DRL-Assisted Resource Allocation for NOMA-MEC Offloading with Hybrid SIC

Multi-access edge computing (MEC) and non-orthogonal multiple access (NOMA) are regarded as promising technologies to improve the computation capability and offloading efficiency of mobile devices in the sixth-generation (6G) mobile system. This paper mainly focused on the hybrid NOMA-MEC system, where multiple users were first grouped into pairs, and users in each pair offloaded their tasks simultaneously by NOMA, then a dedicated time duration was scheduled to the more delay-tolerant user for uploading the remaining data by orthogonal multiple access (OMA). For the conventional NOMA uplink transmission, successive interference cancellation (SIC) was applied to decode the superposed signals successively according to the channel state information (CSI) or the quality of service (QoS) requirement. In this work, we integrated the hybrid SIC scheme, which dynamically adapts the SIC decoding order among all NOMA groups. To solve the user grouping problem, a deep reinforcement learning (DRL)-based algorithm was proposed to obtain a close-to-optimal user grouping policy. Moreover, we optimally minimized the offloading energy consumption by obtaining the closed-form solution to the resource allocation problem. Simulation results showed that the proposed algorithm converged fast, and the NOMA-MEC scheme outperformed the existing orthogonal multiple access (OMA) scheme.     

A joint resource allocation method for hybrid NOMA MEC offloading

In this article, a joint resource allocation of power, time, and sub-channels that minimizes the total energy consumption of users for Hybrid NOMA MEC Offloading is proposed. By formulating and solving the joint optimization problem, first we propose a novel optimal Hybrid NOMA scheme referred to as Switched Hybrid NOMA (SH-NOMA) for power and time allocation. Subsequently, we address sub-channel allocation as a three-dimensional assignment problem, and propose the Total-Reward Exchange Stable (TES) algorithm to solve it. Analytically, we show that SH-NOMA is more energy efficient than the Hybrid NOMA scheme in the literature and that the TES algorithm converges to a solution with less energy consumption than the widely used two-sided exchange stable algorithm. Finally, via simulations we demonstrate that the proposed methods outperform the results in the literature.     

Resource optimization for UAV-assisted mobile edge computing system based on deep reinforcement learning

Computational efficiency is a direction worth considering in moving edge computing (MEC) systems. However, the computational efficiency of UAV-assisted MEC systems is rarely studied. In this paper, we maximize the computational efficiency of the MEC network by optimizing offloading decisions, UAV flight paths, and allocating users’ charging and offloading time reasonably. The method of deep reinforcement learning is used to optimize the resources of UAV-assisted MEC system in complex urban environment, and the user’s computation-intensive tasks are offloaded to the UAV-mounted MEC server, so that the overloaded tasks in the whole system can be alleviated. We study and design a framework algorithm that can quickly adapt to task offload decision making and resource allocation under changing wireless channel conditions in complex urban environments. The optimal offloading decisions from state space to action space is generated through deep reinforcement learning, and then the user’s own charging time and offloading time are rationally allocated to maximize the weighted sum computation rate. Finally, combined with the radio map to optimize the UAC trajectory to improve the overall weighted sum computation rate of the system. Simulation results show that the proposed DRL+TO framework algorithm can significantly improve the weighted sum computation rate of the whole MEC system and save time. It can be seen that the MEC system resource optimization scheme proposed in this paper is feasible and has better performance than other benchmark schemes.     

Fairness optimization in IRS-assisted MEC computational offloading

Nowadays, more and more multimedia services are supported by Mobile Edge Computing (MEC). However, the instability of the wireless environment brings a lot of uncertainty to the computational offloading. Additionally, intelligent reflecting surface (IRS) is considered as a potential technology to enhance Quality of Service (QoS). Therefore, in this paper, we establish a framework for IRS-assisted MEC computational offloading to solve this problem and take fairness optimization as a key point involving communication and computing resources. Minimize user consumption by optimizing bandwidth allocation, task offloading ratio, edge computing resources, transmission power and IRS phase shifts. Firstly, we decompose the problem into three aspects, such as bandwidth allocation, computing resource allocation, transmission power and IRS phase shifts. Then, an alternative optimization algorithm is proposed to find the optimum solution and its convergence is proved. Secondly, since the optimization problem on transmission power and IRS phase shifts is non-convex, we propose Riemann gradient descent (R-SGD) algorithm to solve it. Finally, numerical results show that our proposed algorithm performs better than other algorithms and achieves a superiority in the framework.     

Subchannel and power optimization for sum rate maximization in downlink multicarrier NOMA networks

In a multicarrier NOMA system, the subchannel allocation (SA) and power allocation (PA) are intricately linked and essential for improving system throughput. Also, for the successful execution of successive interference cancellations (SIC) at the receiver, a minimum power gap is required among users. As a result, this research comes up with optimization of the SA and PA to maximize the sum rate of the NOMA system while sticking to the minimum power gap constraint in addition to minimum user rate, maximum number of users in a subchannel and power budget constraints for downlink transmission in multicarrier NOMA networks. To ensure that the formulated problem can be solved in polynomial time, we propose solving it in two stages; SA followed by PA. To obtain SA, we investigate four algorithms: Greedy, WSA, WCA, and WCF. For PA, we propose a low-complexity algorithm. We compare the performance of the proposed method with benchmark method that does not consider the minimum power gap constraint. We conclude that employing WCF algorithm with the PA algorithm gives the best sum rate performance.     

Deep reinforcement learning based IRS-assisted mobile edge computing under physical-layer security

In this paper, we investigate an intelligent reflecting surface (IRS)-assisted mobile edge computing (MEC) network under physical-layer security, where users can partially offload confidential and compute-intensive tasks to a computing access point (CAP) with the help of the IRS. We consider an eavesdropping environment, where an eavesdropper steals information from the communication. For the considered MEC network, we firstly design a secure data transmission rate to ensure physical-layer security. Moreover, we formulate the optimization target as minimizing the system cost linearized by the latency and energy consumption (ENCP). In further, we employ a deep deterministic policy gradient (DDPG) to optimize the system performance by allocating the offloading ratio and wireless bandwidth and computational capability to users. Finally, considering the impacts from different resources, based on DDPG, seeing our optimization strategy as one criterion, we designed other criteria with different resource allocation schemes. And some simulation results are given to demonstrate that our proposed criterion outperforms other criteria.     

Secure computation offloading assisted by intelligent reflection surface for mobile edge computing network

As an novel paradigm, computation offloading in the mobile edge computing (MEC) system can effectively support the resource-intensive applications for the mobile devices (MD) equipped with limited computing capability. However, the hostile radio transmission and data leakage during the offloading process may erode the MEC system’s potential. To tackle these hindrances, we investigate an IRS-assisted secure MEC system with eavesdroppers, where the intelligent reflecting surface (IRS) is deployed to enhance the communication between the MD and the AP equipped with edge servers and the malicious eavesdroppers may attack the wireless data offloaded by MD. The MD opt for offloading part of the tasks to the edge server for execution to support the computation-intensive applications. Moreover, the relevant latency minimization problem is formulated by optimizing the offloading ratio, the allocation of edge server computing capability, the multiple-user-detection (MUD) matrix and the IRS phase shift parameters, subject to the constraints of edge computation resource and practical IRS phase shifts. Then, the original problem is decouple into two subproblem, and the computing and communication subproblems are alternatively optimized by block coordinate descent (BCD) method with low complexity. Finally, simulation results demonstrate that the proposed scheme can significantly enhance the performance of secure offloading in the MEC system.     

Time allocation improvement method for UAV-based wireless energy transfer cooperative mobile edge

Wireless power transfer (WPT) and mobile edge computing (MEC) are two advanced technologies that could improve the computing power and range of mobile devices. However, by integrating the unmanned aerial vehicle (UAV) into wireless powered MEC systems, wireless energy transfer will be susceptible to the “double near–far” effect. Therefore, in order to further overcome the influence of the “double near–far” effect, this paper considers the optimization of time slot allocation for UAV-assisted wireless powered cooperative MEC system, which includes an access point (UAV) and two mobile devices. The purpose of the study is to minimize the total transmission energy of the UAV while satisfying the delay and size of the computational tasks, so this paper proposed a 2:1:1 time-slot optimization allocation method. The method exploits the synergy of users so that the mobile device which is closer to the UAV acts as an offloading relay, by combining power and time slot optimization to minimize the total energy consumption of the UAV. Compared with the equal time slot scheme before the improvement, this method can not only utilize the wireless transmission energy to charge the mobile device for more time in the first period, but also can save the time of data transmission of the closer device in the third period, and it can enhance the rate of data transmission of the mobile devices at the same time. The results show that the task capacity of the system computed will be increased compared to the original scheme; the total transmission rate of the whole system is also improved by the same order of magnitude. The simulation results verify the effectiveness and reliability of the algorithm of the paper, and the comprehensive performance of the system can be maximized by the flexible offloading algorithm.     

Joint user grouping and power allocation in VLC-NOMA system

With the energy consumption of wireless networks increasing, visible light communication (VLC) has been regarded as a promising technology to realize energy conservation. Due to the massive terminals access and increased traffic demand, the implementation of non-orthogonal multiple access (NOMA) technology in VLC networks has become an inevitable trend. In this paper, we aim to maximize the energy efficiency in VLC-NOMA networks. Assuming perfect knowledge of the channel state information of user equipment, the energy efficiency maximization problem is formulated as a mixed integer nonlinear programming problem. To solve this problem, the joint user grouping and power allocation (JUGPA) is proposed including user grouping and power allocation. In user grouping phase, we utilize the average of channel gain among all user equipment and propose a dynamic user grouping algorithm with low complexity. The proposed scheme exploits the channel gain differences among users and divides them into multiple groups. In power allocation phase, we proposed a power allocation algorithm for maximizing the energy efficiency for a given NOMA group. Thanks to the objective function is fraction form and non-convex, we firstly transform it to difference form and convex function. Then, we derive the closed-form optimal power allocation expression that maximizes the energy efficiency by Dinkelbach method and Lagrange dual decomposition method. Simulation results show that the JUGPA can effectively improve energy efficiency of the VLC-NOMA networks.     

On the performance of a virtual user pairing scheme to efficiently utilize the spectrum of unpaired users in NOMA

This paper evaluates the performance of a virtual user pairing scheme that efficiently utilizes the spectrum of unpaired users in non-orthogonal multiple access (NOMA), termed as VP-NOMA. The scheme aims at utilizing the frequency bands of those users which remain unpaired due to the non-uniform distribution of users in a cellular area. We consider a case where the cell edge users are more than the cell center users, so that complete one-to-one correspondence does not exist between all cell center and cell edge users to be accommodated/paired using conventional NOMA (C-NOMA) user pairing. Thus, some cell edge users remain unpaired, and are served using conventional multiple access (OMA) schemes. In such scenario, VP-NOMA pairs a single cell center user with two or more clustered (closely located) cell edge users over non-overlapping frequency bands, thus enabling the cell center user to efficiently use the frequency bands of these previously unpaired cell edge users. Performance of VP-NOMA in terms of ergodic sum capacity (ESC), outage probability (OP), and outage sum capacity (OSC), is analyzed through comprehensive mathematical derivations and simulations for a generalized system model. Moreover, the mathematical analysis is validated through close concordance between analytical and simulation results of ESC, OP, and OSC.     

Computation rate maximization for IRS-enhanced dynamic spectrum access assisted MEC system

Intelligent reflecting surface (IRS)-enhanced dynamic spectrum access (DSA) is a promising technology to enhance the performance of the mobile edge computing (MEC) system. In this paper, we consider the integration of the IRS enhanced DSA technology to a MEC system, and study the pertinent joint optimization of the phase shift coefficients of the IRS, the transmission powers, the central processing unit (CPU) frequencies, as well as the task offloading time allocations of the secondary users (SUs) to maximize the average computation bits of the SUs. Due to the non-convexity, the formulated problem is difficult to solve. In order to tackle this difficulty, we decompose the optimization problem into tractable subproblems and propose an alternating optimization algorithm to optimize the optimization variables in an iterative fashion. Numerical results are provided to show the effectiveness and the correctness of the proposed algorithm.     

Secure energy efficiency maximization for dual-UAV-assisted intelligent reflecting surface system

In this paper, we focus on minimizing energy consumption under the premise of ensuring the secure offloading of ground users. We used dual UAVs and intelligent reflective surfaces (IRS) to assist ground users in offloading tasks. Specifically, one UAV is responsible for collecting task data from ground users, and the other UAV is responsible for sending interference noise to counter potential eavesdroppers. The IRS can not only improve the transmission capacity of ground users, but also reduce the acceptance of eavesdroppers. The original problem is strong non-convex, so we consider using the block coordinate descent method. For the proposed sub-problems, we use Lagrangian duality and first-order Taylor expansion to obtain the results, and finally achieve system design through alternate optimization. The simulation results show that our proposed scheme is significantly better than other existing schemes.     

Computation bits maximization in backscatter-aided wireless powered MEC using binary offloading

This article considers a backscatter-aided wireless powered mobile edge computing (BC-aided WPMEC) network, in which the tasks data of each Internet of Things (IoT) device can be computed locally or offloaded to the MEC server via backscatter communications, and design a resource allocation scheme regarding the weighted sum computation bits (WSCB) maximization of all the IoT devices. Towards this end, by optimizing the mobile edge computing (MEC) server’s transmit power, IoT devices’ power reflection coefficients, local computing frequencies and time, the time allocation between the energy harvesting and task offloading, as well as the binary offloading decision at each IoT device, we built a WSCB maximization problem, which belongs to a non-convex mixed integer programming problem. For solving this, the proof by contradiction and the objective function’s monotonicity are considered to determine the optimal local computing time of each IoT device and the optimal transmit power of the MEC server, and the time-sharing relaxation (TSR) is adopted to tackle the integer variables, which are used to simplify the original problem. Then, we decouple the simplified problem into two sub-problems by means of the block coordinate decent (BCD) technology, and each of the sub-problems is transformed to a convex one by introducing auxiliary variables. Based on this, we design a two-stage alternative (TSA) optimization algorithm to solve the formulated WSCB problem. Computer simulations validate that the TSA algorithm has a fast convergent rate and also demonstrate that the proposed scheme achieves a higher WSCB than the existing schemes.     

Energy efficiency maximization for IRS-assisted NOMA networks

In this paper, we consider a non-orthogonal multiple access (NOMA) system assisted by intelligent reflecting surface (IRS). As an emerging technology that has received widespread attention, IRS can reconfigure the wireless channel environment by adjusting the relationship between the incident angle and the exit angle, thereby improving system performance. Our goal is to use the flexible assistance of IRS to achieve the maximum energy efficiency of the NOMA system. Our design objects are the beam vector design of the base station and the phase matrix design of the IRS. The original problem is highly non-convex. We consider using the block coordinate descent method to design the phase matrix and beam vector separately. Simulation results show that our proposed scheme has better performance than traditional OMA and systems without IRS assistance.     

Multi-Task Offloading Based on Optimal Stopping Theory in Edge Computing Empowered Internet of Vehicles

Vehicular edge computing is a new computing paradigm. By introducing edge computing into the Internet of Vehicles (IoV), service providers are able to serve users with low-latency services, as edge computing deploys resources (e.g., computation, storage, and bandwidth) at the side close to the IoV users. When mobile nodes are moving and generating structured tasks, they can connect with the roadside units (RSUs) and then choose a proper time and several suitable Mobile Edge Computing (MEC) servers to offload the tasks. However, how to offload tasks in sequence efficiently is challenging. In response to this problem, in this paper, we propose a time-optimized, multi-task-offloading model adopting the principles of Optimal Stopping Theory (OST) with the objective of maximizing the probability of offloading to the optimal servers. When the server utilization is close to uniformly distributed, we propose another OST-based model with the objective of minimizing the total offloading delay. The proposed models are experimentally compared and evaluated with related OST models using simulated data sets and real data sets, and sensitivity analysis is performed. The results show that the proposed offloading models can be efficiently implemented in the mobile nodes and significantly reduce the total expected processing time of the tasks.     

Integration of multi access edge computing with unmanned aerial vehicles: Current techniques,open issues and research directions

During the last decade, research and development in the field of multi access edge computing (MEC) has rapidly risen to prominence. One of the factors propelling MEC’s evolution is the ability to deploy edge servers capable of providing both communication and computational services in close proximity to the mobile user terminal. MEC has been regarded as a potentially transformative technique for fifth-generation (5G) and beyond 5G (B5G) wireless communication systems, as well as a possible complement to traditional cloud computing. Additionally, unmanned aerial vehicles (UAVs) integrated with MEC will play a critical role by introducing an additional mobility based computational layer to provide more secure, efficient and faster services. UAV enabled MEC offers seamless connectivity, fulfilling the promise of 5G’s ubiquitous connectivity. Due to the enormous interest in UAV enabled MEC, there has been a tremendous increase in the number of published research articles in this domain; however, the research area still lacks a systematic study and categorization. We present a systematic literature review (SLR) on UAV enabled MEC, examining and analyzing data on the current state of the art using preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. To streamline our assessment, this study analyzes several research papers carefully selected through a multi-stage process satisfying the eligibility criteria defined in the paper. One of the SLR’s primary contributions is to broadly classify the research in the UAV enabled MEC domain into different categories including energy efficiency, resource allocation, security, architecture, and latency. We have identified key findings, technology, and pros and cons for the selected articles under each category. Additionally, we discuss the key open issues related to scalability and fairness, resource allocation and offloading optimization, service delivery with a focus on quality of experience (QoE) and quality of service (QoS), and standardization. Finally, we discuss several future research directions that would address the aforementioned issues and emerging use cases for UAV enabled MEC.     

Deployment and trajectory design of fixed-wing UAVs in NOMA assisted wireless networks

This paper proposes a deployment and trajectory scheme for fixed-wing unmanned aerial vehicles (UAVs) deployed as flying base stations in multi-UAV enabled non-orthogonal multiple access (NOMA) downlink communication. Specifically, the deployment of UAVs and power allocation of users are jointly optimized to maximize the sum-rate. Thereafter, the energy efficiency maximization problem is formulated to optimize the trajectory of UAVs by jointly considering the quality of service (QoS) requirement of users, various flight constraints, limited on-board energy, and users’ mobility. Initially, the existing users are divided into clusters by k-means clustering, where each cluster is served by a single UAV. Then, the clusters are further divided into multiple sub-clusters, each having a pair of near and far users. Orthogonal multiple access (OMA) is applied among sub-clusters and NOMA is applied to intra sub-cluster users. Lastly, the Balanced-grey wolf optimization (B-GWO) algorithm is proposed for solving the non-convex optimization problems. Simulation results prove the superiority of the B-GWO based deployment and trajectory algorithms compared to the benchmarks. In addition, the proposed B-GWO based trajectory algorithm achieves a near-optimal performance with an optimality gap of less than 1.5% compared to the exhaustive search.     

Optimization design of RIS-assisted high-capacity visible light communications based on HDMA

In this paper, a new non-orthogonal multiple access (NOMA) scheme is proposed for the reconfigurable intelligent surface (RIS) assisted high-capacity visible light communication (VLC) system, which is named hybrid domain multiple access (HDMA). HDMA enjoys the benefit of hybrid-domain signals, including the power domain, code domain, and frequency domain, where the message passing algorithm (MPA) and successive interference cancellation (SIC) detectors are jointly used at the HDMA receiver. Furthermore, to achieve a higher communication capacity for the VLC system, we proposed an optimization model by jointly optimizing the power allocation ratio and RIS reflection units. The simulation results verified the proposed scheme. By comparing the system capacity of different RIS allocation schemes and multiple access methods, the VLC system based on HDMA proposed in this paper can significantly improve its communication capacity.     

Secure efficiency maximization for UAV-assisted mobile edge computing networks

This paper considers an unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system, in which the intelligent reflecting surface (IRS) is applied to enhance the performance of the wireless transmission. The role of the UAV is twofold: (1) It is equipped with a MEC server and receives computing tasks from ground users and IRS at the same time; (2) It sends interference signals to counter the potential eavesdropper. Here, the UAV is working as a full duplex equipment, i.e., sending and receiving meanwhile. We comprehensively considered the flight speed constraint of the UAV, the total mission data constraint and the minimum security rate constraint of multiple users on the ground. The phase matrix constraints of IRS are also considered. Our system is dedicated to maximizing the efficiency of secure computing. The formulated problem is highly non-convex, we consider to propose an alternative optimization algorithm. The simulation results show that the proposed scheme not only achieves higher safe computing efficiency, but also has better performance in terms of energy consumption and security rate.     

Game theory-based radio resource allocation in NOMA vehicular communication networks supported by UAV

Vehicular communication networks are emerging as a promising technology to provide high-quality internet service such as entertainment for road users via infrastructure-to-vehicle (I2V) communication, and to guarantee road users’ safety via vehicle-to-vehicle (V2V) communication. Some technical issues that impact the performance of these networks are the lack of or poor communication paths between vehicles, and the limitation of radio resources. Unmanned aerial vehicles (UAVs) as promising solutions for supporting vehicular networks could provide communication coverage in hazardous environments and areas with no capacities for installation or maintenance of ground base stations (BSs). Also, non-orthogonal multiple access (NOMA) methods can improve spectral and energy efficiency and thereby allow more users to be connected to the desired network. In this paper, exploring the NOMA, we develop a scheme for optimum resource allocation in presence of a UAV that supports vehicular communications. Resource allocation for this scenario is formulated as a mixed-integer non-linear programming (MINLP) problem. Due to the high complexity of such problems, we propose two low-complexity near-optimal methods. First, we apply difference-of-concave-functions (DC) approximations to solve the problem in an iterative process. Next, we use Stackelberg game-based method for efficient solving, and then, closed-form expressions of optimal power allocations using KKT-conditions are derived. Simulations illustrate the effectiveness of the proposed scheme along with the Stackelberg game-based method.