Wireless Caching Networks Research Articles

Joint Optimization of User Pairing, Power Allocation and Content Server Deployment in NOMA-Assisted Wireless Caching Networks

Joint Optimization of User Pairing, Power Allocation and Content Server Deployment in NOMA-Assisted Wireless Caching Networks

Latency Minimization for Advanced RSMA-Enabled Wireless Caching Networks

The collaboration between wireless edge caching and rate splitting multiple access (RSMA) can significantly reduce the perceived latency of users. In this letter, an advanced RSMA transmission scheme is proposed with the consideration of users’ content requests. In traditional RSMA, multiple replicas of the same messages are transmitted when users request the same contents, which cause the redundant interference. The advanced RSMA is proposed to reduce such redundant interference. Based on the newly designed transmission paradigm, we investigate the joint cache placement and RSMA resource management problem, aiming to minimize the average latency of all users. The formulated problem is a non-convex mixed-integer nonlinear programming (MINLP) problem. By leveraging its structural features, we decompose the original minimization problem into two types of subproblems, i.e., the long-term caching decision problem and the short-term RSMA resource allocation problem. The caching decision problem is tackled by the dynamic programming approach. Whilst the RSMA resource allocation problem is solved by an alternating optimization method with one-dimensional search. Extensive simulation results validate the superiority of our proposed scheme compared to various benchmark strategies.

Optimal Designs of SVC-Based Content Placement and Delivery in Wireless Caching Networks.

To allieviate the heavy traffic burden over backhaul links and improve the user's quality of service (QoS), edge caching plays an important role in wireless networks. This paper investigated the optimal designs of content placement and transmission in wireless caching networks. The contents to be cached and requested were encoded into individual layers by scalable video coding (SVC), and different sets of layers can provide different viewing qualities to end users. The demanded contents were provided by helpers caching the requested layers, or by the macro-cell base station (MBS) otherwise. In the content placement phase, this work formulated and solved the delay minimization problem. In the content transmission phase, the sum rate optimization problem was established. To effectively solve the nonconvex problem, the methods of semi-definite relaxation (SDR), successive convex approximation (SCA), and arithmetic-geometric mean (AGM) inequality were adopted, after which the original problem was transformed into the convex form. The numerical results show that the transmission delay is reduced by caching contents at helpers. Moreover, the fast convergence of the proposed algorithm for solving the sum rate maximization problem is presented, and the sum rate gain of edge caching is also revealed, as compared to the benchmark scheme without content caching.

Minimizing Delay for MIMO-NOMA Resource Allocation in UAV-Assisted Caching Networks

As one of the important enabling techniques for 6 G, wireless caching network (WCN) attracts significant attentions. In this paper, we jointly apply unmanned aerial vehicle (UAV), millimeter wave (mmWave) multiple input multiple output (MIMO) and non-orthogonal multiple access (NOMA) in WCN. Our aim is to minimize the user delay, which is decomposed into three sub-problems, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , UAV deployment, hybrid beamforming and power allocation. Firstly, to improve the user rate, we apply K-means to reduce the distance between UAVs and users and propose a user pairing method to maintain the channel gain gap in each pair. Then, for increasing UAV hit probability, particle swarm optimization (PSO) and zero forcing are used for analog beamforming and digital beamforming, respectively. Finally, to further improve the user data rate, the genetic algorithm (GA) is applied to calculate the optimal NOMA power allocation factors. Simulation results confirm that the proposed schemes can achieve lower user delay compared with baseline schemes.

Joint Optimization Algorithm for Small Base Station States Control and User Association in Wireless Caching Networks

The energy consumption management of small base stations (SBSs) in wireless caching networks with dense deployment of SBSs is an urgent problem to be solved. This paper jointly optimizes the SBS state control and user association problems in caching networks to reduce network energy consumption while taking into account the average service latency of the network to ensure user experience. First, a new definition of three-state SBSs in caching networks is proposed based on their ability to keep content cache updated. Then, a relaxed threshold setting method is designed and the SBS traffic prediction is used to obtain the initial state information of SBSs in the next period. In order to eliminate the impact on the accessing users when the switched-off base station (BS) wakes up, a SBS state asynchronous switching mechanism is proposed to ensure that the users who switch to the waking SBS can carry out communication services normally, and a user association strategy is constructed with the SBS load as the optimization target. Finally, a joint optimization model of user association and SBS state control (SSC-UA) is constructed to admit and correct the initial state of SBSs to maximize the system gain and obtain the final state strategy for each SBS in the next period. The simulation results show that the proposed SSC-UA algorithm can effectively improve the energy efficiency and reduce the network service delay at the same time.

Joint design of content delivery and recommendation in wireless caching networks

Although content caching and recommendation are two complementary approaches to improve the user experience, it is still challenging to provide an integrated paradigm to fully explore their potential, due to the high complexity and complicated tradeoff relationship. To provide an efficient management framework, the joint design of content delivery and recommendation in wireless content caching networks is studied in this paper. First, a joint transmission scheme of content objects and recommendation lists is designed with edge caching, and an optimization problem is formulated to balance the utility and cost of content caching and recommendation, which is an mixed integer nonlinear programming problem. Second, a reinforcement learning based algorithm is proposed to implement real time management of content caching, recommendation and delivery, which can approach the optimal solution without iterations during each decision epoch. Finally, the simulation results are provided to evaluate the performance of our proposed scheme, which show that it can achieve lower cost than the existing content caching and recommendation schemes.

On the Application of Cooperative NOMA to Spatially Random Wireless Caching Networks

On the Application of Cooperative NOMA to Spatially Random Wireless Caching Networks

Multi-UAV Trajectory and Power Optimization for Cached UAV Wireless Networks With Energy and Content Recharging-Demand Driven Deep Learning Approach

In this paper, we propose a novel joint trajectory and communication scheduling scheme for multiple unmanned aerial vehicles (UAVs) enabled wireless caching networks. To exploit the favorable propagation of air-to-ground channels, we consider an ultra dense UAVs enabled content-centric wireless transmission network, where massive UAVs are deployed to transmit cached contents to a group of random distributed ground users. We formulate the problem as an infinite horizon ergodic stochastic differential game (SDG) for optimizing the users' quality-of-experience (QoE). In particular, stochastic dynamics of channel states, UAVs' mobility, energy queues and content request queues are modeled in this game. To deal with the state coupling between the UAVs, we consider a limiting problem for large number of UAV based on mean field analysis. A reduced-complexity decentralized solution can be obtained through mean-field equilibrium analysis. To further reduce the solution complexity on each UAV, we propose a model-specific deep neural network (DNN) to learn the optimal control solution in an online manner. The DNN is not arbitrarily generated but tailored to the structural properties of the value function and stationary distribution based on the homotopy perturbation method analysis. Finally, simulation results are provided to show that the proposed solution can achieve significant gain over the existing baselines.

Towards Cost Minimization for Wireless Caching Networks With Recommendation and Uncharted Users’ Feature Information

Caching popular contents at the network edge has been considered as a promising enabler to relieve the pressure on networks due to the fact that a substantial portion of global data traffic is repeatedly requested by many subscribers and thus redundantly generated. Recommendation, on the other hand, has attracted spiraling attention for its capability of reshaping users’ contents demand patterns. In this paper, we examine the practicability of recommendation in boosting the gains of edge caching with uncharted users’ feature information. To this end, we first characterize the average system cost for a generic network model, disclosing its dependence on the recommendation and caching strategies. Then, we formulate the joint caching and recommendation decision oriented cost minimization problem, taking the constraints on each content provider’s cache capacity budget, each individual user’s recommendation size and recommendation quality into account. However, the implicit information regarding users’ preference makes the problem inextricable. To address this issue, a versatile long short term memory (LSTM) network assisted prediction paradigm is proposed to attain the preference schema of users with the assistance of their historical behavior data. Based on that, we rigorously prove the NP-hardness of obtaining the optimal recommendation and caching policies that jointly minimize the system cost. Therewith, an iterative suboptimal algorithm is developed, which has provable polynomial time complexity and convergence guarantee. Extensive simulation results validate the effectiveness of our proposed LSTM enabled feature information prediction approach and the convergence performance of the devised joint decision making methodology. In addition, it is shown that the proposed scheme outperforms numerous benchmarks significantly.

Joint Computing and Caching in 5G-Envisioned Internet of Vehicles: A Deep Reinforcement Learning-Based Traffic Control System

Recent developments of edge computing and content caching in wireless networks enable the Intelligent Transportation System (ITS) to provide high-quality services for vehicles. However, a variety of vehicular applications and time-varying network status make it challenging for ITS to allocate resources efficiently. Artificial intelligence algorithms, owning the cognitive capability for diverse and time-varying features of Internet of Connected Vehicles (IoCVs), enable an intent-based networking for ITS to tackle the above-mentioned challenges. In this paper, we develop an intent-based traffic control system by investigating Deep Reinforcement Learning (DRL) for 5G-envisioned IoCVs, which can dynamically orchestrate edge computing and content caching to improve the profits of Mobile Network Operator (MNO). By jointly analyzing MNO's revenue and users' quality of experience, we define a profit function to calculate the MNO's profits. After that, we formulate a joint optimization problem to maximize MNO's profits, and develop an intelligent traffic control scheme by investigating DRL, which can improve system profits of the MNO and allocate network resources effectively. Experimental results based on real traffic data demonstrate our designed system is efficient and well-performed.

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.

Optimal Target User Selection Policy for D2D Wireless Caching Networks

Device-to-device (D2D) communication is one of the core technologies of 5 G and can mitigate the scarcity of spectrum resources for wireless communication systems because it involves direct communication from device to device. However, the instability of the network environment, the explosion in the number of users, the variability of user locations and the existence of selfish users increase the time required to search for target users and data transmission delay. In this paper, an optimal target user local search (OTULS) algorithm with blockchain technology is proposed to reduce the time delay in searching for a target user and the data transmission delay between devices. We shift the target cache users search task from the local area to the base station by the OTULS. Based on the target cache users, we search for the target user with the shortest distance from the requester at local. In addition, to avoid selecting selfish users with the shortest distance from the target cache users, we manage the users’ reputations through a blockchain. The proposed algorithm can reduce the delay in the target user search and in data transmission compared with other algorithms.

Cross-Layer Resource Allocation for UAV-Assisted Wireless Caching Networks With NOMA

Unmanned aerial vehicle (UAV) assisted wireless caching networks (WCN) have been recognized as a promising way to reduce the network load and improve the energy efficiency in the sixth generation (6 G) communication systems. Aiming to improve spectrum efficiency and system capacity, we apply non-orthogonal multiple access (NOMA) in UAV-assisted WCN to serve multiple users on the same spectrum simultaneously and propose the cross-layer resource allocation strategy including the scheduling of UAVs, the grouping of users, and the allocation of power. First, the ρ-K-means algorithm is proposed to assign users to multiple clusters and deploy UAVs according to the distance from UAVs to the base station in the UAV deployment layer. Then, the base station broadcasts the popular files to UAVs via NOMA in the content placement layer. Based on the existing fixed power allocation strategy, we propose a statistic quality of service (QoS) based fixed (SQF) power allocation method to take the statistic QoS of the popular files into consideration and improve the energy efficiency through introducing the discount factor. On the basis of SQF, an instantaneous QoS based adaptive (IQA) strategy allocates power according to the instantaneous QoS of the popular files to reduce the file outage probability. Furthermore, we propose an improved method that is a cross-layer based optimal (CLO) power allocation strategy to maximize the system hit probability. Finally, in the content delivery layer, users in each cluster are grouped according to the channel gain from users to UAVs. In addition, each UAV serves two users on the same time-frequency resource block based on the cognitive radio inspired power allocation for the NOMA user pairs. Simulation results confirm that the proposed ρ-K-means algorithm and CLO strategy reduce the file outage probability and improve the hit probability.

Probabilistic Caching and Dynamic Delivery Policies for Categorized Contents and Consecutive User Demands

Wireless caching networks have been extensively researched as a promising technique for supporting the massive data traffic of multimedia services. Many of the existing studies on real-data traffic have shown that users of a multimedia service consecutively request multiple contents and this sequence is strongly dependent on the related list of the first content and/or the top referrer in the category. This paper thus introduces the notion of “temporary preference”, characterizing the behavior of users who are highly likely to request the next content from a certain target category (i.e., related content list). Based on this observation, this paper proposes both probabilistic caching and dynamic delivery policies for categorized contents and consecutive user demands. The proposed caching scheme maximizes the minimum of the cache hit rates for all users. In the delivery phase, a dynamic helper association policy for receiving multiple contents in a row is designed to reduce the delivery latency. By comparing with the content placement optimized for one-shot requests, numerical results verify the effects of categorized contents and consecutive user demands on the proposed caching and delivery policies.

QoS-Oriented Dynamic Power Allocation in NOMA-Based Wireless Caching Networks

Non-orthogonal multiple access (NOMA) based wireless caching network (WCN) is considered as a promising technology for next-generation wireless communications since it can significantly improve the spectral efficiency. In this letter, we propose a quality of service (QoS)-oriented dynamic power allocation strategy for NOMA-WCN. In content placement phase, base station (BS) sends multiple files to helpers by allocating different powers according to the different QoS targets of files, for ensuring that all helpers can successfully decode the two most popular files. In content delivery phase, helpers serve two users at the same time by allocating the minimum power to far user according to the QoS requirement, and then all the remaining power is allocated to near user. Hence, our proposed method is able to increase the hit probability and drop the outage probability compared with conventional methods. Simulation results confirm that the proposed power allocation method can significantly improve the caching hit probability and reduce the user outage probability.

Dynamic Coded Caching in Wireless Networks

We consider distributed and dynamic caching of coded content at small base stations (SBSs) in an area served by a macro base station (MBS). Specifically, content is encoded using a maximum distance separable code and cached according to a time-to-live (TTL) cache eviction policy, which allows coded packets to be removed from the caches at periodic times. Mobile users requesting a particular content download coded packets from SBSs within communication range. If additional packets are required to decode the file, these are downloaded from the MBS. We formulate an optimization problem that is efficiently solved numerically, providing TTL caching policies minimizing the overall network load. We demonstrate that distributed coded caching using TTL caching policies can offer significant reductions in terms of network load when request arrivals are bursty. We show how the distributed coded caching problem utilizing TTL caching policies can be analyzed as a specific single cache, convex optimization problem. Our problem encompasses static caching and the single cache as special cases. We prove that, interestingly, static caching is optimal under a Poisson request process, and that for a single cache the optimization problem has a surprisingly simple solution.

Online User-AP Association With Predictive Scheduling in Wireless Caching Networks

For wireless caching networks, the scheme design for content delivery is non-trivial in the face of the following tradeoff. On one hand, to optimize overall throughput, users can associate their nearby APs with great channel capacities; however, this may lead to unstable queue backlogs on APs and prolong request delays. On the other hand, to ensure queue stability, some users may have to associate APs with inferior channel states, which would incur throughput loss. Moreover, for such systems, how to conduct predictive scheduling to reduce delays and the fundamental limits of its benefits remain unexplored. In this paper, we formulate the problem of online user-AP association and resource allocation for content delivery with predictive scheduling under a fixed content placement as a stochastic network optimization problem. By exploiting its unique structure, we transform the problem into a series of modular maximization sub-problems with matroid constraints. Then we devise PUARA, a Predictive User-AP Association and Resource Allocation scheme which achieves a provably near-optimal throughput with queue stability. Our theoretical analysis and simulation results show that PUARA can not only perform a tunable control between throughput maximization and queue stability but also incur a notable delay reduction with predicted information.

Revenue Maximization for Content-Oriented Wireless Caching Networks (CWCNs) With Repair and Recommendation Considerations

To maintain reliability of content-oriented wireless caching networks (CWCNs), repair mechanism is of necessity to be considered due to the natural that storage entities are individually unreliable and thus subject to failure on account of hardware error, network congestion or software updating. Meanwhile, recommendation is tunable for edge caching performance improvement. In this paper, we study the revenue maximization problem for CWCNs with both repair and recommendation considerations. The formulated problem is an integer non-convex and non-linear problem, and thus is difficult to be solved. The difficulties are intrinsically derived from the implicit weighted sum costs (WSCs) as regards storage and repair of each content and the coupling among the Boolean variables. For the sake of analytical tractability, a two-step methodology is developed. Specifically, we first explore the optimal storage and repair amount among the content providers to minimize the WSCs in terms of successfully fixing any occurred data corruption for the stored contents. Thereof, an explicit instance is provided to show how the contents can be coded, stored and then repaired in our network given that an error occurs. Based on the obtained storage and repair amount vectors, we solve the resultant joint caching and recommendation decision making problem (DMP). To be more specific, we decouple the DMP into a pair of subproblems, namely a cache placement and a recommendation optimization subproblems. For each subproblem, a globally optimal and a time-efficient suboptimal solutions are developed, respectively. Later, a versatile iterative paradigm is devised to do the decision making jointly. The convergence performance and the complexity analysis of the proposed algorithms are rigorously analyzed. Numerical results confirm the convergence performance of our iterative algorithms and illustrate their revenue improvements compared to various baseline schemes.

Joint Distributed Link Scheduling and Power Allocation for Content Delivery in Wireless Caching Networks

In wireless caching networks, the design of the content delivery method must consider random user requests, caching states, network topology, and interference management. In this article, we establish a general framework for content delivery in wireless caching networks without stringent assumptions that restrict the network structure and interference model. Based on the framework, we propose a dynamic and distributed link scheduling and power allocation scheme for content delivery that is assisted by belief-propagation (BP) algorithms. The proposed scheme achieves three critical purposes of wireless caching networks: 1) limiting the delay of user request satisfactions, 2) maintaining the power efficiency of caching nodes, and 3) managing interference among users. In addition, we address the intrinsic problem of the BP algorithm in our network model, proposing a matching algorithm for one-to-one link scheduling. Simulation results show that the proposed scheme provides almost the same delay performance as the optimal scheme found through an exhaustive search at the expense of a little additional power consumption and does not require a clustering method and orthogonal resources in a large-scale D2D network.

An approximate dynamic programming approach for collaborative caching

In this article, online collaborative content caching in wireless networks is studied from a network economics point of view. The cache optimization problem is first modelled as a finite horizon Markov decision process that incorporates an auto-regressive model to forecast the evolution of the content demands. The complexity of the problem grows exponentially with the system parameters, and even though a good approximation to the cost-to-go can be found, the single-stage decision problem is still NP-hard. To deal with cache optimization in industrial-size networks, a novel methodology called rolling horizon is proposed that solves the dimensionality of the problem by freezing the cache decisions for a short number of periods to construct a value function approximation. Then, to address the NP-hardness of the single-stage decision problem, two simplifications/reformulations are examined: (a) to limit the number of content replicas in the network and (b) to limit the allowed content replacements. The results show that the proposed approach can reduce the communication cost by over 84% compared to that of running least recently used updates on offline schemes in collaborative caching. The results also shed light on the trade-off between the efficiency of the caching policy and the time needed to run the online cache optimization algorithm.