analyze the impacts of BS density and transmit power of the two tiers and the signal-tointerference-plus-noise ratio (SINR) threshold. In general case, we obtain the optimal caching probability that maximizes the lower bound of successful offloading probability and analyze the impact of user density. Simulation and numerical results show that when the ratios of MBS-to-helper density, MBS-tohelper transmit power and user-to-helper density, and the SINR threshold are large, the optimal caching policy tends to cache the most popular files everywhere.

Network utility optimization-based joint user association and content placement in heterogeneous networks

“…In recent years, content deployment strategies with different optimization objectives in HetNets have been studied [11][12][13][14][15][16]. The authors in [11,12] aim to maximize the cache hit probability, defined as the probability that a file requested by the typical user is delivered successfully to the user, to achieve the optimal cache content placement strategies.…”

“…To maximize the traffic volume served from the cache, the authors formulate the femto BSs' decisionmaking process as an Markov decision process (MDP) and develop an efficient online randomized algorithm to achieve the optimal content placement and service scheduling strategy. In [15], the authors investigate the content placement problem in a HetNet where a tier of multi-antenna macro BSs (MBSs) is overlaid with a tier of helpers with caches and propose an optimal content placement strategy which maximizes the successful offloading probability of the MBSs. The authors in [16] study the cache content placement problem in caching enabled small cell networks to minimize the average backhaul load subject to the cache capacity constraints.…”

“…It has been shown in previous research works that the transmission performance of UEs can be enhanced significantly by caching contents at the infrastructures of the RANs [11][12][13][14][15][16]; hence, designing reasonable cache content placement schemes by taking into account both caching constraints and possible performance enhancement is of particular importance. However, while most of previous research works study user association problem or cache content placement problem independently, these two problems are indeed closely related.…”

“…• The problem of user association or content placement in HetNets has been studied separately in previous works [2][3][4][5][6][7] and [11][12][13][14][15][16]. In this paper, we jointly consider the user association and content placement problem in cache-enabled HetNets and design jointly optimal strategies so that the overall performance of the networks can be maximized.…”

Random caching based cooperative transmission in heterogeneous wireless networks

“…In [21] and [22], the authors consider random caching at SBSs, and analyze and optimize the cache hit probability (i.e., the probability that a randomly requested file from the typical user is stored at its serving BS) [21] and successful offloading probability (i.e., the probability that the typical user is associated with the SBS tier and its downlink signal-to-interference-plus-noise ratio is larger than a threshold) [22]. In [23], the authors consider random caching and multicasting at SBSs in a large-scale small cell network, and analyze and optimize the successful transmission probability (i.e., the probability that a randomly requested file from the typical user can be successfully received).…”