User Power Allocation Research Articles

Resource scheduling scheme for 5G mmWave CP‐OFDM based wireless networks with delay and power allocation optimizations

AbstractIn this paper, to optimize the average delay and power allocation (PA) for system users, we propose a resource scheduling scheme for wireless networks based on Cyclic Prefix Orthogonal Frequency Division Multiplexing (CP‐OFDM) according to the first fifth‐generation standards. For delay minimization, we solve a throughput maximization problem that considers CP‐OFDM systems with carrier aggregation (CA). Regarding PA, we consider an approach that involves maximizing goodput using an effective signal‐to‐noise ratio. An algorithm for jointly solving delay minimization through computation of required user rates and optimizing the power allocated to users is proposed to compose the resource allocation approach. In wireless network simulations, we consider a scenario with the following capabilities: CA, 256‐Quadrature Amplitude Modulation, millimeter waves above 6 GHz, and a radio frame structure with 120 KHz spacing between the subcarriers. The performance of the proposed resource allocation algorithm is evaluated and compared with those of other algorithms from the literature using computational simulations in terms of various Quality of Service parameters, such as the throughput, delay, fairness index, and loss rate.

User Scheduling and Power Allocation for Precoded Multi-Beam High Throughput Satellite Systems With Individual Quality of Service Constraints

For extensive coverage areas, multi-beam high throughput satellite (HTS) communication is a promising technology that plays a crucial role in delivering broadband services to many users with diverse Quality of Service (QoS) requirements. This article focuses on multi-beam HTS systems where all beams reuse the same spectrum. In particular, we propose a novel user scheduling and power allocation design capable of providing guarantees in terms of the individual QoS requirements while maximizing the system throughput under a limited power budget. Precoding is employed in the forward link to mitigate mutual interference among the users in multiple-access scenarios over different coherence time intervals. The combinatorial optimization structure from user scheduling requires an extremely high cost to obtain the global optimum even when a reduced number of users fit into a time slot. Therefore, we propose a heuristic algorithm yielding a good trade-off between performance and computational complexity, applicable to a static operation framework of geostationary (GEO) satellite networks. Although the power allocation optimization is signomial programming, non-convex on a standard form, the solution can be lower bounded by the global optimum of a geometric program with a hidden convex structure. A local solution to the joint user scheduling and power allocation problem is consequently obtained by a successive optimization approach. Numerical results demonstrate the effectiveness of our algorithms on GEO satellite networks by providing better QoS satisfaction combined with outstanding overall system throughput.

Joint Optimization Framework for User Clustering, Downlink Beamforming, and Power Allocation in MIMO NOMA Systems

In this paper, we investigate the application of downlink beamforming along with non-orthogonal multiple access (NOMA) in a multi-user multiple-input multiple-output (MIMO) system. The joint optimization framework for user clustering, downlink beamforming and power allocation scheme is formulated as a novel mixed-integer non-linear program (MINLP), where the aim is to minimize the total transmission power while satisfying quality-of-service (QoS), user clustering and power constraints. Owing to the non-convexity and combinatorial nature of the problem, obtaining an optimal solution is challenging. To tackle this issue, we first develop an algorithm based on branch-and-bound (BB), whereby the feasible space is successively partitioned and searched by means of lower and upper bounds on the objective function. While this algorithm is shown to return an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> -optimal solution within a finite number of iterations, it entails high computational complexity. Considering this limitation, we then reformulate the original problem into a more tractable form and conceive a low-complexity algorithm for its solution based on the penalty dual-decomposition technique. The proposed joint design algorithms for MIMO NOMA are evaluated by means of simulations over mmWave channels. Results show significant improvements in terms of total transmit power and spectral efficiency compared to benchmark approaches.

Coexistence of URLLC and eMBB Services in MIMO-NOMA Systems

Enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communications (URLLC) are two major expected services in the fifth-generation mobile communication systems (5G). Specifically, eMBB applications support extremely high data rate communications, while URLLC services aim to provide stringent latency with high reliability communications. Due to their differentiated quality-of-service (QoS) requirements, the spectrum sharing between URLLC and eMBB services becomes a challenging scheduling issue. In this paper, we aim to investigate the URLLC and eMBB co-scheduling/coexistence problem under a puncturing technique in multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) systems. The objective function is formulated to maximize the data rate of eMBB users while satisfying the latency requirements of URLLC users through joint user selection and power allocation scheduling. To solve this problem, we first introduce an eMBB user clustering mechanism to balance the system throughput and computational complexity. Thereafter, we decompose the original problem into two subproblems, namely the scheduling problem of user selection and power allocation. We introduce a Gale-Shapley (GS) theory to solve the user selection problem, and a successive convex approximation (SCA) and a difference of convex (DC) programming to deal with the power allocation problem. Finally, an iterative algorithm is utilized to find the global solution with low computational complexity. Numerical results show the effectiveness of the proposed algorithms, and verify the proposed approach outperforms other baseline methods.

Combination of Throughput-optimal Scheduling and Network Utility Maximization in NOMA Systems with Flow-Level Dynamics

In wireless networks, the network queue stability (throughput-optimality) and the network utility maximization are two crucial tasks for network operators. Besides, new techniques of mobile communications, e.g. the non-orthogonal multiple access (NOMA), have emerged endlessly to meet coming challenges. In this paper, we for the first time, investigate a downlink NOMA system with flow-level dynamics, where long-lived flows and short-lived flows exist simultaneously, and formulate an optimization problem of user selection and power allocation. We aim to maximize the network utility at each time slot and simultaneously guarantee the throughput-optimality, under the quality of service (QoS) constraints. Considering the practicability of algorithm, the suboptimal algorithm containing two stages, is proposed. And we analyze the complexity of proposed algorithm. In the performance evaluation, the benchmark algorithm is provided for comparison. And we take the energy efficiency (EE) as an example of the network utility and define the scheduling delay (SD). Simulation results show that the suboptimal algorithm is throughput-optimal, and understandably, its performances could be better than those in the benchmark algorithm in terms of EE and SD, as long as the appropriate parameter is set.

User Association and Power Allocation in Multi-Connectivity Enabled Millimeter-Wave Networks With Limited Backhaul

User Association and Power Allocation in Multi-Connectivity Enabled Millimeter-Wave Networks With Limited Backhaul

STAR-RISs Assisted NOMA Networks: A Distributed Learning Approach

A novel simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) aided downlink non-orthogonal multiple access (NOMA) communication framework is proposed. Two STAR-RIS protocols are investigated, namely the energy splitting (ES) and the mode switching (MS). However, since the STAR-RIS has a massive number of reconfigurable elements, the passive beamforming problem has enormous action dimensions and extremely high complexity, resulting in an increased training time and performance degradation for the artificial intelligent agent. To resolve this predicament, a partitioning approach is proposed to divide the STAR-RIS into several tiles. A distributed learning approach is conceived for the partitioning and the corresponding tile-based passive beamforming of each STAR-RIS, as well as the power allocation for users to maximize the average throughput achieved by multiple base stations. In particular, the deep reinforcement learning (DRL) agent is employed at each BS, and an access-free federated learning (AFFL) model, which gathers the features of federated learning (FL) and transfer learning (TL) is proposed to accelerate or even exempt the training process for agents. Simulation results indicate that 1) the ES protocol is preferred for being employed in the NOMA networks compared with the MS protocol; 2) the tile-based passive beamforming approach outperforms benchmarks while the STAR-RIS has a large size; 3) the proposed AFFL framework effectively reduces or exempts the training overhead.

Energy-Efficient Pairing and Power Allocation for NOMA UAV Network Under QoS Constraints

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.

Joint Resource Management and Trajectory Optimization for UAV-Enabled Maritime Network.

Due to the lack of places to employ communication infrastructures, there are many coverage blind zones in maritime communication networks. Benefiting from the high flexibility and maneuverability, unmanned aerial vehicles (UAVs) have been proposed as a promising method to provide broadband maritime coverage for these blind zones. In this paper, a multi-UAV-enabled maritime communication model is proposed, where UAVs are deployed to provide the transmission service for maritime users. To improve the performance of the maritime communication systems, an optimization problem is formulated to maximize the minimum average throughput among all users by jointly optimizing the user association, power allocation, and UAV trajectory. To derive the solutions with a low computational complexity, we decompose this problem into three subproblems, namely user association optimization, power allocation optimization, and UAV trajectory optimization. Then, a joint iterative algorithm is developed to achieve the solutions based on the successive convex approximation and interior-point methods. Extensive simulation results validate the effectiveness of the proposed algorithm and demonstrate that UAVs can be used to enhance the maritime coverage.

Multi-objective oriented resource allocation in reconfigurable intelligent surface assisted HCNs

In order to achieve thousand-fold throughput improvements for future applications, heterogeneous cellular networks (HCNs) with ultra-densely deployed small cells have gained a great deal of attention in recent years. However, energy consumption is an increasing concern as base station deployment (BSs) becomes denser. In order to address this issue, a novel framework is proposed for integrating multiple reconfigurable intelligent surfaces (RISs) in heterogeneous cellular networks (HCNs), where the communication in each BS is enhanced by its associated RIS. The multi-objective optimization is formulated to obtain the optimal trade-off between throughput and energy consumption. Moreover, an improved non-dominated sorting genetic algorithm II (NSGA-II) based algorithm is proposed to optimize the sub-carrier assignment, user equipment (UE) association, power allocation, and RISs’ phase shift. The proposed algorithm’s potential solutions are first encoded as individuals and optimized with the conventional NSGA-II algorithm. In order to further tackle the coupling characters of the optimization variables, the Zoutendijk method is further employed for searching for better solutions. Numerical results illustrate that: (i) the proposed multiple-RIS-assisted HCNs outperform conventional HCNs without RIS. (ii) the performance can be improved by deploying the RIS closer to UEs or increasing the number of reflecting elements.

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).

User Subgrouping and Power Control for Multicast Massive MIMO Over Spatially Correlated Channels

Massive multiple-input-multiple-output (MIMO) is unquestionably a key enabler of the fifth-generation (5G) technology for mobile systems, enabling to meet the high requirements of upcoming mobile broadband services. Physical-layer multicasting refers to a technique for simultaneously serving multiple users, demanding for the same service and sharing the same radio resources, with a single transmission. Massive MIMO systems with multicast communications have been so far studied under the ideal assumption of uncorrelated Rayleigh fading channels. In this work, we consider a practical multicast massive MIMO system over spatially correlated Rayleigh fading channels, investigating the impact of the spatial channel correlation on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">favorable propagation</i> , hence on the performance. We propose a subgrouping strategy for the multicast users based on their channel correlation matrices’ similarities. The proposed subgrouping approach capitalizes on the spatial correlation to enhance the quality of the channel estimation, and thereby the effectiveness of the precoding. Moreover, we devise a max-min fairness (MMF) power allocation strategy that makes the spectral efficiency (SE) among different multicast subgroups uniform. Lastly, we propose a novel power allocation for uplink (UL) pilot transmission to maximize the SE among the users within the same multicast subgroup. Simulation results show a significant SE gain provided by our user subgrouping and power allocation strategies. Importantly, we show how spatial channel correlation can be exploited to enhance multicast massive MIMO communications.

User Pairing and Power Allocation for UAV-NOMA Systems Based on Multi-Armed Bandit Framework

In this paper, we investigate the joint user pairing and power coefficient allocation for unmanned aerial vehicle (UAV) systems which employ non-orthogonal multiple access (NOMA) to communicate with multiple ground users. Aiming to maximize achievable sum rate and ensure the users' Quality-of-Service (QoS) requirements, we formulate an optimization problem which relies on reinforcement learning (RL) from Multi-Armed Bandit (MAB) framework to propose a solution based on Upper Confidence Bound (UCB) approach. The proposed solution can successfully identify the best action and selects it more often, which leads to maximum system throughput. The attained results show that the proposed scheme finds the best-performing action fast, while the others methods spend a lot of time exploring non-ideal user pairs. As a result, the proposed method accumulates less regret and achieves satisfactory results in terms of system throughput when compared to other user pairing strategies and power allocation (PA) policies.

Message Passing-Based mmWave MIMO-NOMA With User Grouping and Power Allocation

In this paper, we investigate a downlink non-orthogonal multiple access (NOMA) transmission scheme for hybrid millimeter-wave (mmWave) system and study its user grouping and power allocation. In order to maximize the weighted sum-rate (WSR) of the system, we first propose a user grouping algorithm based on min-sum message passing (MSMP) strategy under the limited number of radio frequency (RF) chains. Then, with the zero-forcing (ZF) digital precoding, we propose a power allocation scheme for multiple-RF-chain NOMA (MRFC-NOMA) structure based on a non-convex transformation method, and design a low-complexity algorithm. The simulation results show that the proposed joint user grouping and power allocation method with lower complexity can achieve better WSR performance than the traditional schemes.

Downlink Resource Allocation With Pilot Length Optimization for User-Centric Cell-Free MIMO Networks

Pilot length is a crucial parameter in cell-free multiple-input multiple-output (MIMO) networks, which could significantly affect the downlink throughput. In this letter, we jointly optimize the user scheduling, power allocation and pilot length for user-centric (UC) cell-free MIMO networks. We consider the frequency band is uniformly split into several sub-channels. As the number of users scheduled on one sub-channel is commonly small, orthogonal pilot sequences are adopted among users on each sub-channel to avoid pilot contamination. To achieve user fairness, we aim to maximize the minimum ergodic user rate in the downlink transmission, yielding a challenging mix-integer non-convex optimization. Inspired by the weighted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula> -norm approximation in compressive sensing together with successive convex approximation (SCA), an iterative scheme is then proposed to solve the problem. Simulation results demonstrate that the proposed scheme outperforms its state-of-the-art counterpart, which is a three-step algorithm including user grouping, sub-channel allocation, and power optimization.

A matching game framework for users clustering and resource allocation with wireless power transfer in a CR‐NOMA network

SummaryThis paper investigates the resource allocation in a massively deployed user cognitive radio enabled non‐orthogonal multiple access (CR‐NOMA) network considering the downlink scenario. The system performance deteriorates with the number of users who are experiencing similar channel characteristics from the base station (BS) in NOMA. To address this challenge, we propose a framework for maximizing the system throughput that is based on one‐to‐one matching game theory integrated with the machine learning technique. The proposed approach is decomposed to solve users clustering and power allocation subproblems. The selection of optimal cluster heads (CHs) and their associated cluster members is based on Gale‐Shapley matching game theoretical model with the application of Hungarian method. The CHs can harvest energy from the BS and transfer their surplus power to the primary user (PU) through wireless power transfer. In return, they are allowed to access the licensed band for secondary transmission. The power allocation to the users intended for power conservation at CHs is formulated as a probabilistic constraint, which is then solved by employing the support vector machine (SVM) algorithm. The simulation results demonstrate the efficacy of our proposed schemes that enable the CHs to transfer the residual power while ensuring maximum system throughput. The effects of different parameters on the performance are also studied.

Throughput fairness trade-offs for downlink non-orthogonal multiple access systems in 5G networks

In this work, user pairing and power allocation are proposed as a hybrid scheme to maximise throughput and achieve system fairness in the non-orthogonal multiple access (NOMA) system in 5G networks. The proposed approach is designed to improve the throughput and fairness performance of the downlink NOMA system in 5G networks. User pairing and power allocation schemes are separated to reduce resource allocation complexity. Integer linear programming is applied to perform user pairing, and particle swarm optimisation is used for power allocation. Moreover, the optimisation problem is formulated by converting multi-objective functions into a single function using the scalarisation of multi-objective optimisation problems, and the penalty function is used to prevent optimisation from violating the power, fairness, and data rate constraints. Simulation results show that the proposed model outperforms the conventional numerical approach by at least 9% of throughput maximisation and achieves an acceptable fairness rate.

An energy‐efficient power allocation scheme for millimeter‐wave MIMO‐NOMA systems

SummaryThere are many challenges in fifth generation (5G) telecommunication systems, due to the increasing demands and applications. The most important of which are need to have higher energy efficiency (EE) and spectral efficiency (SE). They are critical in the practical multiple‐input multiple‐output (MIMO) telecommunication systems. Non‐orthogonal multiple access (NOMA) methods and millimeter‐waves can be used in conjunction with MIMO systems to improve their EE and SE performance. In this paper, we investigate the application of NOMA and mm‐Wave transmission in the downlink of MIMO systems. Then, we formulate the optimization problem for users in MIMO‐NOMA systems to maximize the EE that is subject to minimum data rate to satisfy required quality of service (QoS) and maximum transmission power. To achieve the optimal power allocation for users, we reach a problem for the EE maximization that is non‐convex and solution of the optimization problem is not trivial. We exploit a lower bound of the data rate and the Lagrange dual function to convert it to a convex and unconstrained problem, which is easy to solve. In the next step, we derive a relation for determining the optimal power allocation of users. In addition, a numerical algorithm is presented that can be used to solve the problem. According to the simulation results of the proposed algorithm, our method performs better and provides higher EE than both orthogonal multiple access and equal power allocation schemes.

Learning-Based Resource Allocation in Heterogeneous Ultradense Network

Learning-based resource allocation (LRA) is envisioned as an integral element of 6G. This article proposes a novel learning-based paradigm to address resource allocation problems in heterogeneous ultradense networks (HUDNs). Our paradigm is a highly efficient realization for utilizing the inherence properties in HUDN, which comprise the local validity and correlation attenuation. Concretely, we formulate the HUDNs as a heterogeneous bipartite graph model and propose the corresponding heterogeneous bipartite graph neural network (HBGNN). The local sampling characteristic of HBGNN matches the inherence properties. Meanwhile, our paradigm combines data-driven and model-driven learnings and employs online and offline trainings. Hence, two of LRA’s obstacles: 1) the overreliance on the perfect data set and 2) the low calculation efficiency are mitigated, and the realizability of our paradigm is improved. Besides, entropy regularization is utilized to guarantee the effectiveness of exploration in the configuration space. We apply our approach to a representative resource allocation problem, the jointly user association (UA) and power allocation (JUAPA) problem. We formulate JUAPA as a combination classification and regression problem and adopt a dynamical hyperparameter output layer to address the discrete variable of UA. Simulation results demonstrate that the proposed method has better performance and higher computational efficiency than traditional optimization algorithms.

A novel power allocation strategy for cooperative PDMA systems

Cooperative communication technology is of great importance for increasing the user reachable rate, further improving throughput and reducing the outage probability of non-orthogonal multiple access (NOMA) systems. This paper mainly studies the power allocation optimization method based on amplify-and-forward (AF) pattern division multiple access (PDMA) to obtain the maximum achievable throughput. We formulate an optimization problem of user power allocation in a downlink PDMA system with cooperative relaying, the exact expressions of system throughput and user outage probability of the AF-PDMA system are derived, and a novel power allocation optimization method based on uniform distribution and restricted constraints is proposed. The effectiveness of the restricted constraints and optimization method is verified by theoretical analysis and simulation. The studies we have performed showed that the proposed scheme with uniform distribution and restricted constraints can be significantly improved in terms of the system throughput in comparison to the case with a genetic algorithm (GA) and fixed power allocation scheme. Concerning the proposed method, the search space is reduced to 1/3 of the original feasible region, and the runtime of the algorithm accounts for only 20% of the GA runtime.