Cache-enabled Wireless Networks Research Articles

On the SIR Meta Distribution for Cache-Enabled Wireless Networks With Random Discontinuous Transmission: Analysis and Optimization

A fine-grained analysis of the cache-enabled networks is crucial for system design. In this paper, we focus on the meta distribution of the signal-to-interference ratio for the cache-enabled networks where the locations of the base stations are modeled as a Poisson point process. With the application of the random caching and the random discontinuous transmission schemes, we derive the moments of the conditional successful transmission probability, the exact meta distribution and its beta approximation by utilizing stochastic geometry. The closed-form expressions of the mean and variance of the local delay (i.e., the jitter) are also derived. We then consider the maximization of the mean successful transmission probability and the minimization of the average system transmission delay by jointly optimizing the caching probability and the BS active probability. Finally, the numerical results demonstrate the superiority of the proposed optimization schemes over the existing caching strategies and reveal the impacts of the key network parameters on the cache-enabled networks in terms of successful transmission probability, successful transmission probability variance, meta distribution, mean local delay and jitter.

Performance analysis on a cooperative transmission scheme of multicast and NOMA in cache‐enabled cellular networks

Caching popular contents at base stations (BSs) can effectively avoid redundant data traffic and improve backhaul capacity. Techniques such as multicast (MC) and non-orthogonal multiple access (NOMA) can significantly improve spectral efficiency by delivering popular contents to multiple users using a single channel. Therefore, we propose a cooperative transmission scheme of MC and NOMA in cache-enabled wireless cellular networks. First, we derive the probability mass function (PMF) of the number of channels in the MC mode as well as the joint PMF of the number of channels and number of NOMA users in the NOMA mode. Second, we analyse the successful transmission probabilities of the two modes by utilising tools from stochastic geometry. Finally, with the derived probabilities using the two modes, we obtain the total successful transmission probability based on probability theory. The quasi-closed form expression of the successful transmission probability is obtained in a special case, where the noise is neglected, and the path-loss exponent is set to be four. Simulation results validate the accuracy of the analysis and demonstrate the performance gain due to the proposed cooperative transmission scheme of MC and NOMA.

Effective Cache-Enabled Wireless Networks: An Artificial Intelligence- and Recommendation-Oriented Framework

Caching at the network edge can significantly reduce users’ perceived latency and relieve backhaul pressure, hence invigorating a new set of innovations toward latency-sensitive applications. Nevertheless, the efficacy of caching policies relies on the users’ content preference to be 1) known a priori and 2) highly homogeneous, which is not always the case in the real world. In this article, we explore how artificial intelligence (AI) techniques and recommendation can be leveraged to address those core issues and reap the potentials of cache-enabled wireless networks. Specifically, we present the hierarchical, cache-enabled wireless network architecture, in which AI techniques and recommendation are utilized, respectively, to estimate users’ content requests in real time using historical data and to reshape users’ content preference. Through case studies, we further demonstrate the effectiveness of an AI-based predictor in estimating users’ content requests as well as the superiority of joint recommendation and caching policies over conventional caching policies without recommendation.

Artificial Intelligence for Wireless Caching: Schemes, Performance, and Challenges

Wireless data traffic is growing unprecedentedly and it may impede network performance by consuming an ever-greater amount of bandwidth. With the advancement in technology there exist profound techniques having potentials to improve performance of wireless networks. Artificial Intelligence (AI) is one such evolving technology that enables systems to take intelligent decisions. AI can be incorporated in wireless networks for performing an optimal data caching based on accurate predictions of users' data requests and data popularity profile. AI-based data caching is a promising candidate to effectively harness the issues of rising backhaul data traffic of future wireless networks such as duplicate data transmission and data access delay. In this paper, we provide a systematic survey of state-of-the-art intelligent data caching approaches based on learning mechanism to optimize data caching. First we give an overview of traditional caching approaches and their limitations. Then, after rendering brief introduction of several AI techniques, we introduce state-of-the-art learning approaches in cache-enabled wireless networks. We unfold significant research efforts utilizing AI for efficient data placement for optimizing network performance in terms of cache hit rate, throughput, and offloading etc. Finally, we highlight existing challenges and research directions of AI-based data caching.

Optimized Caching and Spectrum Partitioning for D2D Enabled Cellular Systems With Clustered Devices

Caching at mobile devices and leveraging device-to-device (D2D) communication are two promising approaches to support massive content delivery over wireless networks. The analysis of cache-enabled wireless networks is usually carried out by assuming that devices are uniformly distributed, however, in social networks, mobile devices are intrinsically grouped into disjoint clusters. In this regards, this paper proposes a spatiotemporal mathematical model that tracks the service requests arrivals and account for the clustered devices geometry. Two kinds of devices are assumed, particularly, content clients and content providers. Content providers are assumed to have a surplus memory which is exploited to proactively cache contents from a known library, following a random probabilistic caching scheme. Content clients can retrieve a requested content from the nearest content provider in their proximity (cluster), or, as a last resort, the base station (BS). The developed spatiotemporal model is leveraged to formulate a joint optimization problem of the content caching and spectrum partitioning in order to minimize the average service delay. Due to the high complexity of the optimization problem, the caching and spectrum partitioning problems are decoupled and solved iteratively using the block coordinate descent (BCD) optimization technique. To this end, an optimal and suboptimal solutions are obtained for the bandwidth partitioning and probabilistic caching subproblems, respectively. Numerical results highlight the superiority of the proposed scheme over conventional caching schemes under equal and optimized bandwidth allocations. Particularly, it is shown that the average service delay is reduced by nearly 100% and 350%, compared to the Zipf and uniform caching schemes under equal bandwidth allocations, respectively.

Efficient Betweenness Based Content Caching and Delivery Strategy in Wireless Networks

In this work, we propose a content caching and delivery strategy to maximize throughput capacity in cache-enabled wireless networks. To this end, efficient betweenness (EB), which indicates the ratio of content delivery paths passing through a node, is first defined to capture the impact of content caching and delivery on network traffic load distribution. Aided by EB, throughput capacity is shown to be upper bounded by the minimal ratio of successful delivery probability (SDP) to EB among all nodes. Through effectively matching nodes' EB with their SDP, the proposed strategy improves throughput capacity with low computation complexity. Simulation results show that the gap between the proposed strategy and the optimal one (obtained through exhausted search) is kept smaller than 6%.

Dynamic Resource Allocation for Scalable Video Streaming in OFDMA Wireless Networks

Mobile video streaming is a successful example of Cyber-Physical-Social Systems (CPSS). How to schedule network resources and provide better mobile video streaming services for mobile users are very important. Scalable video streaming is regarded as a promising technology in wireless networks where the cognitive femtocells are overlaid within the coverage area of a macrocell network. In this paper, we study dynamic resource allocation for scalable video streaming over cache-enabled wireless networks with time-varying channel conditions. We formulate the scalable video streaming problem as a stochastic optimization problem which aims at maximizing the time-averaged system utility subject to the time-averaged video cache constraint at the server and the cross-tier interference constraint on the primary user under the sparse deployment scenario of femtocells. By employing the Lyapunov optimization theory, we design a dynamic cache and resource allocation (DCRA) algorithm to solve this problem. Furthermore, the problem is decomposed into three subproblems, i.e., video layer selection, cache placement, and wireless resource allocation. Via solving these subproblems, we derive the video layer selection and cache placement strategies, and a wireless resource allocation algorithm to manage the cross-tier interference. Simulation results demonstrate the advantages of the proposed DCRA for streaming scalable video over time-varying wireless networks.

Coordinated Fronthaul Data Assignment and Multicast Beamforming for Cache-Enabled Wireless Networks

Recently, edge caching and multicasting stand out as two promising technologies to support high-data-rate and low-latency wireless content delivery. In this letter, we investigate coordinated fronthaul data assignment and multicast beamforming for cache-enabled wireless networks. The design goal is to maximize the weighted sum delivery rate via properly assigning the fronthaul links to transfer the uncached requested files to the enhanced remote radio heads. The considered problem is a mixed-integer nonlinear programming problem, and thus is non-convex and challenging to solve. An effective optimization algorithm is proposed by exploiting the weighted regularization approximating $ {\ell _{0}}$ -norm and successive convex approximation. Numerical results show that appropriate fronthaul data assignment can significantly improve the content delivery rate.

User-Centric Delay-Aware Joint Caching and User Association Optimization in Cache-Enabled Wireless Networks

The mobile edge caching is a promising way to reduce the user-perceived delay and improve the transmission data rates for the wireless networks. However, the cache capacities of base stations (BSs) tend to be limited and users’ interests for the contents are diverse, which makes the content placement decision critical for the network performance optimization. Besides, due to the flexible user-BS association, it is more complicated to optimize the content delivery and placement which are coupled with each other. This paper investigates the content placement and content delivery strategies in the cache-enabled wireless networks. In particular, the effective capacity , which can characterize the end-to-end user-perceived delay and data rates simultaneously, is introduced as the user’s utility metric. As the content caching and content delivery operate in different time-scales, they are investigated separately. For the content caching, a content placement problem is formulated, where both users’ different active levels and diverse content preferences are considered. Due to the NP-hard nature, the problem is decomposed into two sub-problems, and an iterative association-aware content placement algorithm is proposed. For the content delivery, the user-BS association problem is formulated, and a cache-aware user-BS association algorithm is designed. The performance of the proposed algorithms is evaluated based on the simulations. The numerical results show that the proposed algorithms have a better capability to cope with users’ diverse active levels, and achieve a better performance in terms of effective capacity and fairness level, compared with the existing algorithms.

A Markovian Model for Analyzing Opportunistic Request Routing in Wireless Cache Networks

In this paper, we investigate the request routing delay of opportunistic routing for cache-enabled wireless networks considering uncorrelated and temporally correlated wireless channels. We model wireless channel variation at different time scales via two approaches: an abstract modeling approach where we model the variation considering Rayleigh fading and shadowing channel models and an empirical approach where the variation is modeled directly using signal strength measurements. We develop Markovian models to analyze the performance of opportunistic forwarding, leverage the wireless channel models to determine the packet transmission success probabilities and then utilize them to obtain the request routing delay. We first perform numerical experiments and simulations considering Rayleigh fading and shadowing channel models and then conduct a trace-based evaluation using signal strength measurements collected over a wireless sensor network testbed. Our experiments demonstrate the validity and effectiveness of our Markovian model in determining the request routing delay in real-world settings. Our work takes a step forward in providing network operators a tool for analyzing network performance before deploying their networks.

Content Delivery in Cache-Enabled Wireless Evolving Social Networks

Cache-enabled wireless networks have gained increasing popularity in recent years for facilitating the delivery of content. Most existing works assumed that the network is static without the change of users and content, which is not the case in the real world. In this paper, we study the content delivery in cache-enabled wireless networks, where the network evolves in terms of users and content. We adopt the affiliation network model to reveal the network evolution process, where new users and content are added into the network randomly and connected with existing nodes via the preferential attachment. Users with strong social relations tend to request the same content objects, thus content popularity is sharply concentrated. Therefore, caching technique is more effective. To maximize the content delivery rate, we formulate the optimization problem of caching replication jointly with routing strategy. We show that network evolution can greatly improve the content delivery rate. In particular, when per-user cache capacity is in the order of $\Theta (1)$ , per-user delivery rate can achieve constant, i.e., the network can scale. Finally, theoretical results are validated based on a real-world data set from Facebook.

Traffic Offloading With Channel Allocation in Cache-Enabled Ultra-Dense Wireless Networks

Traffic offloading via device-to-device communications is expected to play a major role to meet the exponential data traffic growth in wireless networks. In this paper, we focus on the problem of user capacity maximization in ultra-dense heterogeneous networks with device-to-device cooperation, where a large number of users in a given geographical area request common data content, such as video on demand streaming, with strict quality of service guarantees. We aim at finding the best strategy for delivering the content either over long range connectivity from the access points or short range connectivity from peer mobile terminals while meeting a target rate per served user. We formulate the multiuser resource management problem as an optimization problem including traffic offloading and channel allocation considering a very high user density reaching up to 1.75 users per square meter. We extend the system model to include mobile terminals acting as content owners by having the content cached locally, in order to further enhance the network capacity and coverage. We then propose an iterative resource management solution, which first solves the optimization subproblem of traffic offloading with orthogonal channels, and then optimally allocates channels to the transmitters with minimized interference due to channel reuse. We also propose efficient suboptimal hierarchical tree-based algorithms that operate in real time with dynamic and fast solutions for ultra-dense networks. For performance evaluation, we consider a realistic scenario consisting of a stadium topology with thousands of mobile terminals active simultaneously. We generate results as a function of a wide range of system parameters, and demonstrate that the proposed algorithms achieve near-optimal performance with notably low time complexity.

Interference Cancelation at Receivers in Cache-Enabled Wireless Networks

In this paper, we propose to exploit the limited cache packets as side information to cancel incoming interference at the receiver side. We consider a stochastic network where the random locations of base stations and users are modeled using Poisson point processes. Caching schemes to reap both the local caching gain and the interference cancelation gain for the users are developed based on two factors: the densities of different user subsets and the packets cached in the corresponding subsets. The packet loss rate (PLR) is analyzed, which depends on both the cached packets and the channel state information available at the receiver. Theoretical results reveal the tradeoff between caching resource and wireless resource. The performance for different caching schemes is analyzed, and the lowest achievable PLR for the distributed caching is investigated.

Layered Group Sparse Beamforming for Cache-Enabled Green Wireless Networks

The exponential growth of mobile data traffic is driving the deployment of dense wireless networks, which will not only impose heavy backhaul burdens, but also generate considerable power consumption. Introducing caches to the wireless network edge is a potential and cost-effective solution to address these challenges. In this paper, we will investigate the problem of minimizing the network power consumption of cache-enabled wireless networks, consisting of the base station (BS) and backhaul power consumption. The objective is to develop efficient algorithms that unify adaptive BS selection, backhaul content assignment and multicast beamforming, while taking account of user QoS requirements and backhaul capacity limitations. To address the NP-hardness of the network power minimization problem, we first propose a generalized layered group sparse beamforming (LGSBF) modeling framework, which helps to reveal the layered sparsity structure in the beamformers. By adopting the reweighted $\left.\ell_{1}\right/\ell_{2}$-norm technique, we further develop a convex approximation procedure for the LGSBF problem, followed by a three-stage iterative LGSBF framework to induce the desired sparsity structure in the beamformers. Simulation results validate the effectiveness of the proposed algorithm in reducing the network power consumption, and demonstrate that caching plays a more significant role in networks with higher user densities and less power-efficient backhaul links.

Probabilistic Caching in Wireless D2D Networks: Cache Hit Optimal Versus Throughput Optimal

Departing from the conventional cache hit optimization in cache-enabled wireless networks, we consider an alternative optimization approach for the probabilistic caching placement in stochastic wireless D2D caching networks taking into account the reliability of D2D transmissions. Using tools from stochastic geometry, we provide a closed-form approximation of cache-aided throughput, which measures the density of successfully served requests by local device caches, and we obtain the optimal caching probabilities via numerical optimization. Compared with the cache-hit-optimal case, the optimal caching probabilities obtained by cache-aided throughput optimization show notable gain in terms of the density of successfully served user requests, particularly in dense user environments.

Analysis and Optimization of Caching and Multicasting in Large-Scale Cache-Enabled Wireless Networks

Caching and multicasting at base stations are two promising approaches to support massive content delivery over wireless networks. However, existing analysis and designs do not fully explore and exploit the potential advantages of the two approaches. In this paper, we consider the analysis and optimization of caching and multicasting in a large-scale cache-enabled wireless network. We propose a random caching and multicasting scheme with a design parameter. By carefully handling different types of interferers and adopting appropriate approximations, we derive a tractable expression for the successful transmission probability in the general region, utilizing tools from stochastic geometry. We also obtain a closed-form expression for the successful transmission probability in the high signal-to-noise ratio (SNR) and user density region. Then, we consider the successful transmission probability maximization, which is a very complex non-convex problem in general. Using optimization techniques, we develop an iterative numerical algorithm to obtain a local optimal caching and multicasting design in the general region. To reduce complexity and maintain superior performance, we also derive an asymptotically optimal caching and multicasting design in the asymptotic region, based on a two-step optimization framework. Finally, numerical simulations show that the asymptotically optimal design achieves a significant gain in successful transmission probability over some baseline schemes in the general region.

Caching gain in interference‐limited wireless networks

The authors consider the effect of caching in interference-limited wireless networks where fading is the dominant channel effect. First, the authors propose a one-hop transmission strategy for cache-enabled wireless networks, which is based on exploiting multi-user diversity gain. Then, they derive a closed-form result for throughput scaling of the proposed scheme in large networks, which reveals the inherent trade-off between cache memory size and network throughput. Their results show that substantial throughput improvements are achievable in networks with sources equipped with large cache size, compared with previous works where each transmitter could just store one file. Also, the authors provide extensive simulations through which they analyse network throughput and verify their analytical results. Their simulations suggest that the scaling results also hold true even by introducing correlation between channels, making the results more general.