Iterative Power Allocation Algorithm Research Articles

Power allocation scheme for sum rate and fairness trade-off in downlink NOMA networks

Non-orthogonal multiple access (NOMA) is an essential enabler technology that is expected to help satisfy the key requirements of increased system throughput in future wireless networks. However, another equally important aspect that should go hand-in-hand with system throughput is user fairness for any network. But, to the best of our knowledge, even though there have been works that look at system throughput or user fairness maximization for NOMA-based networks, they looked at these as a single objective optimization problem, where one is the objective and the other is one of the constraints. However, quite often, joint optimization of both system throughput and user fairness is required to make optimized decisions in the face of trade-offs between these two equally important but conflicting objectives. In this regard, this paper formulates a multi-objective optimization problem to jointly maximize the sum rate and user fairness in a downlink transmission NOMA system, through optimize power allocation (PA), under system-imposed constraints. A weighted sum approach is used to turn the multi-objective optimization problem into a single-objective optimization problem to make it analytically tractable. The optimized PA is then obtained using Lagrange dual decomposition method and Karush–Kuhn–Tucker (KKT) conditions. Using our derived expressions, we propose an iterative PA algorithm that converges fast enough to be employed in practical NOMA networks. We also present simulation results to highlight the effectiveness of the proposed solution. Further, the performance of the proposed method is compared with that of the benchmark methods.

User scheduling for non-orthogonal multiple access-enabled 5G aeronautical mobile airport communications system

The recent concept of aeronautical mobile airport communications system with the 5th generation mobile communication technology (5G AeroMACS) for enhancing real-time and efficient flight information transmission has attracted wide attention from the civil aviation industry. In addition, non-orthogonal multiple access (NOMA) is also a promising solution to further improve the sum rate. However, the fundamental limit of the conventional 5G AeroMACS is that, the base station (BS) equipped with dozens of antennas cannot support more users than its number of antennas using the same time-frequency resources. To break this limit, for the first time, we investigate user scheduling policies for the integration of NOMA in 5G AeroMACS, i.e., 5G AeroMACS-NOMA, employing a half-duplex (HD) BS for serving multiple HD downlink users simultaneously. The two proposed user scheduling policies are obtained from the sub-optimal solution of mixed combinatorial non-convex optimization problems for the maximization of the achievable sum rate of the entire system. To mitigate the inter-beam interference (IBI), a user clustering algorithm based on the principle of maximizing the channel spatial correlation is designed, and an iterative power allocation algorithm is utilized for maximizing the achievable sum rate of the entire system. Furthermore, to mitigate the serious error propagation caused by the nature in NOMA, a scenario with user division is considered, and an user scheduling policy based on successive convex approximation is designed to strike a balance between computational complexity and optimality. Simulation results show that the proposed 5G AeroMACS-NOMA schemes can achieve a higher achievable sum rate compared with conventional 5G AeroMACS.

User Selection and Power Allocation Scheme With SINR-Based Deep Learning for Downlink NOMA

In non-orthogonal multiple access (NOMA) systems, transmit power allocation plays a crucial role in maximizing the sum rate, while satisfying quality of service requirements of users. In prior study, an interior point method (IPM) has been presented as an iterative algorithm for power allocation, which can maximize the sum rate and minimize the outage rate. However, such iterative algorithms typically suffer from low computational efficiency and excessively high latency. To overcome these limitations, machine learning-based power allocation schemes have been widely studied. Along the same line, in this paper, focusing on unsupervised learning to improve the computational complexity for power allocation as well as user selection, we propose a deep neural network (DNN)-based joint user selection and power allocation technique for the cellular downlink multiple-input single-output(MISO) NOMA systems, where the transmit antenna selection and multi-cell cooperation are considered. Especially, an optimization problem for user selection and power allocation is formulated to maximize the sum rate under the constraint of minimum data-rate requirement. We propose a new loss function and training model for user selection and power allocation to solve the optimization problem, where the training and testing models are based on the signal-to-interference-plus-noise-ratio (SINR) information instead of channel state information, which can significantly reduce the input data size in DNN. Through simulations, we show that our proposed scheme provides comparable performances in terms of sum rate and outage rate to the optimal solutions, while requiring lower computational complexity compared to the optimal ones.

Secrecy Energy Efficiency Maximization for Multi-User Multi-Eavesdropper Cell-Free Massive MIMO Networks

In this paper, the secrecy energy efficiency (SEE) optimization problem is studied in multi-user multi-eavesdropper cell-free massive multiple-input multiple-output (CF-mMIMO) networks, where a large number of distributed access points (APs) equipped with multiple antennas connect to a central processing unit via backhaul links to communicate with multiple users in the presence of multiple active non-colluding eavesdroppers. In order to theoretically analyze the SEE performance of CF-mMIMO systems, we first provide a lower bound on the ergodic secrecy rate. Meanwhile, a realistic model is used to measure the power consumption of CF-mMIMO networks, which takes into account the radio transmit power of all APs, circuit power consumption, and the power consumed by the backhaul link transmissions. Then, we develop a confidential and energy-efficient design for transmit power allocation subject to individual quality-of-service requirements for the users and total transmit power constraint of all APs. For the reason that the proposed SEE maximization (SEEM) problem is non-convex, we give a two-tier iterative power allocation algorithm, capitalizing on Dinkelbach's method and successive convex approximation approach. Simulation results show that the proposed SEEM algorithm converges fast and outperforms the conventional secrecy rate maximization and energy efficiency maximization strategies in terms of SEE performance.

Joint terminal-AP association and power allocation for NOMA-enabled space–air–ground integrated networks

This paper considers a space–air–ground integrated network (SAGIN) to provide network access services for aerial and terrestrial terminals. The non-orthogonal multiple access (NOMA) is used for improving spectral efficiency in the uplink transmission between terminals and access points (APs) in SAGIN. A sum rate maximization optimization problem is formulated by optimizing terminal-AP association and power allocation, while simultaneously satisfying the constraints of transmit power, network coverage characteristics, and quality-of-service (QoS) requirements of both aerial and terrestrial terminals. To deal with the formulated mixed integer nonlinear programming (MINLP) optimization problem, we first decouple it into separated terminal-AP association and power allocation problems. Then, we adopt the Q-learning algorithm to solve the terminal-AP association subproblem. Based on the obtained terminal-AP association solution, an iterative power allocation algorithm is developed by exploiting the Lagrange dual method. Moreover, the computational complexity of the proposed algorithm is further analyzed. Simulation results demonstrate that, compared with other schemes, our proposed algorithm can achieves a better performance in terms of the achievable sum rate, average achievable rate, and outage probability.

Power allocation for offset spatial modulation

AbstractOffset spatial modulation (OSM) is an attractive variant in the family of spatial modulation technology, which is capable of reducing the frequency of radio frequency chain switching in practical implementations. This paper elaborates on the benefits of adaptive power allocation (PA) conceived for OSM systems. First, a closed‐form solution of the optimal PA algorithm is derived for the case of two transmit antennas, which maximizes the minimum Euclidean distance (ED) between the received signal points. Moreover, based on the above solutions, a low‐complexity iterative PA algorithm is proposed for larger numbers of transmit antennas, by exploiting the EDs of a few dominant error vectors. Furthermore, for striking a flexible trade‐off between the bit error performance and complexity, an iterative convex approximation based PA algorithm is also developed. Finally, simulation results are presented to show that the proposed PA algorithms provide considerable improvements on the original OSM in terms of the bit error rate.

NOMA-Based Hybrid Satellite-UAV-Terrestrial Networks for 6G Maritime Coverage

Current fifth-generation (5G) networks do not cover maritime areas, causing difficulties in developing maritime Internet of Things (IoT). To tackle this problem, we establish a nearshore network by collaboratively using on-shore terrestrial base stations (TBSs) and tethered unmanned aerial vehicles (UAVs). These TBSs and UAVs form virtual clusters in a user-centric manner. Within each virtual cluster, non-orthogonal multiple access (NOMA) is adopted for agilely including various maritime IoT devices, which are sparsely distributed over the vast ocean. The nearshore network also shares the spectrum with marine satellites. In such a NOMA-based hybrid satellite-UAV-terrestrial network, interference among different network segments, different clusters, and different users occurs. We thereby formulate a joint power allocation problem to maximize the sum rate of the network. Different from existing studies, we use large-scale channel state information (CSI) only for optimization to reduce system overhead. The large-scale CSI is obtained by using the position information of maritime IoT devices. The problem is non-convex with intractable non-linear constraints. We tackle these difficulties by adopting max-min optimization, the auxiliary function method, and the successive convex approximation technique. An iterative power allocation algorithm is accordingly proposed, which is shown to be effective for coverage enhancement by simulations. This shows the potential of NOMA-based hybrid satellite-UAV-terrestrial networks for maritime on-demand coverage.

Capacity analysis and sum rate maximization for the SCMA cellular network coexisting with D2D communications

Sparse code multiple access (SCMA) is the most concerning scheme among non-orthogonal multiple access (NOMA) technologies for 5G wireless communication new interface. Another efficient technique in 5G aimed to improve spectral efficiency for local communications is device-to-device (D2D) communications. Therefore, we utilize the SCMA cellular network coexisting with D2D communications for the connection demand of the Internet of things (IOT), and improve the system sum rate performance of the hybrid network. We first derive the information-theoretic expression of the capacity for all users and find the capacity bound of cellular users based on the mutual interference between cellular users and D2D users. Then we consider the power optimization problem for the cellular users and D2D users jointly to maximize the system sum rate. To tackle the non-convex optimization problem, we propose a geometric programming (GP) based iterative power allocation algorithm. Simulation results demonstrate that the proposed algorithm converges fast and well improves the sum rate performance.

Min-Max Latency Optimization Based on Sensed Position State Information in Internet of Vehicles

The dual-function radar communication (DFRC) is an essential technology in Internet of Vehicles (IoV). Consider that the road-side unit (RSU) employs the DFRC signals to sense the vehicles’ position state information (PSI), and communicates with the vehicles based on PSI. The objective of this paper is to minimize the maximum communication delay among all vehicles by considering the estimation accuracy constraint of the vehicles’ PSI and the transmit power constraint of RSU. By leveraging convex optimization theory, two iterative power allocation algorithms are proposed with different complexities and applicable scenarios. Simulation results indicate that the proposed power allocation algorithm converges and can significantly reduce the maximum transmit delay among vehicles compared with other schemes.

Improving Quality of Service for Cell-Edge Users in D2D-Relay Networks

In this paper, we build a D2D-relay communications model where the D2D user is selected as a relay to forward data for the users at the edge of the networks. We aim at maximizing the achievable data rates of the cell-edge users, a resource allocation problem is formulated and an iterative power allocation algorithm is proposed. We derive the optimal closed-form power allocation expressions by the Lagrange dual method. Simulation results show that our communication scheme can achieve greater spectral efficiency than the traditional cellular-relay communication scheme.

Resource Allocation in Terrestrial-Satellite-Based Next Generation Multiple Access Networks With Interference Cooperation

In this paper, an uplink non-orthogonal multiple access (NOMA) terrestrial-satellite network is investigated, where the terrestrial base stations (BSs) communicate with satellite by backhaul link, and user equipments (UEs) share spectrum resource of access link. Firstly, a utility function which consists of the achieved terrestrial user rate and cross-tier interference caused by terrestrial BSs to satellite is design. Thus, the optimization problem can be modeled by maximizing the system utility function while satisfying the varying backhaul rate and UEs’ quality of service (QoS) constraints. The optimization problem is highly non-convex and can not be solved directly. Thus, we decouple the original problem into user association sub-problem, bandwidth assignment sub-problem, and power allocation sub-problem. In user association sub-problem, an enhanced-caching, preference relation, and swapping based algorithm is proposed, where the satellite UEs are selected by the channel coefficient ratio. The terrestrial UEs association considers the both caching state and backhaul link. Then we derive the closed-form expression of the bandwidth assignment. In power allocation sub-problem, we convert the non-convex term of the target function into the convex one by the Taylor expansion, and solve the transformed convex problem by an iterative power allocation algorithm. Finally, a three-stages iterative resource allocation algorithm by joint considering the three sub-problems is proposed. Simulation results are discussed to show the effectiveness of the proposed algorithms.

Joint position optimization, user association, and resource allocation for load balancing in UAV-assisted wireless networks

Unbalanced traffic distribution in cellular networks results in congestion and degrades spectrum efficiency. To tackle this problem, we propose an Unmanned Aerial Vehicle (UAV)-assisted wireless network in which the UAV acts as an aerial relay to divert some traffic from the overloaded cell to its adjacent underloaded cell. To fully exploit its potential, we jointly optimize the UAV position, user association, spectrum allocation, and power allocation to maximize the sum-log-rate of all users in two adjacent cells. To tackle the complicated joint optimization problem, we first design a genetic-based algorithm to optimize the UAV position. Then, we simplify the problem by theoretical analysis and devise a low-complexity algorithm according to the branch-and-bound method, so as to obtain the optimal user association and spectrum allocation schemes. We further propose an iterative power allocation algorithm based on the sequential convex approximation theory. The simulation results indicate that the proposed UAV-assisted wireless network is superior to the terrestrial network in both utility and throughput, and the proposed algorithms can substantially improve the network performance in comparison with the other schemes.

Intelligent Resource Management Using Multiagent Double Deep Q-Networks to Guarantee Strict Reliability and Low Latency in IoT Network

With the rapid adoption of the Internet of Things, it is necessary to go beyond fifth-generation applications and apply stringent high reliability and low latency requirements, closely related to strict delay demands. These requirements support massive network connectivity for multiple Internet of Things devices. Hence, in this paper, we optimize energy efficiency and achieve quality-of-service requirements by mitigating co-channel interference, performing efficient power control of transmitters, and harvesting energy using time-slot exchanges. Due to a nonconvex optimization problem, we propose an iterative algorithm for power allocation and time slot interchange to reduce the computational complexity. To achieve a high degree of ultra-reliability and low latency with quality-of-service-aware instantaneous reward under massive connectivity, we efficiently employ multiagent reinforcement learning by addressing the intelligent resource management problem via a novel Double Deep Q Network. The network prioritizes experience replay to exploit the best policy and maximize accumulative rewards. It also learns the optimal policy and enhances learning efficiency by maximizing its reward function to make decisions with high intelligence and guarantee strict ultra-reliability and low latency. The simulation result shows that the Double Deep Q Network with prioritized experience replay can guarantee stringent ultra-reliability and low latency. As a result, the co-channel interference between transmission links and the high-power consumption density associated with the massive connectivity of the Internet of Things devices are mitigated.

Beamspace NOMA Using User Clustering and Throughput Optimisation Algorithms for Massive MIMO

Massive Multiple Input Multiple Output (MIMO) using millimetre wave transmissions received significant attention due to its significance of high data rate. However, achieving energy and spectrum efficient millimetre wave communications is challenging due to the dedicated Radio Frequency (RF) chain. Non-Orthogonal Multiple Access (NOMA) is used in beamspace MIMO (BS MIMO) significantly overcome such challenges. This paper proposes the enhanced approach of beamspace MIMO NOMA using a simple yet effective clustering solution with a C-NOMA throughput optimisation algorithm. This proposal involves the lightweight user clustering, lens antenna, and clustering-based iterative power allocation algorithm to enhance each cluster's spectral and energy efficiency performance. After cluster formation, the throughput optimisation function applies. Iterative power optimisation method is proposed to allocate power to each user in each cluster dynamically. Therefore, compared to recent clustering and NOMA methods, the proposed BS MIMO C-NOMA improves Energy Efficiency (EE) and Spectral Efficiency (SE) with minimum computational overhead. Results demonstrate that high EE and SE, respectively, as compared with the percentage of improvement of 26% and 37% in the existing BS MIMO NOMA and improvement of 16.47 % and 27.72 % in User Clustering based on Channel Gain (UCCG) MIMO NOMA among 50 and 100 users.

Secure communication with joint resource allocation, relay and jammer selection in OFDM‐based cooperative networks

SummaryThis paper considers an orthogonal frequency division multiplexing (OFDM) based cooperative network, where a source node communicates with a destination node with the help of multiple intermediate nodes, in the presence of an eavesdropper. Each intermediate node is assumed to be able to act as a relay or a jammer to improve the physical layer security. The problem of joint subcarrier allocation, power allocation, and mode selection to maximize the sum secrecy rate is investigated. We first reformulate the problem of subcarrier allocation and intermediate node mode selection as a three‐sided matching game and propose an algorithm that achieves a stable matching. Then, the sum secrecy rate is further improved by proposing a heuristic algorithm to switch the modes of the intermediate nodes. Finally, an iterative power allocation algorithm is proposed. It is verified by simulation results that the proposed algorithm outperforms various benchmark algorithms such as the equal subcarrier allocation algorithm, the pure relay selection algorithm, and the random relay selection algorithm, especially when the transmit power limit is high and the number of intermediate nodes is large.

Energy-efficient resource allocation for heterogeneous network with grouping D2D

In this paper, a new communication model is built named grouping D2D (GD2D). Different from the traditional D2D coordination, we proposed GD2D communication in licensed and unlicensed spectrum simultaneously. We formulate a resource allocation problem, which aims at maximizing the energy efficiency (EE) of the system while guaranteeing the quality-of-service (Qos) of users. To efficiently solve this problem, the non-convex optimization problem is first transformed into a convex optimization problem. By transforming the fractional-form problem into an equivalent subtractive-form problem, an iterative power allocation algorithm is proposed to maximize the system EE. Moreover, the optimal closed- form power allocation expressions are derived by the Lagrangian approach. Simulation results show that our algorithm achieves higher EE performance than the traditional D2D communication scheme.

Green communication for OFDMA cellular networks with multiple antennas

The green communication which requires the maximum reduction of energy consumption has become a strong need for the new wireless system designs because of the massive increasing number of mobile subscriptions. Thus, in this paper, energy efficiency (EE) maximization problem is considered for multiple-input single-output orthogonal frequency division multiple access (MISO-OFDMA) cellular networks. The EE maximization problem is defined for a downlink communication model by considering the total transmit power and circuit power constraints. Quality of service (QoS) constraints which guarantee the minimum data rate requirement of the users are also accounted. The considered problem belongs to the category of mixed-integer non-linear programming problem (MINLP), and thus is NP-hard. To cope up with this complexity, a two-step resource allocation algorithm, which contains an heuristic subchannel allocation algorithm and the iterative power allocation algorithm with fast convergence based on the Dinkelbach method, is proposed. Numerical results are provided to demonstrate the effectiveness of the proposed algorithm and the convergence and energy efficiency performance of the proposed scheme is validated.

Joint Uplink Power Allocation Method in Wireless Communication and Positioning Integrated System

With the development of wireless communication, there are usually both communication and positioning requirements. In our previous work, we studied the power allocation algorithm for positioning user (P-User) based on Multi-Scale Non-Orthogonal Multiple Access (MS-NOMA) downlink signal where all communication users (C-Users) have the same power. In the uplink case, the power allocation scheme for both C-User and P-User needs to be considered jointly as all C-Users may have different power. In this paper, we analyze the Quality of Service (QoS) and ranging accuracy based on the MS-NOMA signal by deriving their simple expressions. Then, a two stage joint iterative power allocation algorithm (JIPAA) is proposed. In the first stage, considering the QoS of C-User requirement and total power limitation, the power of P-Users is allocated to obtain optimal ranging performance. In the second stage, the power of C-Users is allocated to minimize the power consumption. With the iterative process, the optimal power of each C-User and P-User is calculated. The numerical results show that the proposed JIPAA algorithm dramatically promotes ranging accuracy under the same energy consumption.

Downlink Power Allocation Optimization in Pattern Division Multiple Access

Non-orthogonal multiple access (NOMA), in particular pattern division multiple access (PDMA), has been recently considered as the critical multiple access technologies deployed for 5G applications. However, optimizing power allocation (PA) at the base station (BS) to increase the sum throughput of the downlink (DL) PDMA system still remains a challenge. Although this problem has been explored to some extent by assuming that the BS can obtain perfect channel state information (CSI), this assumption is unfeasible in harsh environments. Therefore, to effectively improve the sum throughput of communications in the real world, we propose using PDMA with optimized PA. With perfect CSI, iterative power allocation (IPA) that can iteratively update the Lagrange multipliers is proposed. Using the Karush-Kuhn-Tucher (KKT) conditions, a PA solution with closed-form expressions is derived from increasing the sum throughput. With imperfect CSI, the outage probability is considered when maximizing the sum throughput. The probabilistic mixture problem can be converted into a non-probability problem, and thus, PA under imperfect CSI can be optimized with the proposed IPA algorithm. The simulation results demonstrate that the proposed scheme outperforms the existing schemes and can be effective in harsh environments.

Joint Device Assignment and Power Allocation in Multihoming Heterogeneous Multicarrier NOMA Networks

To improve the radio throughput, novel radio access techniques need to be designed. With exploiting the multiuser diversity at power domain, nonorthogonal multiple access (NOMA) is one promising candidate technique for sixth-generation (6G). In this article, a downlink joint device assignment and power allocation problem is investigated in multihoming heterogeneous multicarrier NOMA networks. The joint device assignment and power allocation problem maximizes the network throughput with total power constraint at each base station, required minimum transmission rate for each device, and maximum number of accessed devices at each subchannel. Since the joint device assignment and power allocation problem is a mixed-integer nonlinear problem, the original optimization problem is divided into a device assignment subproblem and a power allocation subproblem. Then, the greedy method is applied to propose a device assignment algorithm, and successive convex approximation theory is adopted to design a power allocation algorithm. Finally, an iterative joint device assignment and power allocation algorithm is proposed. Numerical simulation results demonstrate that the proposed algorithm can improve the throughput and the average satisfactory of devices compared with other algorithms significantly.