Weighted Sum-Rate Maximized Power Allocation in Downlink MIMO-NOMA Systems

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.

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.

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.

Non-Orthogonal Multiple Access (NOMA) has recently been conceived as a new technology for 5G cellular communication which has the potential to provide higher throughput than conventional orthogonal multiple access. In this paper, we deal with sum-rate maximization of a downlink NOMA based system in the presence of channel estimation error which is a more practical scenario, with user's QoS and total BS power constraints. By analyzing KKT conditions, a closed form solution for optimal power allocation aiming to maximize sum-rate is derived. Simulation results depict that in terms of sum-rate, NOMA system outperforms conventional orthogonal multiple access (OMA) systems and the gain of NOMA increases with users.

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

Power allocation is a critical issue in the physical layer of power-domain nonorthogonal multiple access (NOMA) systems. However, existing power allocation schemes have not considered the delay quality of service (QoS) requirement in the datalink layer of users, and hence may not ensure the desired delay QoS requested by the services in the upper layers. Different from existing works, we apply the statistical QoS theory into NOMA systems and formulate the physical-datalink cross-layer power allocation problem as a stochastic optimization problem under different delay QoS constraints. Also, we show that the formulated problem is quasi-concave and propose a bisection-based cross-layer power allocation algorithm. Simulation results show that the proposed algorithm is able to converge to the optimal solution obtained by exhaustive search. Also, the proposed scheme outperforms existing fixed NOMA and time-division multiple access based schemes in terms of max-min effective capacity.

In this article, we address the problem of optimal joint power and resource allocation when Non-Orthogonal Multiple Access (NOMA) and Orthogonal Frequency Multiple Access (OFDMA) are combined for a hybrid downlink multiple access. First, an optimal solution of joint power and resource allocation minimizing the transmit power consumption, is obtained by rewriting the original optimization problem into an equivalent convex one and then by solving it by means of the well-known interior-point method. Second, we analyze the properties of the optimum, which are twofold; we show that at the optimum, the order of the users is preserved from one channel to another. Then, we derive a closed-form expression for the optimal power allocation in the particular case where all users have positive non-zero transmit powers in all channels.

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.

In this paper, we investigate the throughput performance of single-packet and multi-packet hybrid-automatic repeat request (HARQ) with blanking for downlink non-orthogonal multiple access (NOMA) systems. While conventional single-packet HARQ achieves high throughput at the expense of high latency, multi-packet HARQ, where several data packets are sent in the same channel block, can achieve high throughput with low latency. Previous works have shown that multi-packet HARQ outperforms single-packet HARQ in orthogonal multiple access (OMA) systems, especially in the moderate to high signal-to-noise ratio regime. This work amalgamates multi-packet HARQ with NOMA to achieve higher throughput than the conventional single-packet HARQ and OMA, which has been adopted in the legacy mobile networks. We conduct theoretical analysis for the throughput per user and also investigate the optimization of the power and rate allocations of the packets, in order to maximize the weighted-sum throughput. It is demonstrated that the gain of multi-packet HARQ over the single-packet HARQ in NOMA systems is reduced compared to that obtained in OMA systems due to inter-user interference. It is also shown that NOMA-HARQ cannot achieve any throughput gain with respect to OMA-HARQ when the error propagation rate of the NOMA detector is above a certain threshold.

This work proposes a multiuser hybrid beamforming scheme for non-orthogonal multiple access (NOMA) systems using the minimum mean square error (MMSE) approach to weighted sum rate maximization. While NOMA may be effective in terms of enhancing user fairness, hybrid beamforming is necessary to reduce the transceiver cost as the system moves towards higher frequency. The design is divided into two stages. In the first stage, a fully digital multiuser beamformer is derived by maximizing the weighted sum rate of all users under no constraint on the number of RF chains. This problem is then transformed into a weighted sum MSE minimization problem, which facilitates the use of alternating optimization to obtain an efficient local solution. In the second stage, the previously obtained multiuser beamformer is then split into RF and baseband beamformers by using orthogonal matching pursuit (OMP). A user role selection algorithm is then proposed to determine the role of strong and weak users. Simulation results are provided to demonstrate the effectiveness of the proposed schemes.

Recently, non-orthogonal multiple access (NOMA) has been proposed to improve spectral efficiency greatly over conventional orthogonal multiple access (OMA). However, it is still challenging to implement NOMA into transmission of video. In this research, we investigate video broadcasting design over NOMA systems. When video transmission is considered, quality of experience (QoE) is a better metric for NOMA design than throughput. Therefore, we design a QoE-driven scalable video broadcasting (SVB) scheme over NOMA systems. To achieve scalable transmission performance, power allocation is optimized among multiple video layers, which are requested by users with different channel conditions. Specifically, we formulate the power allocation problem to maxmize the system overall QoE while guaranteeing the basic service of users. Although this problem is non-convex and discontinuous, an optimal algorithm is proposed based on the hidden monotonicity of the problem. Simulation results show that the proposed SVB scheme over NOMA systems outperforms existing OMA and NOMA based schemes in various broadcast scenarios in terms of QoE.

Non-orthogonal multiple access (NOMA) has the advantage of increasing throughput and the number of access users than conventional orthogonal multiple access (OMA), which has been paid a lot of attention in multi-beam Low Earth Orbit (LEO) satellite system. Power allocation is an indispensable step in NOMA system, which is closely related to throughput. Aiming at maximizing the throughput of the downlink multi-beam LEO satellite system, power allocation is formulated as a non-convex optimization problem. According to the characteristics of power allocation problem, we propose two heuristic algorithms based on genetic algorithm (GA) and differential evolution (DE) algorithm to solve power allocation problem, which can not only increase system throughput, but also stay robust when faced with different minimum receiving user rate (MRUR) and satellite altitudes. Finally, the simulation results are provided for demonstrating the effectiveness and robustness of the proposed algorithms.

The minimum rate requirements of users are not taken into consideration in the existing power allocation schemes for maximum fairness of non-orthogonal multiple access (NOMA) systems. For this problem, the poa power allocation scheme for fairly improving user rate in NOMA systems is proposed. Firstly, the minimum power required for each cluster is calculated based on channel conditions and rate requirement for each user. Secondly, in the case of meeting the minimum rate requirements of all users, the power allocation optimization problem of improving each user’s rate fairly is established with the constraints of minimum power required by each cluster and the total power required by all clusters. Finally, the power allocation scheme, which satisfies the minimum rate demand of the user and maximizes the user's improved minimum rate, is obtained by adjusting the power of the part clusters. The simulation results show that when the minimum rate requirements of users are different, the increased rates of users and the outage probabilities of the proposed scheme both outperform these of the existing schemes in the same scenario.

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.

Aim: The aim of this research is to increase the system's capacity rate of the system using the novel Modified ESPRIT Multi Signal Classification (Modified ESPRIT-MUSIC) algorithm in comparison with Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT). In this paper, a reliable novel Modified ESPRIT Multi Signal Classification (Modified ESPRIT-MUSIC) direction of arrival (DoA) estimation technique employed to reduce interference in a Digital beamforming Multi-Input Multi-Output Non-Orthogonal Multiple Access (MIMO NOMA) system over Rayleigh fading channel. Materials and Methods: The Modified ESPRIT-MUSIC algorithm is implemented in Digital beamforming at lower signal to noise ratio (SNR) levels. The DoA estimation technique is used in the MIMO NOMA system to decrease interference and increase sum rate. The addition of the estimation process with an additional estimation algorithm gives accurate user detection. The performance of this proposed algorithm is compared with the existing ESPRIT algorithm for the same antenna parameters. Group 1 is the ESPRIT algorithm, and Group 2 is the Modified ESPRIT-MUSIC algorithm. The sample size for each group is 20. The total sample size is 40. The pretest power simulation is 80% with a significance of 0.15. Results: The performance analysis of the proposed system is calculated in terms of capacity vs SNR. The increase in capacity of the proposed system by accurate user detection through beamforming is possible because of the combined ESPRIT-MUSIC algorithm. The mean capacity value for the proposed novel system is 13 bps/Hz at 10 dB and 26 bps/Hz at 20 dB SNR, and the mean capacity value for ESPRIT algorithm is 12 bps/Hz at 10 dB and 23 bps/Hz at 20 dB SNR. Conclusion: From the simulation results and independent sample tests, the novel Modified-ESPRIT MUSIC algorithm detects signals significantly better than the ESPRIT algorithm and increases the system's capacity.