Optimal Probabilistic Caching Research Articles

Multi-Rate Probabilistic Caching Optimized Video Offloading in Dense D2D Networks

In this paper, we propose a multi-rate caching (MRC) scheme for video offloading in dense device-to-device (D2D) networks. The MRC scheme utilises homogeneous Poisson Point Process model, D2D underlay transmission mode, and features of videos encoded into multiple rates (or versions), to derive the cache and spectrum sharing hit probabilities and the cache-based achievable rate. This way, an MRC optimisation problem is formulated for optimal caching probability and encoding rate selection of each video. The MRC optimisation problem further considers the playback resolution and storage resource constraints of the mobile devices to serve the mobile users more efficiently. As a result, the MRC solution gains the highest video offloading playback quality while conserving the bandwidth and caching storage resources of dense D2D networks.

On the Performance of UAV-Aided Content Caching in Small-Cell Networks with Joint Transmission

Unmanned aerial vehicles (UAVs), featured by the high-mobility and high-quality propagation environment, have shown great potential in wireless communication applications. In this paper, a novel UAV-aided small-cell content caching network is proposed and analyzed, where joint transmission (JT) is considered in the dense small-cell networks and mobile UAVs are employed to shorten the serving distance. The system performance is evaluated in terms of the average cache hit probability and the ergodic transmission rate. From the analytical results, we find that (i) the proposed UAV-aided small-cell network shows superior caching performance and, even with a small density of UAVs the system’s cache hit probability, can be improved significantly; (ii) the content’s optimal caching probability to maximize the cache hit probability is proportional to the (K+1)-th root of its request probability, where K is the number of small-cell base stations that serve each user by JT; (iii) caching the most popular content in UAVs may lead to a low transmission rate due to the limited resource offered by the low-density UAVs. Simulation results are presented to validate the theoretical results and the performance gain achieved by the optimal caching strategy.

Analysis and Optimization of Random Caching in mmWave Heterogeneous Networks

In this paper, we investigate the optimal caching policy in a K-tier millimeter wave (mmWave) cache-enabled heterogeneous network. In order to mitigate interferences, we incorporate base station (BS) idling into our analysis. Under the random caching framework, we derive the association probability for each tier as well as the successful transmission probability (STP) by utilizing stochastic geometry and taking the blockage effect into account. In addition, we adopt the Convex-Concave Procedure (CCP) to obtain the locally optimal caching probabilities and propose a two-stage scheme to obtain the globally optimal caching probabilities under the special case where the blockage parameters for K tiers are sufficiently small. Numerical results demonstrate the superiority of the proposed method over the conventional caching strategies such as Most Popular Content (MPC) and Uniform Caching (UC) and the truncated Zipf distribution-based caching schemes.

Optimal Probabilistic Caching in Heterogeneous IoT Networks

With the development of hardware and communication technology, Internet-of-Things (IoT) devices have become more powerful in computing, caching, and communication. The enhancement of equipment makes it possible for IoT devices to act as caching helpers in cache-enabled networks to address the conflict between the limited caching resources and the sharply increasing data traffic. However, how to efficiently utilize the caching resources of IoT devices and edge servers is a challenging problem especially when contents are of different sizes. In this article, we focus on the probabilistic caching for contents of different sizes in heterogeneous IoT networks, aiming at improving the offloading rate for backhaul links. We present a mathematical framework to formulate the hit probability of cache-enabled IoT networks based on stochastic geometry and propose an improved caching probability conversion (CPC) algorithm to derive the closed-form solutions of optimal caching probabilities. Moreover, we further extend the network model with serving capacity constraint, i.e., caching devices can only serve a limited number of users simultaneously and solve the corresponding nonconvex problem by the difference of convex (DC) programming. We validate our analytical framework by the Monte Carlo method and give extensive numerical results to compare the performance of the proposed strategy with that of two existing caching strategies.

The Design of Dynamic Probabilistic Caching with Time-Varying Content Popularity

In this paper, we design dynamic probabilistic caching for the scenario when the instantaneous content popularity may vary with time while it is possible to predict the average content popularity over a time window. Based on the average content popularity, optimal content caching probabilities can be found, e.g., from solving optimization problems, and existing results in the literature can implement the optimal caching probabilities via static content placement. The objective of this work is to design dynamic probabilistic caching that: i) converge (in distribution) to the optimal content caching probabilities under time-invariant content popularity, and ii) adapt to the time-varying instantaneous content popularity under time-varying content popularity. Achieving the above objective requires a novel design of dynamic content replacement because static caching cannot adapt to varying content popularity while classic dynamic replacement policies, such as LRU, cannot converge to target caching probabilities (as they do not exploit any content popularity information). We model the design of dynamic probabilistic replacement policy as the problem of finding the state transition probability matrix of a Markov chain and propose a method to generate and refine the transition probability matrix. Extensive numerical results are provided to validate the effectiveness of the proposed design.

Spatially Cooperative Caching and Optimization for Heterogeneous Network

Compromised by the ever increasing amount of mobile data traffic generated by mobile devices, edge servers are expected to provide caching resources for users so as to enable low-latency content delivery and release the burden on backhaul network. Considering the limited caching capacities of edge servers, it is essential to design an efficient content placement strategy to increase the data offloading ratio, i.e., the proportion of requested contents that are delivered via edge caching rather than backhaul links. In this paper, we propose a spatially cooperative caching strategy for a two-tier heterogeneous network consisting of edge servers and caching helpers aiming at reducing the storage space taken by duplicate contents on caching helpers and increasing hit probability. We develop an analysis framework for the proposed spatially cooperative caching strategy by using tools from stochastic geometry and maximize the hit probability by optimizing the caching probabilities of contents on edge servers and caching helpers. Particular, we derive the closed-form solution of the optimization problem for unit cache size. The simulation results are identical with the analytical ones and the outperformance is observed with the optimal caching probability derived in this paper compared with the existing caching strategies.

Physical Layer Security in Large–Scale Probabilistic Caching: Analysis and Optimization

Edge caching is proposed to offload traffic from backhaul links by storing popular contents on edge servers and reusing these pre-cached contents to meet the frequent requests from local users. Due to the limited computing and caching resources of edge servers, the traditional encryption and decryption technologies, deployed in the upper layers of protocol stacks, appeared difficult in providing secure content delivery for the growing number of mobile users. In this letter, we consider the physical layer security issues for the popular probabilistic caching. Unlike most prior works, which are concentrated on maximizing achievable transmission rate, we analyze the average secure delivery probability of the caching network and jointly optimize the redundant rate and caching probabilities. Simulation results are identical with the theoretical ones and the outperformance over existing caching strategies is observed with the optimal caching probability derived in this letter.

Cooperative Crossing Cache Placement in Cache-Enabled Device to Device-Aided Cellular Networks

In cache-enabled device-to-device (D2D) -aided cellular networks, the technique of caching contents in the cooperative crossing between base stations (BSs) and devices can significantly reduce core traffic and enhance network capacity. In this paper, we propose a scheme that establishes device availability, which indicates whether a cache-enabled device can handle the transmission of the desired content within the required sending time, called the delay, while achieving optimal probabilistic caching. We also investigate the impact of transmission device availability on the effectiveness of a scenario of cooperative crossing cache placement, where content delivery traffic can be offloaded from the local cache, a D2D transmitter’s cache via a D2D link, or else directly from a BS via a cellular link, in order to maximize the offloading probability. Further, we derive the cooperation content offloading strategy while considering successful content transmission by D2D transmitters or BSs to guarantee the delay, even though reducing the delay is not the focus of this study. Finally, the proposed problem is formulated. Owing to the non-convexity of the optimization problem, it can be rewritten as a minimization of the difference between the convex functions; thus, it can be solved by difference of convex (DC) programming using a low-complexity algorithm. Simulation results show that the proposed cache placement scheme improves the offloading probability by 13.5% and 23% compared to Most Popular Content (MPC) scheme, in which both BSs and devices cache the most popular content and Coop. BS/D2D caching scheme, in which each BS tier and user tier applies cooperative content caching separately.

Effect of Retransmissions on Optimal Caching in Cache-Enabled Small Cell Networks

Caching popular content in the storage of small cells is being considered as an efficient technique to complement limited backhaul of small cells in ultradense heterogeneous cellular networks. Limited storage capacity of the small cells renders it important to determine the optimal set of files (cache) to be placed on each small cell. In contrast to prior works on optimal caching, in this work we study the effect of retransmissions on the optimal cache placement policy for both static and mobile user scenarios. With the popularity of files modeled as a Zipf distribution and a maximum $n$ transmissions, i.e., $n-1$ retransmissions, allowed to receive each file, we determine the optimal caching probability of the files that maximizes the hit probability . Our closed-form optimal solutions concretely demonstrate that the optimal caching probabilities are very sensitive to the number of retransmissions.

Fundamentals of Modeling Finite Wireless Networks Using Binomial Point Process

Modeling the locations of nodes as a uniform binomial point process, we present a generic mathematical framework to characterize the performance of an arbitrarily located reference receiver in a finite wireless network. Different from most of the prior works where the serving transmitter (TX) is located at the fixed distance from the reference receiver, we consider two general TX-selection policies: 1) uniform TX-selection : the serving node is chosen uniformly at random from amongst all transmitting nodes and 2) $k$ -closest TX-selection : the serving node is the $k$ th closest node (out of all transmitting nodes) to the reference receiver. The key intermediate step in our analysis is the derivation of a new set of distance distributions that lead not only to the tractable analysis of coverage probability but also enable the analysis of wide range of classical and currently trending problems in wireless networks. Using this new set of distance distributions, we further investigate the diversity loss due to $\mathtt {SIR}$ correlation in a finite network. We then obtain the optimal number of links that can be simultaneously activated to maximize network spectral efficiency. Finally, we evaluate optimal caching probability to maximize the total hit probability in cache-enabled finite networks.

Caching Policy Toward Maximal Success Probability and Area Spectral Efficiency of Cache-Enabled HetNets

In this paper, we investigate the optimal caching policy respectively maximizing the success probability and area spectral efficiency (ASE) in a cache-enabled heterogeneous network (HetNet) where a tier of multi-antenna macro base stations (MBSs) is overlaid with a tier of helpers with caches. Under the probabilistic caching framework, we resort to stochastic geometry theory to derive the success probability and ASE. After finding the optimal caching policies, we analyze the impact of critical system parameters and compare the ASE with traditional HetNet where the MBS tier is overlaid by a tier of pico BSs (PBSs) with limited-capacity backhaul. Analytical and numerical results show that the optimal caching probability is less skewed among helpers to maximize the success probability when the ratios of MBS-to-helper density, MBS-to-helper transmit power, user-to-helper density, or the rate requirement are small, but is more skewed to maximize the ASE in general. Compared with traditional HetNet, the helper density is much lower than the PBS density to achieve the same target ASE. The helper density can be reduced by increasing cache size. With given total cache size within an area, there exists an optimal helper node density that maximizes the ASE.

Probabilistic Caching in Wireless D2D Networks: Cache Hit Optimal Versus Throughput Optimal

Departing from the conventional cache hit optimization in cache-enabled wireless networks, we consider an alternative optimization approach for the probabilistic caching placement in stochastic wireless D2D caching networks taking into account the reliability of D2D transmissions. Using tools from stochastic geometry, we provide a closed-form approximation of cache-aided throughput, which measures the density of successfully served requests by local device caches, and we obtain the optimal caching probabilities via numerical optimization. Compared with the cache-hit-optimal case, the optimal caching probabilities obtained by cache-aided throughput optimization show notable gain in terms of the density of successfully served user requests, particularly in dense user environments.

Cache-Enabled Device to Device Networks With Contention-Based Multimedia Delivery

This paper studies the performance of large-scale cache-enabled device-to-device (D2D) networks with homogeneous Poisson point process distributed mobile helpers(MHs) and user equipments (UEs). The MHs are assumed to have caching capabilities and able to serve the random requests from the UEs within the collaboration distance of D2D transmission. To avoid collisions among the concurrent transmissions, a contention-based multimedia delivery protocol is proposed, under which a MH is allowed to transmit only if its back-off timer is the smallest among its associated contenders. By applying tools from stochastic geometry, the transmission probability of MHs is derived and analyzed. We then characterize the coverage probability of the randomly requested files and thereby the successful content delivery probability of the cache-enabled D2D network. Based on the obtained results of successful content delivery probability, the optimal probabilistic caching strategy of MHs is investigated. Particularly, under the proposed contention-based multimedia delivery protocol, it is recommended to simply cache the most popular file at each MH to maximize the successful content delivery probability of the cache-enabled D2D network. Simulations are provided to validate our analysis.

Caching Placement in Stochastic Wireless Caching Helper Networks: Channel Selection Diversity via Caching

Content delivery success in wireless caching helper networks depends mainly on cache-based channel selection diversity and network interference. For given channel fading and network geometry, both channel selection diversity and network interference dynamically vary according to what and how the caching helpers cache at their finite storage space. We study probabilistic content placement (or caching placement) to desirably control cache-based channel selection diversity and network interference in a stochastic wireless caching helper network, with sophisticated considerations of wireless fading channels, interactions among multiple users such as interference and loads at caching helpers, and arbitrary memory size. Using stochastic geometry, we derive optimal caching probabilities in closed form to maximize the average success probability of content delivery and propose an efficient algorithm to find the solution in a noise-limited network. In an interference-limited network, based on a lower bound of the average success probability of content delivery, we find near-optimal caching probabilities in closed form to control the channel selection diversity and the network interference. We numerically verify that the proposed content placement is superior to other comparable content placement strategies.

Probabilistic Small-Cell Caching: Performance Analysis and Optimization

Small-cell caching utilizes the embedded storage of small-cell base stations (SBSs) to store popular contents for the sake of reducing duplicated content transmissions in networks and for offloading the data traffic from macrocell base stations to SBSs. In this paper, we study a probabilistic small-cell caching strategy, where each SBS caches a subset of contents with a specific caching probability. We consider two kinds of network architectures: 1) The SBSs are always active, which is referred to as the always-on architecture; and 2) the SBSs are activated on demand by mobile users (MUs), which is referred to as the dynamic on–off architecture. We focus our attention on the probability that MUs can successfully download content from the storage of SBSs. First, we derive theoretical results of this successful download probability (SDP) using stochastic geometry theory. Then, we investigate the impact of the SBS parameters, such as the transmission power and deployment intensity on the SDP. Furthermore, we optimize the caching probabilities by maximizing the SDP based on our stochastic geometry analysis. The intrinsic amalgamation of optimization theory and stochastic geometry based analysis leads to our optimal caching strategy, characterized by the resultant closed-form expressions. Our results show that in the always-on architecture, the optimal caching probabilities solely depend on the content request probabilities, while in the dynamic on–off architecture, they also relate to the MU-to-SBS intensity ratio. Interestingly, in both architectures, the optimal caching probabilities are linear functions of the square root of the content request probabilities. Monte-Carlo simulations validate our theoretical analysis and show that the proposed schemes relying on the optimal caching probabilities are capable of achieving substantial SDP improvement, compared with the benchmark schemes.