Wireless Caching Research Articles

Metaheuristic Algorithms for 6G wireless communications: Recent advances and applications

The widespread distribution of intelligent applications and devices, coupled with the vast array of mobile data, technologies, and architectures within Sixth Generation (6G) networks, highlights the importance of optimization strategies. Concurrently, state-of-the-art Metaheuristic Algorithms (MHAs) have emerged as a promising optimization approach for wireless networks, bridging game theory and convex optimization domains. Falling within the domain of Artificial Intelligence (AI), MHAs draw their inspiration from Evolutionary Algorithms (EAs), which are rooted in the principle of natural selection, the principles of Swarm Intelligence (SI), and Trajectory-based Algorithms (TAs). In recent times, MHAs have been implemented in the 6G network. These simple solutions with limited capabilities have proven effective in solving complex, challenging and high-dimensional problems. Nevertheless, the literature has yet to study the MHAs comprehensively, especially within 6G networks. The main objective of this study is to investigate how to merge the 6G and MHAs domains. We begin by giving a comprehensive overview of MHAs, from fundamental to popular optimization methods. Then, we examine the utilization of MHAs to address the challenges in 6G, such as network security, resource allocation, spectrum management, edge computing, wireless caching, and others. Finally, we identify the current literature’s limitations and suggest avenues for further research directions.

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

Mobility-aware caching strategy in an SDN-based cooperative caching network

The upsurge of mobile multimedia traffic puts a heavy burden on the cellular network, and wireless caching has emerged as a powerful technique to overcome the backhaul bottleneck and alleviate the network burden. However, most previous works ignored user mobility, thus not reaping the caching gain from user mobility and having limited practical applications. In this paper, a mobility-aware caching strategy for the software-defined network (SDN)-based network is studied. Firstly, since typical mobile user (MU) has multiple opportunities to connect with the nearby MUs and Small-base stations (SBSs), the contact times between MUs as well as between MU and SBSs are derived as the Poisson distribution and Gamma distribution. Secondly, we propose a two-tier cooperative caching strategy, where SBSs cache the rateless Fountain code encoded video blocks probabilistically and nonrepeatedly while MUs just store the whole encoded video received last time. The corresponding four-stage transmission process is analyzed, where the key intermediate step is the derivation of the service and the failing probabilities of each transmission manner. Finally, we derive the successful offloading rate and average data offloading ratio (ADOR) as performance metrics. A system optimization problem based on ADOR is formulated, and two solutions are proposed, namely, derivative-based solution (DB-Solution) and long-tail distribution approximation (LTD-Approximation). Simulation results demonstrate that the effectiveness of LTD-Approximation is similar to the DB-Solution, and the proposed caching strategy can achieve quasi-optimal performance compared with other contrast schemes.

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.

On the Performance of Cache-Free/Cache-Aided STBC-NOMA in Cognitive Hybrid Satellite-Terrestrial Networks

Future wireless networks pose several challenges such as high spectral efficiency, wide coverage, massive connectivity, low receiver complexity, etc. To this end, this letter investigates an overlay-based cognitive hybrid satellite-terrestrial network (CHSTN) combining non-orthogonal multiple access (NOMA) and conventional Alamouti space-time block coding (STBC) techniques. Herein, a decode-and-forward based secondary terrestrial network cooperates with a primary satellite network for dynamic spectrum access. Further, for reliable content delivery and low latency requirements, wireless caching is employed, whereby the secondary network can store the most popular contents of the primary network. Considering the relevant heterogeneous fading channel models and the NOMA-based imperfect successive interference cancellation, we examine the performance of CHSTN for the cache-free (CF) STBC-NOMA and the cache-aided (CA) STBC-NOMA schemes. We assess the outage probability expressions for primary and secondary networks and further, highlight the corresponding achievable diversity orders. Indicatively, the proposed CF/CA STBC-NOMA schemes for CHSTN perform significantly better than the benchmark standalone NOMA and OMA schemes.

NOMA-Based Integrated Satellite-Terrestrial Networks with Wireless Caching

To decrease the transmission delay and alleviate communication congestion, caching has been applied in the integrated satellite-terrestrial networks (ISTNs). In traditional caching schemes, the cache-enabled relays update files during off-peak hours and push them during on-peak hours. However, for the enormous number of end devices in ISTNs, the long-time waiting for off/on-peak hours would decrease the quality of service. To address this issue, this paper proposes a two-tier nonorthogonal multiple access- (NOMA-) based ISTN with wireless caching. Specifically, data transmission of this model consists of two phases. For the first phase, named the file-push-and-placement (FPAP), the satellite employs the NOMA protocol to send information to both the relay and first-tier user. While for the second phase, which is called the file-push-and-delivery (FPAD), second-tier users are served by the relay employing NOMA. Performance analysis of the proposed configuration is carried out, focusing on the exact and asymptotic outage probability (OP), diversity order, and hit probability. Moreover, the influence of key factors on the system performance is also investigated. Compared with the traditional configuration, it is shown analytically and numerically that the proposed scheme achieves lower OP and higher diversity order.

Intelligent-Reflecting-Surface-Empowered Wireless-Powered Caching Networks

In this article, we propose an intelligent reflecting surface (IRS)-enabled wireless-powered caching system. In the proposed IRS model, a power station (PS) provides wireless energy to multiple Internet of Things (IoT) devices, delivering their information to an access point (AP) by utilizing the harvested power. The AP, equipped with a local cache, stores the IoT data to avoid waking up the IoT devices frequently. Meanwhile, we deploy the IRS involving in the wireless energy and information transfer process for performance enhancements. In this practical system, the PS and AP could belong to different service providers. Also, the AP requires to incentivize the PS to offer a provisional energy service. We model the interaction between the PS and AP as a Stackelberg game that jointly optimizes the transmit power of the PS, the energy price, the phase shifts of the wireless energy transfer (WET) and wireless information transfer (WIT) phases, as well as wireless caching strategies of the AP. In this way, we first derive the optimal solutions of the phase shifts and the transmit power of the PS in a closed form. We propose an alternating optimization (AO) algorithm to optimize the wireless caching strategies and the energy price iteratively. Finally, we present various numerical evaluations to validate the beneficial role of the IRS and the wireless caching strategies and the performance of the proposed scheme compared with the existing benchmark schemes.

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.

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.

Incentive Mechanism Design for Two-Layer Wireless Edge Caching Networks Using Contract Theory

Wireless caching technologies have been proposed to relieve the transmission pressures, especially, the transmission redundancy on back-haul channels. In this paper, we consider a two-layer caching network, consisting of traditional macro-cell base station (MBS) aided back-haul channels and small-cell base stations (SBSs) aided local links. The network service provider (NSP), who is in charge of the two layers, leases its resources of the secondary layer, i.e., coverage of the SBSs, to content providers (CPs) for making extra profits and releasing pressures on the back-haul channels. At the same time, CPs will evaluate whether they are provided with proper incentives to pre-cache their files in the SBSs. Considering different quality of services (QoS) provided by the two layers as well as the economical impact of the traditional layer on the secondary layer, the NSP designs the optimal incentive mechanisms within the framework of contract theory for maximizing its own profits. First, we formulate the utility of the NSP and CPs. Then, the minimum transmission requirement, reserve price and limited resources are considered as constraints in designing the optimal contract. Also, some important properties of these constraints are analyzed to facilitate the optimal contract determination process. At last, an optimal contract determination scheme is proposed, based on which the optimal coverage set is determined first, and then the corresponding optimal prices are derived with the aid of equal cost line. Numerical results are provided to demonstrate the effectiveness of the proposed optimal contract in increasing the NSP's profits and incentivizing CPs to transmit on the secondary layer.

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.

Joint Optimization on Trajectory, Cache Placement, and Transmission Power for Minimum Mission Time in UAV-Aided Wireless Networks

In recent years, due to the strong mobility, easy deployment, and low cost of unmanned aerial vehicles (UAV), great interest has arisen in utilizing UAVs to assist in wireless communication, especially for on-demand deployment in emergency situations and temporary events. However, UAVs can only provide users with data transmission services through wireless backhaul links established with a ground base station, and the limited capacity of the wireless backhaul link would limit the transmission speed of UAVs. Therefore, this paper designed a UAV-assisted wireless communication system that used cache technology and realized the transmission of multi-user data by using the mobility of UAVs and wireless cache technology. Considering the limited storage space and energy of UAVs, the joint optimization problem of the UAV’s trajectory, cache placement, and transmission power was established to minimize the mission time of the UAV. Since this problem was a non-convex problem, it was decomposed into three sub-problems: trajectory optimization, cache placement optimization, and power allocation optimization. An iterative algorithm based on the successive convex approximation and alternate optimization techniques was proposed to solve these three optimization problems. Finally, in the power allocation optimization, the proposed algorithm was improved by changing the optimization objective function. Numerical results showed that the algorithm had good performance and could effectively reduce the task completion time of the UAV.

Performance analysis of cache-aided UAV relaying networks

In this work, we analyze the effect of relaying network where unmanned aerial vehicles (UAVs) act as cache-aided relay nodes. Unmanned aerial vehicle (UAV) has high mobility, which can be flexibly scheduled when unable to communicate so that it can act as a wireless relay to assist communication. It can provide a more flexible deployment mode in different application scenarios to improve the performance of communication. What is more, how to transform the dynamic characteristics of UAVs into channel characteristics is also a challenge. To be more specific, UAVs act as decoded-and-forward (DF) relays to assist the wireless communication between the source and destination, and the speed and flying height remains unchanged. The UAVs are equipped with a wireless cache, which can provide reliable services and expand the channel capacity to reduce the outage probability of the network. Experimental and simulation results verify the effect of UAVs with cache on the outage performance in relaying networks.