Mobile Edge Computing (MEC) is a viable solution in response to the growing demand for broadband services in the new-generation heterogeneous systems. The dense deployment of small cell networks is a key feature of next-generation radio access networks aimed at providing the necessary capacity increase. Nonetheless, the problem of green networking and service computing will be of great importance in the downlink, because the uncontrolled installation of too many small cells may increase operational costs and emit more carbon dioxide. In addition, given the resource and computational limitation of the user layer, energy efficiency (EE) and fairness assurance are critical issues in MEC-based cellular systems. Considering the user fairness criteria, this paper proposes a dynamic optimization model which maximizes the total UL/DL EE along with satisfying the necessary QoS constraints. Based on the non-convex characteristics of the EE maximization problem, the mathematical model can be divided into two separate subproblems, i.e., computational carrier scheduling and resource allocation. So that, a subgradient method is applied for the computational resource allocation and also successive convex approximation (SCA) and dual decomposition methods are adopted to solve the max-min fairness problem. The simulation results exhibit considerable EE improvement for various traffic models in addition to guaranteeing the fairness requirements. It also proved that the proposed computational partitioning scheme managed to significantly improve the total throughput for mobile computing services.

Heterogeneous Computational Resource Allocation for NOMA: Toward Green Mobile Edge-Computing Systems

Nowadays, the application of data caching in mobile edge computing networks is exponentially increasing as a high-speed data storage layer using deep learning (DL) approaches. This paper presents an DL-based resource allocation approach to find the optimal topology for cache-enabled backhaul networks. In the practical scenarios, a numerous of radial configurations of test systems have been applied for training stages. This paper also applied the continuation power flow analysis to achieve the maximum load limit in which the power of macro base stations with the caches of different sizes is provided through either smart grid network or renewable power systems. To increase the power efficiency index of this approach the power sharing capability was enabled among different layer of network components through smart grids. In order to obtain the optimal solution, the DL-based mathematical problem is reformulated into a neural weighting model considering convergence conditions and Lyapunov stability of the mobile-edge-computing under Karush–Kuhn–Tucker optimality constraints. The mathematical analysis and simulation results demonstrate that the performance of the proposed algorithm is better than other energy efficiency algorithms. The proposed approach can effectively increase the total system throughput and network’s utility in addition to guarantee user fairness index.

Towards Optimal Configuration in MEC Neural Networks: Deep Learning-Based Optimal Resource Allocation

Performance analysis of UAV multiple antenna-assisted small cell network with clustered users

A novel approach to fairness-aware energy efficiency in green heterogeneous cellular networks

A novel approach to QoS‐aware resource allocation in NOMA cellular HetNets using multi‐layer optimization

The dense deployment of small-cell networks is a key feature of the next-generation mobile networks employed to provide the necessary capacity increase. The small cells are installed in the areas covered by macro base stations (eNBs) to supply the required local capacity based on the known concept of the hierarchical HetNets. Moreover, small-cell networks use high-capacity backhaul links on millimeter-wave bands to develop multihop topologies to mitigate the costs of data transmission. Nonetheless, green networking gains great importance for the uncontrolled installation of too many small cells may escalate operational costs and emit more carbon dioxide. This article proposes a dynamic optimization model to minimize the overall energy consumption of fifth-generation (5G) heterogeneous networks and provide the essential coverage and capacity. Optimizing carrier allocation and power utilization, the proposed model determines when to turn on or off small cells to meet the quality of service constraints of users with the highest level of energy efficiency. We also proposed a multihop backhauling strategy to effectively use the existing infrastructure of small-cell networks for simultaneous dual-hop transmissions. The numerical results indicated considerable rates of power saving in different traffic models while guaranteeing the throughput requirements for uniform and hotspot user equipment distribution patterns. Also, according to the simulation results, energy efficiency and system data rates can significantly be improved.

Energy-Aware Hierarchical Resource Management and Backhaul Traffic Optimization in Heterogeneous Cellular Networks

5G wireless networks require highly spectral-efficient multiple access techniques, which play an important role in determining the performance of mobile communication systems. Multiple access techniques can be classified into orthogonal and nonorthogonal based on the way the resources are allocated to the users. This paper investigates the joint user association and resource allocation problem in an uplink multicast NOMA system to maximize the power efficiency with guaranteeing the quality-of-experience of all subscribers. We also introduce an adaptive load balancing approach that aspires to obtain “almost optimal” fairness among servers from the quality of service (QoS) perspective in which learning automata (LA) has been used to find the optimal solution for this dynamic problem. This approach contains a sophisticated learning automata which consists of time-separation and the “artificial” ergodic paradigms. Different from conventional model-based resource allocation methods, this paper suggested a hierarchical reinforcement learning based frameworks to solve this non-convex and dynamic power optimization problem, referred to as hierarchical deep learning-based resource allocation framework. The entire resource allocation policies of this framework are adjusted by updating the weights of their neural networks according to feedback of the system. The presented learning automata find the $$\epsilon$$
 -optimal solution for the problem by resorting to a two-time scale-based SLA paradigm. Numerical results show that the suggested hierarchical resource allocation framework in combination with the load balancing approach, can significantly improve the energy efficiency of the whole NOMA system compared with other approaches.

A novel approach to efficient resource allocation in load-balanced cellular networks using hierarchical DRL

Joint energy-efficient resource allocation, subcarrier assignment, and SIC ordering for mmWave-enabled NOMA-UAV networks

Nowadays, data caching is being used as a high-speed data storage layer in mobile edge computing networks employing flow control methodologies at an exponential rate. This study shows how to discover the best architecture for backhaul networks with caching capability using a distributed offloading technique. This article used a continuous power flow analysis to achieve the optimum load constraints, wherein the power of macro base stations with various caching capacities is supplied by either an intelligent grid network or renewable energy systems. This work proposes ubiquitous connectivity between users at the cell edge and offloading the macro cells so as to provide features the macro cell itself cannot cope with, such as extreme changes in the required user data rate and energy efficiency. The offloading framework is then reformed into a neural weighted framework that considers convergence and Lyapunov instability requirements of mobile-edge computing under Karush Kuhn Tucker optimization restrictions in order to get accurate solutions. The cell-layer performance is analyzed in the boundary and in the center point of the cells. The analytical and simulation results show that the suggested method outperforms other energy-saving techniques. Also, compared to other solutions studied in the literature, the proposed approach shows a two to three times increase in both the throughput of the cell edge users and the aggregate throughput per cluster.

Amir Ziaeddini

Papers

Energy-Aware Hierarchical Resource Management and Backhaul Traffic Optimization in Heterogeneous Cellular Networks

Heterogeneous Computational Resource Allocation for NOMA: Toward Green Mobile Edge-Computing Systems

A novel approach to efficient resource allocation in load-balanced cellular networks using hierarchical DRL

Joint energy-efficient resource allocation, subcarrier assignment, and SIC ordering for mmWave-enabled NOMA-UAV networks

An Optimized Multi-Layer Resource Management in Mobile Edge Computing Networks: A Joint Computation Offloading and Caching Solution