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

Multi-carrier based non-orthogonal multiple access (NOMA) is an effective method to meet the ever-increasing demands of both user throughput and energy efficiency by multiplexing multiple users on the same carrier. Since interference from users with a poorer channel gain can be canceled at a user with a strong channel gain by successive interference cancellation, NOMA can enhance the system performance. To improve the downlink system performance, it is crucial to appropriately determine users scheduled on each carrier and power allocation at the base station. However, the existing works are generally either heuristic or local optimal due to the mixed optimization problem. In this paper, we focus on the global optimal solutions to maximize user throughput and energy efficiency in NOMA, respectively. In particular, we first formulate the mixed integer optimization problem which are intractable to be solved. Fortunately, by the provided analytical results, the optimization models can be largely simplified. Then, we propose the architectures of joint user scheduling and power allocation in NOMA, as well as the corresponding optimal algorithms. Simulation results demonstrate that our proposed algorithms indeed outperform existing works in terms of the user throughput and energy efficiency, respectively.

In this article, a comprehensive strategy of user clustering and power allocation is investigated in downlink hybrid non-orthogonal multiple access (NOMA) networks. More particularly, users in the same cluster can receive signals simultaneously by using NOMA techniques, while time-division multiple access (TDMA) schemes are utilized among different clusters. By employing the weight factor, a weighted sum rate maximization problem is formulated and decoupled into user clustering and power allocation problems, where two different schemes for time slot allocation are proposed. The formulated user clustering problems with user-based and cluster-based time slot allocation schemes are respectively considered as coalitional games in characteristic and partition formations, and solved by two different algorithms, where both low-complexity and global optimal methods are proposed. The properties, including complexity, convergence, stability and optimality, are analyzed. To further improve the system performance, the formulated power allocation problem is solved by a successive convex approximation (SCA) based iterative algorithm. Simulation results reveal that: i) the proposed hybrid NOMA system is capable of achieving promising gains over centralized NOMA-based and conventional TDMA-based frameworks; and ii) the developed algorithms can significantly improve the weighted sum rate compared with the random user structure and the fixed power allocation.

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In this paper, we investigate the user scheduling and power allocation scheme for a millimeter wave non-orthogonal multiple access system. To reduce the feedback overhead, random beamforming is adopted at a base station. The optimization problem is formulated to maximize the energy efficiency. To solve this problem, we first address the user scheduling and power allocation problem separately, then an iterative algorithm is proposed to jointly optimize the user scheduling and power allocation. Simulation results show that the proposed scheme achieves higher energy efficiency than the conventional scheme.

In a wireless cellular system, resource allocation among active users is a challenging task. The cellular systems with nonorthogonal multiple access (NOMA) solve the problem of bandwidth and time allocation. All the active users in the same NOMA cluster are allowed to access the complete cluster bandwidth at the same time. However, power allocation in NOMA is a multiconstraint optimization problem. The complexity of power allocation increases with the increase in the number of users inside the cluster. This article presents a novel technique for power allocation in a NOMA downlink cluster, using the magic sum of the magic matrix. The sum throughput (SUMTP) and the min–max fairness index (MMFI) are calculated and compared with an existing method for the downlink NOMA system. The proposed method shows comparable SUMTP performance to the conventional optimized method while ensuring better MMFI. We observed an inverse association between SUMTP and MMFI. In addition, the execution time of the proposed method is much lesser than that of the conventional optimized method owing to lower number of flops required for magic-matrix-based power allocation.

Non-orthogonal multiple access (NOMA) is one of the key technology of the fifth generation mobile communication systems to increase the network capacity and data rate. The combination of multi-input multi-output (MIMO) technique and non-orthogonal multiple access (NOMA) systems can further improve the spectrum efficiency. In this paper, we investigate the power allocation problem in downlink MIMO-NOMA system. We first formulate the power allocation problem as a weighted sum rate maximization problem. Then, a series transformations based on quadratic transformation and Lagrange dual transformation are introduced to convert the original problem into a convex problem. Moreover, we propose an iterative algorithm to obtain the optimal solution of power allocation. Numerical results show the excellent convergence of our proposed power allocation approach and reveal that the proposed scheme improves the weighted sum rate significantly in MIMO-NOMA systems.

Small cell, mmWave, and massive Multi-Input Multi-Output (MIMO) technologies in 5G cellular networks becomes inevitable trend caused by killer applications such as holographic video which is traffic-intensive. In this paper, we study a sequential activation beam selection, user scheduling, and power allocation problem in a mmWave network with massive MIMO utilizing a physical layer preceding technique. We aim to maximize the time-averaged utility of users with a time-averaged transmit power constraint on top of the EdgeSON architecture, which takes advantage of both centralized and distributed characteristics. We decompose the original long-term problem into two-time scales in which solving the problem of choosing beam pattern is run at a slower time scale than solving user scheduling and power allocation. Then, to solve user scheduling and power allocation, we leverage the Lyapunov drift-plus-penalty framework which transforms an original long-term average problem into per-slot modified sub-problems. Since provided per-slot problem to find a set of user scheduling and power allocation is known as NP-hard, we propose a low-complex and practical interference management algorithm, namely CRIM, by introducing two critical users with the highest interference channel gain in intra-BS and inter-BS respectively. Finally, via extensive simulations, we verify and compare the performance of the proposed algorithm and comparing algorithms in a real mmWave network environment.

In non-orthogonal multiple access (NOMA) systems, the outage is regarded to happen when a user cannot correctly decode the messages for users with higher decoding order and hence cannot perform the successive interference cancelation (SIC). However, in this case, the user may still correctly decode its message by treating the uncanceled signal as interference and avoid the outage. By considering this behavior, the outage probability should be redefined. In this paper, we investigate user scheduling and power allocation for a downlink NOMA system with imperfect SIC by using the alternative outage probability as a constraint. In order to tackle the complicated non-convex resource allocation problem, we propose a two-phase algorithm, in which the user scheduling is first optimized through a matching theory based algorithm, and then power allocation is performed with the aid of the branch and bound technique and the concave–convex procedure method. Simulation results show that the performance of the proposed low-complexity algorithm is near-optimal and the algorithm based on the alternative outage probability outperforms that based on the traditional one when the residual interference from imperfect SIC significantly affects the decoding.

In this paper, we investigate the user pairing and power allocation scheme for multiple cellular users (CUs) under the downlink non-orthogonal multiple access (NOMA) system. To maximize the sum-rate of all CUs, we formulate the resource allocation issue through optimizing the user pairing relationship and power allocation method. However, due to the nonconvex property of the formulated problem, the original problem is decoupled into two subproblems. First, the optimal power allocation scheme with a given subchannel assignment is obtained via a closed-form solution. Furthermore, based on the obtained optimal allocation scheme, a classical deep reinforcement learning (DRL) method called Deep Q-Network (DQN) algorithm is adopted to find the optimal user pairing scheme, where the DQN algorithm is characterized by higher learning efficiency and better performance of the features extraction ability compared with traditional reinforcement learning (RL) schemes. Simulation results validate the effectiveness of our proposed resource allocation, as compared against the RL based scheme and conventional orthogonal multiple access (OMA) method.

Nonorthogonal multiple access (NOMA) can effectively improve the spectral efficiency by exploiting the power difference and employing successive interference cancellation (CSI) at receivers. In NOMA, the power allocation is crucial and can be performed with known CSI. It is also possible to carry out the power allocation with statistical CSI to meet target outage probabilities for given transmission rates. In this paper, we consider joint rate and power allocation to minimize the total transmission power with individual throughput constraints in NOMA when statistical CSI is available. An approach to find the optimal solution to the joint rate and power allocation problem is proposed. Based on simulation results, it is shown that NOMA can have a much lower total transmission power than OMA for given target throughput of each user.

Due to the increasing number of unmanned aerial vehicles (UAVs) with generally limited battery power, energy-efficient data transmission schemes with massive connectivity capabilities are required for future wireless networks. Nonorthogonal multiple access (NOMA) is a promising technique that provides such massive connectivity by allowing superposed data transmission of multiple users over the same resource block. This article presents a user pairing and power allocation technique for energy efficiency and Quality-of-Service (QoS)-based NOMA transmission for cellular-connected mobile UAVs and ground users (GUs) in a NOMA UAV network. The aim is to minimize the energy consumption of both aerial users (AUs) and GUs during uplink data transmission and guarantee their required transmission rates through efficient pairing and power optimization. Therefore, a joint pairing and power optimization problem is formulated as a nonconvex optimization problem under QoS constraints. Furthermore, a suboptimal solution is proposed to show that the proposed energy-efficient pairing technique effectively fulfills the QoS requirements of both GUs and AUs in a network with low complexity. The simulation results of the proposed technique show the performance gain in terms of energy efficiency, outage probability, and pairing complexity compared to the conventional channel condition and power-consumption-based NOMA pairing and power allocation techniques.

Non-orthogonal multiple access (NOMA) scheme is seen as a strong candidate for future wireless networks thanks to its spectral efficiency. Thus, NOMA has attracted a great recent attention and the power allocation (PA) in NOMA has been analyzed in terms of various perspectives. However, all these studies analyze only the Shannon limit and the detection at the users have not been considered in detail. In this paper, we propose a true PA constraint for a detectable NOMA signal design in multi-user NOMA schemes with arbitrary M-QAM signalling. With the proposed PA constraint, the successful detection at the receivers are guaranteed. We evaluate the proposed PA with computer simulations. We also compare with other PA strategies in the literature and present that if the provided PA constraint is not satisfied, none of the users can detect it symbols and all users have the worst bit error rate (BER) performance (i.e., 0.5). Finally, we demonstrated that for a reliable communication, the number of users and/or the modulation orders of the users should be limited.

The increasing number of consumers with diverse data rate needs is leading to increased heterogeneity in traditional cellular networks. Nonorthogonal multiple access (NOMA) has emerged as a promising method to serve a large number of users, but research shows that weak users (WU) and strong users (SU) have different throughputs. Intra-group interference reduces WUs throughput due to the superposition of signals. Improper power distribution impacts NOMA performance and lowers the total system rate. The multi-objective sum rate dwarf mongoose optimization algorithm (M-SRDMOA) is implemented as a solution to the NOMA network power allocation problems. The DMOA approach distributes adequate power to all NOMA users to increase the large sum rate. The effectiveness of the M-SRDMOA approach is supported by existing studies on fair NOMA scheduler (FANS) and multi-objective sum rate-based butterfly optimization algorithm (M-SRBOA). The M-SRDMOA’s potential sum rate with an SNR of 9dB and a noise variation=2 is 14.06 bps/Hz, which is high compared to M-SRBOA and FANS.

Non-orthogonal multiple access (NOMA) provides higher spectral efficiency and access to more users than orthogonal multiple access. However, the issue of resource allocation in NOMA is dynamic and produces a high computation burden when using traditional methods. In this paper, a symmetry-aware double deep Q network (DDQN) algorithm in deep reinforcement learning is employed to allocate power to users in NOMA while guaranteeing quality of service for the weakest users. The research process is divided into two parts. Firstly, users in the communication system are grouped using a method that synergistically considers gain difference and similarity, exploiting symmetrical properties within the user groups. Secondly, the DDQN algorithm is used to allocate power to multiple users in a NOMA system, which utilizes the inherent symmetry in the signal-to-interference noise ratio of each user as an objective function. By recognizing and leveraging these symmetrical patterns, the algorithm can dynamically adjust the power allocation to optimize system performance. Finally, the proposed algorithm is compared with conventional NOMA power allocation algorithms and shows significant improvements in system performance. The results of the convergence function show that the algorithm proposed in this paper can converge in approximately 1800 iterations, which effectively solves the problem of large arithmetic and complex processes existing in the traditional method.

This article proposes a novel framework of resource allocation in multi-cell intelligent reflecting surface (IRS) aided non-orthogonal multiple access (NOMA) networks, where an IRS is deployed to enhance the wireless service. The problem of joint user association, subchannel assignment, power allocation, phase shifts design, and decoding order determination is formulated for maximizing the achievable sum rate. The challenging mixed-integer non-linear problem is decomposed into an optimization subproblem (P1) with continuous variables and a matching subproblem (P2) with integer variables. In an effort to tackle the non-convex optimization problem (P1), iterative algorithms are proposed for allocating transmission power, designing reflection matrix, and determining decoding order by invoking relaxation methods such as convex upper bound substitution, successive convex approximation, and semidefinite relaxation. In terms of the combinational problem (P2), swap matching-based algorithms are developed for achieving a two-sided exchange-stable state among users, BSs and subchannels. Numerical results demonstrate that: i) the sum rate of multi-cell NOMA networks is capable of being increased by 35% with the aid of the IRS; ii) the proposed algorithms for multi-cell IRS-aided NOMA networks can enjoy 22% higher energy efficiency than conventional NOMA counterparts; iii) the trade-off between spectrum efficiency and coverage area can be tuned by judiciously selecting the location of the IRS.