Mobility aware caching incentive scheme for D2D cellular networks

Caching at the user devices is a key technology to alleviate backhaul load and improve users' quality of experience in mobile edge networks. However, due to the concern of privacy and limited battery life, users are not willing to share their resources. In this study, the authors jointly design the caching strategy and incentive scheme to encourage the users to share their storage resources and improve the social welfare of the cellular network. First, the user equipments (UEs) are classified into different types according to their preference to share cache resources. Then, based on contract theory, an optimisation problem with necessary constraints is formulated aiming at maximising the base station (BS)'s utility. The reward the BS pays for a UE is based on the total amount of contents it shares with other UEs, which is related to the caching policy of UEs. A heuristic caching strategy is proposed and the optimal contract is obtained. Finally, the system performance of the proposed solution is analysed, simulation results show that the incentive scheme can improve the willingness of users to share contents, and the proposed caching strategy contributes more to the social welfare compared with two baseline caching schemes: fair caching and random caching.

Content caching at base stations (BSs) is a promising technique for future wireless networks by reducing network traffic and alleviating server bottleneck. However, in practice, the content popularity distribution may change with spatio-temporal variation but be unknown for BSs, which is an intractable obstacle for efficient caching strategy design. In this paper, considering unknown popularity distribution, we explore the content caching problem by jointly optimizing the content caching in cooperative BSs, content sharing among BSs, and cost of content retrieving. We tackle the problem from a multi-armed bandit learning perspective, where the learning of the popularity distribution is incorporated with the content caching and sharing process. Specifically, we first propose a centralized algorithm by employing a semidefinite relaxation approach, and we prove that this centralized algorithm learns efficient caching by deriving a sub-linear learning regret bound. To further reduce computational complexity, we propose a distributed algorithm based on alternating direction method of multipliers, where each BS only solves their own problems by exchanging local information with neighbor BSs. Extensive simulation results show the effectiveness of the proposed algorithms in terms of learning content popularity distributions of individual BSs, offloading traffic from the content server, and reducing cost of content retrieving.

A heterogenous network with base stations (BSs), small base stations (SBSs)\nand users distributed according to independent Poisson point processes is\nconsidered. SBS nodes are assumed to possess high storage capacity and to form\na distributed caching network. Popular files are stored in local caches of\nSBSs, so that a user can download the desired files from one of the SBSs in its\nvicinity. The offloading-loss is captured via a cost function that depends on\nthe random caching strategy proposed here. The popularity profile of cached\ncontent is unknown and estimated using instantaneous demands from users within\na specified time interval. An estimate of the cost function is obtained from\nwhich an optimal random caching strategy is devised. The training time to\nachieve an $\\epsilon>0$ difference between the achieved and optimal costs is\nfinite provided the user density is greater than a predefined threshold, and\nscales as $N^2$, where $N$ is the support of the popularity profile. A transfer\nlearning-based approach to improve this estimate is proposed. The training time\nis reduced when the popularity profile is modeled using a parametric family of\ndistributions; the delay is independent of $N$ and scales linearly with the\ndimension of the distribution parameter.\n

This paper studies a multi-tier cache-aided relaying network, where the destination <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$D$ </tex-math></inline-formula> is randomly located in the network and it requests files from the source <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S$ </tex-math></inline-formula> through the help of cache-aided base station (BS) and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> relays. In this system, the multi-tier architecture imposes a significant impact on the system collaborative caching and file delivery, which brings a big challenge to the system performance evaluation and optimization. To address this problem, we first evaluate the system performance by deriving analytical outage probability expression, through fully taking into account the random location of the destination and different file delivery modes related to the file caching status. We then perform the asymptotic analysis on the system outage probability when the signal-to-noise ratio (SNR) is high, to enclose some important and meaningful insights on the network. We further optimize the caching strategies among the relays and BS, to improve the network outage probability. Simulations are performed to show the effectiveness of the derived analytical and asymptotic outage probability for the proposed caching strategy. In particular, the proposed caching is superior to the conventional caching strategies such as the most popular content (MPC) and equal probability caching (EPC) strategies.

In this paper, we propose a novel wireless caching scheme to enhance the physical layer security of video streaming in cellular networks with limited backhaul capacity. By proactively sharing video data across a subset of base stations (BSs) through both caching and backhaul loading, secure cooperative joint transmission of several BSs can be dynamically enabled in accordance with the cache status, the channel conditions, and the backhaul capacity. Assuming imperfect channel state information (CSI) at the transmitters, we formulate a two-stage non-convex mixed-integer robust optimization problem for minimizing the total transmit power while providing the quality of service and guaranteeing communication secrecy during video delivery, where the caching and the cooperative transmission policy are optimized in an offline video caching stage and an online video delivery stage, respectively. Although the formulated optimization problem turns out to be NP-hard, low-complexity polynomial-time algorithms, whose solutions are globally optimal under certain conditions, are proposed for cache training and video delivery control. Caching is shown to be beneficial as it reduces the data sharing overhead imposed on the capacity-constrained backhaul links, introduces additional secure degrees of freedom, and enables a power-efficient communication system design. Simulation results confirm that the proposed caching scheme achieves simultaneously a low secrecy outage probability and a high power efficiency. Furthermore, due to the proposed robust optimization, the performance loss caused by imperfect CSI knowledge can be significantly reduced when the cache capacity becomes large.

In this work we investigate optimal geographical caching in heterogeneous cellular networks where different types of base stations (BSs) have different cache capacities. Users request files from a content library according to a known probability distribution. The performance metric is the total hit probability, which is the probability that a user at an arbitrary location in the plane will find the content that it requires in one of the BSs that it is covered by. We consider the problem of optimally placing content in all BSs jointly. As this problem is not convex, we provide a heuristic scheme by finding the optimal placement policy for one type of base station conditioned on the placement in all other types. We demonstrate that these individual optimization problems are convex and we provide an analytical solution. As an illustration, we find the optimal placement policy of the small base stations (SBSs) depending on the placement policy of the macro base stations (MBSs). We show how the hit probability evolves as the deployment density of the SBSs varies. We show that the heuristic of placing the most popular content in the MBSs is almost optimal after deploying the SBSs with optimal placement policies. Also, for the SBSs no such heuristic can be used; the optimal placement is significantly better than storing the most popular content. Finally, we show that solving the individual problems to find the optimal placement policies for different types of BSs iteratively, namely repeatedly updating the placement policies, does not improve the performance.

Device to Device (D2D) communication in Fifth Generation (5G) and unavoidable in Beyond Fifth Generation (B5G) technology is designed to increase network capacity by offloading backhaul links and base stations traffic and improving the performance of low signal nodes, thereby providing fast and energy-efficient communication. D2D communication has several challenges, out of which network extension and data routing to out-of-coverage nodes is the area of focus for this research. optimized Link State Protocol version 2 (OLSRv2) is a popular Mobile Ad-hoc Network (MANET) and Multi-hop D2D communication routing protocol. However, the provision of the D2D-based OLSRv2 routing protocol presents several issues including energy consumption, routing overhead, and optimum relay selection. Therefore, this paper modifies the OLSRv2 protocol by introducing Node’s Status (NS) according to multiple criteria, namely node’s battery level, mobility speed, node degree, and connection to a base station. The proposed routing protocol employed the supervised machine learning (ML) technique to aggregate the multiple criteria into a single comprehensive metric to minimize routing overhead and energy consumption caused by individually transmitting multiple parameters. To determine the best ML techniques for the proposed protocol, four supervised ML techniques, namely K-Nearest Neighbours (K-NN), Random Forest (RF) Multi-layer Perception (MPL), Gradient Boosting Classifier (GBC) has been used to train the model using training datasets generated from the simulation of D2D network in NS-3. The simulation results show that the RF model performed better than the other three models as it consistently reported 100% accuracy and receiver operating characteristic.

This paper proposes a device-to-device (D2D) communications based scarcity-aware two-stage multicast mechanism for delivering video streaming, in which the Base Station (BS) and D2D perform cooperative retransmissions to improve the mobile video quality and relieve the load of BS as well. In prior works, after BS's one-round multicast, the clients directly resort to D2D communications to obtain the missing frames by forming social groups. However, after the first-round multicast of BS, it is possible that some frames have not been correctly received by most of the clients, especially when the channel conditions are not good. That is, there are scarce frames in the network and the clients have very limited copies of them. In this case, it is inefficient for the clients to form social groups and directly exchange frames through D2D communications. Instead, this paper proposes an adaptive two-stage multicast mechanism: the BS first multicast video packets to the clients who request the same video services, after which the clients collect the information of missing frames and send it to the BS. Then the BS evaluates the scarcity of frames and re-multicast the scarce frames, if any, to the clients. After the BS's second-round multicast, the clients can then form groups to perform D2D communications so as to obtain the remaining missing packets from each other. Numerical studies show that the proposed simple scarcity-aware two-stage multicast mechanism can achieve about 5% video quality improvement than the existing one-stage multicast mechanism and have the potential to enhance the downlink capacity of the future 5G wireless network.

This paper will discuss the distributed caching strategies in clustered heterogeneous cellular networks. Basically, there are two obstacles to accomplish the caching policy in the wireless access networks, i.e., the limited storage capacity of small cells and the too small coverage area of each base station to imply content aggregation effect. Traditionally, the existing caching strategies only concern the local requests within one base station coverage, which neglects the clustered characteristics of network infrastructure and user traffic demand. In this paper, we consider a two-tier clustered cellular networks, within which small cells can exchange content to fulfill each user request. Intuitively, this cooperation between base stations increases the service capacity of clustered cellular networks as testified in our numerical simulation.

We consider a mobile network consisting of both the wireless access network and the backhaul network. All base stations in the access network and gateways in the backhaul network are equipped with caches, so that routing costs for serving content requests can be reduced by caching the requested content items closer to the users. In this case, user association in the wireless access network must be aware of both the quality of wireless channels and the content caching strategy. In this paper, we propose a framework that jointly optimizes wireless user association and content caching in both access and backhaul networks. The resulting problem is NP-hard. We propose a polynomial-time algorithm based on convex approximation and pipage rounding that produces a solution within a constant factor of 1 − 1/e from the optimal. Simulation results show that the proposed joint algorithm outperforms schemes that combine cache-independent user association methods with traditional caching strategies (e.g. LRU) in terms of minimizing the aggregate routing cost and backhaul traffic while achieving a high data sum rate in the access network.

To satisfy rapidly increasing multimedia service requests from mobile users, content caching at the network edges (e.g., base stations) has been regarded as a promising technique in future mobile networks. In this paper, by virtue of Deep Reinforcement Learning (DRL) with respect to solving complicated control problems, we propose a framework on Double Deep Q-Network for cooperative edge caching in mobile networks. Particularly, we aim at minimizing the long-term average content fetching delay of mobile users without requiring any priori knowledge of content popularity distribution. Trace-driven simulation results show that our proposed framework outperforms some existing caching algorithms, including Least Recently Used (LRU), Least Frequently Used (LFU) and First-In First-Out (FIFO) caching strategies by 7%, 11% and 9% improvements, respectively. Besides, our proposed work is further shown that only average 4% performance loss exists compared to an omniscient oracle algorithm.

With the improvement of quality of service requirement and the increase of energy expenditure, integrating caching and energy harvesting (EH) to device-to-device (D2D) communication is a prospective but challenging research. In this paper, we focus on investigating the network performance with stochastic geometry for cache- and EH-enabled D2D cellular network, in which each mobile user (MU) can cache contents from the base station and be charged through EH. Specifically, taking into account the knowledge of the energy states and the requested contents, along with the randomness of content caching, each MU can fetch its requested file via three modes, i.e., self-satisfied, D2D communication and cellular downlink communication. In view of this observation, stochastic geometry is applied to analyze the system transmission model, and from which the content delivery strategies are designed, as well as the cache hit probability and successful transmission probability are characterized under three modes, respectively. Based on the analyzed results, two probabilistic caching strategies with different objectives are further designed to maximize the cache hit probability and the network successful offloading probability. Finally, comprehensive simulations are provided to analyze the effect of different parameters on network performance and verify the superiority of the proposed caching strategies.

This article explores one of the key enablers of beyond 4G wireless networks leveraging small cell network deployments, proactive caching. Endowed with predictive capabilities and harnessing recent developments in storage, context awareness, and social networks, peak traffic demands can be substantially reduced by proactively serving predictable user demands via caching at base stations and users' devices. In order to show the effectiveness of proactive caching, we examine two case studies that exploit the spatial and social structure of the network, where proactive caching plays a crucial role. First, in order to alleviate backhaul congestion, we propose a mechanism whereby files are proactively cached during off-peak periods based on file popularity and correlations among user and file patterns. Second, leveraging social networks and D2D communications, we propose a procedure that exploits the social structure of the network by predicting the set of influential users to (proactively) cache strategic contents and disseminate them to their social ties via D2D communications. Exploiting this proactive caching paradigm, numerical results show that important gains can be obtained for each case study, with backhaul savings and a higher ratio of satisfied users of up to 22 and 26 percent, respectively. Higher gains can be further obtained by increasing the storage capability at the network edge.

Caching on the edge has been recognized as an effective solution to tackle the backhaul constraint of network densification. However, most related works ignored user mobility in wireless networks, which is unreasonable under the background of network densification. For a more flexible and context-aware caching decision, the concept of caching on the edge can be extended to mobile edge computing (MEC) that enables computation and storage resources at mobile edge networks. With MEC servers deployed on base stations, a huge amount of collected radio access network context data can be analyzed and utilized to render a caching scheme adaptive to user’s context-aware information. In this regard, a novel mobility-aware coded probabilistic caching scheme is proposed for MEC-enabled small cell networks (SCNs). Different from previous mobility-aware caching schemes, user mobility and distributed storage are incorporated into a conventional probabilistic caching scheme, with the aim of throughput maximization. Based on stochastic geometry theory and a modified mobility model of discrete random jumps, the explicit expression of throughput is derived. Due to the complexity of the expression, two light-weight heuristic algorithms are provided to numerically obtain the optimal solutions. Moreover, a significant trade-off among the gains of mobility diversity, content diversity, and channel selection diversity is discussed, and we further numerically analyze how such a trade-off is influenced by user mobility, content popularity, and backhaul capacity, with some fundamental insights into the application of the proposed scheme in MEC-enabled SCNs. The superiority of our proposed scheme is demonstrated by the comparisons with the classical M most popular caching scheme and the conventional probabilistic caching scheme. Numerical results show that our proposed caching scheme achieves higher throughput than those of the other two, especially when users of intense mobility request contents, of which the popularity profile is not skewed, in MEC-enabled SCNs with poor backhaul capacity, indicating that the proposed caching scheme is a promising solution for network densification.

The massive data exchange between base stations and network backhaul creates a strong overhead on mobile networks, especially at peak times. This motivates researchers to think about the proactive caching concept which depends mainly on caching some of the expected data items during off-peak times. The caching problem consists of two distinct phases, placement phase and delivery phase. In this work, we consider a mobility-aware device-to-device (D2D) caching network. We assume that data contents were already cached in users devices during the content placement phase and we focus on the content delivery phase. The social relationships between users allows us to predict their meeting probabilities at some places. Moreover, the similarity in users interest increases the probability of finding requested data items at nearby users and hence increases the hit probability. This motivates us to study the effect of sociality and interest similarity on the content delivery. We study how to minimize users average cost by obtaining the requested data items from nearby users, via D2D communication, instead of going back to the base station. Dynamic programming is used to find an optimal content delivery policy. Due to the complexity of the optimal policy, we introduce a suboptimal algorithm and compare between them. We show, through numerical results, that the proposed algorithm has a significant performance, compared to the optimal content delivery policy, for a large enough number of time slots.