NOMA-based Mobile Edge Computing Research Articles

Resource allocation strategy for blockchain-enabled NOMA-based MEC networks

Blockchain technology is getting more and more attention due to its decentralization, independence and security features. However, in wireless networks it faces a computational challenge: the proof-of-work problem. Mobile edge computing (MEC) leads to a vaild scheme by providing cloud computing capabilities to mobile devices. Non-orthogonal multiple access (NOMA) exploits the diversity properties in the power domain to further increase system throughput and spectral efficiency. In this paper, we suggest a new NOMA-based MEC wireless blockchain network to minimize system energy consumption through task offloading decision optimization, user clustering, computing resource and transmit power allocation. In order to effectively figure out this non-convex problem, we first propose a offloading decision and user clustering algorithm, and then propose a computing resource allocation algorithm based on user Quality of Service (QoS) requirements. Finally, the transmission power can be easily determined. The numerical simulation results verify that the proposed joint optimization algorithm can effectively decrease the system energy consumption.

OL-EUA: Online User Allocation for NOMA-Based Mobile Edge Computing

Mobile edge computing (MEC) raises a variety of new challenges for app vendors, including the Edge User Allocation (EUA) problem. EUA aims to allocate as many app users as possible in an MEC system to minimum edge servers in the system. In non-orthogonal multiple access (NOMA)-based MEC system, multiple app users can be allocated to the same subchannel on an edge server through transmit power allocation based on their intra-cell and inter-cell interference. However, allocating excessive app users to the same subchannel may result in severe interference and consequently impact app users’ data rates. In addition, in an MEC system, app users join and depart randomly, and thus need to be allocated in an online manner. Existing EUA approaches suffer from poor performance in dynamic real-world NOMA-based MEC systems because they allocate app users in an offline manner and do not consider the complication caused by NOMA. In this paper, we propose OL-EUA, an OnLine approach for solving dynamic EUA problems in NOMA-based MEC systems. Its performance is theoretically analyzed and experimentally evaluated on a public dataset.

Energy-Efficient Design for IRS-Assisted NOMA-Based Mobile Edge Computing

This letter studies a non-orthogonal multiple-access (NOMA)-based mobile edge computing (MEC) system. In order to enhance communication coverage and energy efficiency, intelligent reflecting surface (IRS) is proposed, which is deployed to assist computation offloading from end users to an access point (AP) equipped with an edge server. We aim to minimize the sum energy consumption by jointly optimizing the transmit powers, transmission time, offloading partitions and IRS phase shift. To address the non-convex formulated problem, we first derive the optimal solution of transmit power in closed-form, and then decouple the reformulated problem into two subproblems and employ alternating optimization (AO) technique to solve it. Numerical results validate that the proposed IRS assisted NOMA-based MEC scheme can yield superior performance in comparison with other benchmark schemes.

Latency and energy aware rate maximization in MC-NOMA-based multi-access edge computing: A two-stage deep reinforcement learning approach

Future network services are emerging with an inevitable need for high wireless capacity along with strong computational capabilities, stringent latency and reduced energy consumption Two technologies are promising, showing potential to support these requirements: multi-access (or mobile) edge computing (MEC) and non-orthogonal multiple access (NOMA). While MEC allows users to access the abundant computing resources at the edge of the network, NOMA technology enables an increase in the density of a cellular network. However, integrating NOMA technology into MEC systems faces challenges in terms of joint offloading decisions (remote or local computation) and inter-user interference management. In this paper, with the objective of maximizing the system-wide sum computation rate under latency and energy consumption constraints, we propose a two-stage deep reinforcement learning algorithm to solve the joint problem in a multicarrier NOMA-based MEC system, in which the first-stage agent handles offloading decisions while the second-stage agent considers the offloading decisions to determine the resource block assignments for users. Simulation results show that compared with other benchmark algorithms, the proposed algorithm improves the sum computation rate while meeting the latency and energy consumption requirements, and it outperforms the approach in which a single agent handles both offloading decisions and resource block assignments due to faster convergence performance.

Energy Consumption Minimization Scheme for NOMA-Based Mobile Edge Computation Networks Underlaying UAV

An unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) networks fulfil the on-demand computation services for the mobile terminals (MTs) with its high mobility and easy deployment. Thus, reduction in the latency is observed but the energy efficiency is still a major issue in this network, as both the UAV and MTs have limited energy storage batteries. Also, to support the massive connectivity, and to handle the data traffic generated by the MTs in an uplink scenario, using nonorthogonal multiple access (NOMA) is accomodated. The NOMA-based MEC provides flexible computing services to MTs. In this article, our goal is to minimize the total energy consumption of the NOMA-based MEC networks underlaying UAV with time, computation capacity, and UAV trajectory. The formulated model is a nonlinear programming problem; for the solution, it is divided into two subproblems: joint time allocation and task computation capacity, and UAV trajectory optimization. To solve the joint time allocation and computation task capacity, we proposed an iterative algorithm with low complexity, where closed-form solutions are obtained in each step. Next, to optimize the trajectory of the UAV, we used the successive convex approximation technique. Numerical results demonstrated that the proposed scheme achieves better results as compared to the orthogonal multiple access and equal resource allocation schemes.

Cost-Effective Optimization for Blockchain-Enabled NOMA-Based MEC Networks

Blockchain technology has been widely used in many fields. However, the proof of work (PoW) problem in the mining process of mobile devices requires a large amount of computing resources and energy consumption, which brings huge challenges to mobile devices. Mobile edge computing (MEC) can effectively solve the above problems, allowing mobile devices to offload tasks to edge servers to relieve the pressure of limited computing resources on mobile devices. Nonorthogonal multiple access (NOMA) is good at improving spectrum efficiency, so that the system can accommodate more users. In this paper, we propose a new NOMA-based MEC-enabled blockchain framework. Under the conditions of a given task execution deadline, the decision of offloading, local computing resource allocation, user clustering and admission control, and transmit power control is jointly optimized to minimize the total cost of the system. Since the problem is hard to solve, we decouple it into subproblems for low-complexity solutions. First, we propose two heuristic algorithms to obtain the binary offloading decision and user association, and then closed-form solutions of local resource allocation and transmit power control are obtained under the required delay constraints. Simulation results show that our proposed algorithms perform good in cost reduction compared with other baseline algorithms.

Cost-Effective User Allocation in 5G NOMA-Based Mobile Edge Computing Systems

Mobile edge computing (MEC) allows edge servers to be placed at cellular base stations. App vendors like Uber and YouTube can rent computing resources and deploy latency-sensitive applications on edge servers for their users to access. Non-orthogonal multiple access (NOMA) is an emerging technique that facilitates the massive connectivity of 5G networks, further enhancing the capability of MEC. The edge user allocation (EUA) problem faces new challenges in 5G NOMA-based MEC systems. In this study, we investigate the EUA problem in a multi-cell multi-channel downlink power-domain NOMA-based MEC system. The main objective is to help mobile app vendors maximize their benefit by allocating maximum users to edge servers in a specific area at the lowest computing resource and transmit power costs. To this end, we introduce a decentralized game-theoretic approach to effectively select a channel and edge server for each user while fulfilling their resource and data rate requirements. We theoretically and experimentally evaluate our solution, which significantly outperforms various state-of-the-art and baseline approaches.

Sub-Channel Scheduling, Task Assignment, and Power Allocation for OMA-Based and NOMA-Based MEC Systems

In this paper, sub-channel scheduling, task assignment and power allocation are investigated for orthogonal multiple access (OMA)-based and non-orthogonal multiple access (NOMA)-based mobile edge computing (MEC) systems. Based on different channel conditions and computational capacities, computational tasks are partially offloaded to the MEC server via OMA or NOMA protocols. In order to minimize the total energy consumption, an optimization problem under the task execution latency constraint is formulated and divided into two sub-problems. By utilizing matching theory, the formulated sub-channel allocation problem is solved by a proposed low-complexity algorithm, where the joint optimization of task assignment and power allocation is performed at each iteration. Based on the delay constraint, some insights are obtained, and the closed-form solutions of task assignment coefficients and transmit power are derived. Furthermore, the offloading strategy in both OMA and NOMA schemes is analyzed, which shows that the optimal task assignment coefficient is decided by the energy consumption efficiency (ECE). Simulation results indicate that: i) the proposed sub-channel allocation algorithm and derived closed-form solutions can significantly improve the MEC system in terms of the energy consumption; ii) the provided offloading strategy can be dynamically and efficiently employed with different channel conditions and computational capacities.

Demand Response in NOMA-based Mobile Edge Computing: A Two-phase Game-theoretical Approach

Similar to cloud servers, edge servers running 24/7 in a mobile edge computing (MEC) system consume a large amount of energy, and thus require demand response management. Demand response has been widely employed to reduce energy consumption at data centers. However, existing demand response approaches for data centers are rendered obsolete by the new and unique characteristics of MEC systems: 1) proximity constraint - mobile users can be served by neighbor edge servers only; 2) latency constraint - mobile users' workloads should be processed by their neighbor edge servers to ensure low latency; and 3) capacity constraint - edge servers have limited computing and communication resources to serve mobile users. Demand response for MEC is further complicated by the non-orthogonal multiple access (NOMA) scheme - the emerging radio access scheme for 5G. Communication resources like channels and transmit power must be systematically considered with computing resources like CPU, memory and storage to fulfil mobile users' resource demands. This paper makes the first attempt to tackle this Edge Demand Response (EDR) problem. We first formulate this problem and prove its NP-hardness. Then, we propose a two-phase game-theoretical approach (EDRGame) to solve the EDR problem. Its performance is theoretically analyzed and experimentally evaluated against state-of-the-art approaches on a widely-used real-world dataset.

System-Centric Computation Energy Efficiency for Distributed NOMA-Based MEC Networks

Energy-efficient communication is one of the most pertaining issues in realizing mobile edge computing (MEC) networks. How to effectively use non-orthogonal multiple access (NOMA) to realize energy efficient MEC networks has been of high interest and calls for in-depth study. In particular, there has been yet no work to study the energy efficient communication scheme from system perspective in this area. In this work, we focus on the design of a system-centric computation energy efficiency (EE) scheme in a NOMA-based MEC network. The system computation EE is maximized by jointly optimizing the computing frequencies of the MEC server and the local users, the offloading time, the transmit powers of local users, as well as the execution time for the local users and the MEC server. Employing the fractional programming theory and introducing auxiliary variables, we develop an efficient Dinkelbach-based iterative scheme to tackle the formulated non-linear fractional programming problem. For obtaining instrumental insights, we further derive closed-form expressions for the partial solutions based on Karush-Kuhn-Tucker conditions. Simulation results are provided to verify the quick convergence of the proposed iterative algorithm, to obtain insights, as well as to demonstrate the superiority of the proposed resource allocation scheme in terms of the system computation EE by comparing it with benchmark schemes.

Analytical Modeling of NOMA-Based Mobile Edge Computing Systems With Randomly Located Users

Non-orthogonal multiple access (NOMA) has been treated as a promising technology to improve the computation offloading performance of mobile edge computing (MEC). A complete understanding of the impact of applying NOMA on MEC is essential for network operators to deploy MEC systems. In this letter, we develop a mathematical framework to analyze the impact of NOMA on offloading decisions in MEC, which captures the interaction among the offloading decisions of multiple co-channel users. Using stochastic geometry and order statistics, we derive the expression for the computation offloading probability of the $n$ -th ranked user in terms of serving distances in a NOMA-based MEC system. Numerical simulations are performed to verify the accuracy of our analysis and demonstrate the performance gain of applying NOMA on MEC.

Enhance Latency-Constrained Computation in MEC Networks Using Uplink NOMA

Non-orthogonal multiple access (NOMA) based mobile edge computing (MEC) networks can enhance delay-constrained computation. Most of the existing works focus on the energy or delay minimization, and the study on NOMA based MEC in terms of successful computation probability has been limited, which motivates this work. In particular, randomly deployed edge computing users are modeled as the homogeneous Poisson Point Process and are divided into two groups namely center group (C-group) and edge group (E-group) for uplink NOMA grouping. We propose a new hybrid offloading scheme in a NOMA-based MEC network that can operate in three different modes, namely partial offloading, complete local computation, and complete offloading. We firstly consider a NOMA grouping scenario where a user from the C-group and a user from the E-group are each given a fixed location. The probability that the computation can be completed within the given delay budget is derived and the optimal parameters, i.e., the time for offloading, the power allocation, and the offloading ratios for the two fixed users, are obtained. It reveals that the optimal offloading ratios are determined by the difference of the computational capability between the edge computing user and the MEC server, and that the locations of users have a big impact on the successful computation probability. Inspired by this, we further study three distance-dependent NOMA grouping schemes in the MEC offloading. Specifically, we provide closed-form mathematical expressions of the successful computation probability and its partial optimal solutions for these three schemes. Simulation results verify the accuracy of the analytical results and compare the performance of the proposed offloading scheme with the existing schemes. Insights on the pros and the cons of different user selection schemes are also provided.

Energy-Aware User Association for NOMA-Based Mobile Edge Computing Using Matching-Coalition Game

Mobile edge computing (MEC) is regarded as a key technology to reduce the network pressure from the computing-intensive and latency-sensitive applications in future wireless networks. Non-orthogonal multiple access (NOMA) can achieve high spectral efficiency by allowing multiple users to reuse the same resources. In this paper, we consider a novel NOMA-based MEC network to improve the energy efficiency during task offloading process. With multiple access points (APs) being deployed, we formulate a joint optimization problem which involves user association, computing resource allocation, and transmit power control, with the objective of minimizing the total energy consumption of all users in such a network. Specifically, the problem can be modeled as a many-to-one matching game with externality due to the co-channel interference and resource competition among users. We then propose a matching-coalition approach coupled with computing resource allocation and power control to find the optimal solution, and also provide the analyses on related properties including stability, convergence, and complexity. Simulation results show that the proposed approach can efficiently reduce the total energy consumption in comparison to other simplified approaches.

Energy-Efficient NOMA-Based Mobile Edge Computing Offloading

Mobile edge computing (MEC) has emerged as a promising technique to enhance computing capacity of mobile devices by offloading tasks to base station for remote execution. This letter considers an MEC system exploiting the non-orthogonal multiple access (NOMA) for both task uploading and result downloading. The total energy consumption is minimized by optimizing the transmit powers, transmission time allocation, and task offloading partitions. Specifically, with the obtained optimal power control solutions, the task offloading partitions and time allocation are obtained by the successive convex approximation algorithm. Numerical results show that the proposed NOMA-based MEC offloading scheme can significantly reduce the system energy consumption compared with a traditional orthogonal multiple access scheme.

Joint Computing Resource, Power, and Channel Allocations for D2D-Assisted and NOMA-Based Mobile Edge Computing

Mobile edge computing (MEC) and non-orthogonal multiple access (NOMA) have been considered as the promising techniques to address the explosively growing computation-intensive applications and accomplish the requirement of massive connectivity in the fifth-generation networks. Moreover, since the computing resources of the edge server are limited, the computing load of the edge server needs to be effectively alleviated. In this paper, by exploiting device-to-device (D2D) communication for enabling user collaboration and reducing the edge server's load, we investigate the D2D-assisted and NOMA-based MEC system. In order to minimize the weighted sum of the energy consumption and delay of all users, we jointly optimize the computing resource, power, and channel allocations. Regarding the computing resource allocation, we propose an adaptive algorithm to find the optimal solution. Regarding the power allocation, we present a novel power allocation algorithm based on the particle swarm optimization (PSO) for the single NOMA group comprised of multiple cellular users. Then, for the matching group comprised of a NOMA group and D2D pairs, we theoretically derive the interval of optimal power allocation and propose a PSO-based algorithm to solve it. Regarding the channel allocation, we propose a one-to-one matching algorithm based on the Pareto improvement and swapping operations and extend the one-to-one matching algorithm to a many-to-one matching scenario. Finally, we propose a scheduling-based joint computing resource, power, and channel allocations algorithm to achieve the joint optimization. The simulation results show that the proposed solution can effectively reduce the weighted sum of the energy consumption and delay of all users.

Joint Radio and Computational Resource Allocation for NOMA-Based Mobile Edge Computing in Heterogeneous Networks

A joint radio and computational resource allocation problem is investigated for non-orthogonal multiple access (NOMA)-based mobile edge computing (MEC) in heterogeneous networks (HetNets). The aim is to minimize the energy consumption of all users under the task execution latency constraint with partial task offloading. Due to the non-convexity of the formulated problem, we propose an iterative algorithm by decomposing the optimization problem into two subproblems, where the computational resource allocation is a convex optimization problem, and the uplink power allocation is solved by the sequential convex programming. Simulation results reveal that the partial offloading is superior to local computing-only and full offloading-only and NOMA-based MEC outperforms the orthogonal multiple access-based MEC in HetNets.