Applying millimeter wave (mmWave) in mobile edge computing (MEC) networks helps to improve task offloading performance from both communication and computing aspects. In this paper, we propose a MEC-enabled heterogeneous network in which mmWave small cells are deployed overlaid sub-6GHz macro cells. Base stations in two tiers are equipped with MEC servers characterized by different computing capacity and computing buffer. An association strategy based on the MEC service area is designed. In order to analyze the offloading performance, we derive the successful offloading probability and offloading latency with the tools of stochastic geometry and queueing theory. Simulation results demonstrate that there exist optimal values of small base station density and MEC service radius to obtain the maximum successful offloading probability and the minimum offloading latency, which provides useful guidance for the practical deployment.

As novel applications spring up in future network scenarios, the requirements on network service capabilities for differentiated services or burst services are diverse. Aiming at the research of collaborative computing and resource allocation in edge scenarios, migrating computing tasks to the edge and cloud for computing requires a comprehensive consideration of energy consumption, bandwidth, and delay. Our paper proposes a collaboration mechanism based on computation offloading, which is flexible and customizable to meet the diversified requirements of differentiated networks. This mechanism handles the terminal's differentiated computing tasks by establishing a collaborative computation offloading model between the cloud server and edge server. Experiments show that our method has more significant improvements over regular optimization algorithms, including reducing the execution time of computing tasks, improving the utilization of server resources, and decreasing the terminal's energy consumption.

With the rapid explosion of data volume from mobile networks, edge caching has received significant attentions as an efficient approach to boost content delivery efficiency by bringing contents near users. In this article, cache-enabled heterogeneous networks (HetNets) considering the limited backhaul are analyzed with the aid of the stochastic geometry approach. A hybrid caching policy, in which the most popular contents are cached in the macro BSs tier with the deterministic caching strategy and the less popular contents are cached in the helpers tier with the probabilistic caching strategy, is proposed. Correspondingly, the content-centric association strategy is designed based on the comprehensive state of the access link, the cache and the backhaul link. Under the hybrid caching policy, new analytical results for successful content delivery probability, average successful delivery rate and energy efficiency are derived in the general scenario, the interference-limited scenario and the mean load scenario. The simulation results show that the proposed caching policy outperforms the most popular caching policy in HetNets with the limited backhaul. The performance gain is dramatically improved when the content popularity is less skewed, the cache capacity is sufficient and the helper density is relatively large. Furthermore, it is confirmed that there exists an optimal helper density to maximize the energy efficiency of the cache-enabled HetNets.

Caching at base stations (BSs) has been proposed as a promising technology to address the challenges of the increasing traffic. In this article, cache-enabled heterogeneous networks (HetNets) considering the limited backhaul is analyzed with the aid of the stochastic geometry approach. A hybrid caching policy, in which the most popular contents are cached in the macro BSs tier with the deterministic caching strategy and the less popular contents are cached in the helpers tier with the probabilistic caching strategy, is proposed. Correspondingly, the content-centric association strategy is designed based on the comprehensive state of the access link, the cache and the backhaul link. By utilizing the hybrid caching policy, analytical expression of successful content delivery probability (SCDP) is derived. The simulation results show that the proposed caching policy outperforms the most popular caching policy in HetNets with the limited backhaul. SCDP increases with the increasing backhaul capacity until converging, whose convergence rate increases with the increment of cache capacity.

Caching at the edge has emerged as a promising technology to enhance the quality of service (QoS) of users and mitigate the backhaul load. Since the cache capacity is not arbitrarily large to store all contents, the backhaul can assist to fetch the contents from the core network. The network performance analysis of base station (BS) caching should consider the limited backhaul capacity. In this paper, we analyze the energy efficiency of the cache-enabled cellular networks considering the backhaul with stochastic geometry. First, the content coverage probability (CCP) based on the limited backhaul is analyzed. With the obtained CCP results, the expressions of throughput, power consumption and energy efficiency for a general case and a specific case with the mean load approximation are derived respectively. Simulation results confirm the accuracy of theoretical analysis and verify that BSs caching can dramatically improve energy efficiency on the condition that the content popularity is skewed and the backhaul capacity is relatively small.

Caching in the cellular networks has been proposed as a promising technology for reducing the content delivery latency and backhaul cost. Since the backhaul capacity is limited in the practical scenario, the network performance analysis of base station (BS) caching should address the effects of the limited backhaul. This paper investigates the energy efficiency of the cache‐enabled cellular networks with the limited backhaul based on the stochastic geometry method. First, the successful content delivery probability (SCDP), which depends on the successful access delivery probability, successful backhaul delivery probability, and cache hit ratio, is analyzed under the limited backhaul. Based on the obtained SCDP results, we derive the analytical expressions of throughput, power consumption, and energy efficiency for various scenes including the general case, the interference‐limited case, and the mean load approximation case. The accuracy of theoretical analysis is verified by the Monte Carlo simulation. The simulation results show that BS caching can dramatically improve energy efficiency when the content popularity is skewed, the content library size is small, and the backhaul capacity is relatively small. Furthermore, it is confirmed that there exists an optimal BS density which maximizes the energy efficiency of the cache‐enabled cellular networks.

Heterogeneous network (HetNet) has been widely accepted as a viable way to deal with the increasing traffic demand. However, the deployment of small cell base stations (SBSs) inevitably triggers a tremendous escalation of energy consumption. In this paper, we study the energy efficient HetNet deployment problem taking the disparities of the traffic load distribution into account. The optimization task is to select a set of SBSs from the candidate sites to maximize the energy efficiency while satisfying the traffic load requirement of the traffic demand points (TDPs). A two- stage approximation algorithm is introduced to solve the optimization problem by decomposing the original problem into two sub problems and settling with the greedy algorithm and the modified matching algorithm respectively. Simulation results verify that the proposed deployment strategy can greatly improve the energy efficiency compared with the random deployment strategy and is suitable for the scene with a large number of TDPs in the serving area whose minimum traffic load is large and the traffic load disparities are great.

With the diversification of mobile services, mobile traffic exhibits new characteristics which is different from traditional voice traffic. In this paper, we investigate the optimal base station (BS) density in cellular networks by taking into account the self- similar characteristics of mobile traffic using stochastic geometry approach. Based on the traffic load distribution satisfying self-similar characteristics, traffic coverage probability is defined and its relation with BS density is derived. An optimization problem with the aim of minimizing the BS density is formulated under the constraint of traffic coverage probability. Using binary search algorithm, the optimal BS density and the bounds (both upper and lower bound) for simplified analysis is obtained. The simulation results confirm the accuracy of theoretical analysis and verify the impact of the self-similar characteristics on the optimal BS density.