Multi-carrier Non-orthogonal Multiple Access Systems Research Articles

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.

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.

Proportional rate constrained resource allocation in multicarrier nonorthogonal multiple access systems

AbstractIn multicarrier nonorthogonal multiple access (MC‐NOMA) systems, resource allocation for user rate fairness is an inherent challenge due to the huge difference in channel conditions among different users. As the general case of proportional fairness, proportional rate constraint is usually adopted in resource allocation optimization problems to ensure that achievable rates of users are distributed proportionally according to a set of arbitrarily predefined portion. In this article, we investigate joint subcarrier and power allocation in MC‐NOMA systems under proportional rate constraints. The formulated resource allocation optimization problem belongs to a mixed integer nonlinear programming (MINLP) problem due to its binary subcarrier allocation variables and nonaffine equality constraints. By introducing an auxiliary variable in proportional rate constraints and substituting power variable with rate variable, we convert this nonconvex problem into a convex optimization problem and solve it by using Karush‐Kuhn‐Tucker (KKT) conditions and the dual decomposition method. Based on the optimal solutions, we divide the resource allocation process into two phases, that is, subcarrier allocation and power allocation, and develop an optimal subcarrier allocation solution based resource allocation algorithm (OSSRA). Furthermore, we also propose a heuristic algorithm, referred to as proportional rate constrained subcarrier allocation algorithm (PCSAA), as a benchmark. Numerical results show that, substantial gains can be achieved by OSSRA over the existing work and the traditional orthogonal multiple access (OMA) schemes in terms of both sum rate and user fairness. Besides, PCSAA has better user fairness performance than OSSRA.

User scheduling and power allocation for downlink multi-cell multi-carrier NOMA systems

In Non-Orthogonal Multiple Access (NOMA), the best way to fully exploit the benefits of the system is the efficient resource allocation. For the NOMA power domain, the allocation of power and spectrum require solving the mixed-integer nonlinear programming NP-hard problem. In this paper, we investigate user scheduling and power allocation in Multi-Cell Multi-Carrier NOMA (MCMC-NOMA) networks. To achieve that, we consider Weighted Sum Rate Maximization (WSRM) and Weighted Sum Energy Efficiency Maximization (WSEEM) problems. First, we tackle the problem of user scheduling for fixed power using Fractional Programming (FP), the Lagrange dual method, and the decomposition method. Then, we consider Successive Pseudo-Convex Approximation (SPCA) to deal with the WSRM problem. Finally, for the WSEEM problem, SPCA is utilized to convert the problem into separable scalar problems, which can be parallelly solved. Thus, the Dinkelbach algorithm and constraints relaxation are used to characterize the closed-form solution for power allocation. Extensive simulations have been implemented to show the efficiency of the proposed framework and its superiority over other existing schemes.

Resource Allocation in Multicarrier NOMA Systems Based on Optimal Channel Gain Ratios

The application of non-orthogonal multiple access (NOMA) to multicarrier systems can improve the spectrum efficiency and enable massive connectivity in future mobile systems. Resource allocation in multicarrier NOMA systems is a non-deterministic polynomial time-hard problem requiring exhaustive search, which has prohibitive computational complexity. Instead, efficient algorithms that provide a good trade-off between system performance and implementation practicality are needed. In this paper, exact values of the optimal channel gain ratios between a pair of NOMA users are presented for the first time for quadrature amplitude modulation (QAM) schemes. Further, numerical limits are derived for the values of channel gain ratios that fulfill the system constraints. These findings are used to propose a user pairing algorithm with quasi-linear complexity. Further, a novel scheme for data rate and continuous power allocation is proposed. Through numerical simulations, it is proved that the proposed scheme yields an achievable sum-rate close to the performance of exhaustive search, and it outperforms other suboptimal resource allocation schemes.

Efficient Bit Loading Algorithm for OFDM-NOMA Systems With BER Constraints

This paper proposes a novel bit loading algorithm to multicarrier non-orthogonal multiple access (NOMA) systems to maximize the system total throughput while satisfying the users’ individual quality of service (QoS) constraints. Although bit loading is generally a non-deterministic polynomial-time (NP)-hard problem, even for orthogonal multiple access (OMA), the mutual interference between the users, and the dependence of power coefficients and modulation orders are additional challenges that add substantial complexity to the optimization problem. Therefore, in this paper, we propose an efficient bit loading algorithm for multicarrier NOMA systems and compare the complexity and throughput with a corresponding OMA system. The obtained results show that for the case of NOMA, bit loading provides an additional degree of freedom that allows non-uniform spectrum sharing among the users. In particular scenarios, the system may experience hybrid modes of operation, where the system switches between NOMA and OMA. The obtained numerical results show that NOMA can outperform OMA by 100% in terms of spectral efficiency, for a two-user scenario. The complexity of the loading process for NOMA is noticeably higher than OMA, which is due to the high complexity associated with the bit error rate (BER) computation in the case of NOMA.

Downlink Multi-Carrier NOMA With Opportunistic Bandwidth Allocations

Multi-carrier non-orthogonal multiple access (MC-NOMA) system has been considered as a promising candidate in future wireless networks. In a MC-NOMA system, the available bandwidth of transmission is divided into several sub-bands, such that multiple users in each sub-band are served based on power-domain NOMA. Unlike the equal sub-band allocations, we propose a sum-rate maximization technique that jointly allocates the available power and bandwidth with opportunistic sharing between the sub-bands. A second-order cone program approach is exploited to deal with the non-convexity issues of the corresponding optimization problem. Simulation results reveal that the MC-NOMA system with opportunistic bandwidth allocation outperforms the scheme with the equal bandwidth allocation in terms of achieved sum-rate.

Joint subcarrier and power allocation with proportional fairness in multicarrier nonorthogonal multiple access systems

SummaryIn this paper, we investigate joint subcarrier and power allocation in multicarrier nonorthogonal multiple access (MC‐NOMA) systems aims to guarantee rate proportional fairness among all users. To this end, we adopt the sum logarithmic rate function as the objective and formulate a resource optimization problem which is a mixed‐integer nonlinear programming (MINLP) problem. Based on variable relaxation and replacement, we convert this problem into a convex optimization problem. On this basis, the expressions of joint subcarrier and power allocation solutions are obtained by using convex optimization theory and the Karush–Kuhn–Tucker (KKT) condition. However, power and subcarrier allocation solutions are highly coupled, and it is difficult to derive both solutions simultaneously. Fortunately, when the subcarrier allocation strategy is given, the optimal power allocation solution can be derived by solving the equations. Furthermore, we develop an iterative resource allocation algorithm to implement joint subcarrier and power allocation among users. Numerical results reveal that the proposed algorithm is effective in general multiuser and multicarrier scenarios, and a substantial performance improvement can be achieved by the proposed algorithm in contrast with the traditional orthogonal multiple access (OMA) schemes. In specific, the proposed algorithm achieves 63% and 59% gains in terms of utility and fairness performance, respectively.

Active user and data detection for uplink grant-free NOMA systems

This paper proposes some low complexity algorithms for active user detection (AUD), channel estimation (CE) and multi-user detection (MUD) in uplink non-orthogonal multiple access (NOMA) systems, including single-carrier and multi-carrier cases. In particular, we first propose a novel algorithm to estimate the active users and the channels for single-carrier based on complex alternating direction method of multipliers (ADMM), where fast decaying feature of non-zero components in sparse signal is considered. More importantly, the reliable estimated information is used for AUD, and the unreliable information will be further handled based on estimated symbol energy and total accurate or approximate number of active users. Then, the proposed algorithm for AUD in single-carrier model can be extended to multi-carrier case by exploiting the block sparse structure. Besides, we propose a low complexity MUD detection algorithm based on alternating minimization to estimate the active users' data, which avoids the Hessian matrix inverse. The convergence and the complexity of proposed algorithms are analyzed and discussed finally. Simulation results show that the proposed algorithms have better performance in terms of AUD, CE and MUD. Moreover, we can detect active users perfectly for multi-carrier NOMA system.

User Selection and Resource Allocation Algorithms for Multicarrier NOMA Systems on Downlink Beamforming

In this paper, we propose user grouping, subcarrier allocation, and bit allocation schemes in multicarrier nonorthogonal multiple access (NOMA) systems on downlink beamforming to reduce the total transmit power while considering the data-rate and the error-rate constraints of practical modulation types. Power control is also considered. In this system, each subcarrier can be assigned to multiple users for data transmission. Each subcarrier is allocated to a cluster that contains groups of two users for data transmission. A subset of subcarriers is assigned to the same cluster for sharing. The system is based on orthogonal frequency division multiple access (OFDMA), in which a primary user (PU) is more important than a secondary user, and we ensure that all assigned subcarriers transmit data to the PU in the system. In the proposed schemes, the user-grouping operation is performed first and the subcarrier allocation along with the bit loading and power assignment are performed subsequently based on the user-grouping results. The simulation results obtained using the proposed schemes in conjunction with the nonorthogonal multiple access to allocate bits, subcarriers, and transmit power show that the proposed schemes outperform the conventional OFDMA system in terms of transmit power.

Optimal Resource Allocation for Multicarrier NOMA in Short Packet Communications

In this paper, we study the resource allocation problem for downlink multicarrier non-orthogonal multiple access systems with short-packet communications (MC-NOMA-SPC). In contrast to long-packet communications in conventional wireless systems, SPC suffers from a transmission rate degradation and an unavoidable decoding error rate. Thus conventional resource allocation based on the Shannon capacity assuming infinite blocklength is no longer optimal. In this paper, we propose to use the effective-throughput as the performance metric to evaluate the tradeoff between the transmission rate and the decoding error rate. Building on that, we jointly optimize the subcarrier assignment, transmission power allocation, and transmission rate adaptation of each user to maximize the total weighted effective-throughput subject to transmission reliability and various practical constraints. Since the formulated problem belongs to a non-convex mixed integer nonlinear programming (MINLP) problem, we develop an efficient algorithm based on the dynamic programming (DP) recursion framework to obtain its optimal solutions. We further propose a low-complexity algorithm based on the principles of block coordinate descent (BCD) and concave-convex procedure (CCCP). Finally, the simulation results show that the proposed low-complexity algorithm can achieve similar performance as that of the optimal solution and outperform the other baseline schemes significantly.

Joint Power Allocation and Channel Assignment for NOMA With Deep Reinforcement Learning

Non-orthogonal multiple access (NOMA) has been considered as a significant candidate technique for the next generation wireless communication to support high throughput and massive connectivity. It allows different users to be multiplexed on one channel through applying superposition coding at the transmitter and successive interference cancellation (SIC) at the receiver. To fully utilize the benefit of the NOMA technique, the key problem is how to optimally allocate resources, such as power and channels, to users to maximize the system performance. There have been some existing works on the power allocation for the single-carrier NOMA system. However, how to optimally assign channels in the multi-carrier NOMA system is still unclear. In this paper, we propose a deep reinforcement learning framework to allocate resources to users in a near optimal way. Specifically, we exploit an attention-based neural network (ANN) to perform the channel assignment. Simulation results show that the proposed framework can achieve better system performance, compared with the state-of-the-art approaches.

A Deep Learning-Based Approach to Power Minimization in Multi-Carrier NOMA With SWIPT

Simultaneous wireless information and power transfer (SWIPT) and multi-carrier non-orthogonal multiple access (MC-NOMA) are promising technologies for future fifth generation and beyond wireless networks due to their potential capabilities in energy-efficient and spectrum-efficient system designs, respectively. In this paper, the joint downlink resource allocation problem for a SWIPT-enabled MC-NOMA system with time switching-based receivers is investigated, where pattern division multiple access (PDMA) technique is employed. We focus on minimizing the total transmit power of the system while satisfying the quality-of-service requirements of each user in terms of data rate and harvested power. The corresponding optimization problem is a non-convex and a mixed integer programming problem which is difficult to solve. Different from the conventional iterative searching-based algorithms, we propose an efficient deep learning-based approach to determine an approximated optimal solution. Specifically, we employ a typical class of deep learning model, namely, deep belief network (DBN), where the detailed procedure of the developed approach consists of three parts, i.e., data preparation, training, and running. The simulation results demonstrate that the proposed DBN-based approach can achieve similar performance of power consumption to the exhaustive search method. Furthermore, the results also confirm that MC-NOMA with PDMA outperforms MC-NOMA with sparse code multiple access, single-carrier non-orthogonal multiple access, and orthogonal frequency division multiple access in terms of power consumption in SWIPT-enabled systems.

Dynamic Power Allocation for Downlink Multi-Carrier NOMA Systems

In this letter, the performance of optimal dynamic power allocation (PA) is analyzed for downlink multi-carrier non-orthogonal multiple access (MC-NOMA) systems. We study the PA optimization problem with a pair of users and weighted-sum-rate utility. A novel low-complexity algorithm is designed to solve the problem with a closed-form PA expression. The analytical data rate performance is derived and verified by simulation results. The numerical results indicate that a large difference in user channel states improves performance by enhancing the user diversity gain in NOMA systems. Moreover, the channel diversity gain can be achieved by multi-carrier transmission and increases with the number of subcarriers. Finally, the number of subcarriers for dynamic PA in MC-NOMA systems is discussed for practical applications.

Performance Analysis of Non-Orthogonal Multiple Access Under I/Q Imbalance

Non-orthogonal multiple access (NOMA) has been recently proposed as a viable technology that can potentially improve the spectral efficiency of fifth generation wireless networks and beyond. However, in practical communication scenarios, transceiver architectures inevitably suffer from radiofrequency (RF) front-end related impairments that can lead to degradation of the overall system performance with in-phase/quadrature-phase imbalance (IQI) constituting a major impairment in direct-conversion transceivers. In this paper, we quantify the effects of IQI on the performance of NOMA-based single-carrier (SC) and multi-carrier (MC) systems under multipath fading conditions. This is realized by first deriving analytic expressions for the signal-to-interference-plus-noise ratio and the outage probability of both SC and MC NOMA systems subject to IQI at the transmitter and/or receiver (RX) side. Furthermore, we derive asymptotic diversity orders for all considered impairment scenarios. Capitalizing on these results, we demonstrate that the effects of IQI differ considerably among NOMA users and depend on the underlying systems' parameters. For example, for a target data rate and power allocation ratio satisfying a given condition, IQI does not affect the asymptotic diversity of SC NOMA systems, whereas the asymptotic diversity of MC NOMA systems, suffering from RX IQI, is always zero. Moreover, it is shown that for both SC and MC NOMA systems, the first sorted user appears more robust to IQI, which indicates that higher order users are more sensitive to the considered impairment.

Optimal Joint Power and Subcarrier Allocation for Full-Duplex Multicarrier Non-Orthogonal Multiple Access Systems

In this paper, we investigate resource allocation algorithm design for multicarrier non-orthogonal multiple access (MC-NOMA) systems employing a full-duplex (FD) base station for serving multiple half-duplex (HD) downlink and uplink users simultaneously. The proposed algorithm is obtained from the solution of a non-convex optimization problem for the maximization of the weighted sum system throughput. We apply monotonic optimization to develop an optimal joint power and subcarrier allocation policy. The optimal resource allocation policy serves as a system performance benchmark due to its high computational complexity. Furthermore, a suboptimal iterative scheme based on successive convex approximation is proposed to strike a balance between computational complexity and optimality. Our simulation results reveal that the proposed suboptimal algorithm achieves a close-to-optimal performance. In addition, FD MC-NOMA systems employing the proposed resource allocation algorithms provide a substantial system throughput improvement compared with conventional HD multicarrier orthogonal multiple access (MC-OMA) systems and other baseline schemes. In addition, our results unveil that FD MC-NOMA systems enable a fairer resource allocation compared with traditional HD MC-OMA systems.

Optimal Joint Power and Subcarrier Allocation for Full-Duplex Multicarrier Non-Orthogonal Multiple Access Systems

In this paper, we investigate resource allocation algorithm design for multicarrier non-orthogonal multiple access (MC-NOMA) systems employing a full-duplex (FD) base station (BS) for serving multiple half-duplex (HD) downlink (DL) and uplink (UL) users simultaneously. The proposed algorithm is obtained from the solution of a non-convex optimization problem for the maximization of the weighted sum system throughput. We apply monotonic optimization to develop an optimal joint power and subcarrier allocation policy. The optimal resource allocation policy serves as a system performance benchmark due to its high computational complexity. Furthermore, a suboptimal iterative scheme based on successive convex approximation is proposed to strike a balance between computational complexity and optimality. Our simulation results reveal that the proposed suboptimal algorithm achieves a close-to-optimal performance. Besides, FD MC-NOMA systems employing the proposed resource allocation algorithms provide a substantial system throughput improvement compared to conventional HD multicarrier orthogonal multiple access (MC-OMA) systems and other baseline schemes. Also, our results unveil that the proposed FD MC-NOMA systems achieve a fairer resource allocation compared to traditional HD MC-OMA systems.