Multi-carrier Non-orthogonal Multiple Access Research Articles

Fair resource allocation and SBS selection for MC-NOMA-based HetNets supporting D2D communications

This paper studies the application of downlink multi-carrier non-orthogonal multiple access (MC-NOMA) and device-to-device (D2D) in heterogeneous networks (HetNets). In the considered model, the small base stations (SBSs) can reuse the sub-channels (SCs) of the macro base stations (MBS). The paper aims to maximize the system sum-rate while managing co-tier and co-channel interference by optimizing joint SBS selection, fair SC allocation, and power allocation, under the constraints of guaranteeing a certain amount of resources to all cellular users (CUs)and D2D pairs and satisfying CUs’ minimum signal-to-interference-plus-noise ratio (SINR) requirements. However, obtaining an efficient exact solution is challenging due to the non-convexity of the mixed integer optimization problem. To deal with this issue, the problem is decoupled into two sub-problems: 1- SBS selection and fair SC allocation problem and 2- power allocation problem. Then, a four-sided many-to-one matching method is proposed to solve the SBS selection and fair SC allocation problem jointly. The performance of the SC fair allocation algorithm is investigated in terms of the system quality of services (QoS) using a proposed mathematical expression. Furthermore, a novel dynamic search operator is introduced to enhance the performance of the matching-based algorithm. Next, the non-convex power allocation problem is transformed into a deep reinforcement learning (DRL) based framework, and the deep deterministic policy gradient (DDPG) approach is employed to solve the problem. Moreover, a joint SBS selection, SC allocation, and power allocation iterative algorithm is proposed to improve the system sum-rate. Simulation results validate our analysis and indicate the superiority of the proposed algorithms in terms of the system sum-rate compared to the benchmark algorithms.

A DDPG-based energy efficient federated learning algorithm with SWIPT and MC-NOMA

Federated learning (FL) has emerged as a promising distributed machine learning technique. It has the potential to play a key role in future Internet of Things (IoT) networks by ensuring the security and privacy of user data combined with efficient utilization of communication resources. This paper addresses the challenge of maximizing energy efficiency in FL systems. We employed simultaneous wireless information and power transfer (SWIPT) and multi-carrier non-orthogonal multiple access (MC-NOMA) techniques. Also, we jointly optimized power allocation and central processing unit (CPU) resource allocation to minimize latency-constrained energy consumption. We formulated an optimization problem using a Markov decision process (MDP) and utilized a deep deterministic policy gradient (DDPG) reinforcement learning algorithm to solve our MDP problem. We tested the proposed algorithm through extensive simulations and confirmed it converges in a stable manner and provides enhanced energy efficiency compared to conventional schemes.

Multi-Carrier NOMA-Empowered Wireless Federated Learning With Optimal Power and Bandwidth Allocation

Wireless federated learning (WFL) undergoes a communication bottleneck in uplink, limiting the number of users that can upload their local models in each global aggregation round. This paper presents a new multi-carrier non-orthogonal multiple-access (MC-NOMA)-empowered WFL system under an adaptive learning setting of Flexible Aggregation. Since a WFL round accommodates both local model training and uploading for each user, the use of Flexible Aggregation allows the users to train different numbers of iterations per round, adapting to their channel conditions and computing resources. The key idea is to use MC-NOMA to concurrently upload the local models of the users, thereby extending the local model training times of the users and increasing participating users. A new metric, namely, Weighted Global Proportion of Trained Mini-batches (WGPTM), is analytically established to measure the convergence of the new system. Another important aspect is that we maximize the WGPTM to harness the convergence of the new system by jointly optimizing the transmit powers and subchannel bandwidths. This nonconvex problem is converted equivalently to a tractable convex problem and solved efficiently using variable substitution and Cauchy’s inequality. As corroborated experimentally using a convolutional neural network and an 18-layer residential network, the proposed MC-NOMA WFL can efficiently reduce communication delay, increase local model training times, and accelerate the convergence by over 40%, compared to its existing alternative.

Joint power and subchannel allocation regarding energy harvesting in MC-NOMA based wireless networks

We propose resource allocation schemes for a multi-carrier non-orthogonal multiple access (MC-NOMA) system with the consideration of energy harvesting. The resource allocation schemes includes an optimization scheme for maximizing the total system capacity, and an optimization scheme for maximizing the fairness among users, such that both schemes guarantee minimum data rate and harvested energy to each user. Because NOMA is one of the key technologies for enhancing system capacity, we apply a NOMA scheme to an Energy Harvesting Wireless Network (EHWN) to increase the capacity whilst at the same time supplying sufficient harvested energy and data rate for each user. The proposed resource allocation schemes are based on a block coordinate descent (BCD) algorithm that can effectively solve problems in which the objective function is not jointly concave. The performance of each proposed scheme is evaluated using simulations, and we show that the capacity maximization and fairness schemes outperform orthogonal multiple access (OMA) schemes in terms of capacity and fairness, respectively.

Outage Analysis of Sparse Vector Coding-Based Downlink Multicarrier NOMA for URLLC

Ultra-reliable and low latency communication (uRLLC) is a promising use case in the fifth generation and beyond systems for critical and delay-sensitive applications with aid of short packet transmission. Nevertheless, multi-carrier non-orthogonal multiple access (MCNOMA) is a promising technique to achieve spectral efficiency and massive connectivity, however, inter-carrier interference (ICI) stands out to be a major downside. Recently, sparse vector coding (SVC) is proposed to fulfill the trade-off between reliability and latency, thereby achieving the benchmarks of uRLLC. In this paper, SVC based downlink MCNOMA (SVC-MCNOMA) system is considered and first it is shown that SVC-MCNOMA is free from ICI due to the sparse nature of the information. Further, compressed sensing-based recovery at the receiver end helps in reducing the latency as well as enhances reliability. Moreover, to study the system performance, this paper derives the closed form outage expressions of the SVC-MCNOMA system over the Rician fading channel and through Monte-Carlo simulation, the analytical results are validated. The analyses demonstrate that SVC-MCNOMA outperforms conventional MCNOMA (C-MCNOMA), thereby achieving spectral efficacy, ultra-reliability with low latency and eradicates the effect of ICI.

Maximum Sum Rate of MCM–NOMA in Future Vehicular Sensor Networks

This letter addresses the challenge of determining the maximum sum rate achievable in high mobility scenarios with time and frequency selectivity in 5G V2X communications, particularly in the context of IoT/sensor networks. We derive a closed-form expression for the maximum sum rate of a multicarrier non-orthogonal multiple access (NOMA) system with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> users, and validate our theoretical results through simulations by comparing them with traditional orthogonal schemes. Our work provides valuable insights into the potential of NOMA for high-speed V2X communications, and offers a novel contribution to the existing literature by presenting a closed-form expression allowing the development of efficient and reliable 5G V2X systems.

AlexNet Classifier and Support Vector Regressor for Scheduling and Power Control in Multimedia Heterogeneous Networks

In this paper, the downlink transmission of a two-tier heterogeneous network (HetNet) is considered in which a macro base station (MBS) serves the macro users using orthogonal frequency division multiple access (OFDMA) and small base stations (SBSs) serve the small-cell users through multi-carrier non-orthogonal multiple access (MC-NOMA) and joint transmission (JT). In particular, assuming the subcarriers are already allocated to macro users, the problem of scheduling (i.e., joint user association and subcarrier allocation) and power control is studied with the goal of maximizing the total users perceived quality-of -experience (QoE) for small-cell users, while a minimum data rate for macro users is guaranteed. To solve the joint optimization problem, a near-optimal and computationally efficient two-phase solution approach is proposed based on the tools from optimization and machine learning (ML). In the first phase, the optimization problem is solved to obtain the scheduling decisions and transmit power variables. In the second phase, the optimized scheduling decisions and transmit power variables serve as training samples for an AlexNet classifier and support vector regressor (SVR), respectively. Simulation results reveal that the integration of JT into MC-NOMA, outperforms the conventional MC-NOMA scheme by up to 24%, 19%, and 21% for the web, video and audio multimedia services, respectively.

Spectrum-efficient mode selection and fair resource allocation for D2D-enabled uplink/downlink MC-NOMA networks

The application of device-to-device (D2D) communication and multi-carrier (MC) non-orthogonal multiple access (NOMA) have been considered as promising technologies to enhance energy efficiency, network connectivity, and spectrum efficiency in the next generation of communication systems. This paper investigates a joint optimization problem of spectrum-efficient mode selection, fair sub-channel (SC) allocation, and power allocation of user pairs for a D2D-enabled MC-NOMA network. In the proposed network, user pairs are enabled to transmit and receive data in either underlaid D2D mode or cellular mode, and the NOMA technique is adopted in both downlink and uplink transmission. To maximize the throughput of the system and to manage the co-channel interference, the joint optimization problem is formulated under the constraint of guaranteeing all users with a certain amount of resources while satisfying the minimum signal-to-interference-plus-noise ratio (SINR) requirements of users. As an NP-hard problem, the optimization problem is first transformed into two sub-problems namely 1- the joint SC allocation and mode selection and 2- the power allocation. Then, to jointly solve the SC allocation and mode selection sub-problem, an enhanced three-side many-to-one matching algorithm is proposed and to obtain the solution to the power allocation problem, the known sequential convex programming method is used. Moreover, an iterative algorithm is used to enhance the performance of the system in terms of sum-rate. Finally, a mathematical expression is introduced to check the quality of service (QoS) of the proposed fair resource allocation algorithm and the results of the simulation are stated to indicate improved system performance.

Dynamic User Resource Allocation for Downlink Multicarrier NOMA with an Actor–Critic Method

Future wireless communication systems require higher performance requirements. Based on this, we study the combinatorial optimization problem of power allocation and dynamic user pairing in a downlink multicarrier non-orthogonal multiple-access (NOMA) system scenario, aiming at maximizing the user sum rate of the overall system. Due to the complex coupling of variables, it is difficult and time-consuming to obtain an optimal solution, making engineering impractical. To circumvent the difficulties and obtain a sub-optimal solution, we decompose this optimization problem into two sub-problems. First, a closed-form expression for the optimal power allocation scheme is obtained for a given subchannel allocation. Then, we provide the optimal user-pairing scheme using the actor–critic (AC) algorithm. As a promising approach to solving the exhaustive problem, deep-reinforcement learning (DRL) possesses higher learning ability and better self-adaptive capability than traditional optimization methods. Simulation results have demonstrated that our method has significant advantages over traditional methods and other deep-learning algorithms, and effectively improves the communication performance of NOMA transmission to some extent.

Energy Efficient Resource Allocation for Uplink MC-NOMA Based Heterogeneous Small Cell Networks With Wireless Backhaul

In this paper, we investigate energy efficient resource allocation for uplink multi-carrier non orthogonal multiple access (MC-NOMA) based two-tier heterogeneous networks (HetNets) with wireless backhaul. We have exploited channel diversity by assigning multiple subchannels to various MC-NOMA users (NUs), while allowing multiple NUs to use the same subchannel. We formulate a joint NU clustering, and power and subchannel allocation problem for system's energy efficiency (EE) maximization under the constraints of minimum quality of service (QoS) requirement, maximum allowable transmit power for each NU, and successive interference cancelation. The formulated problem is non-convex with fractional objective function, and therefore, to convert it into a tractable from, it is divided into two subproblems, i.e., subchannel allocation and NU clustering subproblem, and power allocation subproblem. The subchannel allocation and NU clustering subproblem is solved through a suboptimal algorithm, which exploits the channel diversity by maintaining maximum possible difference in transmit power levels of clustered NUs. Whereas for the power allocation subproblem, the EE optimization problem is convexified through Dinkelbach and successive convex approximation techniques and a closed-form suboptimal solution is obtained. The simulation results show that the proposed energy efficient algorithm outperforms the existing NOMA and orthogonal multiple access (OMA) algorithms. The proposed approach also guarantees user fairness by giving priority to the NUs with unfulfilled throughput requirements.

Power Allocation and User Grouping for NOMA Downlink Systems

Non-orthogonal multiple access (NOMA) technology allows multiple users to use the same time-frequency resource to send signals, which can improve spectral efficiency and throughput. We study the problems of user grouping and power allocation in the downlink of a multi-carrier NOMA system. The sum rate is the optimization goal. A step-by-step optimization is adopted. Users are grouped first and then power is allocated. For user grouping, the user grouping method based on the maximum channel gain difference is improved to prevent users with similar channel gains from being grouped together. For power allocation, the deep learning power allocation algorithm is used among subcarriers. Then, the closed-form expressions of power allocation between multiplexed users are derived according to the minimum transmission rate constraint. The simulation results show that compared with the fractional transmit power allocation method and fixed power allocation method, the deep learning power allocation method improves the system sum rate by about 2.2% and 19%, respectively. The power allocation methods we propose improve the system sum rate by about 10% compared to the fractional transmit power allocation method used among subcarriers and between multiplexed users.

Hybrid optimization using lion and dragonfly for enhanced resource allocation in fifth-generation networks

“5G (Fifth Generation) network promises Enhanced Mobile Communications (EMC), Massive Machine Type Communications (MMTC) and Ultra-Reliable Low Latency ygCommunications (URLLC)”. For enhancing communication, resource allocation is a crucial factor that guarantees the partitioning of channels into resource blocks, which were allocated to diverse users. Different methods have been introduced for improving resource scheduling however, they do not reach the optimum solution. This work aims to scrutinize the resource allocation problems in the “hybrid multi-carrier non-orthogonal multiple access (MC-NOMA) systems” that comprise the OMA and NOMA modes. It is used to achieve the EE and SE trade-off with minimal rate requirements of users. In addition, the total degree of freedom includes “user clustering, power allocation, choice of Multiple Access (MA) modes, and subcarrier assignment as well”. Accordingly, the optimization concept is integrated that offers the most noteworthy way of resolving multi-objective issues. In this context, resource allocation with power allotment is considered a vital factor, which is determined as a single-objective issue. Thereby this paper ensures the optimal allocation of power and subcarriers by a novel algorithm termed as Dragon Levy-based Lion Cub Generation Model (DL-LCG). Finally, the presented model is evaluated over existing models regarding cost analysis

Harvested Power Fairness-Based Multi-Carrier NOMA IoT Networks With SWIPT

The integration of simultaneous wireless information and power transfer (SWIPT) in multi-carrier non-orthogonal multiple access (NOMA) systems has been recently investigated as a potential paradigm to improve the lifespan of energy-constrained Internet-of-Things networks. In this letter, we develop a fairness-aware design for a multi-carrier NOMA SWIPT-based system that attempts to maximize the minimum harvested power subject to quality-of-service constraints. With this design, each user’s power allocation, and the power splitting ratio are jointly computed by solving the corresponding optimization problem. To deal with the non-convexity nature of the formulated max-min optimization problem, an iterative mechanism with a second-order cone method is proposed. Simulation results indicate that our design maintains a better user fairness with respect to the harvested power.

Resource Allocation in MU-MISO Rate-Splitting Multiple Access With SIC Errors for URLLC Services

Rate-splitting multiple access (RSMA) is promising to be developed as a key enabling technology for 6G. This paper investigates the resource allocation problem in a downlink multi-user and multiple-input single-output (MU-MISO) RSMA system with successive interference cancellation (SIC) errors for ultra-reliable and low-latency communications (URLLC). The single-carrier RSMA (SC-RSMA) resource allocation scheme with URLLC is first given, where the beamforming vector and the transmission rate are optimized to maximize the effective throughput (ET). To solve the non-convex optimization problem formulated in the SC-RSMA scheme, an iterative algorithm based on block coordinate descent (BCD) and successive convex approximation (SCA) is proposed to alternately optimize the beamforming factor and the transmission rate. Furthermore, the multicarrier RSMA (MC-RSMA) scheme is developed for the URLLC access of large-scale users. Based on the results of the SC-RSMA scheme, a low-complexity three-step optimization algorithm is proposed to solve the resource allocation problem formulated in the MC-RSMA scheme. Finally, the simulation results show that the RSMA scheme and MC-RSMA scheme can respectively achieve higher ET than the non-orthogonal multiple access (NOMA) scheme and multi-carrier NOMA (MC-NOMA) scheme in the URLLC scenario, and verify that RSMA can reduce the latency and improve the reliability of the system.

Energy-Efficient Resource Allocation for Short Packet Transmission in MISO Multicarrier NOMA

In this article, we investigate energy efficiency (EE) maximization of short packet transmission (SPT) in a downlink multiple-input single-output (MISO) multi-carrier non-orthogonal multiple access (MC-NOMA) communication system. We use the ratio of the effective throughput to the power as a performance metric to formulate a non-convex mixed-integer nonlinear programming (MINLP) problem. Then, a low-complexity three-step optimization framework is proposed for MINLP optimization. Under a given maximum transmit power and channel blocklength of each subcarrier, a block coordinate descent (BCD) based method is proposed to jointly optimize the power allocation coefficient, transmission rate, and transmit power within a single subcarrier. Then, we transform the subcarrier allocation problem into a weighted bipartite graph matching problem and obtain the optimal subcarrier allocation scheme using the Kuhn-Munkres (KM) algorithm. Finally, a dynamic programming (DP) method is proposed to obtain the optimal channel blocklength allocation scheme. The simulation results validate the effectiveness of the proposed three-step optimization framework and indicate that the MC-NOMA system achieves higher EE and higher effective throughput than the orthogonal frequency-division multiple access (OFDMA) system in SPT.

Energy-Efficient Power Allocation in Downlink Multi-Cell Multi-Carrier NOMA: Special Deep Neural Network Framework

Energy-efficient resource allocation for multi-cell multi-carrier non-orthogonal multiple access (MCMC-NOMA) is a challenging task due to the interference and other related factors, which makes obtaining an applicable solution in the real-time is even more challenging. In this paper, we aim to design a model capable of efficient resource allocation in real-time. We formulate our problem as energy efficiency (EE) maximization. First, we propose an iterative solution to handle user scheduling and power allocation, which is not suitable for real-time application. Next, we design a dual-pipeline augmented deep convolutional neural network (ADCNN) to handle the power allocation in real-time. The first pipeline is to extract high quality features and spatially connect and refine them using attention-based network. The second pipeline is to extract low-quality spatial features. Because more discriminative features are obtained through fusion of the high and low quality features, a better prediction of power allocation can be obtained. Simulation results show the adequacy of the proposed model for the real-time application and larger problems compared with other models such as deep neural network (DNN).

Energy efficiency maximization for multi-carrier cooperative non-orthogonal multiple access systems

Driven by massive connections and exponential growth of data in mobile devices, Non-orthogonal multiple access (NOMA) has become a candidate access technology to meet the requirement of Internet of things (IoT) in the future network. The combination of multi-carrier technology and cooperative communication is an efficient solution to significantly improve the energy efficiency of NOMA systems. Hence, we consider the energy efficiency (EE) maximization problem for the multi-carrier cooperative NOMA (MC-C-NOMA) systems with amplifying-and-forward (AF) protocol. As the EE maximization problem is mixed-integer nonlinear programming (MINLP), it is difficult to design the resource allocation algorithm to find the optimal solution. To make the EE maximization problem easier to handle, we decouple the EE maximization problem into sub-channel selection and power allocation sub-problems. First, under each user's minimum data rate and maximum transmit power constraints, a matching algorithm is proposed to achieve sub-channel allocation, which first matches the user with the sub-channel. Secondly, the Dinkelbach-like algorithm is used to achieve power allocation. Then, a joint sub-channel selection and power allocation algorithm is proposed to maximize the total energy efficiency (EE) of users in the MC-C-NOMA system. Finally, simulation results are presented to show that the proposed algorithm can achieve higher energy efficiency compared with the existing algorithms.

Energy Harvesting for TDS-OFDM in NOMA-Based Underwater Communication Systems.

Non-orthogonal multiple access (NOMA) is considered a promising multiple access technique for fifth generation (5G) mobile networks and tactical internet due to its high spectral efficiency. Thanks to the high spectral efficiency of NOMA, it can be a strong candidate suitable for the limited channel bandwidth of underwater acoustic communication. The NOMA transmitter is employing superposition coding (SC). The NOMA receiver is based on the successive interference cancellation (SIC) technique. The multicarrier NOMA adopts orthogonal frequency division multiplexing (OFDM) as a multicarrier modulation (MCM) technique; however, conventional cyclic prefix OFDM (CP-OFDM) and zero padding (ZP-OFDM) have inefficient spectral efficiency. Thanks to efficient synchronization and high energy-spectral efficiency of the time-division synchronization OFDM (TDS-OFDM), it is a significant attractive candidate for underwater multicarrier communication. However, wasting the power transmission of long guard intervals in the battery-based underwater communication is represented as one of the TDS-OFDM main drawbacks. Harvesting energy and improving the energy efficiency of acoustic-based TDS-OFDM-NOMA represent high achievement goal battery recharging challenges due to the ocean environment. This paper proposes time switching simultaneous wireless information and power transfer (TS-SWIPT) to harvest the energy of transmitted power over the guard interval in the TDS-OFDM-NOMA scheme. The proposed energy harvested scheme harvests the energy from the wasted power in the long guard interval and improves the energy efficiency of the TDS-OFDM multicarrier scheme. This study demonstrates the superiority of the proposed TDS-OFDM-NOMA over the underwater acoustic channel by revealing high energy efficiency, high spectral efficiency, better bit error rate performance, and high system data throughput.

Intelligent radio resource management in reconfigurable IRS-enabled NOMA networks

Intelligent reflecting surfaces (IRSs) are anticipated to provide reconfigurable propagation environment for next generation communication systems. In this paper, we investigate a downlink IRS-aided multi-carrier (MC) non-orthogonal multiple access (NOMA) system, where the IRS is deployed to especially assist the blocked users to establish communication with the base station (BS). To maximize the system sum rate under network quality-of-service (QoS), rate fairness and successive interference cancellation (SIC) constraints, we formulate a problem for joint optimization of IRS elements, sub-channel assignment and power allocation. The formulated problem is mixed non-convex. Therefore, a novel three stage algorithm is proposed for the optimization of IRS elements, sub-channel assignment and power allocation. First, the IRS elements are optimized using the bisection method based iterative algorithm. Then, the sub-channel assignment problem is solved using one-to-one stable matching algorithm. Finally, the power allocation problem is solved under the given sub-channel and optimal number of IRS elements using Lagrangian dual-decomposition method based on Lagrangian multipliers. Moreover, in an effort to demonstrate the low-complexity of the proposed resource allocation scheme, we provide the complexity analysis of the proposed algorithms. The simulated results illustrate the various factors that impact the optimal number of IRS elements and the superiority of the proposed resource allocation approach in terms of network sum rate and user fairness. Furthermore, we analyze the proposed approach against a new performance metric called computational efficiency (CE).

Robust Resource Allocation for Lightweight Secure Transmission in Multicarrier NOMA-Assisted Full Duplex IoT Networks

In this article, with the aim to enhance the secure transmission and improve the utilization of spectrum resources in Internet of Things (IoT), a multicarrier nonorthogonal multiple access (MC-NOMA)-assisted full duplex (FD) network is investigated, in which nonorthogonal multiple access (NOMA) is implemented in both uplink and downlink transmissions. The lightweight and low-power physical layer security (PLS) technology is employed to protect the information from eavesdropping. Taking the imperfect channel state information (CSI) into account, we formulate a problem to optimize the beamforming vector, artificial noise (AN), transmit power, and subcarrier assignment policy aiming to maximize the worst case sum secrecy rate under the Quality of Service (QoS) and power consumption constraints. Since the formulated problem is nonconvex and difficult to be solved, we decompose it into two joint optimization subproblems. The first is resource allocation with given subcarrier assignment, which is solved by using the block coordinate descent (BCD) approach. The second is subcarrier assignment solved by the matching theory. Our simulation shows that the proposed scheme is robust against the CSI imperfectness of the eavesdropping and self-interference channels, while providing significant sum secrecy rate improvement compared with the orthogonal multiple access (OMA), half duplex (HD) systems, and other benchmark schemes.