Energy-efficient Power Allocation Research Articles

Empowering Light Fidelity-Enabled Internet of Things with Hybrid Fuzzy-Particle Swarm Optimization Power Allocation for Enhanced Energy Efficiency and Quality of Service

This paper proposes a hybrid fuzzy-Particle Swarm Optimization (PSO) approach for power allocation in bidirectional Light Fidelity (LiFi)-enabled Internet of Things (IoT) communication systems. The approach integrates fuzzy logic and PSO to achieve efficient and robust power allocation, considering energy efficiency and Quality of Service (QoS) requirements. The hybrid approach leverages flexibility of fuzzy logic to handle uncertainties and variations in system parameters, such as channel conditions and QoS requirements. Fuzzy sets and membership functions are defined to represent linguistic variables, and fuzzy rules are formulated based on expert knowledge and system-specific considerations. This allows the approach to adaptively adjust power allocation based on varying channel conditions and QoS requirements, enhancing the robustness of the power allocation strategy. The PSO algorithm is employed for iterative optimization to explore for ideal power allocation solution. The particles in swarm signify possible power allocation configuration, and its position in the search space corresponds to a specific power allocation. The particles update their positions based on local best-known positions and globally best-known positions, allowing the swarm to converge toward the optimal solution. Through extensive simulations and analysis, the proposed hybrid fuzzy-PSO approach is evaluated with regard to energy efficiency and QoS satisfaction. The results demonstrate its effectiveness in achieving energy-efficient power allocation while meeting diverse QoS requirements. The proposed approach outperforms existing methods such as channel- and Orthogonal Multiple Access (OMA)-based power allocation schemes.

Energy-Efficient Power Allocation Maximization for Multi-User MIMO Broadcast Channel

Energy-Efficient Power Allocation Maximization for Multi-User MIMO Broadcast Channel

Pareto Optimal Energy Efficiency Power Allocation: Coping With Highly Constraints in NOMA Uplink Dense Access VLC Network

Pareto Optimal Energy Efficiency Power Allocation: Coping With Highly Constraints in NOMA Uplink Dense Access VLC Network

Enhancing Energy-Efficient Power Allocation for IoT URLLC through Action Space Reduction in Energy Harvesting Devices

Enhancing Energy-Efficient Power Allocation for IoT URLLC through Action Space Reduction in Energy Harvesting Devices

Energy efficient power allocation for MIMO‐NOMA‐based integrated terrestrial‐satellite networks

SummaryMillimeter‐wave (mmWave) massive MIMO and non‐orthogonal multiple access (NOMA) are promising ways for improving energy efficiency of integrated terrestrial‐satellite networks. In this paper, we investigate the performance of integrated terrestrial‐satellite networks, in which MIMO‐NOMA‐based terrestrial networks and satellite network cooperatively provide ubiquitous services for ground users while reusing the entire bandwidth. Both base stations (BSs) and satellite are equipped with multiple antennas, and beamforming technology is utilized to serve multiple users simultaneously. First, a user selection scheme based on channel gain ratio is proposed to select satellite users. Then, we design a user clustering algorithm for reducing the intra‐beam and inter‐beam interference of BSs users. Analog and digital beamforming techniques are also adopted to further increase beam gain and improve system energy efficiency. The terrestrial networks and satellite networks energy efficiency are separately optimized through beamforming design and power allocation scheme, and then, a joint iterative algorithm is proposed to maximize the system energy efficiency. In addition, we introduce the interference temperature threshold to limit the satellite interference to BSs users. Finally, the performance of the proposed algorithm is simulated in comparison with the existing algorithm. The results indicate that the presented algorithm has high superiority in system energy efficiency and it can be applied to integrated terrestrial‐satellite networks.

Energy-Efficient Power Allocation for Full-Duplex Device-to-Device Underlaying Cellular Networks with NOMA

Full-duplex (FD), Device-to-Device (D2D) and non-orthogonal multiple access (NOMA) are promising wireless communication techniques to improve the utilization of spectrum resources. Meanwhile, introducing FD, D2D and NOMA in cellular networks is very challenging due to the complex interference problem. To deal with the complex interference of FD D2D underlaying NOMA cellular networks, power allocation (PA) is extensively studied as an efficient interference management technique. However, most of the previous research works on PA to optimize energy efficiency only consider the system framework of partially joint combining techniques of FD, D2D and NOMA, and the constraints of optimization problem are very different. In this paper, in order to further improve the energy efficiency of a system, a dual-layer iteration power allocation algorithm is proposed to eliminate the complex interference. The outer-layer iteration is to solve the non-linear fractional objective function based on Dinkelbach, and the inner-layer iteration is to solve the non-convex optimization problem based on D.C. programming. Then, the non-convex and non-linear fractional objective function is transformed into a convex function to solve the optimal power allocation. In this approach, FD D2D users reuse the spectrum with downlink NOMA cellular users. Imperfect self-interference (SI) cancellation at the FD D2D users and the successive interference cancellation (SIC) at the strong NOMA user are considered in the system framework. The optimization problem is constructed to maximize the system’s energy efficiency with the constraints of successful SIC, QoS requirements, the maximum transmit power of BS and FD D2D users. Numerical results demonstrate that the proposed algorithm outperforms the traditional orthogonal multiple access (OMA) in terms of energy efficiency with a higher system sum rate.

Coalition Game Theoretic Power Allocation Strategy for Multi-Target Detection in Distributed Radar Networks

This paper studies a coalition game theoretic power allocation algorithm for multi-target detection in radar networks based on low probability of intercept (LPI). The main goal of the algorithm is to reduce the total radiated power of the radar networks while satisfying the predetermined target detection performance of each radar. Firstly, a utility function that comprehensively considers both target detection performance and the radiated power of the radar networks is designed with LPI performance as the guiding principle. Secondly, it causes a coalition to form between cooperating radars, and radars within the same coalition share information. On this basis, a mathematical model for power allocation in radar networks based on coalition game theory is established. The model takes the given target detection performance as a constraint and maximizing system energy efficiency and optimal power allocation as the optimization objective. Furthermore, this paper proposes a game algorithm for joint coalition formation and power allocation in a multi-target detection scenario. Finally, the existence and uniqueness of the Nash equilibrium (NE) solution are proven through strict mathematical deduction. Simulation results validate the effectiveness and feasibility of the proposed algorithm.

Throughput maximization and reliable wireless communication in NOMA using chained fog structure and weighted energy efficiency power allocation approach

Novel radio access strategies must be developed to support an unprecedented number of connected devices to increase radio spectral efficiency of 5G and outside, and non-orthogonal multiple features (NOMA) arisen as a possible contender. To support cognitive NOMA networks, power allocation and NOMA-secondary user allocation is the effective technique for enhancing the resource utilization proficiency in power and spectrum domain. NOMA approach provides throughput enhancement to fulfil the demands of next group of Wireless Communication Networks. In this manuscript, a Chained Fog Structure (CFS) with Weighted Energy Efficiency Power Allocation (WEE-PA) method is proposed for throughput maximization and reliable wireless communication (CFS-WEE-NOMA). The aim of this study is “to enhance the throughput by involving underlay NOMA with Chained Fog Structure and WEE-PA method”. Here, the proposed WEE-PA approach is applied to multicarrier NOMA with chained fog structure for enhancing the throughput. During this analysis, the computational complexity due to the consideration of high users and different subcarrier values are solved by the proposed CFS-WEE-NOMA approach. Thus, the proper user pairing, power allocation, and bandwidth sharing of the proposed methodology ensure seamless communication. The simulation of this work is done in MATLAB and the performance metrics like throughput, computational complexity, power consumption, energy efficiency, delay, sum rate is analysed. The proposed CFS-WEE-NOMA approach has achieved 8.6%, 7.7%, and 6.7% high throughput based on transmitted power, 13.6%, 8.6%, and 11.7% high throughput based on subcarrier, and 13.6%, 9.6%, and 12.7% high energy efficiency compared with the existing methods, like Dynamic Network Resource Allocation (DNRA) algorithm (DNRA-NOMA), Multiple Carrier Cell-Less Non-Orthogonal Multiple Access (MC-CL-NOMA), dynamic programming (DP) recursion framework based multicarrier NOMA systems (DPRF-MC-NOMA) methods respectively.

Energy-efficient power allocation algorithm based on the BFGS algorithm and the armijo criterion

In high-speed railway (HSR), the increasing demand for wireless communication and high-speed broadband leads to higher energy consumption by the system. To improve energy efficiency (EE) and fair allocation of resources in multi-user communication, an algorithm combining the BFGS algorithm and the Armijo criterion is used. Firstly, an EE optimisation model of the system is constructed by considering the constraints, such as the average transmitting power of the evolved Node B (eNB), the minimum transmission rate and fairness among users. Secondly, the objective function is transformed into a convex optimisation problem through equivalent transformation. Finally, an algorithm combining the BFGS algorithm and the Armijo criterion is used to solve the objective function and then determine the power allocation scheme for each moment of the system. The simulation results show that the proposed scheme shows a better performance in terms of EE than the traditional algorithm in the same scenario over time. Under different user numbers and proportional rate constraint conditions, the adopted scheme can optimise system EE while satisfying the user communication fairness threshold. In addition, compared with the traditional power allocation scheme, the proposed algorithm is significantly improved in terms of eNB transmit power, fairness and EE performance.

Energy efficient power allocation for ultra‐reliable and low‐latency communications via unsupervised learning

AbstractEnergy efficiency (EE) is an important indicator in ultra‐reliable and low‐latency communication (URLLC). Power allocation is considered as an effective method to achieve high EE in URLLC. However, since the EE optimization problem is non‐convex, it is difficult to obtain the analytical solution efficiently. Moreover, to ensure reliable and low‐latency communication within a finite blocklength, the Shannon formula becomes impractical for URLLC. Therefore, finite blocklength coding theory is used to meet the requirements of URLLC. In this paper, the EE problem of URLLC is formulated and the power allocation function is parameterized to be optimized through a deep neural network (DNN). The DNN is trained through the primal‐dual iterative algorithm offline in the unsupervised manner, and can be deployed online to achieve real time power allocation results. The numerical results show the effectiveness of the proposed method.

Optimization-enabled user pairing algorithm for energy-efficient resource allocation for noma heterogeneous networks

Abstract In recent times, nonorthogonal multiple access (NOMA) has appeared as an encouraging system for satisfying the requirements of 5G communications in alleviating the spectrum insufficiency problems. The purpose of NOMA in heterogeneous networks (HetNets) is to increase the spectrum exploitation with the cost of proficient allotment of resources. Therefore, to achieve effective resource assignments for NOMA HetNets, this study develops the best user pairing and efficient power allocation approach. Here, the newly devised optimization method, Feedback Sea Lion Optimization (FSLnO), is employed for achieving a less-difficult optimal solution when user pairing. In addition, the designed FSLnO is also accomplished for performing the energy-efficient power allocation process by enhancing the lesser energy effectiveness of the femtocell users. The Feedback Artificial Tree (FAT) and Sea Lion Optimization (SLnO) are combined to create the developed FSLnO algorithm. Additionally, according to evaluation metrics like achievable rate, energy efficiency, sum rate, and throughput, the developed approach performed better, with maximum values of 2.384 Mbits/s, 0.028 Mbits/Joules, 13.27 5 Mbits/s, and 0.154 Mbps, respectively.

Energy efficient power allocation in MIMO relaying systems with the MRT precoding

SummaryMultiple‐input multiple‐output (MIMO) systems perform well from the energy efficiency (EE) and the spectral efficiency (SE) points of view in fifth generation (5G) communication systems. This paper considers the operation of a MIMO system with a relay. The optimization problem relates to the EE maximization. This problem has two types of limitations, which provide a maximum transmission power and a minimum data rate for users. The encountered objective function is in a fractional form and thus it is a non‐convex function. Besides, the problem is constrained. We utilize a lower bound analysis for the data rates, some properties of the linear programming, and the maximum ratio transmission (MRT) precoding scheme to obtain a convex objective function. Using the Lagrange dual function, we obliterate the constraints of the problem and then it is easy to solve. To improve system performance, users are divided into two groups based on their channel gains, and the maximum transmission power is reasonably divided between them. Two iterative algorithms are proposed to solve the optimization problem numerically, and finally we investigate performance of the proposed method.

Energy-Efficient Power Allocation for Multiuser Distributed MISO Systems With Beamforming and Imperfect CSI

In this article, we study the energy-efficient power allocation (PA) for multiuser distributed multiple-input-single-output (D-MISO) system with beamforming (BF) and imperfect channel state information (CSI). Given a target bit error rate, the system energy efficiency (EE) with imperfect CSI is deduced. Subject to the power budget constraints, a constrained optimization problem is first formulated to maximize EE of single-user D-MISO. The optimal BF is shown to be aligned with the channel direction that renders the joint optimization problem to be a PA problem. A near-optimal PA scheme is proposed to tackle this problem by using the concave–convex procedure method and fractional programming (FP) and block coordinate descent (BCD) method, and it can provide nearly optimal performance close to that of optimal exhaustive search scheme with less complexity. On this basis, a suboptimal PA scheme based on the FP and BCD method is developed to further decrease the complexity and maintain similar performance to near-optimal scheme, and it is suitable for the system with more distributed antennas. With these results, two PA schemes are, respectively, proposed for multiuser D-MISO with imperfect CSI to maximize EE. The first scheme can obtain better performance with relatively higher complexity, and the second scheme can gain the performance close to the first scheme with lower complexity, and only smaller performance loss is found. Interestingly, these two schemes have the same optimal performance for perfect CSI, and include the existing ones under perfect CSI as special cases. Simulation results verify the effectiveness of the developed schemes.

Energy-efficient power allocation for cross-media communications with hybrid VLC/RF

Energy-efficient power allocation for cross-media communications with hybrid VLC/RF

Energy efficient power allocation with joint antenna and user selection in massive MIMO systems

The problem of maximizing energy efficiency (EE) is considered in this paper. The system in question is a massive multiple-input multiple-output (MIMO) system that transmits information simultaneously in the uplink and downlink. To maximize EE, an optimal antenna selection strategy is used to send information. Furthermore, we propose an optimal user selection method and then obtain their optimal allocated power. The optimization problem of the system has two constraints, including power limitations in the uplink and downlink. Due to the non-convexity and being constrained of the optimization problem, in order to obtain the optimal power of users, it must first be converted to a convex problem form and then turned into an unconstrained problem using the Lagrange dual function method. The bisection algorithm is utilized to select the optimal set of active antennas for sending information. The cross-entropy algorithm with the proposed algorithm is used simultaneously to select the set of users and determine their optimal power values. The results of computer simulations show that the proposed selection schemes of antennas and users significantly improve the system performance.

Energy-Efficient Power and Subcarrier Allocation in Downlink OFDMA Systems with Channel Estimation Errors

In this paper, we investigate the joint optimization of power and subcarrier allocation for maximizing the energy efficiency (EE) in downlink OFDMA systems. The problem of EE maximization is formulated as a stochastic optimization to determine the optimal power and subcarrier allocation, in which many practical factors including channel estimation errors, delay requirements, and time-varying channel are considered. We first propose a dynamic power and subcarrier allocation (DPSA) algorithm to solve the formulated problem. Then, we derive the two bounds of EE and delay and reveal the tradeoff between them. The theoretical analysis and simulation results demonstrate the variations of EE and delay with channel estimation errors.

Deep Learning Based Power Allocation Schemes for NOMA System with Imperfect SIC

Non-orthogonal multiple access (NOMA) has been a promising technology for 5G communication system. NOMA allows more than one user to utilize the same resource at the same time, which can lead to high performance in terms of spectral efficiency, energy efficiency or fairness. NOMA enables power domain multiplexing and separates the multiplexed signal by using successive interference cancellation (SIC). Therefore, the full benefit of NOMA depends on power allocation. However, in practical system, residual interference caused by SIC process can severely degrade a system performance. In this paper, we propose two power allocation schemes based on deep learning to alleviate the effect of imperfect SIC for downlink NOMA system, including a deep learning-based sum rate power allocation scheme (DL-SRPAS) and a deep learning-based energy-efficient power allocation scheme (DL-EEPAS). our two proposed schemes learn two optimal power allocation schemes provided through exhaustive search. We search the solution of the optimization problems, where two optimization problems are formulated to maximize the sum rate and the energy efficiency subject to a minimum user data rate requirement. The simulation results verify that our two proposed schemes can alleviate the effect of imperfect SIC and outperform two conventional power allocation schemes which maximize the sum rate and the energy efficiency without considering imperfect SIC. In addition, our proposed schemes achieve near-optimal performance with very low computational time.

Adaptive power allocation with energy efficiency in 5 g multitier networks using a hybrid heuristic approach

Nowadays, the cellular network has become a part of every human being's life. Cellular communication, hand in hand with information technology, has reduced the physical work performed by people. As there is great improvement and development in the fields of electronics and communication, the accumulation of data has also increased at a high rate. One of the cons observed due to the development of huge data is data traffic. The currently used 4G mobile communication technology is facing congestion, reduced capacity, shortages in bandwidth, slower data rate problems, and interference. The technology is equipped to connect with a large number of devices, overcoming the issue of network traffic that arises in 4G communications. Recent growth in technology has given the best solution with 5G communication. This proposed study focuses on the energy-efficient power allocation in a 5G multitier heterogeneous network, which consists of relays, deployment of small cells, and device-to-device communication. Here, a new methodology like Hybrid Heuristic algorithm is proposed for Adaptive Power Allocation with Energy Efficiency in 5G Multitier Networks which is a combination of ACO (Ant Colony Optimization) and PSO (Particle Swarm Optimization) algorithms to produce an efficient power allocation scheme for 5G downlink systems. The novelty of this research work is the integration of two optimization algorithm for adaptive power allocation in 5G network. The average SNIR value of the proposed Heuristic Approach is compared with the existing algorithms like SOA, and NCOL based on D2D user, micro user and pico user. The proposed Heuristic technique average system energy efficiency, and average system spectral efficiency also calculated and compared with the existing techniques such as SOA and NOCL.

Energy Efficient Power Allocation in MIMO-NOMA Systems with ZF Precoding Using Cell Division Technique

Energy Efficient Power Allocation in MIMO-NOMA Systems with ZF Precoding Using Cell Division Technique

Energy-Efficient NOMA Multicasting System for Beyond 5G Cellular V2X Communications With Imperfect CSI

The integration of non-orthogonal multiple access (NOMA) in vehicle-to-everything (V2X) communications has recently shown great potential to improve traffic efficiency, control, and reliability of beyond 5G transportation systems. In V2X communications, it is vital to inspect imperfect channel state information (CSI) because the high mobility of vehicles leads to more channel estimation uncertainties. This paper proposes an energy-efficient power allocation scheme for the road-side unit (RSU) assisted NOMA multicasting in beyond 5G cellular V2X networks. In particular, the energy efficiency maximization problem is investigated under the outage probability of vehicles under imperfect CSI, quality of services (QoS), and power limit constraints. Since the problem is non-convex and difficult to solve directly, we first convert outage probability constraint to non-probabilistic constraint through approximation and adopt a low complexity gradient assisted binary search (GABS) method to obtain the efficient power allocation at RSUs. Then, a successive convex approximation (SCA) technique is exploited to transform the power allocation problem of vehicles associated with each RSU into a tractable concave-convex fractional programming (CCFP) problem. The optimal solution to the CCFP problem is achieved through Dinkelbach and the dual decomposition method. The global optimal power allocation through the GABS-Exhaustive scheme act as a benchmark, which has considerable computational complexity. Simulation results unveil that the proposed suboptimal scheme (GABS-Dinkelbach) can achieve near-optimal performance with very low complexity.