Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research.

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

Caching popular contents at base stations (BSs) can reduce the backhaul cost and improve the network throughput. Yet whether locally caching at the BSs can improve the energy efficiency (EE), a major goal for fifth generation cellular networks, remains unclear. Due to the entangled impact of various factors on EE such as interference level, backhaul capacity, BS density, power consumption parameters, BS sleeping, content popularity, and cache capacity, another important question is what are the key factors that contribute more to the EE gain from caching. In this paper, we attempt to explore the potential of EE of the cache-enabled wireless access networks and identify the key factors. By deriving closed-form expression of the approximated EE, we provide the condition when the EE can benefit from caching, find the optimal cache capacity that maximizes the network EE, and analyze the maximal EE gain brought by caching. We show that caching at the BSs can improve the network EE when power efficient cache hardware is used. When local caching has EE gain over not caching, caching more contents at the BSs may not provide higher EE. Numerical and simulation results show that the caching EE gain is large when the backhaul capacity is stringent, interference level is low, content popularity is skewed, and when caching at pico BSs instead of macro BSs.

Energy Efficiency of Downlink Networks With Caching at Base Stations

Caching popular contents at base stations (BSs) can reduce the backhaul cost and improve the network throughput. Yet whether locally caching at the BSs can improve the energy efficiency (EE), a major goal for 5th generation cellular networks, remains unclear. Due to the entangled impact of various factors on EE such as interference level, backhaul capacity, BS density, power consumption parameters, BS sleeping, content popularity and cache capacity, another important question is what are the key factors that contribute more to the EE gain from caching. In this paper, we attempt to explore the potential of EE of the cache-enabled wireless access networks and identify the key factors. By deriving closed-form expression of the approximated EE, we provide the condition when the EE can benefit from caching, find the optimal cache capacity that maximizes the network EE, and analyze the maximal EE gain brought by caching. We show that caching at the BSs can improve the network EE when power efficient cache hardware is used. When local caching has EE gain over not caching, caching more contents at the BSs may not provide higher EE. Numerical and simulation results show that the caching EE gain is large when the backhaul capacity is stringent, interference level is low, content popularity is skewed, and when caching at pico BSs instead of macro BSs.

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Energy Efficiency of Downlink Networks with Caching at Base Stations

A survey on green communication and security challenges in 5G wireless communication networks

Femtocell architecture involves the use of two separate layers—the macrocell and femtocell layers. In this architecture, the former is the conventional cellular network, whereas the latter incorporates a range of shorter range cells. Femtocells are designed to coexist alongside macrocells, providing spatial frequency reuse, higher spectrum efficiency, and cover areas where macrocells cannot. Femtocells positioned in the macrocell considerably improve the indoor coverage and provide better user experience. However, interference between the two layers is imminent; therefore, ways to manage it must be employed to efficiently avoid problems such as coverage holes in the macrocells. Essential limits of capacity and attainable data rates also mainly depend on the interference faced by a femtocell network. Recently, cognitive radio (CR), which has the ability to sense its environment and accordingly alter its characteristics, e.g., transmission parameters, has been merged with femtocells to exploit the capabilities of the former in the latter. CR-enabled femtocells in a two-tier network can sense the environment and opportunistically allocate both licensed and unlicensed frequency bands to user equipments to avoid interference. This paper examines interference mitigation in femtocells using CR and provides comprehensive survey of different CR-enabled interference mitigation schemes. Presented schemes such as power control, spectrum access, antenna, and joint schemes are classified before they are compared for pros and cons. Finally, tradeoffs and cost of using CR in femtocells are highlighted with some insight into future research issues and challenges.

Interference Mitigation in Cognitive-Radio-Based Femtocells

Data traffic continues to increase exponentially and operators are continuously upgrading their networks to meet this demand. The resulting capital and operational expenditures have limited revenues and the associated energy costs and CO 2 emissions have raised economic and ecological concerns. In this paper, we use the stochastic geometry approach to investigate different sleep mode mechanisms that can address both capacity and energy efficiency (EE) objectives in dense small cell networks. We derive a multi-user connectivity model that facilitates the study of sleep mode mechanisms and manages the blocking rate of the network. We formulate an optimization framework that minimizes area power consumption using appropriate constraints. Numerical results show that sleep mode mechanisms enhance the EE of dense small cell networks and that the selection criterion of sleep mode candidate base stations is very important.

Sleep mode mechanisms in dense small cell networks

Mobile traffic demand has been increasing exponentially over the last few years and forecasts show that this trend will continue in the foreseeable future. As a result, operators are forced to densify and upgrade their networks to meet this demand. This has created concerns, such as increasing greenhouse gas emissions, high capital expenditures, and associated energy costs. This paper uses tools from stochastic geometry to analyze and formulate energy-efficient deployment strategies for multi-tier heterogeneous networks (HetNets) using various user association schemes. We use simple approximations to combine the required base station (BS) density and associated transmit power per tier subject to both coverage probability and average user rate constraints. In this paper, this combination is called the deployment factor and it can be expressed in closed form for unbiased HetNets. We then formulate area power consumption (APC) minimization framework, which optimizes the deployment factor to derive specific optimal BS density and transmit power values. Furthermore, we perform a comprehensive study of the effect of biasing on the APC performance of biased HetNets. Our results show that for HetNets using the maximum average-biased-received-power association scheme, significant energy savings are possible with appropriate biasing.

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User Association in Energy-Aware Dense Heterogeneous Cellular Networks

Mobile network operators are currently faced with exponentially-increasing data traffic demand which necessitates significant investment in network expansion and upgrades. The required capital expenditure (CAPEX) and operational expenditure (OPEX) have reduced revenues and the associated CO2 emissions continue to raise ecological concerns. In this paper, we investigate the effect of varying user densities on the energy efficiency (EE) performance of the network. Using Poisson point process (PPP) theory, we derive simple analytical approximations that allow important insights to be made on the spectral efficiency (SE) and EE performance of a typical dense small cell network. We also study the impact of the sleep mode power consumption on the network EE especially in dense networks where numerous base stations may be idle simultaneously.

Spectral and Energy Efficiency Analysis of Dense Small Cell Networks

Traffic demand is increasing exponentially and operators will need to densify their networks with small cells to meet this demand. This has created economic and ecological concerns due to the high cost of energy and the associated greenhouse gas emissions. In this paper, we apply tools from stochastic geometry to design a deployment strategy for multi-tier heterogeneous networks (HetNets). Using appropriate approximations, we derive simple expressions that allow insights into the energy efficiency (EE) performance of biased and unbiased HetNets. We perform a comprehensive analysis on the effect of biasing on the EE performance of HetNets. We also formulate an optimization framework that minimizes the area power consumption (APC) of the HetNet subject to appropriate performance constraints. Our results show that significant energy savings can be made by appropriately biasing the HetNet and optimizing the BS densities and their transmit powers subject to the performance constraints.

Energy Efficient Deployment of Dense Heterogeneous Cellular Networks

Data traffic demand in cellular networks continues to increase exponentially leading to high capital expenditure and operational costs such as electricity cost. Femtocells have been proposed as a solution to enhance network capacity without significantly increasing energy consumption and associated network deployment costs. In this paper, we analyze the network capacity and energy consumption aspects of a joint macrocell and femtocell network based on the LTE standard. Femtocells and macrocells are operated in the same frequency band to enhance spectral efficiency. Femtocell users are secondary users who only access the channel opportunistically when macrocell users are idle. We propose the use of cooperative spectrum sensing to manage adverse interference emanating from the co-channel operation of both macrocells and femtocells. Simulation results show that cooperative sensing is very effective at managing this cross-tier interference to maximize the throughput of both the macrocell and femtocell layers. This throughput maximization allows the lowest energy consumption ratio of such a heterogeneous network.

Edwin Mugume

Papers

Sleep mode mechanisms in dense small cell networks

User Association in Energy-Aware Dense Heterogeneous Cellular Networks

Spectral and Energy Efficiency Analysis of Dense Small Cell Networks

Energy Efficient Deployment of Dense Heterogeneous Cellular Networks

Cooperative spectrum sensing for green cognitive femtocell network