Proposed Power Control Algorithm Research Articles

Development and Validation of Control Algorithm for Variable Speed Fixed Pitch Small Wind Turbine

In this study, a power control algorithm of a variable-speed fixed-pitch horizontal-axis lift-type 20 kW small wind turbine (SWT) was proposed and verified through dynamic simulations. The power control algorithm proposed in this study consists of algorithms for Region II to track the maximum power coefficient, for Region II-1/2 to maintain the rated rotor speed, and for Region III to maintain the rated power. To verify the proposed power control algorithm, simulations were performed at the rated wind speed and above the rated wind speed, to which turbulence intensity based on the IEC regulation’s normal turbulence model was applied. As a result, it was confirmed that the proposed controller operates properly in the whole three regions including Regions II, II-1/2, and III. The controller performance was then compared with the variable-speed variable-pitch power controller. Although the performance of the proposed controller was considered good for the target VSVP wind turbine, it was lower than that of the conventional controller applied to the same wind turbine. Compared to the VSVP wind turbine, the VSFP wind turbine with the proposed controller was found to have higher mean loads on the blade and the tower but the fatigue loads in terms of Damage Equivalent Load (DEL) were found to be reduced.

Mean Field Game-Theoretic Framework for Distributed Power Control in Hybrid NOMA

The steady expansion of the number of wireless devices and the ubiquity of the networks give rise to various interesting challenges for the future sixth generation (6G) of wireless communication systems. Particularly, the operators have to handle massive connectivity among Machine Type Devices (MTDs) and increasing demand for eMMB through limited spectrum resources. Non-Orthogonal Multiple Access (NOMA) has been spotlighted as an emerging technology to meet the above-mentioned challenges. In this paper, we consider a densely deployed network in which users are divided into NOMA coalitions. Firstly, we model the power allocation problem as a differential game. Then, we extend the formulated game using a Mean Field Game (MFG) theoretic framework by considering the effect of the collective behavior of devices. Furthermore, we derive a distributed power control algorithm that enables the users to appropriately regulate their transmit power according to brief information received from the BS. Indeed, the analysis of the proposed approach is governed by the two fundamental Hamilton- Jacobi-Bellman (HJB) and Fokker-Planck-Kolmogorov (FPK) equations. Numerical results are presented to analyze the equilibrium behaviors of the proposed power control algorithm and to demonstrate the effectiveness of the formulated MFG compared to existing works in the literature.

Energy Efficiency Maximization in Large-Scale Cell-Free Massive MIMO: A Projected Gradient Approach

This paper considers the fundamental power allocation problem in cell-free massive mutiple-input and multiple-output (MIMO) systems which aims at maximizing the total energy efficiency (EE) under a sum power constraint at each access point (AP) and a quality-of-service (QoS) constraint at each user. Existing solutions for this optimization problem are based on solving a sequence of second-order cone programs (SOCPs), whose computational complexity scales dramatically with the network size. Therefore, they are not implementable for practical large-scale cell-free massive MIMO systems. To tackle this issue, we propose an iterative power control algorithm based on the frame work of an accelerated projected gradient (APG) method. In particular, each iteration of the proposed method is done by simple closed-form expressions, where a penalty method is applied to bring constraints into the objective in the form of penalty functions. Finally, the convergence of the proposed algorithm is analytically proved and numerically compared to the known solution based on SOCP. Simulations results demonstrate that our proposed power control algorithm can achieve the same EE as the existing SOCPs-based method, but more importantly, its run time is much lower (one to two orders of magnitude reduction in run time, compared to the SOCPs-based approaches).

Security performance analysis for cell-free massive multiple-input multiple-output system with multi-antenna access points deployment in presence of active eavesdropping

This article investigates the influence of multiple-antenna deployment at access points on physical layer security under cell-free massive multiple-input multiple-output systems. To embody the concept of network multiple-input multiple-output, cell-free massive multiple-input multiple-output employs a great deal of geographically distributed access points, which jointly serve multiple users in the same frequency-time resource. Assuming a malicious active eavesdropper having knowledge of instantaneous channel state information, we derive the asymptotic closed-form expression of the achievable secrecy rate, which is used to evaluate network security performance. A distributed power allocation scheme is developed based on channel fading degrees of different users. All of access points distribute a larger proportion of local transmission power to the users with better channel conditions. We verified that the security performance of the multi-antenna access points outperforms that of the single one. Compared to equal power allocation, the proposed power control algorithm can further boost the network security performance.

Cell-Free Massive MIMO Systems With Non-Ideal Hardware: Phase Drifts and Distortion Noise

In this paper, a generalized uplink cell-free massive multiple-input multiple-output (mMIMO) system with hardware imperfections is investigated. Specifically, the hardware impairments consist of multiplicative phase drifts, additive distortion, and amplified thermal noise. Leveraging on a generic hardware impairment model, closed-form expressions for the channel estimation and achievable rate are presented. Based on these tractable results, we explore the uplink achievable rate surrounding the key design parameters, i.e., the channel estimation error, the number of antenna arrays, the local oscillator (LO) setup, and the variance of phase drifts. Aside from this, we also design a power optimization algorithm to maximize the sum-rate under variant channel use. The weighted minimum mean square error (MMSE) methodology is introduced to iteratively determine the solution in closed form. Numerical simulations validate the analytical analyses and examine the effectiveness of the proposed power control algorithm.

Power Control for Cognitive Users of Perception Layer in Complex Industrial CPS Based on DQN

Wireless communication is a significant auxiliary technology of data transmission for industrial Cyber-Physical system (CPS). While for the complex industrial scenario of coal mine with long and narrow laneway, lifetime of wireless perception nodes is a potential and nonnegligible problem for safety production. In order to deal with this problem, a power control algorithm based on deep Q network (DQN) is adopted to train micro base station (MBS) by two steps so that the MBS can learn an optimal policy to help the cognitive users (CUs) communicate with a proper transmit power. Firstly, the selection range of transmit power for CUs is calculated by the lower bound of Signal-to-Interference plus-Noise Ratio (SINR) to guarantee the transmission condition of both users. Then, the power control problem is modeled as a Markov Decision Process (MDP) with unknown transition function, where the energy consumption is decreased by giving the upper bound of CUs’ SINR or threshold of transmit power in formulation of reward. In modeled MDP, the system state, which collected by primary users (PUs) and fed back to MBS, is reduced dimension by method of principal component analysis and then treated as the input of DQN. After that, DQN is used to train a power control optimal policy by minimizing the loss function. Simulation results demonstrate that the proposed power control algorithm based on DQN has a good performance that the average transition step and energy utility are 3.56 and 1580h, which is better than the existing solutions.

Improving Coverage Probability in Heterogeneous Networks Based on Poisson Point Process

Introducing heterogeneous networks as a solution for increasing capacity and data rate in cellular networks faces several challenges to be addressed by network designers. Among them, interference is an important issue leading to the reduction in coverage probability or equivalently, the increase in outage probability. Hence, development of schemes that increase the coverage probability in networks seems essential. In this paper, a power control algorithm based on Poisson Point Process (PPP) is proposed to increase the coverage probability in networks. PPP has been shown to accurately model real networks. We provide numerical results to show that our proposed power control algorithm converges to an optimal point and increases the coverage probability, compared to previous works that apply constant transmit power for each tier of the network.

Energy Efficient Cross Layer Multipath Routing for Image Delivery in Wireless Sensor Networks

Owing to limited energy in wireless devices power saving is very critical to prolong the lifetime of the networks. In this regard, we designed a cross-layer optimization mechanism based on power control in which source node broadcasts a Route Request Packet (RREQ) containing information such as node id, image size, end to end bit error rate (BER) and residual battery energy to its neighbor nodes to initiate a multimedia session. Each intermediate node appends its remaining battery energy, link gain, node id and average noise power to the RREQ packet. Upon receiving the RREQ packets, the sink node finds node disjoint paths and calculates the optimal power vectors for each disjoint path using cross layer optimization algorithm. Sink based cross-layer maximal minimal residual energy (MMRE) algorithm finds the number of image packets that can be sent on each path and sends the Route Reply Packet (RREP) to the source on each disjoint path which contains the information such as optimal power vector, remaining battery energy vector and number of packets that can be sent on the path by the source. Simulation results indicate that considerable energy saving can be accomplished with the proposed cross layer power control algorithm

Energy-efficient power control for two-tier femtocell networks with block-fading channels

Femtocell network is an effective technique to improve spectrum utilization and indoor coverage. How to allocate users power while keeping acceptable performance under dynamic environment is still a challenge in practice. In this article, we study the problem of energy efficiency in a two-tier network. The objective of the article is to maximize energy efficiency for the two-tier femtocell network with the consideration of uncertain channels. The complex and dynamic communication environment will lead to the imperfect channel state information. In order to enhance the robustness of the two-tier network, we consider the imperfect channel state information of interference links in block-fading channels using probability constraints. We propose a novel scheme to derive optimal energy efficiency by transforming probability constraints into indicator function. A distributed iterative algorithm is proposed to obtain the optimal power solution. Numerical results illustrate the effectiveness of the proposed power control algorithm.

Energy Control in 4G Networks

Objective: The design of wireless networks mainly focuses on resource allocation and energy efficient schemes to meet the increasing demands. Methods/Analysis: In this paper, we proposed an algorithm for Resource block (RB) allocation and Transmit power control in Long term evolution (LTE) downlink heterogeneous networks. Findings: Energy efficiency is increased by increasing the number of smaller cells. To satisfy the required user throughput energy efficiency is minimized and suitable resource block allocation is maximized. Novelty/Improvement: In the proposed power control algorithm, user is allowed to select minimum powers to reduce power of Evolved NodeB (eNB).

EFPC: An Environmentally Friendly Power Control Scheme for Underwater Sensor Networks.

In oceans, the limited acoustic spectrum resource is heavily shared by marine mammals and manmade systems including underwater sensor networks. In order to limit the negative impact of acoustic signal on marine mammals, we propose an environmentally friendly power control (EFPC) scheme for underwater sensor networks. EFPC allocates transmission power of sensor nodes with a consideration of the existence of marine mammals. By applying a Nash Equilibrium based utility function with a set of limitations to optimize transmission power, the proposed power control algorithm can conduct parallel transmissions to improve the network’s goodput, while avoiding interference with marine mammals. Additionally, to localize marine mammals, which is a prerequisite of EFPC, we propose a novel passive hyperboloid localization algorithm (PHLA). PHLA passively localize marine mammals with the help of the acoustic characteristic of these targets. Simulation results show that PHLA can localize most of the target with a relatively small localization error and EFPC can achieve a close goodput performance compared with an existing power control algorithm while avoiding interfering with marine mammals.

Energy-Efficient Resource Allocation for Fractional Frequency Reuse in Heterogeneous Networks

Next generation wireless networks face the challenge of increasing energy consumption while satisfying the unprecedented increase in the data rate demand. To address this problem, we propose a utility-based energy-efficient resource allocation algorithm for the downlink transmissions in heterogeneous networks (HetNets). We consider the fractional frequency reuse (FFR) method in order to mitigate the intra- and inter-cell interference. The proposed algorithm divides the resource allocation problem into frequency and power assignment problems and sequentially solves them. The proposed power control algorithm uses the gradient ascent method to control the transmit power of macrocell base stations (MeNBs) as most of the power in the network is consumed there. We present the optimality conditions of the resource allocation problem and the convergence of the proposed algorithm. In order to mitigate the inter-cell interference further, we study the interference pricing mechanisms and obtain an upper bound to the maximum energy efficiency problem including the inter-cell interference contributions. The performance of the proposed algorithm is studied in a Long Term Evolution (LTE) system. Our simulation results demonstrate that the proposed algorithm provides substantial improvements in the energy efficiency and throughput of the network. It is also shown that interference pricing provides only marginal improvements over the proposed algorithm.

A novel utility function for energy-efficient power control game in cognitive radio networks.

Spectrum scarcity is a major challenge in wireless communications systems requiring efficient usage and utilization. Cognitive radio network (CRN) is found as a promising technique to solve this problem of spectrum scarcity. It allows licensed and unlicensed users to share the same licensed spectrum band. Interference resulting from cognitive radios (CRs) has undesirable effects on quality of service (QoS) of both licensed and unlicensed systems where it causes degradation in received signal-to-noise ratio (SIR) of users. Power control is one of the most important techniques that can be used to mitigate interference and guarantee QoS in both systems. In this paper, we develop a new approach of a distributed power control for CRN based on utility and pricing. QoS of CR user is presented as a utility function via pricing and a distributed power control as a non-cooperative game in which users maximize their net utility (utility-price). We define the price as a real function of transmit power to increase pricing charge of the farthest CR users. We prove that the power control game proposed in this study has Nash Equilibrium as well as it is unique. The obtained results show that the proposed power control algorithm based on a new utility function has a significant reduction in transmit power consumption and high improvement in speed of convergence.

Distributed Power Control for Cognitive Satellite Networks

In this paper, we considered the problem of distributed power control for cognitive satellite networks. We defined a utility function that measures the user’s satisfaction, as a function of signal to interference plus noise ratio. And then a distributed power control algorithm was proposed based on the defined utility function. This algorithm can guarantee the protection of the primary user and meet quality of service requirements of the cognitive users. Simulation results show the performance of the proposed power control algorithm.

Power control algorithm in cognitive radio system based on modified Shuffled Frog Leaping Algorithm

Based on the non-cooperative power control game introduced by David Goodman, in this paper, we will introduce the concept of target SIR, modify the utility function, and propose a modified power control game algorithm. In this proposed power control game algorithm, it will be proved that the Nash equilibrium exists and is unique. To further improve the accuracy of the solution, the Shuffled Frog Leaping Algorithm (SFLA) will be modified and adopted by incorporating the basic ideas of Artificial Fish (AF). It can be shown that the proposed algorithm will have better global convergence and will have less possibility to be tripped in local optimum. Simulation results show that the proposed power control algorithm based on modified Shuffled Frog Leaping Algorithm (MSFLA) can not only increases the controllability on the target SIR but also reduces the user transmission power and improves system performance.

A Dynamic MAC Protocol with Adaptive Power Control for WCDMA Networks

In Wideband code-division multiple-access (WCDMA) systems, all users share the same time and frequency resources and may lead to near far problem, where power control forms an effective solution. A power control algorithm aims to reduce transmission power and interference level, and to maximize system capacity. In this paper, we propose an adaptive power control mechanism for multimedia traffic in WCDMA networks. The proposed algorithm uses two most recent Transmit Power Control (TPC) commands to compute the Adaptive Factor (AF) based on a predefined Adaptive Control Factor (ACF). We introduce another parameter, Power Determining Factor (PDF) based on the data traffic rate to determine the power. Based on this parameter, the power is increased or decreased. Depending on the traffic rate, the PDF factor is updated i.e., if the observed traffic rate is high, then it will increase the parameter and subsequently increases the power and if it is low, then the parameter will be decreased and correspondingly the power also. By simulation results, we show that the proposed power control algorithm reduces the power consumption of multimedia traffic.

Power Control for Radio-Over-Fiber Downlinks in Frequency Division Multiplexing Cellular Communication Systems

This paper, for the first time to our knowledge, introduces a power control technique for radio-over-fiber (RoF) downlinks in frequency division multiplexing (FDM) cellular communication systems. The power control technique, which has been widely used in traditional wireless systems, is to radiate sufficient power to establish an adequate service for a particular distance. Due to RoF link nonlinearities, RoF systems can be limited by nonlinear distortion that is dependent on the input RF power. The power control technique can manage the input power of RF signals to the RoF links, and thus nonlinear distortion and interchannel interference can be diminished. In this paper, we theoretically analyze the necessity of power control in FDM-RoF systems and then propose a simple power control algorithm. Finally, the proposed power control algorithm is verified in four- and nine-channel orthogonal frequency division multiplexing ultrawideband (OFDM UWB) wireless systems with RoF distribution.

Optimal power control game for primary-secondary user in cognitive radio network

In cognitive radio network (CRN), the utilities results in Nash equilibrium of power control game without using pricing are inefficient. In this paper, a distributed power control algorithm is proposed to improve the utilities of both primary user (PU) and secondary users (SUs) in the CRN based on game theoretic framework. A distributed power control is a non-cooperative power control game, and the quality of service (QoS) received by PU and SUs terminals are referred to as the utility function. PU and SUs act as decision makers in the game and they maximize their utilities in a distributed fashion. We introduce a new pricing function for SUs as a function of transmit power and square amount of interference in order to guide SUs to an efficient Nash equilibrium point. Analysis of the existence and uniqueness of Nash equilibrium for the proposed power control game with pricing is presented. Simulation results show that the proposed power control algorithm via a new pricing function maximizes the number of SUs access the unused spectrum, and improves the utilities of PU and SUs.

Delay-optimal power and subcarrier allocation for OFDMA systems via stochastic approximation

In this paper, we consider delay-optimal power and subcarrier allocation design for OFDMA systems with N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</sub> subcarriers, K mobiles and one base station. There are K queues at the base station for the downlink traffic to the K mobiles with heterogeneous packet arrivals and delay requirements. We shall model the problem as a K-dimensional infinite horizon average reward Markov Decision Problem (MDP) where the control actions are assumed to be a function of the instantaneous Channel State Information (CSI) as well as the joint Queue State Information (QSI). We propose an online stochastic value iteration solution using stochastic approximation. The proposed power control algorithm, which is a function of both the CSI and the QSI, takes the form of multi-level water-filling. We prove that under two mild conditions in Theorem 1, the proposed solution converges to the optimal solution almost surely (with probability 1) and the proposed framework offers a possible solution to the general stochastic NUM problem. By exploiting the birth-death structure of the queue dynamics, we obtain a reduced complexity decomposed solution with linear O(KN <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</sub> ) complexity and O(K) memory requirement.

Adaptive Power Control with Overhearing Avoidance for Wireless Sensor Networks

In this paper, Adaptive Power Control with Overhearing Avoidance (APC+OA) has been proposed. The proposed power control algorithm has been implemented in PICSENSE wireless sensor nodes ...