Energy-efficient Power Allocation Scheme Research Articles

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.

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.

An Energy-Efficient Power Allocation Scheme for NOMA-Based IoT Sensor Networks in 6G

Non-orthogonal multiple access (NOMA) is an upcoming technique to boost the capacity of visible light communication (VLC) systems which is otherwise limited by the slow modulation response of light-emitting diode (LED) sources used therein. We propose to enhance the energy efficiency of NOMA-based VLC systems, thereby making them suitable for green Internet of things (IoT) in the sixth generation (6G) networks. The proposed energy-efficient power allocation (EPA) scheme reduces the average bit error rate (ABER) and augments the system’s energy efficiency as well as its capacity and fairness. However, at high values of transmitted power, there is a trade-off between energy efficiency and ABER. We define and evaluate the energy utilization factor to study this trade-off and show that the EPA scheme has superior energy utilization than the state-of-the-art power allocation schemes. Moreover, its performance does not deteriorate appreciably when the number of connected users (or sensor nodes) is increased. Based on numerical and simulation results, we deduce that the proposed EPA scheme meets the requirements of green IoT systems in 6G networks.

Energy-efficient power scheduling and allocation scheme for wireless sensor networks

The focus in this paper is on coverage energy-efficient of WSN (wireless sensor networks). Communication path lengths of a randomly distributed BSs and sensor nodes field are characterized by distance distributions. For sensor nodes, this paper will use theory of Poisson point process (PPP) and Boolean Model (BM) to purpose a power allocation scheme results in green communication of sensor nodes. The energy allocation scheme of wireless sensor networks can help to prolong the life of the whole network. In this paper, we prove that the scheduling and allocation scheme can achieve energy saving, so as to prolong the life cycle of the whole wireless sensor networks. In the uniform placement migration case, we simulation 1000 times. In Boolean Model, There is 71.3% for power scheduling and allocation schemes and 28.7% is not. From these results, we see that, in wireless sensor networks, 20%–30% of total energy, which was being consumed from the part of power scheduling and allocation. The scheduling scheme improves response time, throughput and overall energy consumption over base case. Compared with other allocation schemes, the advantage of the scheme proposed in this paper is that the less sensors selected can still provide more than 95% detection area coverage. It will enable robust communication against cross-tier interference and co-layer interference thereby obtaining a substantial improvement in link quality.

Energy-efficient resource optimization using game theory in hybrid NOMA assisted cognitive radio networks

Presently, Secondary Networks (SNs) are gleaned from rising capacity with reducing interference together instead of making them energy-efficient for green communication. This paper proposes an energy-efficient optimization for resources using game in Hybrid Non-orthogonal Multiple Access (HNOMA) based Cognitive Radio Networks (CRNs). For this, also proposes SUs clustering and energy-efficient Power Allocation (PA) scheme together to enhance the throughput. Whereas, clustering and PA schemes together are incorporated to broken SUs in distinct coalitions and allocating efficient powers to them simultaneously over various Frequency Bands (FBs). In addition, PA to users happens in a distributed manner (without communicating other BSs) in which each BS is updating PA strategies for its utility maximization. The stability, existence and uniqueness of both schemes are being derived and proved. Simulations are taken out with various channel gains to evaluate analytical outcome, and uphold the success of both schemes together over existing ones.

An energy-efficient power allocation scheme for Massive MIMO systems with imperfect CSI

As a design goal of green wireless communication, it is an important consideration to maximize energy efficiency of a Massive multiple-input multiple-output (MIMO) downlink system. By considering the case where the base station and all the users have the imperfect channel state information, a power allocation optimization problem is formulated to achieve the maximization of energy efficiency. Also, we derive the tractable closed-form expressions of lower bound on the achievable downlink rate utilizing maximum-ratio transmission (MRT) and zero-forcing precoding. Since the proposed optimization problem is non-convex, we strive to find a sub-optimal power allocation scheme. By decomposing the expression of the non-convex objective function, an energy-efficient power allocation algorithm that maximizes the energy efficiency is developed based on the difference of convex programming. Numerical results verify the tightness of the derived closed-form expressions, and show the performance improvement of the proposed energy-efficient power allocation scheme over the existing power allocation schemes.

Maximum-Throughput Sidelink Resource Allocation for NR-V2X Networks With the Energy-Efficient CSI Transmission

New radio vehicle-to-everything (NR-V2X) is an emerging technology based on the 5G cellular mobile communication networks. In this paper, the sidelink (SL) resource allocation for NR-V2X in Mode 1 is considered, where 5G base station (gNB) schedules SL resources to be used by V2X users (VUs) based on the periodically reported channel state information (CSI). To reduce the overhead, an energy efficient power allocation scheme of CSI transmission is proposed using hybrid spectrum access technology. With the knowledge of CSI, SL resource allocation is modeled as a mixed binary integer nonlinear programming (MBINP). It is designed to maximize the sum throughput of NR-V2X networks among different subcarriers subject to the total available power and the minimal transmission rate. To achieve that, the original MBINP is decomposed into two sub-problems, namely, subcarrier assignment and power allocation. Firstly, the appropriate subcarriers are quickly obtained by averaging the power allocation. Secondly, an alternative optimization mechanism is proposed to the power allocation. Simulation results show that the proposed power allocation scheme of CSI transmission can obviously save the energy consumption, and the proposed suboptimal SL resource allocation algorithm achieves better performance than the compared methods with relatively low complexity.

Power Allocation for an Energy-Efficient Massive MIMO System With Imperfect CSI

Massive multiple-input and multiple-output (MIMO) has been recognized as a core technology for the fifth generation networks due to its huge spectral utilization brought by multiplexing gain and array gain. Most previous works on Massive MIMO systems have focused on the power optimization to maximize the system energy efficiency, in which the ergodic achievable sum-rate is replaced with a lower bound closed-form expression, which can result in suboptimal performance. In this paper, we aim to optimize the power assignment to achieve the maximum energy efficiency for a downlink system of single-cell Massive MIMO based on a tight approximate expression for the achievable sum-rate. Considering two linear precoding strategies at base station, i.e., maximum ratio transmission and zero-forcing, we first derive the corresponding approximate closed-form of the achievable sum-rate under imperfect channel state information. Furthermore, an energy-efficient power allocation scheme is formulated as a non-convex optimization problem. An iterative power allocation algorithm that maximizes the system energy efficiency is developed based on the Lagrangian dual approach. Simulation results demonstrate the tightness of the proposed approximate closed-form expressions, and show that our designed algorithm yields much better energy efficiency performance over the existing algorithms.

A Power Control Mean Field Game Framework for Battery Lifetime Enhancement of Coexisting Machine-Type Communications

Machine-type communications (MTC) enable the connectivity and control of a vast category of devices without human intervention. This study considers a hybrid coexisting wireless cellular network for traditional and MTC devices along with the need for an energy efficient power allocation mechanism for MTC devices. A model is presented for the interference and battery lifetime of MTC devices and a battery lifetime maximization problem is formulated. Conventional game designs are unable to address the demands of a densified user environment because of the dimensional difficulty presented when attempting to achieve a converged solution that would lead to a stable equilibrium. The MTC power control problem is modeled as a differential game and a mean field game (MFG) for massive number of MTC nodes estimates the power allocation policy with system utility defined in terms of the experienced interference and reliability. The formulated power control MFG is solved using a finite difference method and analyzed using extensive simulations. The solution provides an optimal power control strategy for MTC devices, enabling them to prolong their battery lives with the implemented energy efficient power allocation scheme.

Power Allocation for Energy Efficient Optimization of Distributed MIMO System With Beamforming

This paper presents three energy-efficient power allocation (PA) schemes for the downlink of a distributed multiple-input multiple-output (D-MIMO) system with beamforming at distributed antenna ports and antenna selection (AS) at a mobile station over composite Rayleigh channels. Subject to maximum power constraints, a constrained maximization problem on energy efficiency (EE) is formulated. Based on theoretical analysis, the optimal beamforming is shown to be aligned with the channel direction that renders the joint design problem to be a PA optimization one. It is shown that the optimization problem is strictly pseudo-concave with a unique maximum. Thus, an optimal PA scheme based on a gradient projection method is proposed to obtain the global solution. To speed up the gradient projection method, a closed-form initialization is derived. Furthermore, a low-complexity optimal PA schemes based on the Karush–Kuhn–Tucker conditions is proposed. This optimal scheme has a closed-form PA expression for a certain condition or else computes the PA by the Lambert function or the bisection method, and gives the same EE performance as that of the optimal gradient-based scheme. The performances of these two optimal schemes are optimal and verified to be valid in contrast to the inaccurate optimal scheme in the literature. For large maximum power, the closed-form initial PA will become the optimal solution and can be considered as a suboptimal solution with no iterative computation. Making use of the tight upper and lower bounds of the Lambert function, a very low-complexity suboptimal PA scheme is developed. Simulation results show that the proposed suboptimal scheme can achieve nearly optimal performance. Maximum EE and maximum channel power, which are optimal and suboptimal criteria, are adopted for antenna selection. The simple suboptimal criterion can provide EE performance very close to that of the optimal one. The D-MIMO with AS is shown to give considerable EE gain over the single receive antenna system. Moreover, the proposed three PA schemes can be used for multiuser diversity D-MIMO systems to achieve superior EE performance.

Energy-Efficient Power Allocation Scheme for Uplink Distributed Antenna System with D2D Communication

In this paper, the performance of uplink distributed antenna system (DAS) with Device-to-Device (D2D) communication is investigated over composite Rayleigh fading channels, and an energy-efficient power allocation (PA) scheme is developed for D2D communication underlaying DAS. Firstly, we establish the uplink DAS model with D2D communication. Then, the optimization problem for energy efficiency (EE) maximization subject to maximum total power constraint and the minimal rate constraints of cellular user and D2D user is formulated. Based on the pseudo-concave of objective function in optimization problem, we propose an optimal PA scheme with the bisection method to obtain the optimal solution of the optimization problem. The simulation results demonstrate the effectiveness of our proposed scheme. The proposed optimal PA scheme can achieve better EE performance than the conventional equal PA scheme, and the same EE as the PA scheme based on two-dimensional search method but with lower complexity.

Power Allocation for Energy Efficiency Optimization in Multi-User mmWave-NOMA System With Hybrid Precoding

Recently, the non-orthogonal multiple access (NOMA) scheme has been applied in millimeter-wave (mmWave) communication system to support more users and further improve the performance. In this paper, an energy-efficient power allocation (PA) scheme is designed for a downlink multi-user mmWave-NOMA system with hybrid precoding (HP), where two typical HP architectures, namely the fully-connected HP architecture (FHPA) and the sub-connected HP architecture (SHPA), are both considered. Firstly, we pair users in term of their channel difference and correlation. Then, analog beamforming schemes are proposed for the system with fully-connected and sub-connected HP architectures, respectively. Based on this, a two-step HP design with the proposed analog beamforming and zero-forcing precoding is presented. With these results, the optimization problem is formulated to maximize the energy efficiency (EE) of the system. This is a non-convex optimization problem, and can be approximately decomposed into independent convex sub-problems by applying the fractional programming theory. Using the coordinate descent method, the closed-form solutions of each sub-problem are derived. On these basis, an effective iterative algorithm is proposed to obtain the suboptimal power allocation. Simulation results verify the effectiveness of the proposed PA scheme, it has the same EE performance as the existing scheme with relatively low complexity, and can obtain the EE close to the exhaustive search scheme as well as the chaotic accelerated particle swarm optimization scheme. Moreover, the proposed analog beamforming obviously outperforms the conventional finite resolution analog beamforming, and the system with SHPA has higher EE than that with FHPA.

Energy-Efficient Power Allocation for Millimeter Wave Beamspace MIMO-NOMA Systems

Massive multi-input multi-output (MIMO) is envisioned as a key technology for the emerging fifth generation of communication networks (5G). However, considering the energy consumption of the large number of radio frequency (RF) chains, massive MIMO poses a problem to energy efficiency (EE) requirement of 5G. In this paper, we propose an energy-efficient power allocation method for millimeter-wave (mmWave) beamspace MIMO non-orthogonal multiple access (NOMA) systems, where there may be multiple users in each selected beam. First, according to the beam selection (BS) results, we get the precoding matrix through zero-forcing (ZF) beamforming method. Second, we formulate the energy efficiency (EE) maximization optimization problem as a fractional programming. Through sequential convex approximation (SCA) and second-order cone (SOC) transformation, the original optimization problem can be transformed to a convex optimization problem. By using iterative optimization algorithm, we can get the power allocation results. Then, we analyze the convergence of our proposed iterative optimization method and get that the solution in each iteration is a suboptimal solution to the original non-convex optimization problem. Simulation results show that the proposed energy-efficient power allocation scheme has better EE performance comparing with the conventional methods when the transmitted power exceeds the power threshold.

Energy-Efficient Resource Allocation in Multicell Large-Scale Distributed Antenna System with Imperfect CSI

ABSTRACT In this paper, we propose an energy-efficient resource allocation algorithm in the downlink of a multicell large-scale distributed antenna system. A non-convex optimization problem is formulated to maximize the energy efficiency (EE) of the entire network. The optimization problem considers the circuit power consumption, the minimum data rate requirements, and the maximum allowed transmit power per remote radio head. The optimization problem is transformed into an equivalent optimization problem in the subtractive form using the Dinkelbach method. An iterative algorithm for the optimization solution is proposed in which antenna selection with user scheduling and power allocation are implemented separately. To tackle this significant computational complexity, a norm-based joint antenna and the user selection algorithm that uses Tabu Search are proposed to select optimum antennas and users for transmission. Next, a Lagrangian dual decomposition method is adopted to derive a power allocation strategy that can run independently in each cell. Simulation results show that the proposed algorithm can achieve its maximum EE in several iterations, and that increasing the number of antennas or users does not necessarily result in a higher EE. In addition, the proposed iterative scheme is always able to achieve a better performance than the traditional norm-based algorithm and the simplified cloud radio access network based energy efficient power allocation scheme, no matter how many antennas or users are selected.

Energy-Efficient Power Allocation and Relay Selection Schemes for Relay-Assisted D2D Communications in 5G Wireless Networks

Device-to-device (D2D) communications allows user equipment (UE) that are in close proximity to communicate with each other directly without using a base station. Relay-assisted D2D (RA-D2D) communications in 5G networks can be applied to support long-distance users and to improve energy efficiency (EE) of the networks. In this paper, we first establish a multi-relay system model where the D2D UEs can communicate with each other by reusing only one cellular uplink resource. Then, we apply an adaptive neuro-fuzzy inference system (ANFIS) architecture to select the best D2D relay to forward D2D source information to the expected D2D destination. Efficient power allocation (PA) in the D2D source and the D2D relay are critical problems for operating such networks, since the data rate of the cellular uplink and the maximum transmission power of the system need to be satisfied. As is known, 5G wireless networks also aim for low energy consumption to better implement the Internet of Things (IoT). Consequently, in this paper, we also formulate a problem to find the optimal solutions for PA of the D2D source and the D2D relay in terms of maximizing the EE of RA-D2D communications to support applications in the emerging IoT. To solve the PA problems of RA-D2D communications, a particle swarm optimization algorithm is employed to maximize the EE of the RA-D2D communications while satisfying the transmission power constraints of the D2D users, minimum data rate of cellular uplink, and minimum signal-to-interference-plus-noise-ratio requirements of the D2D users. Simulation results reveal that the proposed relay selection and PA methods significantly improve EE more than existing schemes.

Energy-efficient power allocation for massive MIMO-enabled multi-way AF relay networks with channel aging

In this paper, we consider a massive MIMO-enabled multi-way amplify-and-forward relay network with channel aging, where multiple users mutually exchange information via an intermediate relay equipped with massive antennas. For this system, we propose an energy-efficient power allocation scheme for the optimization of energy efficiency (EE). Specifically, we firstly derive accurate closed-form expressions of the system sum rate with aged channel state information (CSI) and predicted CSI. Secondly, based on the derived analytical results, a unified power allocation optimization problem with aged/predicted CSI is formulated for maximizing the system EE. To solve this challenging problem, the successive convex approximation technique is invoked to transform the original optimization problem into a tractable concave fractional programming problem. Then, Dinkelbach’s algorithm and Lagrangian dual method are adopted to find the optimal solution. In addition, to strike a balance between the computational complexity and the optimality, the EE maximization problem using the equal power allocation scheme is solved by extreme value theorem, leading to a closed-form optimal solution. Numerical results demonstrate the accuracy of our analytical results and the effectiveness of the proposed algorithms. Moreover, the impact of several important system parameters on the system performance achieved by the proposed algorithms is also illustrated.

Power Allocation in Land Mobile Satellite Systems: An Energy-Efficient Perspective

Energy-efficient resource allocation schemes have gained considerable attention in satellite communications since the energy consumption powered by solar panels or batteries strongly affect the on-board payload and operation lifetime. This letter proposes an energy-efficient power allocation scheme in a downlink land mobile satellite (LMS) system, which aims at maximizing the ratio of average rate over the total consumed power. With the aid of fractional programming, the energy-efficient optimal power allocation coefficient is first obtained for the LMS system by considering the practical propagation environment and maximum transmit power constraint. Then, analytical result for the optimal mean energy efficiency (EE) of the considered system is derived, which provides an effective approach to evaluate the satellite beam angle, terminal’s elevation angle, and constant circuit power consumption on the EE of the LMS system.

Energy-efficient power allocation scheme for distributed MISO system with transmit correlation

ABSTRACTThe energy efficiency (EE) of distributed multiple-input single-output (MISO) system is investigated in spatially-correlated Rayleigh channels, where large-scale fading including the path loss and the shadow fading is considered. The target is to maximize the EE which is defined as the ratio of the transmission rate to the total power consumed by the system subject to the maximum transmit power of each remote antenna unit (RAU) constraint. The EE optimization can be formulated as a nonlinear fractional programming problem. An optimal power allocation (PA) scheme for maximizing the EE is proposed. With this scheme, an iterative algorithm using Dinkelbach method is presented. To avoid iterative calculation, a low-complexity simplified PA scheme is developed based on the good channel information for antenna selection; it can provide a computational efficient method to acquire the optimal numbers of active RAUs. Therefore, the multidimensional optimization problem is converted to a single-dimensional optimization problem. As a result, the closed-form PA is attained. The simulation results validate the effectiveness of the proposed two schemes. The results indicate that these two schemes can achieve very similar performance, and the simplified scheme has lower complexity because no iteration is required.

Low‐complexity energy efficient power allocation scheme for DAS with maximum power constraint

SummaryIn this paper, a low‐complexity optimal power allocation (PA) scheme is developed to maximize energy efficiency (EE) in a distributed antenna system (DAS) under maximum power constraint and target bit error rate (BER) requirement. Composite Rayleigh fading, multiple receive antennas, and dynamic circuit power consumption are all considered in the system. Unlike conventional schemes, the presented scheme provides a closed‐form expression of PA. Firstly, the optimization problem is formulated according to the definition of EE. Using the Karush‐Kuhn‐Tucker conditions, a general form of the optimal PA, in which the number of active antennas and corresponding power allocation are required only, is then proposed. With this general form, an effective algorithm is presented to yield the closed‐form PA. The proposed scheme can be applied to the system with static circuit power consumption and/or without target BER constraint to obtain optimal PA. Simulation results corroborate the effectiveness of the developed scheme, and the scheme can achieve the same EE performance as the existing optimal schemes with lower complexity. Moreover, the distributed antenna system with multiple receive antennas has higher EE than that with single receive antenna.

Energy-Efficient Power Allocation Scheme for Distributed MISO System With OFDM Over Frequency-Selective Fading Channels

Considering multiple transmit antennas in each distributed antenna unit (DAU), two power allocation (PA) schemes are proposed for energy efficiency (EE) maximization for downlink distributed multiple-input single-output (DMISO) systems with orthogonal frequency-division multiplexing (OFDM) over frequency-selective fading channels, where the power constraints for individual antenna units are addressed. The optimization problem for the maximization of the EE subject to per-antenna maximum power constraints is formulated. By means of linear programming, the optimization is simplified to as if for a DMISO system whose DAUs use a single antenna corresponding to the largest channel-gain-to-noise ratio (CGNR) for transmission. Using the block coordinate descent (BCD) method, an iterative optimal PA scheme to the simplified optimization problem is derived, where an efficient procedure for determining the number of effective subcarriers and the optimized PA is developed. Since the optimal scheme needs iterative calculations, a closed-form suboptimal PA scheme is also derived by sorting the total CGNR and using the principle of the BCD method. Interestingly, this suboptimal scheme has small performance loss in comparing with the optimal scheme, and its relative EE loss is decreased with the number of subcarriers. Moreover, these two schemes include the ones with single transmit antenna for distributed antenna systems as special cases. Computer simulations verify the effectiveness of the two proposed schemes, and the proposed optimal one can obtain the same performance as the existing optimal scheme for DMISO-OFDM but with lower complexity.