Information centric modeling for two-tier cache enabled cellular networks

Abstract

Abstract-In this article, we introduce a new metric called 'information centric coverage probability' to characterize the performance of a two-tier cache enabled cellular network. The proposed metric unifies the dynamics of in-network caching and heterogeneous networking to provide a unified performance measure. Specifically, it quantifies the probability that a mobile user (MU) is covered at a desired rate when a certain content is requested from a global content library. In other words, it quantifies the percentage of time when an MU can be served locally without paying the traffic penalties at backhaul, fronthaul and core networks. Caching dynamics are modeled by considering that the content which is least recently used (LRU) is evicted while the requested content is stored in the cache. The considered two-tier cellular model leverages coordination between the macro base-station (MBS) and the small cell base-stations (SBSs) to maximize the resource efficiency. More specifically, coordination between macro and small cells enables an arbitrary SBS to exploit the caches at other SBSs in the neighborhood. Thus reducing the requirement for huge and expensive memory modules at individual SBSs. The spatial dynamics of cellular network are modeled by borrowing well established tools from stochastic geometry. Propagation uncertainties are explicitly factored in characterization by considering the small scale Rayleigh fading and the large scale power-law path-loss model. It is shown that the information centric coverage probability is a function of (i) the size of caches at the SBSs and the MBS; (ii) the content eviction strategy; (iii) the underlying popularity law for referenced objects; (iv) the size of the global content library; (v) desired downlink transmission rate; (vi) the amount of spectrum allocated to each tier; (vii) pathloss exponent; and (viii) the deployment density of the SBSs and the MBSs. Our analysis reveals that significant performance gains can be harnessed with appropriate dimensioning of both cache sizes and deployment density. Finally, identification of memory limited vs. QoS limited operational regime for two-tier cellular networks is considered.

On the analysis of cellular networks with caching and coordinated device-to-device communication

“…A sharp decrease in p (k) d,N S (c) is observed as the requested content becomes less popular or the skewness parameter ζ decreases. As established in (12), we see that there is no effect of increasing k on p (k) d,U S (c). In Fig.…”

“…In Fig. 4, we plot the D2D mode probabilities p (11) and (12). We see that the deviation for the NS scheme becomes large as the value of k increases, but convergence is fast and the bounds are fairly tight for η d ≥ 10.…”

“…For the case of multi-tier analysis with caching, the authors in [12] and [13] consider distributed caching, where a user can access data from the caches of multiple small base stations (SBSs) inside a cell. However, in case of the content availability in any one of the SBSs within the cell, the user is always served by its nearest SBS assuming that the content transfer takes place among the SBSs.…”

Exploiting Transmission and Caching Diversity in Cache-Enabled User-Centric Network: Analysis and Optimization

“…Recently, stochastic geometry has been widely used to model cache-enabled wireless networks. In [7], information centric coverage probability is introduced to unify signal-level and cache-level performance. Closed-form expressions for coverage [8]- [11] and throughput [8], [10], [11] are obtained respectively.…”

“…Furthermore, in [9], [11]- [13], the optimal caching distribution is investigated to maximize the analytical results of coverage and throughput. However, in [7]- [13], the UE only concerns the caching contents of its tagged BSs, but fails to benefit from the increased BS diversity of potential BSs. In addition, only a few works (e.g., [13]) derive the optimal caching distribution in closed-form.…”