Cache-enabled Small Cell Networks Research Articles

Distributed content placement in cache-enabled small cell networks in the presence of malicious users

Distributed content placement in cache-enabled small cell networks in the presence of malicious users

Multi-agent learning algorithms for content placement in cache-enabled small cell networks: 4G and 5G use cases

Multi-agent learning algorithms for content placement in cache-enabled small cell networks: 4G and 5G use cases

A Sleeping Mechanism for Cache-Enabled Small Cell Networks With Energy Harvesting Function

In the 5G small cell networks, the network edge has been largely enhanced by the rich network functions, which makes small cell base stations (SBSs) the major energy consumption components. To overcome this challenge, the energy harvesting technology has been attracting researchers’ attention. However, how to exploit the density characteristic of the small cell networks and match the dynamics of the arrival energy to the user requirements are still remained to be investigated. In our work, we propose a sleeping mechanism in the 5G small cell networks with energy harvesting function. In addition, the cooperative caching is jointly considered. Firstly, we formulate the energy consumption minimization problem considering both the energy cost by the SBSs and backhaul. And secondly, to solve the formulated problem, we decompose the original problem into two subproblems, which are the SBS status decision and the cooperative content caching, respectively. Thirdly, the solution which is composed of the binary particle swarm optimization (BPSO) and the linear programming tool, is proposed. Finally, simulation results show that our proposed algorithm has the advantage of reducing the energy consumption compared with other algorithms.

A Reinforcement Learning-Based User-Assisted Caching Strategy for Dynamic Content Library in Small Cell Networks

This paper studies the problem of joint edge cache placement and content delivery in cache-enabled small cell networks in the presence of spatio-temporal content dynamics unknown a priori . The small base stations (SBSs) satisfy users’ content requests either directly from their local caches, or by retrieving from other SBSs’ caches or from the content server. In contrast to previous approaches that assume a static content library at the server, this paper considers a more realistic non-stationary content library, where new contents may emerge over time at different locations. To keep track of spatio-temporal content dynamics, we propose that the new contents cached at users can be exploited by the SBSs to timely update their flexible cache memories in addition to their routine off-peak main cache updates from the content server. To take into account the variations in traffic demands as well as the limited caching space at the SBSs, a user-assisted caching strategy is proposed based on reinforcement learning principles to progressively optimize the caching policy with the target of maximizing the weighted network utility in the long run. Simulation results verify the superior performance of the proposed caching strategy against various benchmark designs.

Cache-Enabled Millimeter Wave Cellular Networks With Clusters

Wireless content caching in cellular networks is an efficient way to reduce the service delay and alleviate backhaul pressure. For the benefits of sharing spectral and storage resources, clustering in cached networks has recently attracted significant research interests. Meanwhile, since the multimedia content (e.g., video) of caching networks may require a huge transmission rates, millimeter wave (mmWave) communication is considered to be an efficient transmission scheme for cache-enabled networks. We investigate the ergodic rate and average service delay for typical user terminal (UT) in the clustered cache-enabled small cell networks (SCN) and ultra dense networks (UDN) with mmWave channels. In SCN, each cluster consists of cache-enabled UTs, and in the UDN a cluster is formed by cache-enabled UTs and small base stations (SBSs) with non-uniform caching capacity. The clusters are assumed to be discs and content sharing is only possible within clusters through mmWave device-to-device (D2D) tier and SBS tier communications. With stochastic geometry methods, the distributions of content sharing distance and signal-to-interference-noise-ratio (SINR) of typical UT in a cluster are derived for both SCN and UDN scenarios. To minimize the average service delay in high SINR region, we provide an algorithm to jointly optimize caching scheme for SBSs and UTs. By simulations, we validate our theoretical analysis and the performance of proposed caching scheme. The numerical results also show that there exists best radius in the design of cluster for UDNs.

Modeling, Analysis, and Optimization of Caching in Multi-Antenna Small-Cell Networks

In traditional cache-enabled small-cell networks (SCNs), a user can suffer strong interference due to content-centric base station association. This may degenerate the advantage of collaborative content caching among multiple small base stations (SBSs), including probabilistic caching and coded caching. In this work, we tackle this issue by deploying multiple antennas at each SBS for interference management. Two types of beamforming are considered. One is matched-filter (MF) to strengthen the effective channel gain of the desired signal, and the other is zero-forcing (ZF) to cancel interference within a selected SBS cooperation group. We apply these two beamforming techniques in both probabilistic caching and coded caching, and conduct performance analysis using stochastic geometry. We obtain exact and approximate compact integral expressions of system performances measured by average fractional offloaded traffic (AFOT) and average ergodic spectral efficiency (AESE). Based on these expressions, we then optimize the caching parameters for AFOT or AESE maximization. For probabilistic caching, optimal caching solutions are obtained. For coded caching, an efficient greedy-based algorithm is proposed. Numerical results show that multiple antennas can boost the advantage of probabilistic caching and coded caching over the traditional most popular caching with the proper use of beamforming.

Near-Optimal Layer Placement for Scalable Videos in Cache-Enabled Small-Cell Networks

We investigate the near-optimal video layer placement in cache-enabled small-cell networks to provide on-demand video services with differential perceptual experiences. By employing scalable video coding, each video file is encoded into multiple layers, which are then stored in the local cache of small-cell base stations (SBSs). Based on the proposed caching scheme, we define the concept of average offloaded traffic (AOT), which quantitatively indicates the total amount of data traffic that can be sourced from the local cache of SBSs. Relying on stochastic geometry, we derive the expression for the successful transmission probability, based on which the closed form of AOT is obtained. Afterwards, under the cache size constraint of each SBS, the AOT maximization problem is formulated. Due to the NP-hard property of this integer programming problem, it is first relaxed into the convex form. Then, a greedy-strategy based heuristic algorithm is proposed, which can yield the near-optimal layer caching indicators. Numerical results validate our derivations and demonstrate that the proposed layer caching scheme outperforms the benchmark in terms of the AOT performance.

Minimum Cache Size and Backhaul Capacity for Cache-Enabled Small Cell Networks

In cache enabled networks, either memory or backhaul is used to deliver files from a core network to a transmitter. To support a high level of wireless quality of service (QoS), network resources including small base stations, memory, and backhaul are required. In this letter, while satisfying a wireless QoS constraint, memory, and backhaul for the whole network are minimized. For a given density of small base stations, optimal memory size is found in closed-form. Also, the optimal density of SBSs is numerically obtained to minimize the total memory and backhual required to satisfy the QoS.

Secure Video Streaming in Heterogeneous Small Cell Networks With Untrusted Cache Helpers

This paper studies secure video streaming in cache-enabled small cell networks, where some of the cache-enabled small cell base stations (BSs) helping in video delivery are untrusted . Unfavorably, caching improves the eavesdropping capability of these untrusted helpers as they may intercept both the cached and the delivered video files. To address this issue, we propose joint caching and scalable video coding of video files to enable secure cooperative multiple-input multiple-output transmission and, at the same time, exploit the cache memory of both the trusted and untrusted BSs for improving the system performance. Considering imperfect channel state information at the transmitters, we formulate a two-timescale non-convex mixed-integer robust optimization problem to minimize the total transmit power required for guaranteeing the quality of service and secrecy during video streaming. We develop an iterative algorithm based on a modified generalized Benders decomposition to solve the problem optimally, where the caching and the cooperative transmission policies are determined via offline (long-timescale) and online (short-timescale) optimization, respectively. Furthermore, inspired by the optimal algorithm, a low-complexity suboptimal algorithm based on a greedy heuristic is proposed. Simulation results show that the proposed schemes achieve significant gains in power efficiency and secrecy performance compared to several baseline schemes.

Effect of Retransmissions on Optimal Caching in Cache-Enabled Small Cell Networks

Caching popular content in the storage of small cells is being considered as an efficient technique to complement limited backhaul of small cells in ultradense heterogeneous cellular networks. Limited storage capacity of the small cells renders it important to determine the optimal set of files (cache) to be placed on each small cell. In contrast to prior works on optimal caching, in this work we study the effect of retransmissions on the optimal cache placement policy for both static and mobile user scenarios. With the popularity of files modeled as a Zipf distribution and a maximum $n$ transmissions, i.e., $n-1$ retransmissions, allowed to receive each file, we determine the optimal caching probability of the files that maximizes the hit probability . Our closed-form optimal solutions concretely demonstrate that the optimal caching probabilities are very sensitive to the number of retransmissions.

Coding, Multicast, and Cooperation for Cache- Enabled Heterogeneous Small Cell Networks

Caching at the wireless edge is a promising approach to dealing with massive content delivery in heterogeneous wireless networks, which have high demands on backhaul. In this paper, a typical cache-enabled small cell network under heterogeneous file and network settings is considered using maximum distance separable (MDS) codes for content restructuring. Unlike those in the literature considering online settings with the assumption of perfect user request information, we estimate the joint user requests using the file popularity information and aim to minimize the long-term average backhaul load for fetching content from external storage subject to the overall cache capacity constraint by optimizing the content placement in all the cells jointly. Both multicast-aware caching and cooperative caching schemes with optimal content placement are proposed. In order to combine the advantages of multicast content delivery and cooperative content sharing, a compound caching technique, which is referred to as multicast-aware cooperative caching, is then developed. For this technique, a greedy approach and a multicast-aware in-cluster cooperative approach are proposed for the small-scale networks and large-scale networks, respectively. Mathematical analysis and simulation results are presented to illustrate the advantages of MDS codes, multicast, and cooperation in terms of reducing the backhaul requirements for cache-enabled small cell networks.

A distributed user association and resource allocation method in cache-enabled small cell networks

Cache-enabled small cell networks have been regarded as a promising approach for network operators to cope with the explosive data traffic growth in future 5G networks. However, the user association and resource allocation mechanism has not been thoroughly studied under given content placement situation. In this paper, we formulate the joint optimization problem of user association and resource allocation as a mixed integer nonlinear programming (MINLP) problem aiming at deriving a balance between the total utility of data rates and the total data rates retrieved from caches. To solve this problem, we propose a distributed relaxing-rounding method. Simulation results demonstrate that the distributed relaxing-rounding method outperforms traditional max-SINR method and range-expansion method in terms of both total utility of data rates and total data rates retrieved from caches in practical scenarios. In addition, effects of storage and backhaul capacities on the performance are also studied.

Network Utility Aware Traffic Load Balancing in Backhaul-Constrained Cache-Enabled Small Cell Networks with Hybrid Power Supplies

Explosive data traffic growth leads to a continuous surge in capacity demands across mobile networks. In order to provision high network capacity, small cell base stations (SCBSs) are widely deployed. Owing to the close proximity to mobile users, SCBSs can effectively enhance the network capacity and offloading traffic load from macro BSs (MBSs). However, the cost-effective backhaul may not be readily available for SCBSs, thus leading to backhaul constraints in small cell networks (SCNs). Enabling cache in BSs may mitigate the backhaul constraints in SCNs. Moreover, the dense deployment of SCBSs may incur excessive energy consumption. To alleviate brown power consumption, renewable energy will be explored to power BSs. In such a network, it is challenging to dynamically balance traffic load among BSs to optimize the network utilities. In this paper, we investigate the traffic load balancing in backhaul-constrained cache-enabled small cell networks powered by hybrid energy sources. We have proposed a network utility aware (NUA) traffic load balancing scheme that optimizes user association to strike a tradeoff between the green power utilization and the traffic delivery latency. On balancing the traffic load, the proposed NUA traffic load balancing scheme considers the green power utilization, the traffic delivery latency in both BSs and their backhaul, and the cache hit ratio. The NUA traffic load balancing scheme allows dynamically adjusting the tradeoff between the green power utilization and the traffic delivery latency. We have proved the convergence and the optimality of the proposed NUA traffic load balancing scheme. Through extensive simulations, we have compared performance of the NUA traffic load balancing scheme with other schemes and showed its advantages in backhaul-constrained cache-enabled small cell networks with hybrid power supplies.

Energy Efficiency in Cache-Enabled Small Cell Networks With Adaptive User Clustering

Using a network of cache enabled small cells, traffic during peak hours can be reduced by proactively fetching the content that is most likely to be requested. In this paper, we aim to explore the impact of proactive caching on an important metric for future generation networks, namely, energy efficiency (EE). We argue that, exploiting the spatial repartitions of users in addition to the correlation in their content popularity profiles, can result in considerable improvement of the achievable EE. In this paper, the optimization of EE is decoupled into two related subproblems. The first one addresses the issue of content popularity modeling. While most existing works assume similar popularity profiles for all users, we consider an alternative framework in which, users are clustered according to their popularity profiles. In order to showcase the utility of the proposed clustering, we use a statistical model selection criterion, namely, Akaike information criterion. Using stochastic geometry, we derive a closed-form expression of the achievable EE and we find the optimal active small cell density vector that maximizes it. The second subproblem investigates the impact of exploiting the spatial repartitions of users. After considering a snapshot of the network, we formulate a combinatorial problem that optimizes content placement in order to minimize the transmission power. Numerical results show that the clustering scheme considerably improves the cache hit probability and consequently the EE, compared with an unclustered approach. Simulations also show that the small base station allocation algorithm improves the energy efficiency and hit probability.

The 5G Cellular Backhaul Management Dilemma: To Cache or to Serve

To reap the benefits of cache-enabled small cell networks, new backaul management mechanisms are needed to prevent the predicted files that are downloaded at the small base stations (SBSs) for caching purposes from jeopardizing the urgent requests that need to be served via the backhaul. Such mechanisms must account for the heterogeneity of the backhaul that will encompass both wireless backhaul links (at various frequency bands) and a wired backhaul component. In this paper, the heterogeneous backhaul management problem is formulated as a minority game in which each SBS has to define the number of predicted files to download, without affecting the required transmission rate of the current requests. For the formulated game, it is shown that a unique fair proper mixed Nash equilibrium (PMNE) exists. A self-organizing reinforcement learning algorithm is then proposed and shown to converge to a unique Boltzmann-Gibbs equilibrium, which approximates the desired PMNE. Simulation results show that the performance of the proposed approach can be close to that of the ideal optimal algorithm while it outperforms a centralized greedy approach in terms of the amount of data that is cached without jeopardizing the quality-of-service of current requests.

Coverage analysis of cache-enabled small cell networks with stochastic geometry methods

Caching popular content in the storage of small cells is deemed as an efficient way to decrease latency, offload backhaul and satisfy user's demands. In order to investigate the performance of cache-enabled small cell networks, coverage probability is studied in both single-point transmission and cooperative multipoint (CoMP) transmission scenarios. Meanwhile, the caching distribution modeled as Zipf and uniform distribution are both considered. Assuming that small base stations (SBSs) are distributed as a homogeneous Poisson point process (HPPP), the closed-form expressions of coverage probability are derived in different transmission cases. Simulation results show that CoMP transmission achieves a higher coverage probability than that of single-point transmission. Furthermore, Zipf distribution-based caching is more preferable than uniform distribution-based caching in terms of coverage probability.

Cache-enabled small cell networks: modeling and tradeoffs.

We consider a network model where small base stations (SBSs) have caching capabilities as a means to alleviate the backhaul load and satisfy users’ demand. The SBSs are stochastically distributed over the plane according to a Poisson point process (PPP) and serve their users either (i) by bringing the content from the Internet through a finite rate backhaul or (ii) by serving them from the local caches. We derive closed-form expressions for the outage probability and the average delivery rate as a function of the signal-to-interference-plus-noise ratio (SINR), SBS density, target file bitrate, storage size, file length, and file popularity. We then analyze the impact of key operating parameters on the system performance. It is shown that a certain outage probability can be achieved either by increasing the number of base stations or the total storage size. Our results and analysis provide key insights into the deployment of cache-enabled small cell networks (SCNs), which are seen as a promising solution for future heterogeneous cellular networks.