Proposed Power Allocation Algorithm Research Articles

To Charge In-Flight or Not: A Comparison of Two Parallel Hybrid Electric Aircraft Configurations via Optimal Control

This study examines two configurations for parallel hybrid electric aircraft, one with a mechanical connection between the engines and the electric motors and the other without. A previously proposed power allocation algorithm in the context of aircraft energy management is applied to these two configurations for a 19-seat conceptual hybrid electric aircraft. The original optimal control problem is then transformed into a finite-dimensional optimization, and the second-order sufficient conditions for the global optimality of the obtained solution are validated. This is followed by a sensitivity analysis of the fuel consumption to the initial aircraft weight and flight duration. Simulation and theoretical results clarify the limited benefits of charging the battery in-flight for this class of hybrid electric aircraft for the purpose of reducing fuel consumption and CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emissions.

Power Optimization of Cell-Free Massive MIMO With Full-Duplex and Low-Resolution ADCs

This paper analyzes the spectral/energy efficiency (SE/EE) of a full-duplex (FD) cell-free (CF) massive multiple-input multiple-output (mMIMO) over Rician fading channels. Due to the FD radios, the access points (APs) suffer from self-interference (SI) and inter-AP interference (IAI) while the downlink (DL) users’ signals are corrupted by the uplink (UL) users’ transmissions. We consider the case, where low-resolution analog-to-digital converters (ADCs) are utilized at the APs and DL users, which introduces the quantization noise (QN). The combined effects of Rician κ-factor, residual SI/IAI, UL-to-DL interference, multi-user interference, and QN on the UL/DL SEs are characterized. The UL SE is degraded by the increase in DL power, whereas the growth in UL power deteriorates the DL SE. The effects of the residual SI/IAI and UL-to-DL interference are worsened by the low-resolution ADCs. We optimize the UL/DL transmit powers to maximize the overall sum SE of the network. It is observed that the proposed power allocation algorithm brings substantial sum SE gain. A trade-off analysis between the EE and SE, as a function of the ADCs’ resolution, shows that the entire envelope of the operating region of FD CF mMIMO is enhanced in Rician channels.

Improved physical layer secrecy fairness in a realistic indoor NOMA VLC network under users’ mobility

This paper investigates the improvement of the secrecy rate fairness in a cellular non-orthogonal multiple access (NOMA) visible light communication (VLC) network. We formulate the users’ secrecy rate by taking into account the users (legitimate and illegitimate) mobility according to the random way-point (RWP) model. We propose an optimization problem to maximize the minimum secrecy rate for two legitimate users per cell, and then we generalize it for an arbitrary number of users. Moreover, utilizing secrecy considerations, we customize two well-known power allocation methods of fixed power allocation (FPA) and gain ratio power allocation (GRPA) as benchmarks for comparison of the proposed maximization power allocation method. Our numerical results demonstrate the superiority of the proposed power allocation algorithm compared with FPA and GRPA methods. Moreover, for the higher number of users, our results indicate the advantages of the proposed algorithm over modified FPA and GRPA methods. Finally, we investigate the effect of the optical receiver’s field of view fluctuations on the performance of the VLC network.

Power allocation optimization for hybrid information and energy transfer with massive MIMO downlink

AbstractThis paper investigates the power allocation in a massive multiple‐input multiple‐output (MIMO) downlink system, where the base station (BS) simultaneously transmits information and energy to information terminals and energy terminals without resorting to any wireless energy harvesting (EH) protocol, respectively. First, with respect to this system, the closed‐form expressions of achievable rate and harvested energy are, respectively, derived for Rayleigh fading channels. Then, from these expressions, three optimization problems are formulated based on quality‐of‐service (QoS) requirements, with the purposes of maximizing achievable sum rate, harvested sum energy and joint QoS requirements, respectively. The optimization problem of maximizing achievable sum rate is transformed into a sequence of geometric programmings (GPs), which can be solved efficiently with standard convex optimization tools. The optimization problem of maximizing harvested sum energy is proved to be solvable as a linear program. The optimization problem of maximizing joint QoS requirements is shown to be a multi‐objective optimization problem (MOOP) and solved by a one‐dimensional search based on GP. Finally, numerical results manifest that the proposed power allocation algorithms can provide good system performances of achievable sum rate and harvested sum energy, and jointly guarantee QoS fairness in terms of achievable rate and harvested energy.

Pareto-based bi-objective optimization for robust power allocation in hybrid MIMO phased-array radar system under air defense maneuvering tracking

Recently, Hybrid MIMO phased-array (H-MIMO-PA) radar systems have been demonstrated to offer enhanced target tracking capabilities. In practice, an effective radar resource allocation (RA) strategy can ensure the quality of service (QoS) of subtasks and improve the system resource utilization in multiple target tracking (MTT) applications. In this paper, a robust power allocation strategy is developed with the objectives of enhancing target tracking accuracy (TTA) and low probability of intercept (LPI) performance in H-MIMO-PA radar. Firstly, in order to accurately grasp the air defense battlefield situation and improve the QoS for MTT, we propose a threat assessment model based on the intuitionistic fuzzy entropy (IFE) to calculate the comprehensive threat degree of each target in real time. Next, since the predicted conditional Cramér-Rao lower bound (PC-CRLB) is based on the most recently realized measurement and provides an accurate lower bound, the PC-CRLB in clutter is derived and utilized to formulate the task utility function for TTA. In addition, the probability of intercept is derived and adopted as the performance metric for LPI. Due to power allocation process may become conflicting for achieving better TTA and LPI performance at the same time, the Pareto-based bi-objective optimization scheme is adopted, which formulates a non-convex optimization problem. Finally, to solve the formulated model, we propose the modified bi-objective particle swarm optimization (MBOPSO) algorithm. Simulation results are provided to verify the effectiveness of the proposed power allocation algorithm.

Secrecy Outage Performance and Power Allocation for Three Secondary Users CR-NOMA Networks with Transmit Antenna Selection

Cognitive radio inspired non-orthogonal multiple access (CR-NOMA) networks are a research focus in the wireless communication field. The secure communication of wireless networks has become a pressing issue due to the openness of the wireless channel. Multiple-antenna technology can enhance the secrecy performance in CR-NOMA networks, thus we propose a multiple-input-single-output (MISO) CR-NOMA network where the base station (BS) is equipped with multiple antennas, while others have a single antenna in the paper. The BS serves three secondary users in the presence of an eavesdropper and a primary user. We propose two transmit antenna selection (TAS) schemes, namely the space-time transmission (ST) scheme and the maximum channel capacity (MCC) scheme, respectively. Firstly, we obtain an exact closed-form expression for the secrecy outage probability (SOP) of three secondary users and the overall SOP of the networks with the two schemes, respectively. To gain further insights, the present study analyzes the asymptotic SOP performance to analyze the relationship between the network parameters and secrecy outage performance. Based on this, we propose a power allocation algorithm to further improve the secrecy outage performance of the networks. Finally, we verify the analyses with Monte Carlo simulations. The numerical and simulation results demonstrate that: (1) The MCC scheme outperforms the ST scheme on the secrecy outage performance. (2) The proposed power allocation algorithm optimizes the secrecy outage performance of the networks. (3) There exists a sole respective optimal power allocation factor for a BS different transmission power.

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.

Multi-stream receive-spatial modulation based cell-free MIMO downlink transmission method

Multi-stream receive-spatial modulation (MSR-SM) technology is applied to the cell-free multiple-input multiple-output (MIMO) downlink transmission, where both users and access points (APs) are multi-antenna, providing a new idea for green and energy-saving wireless communication. The power control coefficients of each AP are obtained using the sequential convex approximation (SCA) algorithm to optimize energy efficiency (EE) under the constraints of the highest transmitting power of the AP and the lowest signal interference-to-noise ratio of the user. This paper proposes an access point selection scheme based on a water-filling algorithm to reduce the number of unnecessary service APs and the total power consumption of the system. The simulation results show that the proposed power allocation algorithm outperforms the max-min power control algorithm in terms of energy efficiency. The MSR-SM-based downlink transmission mode is more energy efficient than the traditional space division multiplexing MIMO digital modulation mode. Furthermore, the SCA algorithm based on AP selection can improve energy efficiency performance compared with the power control algorithm without AP selection.

User Pairing and Power Allocation for IRS-Assisted NOMA Systems With Imperfect Phase Compensation

In this letter, we analyze the performance of the intelligent reflecting surface (IRS) assisted downlink non-orthogonal multiple access (NOMA) systems in the presence of imperfect phase compensation. We derive an upper bound on the imperfect phase compensation to achieve minimum required data rate for each user. Using this bound, we propose an adaptive user pairing algorithm to maximize the network throughput. We then derive bounds on the power allocation factors and propose power allocation algorithms for the paired users to achieve the maximum sum rate or ensure fairness. Through extensive simulations, we show that the proposed algorithms significantly outperform the state-of-the-art algorithms in the presence of phase imperfections.

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.

Robust and Adaptive Power Allocation Techniques for Rate Splitting Based MU-MIMO Systems

Rate splitting (RS) systems can better deal with imperfect channel state information at the transmitter (CSIT) than conventional approaches. However, this requires an appropriate power allocation that often has a high computational complexity, which might be inadequate for practical and large systems. To this end, adaptive power allocation techniques can provide good performance with low computational cost. This work presents novel robust and adaptive power allocation technique for RS-based multiuser multiple-input multiple-output (MU-MIMO) systems. In particular, we develop a robust adaptive power allocation based on stochastic gradient learning and the minimization of the mean-square error between the transmitted symbols of the RS system and the received signal. The proposed robust power allocation strategy incorporates knowledge of the variance of the channel errors to deal with imperfect CSIT and adjust power levels in the presence of uncertainty. An analysis of the convexity and stability of the proposed power allocation algorithms is provided, together with a study of their computational complexity and theoretical bounds relating the power allocation strategies. Numerical results show that the sum-rate of an RS system with adaptive power allocation outperforms RS and conventional MU-MIMO systems under imperfect CSIT.

Adaptive Coordinated Direct and Relay Transmission for NOMA Networks: A Joint Downlink-Uplink Scheme

Coordinated direct and relay transmission (CDRT) is a promising strategy to extend cell coverage. However, higher spectral efficiency (SE) cannot be guaranteed in CDRT due to the cooperative process. Thus, non-orthogonal multiple access (NOMA) based CDRT is investigated, where a relaying user assists a base station (BS) in communicating with a cell-edge user (CEU) while the BS directly communicates with a cell-center user (CCU). Specifically, a novel joint downlink-uplink adaptive scheme is proposed for NOMA-based CDRT to improve the SE further. The proposed scheme jointly designs uplink and downlink via NOMA and adaptive transmission to implement bidirectional communication. First, we study the performance of the proposed scheme under both perfect and imperfect successive interference cancellation (SIC), and closed-form expressions are derived for the outage probability, diversity order, outage throughput (OT), ergodic sum rate (ESR), and energy efficiency (EE). Then, an optimal power allocation algorithm using the segment optimization and a modified particle swarm optimization algorithm are proposed to maximize the ESR under perfect and imperfect SIC, respectively. Numerical results verify the superiority of the proposed scheme in terms of the ESR, OT, EE, and the outage performance of CEU. The effectiveness of the proposed power allocation algorithms is also validated.

Energy-Efficient Power Allocation for D2D Communication underlaying Cellular Networks

This paper considers the power allocation problem in device-to-device (D2D) communication underlaying a cellular network and investigates the impact of different transmitting and interference power constraints on the energy efficiency and spectral efficiency of the network. We formulate the power allocation problem in D2D communication as a nonlinear fractional programming problem with an objective to maximize the energy efficiency of a D2D communication link subject to four different combinations of transmitting and interference power constraints. To solve the original formulated nonlinear fractional programming problem, we first convert it into a dual nonlinear parametric programming problem, and then decouple the dual problem into several solvable concave problems. Further, a closed-form solution is derived to each dual problem and an efficient power allocation algorithm is proposed to find a numerical solution to the formulated problem. Simulation results show that the proposed power allocation algorithm outperforms an ergodic capacity maximization algorithm and a uniform power distribution algorithm in terms of the energy efficiency of a D2D link, and can efficiently improve the overall ergodic capacity of a cellular network.

Optimal Power Allocation in Downlink Non-Orthogonal Multiple Access (NOMA)

Fifth generation of wireless cellular networks promise to enable better services anytime and anywhere. Non-orthogonal multiple access (NOMA) stands as a suitable multiple accessing scheme due to the ability to allow multiple users to share the same radio resource simultaneously via different domains (power, code, etc.). Through the introduced power domain, users multiplexed at the radio resource within different power levels. This paper studies power allocation in downlink NOMA, an optimization problem formulated that aims to maximize the system's sum rate. To solve the problem, a genetic algorithm based power allocation (GAPA) was proposed that uses genetic algorithm (GA) that employs heuristics to search for suitable solutions. The performance of the proposed power allocation algorithm compared with full search power allocation (FSPA) that gives an optimal performance. Results show that GAPA reaches a performance near to FSPA with lower complexity. In addition, GAPA simulated with various user paring algorithms. Channel state sorting based user pairing with GAPA achieves the best performance comparing to random user pairing algorithm and exhaustive user pairing.

Energy efficiency optimization for amplify and forward relay networks with channel estimation errors

Due to the rapidly increasing energy consumption of the wireless communication systems, designing an energy-efficient system has become an important challenge. One can consider cooperative communication as a promising solution to reduce energy consumed by wireless networks. Hence, in this paper, we study the problem of maximizing energy efficiency by optimally allocating power in three cooperative channel scenarios. Unlike the previous works, imperfect channel state information is considered for all channels. Therefore, energy efficiency functions are derived considering channel estimation errors, and the following optimization problems are formulated to maximize the energy efficiency under the maximum transmission power and the minimum spectral efficiency constraints. Regarding the energy efficiency function behavior, an alternating optimization algorithm is used to find pilot and data transmission power at the source node. It also optimizes the power amplification gain of pilot and data at the relay node. Simulation results demonstrate that the imperfect channel state information condition can degrade the energy efficiency performance significantly. However, using the proposed power allocation method can improve the performance and compensate for the negative effect of channel estimation error. Moreover, the effect of circuit power on the optimal energy efficiency is investigated. It is shown that for a lower range of spectral efficiency, the negative effect of circuit power is greater than the channel estimation error’s negative effect, while for the higher values, the estimation error dominates the circuit power. We also observe that the results obtained from the proposed power allocation algorithms are close to the results of the global search method.

Power Allocation for Multiple User-Type Massive MIMO Systems

In this paper, we propose a multiple user-type massive multiple-input-multiple-output (MIMO) system and present power allocation solutions for this proposed network. Unlike conventional massive MIMO systems in which all users are treated equally, the proposed system consists of two types of users: quality-of-service (QoS) users, who are guaranteed to have good and stable quality, and non-QoS users, who are similar to users in conventional massive MIMO systems. We formulate two power allocation optimization problems which guarantee quality-of-service for QoS users and maximize the sum-rate of non-QoS users simultaneously. These two problems are solved by two proposed successive convex approximation algorithms, and we show that these two algorithms surely converge to a local maximum Karush-Kuhn-Tucker point. The numerical results show the effectiveness of our proposed power allocation algorithms in multiple user-type massive MIMO systems.

Rate Analysis of Intensity Modulated Broadcast Optical Mobile Communication System With User Mobility

Optical mobile communication (OMC) provides a hopeful vision to handle with wireless communication scenarios where multiple mobile users are involved. In previous work, achievable rate analysis for broadcast OMC system with coherent transmission and detection is given. Although it was shown that OMC has superior performance compared with conventional schemes, coherent processing imposes strong challenges on the hardware design. In practice, intensity modulation (IM) and direct detection (DD) is more implementation-friendly. However, the result in coherent processing cannot be migrated to IM/DD scenario since IM imposes new constraints on the signal model, making it more challenging to evaluate the rate performance. Motivated by this, in this paper, we investigate the rate performance of broadcast OMC in IM/DD mode with user mobility. The aim of this work is to reveal the potential gain of OMC with IM/DD over some benchmark schemes. By assuming dense pulse amplitude modulation (PAM), the rate is assessed through mutual information with proposed power allocation algorithm. Through simulation results, it is found that OMC outperforms conventional free space optical (FSO) communication in time division mode at high SNR region with not-so-large number of users.

Sum Power Minimization With Mixed Power and QoS Bounded Constraints

In the incoming communication system, especially for the battery constrained Internet of Things devices, consumption of power resources will be a critical performance metric. This point shows importance when throughput minimal requirement and interference limit have been carried out. This paper investigates such a power allocation problem in a multiple-parallel-channel wireless system to minimize the sum power consumed by the entire system, while meeting the sum power constrains for each group of channels and the whole system as well as meeting the throughput constraints for each of the groups and the system. Sum power minimization itself is also a key issue for margin-adaptive loading. Resorting to geometric concepts, an algorithm named as the group virtual bottom power water-filling (GVB-PWF) is proposed to solve the problem, including the large-scale problems, which computes the exact solution with a low degree of the polynomial computational complexity. Optimality of the proposed algorithm is also proved strictly. To the best of our knowledge, no prior algorithm in the open literature offered such an optimal solution to the proposed problem, with the merit of exactness and efficiency. Simulation results demonstrate that the proposed power allocation algorithm uses less power about 25%, compared with the popular primal-dual interior-point method with the same amount of computations.

Energy Efficient Uplink Transmissions in LoRa Networks

LoRa has been recognized as one of the most promising low-power wide-area (LPWA) techniques. Since LoRa devices are usually powered by batteries, energy efficiency (EE) is an essential consideration. In this paper, we investigate the energy efficient resource allocation in LoRa networks to maximize the system EE (SEE) and the minimal EE (MEE) of LoRa users, respectively. Specifically, our objective is to maximize the corresponding EE by jointly exploiting user scheduling, spreading factor (SF) assignment, and transmit power allocations. To solve them efficiently, we first propose a suboptimal algorithm, including the low-complexity user scheduling scheme based on matching theory and the heuristic SF assignment approach for LoRa users scheduled on the same channel. Then, to deal with the power allocation, an optimal algorithm is proposed to maximize the SEE. To maximize the MEE of LoRa users assigned to the same channel, an iterative power allocation algorithm based on the generalized fractional programming and sequential convex programming is proposed. Numerical results show that the proposed user scheduling algorithm achieves near-optimal EE performance, and the proposed power allocation algorithms outperform the benchmarks.

An improved water-filling algorithm based on power allocation in network MIMO

Inter-Cell Interference (ICI) could affect the performance of the LTE-Advanced (LTE-A) system and can be evaluated according to the user’s Signal to Interference and Noise Ratio (SINR). Owing to this, the power and frequency allocation schemes can be utilized in order to attain an optimum trade-off within the obtained SINR and the resulting interference. In this study, power allocation was integrated into the Dynamic Fractional Frequency Reuse (DFFR) scheme and Network MIMO (NetMIMO) to reduce ICI and enhance the performance of LTE-A downlink. Moreover, this study focuses on the design and development of a novel power allocation algorithm, which is an extension to the previous Improved Water-Filling algorithm (IWF), namely, Low-Complexity Improved Water-Filling algorithm (LC-IWF). Results indicated a reduction in computations and enhancement in interference rejection performance in comparison to the existing algorithms by removing the Lagrange multiplier and water level. Moreover, simulation results revealed that the proposed algorithm enhances the throughput and fairness of up to 20% and 25%, respectively. Furthermore, the power consumption was reduced by 41% and the running time was 5–10 times faster than the IWF algorithm while attaining the best solution. The proposed power allocation algorithm provided a significant improvement in the performance of the NetMIMO LTE-A system, which can be extended for future 5G networks.