Resource Allocation For Non-orthogonal Multiple Access Research Articles

Dynamic resource allocation for energy-efficient downlink NOMA systems in 5G networks

Non-Orthogonal Multiple Access (NOMA) is a promising energy-efficient technology designed to satisfy the demands of future networks by efficiently sharing radio resources. In NOMA, the same radio resource is simultaneously assigned to two users at different power levels based on the NOMA-power domain. Resource allocation in NOMA presents a non-convex challenge, characterized as a non-deterministic polynomial (NP-hard) problem. This involves user and channel assignment and power allocation, making it an extraordinarily complex task to achieve an optimal solution. In this work, Simulated Annealing (SA) is proposed as an optimization technique for resource allocation in an energy-efficient downlink NOMA system. This resource allocation scheme addresses user and channel assignment, as well as power allocation, using SA as an efficient standalone approach to maximize energy efficiency in NOMA. SA is utilized to execute the assignment of users to channels, distribute the necessary power for each channel, and determine the power ratio among users sharing the same channel. The results obtained demonstrate a significant improvement in energy efficiency, outperforming the existing numerical methods by 22 %. The proposed SA scheme not only achieves a close optimal solution but also in less computational time, offering sufficient reliability in terms of energy efficiency enhancement when compared to the existing numerical method.

Computation Offloading and Resource Allocation in NOMA–MEC: A Deep Reinforcement Learning Approach

Multi-access edge computing (MEC) has emerged as a powerful paradigm for increasing the computation performance of mobile devices (MDs). Applying non-orthogonal multiple access (NOMA) to MEC can further improve the spectrum efficiency and reduce offloading delays caused by the upload congestion. In this paper, we examine the joint computation offloading and resource allocation problem in the NOMA-MEC system, which benefits from the combination of NOMA and MEC. Our optimization objective is to minimize the computational overhead (the weighted sum of the execution delay and the energy consumption) in dynamic environments with time-varying wireless fading channels. The optimization problem is formulated as a mixed integer programming (MIP), which involves jointly optimizing the task offloading decisions, channel assignment, and transmit power allocation. To solve such an optimization problem, we formalize the task offloading and the resource allocation as a Markov decision process (MDP). Then, we propose a deep reinforcement learning (DRL) based approach, which combines multiple deep neural networks (DNNs) to directly approximate different statistical models for continuous and discrete control. The simulation results demonstrate that the proposed approach can rapidly converge and efficiently decrease the total computational overhead compared to other baseline approaches in different scenarios.

Performance Study of Downlink Users in Non-Orthogonal Multiple Access (NOMA) for 5G Communications

An outline of NOMA principles is provided in this article. Furthermore, this page discusses cooperative NOMA and its variations, explains power allocation in detail as a technique of resource allocation for NOMA, and gives an overview of the research challenges related to NOMA. We then, use a two-case scenario, to suggest a dynamic power allocation (DPA) plan for the downlink NOMA users, albeit it can also be expanded to many use cases. The DPA relies on channel state information (CSI) to guarantee the quality of service (QOS) for cell center customers (user c). The Outage probability (OP) as a critical performance criterion simulation data is also provided, and they demonstrate a notable performance improvement when DPA is employed compared to fixed power allocation.

Joint resource and power allocation for 5G enabled D2D networking with NOMA

Paramount data transfer among users in social groups and concerts imposes enormous traffic at the base station (BS), depleting the available resources and forcing fifth-generation (5G) researchers to seek communion with alternative technologies. Non-Orthogonal Multiple Access (NOMA) is determined as a potential technology for supporting multiple users on the same sub-channels using power domain multiplexing. Integration of NOMA with a Device-to-Device (D2D) communication technology provides lower latency for supporting proximity communication. Significant research has been carried out till date in device pairing for resource allocation in NOMA and D2D. This paper proposes a joint resource and power allocation in the NOMA (JRPAN) algorithm for resource and power allocation to cellular and D2D users. Resources and power are adaptively assigned to the users depending upon the user's application. A proposal of group formation for resource reuse in NOMA with prioritized user application is established. The Simulations have been performed to ascertain the fair allocation of resources to the users in conformity to the application. The proposed algorithm helps in conserving power as compared to orthogonal frequency division multiplexing (OFDMA) with the Hidden Markov Model (HMM). The results demonstrate that JRPAN achieves higher system throughput and saves power while ensuring the requirement of Quality of Experience and Quality of service in comparison to OFDMA-HMM and proportional algorithm.

Deep learning for online computation offloading and resource allocation in NOMA

The limited battery capacity and low computing capability of wireless Internet of Things (IoT) devices can hardly support computation-intensive and delay-sensitive applications. While recent development of wireless power transfer (WPT) and mobile edge computing (MEC) technologies help IoT devices harvest energy and offload computation tasks to edge servers. While it is still challenging to design an efficient offloading policy to improve the performance of the IoT network. In this article, we consider a MEC network that has WPT capability and adopts the non-orthogonal multiple access (NOMA) technology to offload tasks partially. Our goal is to propose an online algorithm to optimize resource allocation under a wireless dynamic channel scenario. In order to obtain the optimal offloading decision and resource allocation efficiently, we propose a Deep Reinforcement learning-based Online Sample-improving (DROS) framework which implements a deep neural network to input the discretized channel gains to obtain the optimal WPT duration. Based on the WPT duration derived by DNN, we design an optimization algorithm to derive the optimal energy proportion for offloading data. Numerical results verify that compared with traditional optimization algorithms, our proposed DROS has significantly sped up convergence for better solutions.

Underpinnings of User-Channel Allocation in Non-orthogonal Multiple Access for 5G

Non-orthogonal multiple access (NOMA) is a part of 5th generation (5G) communication systems. This article presents the underpinnings and underlying structures of the problem of NOMA user-channel allocation. Unlike the heuristics for NOMA user-channel allocation, the presented results are guaranteed to converge to a solution. In addition, the solutions are stable. More specifically, the deployment of results to cellular vehicular communication systems is shown as a use case of 5G technology in smart transport. Generally, the results apply to any NOMA system. Unlike the orthogonal frequency division multiple access resource allocation problem, the core matching is not the solution to NOMA resource allocation. The conditions under which the fix-point NOMA resource allocation is guaranteed to be stable from the viewpoint of both the base station and the NOMA users are described. In addition, relationships of NOMA user-channel resource allocation to game models and subgame perfect Nash equilibria are elucidated.

Joint Uplink and Downlink Resource Allocation in NOMA for End-to-End URLLC Services

Non-orthogonal multiple access (NOMA) has great potential to increase spectral efficiency and reduce latency. However, reliability problems of NOMA limit its applications in ultra-reliable and low-latency communications (URLLC). For the first time in this letter, the optimal resource allocation scheme is investigated for uplink (UL) and downlink (DL) NOMA in URLLC communications. Specifically, we consider an UL and DL NOMA system with two-pair of users, and try to minimize the overall error probability under stringent URLLC requirements. Then, an optimal scheme for both UL and DL resources allocation is proposed, and a global optimal solution for both power control and channel blocklength allocation is obtained. Finally, simulation results show that NOMA significantly outperforms the orthogonal multiple access (OMA) subjected to the same power and channel blocklength levels and demonstrate the availability of NOMA for end-to-end (E2E) URLLC services.

NOMA resource allocation method in IoV based on prioritized DQN-DDPG network

To meet the demands of massive connections in the Internet-of-vehicle communications, non-orthogonal multiple access (NOMA) is utilized in the local wireless networks. In NOMA technique, various optimization methods have been proposed to provide optimal resource allocation, but they are limited by computational complexity. Recently, the deep reinforcement learning network is utilized for resource optimization in NOMA system, where a uniform sampled experience replay algorithm is used to reduce the correlation between samples. However, the uniform sampling ignores the importance of sample. To this point, this paper proposes a joint prioritized DQN user grouping and DDPG power allocation algorithm to maximize the system sum rate. At the user grouping stage, a prioritized sampling method based on TD-error (temporal-difference error) is proposed. At the power allocation stage, to deal with the problem that DQN cannot process continuous tasks and needs to quantify power into discrete form, a DDPG network is utilized. Simulation results show that the proposed algorithm with prioritized sampling can increase the learning rate and perform a more stable training process. Compared with the previous DQN algorithm, the proposed method improves the sum rate of the system by 2% and reaches 94% and 93% of the exhaustive search algorithm and optimal iterative power optimization algorithm, respectively. Although the sum rate is improved by only 2%, the computational complexity is reduced by 43% and 64% compared to the exhaustive search algorithm and the optimal iterative power optimization algorithm, respectively.

Coalitional game‐based user pairing and power allocation in downlink non‐orthogonal multiple access networks

AbstractThis article studies the user pairing and power allocation in a downlink non‐orthogonal multiple access (NOMA) network in which users form different pairs accessing orthogonal channels and NOMA is utilized in each channel. Existing studies mainly focused on throughput optimization, while throughput cannot completely reflect users' quality of experience. Therefore, we aim to maximize the network's sum of mean opinion scores rather than throughput to meet different traffic demands of users. The mixed integer nonlinear programming problem is decomposed into two subproblems. The first user pairing problem is modeled as a coalition formation game, and a Nash‐stable suboptimal pairing scheme is obtained via proposed coalition formation algorithm. The second power allocation problem is modeled as a knapsack problem, and we obtain the optimal power allocation scheme through dynamic programming with bisection method. These two subproblems are solved alternately until converging to the final resource allocation strategy. Simulation results show that the proposed strategy outperforms existing NOMA resource allocation techniques and achieves near‐optimal performance.

Height Optimization and Resource Allocation for NOMA Enhanced UAV-Aided Relay Networks

In this paper, we investigate the application of the non-orthogonal multiple access (NOMA) technique into the unmanned aerial vehicle (UAV) aided relay networks. Specifically, we first incorporate the NOMA protocol with the decode-and-forward (DF) relay protocol to enhance the performance of the cell edge users in a macrocell network. Theoretical analysis indicates that the NOMA-DF-relay protocol outperforms the conventional orthogonal multiple access (OMA) based DF-relay protocol in terms of data rate. To fully exploit the advantages of the proposed protocol, we formulate a joint UAV height optimization, channel allocation, and power allocation problem with the objective to maximize the total data rate of the cell edge users under the coverage of the UAV. For solving the formulated problem effectively, we first analyze its property and employ the golden section method to propose a general framework to obtain the optimal height of the UAV. Then, we design a low-complexity iterative algorithm to solve the joint channel-and-power allocation problem based on the matching theory and the Lagrangian dual decomposition technique. Finally, simulation results demonstrate that the NOMA-DF-relay protocol is superior to the OMA-DF-relay protocol even when the system parameters are not optimized, and the proposed algorithms can further significantly improve the network performance in comparison with the other schemes.

Network Utility Maximization Resource Allocation for NOMA in Satellite-Based Internet of Things

High-throughput satellite (HTS) is viewed as a promising solution for the next generation of satellite-based Internet of Things (S-IoT). Considering that the onboard communication resources, such as power and storage, are limited, we formulate a joint network stability and resource allocation optimization problem to maximize the long-term network utility of a nonorthogonal multiple access (NOMA) S-IoT downlink system. First, we establish two virtual queues for both the data queueing and power expenditure. Then, a joint optimal problem can be formulated as a problem that optimizes the time average of network utility, which perfectly matches the Lyapunov optimization framework. Therefore, by taking into account the condition of successive interference cancellation decoding, we propose a practical solution under the Karush–Kuhn–Tucker (KKT) conditions, and further introduce an optimal solution by using the particle swarm optimization (PSO) algorithm for the joint resource allocation problem. The simulation results demonstrate that our joint optimization allocation schemes outperform the existing benchmark schemes.

Sparse Code Multiple Access-Based Edge Computing for IoT Systems

In this paper, a sparse code multiple access (SCMA)-based edge computing scheme is proposed for Internet-of-Things (IoT) systems. The aim of implementing SCMA, which is a nonorthogonal multiple access resource allocation technique, is to improve network connectivity and maximize data rate provision. The proposed edge-IoT system is investigated under different SCMA configurations to explore the various performance aspects such as connectivity, throughput, task completion time, and complexity. First, the problem is formulated as a data rate maximization problem for SCMA-based heterogeneous networks under power constraints. Then, the problem is subdivided into a power allocation problem, which is solved using the water filling approach, and a codebook allocation problem that is solved using a heuristic algorithm. The results show that the SCMA scheme can significantly improve the IoT performance compared to the conventional orthogonal frequency-division multiple access resource allocation scheme in terms of connectivity, throughput, and task completion time provided that SCMA configurations are suitable with IoT processing capabilities to avoid undesired detection latency.

Energy-Efficient and Secure Resource Allocation for Multiple-Antenna NOMA With Wireless Power Transfer

Non-orthogonal multiple access (NOMA) is considered as one of the promising techniques for providing high data rates in the fifth generation mobile communication. By applying successive interference cancellation schemes and superposition coding at the NOMA receiver, multiple users can be multiplexed on the same subchannel. In this paper, we investigate resource allocation algorithm design for an OFDM-based NOMA system empowered by wireless power transfer (WPT). In the considered system, users who need to transmit data can only be powered by the WPT. With the consideration of an existing eavesdropper, the objective is to obtain secure and energy efficient transmission among multiple users by optimizing time, power and subchannel allocation. Moreover, we also take into consideration for the practical case that the statistics of the channel state information of the eavesdropper is not available. In order to address the optimization problem and its high computational complexity, we propose an iterative algorithm with guaranteed convergence to deliver an upper bound and a suboptimal solution in more general cases. For some special cases, we identify the optimality condition that ensures the global optimum in our algorithm. Extensive simulation studies demonstrate the competitiveness and effectiveness of the proposed algorithmic solution over conventional OFDMA systems as well as over other existing NOMA resource allocation schemes.

‐Fair resource allocation in non‐orthogonal multiple access systems

A non-orthogonal multiple access (NOMA) system is now considered as a promising radio access technique for next-generation networks owing to its benefits, e.g. spectral efficiency improvement. Due to the successive interference cancellation order at receivers, fairness among users in NOMA may not be guaranteed. In this study, the authors focus on α -fair resource allocation in NOMA. The complexity of the considered problem is then analysed. In particular, the problem is shown to be convex when 1 ≤ α < ∞ and α = ∞ , non-deterministic polynomial-time (NP)-hard when 0 < α < 1 , and polynomial-time solvable when α = 0 . Finally, simulation results are provided to examine the effects of the fairness degree on the system performance and verify the effectiveness of the proposed algorithms.

Joint User Scheduling and Power Allocation Optimization for Energy-Efficient NOMA Systems With Imperfect CSI

Non-orthogonal multiple access (NOMA) exploits successive interference cancellation technique at the receivers to improve the spectral efficiency. By using this technique, multiple users can be multiplexed on the same subchannel to achieve high sum rate. Most previous research works on NOMA systems assume perfect channel state information (CSI). However, in this paper, we investigate energy efficiency improvement for a downlink NOMA single-cell network by considering imperfect CSI. The energy efficient resource scheduling problem is formulated as a non-convex optimization problem with the constraints of outage probability limit, the maximum power of the system, the minimum user data rate, and the maximum number of multiplexed users sharing the same subchannel. Different from previous works, the maximum number of multiplexed users can be greater than two, and the imperfect CSI is first studied for resource allocation in NOMA. To efficiently solve this problem, the probabilistic mixed problem is first transformed into a non-probabilistic problem. An iterative algorithm for user scheduling and power allocation is proposed to maximize the system energy efficiency. The optimal user scheduling based on exhaustive search serves as a system performance benchmark, but it has high computational complexity. To balance the system performance and the computational complexity, a new suboptimal user scheduling scheme is proposed to schedule users on different subchannels. Based on the user scheduling scheme, the optimal power allocation expression is derived by the Lagrange approach. By transforming the fractional-form problem into an equivalent subtractive-form optimization problem, an iterative power allocation algorithm is proposed to maximize the system energy efficiency. Simulation results demonstrate that the proposed user scheduling algorithm closely attains the optimal performance.

Power and Channel Allocation for Non-Orthogonal Multiple Access in 5G Systems: Tractability and Computation

Network capacity calls for significant increase for 5G cellular systems. A promising multi-user access scheme, non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC), is currently under consideration. In NOMA, spectrum efficiency is improved by allowing more than one user to simultaneously access the same frequency-time resource and separating multi-user signals by SIC at the receiver. These render resource allocation and optimization in NOMA different from orthogonal multiple access in 4G. In this paper, we provide theoretical insights and algorithmic solutions to jointly optimize power and channel allocation in NOMA. For utility maximization, we mathematically formulate NOMA resource allocation problems. We characterize and analyze the problems' tractability under a range of constraints and utility functions. For tractable cases, we provide polynomial-time solutions for global optimality. For intractable cases, we prove the NP-hardness and propose an algorithmic framework combining Lagrangian duality and dynamic programming (LDDP) to deliver near-optimal solutions. To gauge the performance of the obtained solutions, we also provide optimality bounds on the global optimum. Numerical results demonstrate that the proposed algorithmic solution can significantly improve the system performance in both throughput and fairness over orthogonal multiple access as well as over a previous NOMA resource allocation scheme.

Dynamic Resource Allocation for Transmit Power Minimization in OFDM-Based NOMA Systems

In this letter, resource allocation in a downlink orthogonal frequency division multiplexing (OFDM)-based non-orthogonal multiple access (NOMA) system is studied. We investigate an optimization problem of minimizing the total transmit power under quality of service requirements, by jointly searching for optimal subcarrier assignments of each user and power allocations over subcarriers. We first present an optimal solution of power allocation with fixed subcarrier assignments, based on which we next propose a low-complexity algorithm of jointly optimizing the subcarrier assignments and power allocations. Numerical results show that the performance of the proposed algorithm is near-optimal and the power consumption is significantly reduced compared with both the conventional OFDM-based frequency division multiple access and the static NOMA resource allocation schemes.