Base Station Caching Research Articles

User-Assisted Base Station Caching and Cooperative Prefetching for High-Speed Railway Systems

With the aim of reducing the transmission latency, this paper proposes a scheme of user-assisted base station (BS) caching and cooperative prefetching for high-speed railway (HSR) communications. In the model under consideration, neighboring BSs periodically exchange information, including the coverage area and communication rate, to facilitate content caching and prefetching. As an additional means, users can cache contents that are different from BSs. Specifically, we construct an optimization problem for content caching and prefetching that minimizes the overall transmission latency. Moreover, this problem is decomposed into two subproblems, namely the one aiming to maximize the available throughput of BSs and the other attempting to maximize the user cache utilization subject to the constraint of the BS cache. We demonstrate that the objective function of the first subproblem is equivalent to a monotone submodular function subject to matroid constraints. Therefore, it can be efficiently solved with a greedy algorithm. Meanwhile, the user cache issue can be viewed as a weighted sum maximization problem. Simulation results are presented to verify that the proposed scheme can not only reduce the average transmission latency but also improve the hit probability and system throughput.

Joint Optimization of Transmit Beamforming and Base Station Cache Allocation in Multi-Cell C-RAN

This paper studies a downlink cloud radio access network (C-RAN) consisting of a centralized processor (CP) and multiple cells each one of which has one cache-enabled base station (BS) connected to the CP through wireless backhaul links. In such a network, we aim to determine sizes of the cache allocated to the BSs such that the long-term delivery time of the available files is minimized. Considering the two-stage nature of this problem, we formulate a beamforming optimization problem and a cache allocation problem. The purpose of the first one is to deliver un-cached portions of files to the BSs in the shortest possible time during the beamforming stage. The second problem aims to minimize long-term expectation of delivery time through optimal cache allocation during the cache allocation stage. We rigorously prove that the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">time-varying beamformers</i> can be assumed to be piece-wise constant functions of time. Based on this finding, the beamforming optimization problem does not appear to be amenable to a computationally efficient solution. Hence, we resort to the zero-forcing (ZF) beamforming approach to tackle the beamforming optimization problem. Using the results of the beamforming optimization problem, we prove that the cache allocation problem is a convex optimization problem; hence it can be solved by any convex optimization solvers. Simulation results show that the loss of optimality due to adopting ZF beamforming is negligible. Moreover, the superiority of our proposed cache allocation scheme over several other heuristic schemes including proportional cache size allocation is shown.

Cooperative Multilayer Edge Caching in Integrated Satellite-Terrestrial Networks

The integrated satellite-terrestrial network is promising to provide global broadband communication service. However, the long propagation delay of satellite-terrestrial links will lead to high communication delay when users access the Internet via satellites. In this paper, we investigate the cooperative multilayer edge caching in the integrated satellite-terrestrial network to reduce the communication delay, in which the base station cache, the satellite cache, and the gateway cache cooperatively provide content service for ground users. We first propose the three-layer cooperative caching model of the network, based on which we analyze the content retrieving process and derive the cache hit probability for different caching locations. Considering limited cache sizes, we formulate the content placement problem to minimize the average content retrieving delay of users. Then, two caching strategies, the non-cooperative caching strategy and the cooperative caching strategy, are proposed with exhaustive theoretical analysis. By introducing the concept of delay reduction gains, the optimal caching strategies are obtained based on the proposed iterative algorithms. Finally, numerical results are presented to demonstrate the performance of the proposed cooperative caching architecture and the caching strategies.

Joint Optimal Multicast Scheduling and Caching for Improved Performance and Energy Saving in Wireless Heterogeneous Networks

Base station caching and multicast are two promising methods to support mass content delivery in future wireless network environments. However, existing scheduling designs do not take full advantage of the two methods. This article focuses on employing multicast scheduling and caching in a network architecture which involves both macro cell base stations (MBS) and small cell base stations (SBS) in order to achieve joint optimization of average delay and power consumption. We describe this co-optimization problem as the Multicast-Aware Caching Scheduling Problem (MACSP). This article proposes a novel pending request queue model, which aims to solve the problem of long waiting time for non-popular content, and transform this collaborative multicast-cache scheduling problem into a Markov Decision Process that can be solved using reinforcement learning methods. For actual deployment, the paper further introduces a Distributed Policy Gradient algorithm (DPG) with similar performance and lower complexity. The simulation-based testing results demonstrate that our model and algorithm have better performance and lower energy consumption than existing state-of-the-art approaches.

Reinforcement Learning-Based Optimal Computing and Caching in Mobile Edge Network

Joint pushing and caching are commonly considered an effective way to adapt to tidal effects in networks. However, the problem of how to precisely predict users' future requests and push or cache the proper content remains to be solved. In this paper, we investigate a joint pushing and caching policy in a general mobile edge computing (MEC) network with multiuser and multicast data. We formulate the joint pushing and caching problem as an infinite-horizon average-cost Markov decision process (MDP). Our aim is not only to maximize bandwidth utilization but also to decrease the total quantity of data transmitted. Then, a joint pushing and caching policy based on hierarchical reinforcement learning (HRL) is proposed, which considers both long-term file popularity and short-term temporal correlations of user requests to fully utilize bandwidth. To address the curse of dimensionality, we apply a divide-and-conquer strategy to decompose the joint base station and user cache optimization problem into two subproblems: the user cache optimization subproblem and the base station cache optimization subproblem. We apply value function approximation Q-learning and a deep Q-network (DQN) to solve these two subproblems. Furthermore, we provide some insights into the design of deep reinforcement learning in network caching. The simulation results show that the proposed policy can learn content popularity very well and predict users' future demands precisely. Our approach outperforms existing schemes on various parameters including the base station cache size, the number of users and the total number of files in multiple scenarios.

Degrees of Freedom of Cache-Aided Wireless Cellular Networks

This work investigates the degrees of freedom (DoF) of a downlink cache-aided cellular network where the locations of base stations (BSs) are modeled as a grid topology and users within a grid cell can only communicate with four nearby BSs. We adopt a cache placement method with uncoded prefetching tailored for the network with partial connectivity. According to the overlapped degree of cached contents among BSs, we propose transmission schemes with no BS cooperation, partial BS cooperation, and full BS cooperation, respectively, for different cache sizes. In specific, the common cached contents among BSs are utilized to cancel some undesired signals by interference neutralization while interference alignment is used to coordinate signals of distinct cached contents. Our achievable results indicate that the reciprocal of per-user DoF of the cellular network decreases piecewise linearly with the normalized cache size $\mu$ at each BS, and the gain of BS caching is more significant for the small cache region. Under the given cache placement scheme, we also provide an upper bound of per-user DoF and show that our achievable DoF is optimal when $\mu\in\left[\frac{1}{2},1\right]$, and within an additive gap of $\frac{4}{39}$ to the optimum when $\mu\in\left[\frac{1}{4},\frac{1}{2}\right)$.

Research on Cache Strategy of Supporting D2D Communication Based on Recommendation Mechanism

In D2D wireless cache network, a good cache strategy is very important, but the current cache strategy has the problems of low cache hit rate and high base station cost. Aiming at this problem, this paper proposes a smart base station cache strategy based on recommendation mechanism. Through the Q-learning algorithm to learn user mobility and file popularity information, a base station cache strategy with the goal of minimizing long-term base station cost is obtained. How to formulate the base station cache strategy and set the recommendation mechanism under different user movement scenarios in the cellular network. The simulation results show that the lower the user’s mobility rate is, the smaller the optimal recommendation strength is. In the four user mobility scenarios in the simulation, the proposed cache strategy reduces the core network cost by up to 22.4% compared with the greedy cache, and it is reduced by up to 18.6% recommended strategy.

Dynamic Content Update for Wireless Edge Caching via Deep Reinforcement Learning

This letter studies a basic wireless caching network where a source server is connected to a cache-enabled base station (BS) that serves multiple requesting users. A critical problem is how to improve cache hit rate under dynamic content popularity. To solve this problem, the primary contribution of this work is to develop a novel dynamic content update strategy with the aid of deep reinforcement learning. Considering that the BS is unaware of content popularities, the proposed strategy dynamically updates the BS cache according to the time-varying requests and the BS cached contents. Towards this end, we model the problem of cache update as a Markov decision process and put forth an efficient algorithm that builds upon the long short-term memory network and external memory to enhance the decision making ability of the BS. Simulation results show that the proposed algorithm can achieve not only a higher average reward than deep Q-network, but also a higher cache hit rate than the existing replacement policies such as the least recently used, first-in first-out, and deep Q-network based algorithms.

Cooperative Base Station Caching and X2 Link Traffic Offloading System for Video Streaming Over SDN-Enabled 5G Networks

This paper presents a cooperative base station caching and X2 link traffic offloading system for video streaming services over software-defined networking (SDN)-enabled 5G networks. The goal of the proposed system is to provide seamless video streaming services to users while alleviating the backhaul traffic in the cellular network by effectively caching contents at the base stations and appropriately offloading parts of the backhaul traffic to X2 links. In the proposed system, the main functionalities of content-centric networking (CCN) are deployed at the base stations to take advantage of the attributes offered by CCN such as in-network caching and name-based forwarding. Furthermore, SDN technology is adopted not only to solve the practical issues of CCN, but also to control network resources efficiently. The proposed system is designed to determine near optimal caching positions and routes for all content in two phases to reduce the computational complexity for the real-time processing. The proposed system is fully implemented using well-known open sources, e.g., CCNx and ONOS, and ${\mathrm{C/ C}}+ +$C/C++. Finally, the system is examined in real wireless network environments. During the experiment, the proposed system can reduce cellular traffic by more than 13 percent over existing traffic offloading algorithms.

Joint Pricing and Cache Placement for Video Caching: A Game Theoretic Approach

Caching can effectively smooth the temporal traffic variability and decrease the redundant data transmission in mobile video delivery. In this paper, we consider a video caching system consisting of a video provider (VP), a mobile network operator (MNO) with a set of cache-enabled base stations (BSs), and multiple mobile users. The VP leases some popular videos to the MNO, while the MNO places these rented videos in local caches of its BSs to save expensive backhaul transmission cost. However, in such a two-sided market, these two entities are competing with each other for their own profit due to their opposite expectation on the video pricing. To address this, we model the competition between the two entities using the framework of Stackelberg games and propose a joint video pricing and cache placement strategy by considering the heterogeneity of video file sizes and exploiting the classic law of demand from the field of economics. The proposed optimization problem is able to jointly maximize the profit of the VP and the MNO by the optimal selection of the video pricing and the cache placement strategy given that price, for both noncooperative BS caching and cooperative BS caching cases. We then develop iterative algorithms based on dynamic programming and gradient ascent, respectively, for these two cases to find the Stackelberg equilibrium (SE). The simulation results further show that the proposed joint optimization formulation follows the law of demand in economics, and the proposed algorithms for both cases can efficiently converge to the SE point that jointly maximizes the profit for both the VP and the MNO.

Energy Efficient Downlink Resource Allocation for D2D-Assisted Cellular Networks With Mobile Edge Caching

In this paper, a downlink device-to-device (D2D)-assisted cellular networks with mobile edge caching, where most popular video files are independently cached in D2D users and cellular base station (BS), are studied. In the considered system model, each user may obtain the requested video from the cache of BS or/and D2D users surrounding them. According to the different collaborative schemes of BS caching and D2D caching, it can be divided into two different resource allocation schemes. In the hybrid caching transmission scheme, users could adopt the BS caching mode or alternatively the D2D caching mode. In the joint caching transmission scheme, each user may obtain the requested files from the BS server and the adjacent D2D users, simultaneously. By taking the required data rate and the interference constraint into account, we formulate two joint resource allocation problems integrating link selection, channel allocation, and power control to maximize the system energy efficiency (EE). Leveraging on the Dinkelbach method, the EE optimization problems are transformed into mixed-integer nonlinear programming problems and can be decomposed into three subproblems: link selection, channel allocation, and power control. To solve these complicated problems, we propose two optimization algorithms that consist of a modified branch and bound method as well as Lagrange dual decomposition approach. The simulation results demonstrate the superiority of these two proposed algorithms in improving system throughput and EE compared with other algorithms.

Optimized Base-Station Cache Allocation for Cloud Radio Access Network With Multicast Backhaul

The performance of cloud radio access network (C-RAN) is limited by the finite capacities of the backhaul links connecting the centralized processor (CP) with the base-stations (BSs), especially when the backhaul is implemented in a wireless medium. This paper proposes the use of wireless multicast together with BS caching, where the BSs pre-store contents of popular files, to augment the backhaul of C-RAN. For a downlink C-RAN consisting of a single cluster of BSs and wireless backhaul, this paper studies the optimal cache size allocation strategy among the BSs and the optimal multicast beamforming transmission strategy at the CP such that the user's requested messages are delivered from the CP to the BSs in the most efficient way. We first state a multicast backhaul rate expression based on a joint cache-channel coding scheme, which implies that larger cache sizes should be allocated to the BSs with weaker channels. We then formulate a two-timescale joint cache size allocation and beamforming design problem, where the cache is optimized offline based on the long-term channel statistical information, while the beamformer is designed during the file delivery phase based on the instantaneous channel state information. By leveraging the sample approximation method and the alternating direction method of multipliers (ADMM), we develop efficient algorithms for optimizing cache size allocation among the BSs, and quantify how much more cache should be allocated to the weaker BSs. We further consider the case with multiple files having different popularities and show that it is in general not optimal to entirely cache the most popular files first. Numerical results show considerable performance improvement of the optimized cache size allocation scheme over the uniform allocation and other heuristic schemes.

Hybrid Content Caching in 5G Wireless Networks: Cloud Versus Edge Caching

Most existing content caching designs require accurate estimation of content popularity, which can be challenging in the dynamic mobile network environment. Moreover, emerging hierarchical network architecture enables us to enhance the content caching performance by opportunistically exploiting both cloud-centric and edge-centric caching. In this paper, we propose a hybrid content caching design that does not require the knowledge of content popularity. Specifically, our design optimizes the content caching locations, which can be original content servers, central cloud units (CUs) and base stations (BSs) where the design objective is to support as high average requested content data rates as possible subject to the finite service latency. We fulfill this design by employing the Lyapunov optimization approach to tackle an NP-hard caching control problem with the tight coupling between CU caching and BS caching control decisions. Toward this end, we propose algorithms in three specific caching scenarios by exploiting the submodularity property of the sum-weight objective function and the hierarchical caching structure. Moreover, we prove the proposed algorithms can achieve finite content service delay for all arrival rates within the constant fraction of capacity region using Lyapunov optimization technique. Furthermore, we propose practical and heuristic CU/BS caching algorithms to address a general caching scenario by inheriting the design rationale of the aforementioned performance-guaranteed algorithms. Trace-driven simulation demonstrates that our proposed hybrid CU/BS caching algorithms outperform the general popularity based caching algorithm and the independent caching algorithm in terms of average end-to-end service latency and backhaul/fronthaul load reduction ratios.

Optimization and Analysis of Probabilistic Caching in $N$ -Tier Heterogeneous Networks

In this paper, we study the probabilistic caching for an $N$ -tier wireless heterogeneous network (HetNet) using stochastic geometry. A general and tractable expression of the successful delivery probability (SDP) is first derived. We then optimize the caching probabilities for maximizing the SDP in the high signal-to-noise ratio regime. The problem is proved to be convex and solved efficiently. We next establish an interesting connection between $N$ -tier HetNets and single-tier networks. Unlike the single-tier network where the optimal performance only depends on the cache size, the optimal performance of $N$ -tier HetNets depends also on the base station (BS) densities. The performance upper bound is, however, determined by an equivalent single-tier network. We further show that with uniform caching probabilities regardless of content popularities, to achieve a target SDP, the BS density of a tier can be reduced by increasing the cache size of the tier when the cache size is larger than a threshold; otherwise, the BS density and BS cache size can be increased simultaneously. It is also found analytically that the BS density of a tier is inverse to the BS cache size of the same tier and is linear to BS cache sizes of other tiers.

Energy Efficiency Analysis of Cache‐Enabled Cellular Networks with Limited Backhaul

Caching in the cellular networks has been proposed as a promising technology for reducing the content delivery latency and backhaul cost. Since the backhaul capacity is limited in the practical scenario, the network performance analysis of base station (BS) caching should address the effects of the limited backhaul. This paper investigates the energy efficiency of the cache‐enabled cellular networks with the limited backhaul based on the stochastic geometry method. First, the successful content delivery probability (SCDP), which depends on the successful access delivery probability, successful backhaul delivery probability, and cache hit ratio, is analyzed under the limited backhaul. Based on the obtained SCDP results, we derive the analytical expressions of throughput, power consumption, and energy efficiency for various scenes including the general case, the interference‐limited case, and the mean load approximation case. The accuracy of theoretical analysis is verified by the Monte Carlo simulation. The simulation results show that BS caching can dramatically improve energy efficiency when the content popularity is skewed, the content library size is small, and the backhaul capacity is relatively small. Furthermore, it is confirmed that there exists an optimal BS density which maximizes the energy efficiency of the cache‐enabled cellular networks.

DeepMEC: Mobile Edge Caching Using Deep Learning

Caching popular contents at edge nodes such as base stations is a crucial solution for improving users’ quality of services in next-generation networks. However, it is very challenging to correctly predict the future popularity of contents and decide which contents should be stored in the base station cache. Recently, with the advances in big data and high computing power, deep learning models have achieved high prediction accuracy. Hence, in this paper, deep learning is used to learn and predict the future popularity of contents to support cache decision. First, deep learning models are trained and utilized in the cloud data center to make an efficient cache decision. Then, the final cache decision is sent to each base station to store the popular contents proactively. The proposed caching scheme involves three distinct parts: 1) predicting the future class label of each content; 2) predicting the future popularity score of contents based on the predicted class label; and 3) caching the predicted contents with high popularity scores. The prediction models using the Keras and Tensorflow libraries are implemented in this paper. Finally, the performance of the caching schemes is tested with a Python-based simulator. In terms of a cache hit, simulation results show that the proposed scheme outperforms 38%, convolutional recurrent neural network-based scheme outperforms 33%, and convolutional neural network-based scheme outperforms 25% compared to the baseline scheme.

Hybrid Cache Placement for Physical-Layer Security in Cooperative Networks

Due to the broadcast nature of wireless transmission, traditional wireless two-hop relaying is vulnerable for eavesdroppers to overhear messages. In order to enhance the physical-layer security, this paper studies one cooperative network with caching relays to reduce the transmission links overheard by the eavesdropper. Relays are grouped into different clusters so that the secrecy outage probability of any cluster is better than the traditional two-hop transmission. Based on the secrecy outage probability of clusters and the groups of files, we propose a novel hybrid cache placement, which combines the traditional base station caching strategy, most popular content caching strategy and largest content diversity caching strategy to cache the popular contents. The performance analysis and optimal design results are provided on the two objective functions: the secrecy outage probability and average secrecy capacity. From these two objective functions, some interesting observations are reached: for the function of secrecy outage probability, we can always find an optimal value of most popular files to minimize the secrecy outage probability; and for the function of average secrecy capacity, we can also find an optimal value of most popular files to maximize the average secrecy capacity. The numerical and simulation results are finally demonstrated to verify the proposed studies.

Cache-Enabled Heterogeneous Cellular Networks: Optimal Tier-Level Content Placement

Caching popular contents at base stations (BSs) of a heterogeneous cellular network (HCN) avoids frequent information passage from content providers to the network edge, thereby reducing latency and alleviating traffic congestion in backhaul links. In general, the optimal strategies for content placement in HCNs remain largely unknown and deriving them forms the theme of this paper. To this end, we adopt the popular random HCN model where $K$ tiers of BSs are modelled as independent Poisson point processes distributed in the plane with different densities. Further, the random caching scheme is considered where each of a given set of $M$ files with corresponding popularity measures is placed at each BS of a particular tier with a corresponding probability, called placement probability. The probabilities are identical for all BSs in the same tier but vary over tiers, giving the name tier-level content placement. We consider the network performance metric, hit probability, defined as the probability that a file requested by the typical user is delivered successfully to the user. We maximize the hit probability over content placement probabilities, which yields the optimal tier-level placement policies. For the case of uniform received signal-to-interference thresholds for successful transmissions for BSs in different tiers, the policy is in closed-form where the placement probability for a particular file is proportional to the square-root of the corresponding popularity measure with an offset depending on BS caching capacities. For the general case of non-uniform SIR thresholds, the optimization problem is non-convex and a sub-optimal placement policy is designed by approximation, which has a similar structure as in the case of uniform SIR thresholds and shown by simulation to be close-to-optimal.

Understanding Performance of Edge Content Caching for Mobile Video Streaming

Today’s Internet has witnessed an increase in the popularity of mobile video streaming, which is expected to exceed 3/4 of the global mobile data traffic by 2019. To satisfy the considerable amount of mobile video requests, video service providers have been pushing their content delivery infrastructure to edge networks—from regional content delivery network (CDN) servers to peer CDN servers (e.g., smartrouters in users’ homes)—to cache content and serve users with storage and network resources nearby. Among the edge network content caching paradigms, Wi-Fi access point caching and cellular base station caching have become two mainstream solutions. Thus, understanding the effectiveness and performance of these solutions for large-scale mobile video delivery is important. However, the characteristics and request patterns of mobile video streaming are unclear in practical wireless network. In this paper, we use real-world data sets containing 50 million trace items of nearly 2 million users viewing more than 0.3 million unique videos using mobile devices in a metropolis in China over two weeks, not only to understand the request patterns and user behaviors in mobile video streaming, but also to evaluate the effectiveness of Wi-Fi and cellular-based edge content caching solutions. To understand the performance of edge content caching for mobile video streaming, we first present temporal and spatial video request patterns, and we analyze their impacts on caching performance using frequency-domain and entropy analysis approaches. We then study the behaviors of mobile video users, including their mobility and geographical migration behaviors, which determine the request patterns. Using trace-driven experiments, we compare strategies for edge content caching, including least recently used (LRU) and least frequently used (LFU), in terms of supporting mobile video requests. We reveal that content, location, and mobility factors all affect edge content caching performance. Moreover, we design an efficient caching strategy based on the measurement insights and experimentally evaluate its performance. The results show that our design significantly improves the cache hit rate by up to 30% compared with LRU/LFU.

Exploiting Base Station Caching in MIMO Cellular Networks: Opportunistic Cooperation for Video Streaming

We propose a novel MIMO cooperation framework called the cache-induced opportunistic cooperative MIMO (Coop-MIMO) for video streaming in backhaul limited multicell MIMO networks. By caching a portion of the video files, the base stations (BSs) opportunistically employ Coop-MIMO transmission to achieve MIMO cooperation gain without expensive payload backhaul. We derive closed form expressions of various video streaming performance metrics for cache-induced opportunistic Coop-MIMO and investigate the impact of BS level caching and key system parameters on the performance. Specifically, we first obtain a mixed fluid-diffusion limit for the playback buffer queueing system. Then we derive the approximated video streaming performance using the mixed fluid-diffusion limit. Based on the performance analysis, we formulate the joint optimization of cache control and playback buffer management as a stochastic optimization problem. Then we derive a closed form solution for the playback buffer thresholds and develop a stochastic subgradient algorithm to find the optimal cache control. The analysis shows that the video streaming performance improves linearly with the BS cache size BC, the transmit power cost decreases exponentially with BC, and the backhaul cost decreases linearly with BC.