Resource Allocation Subproblem Research Articles

A Benders decomposition algorithm for resource allocation with multi-resource operations

This paper addresses a real life scheduling problem characterised by multi-resource operations whose completion simultaneously requires more than one (renewable) resource of different types. Such problems arise in various companies not only in manufacturing but also in services. Solving such a problem needs to address two interconnected subproblems: a sequencing subproblem and a resource-allocation subproblem if more than one resource is available in some types. The resource-allocation subproblem consists of allocating to each operation the resources it requires while the sequencing subproblem consists of determining the order in which each resource performs the operations assigned to it. This paper focuses on the resource-allocation subproblem. It generalises the basic scheduling problems which consist of determining the operations' starting or completion times for a given processing sequence for every resource. We consider a cost function taking into account the makespan, the cost of resource utilisation, and the load imbalance among the resources of the same type. We first formulate the problem into a mixed-integer linear program (MILP). To efficiently solve it, even in practical-sized instances, an exact algorithm called BD (Benders decomposition with enhancing cuts) is developed where the master problem only considers integer variables. We prove that the slave problem can be transformed into finding the longest paths in a digraph and therefore can be solved with the Bellman–Ford algorithm. To enhance the efficiency of the method, equivalent solutions are limited in the master problem. The performance of the approach is evaluated by comparing it against CPLEX, a state-of-the-art commonplace MILP solver, used to directly solve the initial MILP. The computational results demonstrate that BD provides competitive solutions in all upper and lower bounds. In particular, it improves, compared with CPLEX, the upper and lower bounds by 5.07% and 4.63%, respectively, in solving practical-sized instances. The experiment also shows that considering load balancing can make more rational use of resources and avoid adverse effects caused by excessive workload of staff and imbalanced use of equipment, which is very important in real-world production.

Fairness‐aware eMBB/URLLC spectrum resource multiplexing to ensure reliability in vehicular networks

AbstractSpectrum resource multiplexing of enhanced Mobile BroadBand (eMBB) and Ultra‐Reliable and Low Latency Communications (URLLC) via puncturing in vehicular networks has become an important research direction. The main issue in this direction is how to minimize the adverse impact of preemptive puncturing on eMBB traffic. However, the available eMBB/URLLC coexisting schemes lack considerations on the quality of service of a single eMBB user and fairness between users. In view of this, we formulate the eMBB/URLLC multiplexing problem to maximize eMBB reliability and fairness under the premise of meeting the requirements of URLLC as a nonlinear integer programming (NIP) in this article. To balance the performance and computational complexity, we decompose this NIP into two subproblems that be solved efficiently. For the eMBB spectrum resource allocation subproblem of maximizing the reliability and fairness under resource constraints, we prove that this problem is NP‐hard, and design a Deep Q‐Network (DQN)‐based spectrum resource allocation algorithm and a two‐stage heuristic spectrum resource allocation algorithm, respectively. For the URLLC preemption subproblem, we further propose bandwidth‐sensitive URLLC spectrum resource preemption algorithm and time‐sensitive URLLC spectrum resource preemption algorithm, respectively. Simulation results show that the proposed scheme can guarantee better fairness and reliability for eMBB users compared with the comparison schemes. Besides, for the eMBB resource allocation problem, the proposed scheme can achieve near‐optimal performance with low complexity.

An Energy-Efficient Multimode Transmission Scheme for Underwater Sensor Network

Underwater sensor network plays a critical role in the ocean data collection owing to its capability of providing reliable and wide communication coverage. How to assure lifetime performance of the network with limited energy supply has always been a key research issue. In this paper, we propose an underwater acoustic sensor network model which supports data delivery from each underwater sensor node (USN) to sea surface sink node (SN) in either direct or relay-assisted transmission mode, i.e., transmission mode between each USN and the SN can be dynamically selected according to network’s current state. To optimize its lifetime performance, we model the mode selection and resource allocation issue jointly into a non-convex and mixed integer programming problem. In order to efficiently solve it, we further divide the original problem into two subproblems: resource allocation subproblem and joint mode and relay selection subproblem. We prove that the reformulated non-convex resource allocation problem can be equivalent to a convex optimization problem, and its optimal solution can be found by using Lagrange dual decomposition method. For the second subproblem, some critical conditions are analyzed to determine optimal relays with the criterion of balancing energy consumption between USNs and a matching approach is designed to obtain the mode and relay selection results based on USNs’ priorities. Simulation results validate that the proposed transmission scheme is effective and superior to the traditional time division multiple access scheme.

FEDRESOURCE

Deep reinforcement learning can effectively deal with resource allocation (RA) in wireless networks. However, more complex networks can have slower learning speeds, and a lack of network adaptability requires new policies to be learned for newly introduced systems. To address these issues, a novel federated learning-based resource allocation (FEDRESOURCE) has been proposed in this paper which efficiently performs RA in wireless networks. The proposed FEDRESOURCE technique uses federated learning (FL) which is a ML technique that shares the DRL-based RA model between distributed systems and a cloud server to describe a policy. The regularized local loss that occurs in the network will be reduced by using a butterfly optimization technique, which increases the convergence of the FL algorithm. The suggested FL framework speeds up policy learning and allows for adoption by employing deep learning and the optimization technique. Experiments were conducted using a Python-based simulator and detailed numerical results for the wireless RA sub-problems. The theoretical results of the novel FEDRESOURCE algorithm have been validated in terms of transmission power, convergence of algorithm, throughput, and cost. The proposed FEDRESOURCE technique achieves maximum transmit power up to 27%, 55%, and 68% energy efficiency compared to Scheduling policy, Asynchronous FL framework, and Heterogeneous computation schemes respectively. The proposed FEDRESOURCE technique can increase discrimination accuracy by 1.7%, 1.2%, and 0.78% compared to the scheduling policy framework, Asynchronous FL framework, and Heterogeneous computation schemes respectively.

MSEC-D based energy consumption optimization strategy in satellite communication systems

In this paper, we consider a satellite communication system that consists of a source low earth orbit (SLEO) satellite, several relay low earth orbit (RLEO) satellites, and a destination ground station (GS). Under the assumption that the data can be split into different sizes of data flow while transmitted through the system in different time slots, a joint route selection and time-slot allocation problem are formulated. Considering the constraints on the availability of transmission links, conservation of data flow, and system resources, an optimization problem that minimizes the total system energy consumption is formulated. As the original optimization problem is NP-hard, which cannot be solved conveniently, we transform the problem to the maximum transmission rate route selection subproblem and energy-efficient resource allocation subproblem. To solve these subproblems, we propose two algorithms, namely the multi-time-expanding graph-based maximum transmission rate route selection (MTEG-R) algorithm and the minimum system energy consumption - Dijkstra (MSEC-D) algorithm. The proposed algorithms enable the determination of routes selection and time slots allocation for data transmission. Simulation results demonstrate the effectiveness of the proposed scheme.

Resource Allocation for RIS-Assisted Device-to-Device Communications in Heterogeneous Cellular Networks

In recent years, with the explosive growth of data traffic, communication base stations (BSs) need to serve more and more users. Offloading traffic from BSs has become an efficient way to reduce the burden on BSs. Device-to-Device (D2D) communications have emerged to improve spectrum utilization by reusing the frequency spectrum of the cellular frequency band. In the general environment, Heterogeneous Cellular Networks (HCNs) including millimeter wave (mm-wave) have appeared. Since the D2D link allows to share of spectrum resources with the cellular user, it will bring potential interference to the cellular user. Fortunately, an emerging technology called Reconfigurable Intelligent Surface (RIS) can mitigate the severe interference caused by D2D links by shaping the incident beam and improving the multipath phase shift. In this paper, we study the resource allocation scheme to maximize the system sum rate, in the RIS-assisted single-cell heterogeneous D2D communication scenario. To solve the Block Coordinate Descent (BCD) problem, the problem of maximizing the sum rate is decomposed into three sub-problems. The resource allocation sub-problem is solved by a coalitional game method based on the game theory. The power allocation problem of the coalition converts the concave function into a convex optimization by mathematical transformation. The problem is solved by the gradient descent method. The local search method is adopted to find the optimum for the phase conversion problem. Then iterate until the difference of sum rate is less than the threshold. The simulation results show that the designed algorithm is superior to other benchmark schemes in the literature.

Deep Reinforcement Learning-Based Energy Minimization Task Offloading and Resource Allocation for Air Ground Integrated Heterogeneous Networks

With the popularization of Internet of Things, a number of emerging applications all require both efficient communication and computing service, which poses enormous challenges to the computing ability and battery capacity of terminal equipment. Moreover, the ground-based 5G system cannot provide seamless service especially for hotspot and remote area. In order to deal with the mentioned issues, we first propose an air ground integrated heterogeneous networks model, which consists of multiple UAVs and GBSs equipped with edge servers. Then, by jointly taking into account terminal power control, computing resource allocation, and task offloading decision, an energy-minimization issue is formulated, which, however, is a mixed integer nonlinear programming problem due to the tight coupling between optimization variables. Therefore, we decompose it into two subproblems and design a deep actor-critic-based online offloading algorithm to solve the first offloading decision-making issue facing dimensionality curse. For the second power control and computing resource allocation subproblem, a difference-of-convex-based solution is presented. The proposed approach can achieve superior performance in terms of terminal energy consumption and convergence speed with lower complexity compared with other benchmark methods. Specifically, it outperforms DDQN, AC&Greedy, and UCB algorithm by 7.62%, 17.32%, and 23.14%, respectively.

Energy-efficient task scheduling for mobile edge computing with virtual machine I/O interference

Mobile edge computing (MEC) is expected to support the computation-intensive and delay-sensitive applications of mobile internet users. In this paper, we investigate the resource allocation of MEC with the effect of I/O interference among parallel virtual machines (VMs) while satisfying the quality of service (QoS) of tasks. Different from existing works, we propose a flexible task scheduling approach that combining parallel and sequential computing to minimize the computing energy consumption of MEC server. We formulate the task scheduling problem as a mixed-integer nonlinear programming (MINLP) and decompose it as a CPU resource allocation subproblem, a computing time slot subproblem, and a VM selection subproblem. We show the first subproblem is a convex problem and propose a CPU frequency allocation (CFA) algorithm based on the Karush–Kuhn–Tucker (KKT) conditions to obtain the optimal CPU frequency resource allocation. For the time slot allocation and VM selection subproblems, we propose the three step allocation (TSA) and urgency based adjusting (UBA) algorithms to obtain the near-optimal solutions, respectively. Simulation results show that compared with several time slot allocations and VM selections, the proposed TSA and UBA algorithms can save up to 21.7% and 95.8% of energy consumption, respectively.

Resource allocation and device pairing for energy-efficient NOMA-enabled federated edge learning

In the era of Fifth Generation of cellular networks (5G), Internet of Things (IoT) devices are becoming much more popular and accumulate a lot of data. With the advantages of privacy protection and resource saving, Federated Edge Learning (FEEL) is a promising machine learning paradigm which helps improve the service quality of deep learning-based applications and makes people’s lives intelligent by processing these data. In FEEL, users only train the models locally without uploading their own data, thus alleviating the data security issue. Nevertheless, this paradigm also faces a major challenge. The training tasks are carried out on the mobile devices, which poses great pressure to their limited battery lives. In 5G, Non-Orthogonal Multiple Access (NOMA) has become an enabling technology to support massive connections because of its higher spectral efficiency and throughput over OMA technologies. Considering the potential of FEEL and the broad application prospects of NOMA, we design a NOMA-enabled FEEL framework and minimize its Energy Consumption (EC) by optimizing the device pairing, communication resource (i.e., transmission powers and time slots) and computation resource (i.e., CPU frequencies and local training threshold). The original problem can be decomposed into two subproblems: resource allocation and device pairing subproblems. For the resource allocation subproblem, with the help of the linear search method, we can find a near-optimal solution. Then, the optimality of the proposed solution (i.e., how far is the near-optimal from the optimum) is proved by sensitivity analysis. For the device pairing subproblem, we propose two low-complexity pairing schemes, which have their own characteristics. Simulation results demonstrate the effectiveness of the proposed resource allocation and device pairing strategies and highlight the advantage of the NOMA-enabled FEEL framework over the existing TDMA-enabled framework in terms of EC.

Energy-Efficient Collaborative Multi-Access Edge Computing via Deep Reinforcement Learning

The joint problem of task offloading, collaborative computing and resource allocation for Multi-access Edge Computing (MEC) is a challenging issue. In this paper, splitting computing tasks at MEC servers through collaboration among MEC servers and a cloud server, we investigate the joint problem of collaborative task offloading and resource allocation. A collaborative task offloading, computing resource allocation, and subcarrier and power allocation problem in MEC is formulated. The goal is to minimize the total energy consumption of the MEC system while satisfying a delay constraint. The formulated problem is a non-convex mixed-integer optimization problem. In order to solve the problem, we propose a Deep Reinforcement Learning (DRL) based bi-level optimization framework. The task offloading decision, computing collaboration decision, and power and subcarriers allocation subproblems are solved at the upper-level, while the computing resource allocation subproblem is solved at the lower-level. We combine Dueling-Deep-Q-Network (DQN), Double-DQN and add adaptive parameter space Noise (D <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> N-DQN) to improve DRL performance in MEC. Simulation results demonstrate that the proposed algorithm achieves near-optimal performance in Energy Efficiency (EE) and task completion rate compared to other DRL-based approaches and other benchmark schemes under various network parameter settings.

New Bridge to Cloud: An Ultra-Dense LEO Assisted Green Computation Offloading Approach

Mobile edge computing and cloud computing have emerged as effective technologies to alleviate the increasing computational workload of mobile devices. As a promising enabling 6G technology, the ultra-dense (UD) low earth orbit (LEO) satellite network with low communication latency and high throughput is considered a new bridge for cloud computation offloading. In this paper, we investigate energy-efficient cloud and edge computing in UD-LEO-assisted terrestrial-satellite networks. An optimization problem aiming at minimizing the energy consumption of the computation tasks is formulated. The optimization problem is a mixed-integer non-linear programming problem. To solve this problem, we decompose it into two subproblems, i.e., a joint user association and task scheduling subproblem, and an adaptive computation resource allocation subproblem. For the first subproblem, we model the input of a forward neural network (NN) as the large-scale information (i.e., channel gain and task arrival rates) and obtain the optimal solution by transforming the direct output of the NN. For the second subproblem, we introduce a successive convex approximation method to optimize it iteratively. The simulation results show that our proposed user association and task scheduling strategy outperforms two benchmark algorithms in terms of energy consumption under a strict delay bound and high user density.

Intelligent resource allocation scheme for cloud-edge-end framework aided multi-source data stream

To support multi-source data stream generated from Internet of Things devices, edge computing emerges as a promising computing pattern with low latency and high bandwidth compared to cloud computing. To enhance the performance of edge computing within limited communication and computation resources, we study a cloud-edge-end computing architecture, where one cloud server and multiple computational access points can collaboratively process the compute-intensive data streams that come from multiple sources. Moreover, a multi-source environment is considered, in which the wireless channel and the characteristic of the data stream are time-varying. To adapt to the dynamic network environment, we first formulate the optimization problem as a markov decision process and then decompose it into a data stream offloading ratio assignment sub-problem and a resource allocation sub-problem. Meanwhile, in order to reduce the action space, we further design a novel approach that combines the proximal policy optimization (PPO) scheme with convex optimization, where the PPO is used for the data stream offloading assignment, while the convex optimization is employed for the resource allocation. The simulated outcomes in this work can help the development of the application of the multi-source data stream.

Joint dynamic routing and resource allocation in satellite-terrestrial integrated networks

The Satellite-Terrestrial Integrated Networks (STINs) is considered as a reliable and agile next-generation communication network scheme, because it is flexible to deploy and robust to disasters (e.g., earthquakes, floods, and volcanic eruptions). The routing and resource allocation directly affect the power consumption of satellites in the STINs. However, most of existing research works study these two issues separately, and neglect the impact of path selection on the satellite power consumption. This paper investigates the joint optimization problem of routing, bandwidth allocation, user association, channel allocation, and power allocation of the STINs. We aim at minimizing satellites’ power consumption while satisfying user's Quality of Service (QoS). Because the power consumption of satellites for routing in the space segment is involved with the data rate through satellite gateways in the user segment, this optimization problem is a Multi-Objective Optimization (MOO) problem. To this end, we convert the MOO problem to a Single-Objective Optimization (SOO) problem by linear weighted method. Considering that the SOO problem is a Mixed Integer Nonlinear Programming (MINLP) problem, we decompose it into the resource allocation sub-problem in the user segment and the routing optimization sub-problem in the space segment. And we solve these two sub-problems iteratively. Specifically, we convert the first sub-problem into a convex optimization problem and obtain the current user association and channel allocation. Then we formulate the second sub-problem into a multi-commodity flow problem and solve it based on the obtained user association and channel allocation. The SOO problem is solved by iteratively optimizing these two sub-problems. Experimental results demonstrate that our proposed algorithm is able to get an approximate optimal solution quickly and effectively reduce the power consumption of satellites compared with benchmark algorithms in the STINs.

Joint Task Offloading and Resource Allocation for Quality-Aware Edge-Assisted Machine Learning Task Inference

Edge computing is essential to enhance delay-sensitive and computation-intensive machine learning (ML) task inference services. Quality of inference results, which is mainly impacted by the task data and ML models, is an important indicator impacting the system performance. In this paper, we consider a quality-aware edge-assisted ML task inference scenario and propose a resource management scheme to minimize the total task processing delay while guaranteeing the stability of all the task queues and the inference accuracy requirements of all the tasks. In our scheme, the task offloading, task data adjustment, computing resource allocation, and wireless channel allocation are jointly optimized. The Lyapunov optimization technique is adopted to transform the original optimization problem into a deterministic problem for each time slot. Considering the high complexity of the optimization problem, we design an algorithm that decomposes the problem into a task offloading and channel allocation (TOCA) sub-problem, a task data adjustment sub-problem, and a computing resource allocation sub-problem, and then solves them iteratively. A low-complexity heuristic algorithm is also designed to solve the TOCA sub-problem efficiently. Extensive simulations are conducted by varying different crucial parameters. The results demonstrate the superiority of our scheme in comparison with 4 other schemes.

Dynamic Transmission and Computation Resource Optimization for Dense LEO Satellite Assisted Mobile-Edge Computing

A dense satellite-terrestrial integrated mobile-edge computing network (SATIMECN) architecture is developed to meet the computing demands for next generation networks. We formulate an average weighted sum energy consumption minimization problem by jointly considering task ratio allocation of computing or offloading at local and the gateway (GW), ground user terminal (GUT)-satellite association relation, GUT multiple-input and multiple-output (MIMO) precoding, and computation resource allocation at local and the GW. Due to the stochastic property of the optimization problem, we adopt Lyapunov optimization theory to transform it into a deterministic one. Then, we decompose the optimization problem into four subproblems and solve each one iteratively. Specifically, task ratio allocation of computing or offloading at local and the GW is obtained in a closed-form expression using the delay constraint. Then, the binary GUT-satellite association subproblem is solved by the weighted minimum mean-squared error and quadratic transform based fractional programming (QTFP) methods. Moreover, the MIMO precoding subproblem is solved by QTFP and interior point methods. Finally, the computation resource allocation subproblem for local and edge computing is derived in closed-form expressions. Simulation results demonstrate that the tradeoff between the average weighted sum energy consumption and the average queue length can be realized by adjusting the Lyapunov control parameter. Moreover, the proposed MIMO communication and frequency reuse schemes for dense satellite network can realize efficient computation offloading with relative low cost.

Energy-Efficient Multiprocessor-Based Computation and Communication Resource Allocation in Two-Tier Federated Learning Networks

In conventional federated learning (FL), multiple edge devices holding local data jointly train a machine learning model by communicating learning updates with a centralized aggregator without exchanging their data samples. Owing to the communication and computation bottleneck at the centralized aggregator and inaccurate learning model caused by the non-IID data, we here consider a two-tier FL network, in which IoT nodes are the core clients that hold data, the model aggregators at the middle tier are the low altitude aerial platforms (UAVs), and the model aggregator at the top-most layer is the high altitude aerial platform (UAV with relatively high altitude). Under the assumption that each IoT node has parallel computing ability, we study the energy-efficient computation and communication resource allocation in such a network within some time budget. Upon formulating the problem as an optimization problem, we solve the computation and communication resource allocation problems as the separate subproblems within a time frame, and then propose an iterative algorithm to solve the entire problem jointly. More specifically, We solve both the energyefficient computation and communication resource allocation subproblems using the dual decomposition technique, and then apply a bisection search-based recursive technique to solve the entire energy efficiency problem jointly. Moreover, we propose offline and online client scheduling schemes that not only select the optimal edge nodes for association but also assign workload to each client based on the data quality and workload constraint. With real data, extensive simulations are conducted to verify the effectiveness of the proposed resource allocation scheme. The results further reveal that the learning performance not only is dependent on the computation and communication energy consumption of the FL process but also the model divergence weight owing to the non-IID data at client IoT nodes.

Joint Allocations of Radio and Computational Resource for User Energy Consumption Minimization Under Latency Constraints in Multi-Cell MEC Systems

This paper investigates the joint allocation of radio resources (i.e., base stations (BSs), sub-channels, and uplink transmission powers) and computational resources for a multicell mobile edge computing (MEC) system, aimed at minimizing the sum energy consumption of all mobile terminals under strict delay and signal-to-interference-plus-noise ratio (SINR) constraints. The problem is challenging to solve due to the highly coupled mixed-integer variables and the complicated expressions of the constraints. To tackle the difficulty, we decompose the problem into two sub-problems: the power and computational resource allocation sub-problem and the user-BS-subchannel association sub-problem. For the first sub-problem, we derive the optimal computational resource as a function of the optimal transmission power. Then the power optimization problem is distributed to each cell, and the local-optimal power solution within each cell is derived in closed-form expressions. For the second sub-problem, we design an optimal modified-cutting-plane (MCP) algorithm with exponential time-complexity. A polynomial-time near-optimal pivoting-and-subgradient (PS) algorithm is further proposed to reduce the complexity. Simulation results demonstrate that the PS algorithm achieves comparable performance to the optimal MCP algorithm, while they both outperform the benchmark schemes in most cases.

Joint C-V2X Based Offloading and Resource Allocation in Multi-Tier Vehicular Edge Computing System

Emerging intelligent transportation services are latency-sensitive with heavy demand for computing resources, which can be supported by a multi-tier computing system composed of vehicular edge computing (VEC) servers along the roads and micro servers on vehicles. In this work, we investigate the dual Uu/PC5 interface offloading and resource allocation strategy in Cellular Vehicle-to-Everything (C-V2X) enabled multi-tier VEC system. The successful transmission probability is characterized to obtain the normalized transmission rate of PC5 interface. We aim to minimize the system latency of task processing while satisfying the resource requirements of Uu and PC5 interfaces. Due to the non-convex and variables coupling, we decompose the original problem into two subproblems, i.e., resource allocation and offloading strategy subproblems. Specifically, we derive the closed-form expressions of packet transmit frequency of PC5 interface, transmission power of Uu interface, and CPU computation frequency in the resource allocation subproblem. Moreover, for the offloading strategy subproblem, the offloading ratio matrix is obtained by proposing the PC5 interface based greedy offloading (PC5-GO) algorithm, which concludes offloading decision and ratio. Simulation results are provided that the proposed PC5-GO algorithm can significantly improve the system performance compared with other baseline schemes by 13.7% at least.

Task Offloading and Resource Allocation for Tasks with Varied Requirements in Mobile Edge Computing Networks

Edge computing enables devices with insufficient computing resources to offload their tasks to the edge for computing, to improve the service experience. Some existing work has noticed that the data size of offloaded tasks played a role in resource allocation shares but has not delved further into how the data size of an offloaded task affects resource allocation. Among offloaded tasks, those with larger data sizes often consume a larger share of system resources, potentially even monopolizing system resources if the data size is large enough. As a result, tasks with small or regular sizes lose the opportunity to be offloaded to the edge due to their limited data size. To address this issue, we introduce the concept of an emergency factor to penalize tasks with immense sizes for monopolizing system resources, while supporting tasks with small sizes to contend for system resources. The joint offloading decision and resource allocation problem is formulated as a mixed-integer nonlinear programming (MINLP) problem and further decomposed into an offloading decision subproblem and a resource allocation subproblem. Using the KKT conditions, we design a bisection search-based algorithm to find the optimal resource allocation scheme. Additionally, we propose a linear-search-based coordinate descent (CD) algorithm to identify the optimal offloading decision. Numerical results show that our proposed algorithm converges to the optimal scheme (for the minimal delay) when tasks are of regular size. Moreover, when tasks of immense, small and regular sizes coexist in the system, our scheme can exclude tasks of immense size from edge resource allocation, while still enabling tasks of small size to be offloaded.

Joint computation offloading and resource allocation strategy for D2D-assisted and NOMA-empowered MEC systems

Multi-access edge computing (MEC) emerged as a promising network paradigm that provides computation, storage and networking features within the edge of the pervasive mobile radio access network. This paper jointly considers computation offloading and resource allocation problem in device-to-device (D2D)-assisted and non-orthogonal multiple access (NOMA)-empowered MEC systems, where each mobile device (MD) is allowed to execute its task in one of the three ways, i.e., local computing, MEC offloading or D2D offloading. We invoke orthogonal multiple access (OMA) and NOMA schemes for MDs that select D2D offloading mode, allowing them to assign tasks to their peers using OMA or NOMA. The original problem is formulated as an overall energy consumption minimization problem, which proves to be NP-hard, making it intractable to solve optimally. We start from a simple case, OMA case and transform the original problem into two sub-problems, i.e., resource allocation sub-problem and computation offloading sub-problem and propose two heuristic algorithms to obtain the sub-optimal solutions of both sub-problems. Then, for the MDs selecting D2D offloading mode, we conduct user pairing and apply the NOMA scheme. Finally, simulation results demonstrate the efficiency of the proposed scheme when compared with the related schemes.