Cache-enabled D2D Research Articles

On cost minimization for cache-enabled D2D networks with recommendation

To accommodate the tremendous increase of mobile data traffic, cache-enabled device-to-device (D2D) communication has been taken as a promising technique to release the heavy burden of cellular networks since popular contents can be pre-fetched at user devices and shared among subscribers. As a result, cellular traffic can be offloaded and an enhanced system performance can be attainable. However, due to the limited cache capacity of mobile devices and the heterogeneous preferences among different users, the requested contents are most likely not be proactively cached, inducing lower cache hit ratio. Recommendation system, on the other hand, is able to reshape users' request schema, mitigating the heterogeneity to some extent, and hence it can boost the gain of edge caching. In this paper, the cost minimization problem for the social-aware cache-enabled D2D networks with recommendation consideration is investigated, taking into account the constraints on the cache capacity budget and the total number of recommended files per user, in which the contents are sharing between the users that trust each other. The minimization problem is an integer non-convex and non-linear programming, which is in general NP-hard. Therewith, we propose a time-efficient joint recommendation and caching decision scheme. Extensive simulation results show that the proposed scheme converges quickly and significantly reduces the average cost when compared with various benchmark strategies.

Energy Consumption in Cache-Enabled D2D Networks Based on Matérn Cluster Process

Device-to-device (D2D) communications have significant advantages such as short range and low energy consumption. Caching at mobile user devices by leveraging D2D communications relieves the heavy load on backhaul links and enhances users’ quality of service. Therefore, in a cache-enabled clustered D2D network, we analyze the probability of successful transmission and propose a jointly optimizing bandwidth partitioning and caching policy strategy to solve the average energy consumption minimization problem. To simultaneously have the clustered and bounded characteristics of D2D communication in real hotspots areas, we use Matérn cluster process (MCP) to model the cache-enabled clustered D2D network. For a given D2D receiver (DR), we derive the successful transmission probability of getting its requested content. Moreover, leveraging the analysis of successful transmission probability, a joint bandwidth partitioning and caching policy optimization problem is formulated to reduce average energy consumption. Considering the complexity of directly solving the optimization problem, two sub-problems are decoupled and solved iteratively. The optimized solutions of bandwidth partitioning and caching policy sub-problems are obtained by taking the derivative and adaptive mutation particle swarm optimization (AMPSO) algorithm, respectively. The simulations show the influence of the related parameters on system performance and illustrate that the strategy to jointly optimize bandwidth partitioning and caching policy can significantly reduce energy consumption.

A Joint Reinforcement-Learning Enabled Caching and Cross-Layer Network Code in F-RAN With D2D Communications

In this paper, we leverage reinforcement learning (RL) and cross-layer network coding (CLNC) for efficiently pre-fetching requested contents to the local caches and delivering these contents to requesting users in a downlink fog-radio access network (F-RAN) with device-to-device (D2D) communications. In the considered system, fog access points (F-APs) and cache-enabled D2D (CE-D2D) users are equipped with local caches that alleviate traffic burden at the fronthaul and facilitate rapid delivery of the users’ contents. To this end, the CLNC scheme optimizes the coding decisions, transmission rates, and power levels of both F-APs and CE-D2D users, and RL scheme optimizes caching strategy. A joint content placement and delivery problem is formulated as an optimization problem with a goal to maximize system sum-rate. The problem is an NP-hard problem. To efficiently solve it, we first develop an innovative decentralized CLNC coalition formation (CLNC-CF) switch algorithm to obtain a stable solution for the content delivery problem, where F-APs and CE-D2D users utilize CLNC resource allocation. By considering statistics of channel and users’ content request into account, we then develop a multi-agent RL algorithm for optimizing the content placement at both F-APs and CE-D2D users. Simulation results show that the proposed joint CLNC-CF-RL framework can effectively improve the sum-rate by up to 30%, 60%, and 150%, respectively, compared to: 1) an optimal uncoded algorithm, 2) a standard rate-aware-NC algorithm, and 3) a benchmark classical NC with network-layer optimization.

Joint Content Sharing and Incentive Mechanism for Cache-Enabled Device-to-Device Networks

Cache-enabled device-to-device (C-D2D) networks allow the constituent user devices to share their cached content with other user devices through D2D communication. As a result, the communication delay of participating users is minimized in C-D2D networks. Clearly, the success of C-D2D networks relies on the willingness of the participating users to share their cached content. In this paper, we analyze the interaction among participating cache-enabled D2D users and determine their caching, sharing, and reward decisions to minimize the users' total cost. In view of the fact that the participating users have heterogeneous content interest profile (CIP) and storage capacity, and are self-centric, there is a need to design a fair incentive mechanism to encourage cooperation among users. To this end, we model the interaction among the D2D users as a multi-person bargaining game and design a novel incentive mechanism using the Nash bargaining solution (NBS) approach. The proposed incentive mechanism is capable of minimizing users' total costs while ensuring fair reward transfer among participating users. Further, we proposed a distributed algorithm which allows the execution of the proposed mechanism without any involvement of base station (BS), which is much needed for autonomous D2D networks. The simulation results demonstrate that the proposed mechanism improves fairness by at least 74% and reduces the users' total cost by at least 13.83% compared to the benchmark schemes.

Performance Analysis and Optimization of Cache-Assisted CoMP for Clustered D2D Networks

Caching at mobile devices and leveraging cooperative device-to-device (D2D) communications are two promising approaches to support massive content delivery over wireless networks while mitigating the effects of interference. To show the impact of cooperative communication on the performance of cache-enabled D2D networks, the notion of device clustering must be factored in to convey a realistic description of the network performance. In this regard, this paper develops a novel mathematical model, based on stochastic geometry and an optimization framework for cache-assisted coordinated multi-point (CoMP) transmissions with clustered devices. Devices are spatially distributed into disjoint clusters and are assumed to have a surplus memory to cache files from a known library, following a random probabilistic caching scheme. Desired contents that are not self-cached can be obtained via D2D CoMP transmissions from neighboring devices or, as a last resort, from the network. For this model, we analytically characterize the offloading gain and rate coverage probability as functions of the system parameters. An optimal caching strategy is then defined as the content placement scheme that maximizes the offloading gain. For a tractable optimization framework, we pursue two separate approaches to obtain a lower bound and a provably accurate approximation of the offloading gain, which allows us to obtain optimized caching strategies.

Deep Reinforcement Learning Approaches for Content Caching in Cache-Enabled D2D Networks

Internet of Things (IoT) technology suffers from the challenge that rare wireless network resources are difficult to meet the influx of a huge number of terminal devices. Cache-enabled device-to-device (D2D) communication technology is expected to relieve network pressure with the fact that the requesting contents can be easily obtained from nearby users. However, how to design an effective caching policy becomes very challenging due to the limited content storage capacity and the uncertainty of user mobility pattern. In this article, we study the jointly cache content placement and delivery policy for the cache-enabled D2D networks. Specifically, two potential recurrent neural network approaches [the echo state network (ESN) and the long short-term memory (LSTM) network] are employed to predict users’ mobility and content popularity, so as to determine which content to cache and where to cache. When the local cache of the user cannot satisfy its own request, the user may consider establishing a D2D link with the neighboring user to implement the content delivery. In order to decide which user will be selected to establish the D2D link, we propose the novel schemes based on deep reinforcement learning to implement the dynamic decision making and optimization of the content delivery problems, aiming at improving the quality of experience of overall caching system. The simulation results suggest that the cache hit ratio of the system can be well improved by the proposed content placement strategy, and the proposed content delivery approaches can effectively reduce the request content delivery delay and energy consumption.

Energy Minimization in D2D-Assisted Cache-Enabled Internet of Things: A Deep Reinforcement Learning Approach

Mobile edge caching (MEC) and device-to-device (D2D) communications are two potential technologies to resolve traffic overload problems in the Internet of Things. Previous works usually investigate them separately with MEC for traffic offloading and D2D for information transmission. In this article, a joint framework consisting of MEC and cache-enabled D2D communications is proposed to minimize the energy cost of systematic traffic transmission, where file popularity and user preference are the critical criteria for small base stations (SBSs) and user devices, respectively. Under this framework, we propose a novel caching strategy, where the Markov decision process is applied to model the requesting behaviors. A novel scheme based on reinforcement learning (RL) is proposed to reveal the popularity of files as well as users’ preference. In particular, a $Q$ -learning algorithm and a deep $Q$ -network algorithm are, respectively, applied to user devices and the SBS due to different complexities of status. To save the energy cost of systematic traffic transmission, users acquire partial traffic through D2D communications based on the cached contents and user distribution. Taking the memory limits, D2D available files, and status changing into consideration, the proposed RL algorithm enables user devices and the SBS to prefetch the optimal files while learning, which can reduce the energy cost significantly. Simulation results demonstrate the superior energy saving performance of the proposed RL-based algorithm over other existing methods under various conditions.

User Preference Aware Caching Deployment for Device-to-Device Caching Networks

Content caching in the device-to-device (D2D) cellular networks can be utilized to improve the content delivery efficiency and reduce traffic load of cellular networks. In such cache-enabled D2D cellular networks, how to cache the diversity contents in the multiple cache-enabled mobile terminals, namely, the caching deployment, has a substantial impact on the network performance. In this paper, a user preference aware caching deployment algorithm is proposed for D2D caching networks. First, the definition of the user interest similarity is given based on the user preference. Then, a content cache utility of a mobile terminal is defined by taking the transmission coverage region of this mobile terminal and the user interest similarity of its adjacent mobile terminals into consideration. A general cache utility maximization problem with joint caching deployment and cache space allocation is formulated, where the special logarithmic utility function is integrated. In doing so, the caching deployment and the cache space allocation can be decoupled by equal cache space allocation. Subsequently, we relax the logarithmic utility maximization problem, and obtain a low complexity near-optimal solution via a dual decomposition method. Compared with the existing caching placement methods, the proposed algorithm can achieve significant improvement on cache hit ratio, content access delay, and traffic offloading gain.

On the Analysis of Content Dissemination with Reactive Content Pushing in Cache-Enabled D2D Networks

Device-to-device (D2D) communication has huge potential to offload ever increasing cellular traffic. As a premise, devices should store as many popular contents as possible. For augmenting the number of cached contents, in this paper, we propose a reactive content pushing (RCP) mechanism in cache-enabled D2D networks, in which some devices overhear the transmitted contents utilizing the broadcast nature of wireless medium. Since no proactive content pushing is involved, it doesn’t generate interference to the D2D links for normal content requests and its implementation is quite simple. By modeling the D2D network by Poisson point process (PPP), we derive the probabilities that users are in the defined states and the law of evolution of content caching distribution. Moreover, based on the PPP model, we derive the expressions of the successful offloading probability, the average delay and the upper bound of average energy consumption of a request. Simulation results demonstrate the accuracy of the theoretical results and the superiority of the RCP mechanism compared with general content dissemination process.

Incentive Design for Cache-Enabled D2D Underlaid Cellular Networks Using Stackelberg Game

Caching in wireless device-to-device (D2D) networks can bring contents closer to users and reduce backhaul traffic. However, the major obstacle to realize cache-enabled D2D communications is the selfish nature of users. In this paper, we propose an incentive mechanism for pricing the contributions of D2D transmitters in cache-enabled D2D-underlaid cellular networks modeled by stochastic geometry. A Stackelberg game is employed to resolve the conflict of interests between the operator and D2D transmitters. The main objective of this game is to reach the Stackelberg equilibrium, in which both the operator and D2D transmitters achieve the maximum profit by designing the optimal incentive price and the optimal caching strategy. We show the existence of the equilibrium and propose a low-complexity algorithm to obtain the near-optimal caching strategy and incentive price. Numerical results verify the existence of the SE and the advantages of the proposed scheme in terms of the utilities for the operator and D2D transmitters over existing caching schemes with fixed incentive.

Caching Policy for Cache-Enabled D2D Communications by Learning User Preference

Prior works in a designing caching policy do not distinguish content popularity with user preference. In this paper, we illustrate the caching gain by exploiting individual user behavior in sending requests. After showing the connection between the two concepts, we provide a model for synthesizing user preference from content popularity. We then optimize the caching policy with the knowledge of user preference and activity level to maximize the offloading probability for cache-enabled device-to-device communications, and develop a low-complexity algorithm to find the solution. In order to learn user preference, we model the user request behavior resorting to probabilistic latent semantic analysis, and learn the model parameters by the expectation maximization algorithm. By analyzing a Movielens data set, we find that the user preferences are less similar, and the activity level and topic preference of each user change slowly over time. Based on this observation, we introduce a prior knowledge-based learning algorithm for user preference, which can shorten the learning time. Simulation results show a remarkable performance gain of the caching policy with user preference over existing policy with content popularity, both with realistic data set and synthetic data validated by the real data set.

Joint cache policy and transmit power for cache‐enabled D2D networks

The cache-enabled device-to-device (D2D) communication is a burgeoning technique to offload cellular traffic. In this study, the authors conducted the joint cache policy and transmit power to maximise the content-related energy efficiency (CREE) for the cache-enabled D2D network. Specifically, the authors model the random distribution of mobile users by the Poisson point process and derive the closed-form CREE via considering the D2D establishment threshold and signal-to-interference ratio threshold at the same time. Then the authors aim to determine the optimal cache policy and transmit power for maximising the CREE in two practical cases, respectively. Since the optimisation problem in both cases are non-linear non-convex problems and share the same mathematical form, the authors focus on the first case and propose a two-step iterative algorithm to find a stationary solution due to its intractability. This iterative algorithm builds on two subproblems which are proved to have optimal solutions. Numerical simulations show that the joint optimisation can obtain more than two times the CREE compared with either of the single optimisation for cache policy or transmit power.

Cooperative Caching Placement in Cache-Enabled D2D Underlaid Cellular Network

In a cache-enabled D2D underlaid cellular network, cooperation of BS caching and D2D caching can further exploit the limited storage capacity and achieve more efficient wireless resource utilization. In this complex network scenario, the closed form expression for successful transmission probability is firstly derived using stochastic geometry, to measure the proportion of users satisfying the delay guarantee. On this basis, a cooperative caching placement problem is formulated to maximize the successful transmission probability. We prove it can be transformed equivalently into a biconvex problem and then a block coordinate descent based algorithm is proposed. Simulation results demonstrate the performance gain of the proposed cooperative caching placement.

Optimal Caching and Scheduling for Cache-Enabled D2D Communications

To maximize offloading gain of cache-enabled device-to-device (D2D) communications, content placement and delivery should be jointly designed. In this letter, we jointly optimize caching and scheduling policies to maximize successful offloading probability, defined as the probability that a user can obtain desired file in local cache or via D2D link with data rate larger than a given threshold. We obtain the optimal scheduling factor for a random scheduling policy that can control interference in a distributed manner, and a low complexity solution to compute caching distribution. We show that the offloading gain can be remarkably improved by the joint optimization.

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.