D2D Caching Research Articles

Mixed Micro/Macro Cache for Device-to-Device Caching Systems in Multi-Operator Environments.

In a device-to-device (D2D) caching system that utilizes a device's available storage space as a content cache, a device called a helper can provide content requested by neighboring devices, thereby reducing the burden on the wireless network. To enhance the efficiency of a limited-size cache, one can consider not only macro caching, which is content-based caching based on content popularity, but also micro caching, which is chunk-based sequential prefetching and stores content chunks slightly behind the one that a nearby device is currently viewing. If the content in a cache can be updated intermittently even during peak hours, the helper can improve the hit ratio by performing micro caching, which stores chunks that are expected to be requested by nearby devices in the near future. In this paper, we discuss the performance and effectiveness of micro D2D caching when there are multiple operators, the helpers can communicate with the devices of other operators, and the operators are under a low load independently of each other. We also discuss the ratio of micro caching in the cache area when the cache space is divided into macro and micro cache areas. Good performance can be achieved by using micro D2D caching in conjunction with macro D2D caching when macro caching alone does not provide sufficient performance, when users are likely to continue viewing the content they are currently viewing, when the content update cycle for the cache is short and a sufficient number of chunks can be updated for micro caching, and when there are multiple operators in the region.

A decision model for OTT service users to adopt wireless D2D caching networks: Exploring the Korean case

As many Over-The-Top (OTT) users opt for mobile devices, mobile network traffic surges. However, current mobile communication technology faces difficulties. To provide a stable video content consumption experience to users, reliable alternatives must be found to resolve the limitation of the conventional approach. Accordingly, wireless Device-to-Device (D2D) caching networks have gained attention as a reliable alternative. Considering it can reduce network-related overheads, save related costs, and improve OTT video consumption environment, there is a high possibility that telecommunication operators want to commercialize this technology. To commercialize wireless D2D caching networks, users need to share their resources, such as personal information, smartphone storage, and battery. This study explores the decision-making process of mobile OTT users regarding D2D caching adoption on their mobile devices in Korea. An online survey of 94 respondents was conducted in South Korea. By using the analytic hierarchy process (AHP) approach, this study devised an evaluation model that includes three major factors: perceived benefits, perceived costs, and perceived situational conditions. The results reveal that risks, rather than situational factors and benefits, predominantly influence user intentions and strategies. Streaming video content via mobile data or Wi-Fi is viewed as the most feasible choice. Moreover, users show a preference for pre-downloading video content over adopting wireless D2D caching networks on their mobile devices. The findings provide a guideline for understanding the responses of mobile OTT service users and their priorities in responding to adopting wireless D2D caching networks.

Understanding Mobile OTT Service Users' Resistance to Participation in Wireless D2D Caching Networks.

With the explosive pace of mobile over-the-top (OTT) video content streaming services, mobile network traffic has seen unprecedented growth in recent years. However, the limitation of antenna performance, the burden of investment cost, and restricted resources hinder improving the current mobile networks' functionality. Accordingly, wireless device-to-device (D2D) caching networks came to the fore as one of the competitive alternatives for alleviating the overloads of mobile network traffic. Wireless D2D caching networks can be a desirable alternative for OTT service providers and telecommunication operators, but the problem is user resistance. User participation is imperative to deliver wireless D2D caching network functionality successfully. Thus, to gain a deeper understanding of user resistance toward wireless D2D caching networks and their underlying sources, this study introduces two perceived cost factors contributing to this resistance and one perceived benefit that mitigates such resistance. Based on an online survey, this study found new theoretical links among perceived costs and benefits, resistance, and participation intention. The findings reveal that user resistance is predicted by perceived costs, encompassing resource sacrifices and privacy concerns, whereas perceived benefits-specifically, perceived usefulness-did not significantly influence resistance. This implies that telecommunication operators should prioritize market requirements over technological advantages, emphasizing the potential for successful commercialization of wireless D2D caching networks.

Performance Analysis of D2D Cache Network for Total Hit Probability and Energy Consumption

Performance Analysis of D2D Cache Network for Total Hit Probability and Energy Consumption

A Deep-Reinforcement-Learning-Based Social-Aware Cooperative Caching Scheme in D2D Communication Networks

Device-to-Device (D2D) caching is becoming prevalent in relieving network congestion. However, there remain challenges in exploring efficient D2D caching strategies due to the diverse user requirements. In this paper, we propose a social-aware D2D caching scheme that integrates the concept of social incentive and recommendation with D2D caching decision-making. Firstly, we investigate federated learning (FL)-based prediction method to achieve the social-aware in a privacy-preserving manner. Then, the predicted social relationship provides prior knowledge for deep reinforcement learning (DRL) to make optimal D2D caching decisions. The optimization problem of this paper is to maximize the data offloading probability, which can be formulated as a Markov decision process. To solve it, we propose a double deep Q-learning network (DDQN)-based D2D caching algorithm. At last, simulation results validate the prediction and convergence performance of the proposed scheme. Besides, the scheme also shows superior caching performance in reducing the average delay and improving overall offloading probability.

Blockchain-Empowered Federated Learning Approach for an Intelligent and Reliable D2D Caching Scheme

Cache-enabled device-to-device (D2D) communication is a potential approach to tackle the resource shortage problem. However, public concerns of data privacy and system security still remain, which thus arises an urgent need for a reliable caching scheme. Fortunately, federated learning (FL) with a distributed paradigm provides an effective way to privacy issue by training a high-quality global model without any raw data exchanges. Besides the privacy issue, blockchain can be further introduced into the FL framework to resist the malicious attacks occurred in D2D caching networks. In this study, we propose a double-layer blockchain-based deep reinforcement FL (BDRFL) scheme to ensure privacy-preserved and caching-efficient D2D networks. In BDRFL, a double-layer blockchain is utilized to further enhance data security. Simulation results first verify the convergence of the BDRFL-based algorithm, and then demonstrate that the download latency of the BDRFL-based caching scheme can be significantly reduced under different types of attacks when compared to some existing caching policies.

Joint Optimization of Edge Computing Resource Pricing and Wireless Caching for Blockchain-Driven Networks

In wireless communication networks, traffics of base stations (BS) can be effectively offloaded by sharing the cached content between mobile devices through wireless device-to-device (D2D) communication. To encourage the content caching of D2D users, a blockchain incentive scheme is proposed; the game theory is introduced to optimize the benefits for both the edge computing server (ECS) and D2D users in the blockchain network. In Stackelberg game, D2D users act as followers. After the successful implementation of the cache strategy, D2D users are rewarded by the system, which is a new block mining process. In this process, the required computing power is provided by the ECS. Therefore, D2D cache users need to pay the cost to purchase computing power from the ECS. As the game leader, the ECS prices the computing resources, and D2D users adjust their caching strategies based on the price. In this paper, we propose four algorithms based on different pricing methods and different cache reward methods. Simulations are performed to compare the cache quality and cache content dispersion of the four algorithms. The results show that the uniform pricing scheme with a linear reward relationship is more suitable for the scenarios that require higher cache quality, and the discriminatory pricing scheme with a nonlinear reward relationship is more suitable for the scenarios that require more evenly cache content distribution.

Adapting Deep Learning for Content Caching Frameworks in Device-to-Device Environments

Recently, we have witnessed an expeditious growth in the intelligence and processing ability of user equipment accompanied by the explosive increase of wireless data and traffic. With such a huge demand, and a shortage of resources to fulfill that need, device-to-device (D2D) communication has surfaced as a propitious solution to reduce costs associated with backhaul links by collaboratively caching files. This paper explores the existing content caching frameworks in D2D communication environments. Further, it proposes two novel frameworks -one leveraging a combination of recurrent and deep neural networks and another based on the latest deep language models that use attention mechanisms known as transformers. The developed frameworks require minimum apriori knowledge about the environment and utilize the growth of user data to achieve performance enhancement. Our experiments show that the proposed frameworks are adaptive in nature and learn from the historical data of the environment. Further, we achieve an overall 25% increase in the D2D cache hit rate over a recently proposed framework that uses neural networks for collaborative filtering (NCF) to make caching decisions.

Optimizing joint probabilistic caching and channel access for clustered D2D networks

Caching at mobile devices and leveraging device-to-device (D2D) communication are two promising approaches to support massive content delivery over wireless networks. Analysis of such D2D caching networks based on a physical interference model is usually carried out by assuming uniformly distributed devices. However, this approach does not capture the notion of device clustering. In this regard, this paper proposes a joint communication and caching optimization framework for clustered D2D networks. Devices are spatially distributed into disjoint clusters and are assumed to have a surplus memory that is utilized to proactively cache files, following a random probabilistic caching scheme. The cache offloading gain is maximized by jointly optimizing channel access and caching scheme. A closed-form caching solution is obtained and bisection search method is adopted to heuristically obtain the optimal channel access probability. Results show significant improvement in the offloading gain reaching up to 10% compared to the Zipf caching baseline.

Data-Supported Caching Policy Optimization for Wireless D2D Caching Networks

In this paper we study a data-supported caching policy design for wireless D2D caching networks, which is based on a dataset collected from a campus Wi-Fi network. After a well-designed preprocessing for the dataset, for the first time, we conduct a real dataset based performance evaluation for the caching policies designed based on the homogeneous Poisson Point Process (PPP) model and a clustered PPP model. We proceed to propose a novel approach for the design of the D2D caching policy. It directly models the number of D2D neighbours, instead of characterizing the locations of users as the PPP models. We show that the number of D2D neighbours can be well modeled by a discrete Gamma distribution. Given the model, we develop an iterative algorithm to optimize the D2D caching policy, and also provide a method to optimize the cache update time in order to balance the caching gain and overhead. Simulation results based on the dataset show that the proposed caching policy can achieve good performance with low cost of cache updating.

Secure D2D caching framework inspired on trust management and blockchain for Mobile Edge Caching

Device-to-Device communication (D2D), combined with edge caching and mobile edge computing, is a promising approach to allow offloading data from the wireless mobile network in light of the growth of the number of connected devices and the increase in traffic. However, user security is still an open issue in D2D communication. Security vulnerabilities remain possible owing to easy, direct and spontaneous interactions between untrusted users as well as mobility. In this article, we establish a Secure D2D Caching framework inspired on trUst management and Blockchain (SeCDUB) to improve the security of D2D communication in video caching, through an approach that combines direct and indirect observations. In addition, blockchain concepts were adapted to the dynamic and restricted scenario of the D2D networks to prevent data interception and the alteration of indirect observations. Two uncertainty mathematical models were used to infer direct and indirect trust values: Bayesian inference and the Dempster Shafer Theory (DST) respectively. In addition, we have designed a clustering scheme that enables a faster and more lightweight blockchain for D2D networks. This ensures overhead will not increase with the number of nodes, and thus offers a scalable security solution. Although the average throughput and packet loss increase when SeCDUB is employed, it is not significant in dense scenarios. Nonetheless, there is an improvement in the goodput for all the accessed network scenarios.

Multi-Agent Reinforcement Learning Based 3D Trajectory Design in Aerial-Terrestrial Wireless Caching Networks

This paper investigates a dynamic 3D trajectory design of multiple cache-enabled unmanned aerial vehicles (UAVs) in a wireless device-to-device (D2D) caching network with the goal of maximizing the long-term network throughput. By storing popular content at the nearby mobile user devices, D2D caching is an efficient method to improve network throughput and alleviate backhaul burden. With the attractive features of high mobility and flexible deployment, UAVs have recently attracted significant attention as cache-enabled flying base stations. The use of cache-enabled UAVs opens up the possibility of tracking the mobility pattern of the corresponding users and serving them under limited cache storage capacity. However, it is challenging to determine the optimal UAV trajectory due to the dynamic environment with frequently changing network topology and the coexistence of aerial and terrestrial caching nodes. In response, we propose a novel multi-agent reinforcement learning based framework to determine the optimal 3D trajectory of each UAV in a distributed manner without a central coordinator. In the proposed method, multiple UAVs can cooperatively make flight decisions by sharing the gained experiences within a certain proximity to each other. Simulation results reveal that our algorithm outperforms the traditional single- and multi-agent Q-learning algorithms. This work confirms the feasibility and effectiveness of cache-enabled UAVs which serve as an important complement to terrestrial D2D caching nodes.

Clustering-based device-to-device discovery and content delivery in wireless networks

Device-to-device (D2D) cache placement and content delivery is a recent technology that exploits idle memory capacity of mobile devices and promises to fulfill users' content needs with very low delay and without using the scarce cellular network capacity. Recent literature on D2D caching mostly concentrated on optimal cache placement and mostly disregarded the required discovery mechanisms and the accompanying overhead. In this work we propose a clustering-based procedure that is used to cache content at the helper nodes along with advertisement and discovery procedures for content delivery. We implement a Long Term Evolution (LTE)-based simulation setup to evaluate the proposed schemes. Numerical results reveal that the clustering-based user and content discovery significantly improves cellular offloading and reduces the discovery overhead with a minimal sacrifice from delay.

A Privacy-Preserving Caching Scheme for Device-to-Device Communications

With device-to-device (D2D) communication, user equipment can share data with each other without the involvement of network infrastructures. In order to maintain the Quality of Service (QoS) and Quality of Experience (QoE) for user applications in D2D communications, most existing schemes use proactive content caching that needs to predict content popularity before making caching decisions which may result in privacy leakage, since the information of users is collected to train a deep learning-based model to predict content popularity. Therefore, it is crucial to guarantee secure data collection in machine learning-based framework. In this paper, we propose a privacy-preserving D2D caching scheme with a passive content caching strategy based on node importance, which can deliver more efficient caching and prevent the potential leakage of user privacy. The scheme is based on softwaredefined networking (SDN), in which the controller is responsible for calculating node importance of devices according to the information of requests and encounters collected by SDN switches. Base station will decide which device can establish reliable and secure communication with content requester based on historical information. The simulation results show that the proposed strategy can outperform other D2D caching strategies in terms of cache hit rate and data rate.

Efficient Algorithms for Cache-Throughput Analysis in Cellular-D2D 5G Networks

In this paper, we propose a two-tiered segment-based Device-to-Device (S-D2D) caching approach to decrease the startup and playback delay experienced by Video-on-Demand (VoD) users in a cellular network. In the S-D2D caching approach cache space of each mobile device is divided into two cache-blocks. The first cache-block reserve for caching and delivering the beginning portion of the most popular video files and the second cache-block caches the latter portion of the requested video files ‘fully or partially’ depending on the users’ video watching behaviour and popularity of videos. In this approach before caching, video is divided and grouped in a sequence of fixed-sized fragments called segments. To control the admission to both cache-blocks and improve the system throughput, we further propose and evaluate three cache admission control algorithms. We also propose a video segment access protocol to elaborate on how to cache and share the video segments in a segmentation based D2D caching architecture. We formulate an optimisation problem and find the optimal cache probability and beginning-segment size that maximise the cache-throughput probability of beginning-segments. To solve the non-convex cache-throughout maximisation problem, we derive an iterative algorithm, where the optimal solution is derived in each step. We used extensive simulations to evaluate the performance of our proposed S-D2D caching system.

Cache Partitioning and Caching Strategies for Device-to-Device Caching Systems

The amount of traffic in wireless networks is increasing exponentially, and this problem can be mitigated using device-to-device (D2D) caching technology, which installs a cache on a mobile end device. Devices can reduce the cell load through self-offloading via content in their own cache and D2D offloading using content in others' caches. However, especially in the early stage of D2D caching systems, a limited number of devices with a small storage might be used, and it is required to develop a caching scheme with excellent performance despite the small cache size. Regarding content popularity, which is common to most users, the preference probability values are not concentrated on some pieces of content, making it difficult to achieve satisfactory performance using a small cache. On the other hand, when considering individual users, content preferences may contain large values for specific content based on individual characteristics. In addition, the performance can be improved by considering short-term content preferences that reflect changes in content preferences over time or newly created content during peak hours. In this article, the hit ratio is divided into six parts considering self- and D2D offloading, common and individual user preferences, and little and large temporal changes in content preferences during peak hours. We also conceptually divide the cache of a helper into six areas in relation to the six parts of the hit ratio, and discuss cache partitioning and proactive caching strategies according to the environment.

A completely distributed transmission algorithm for mobile device-to-device caching networks

We investigate mobile device-to-device (D2D) caching networks where mobiles called receiving devices (RDs) can receive data directly from caching server devices (CSDs). In mobile D2D caching networks, the quality of communications can be greatly enhanced because end-to-end communication ranges are shortened compared to cellular communications. In this paper, we propose a completely distributed transmission algorithm for mobile caching networks (DMC) based on signal-to-generating interference ratio (SGIR) that can eliminate overhead for signaling and feedback. In the DMC, each CSD autonomously chooses an RD to transmit data to and the CSD multicasts the data to RDs that requested the same data. One CSD is only activated to transmit data to each RD in a distributed manner based on timers. Our numerical results show that the DMC can yield comparable average sum-rates to a centralized scheme in various environments with no extra overhead.

Competition Analysis of Diverse Request-Aware Packet Caching Policy for D2D Communication

Device-to-device (D2D) based packet caching technologies recently attract increasing attention, thanks to their great potentials to facilitate network traffic offloading. Despite the many popular issues arising in the D2D caching area, one of the new perspectives, namely the competition due to the packet request diversity originating from various D2D user equipment (UE) groups is not sufficiently investigated. In this work, we analyze several key aspects of the competition for packet allocation among diverse packet requests. Firstly, we study the impact from diverse group proportions on the system throughput and the packet allocation fairness. Particularly, the novel group separation index (GSI) is introduced, which helps to reflect the packet allocation fairness. We derive and analyze both the upper and lower bounds of GSI. Secondly, we investigate how the concentration levels of diverse packet requests may affect the system performance, such as the impact from the caching size limit on packet allocation. Thirdly, we derive the average energy consumption metric using the binomial point process based network model, which facilitates a comprehensive evaluation of the competition among UE groups. Finally, simulations validate our proposed analysis method, which may provide important design hints for improving existing D2D caching schemes.

Loss‐aware adaptive caching scheme for device‐to‐device communications

In current research of device-to-device (D2D) caching strategies, when terminals request and receive files within the transmission range, the effect of file redundancy and idle loss rate of the cache in terminals are rarely considered. In this study, the authors establish a motion and the transfer model of D2D terminals, and define a loss function considering cache idle loss and file redundancy. Then the relationship between the cache size and the number of terminals is analysed, and the optimal cache size is calculated according to the loss function. Through the analysis, the authors can see that the performance of the D2D caching network is almost constant when the product of the buffer size and the number of terminals is constant. Therefore, inspired by the above analysis and results, the authors devise an adaptive caching strategy, which determines the file caching method according to the comparison of the actual size of the terminal cache and the optimal cache size with respect to the loss function. Simulation results indicate that, compared with other caching schemes, the proposed adaptive strategy yields a significantly higher cache-hitting rate and less delay.

Blockchain-Incentivized D2D and Mobile Edge Caching: A Deep Reinforcement Learning Approach

D2D caching assists mobile edge caching in offloading inter-domain traffic by sharing cached items with nearby users, while its performance relies heavily on caching nodes' sharing willingness. In this article, a Blockchain-based CDM is proposed as an incentive mechanism for the distributed caching system. Under given incentive mechanisms, both D2D and mobile edge caching nodes' willingness is guaranteed by satisfying their expected reward for cache sharing. Besides, for the distributed CDM, content delivery related transactions are executed by smart contracts. To achieve consensus on the transactions and prevent frauds, a consensus protocol among the SCENE is necessary. The pPBFT protocol is proposed to minimize the latency of reaching consensus while guaranteeing its confidence level. We build the model of cache sharing and transaction execution consensus, and we further formulate cache placement and SCENE selection as Markov Decision Process problems. Considering the complexity and dynamics of the problems, a deep reinforcement learning approach is adopted to solve the problems. Simulation results show that, compared to conventional solutions, the proposed schemes achieve efficient traffic offloading, and significantly improve the transaction execution consensus speed.