User Clustering and Power Allocation for Hybrid Non-Orthogonal Multiple Access Systems

International audience

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.

Non-orthogonal multiple access (NOMA) emerges as a promising candidate for 5G, which radically alters the way users share the spectrum. In the NOMA system, user clustering (UC) becomes another research issue as grouping the users on different subcarriers with different power levels has a significant impact on spectral utilization. In previous literature, plenty of works have been devoted to solving the UC problem. Recently, the artificial neural network (ANN) has gained considerable attention due to the availability of UC datasets, obtained from the Brute-Force search (BF-S) method. In this paper, deep neural network-based UC (DNN-UC) is employed to effectively characterize the nonlinearity between the cluster formation with channel diversity and transmission powers. Compared to the ANN-UC, the DNN-UC is more competent as UC is a non-convex NP-complete problem, which cannot be entirely captured by the ANN model. In this work, the DNN-UC is first trained with the training samples and then validated with the testing samples to examine its mean square error (MSE) and throughput performance in an asymmetrical fading NOMA channel. Unlike the ANN-UC, the DNN-UC model offers greater room for hyper-parameter optimizations to maximize its learning capability. With the optimized hyper-parameters, the DNN-UC can achieve near-optimal throughput performance, approximately 97% of the throughput of the BF-S method.

Reconfigurable Intelligent Surfaces (RIS) and Non-Orthogonal Multiple Access (NOMA) are considered key enabling technologies for sixth-generation (6G) wireless networks due to their ability to improve spectral efficiency, coverage, and energy efficiency. However, the joint optimization of user clustering and power allocation in RIS-assisted NOMA systems leads to highly complex and non-convex optimization problems, especially in dynamic and large-scale network environments. Recently, Deep Reinforcement Learning (DRL) has emerged as a powerful tool for solving such complex resource allocation problems without relying on accurate mathematical models. In this paper, an in-depth review of existing research on RIS, NOMA, and DRL-based optimization techniques is presented. The literature is analyzed and classified based on system models, optimization objectives, and learning methods. To illustrate the benefits and limitations of current methods, a comparative analysis is given. Finally, open challenges and future research directions are identified, with a focus on the cooperative optimization of power distribution and user clustering for 6G networks.

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

The emerging non-orthogonal multiple access (NOMA), which enables mobile users (MUs) to share same frequency channel simultaneously, has been considered as a spectrum-efficient multiple access scheme to accommodate tremendous traffic growth in future cellular networks. In this paper, we investigate the NOMA downlink relay-transmission, in which the macro base station (BS) first uses NOMA to transmit to a group of relays, and all relays then use NOMA to transmit their respectively received data to an MU. In specific, we propose an optimal power allocation problem for the BS and relays to maximize the overall throughput delivered to the MU. Despite the non-convexity of the problem, we adopt the vertical decomposition and propose a layered-algorithm to efficiently compute the optimal power allocation solution. Numerical results show that the proposed NOMA relay-transmission can increase the throughput up to 30 percent compared with the conventional time division multiple access (TDMA) scheme, and we find that increasing the relays' power capacity can increase the throughput gain of the NOMA relay against the TDMA relay. Furthermore, to improve the throughput under weak channel power gains, we propose a hybrid NOMA (HB-NOMA) relay that adaptively exploits the benefit of NOMA relay and that of the interference-free TDMA relay. By using the throughput provided by the HB-NOMA relay for each individual MU, we study the multi-MUs scenario and investigate the multi-MUs scheduling problem over a long-term period to maximize the overall utility of all MUs. Numerical results demonstrate the performance advantage of the proposed multi-MUs scheduling that adopts the HB-NOMA relay-transmission.

To address the straggler problem in wireless federated learning (WFL) networks, a novel hybrid non-orthogonal multiple access (NOMA) - time division multiple access (TDMA) scheme is proposed, where not only local model training and uploading of different users are performed in parallel, but also the model uploading of different users is performed in a hybrid NOMA-TDMA manner. Particularly, users that complete their local training form dynamic NOMA groups across different time slots in a TDMA fashion to upload their models efficiently. The latency per training round minimization problem is formulated to jointly optimize the user scheduling, time allocation, computing frequency allocation, and energy allocation. An efficient algorithm, combining one-dimensional search and successive convex approximation, is developed. Simulation results demonstrate that the proposed scheme can outperform the state-of-the-art NOMA and TDMA schemes under various system parameter setups.

Non-orthogonal multiple access (NOMA) and mmWave are considered to be promising techniques for 5G and beyond cellular network communication systems. While the mmWave band provides a very wide under-utilized bandwidth, NOMA enhances the spectral efficiency compared to orthogonal multiple access (OMA). The combination of these two techniques could be considered as the key solution to the high required data rates in next-generation communication systems. The implementation of NOMA is studied and verified under an ideal condition with perfect knowledge of the channel state information (CSI) at the base station (BS). However, under the practical conditions, the fluctuation of the wireless channel makes perfect CSI unachievable at the BS. Hence, we proposed to use the angle of departure (AoD) as feedback information from UEs to the BS. We assume that, mobile users (UEs) perfectly estimate the channel by detecting the pilot signals. Then, UEs quantize the AoD and feed back to the BS. Finally, the BS uses the AoD to perform user clustering, power allocation and beamforming. To reduce the feedback overhead further, we proposed a user clustering algorithm which uses one-bit feedback to determine the change of the AoD. Numerical results demonstrate that the proposed NOMA system outperforms the conventional orthogonal multiple-access (OMA) system with the same amount of feedback information.

By enabling multiple non-orthogonal transmissions, power domain non-orthogonal multiple access (PD-NOMA) potentially increases a system’s spectral efficiency. This technique can become an alternative for future generations of wireless communication networks. The efficiency of this method fundamentally depends on two previous processing steps: an appropriate grouping of users (transmission candidates) as a function of the channel gains and the choice of power levels that will be used to transmit each signal. Thus far, the solutions presented in the literature to address the problems of user clustering and power allocation do not consider the dynamics of communication systems, i.e., the temporal variation in the number of users and the channel conditions. In order to consider these dynamic characteristics in the clustering of users in NOMA systems, this work proposes a new clustering technique based on a modification of the DenStream evolutionary algorithm, chosen for its evolutionary capacity, noise robustness and online processing. We evaluated the performance of the proposed clustering technique considering, for simplicity, the use of an already widely known power allocation strategy called improved fractional strategy power allocation (IFSPA). The results show that the proposed clustering technique can follow the system dynamics, clustering all users and favoring the uniformity of the transmission rate between the clusters. Compared to orthogonal multiple access (OMA) systems, the proposed model’s gain was approximately 10%, obtained on a challenging communication scenario for NOMA systems since the channel model adopted does not favor a large difference in the channel gains between users.

Massive connectivity is one of the killer use cases for fifth generation (5G) wireless systems over the millimeter wave (mmWave) band. Due to the sparse nature of mmWave channel, several users may choose the same beam as the strongest one, leading to severe intra-beam interference (intra-BI). Although intra-BI can be mitigated through digital precoding, the required number of RF chains for fulfilling massive connectivity becomes large. By applying non-orthogonal multiple access (NOMA) and properly allocating transmit power to each beam and user, the sum rate can be maximized using a smaller number of RF chains. However, the power allocated to each user depends on that allocated to other users within the same beam. Since the joint beam selection and power allocation problem contains a non-convex objective function with a large set of coupled and mixed integer variables, direct solutions may not exist particularly when both the number of users and antennas are large. In this work, we decompose the aforementioned problem into two subproblems, where the beam selection sub-problem is first solved under an equal power allocation. The outcome of beam selection is then used as the input to the power allocation sub-problem. For both subproblems, we develop numerous efficient algorithms to find the active beam set and the user power allocation, respectively. The notions of embedded methods from machine learning and that of intelligent searching from metaheuristics are adopted in our work as the key ingredient for algorithm constructions. Numerical results demonstrate that in the region of large user population, the proposed beam selection and power allocation algorithms can effectively improve the sum rate using a less number of RF chains in comparison with some existing solutions.

In this paper, we aim to design an adaptive power allocation scheme to minimize the average transmit power of a hybrid automatic repeat request with chase combining (HARQ-CC) enabled non-orthogonal multiple access (NOMA) system under strict outage constraints of users. Specifically, we assume that the base station only knows the statistical channel state information of the users. To achieve power efficient design and cope with the reliable transmissions of users, a partial HARQ-CC scheme is proposed. We first focus on the two-user case. To evaluate the performance of the two-user partial HARQ-CC enabled NOMA system, we first analyze the outage probability of each user. Then, an average power minimization problem is formulated. However, the attained expressions of the outage probabilities are nonconvex, and thus make the problem challenging to solve. Hence, we propose to use a successive convex approximation (SCA) based algorithm to solve the problem iteratively. Meanwhile, we prove that the proposed algorithm can converge to a Karush-Kuhn-Tucker point of the original problem. For more practical applications, we also investigate the partial HARQ-CC enabled transmissions in the multi-user scenario. The user pairing and power allocation problem is considered. With the aid of matching theory, a low complexity algorithm is presented to first handle the user pairing problem. Then the power allocation problem for each user pair is solved by the proposed SCA-based algorithm. Simulation results show the efficiency of the proposed transmission strategy and the near-optimality of the proposed algorithms.

Recently, nonorthogonal multiple access (NOMA) has been proposed as a key enabler and candidate to overcome the spectral‐inefficiency of the conventional orthogonal multiple‐access (OMA) techniques, and meet the pressing demands for massive connectivity in 5G and beyond cellular networks. Despite the numerous merits of NOMA, power allocation must be optimized. In addition, the scarcity of the frequency spectrum is still a major bottleneck, and thus, bandwidth must also be carefully utilized. This paper investigates the problem of joint power allocation and subcarrier assignment (J‐PA‐SA) in uplink clustered multicarrier NOMA networks. Specifically, the objective is to maximize the network sum‐rate subject to users' minimum rate requirements. Two novel network‐coded single and multiple subcarrier assignment schemes are proposed to improve bandwidth utilization over the conventional single and multiple subcarrier assignment schemes. In general, the J‐PA‐SA problem is nonconvex and NP‐hard (i.e., excessively complex). To effectively solve it, it is decomposed into two subproblems: (1) power allocation per (user cluster, subcarrier) combination, and (2) subcarrier assignment. Solution procedures are proposed to solve the J‐PA‐SA problem for the conventional and network‐coded SA schemes. Simulation results revealed that the proposed solution procedures yield the optimal network sum‐rate solutions with low‐complexity, while satisfying the users' minimum rate requirement. Lastly, the network‐coded SA schemes are shown to significantly outperform to the conventional SA schemes for the NOMA and OMA networks.

In this letter, we explore user pairing in a downlink non-orthogonal multiple access (NOMA) network. As power allocation inherently intertwines with user pairing, a joint user pairing and power allocation problem is considered to optimize the achievable sum rate (ASR) with minimum rate constraint for each user, which is a mixed integer programming problem. To solve this non-convex problem, we first obtain the optimal power allocation in an NOMA system with only 2 users; then analyze the user pairing problem in a simplified situation, i.e., an NOMA system with four users. Finally, we obtain the closed-form globally optimal solution in a general NOMA system. Extensive performance evaluations are conducted to compare the ASRs of the NOMA and OMA systems. Results show that the performance of the NOMA system with the proposed optimal user pairing is significantly better than that of the OMA system, as well as the performance of the NOMA system with random user pairing.

In this paper, we investigate the bandwidth and power allocation problem in remote radio head cluster (RRHC)-based millimeter wave (mm-wave) massive MIMO heterogeneous cloud radio access networks with limited fronthaul capacity. The coordinated multipoint transmission is applied in each RRHC for cancelling the intra-cluster interference. To avoid the inter-tier interference, distinct bandwidths are allocated to macro base station and RRHs. Following this, we formulate a bandwidth and power allocation optimization problem to maximize the downlink weighted sum rate of the system subject to per-RRHC power and fronthaul capacity constraints, which is a non-convex optimization problem and is difficult to directly solve. Next, we fix the bandwidth allocation and the original problem can be divided into two independent optimization problems, i.e., the weighted sum rate maximization problems of MUs and RRH users, respectively. For the former, the convex optimization technique can be used to solve it. As for the latter, a two-loop iterative algorithm is proposed to deal with it. Specifically, we propose the price-based outer iteration to control the fronthaul capacity and the weighted minimum mean square error-based inner iteration to obtain the power allocation. To this end, a 1-D search method is adopted to find the optimal bandwidth allocation. Finally, numerical results are conducted to verify the effectiveness of the proposed algorithms under different parameters.

This paper investigates the optimal resource allocation of a downlink non-orthogonal multiple access (NOMA) system consisting of one base station and multiple users. Unlike existing short-term NOMA designs that focused on the resource allocation for only the current transmission timeslot, we aim to maximize a long-term network utility by jointly optimizing the data rate control at the network layer and the power allocation among multiple users at the physical layer, subject to practical constraints on both the short-term and long-term power consumptions. To solve this problem, we leverage the recently-developed Lyapunov optimization framework to convert the original long-term optimization problem into a series of online rate control and power allocation problems in each timeslot. The power allocation problem, however, is shown to be non-convex in nature and thus cannot be solved with a standard method. However, we explore two structures of the optimal solution and develop a dynamic programming based power allocation algorithm, which can derive a globally optimal solution, with a polynomial computational complexity. Extensive simulation results are provided to evaluate the performance of the proposed joint rate control and power allocation framework for NOMA systems, which demonstrate that the proposed NOMA design can significantly outperform multiple benchmark schemes, including orthogonal multiple access (OMA) schemes with optimal power allocation and NOMA schemes with non-optimal power allocation, in terms of average throughput and data delay.