Power Control Algorithm Research Articles

Cooperative UAV Enabled Relaying Systems: Joint Trajectory and Transmit Power Optimization

Unmanned aerial vehicles (UAVs) can be used as aerial relays to provide quick and on-demand wireless connections for the area without network coverage. Since a UAV’s onboard energy is limited, how to prolong the duration of UAV relaying communication is challenging. In this paper, we consider applying multiple UAVs to extend the duration of communication via UAV collaboration. We first propose a UAV collaboration scheme, namely heuristic UAV substitution, which lets the UAV relays work one by one individually. Then, we propose another scheme called spectral efficient UAV substitution to improve the spectrum efficiency. Furthermore, under these two proposed schemes, we jointly design the trajectories of the UAV relays and the transmit power of the source and the UAV relays, with the goal of end-to-end throughput maximization. Although the considered optimization problem is non-convex and difficult to solve, we propose an efficient algorithm to find a suboptimal solution to it by applying the block coordinate ascent and successive convex approximation methods. Simulation results show that the proposed UAV substitution schemes can effectively extend the relaying communication duration, and the throughput performance of the proposed joint trajectory optimization and transmit power control algorithm outperforms some benchmark algorithms without considering joint design.

Energy-Efficient Optimization for Energy-Harvesting-Enabled mmWave-UAV Heterogeneous Networks.

Energy Harvesting (EH) is a promising paradigm for 5G heterogeneous communication. EH-enabled Device-to-Device (D2D) communication can assist devices in overcoming the disadvantage of limited battery capacity and improving the Energy Efficiency (EE) by performing EH from ambient wireless signals. Although numerous research works have been conducted on EH-based D2D communication scenarios, the feature of EH-based D2D communication underlying Air-to-Ground (A2G) millimeter-Wave (mmWave) networks has not been fully studied. In this paper, we considered a scenario where multiple Unmanned Aerial Vehicles (UAVs) are deployed to provide energy for D2D Users (DUs) and data transmission for Cellular Users (CUs). We aimed to improve the network EE of EH-enabled D2D communications while reducing the time complexity of beam alignment for mmWave-enabled D2D Users (DUs). We considered a scenario where multiple EH-enabled DUs and CUs coexist, sharing the full mmWave frequency band and adopting high-directive beams for transmitting. To improve the network EE, we propose a joint beamwidth selection, power control, and EH time ratio optimization algorithm for DUs based on alternating optimization. We iteratively optimized one of the three variables, fixing the other two. During each iteration, we first used a game-theoretic approach to adjust the beamwidths of DUs to achieve the sub-optimal EE. Then, the problem with regard to power optimization was solved by the Dinkelbach method and Successive Convex Approximation (SCA). Finally, we performed the optimization of the EH time ratio using linear fractional programming to further increase the EE. By performing extensive simulation experiments, we validated the convergence and effectiveness of our algorithm. The results showed that our proposed algorithm outperformed the fixed beamwidth and fixed power strategy and could closely approach the performance of exhaustive search, particle swarm optimization, and the genetic algorithm, but with a much reduced time complexity.

Deep Q-Learning-Based Transmission Power Control of a High Altitude Platform Station with Spectrum Sharing.

A High Altitude Platform Station (HAPS) can facilitate high-speed data communication over wide areas using high-power line-of-sight communication; however, it can significantly interfere with existing systems. Given spectrum sharing with existing systems, the HAPS transmission power must be adjusted to satisfy the interference requirement for incumbent protection. However, excessive transmission power reduction can lead to severe degradation of the HAPS coverage. To solve this problem, we propose a multi-agent Deep Q-learning (DQL)-based transmission power control algorithm to minimize the outage probability of the HAPS downlink while satisfying the interference requirement of an interfered system. In addition, a double DQL (DDQL) is developed to prevent the potential risk of action-value overestimation from the DQL. With a proper state, reward, and training process, all agents cooperatively learn a power control policy for achieving a near-optimal solution. The proposed DQL power control algorithm performs equal or close to the optimal exhaustive search algorithm for varying positions of the interfered system. The proposed DQL and DDQL power control yields the same performance, which indicates that the actional value overestimation does not adversely affect the quality of the learned policy.

An Interference-Managed Hybrid Clustering Algorithm to Improve System Throughput.

In the current smart era of 5G, cellular devices and mobile data have increased exponentially. The conventional network deployment and protocols do not fulfill the ever-increasing demand for mobile data traffic. Therefore, ultra-dense networks have widely been suggested in the recent literature. However, deploying an ultra-dense network (UDN) under macro cells leads to severe interference management challenges. Although various centralized and distributed clustering methods have been used in most research work, the issue of increased interference persists. This paper proposes a joint small cell power control algorithm (SPC) and interference-managed hybrid clustering (IMHC) scheme, to resolve the issue of co-tier and cross-tier interference in the small cell base station cluster tiers. The small cell base stations (SBSs) are categorized based on their respective transmitting power, as high-power SBSs (HSBSs) and low-power SBSs (LSBSs). The simulation results show that by implementing the IMHC algorithm for SBSs in a three-tier heterogeneous network, the system throughput is improved with reduced interference.

Research on Zoning, Optimization, Stability, and Nonlinear Control of Wireless Network in Power Grid Communication

To explore the partition optimization and stability of telecommunication network under nonlinear control, a routing optimization algorithm for power control of distribution communication network is proposed, which is based on the Ad-hoc On-demand Distance Vector (AODV) routing optimization algorithm, based on the energy consumption model of wireless sensor network, the optimal routing mechanism for power control is constructed, and the optimal routing mechanism is used to select the route with low energy consumption to solve the problem of energy limitation of wireless sensor nodes. This paper aims at the network routing selection problem in the terminal information collection of the distribution network and designs the routing optimization algorithm of the wireless sensor network. Experimental results show that the model accuracy of the optimized model is 99.8%, the average residual error of the model is about 5.5[Formula: see text]nm, the model accuracy meets the requirements, and the method is effective. According to the confidence interval prediction results of wireless communication link reliability, a node power control system structure of wireless sensor network is proposed, and an optimal control algorithm of communication link quality and reliability of microgrid wireless sensor network based on fuzzy control is studied.

Large-Scale Fading Coefficients Classification-Aided Power Control Strategy for Mitigating the Pilot Contamination in Massive MIMO Systems

Deploying base stations with a massive number of antennas give birth to massive multi-input multi-output technology, which can boost the quality of the wireless communication; however, the limitations imposed by the length of the coherence interval has, directly, limited the number of the orthogonal pilot sequences (OPSs) that can be exploited for acquiring such accurate channel state information at each base station (BS). Hence, the available pilot resources are reused among several cells, which causes the problem of pilot contamination (PC). In this paper, the PC problem is analyzed, and by exploiting the large-scale fading coeffificients of the users’ equipment (UEs), we have classifified the users within each cell; therefore, the same OPS is, solely, reused by the far-distant UEs; hence, the inflfluence of the PC problem is made as small as possible. Since the UEs are subject to different channel conditions, we have consolidated our proposal by a power control algorithm, which boosts the performance of our proposal. Simulation results prove the effectiveness of our proposed decontaminating strategy.

Dynamic Channel Access and Power Control in Wireless Interference Networks via Multi-Agent Deep Reinforcement Learning

Due to the scarcity in the wireless spectrum and limited energy resources especially in mobile applications, efficient resource allocation strategies are critical in wireless networks. Motivated by the recent advances in deep reinforcement learning (DRL), we address multi-agent DRL-based joint dynamic channel access and power control in a wireless interference network. We first propose a multi-agent DRL algorithm with centralized training (DRL-CT) to tackle the joint resource allocation problem. In this case, the training is performed at the central unit (CU) and after training, the users make autonomous decisions on their transmission strategies with only local information. We demonstrate that with limited information exchange and faster convergence, DRL-CT algorithm can achieve 90% of the performance achieved by the combination of weighted minimum mean square error (WMMSE) algorithm for power control and exhaustive search for dynamic channel access. In the second part of this paper, we consider distributed multi-agent DRL scenario in which each user conducts its own training and makes its decisions individually, acting as a DRL agent. Finally, as a compromise between centralized and fully distributed scenarios, we consider federated DRL (FDRL) to approach the performance of DRL-CT with the use of a central unit in training while limiting the information exchange and preserving privacy of the users in the wireless system. Via simulation results, we show that proposed learning frameworks lead to efficient adaptive channel access and power control policies in dynamic environments.

Data and Channel-Adaptive Sensor Scheduling for Federated Edge Learning via Over-the-Air Gradient Aggregation

Over-the-air gradient aggregation and data-aware scheduling have recently drawn great attention due to the outstanding performance in improving communication efficiency for federated edge learning applications. However, in this case, the estimated gradient suffers from the channel and data distortion induced by channel fading and data-aware scheduling, which introduces significant bias and harms the training performance. To solve these problems, we propose a dynamic data and channel adaptive sensor scheduling and power control algorithm combining a residual feedback mechanism. Instead of discarding the gradients not transmitted to the central server, each sensor keeps track of a local residual to store these gradients. Furthermore, by connecting the model update iterations to a dynamic evolution process, we utilize the Lyapunov drift optimization method to analyze the relationship between the training gain and resource allocation. The derived decentralized optimal solution is adaptive to both the channel state information and data importance to seize good transmission opportunity and important gradients. Theoretical analysis is provided on the convergence of the proposed algorithm in practical training scenarios. Simulation results further illustrate that under the same power cost, the proposed scheme has a much faster convergence rate and lower training loss compared to existing baselines.

Multimode Coordination Control Method for Microgrid Cluster Based on Adaptive Power Control and Routing Algorithm

Aiming at the coordinated control problem caused by distributed energy belonging to different agents, a multimode coordinated control method of microgrid group based on adaptive power control and routing algorithm is studied. According to the characteristics of microgrid group, a method combining adaptive power control with routing algorithm is proposed to predict the channel interference of nodes and select the next hop of nodes. The nodes adjust their power to adapt to their own surrounding environment to ensure the maximum throughput of microgrid under the minimum channel interference; on this basis, a multi-agent system is introduced to establish the energy’s intelligent coordinated control strategy of multi microgrid, a three-layer coordinated control system framework is designed, and the solution process of regional autonomy and global collaborative optimal control is proposed. The results show that the proposed multimode coordination control strategy can be used to optimize the multi microgrid scheduling in grid connected and islanded modes. The energy storage system can obtain better economic benefits in the economic optimal mode, while the longest life mode can reduce the economic benefits in order to ensure the smooth operation of the energy storage system.

Hybrid Power Supply System with Fuzzy Logic Controller: Power Control Algorithm, Main Properties, and Applications

This paper presents a novel power supply system based on the use of fuzzy inference logic to improve the power control of renewable energy sources. The system comprises renewable solar and wind sources, and an accumulator battery is used as an additional power source. The procedure for the parallel connection of multiple energy sources provides a stable power supply and optimal charging of the accumulative element. Renewable energy sources are connected in parallel using two serial converters and controlled by the controller based on the fuzzy logic. The reference voltage control of the serial converter enables an optimal use of available energy sources. The accumulative element is connected in parallel to compensate for the shortage of solar and wind energies, whereas if the available renewable energy exceeds the needs of the consumers, the surplus energy is accumulated in the battery. All measurements are conducted on the prototype of the hybrid power system under real conditions and compared with the applied systems of this type. This novel system is mainly used in remote telecom locations where there is no power distribution network.

Efficient Secure NOMA Schemes Based on Chaotic Physical Layer Security for Wireless Networks

Non-orthogonal multiple access (NOMA) is considered by the 3GPP as a potential technology for beyond 5G wireless networks to support massive connectivity with robust reliability. However, the massive increase in connected users critically leads to data security problems owing to the high possibility of eavesdropping. In this paper, uplink secure NOMA (sNOMA) schemes based on chaotic physical layer security (PLS) are proposed to achieve two-fold secrecy of integrated channel coding and secret key approaches. Different code-domain and power-domain techniques are employed for sNOMA designs over realistic fading channel environments. Equal power allocation strategy is used for the transmitted chaotic signals in code-domain sNOMA (CD-sNOMA), whereas dynamic power control is employed in power-domain sNOMA (PD-sNOMA) and the hybrid code-power-domain sNOMA (CPD-sNOMA) approaches. The former design adopts joint maximum Likelihood (ML) signal detection while the later scenarios utilize integrated receiver designs based on successive interference cancellation (SIC) and ML techniques. For the proposed sNOMA schemes, power control algorithms are presented to optimize the system performance over constrained total received power and target error rate. Numerical results validate the effectiveness of sNOMA designs compared with the benchmark systems under the worst-case secrecy of unauthorized receiver with complete knowledge of the transmission scheme and associated channel,. Valuable tradeoffs are demonstrated between the achieved error rate, connectivity, security gap, and complexity. Moreover, the utilized chaotic signals offer robust and cost-effective PLS solutions with a huge key-space to combat the most powerful brute-force eavesdropping attacks.

Interference Management in Small Cell System

The rapid increase in quality performance and high data rate make the small cell an attractive solution in the next generation of a cellular mobile network. The Femtocell is a small cell that can boost services to four or five users in low price, and it can guarantee high data rate and capacity within the indoor residential. The Macrocell is a base station with high power that can cover up to 200 miles of cell radius. The propagative of Femtocell over macrocell can cause interference between them as they use the same spectrum. In our paper, first we explain interference management algorithms; such as power control algorithm, Fractional Frequency Reuse (FFR) algorithm and resource scheduling algorithm. Also, we propose an algorithm for interference management that combines the fixed power control algorithm, Dynamic Fractional Frequency Reuse (DFFR) algorithm, and resource scheduling algorithm to mitigate the interference. Simulation results show that the proposed algorithm improves the signal to interference plus noise ratio (SINR), throughput and reduces the interference from neighbor cells.

A review of emerging design concepts in applied microgrid technology

<abstract> <p>Most of the research in distributed generation focuses on power flow optimization and control algorithm development and related fields. However, microgrids are evolving on multiple levels with respect to the chemical processes used to manufacture the underlying technologies, deployment strategies, physical architecture (which is important to the economic factor) as well as environmental impact mitigation of microgrids. Special use cases and paradigms of deploying Distributed Generation (DG) in harmony with agricultural or decorative purposes for existing spaces are emerging, propelled by research in frontiers that the DG engineer would benefit from being aware of. Also, offshore photovoltaic (PV) has emerged as an increasingly important research area. Many nascent technologies and concepts have not been techno-economically analyzed to determine and optimize their benefits. These provide ample research opportunities from a big-picture perspective regarding microgrid development. This also provides the avenue for research in distributed generation from a physical integration and space use perspective. This study reviews a selection of developments in microgrid technology with the themes of manufacturing technology, optimal deployment techniques in physical spaces, and impact mitigation approaches to the deployment of renewable energy from a qualitative perspective.</p> </abstract>

The Fixed Point Iteration of Positive Concave Mappings Converges Geometrically if a Fixed Point Exists: Implications to Wireless Systems

We prove that the fixed point iteration of arbitrary positive concave mappings with nonempty fixed point set converges geometrically for any starting point. We also show that positivity is crucial for this result to hold, and the concept of (nonlinear) spectral radius of asymptotic mappings provides us with information about the convergence factor. As a practical implication of the results shown here, we rigorously explain why some power control and load estimation algorithms in wireless networks, which are particular instances of the fixed point iteration, have shown geometric convergence in simulations. These algorithms have been typically derived by considering fixed point iterations of the general class of standard interference mappings, so the possibility of sublinear convergence rate could not be ruled out in previous studies, except in special cases that are often more restrictive than those considered here.

Intelligent IoT Wind Emulation System Based on Real-Time Data Fetching Approach

The fundamental challenge for the development of the wind energy conversion system is the natural instability of this resource, which is now at the forefront of the debate. The Internet of Things (IoT) has emerged as a smart and practical approach to renewable energy system management. In addition, the Internet of Things is an important study area with several possibilities for advancement and demanding work. To improve the dependability of wind energy conversion systems, researchers may simulate all potential wind turbine operating scenarios and design power electronics topologies and control algorithms appropriately. The proposed wind emulation platform is wind-turbine-oriented, with significantly increased traceability and efficiency when compared to existing systems. The wind emulation system is driven with real-time wind velocity data. The real-time and forecasted wind data is fetched from global nodes using an IoT cloud application programming interface. The wind emulation system is modelled in the VEE Pro platform and integrated with IoT cloud API and FPGA controller. Data obtained from IoT is converted into actionable information to analyse and investigate wind turbine performance. Also, effective security measures have been taken for ESP8266 communication with the Host Computer. Adapting the proposed IoT algorithm with an associated hardware prototype, the condition of the wind energy conversion system and the power generation capability shall be analyzed in a real-time environment.

Improving Sum-Rate of Cell-Free Massive MIMO With Expanded Compute-and-Forward

Cell-free massive multiple-input multiple-output (MIMO) employs a large number of distributed access points (APs) to serve a small number of user equipments (UEs) via the same time/frequency resource. Due to the strong macro diversity gain, cell-free massive MIMO can considerably improve the achievable sum-rate compared to conventional cellular massive MIMO. However, the performance of cell-free massive MIMO is upper limited by inter-user interference (IUI) when employing simple maximum ratio combining (MRC) at receivers. To harness IUI, the expanded compute-and-forward (ECF) framework is adopted. In particular, we propose power control algorithms for the parallel computation and successive computation in the ECF framework, respectively, to exploit the performance gain and then improve the system performance. Furthermore, we propose an AP selection scheme and the application of different decoding orders for the successive computation. Finally, numerical results demonstrate that ECF frameworks outperform the conventional CF and MRC frameworks in terms of achievable sum-rate.

Impact of Antenna Distribution on Spectral and Energy Efficiency of Cell-Free Massive MIMO With Transmit Power Control Algorithms

Cell-free massive multiple-input multiple-output (CF-mMIMO) systems are expected to provide faster and more robust connections to user equipments by cooperation of a massive number of distributed access points (APs), and to be one of the key technologies for beyond 5G (B5G). CF-mMIMO systems with multiple-antenna APs have been investigated from various viewpoints recently. However, no comprehensive analysis of the impact of antenna distribution on CF-mMIMO system performance has been done so far, which is important for practical deployment. Besides spectral efficiency, in B5G, energy efficiency of user equipments is one of the key indicators because various kinds of battery-limited devices connect to the network. Thus, this paper provides a comprehensive performance analysis of the impact of antenna distribution on the performance indicators, while considering several combining/precoding schemes and transmit power control algorithms. For uplink maximal-ratio combining, the concentrated deployment has the best performance thanks to the channel hardening and favorable propagation phenomena. On the other hand, the concentrated deployment prominently suffers from shadowing effects. For uplink/downlink minimum mean-square error combining with transmit power control, the semi-distributed deployments show the best performance, and it implies that we can reduce the number of APs to 1/4 for uplink and to 1/2 for downlink while keeping the same performance as the fully-distributed deployment.

마이크로그리드의 수용성 향상을 위한 ESS의 전력제어

Microgrid(MG) pursues its economic power distribution in order to maximize its efficiency and also business expansion. To achieve it, an energy management system(EMS) is key technology. The ultimate pursuit of the MG is to minimize the use of diesel fuel by making the most of renewable energy. This paper proposes a power control algorithm of energy storage system(ESS) in stand-alone MG that considers the hosting capacity as an index to minimize the size of diesel generator(DG) capacity. For this, the ESS is installed at the end of the line of the MG and uses a bi-directional power flow between the DG and the ESS to increase the hosting capacity of the load and renewable energy. In addition, by using ESS, customers and renewable energy exceeding the capacity of DG are stably introduced without adding facilities to the grid. The simulation results using Matlab/Simulk show the effectiveness of the proposed algorithm.

Energy-Efficient Secure Communications for Wireless-Powered Cognitive Radio Networks.

In this study, we investigate energy-efficient secure communications for wireless-powered cognitive ratio networks, in which multiple secondary users (SUs) share the same frequency band with primary users (PUs) and energy harvesting (EH) nodes harvest energy from the transmitted signals, even though information decoding is not permitted. To maximize the average secrecy energy efficiency (SEE) of SUs while ensuring acceptable interference on PUs and the required amount of energy for the EH nodes, we propose an energy-efficient transmit power control algorithm using dual decomposition, wherein suboptimal transmit powers are determined in an iterative manner with low complexity. Through extensive simulations in various scenarios, we verify that the proposed scheme has a higher average SEE than conventional schemes and a considerably shorter computation time than the optimal scheme.

Low-Complexity Transmit Power Control for Secure Communications in Wireless-Powered Cognitive Radio Networks.

In this study, wireless-powered cognitive radio networks (WPCRNs) are considered, in which N sets of transmitters, receivers and energy-harvesting (EH) nodes in secondary networks share the same spectrum with primary users (PUs) and none of the EH nodes is allowed to decode information but can harvest energy from the signals. Given that the EH nodes are untrusted nodes from the point of view of information transfer, the eavesdropping of secret information can occur if they decide to eavesdrop on information instead of harvesting energy from the signals transmitted by secondary users (SUs). For secure communications in WPCRNs, we aim to find the optimal transmit powers of SUs that maximize the average secrecy rate of SUs while maintaining the interference to PUs below an allowable level, while guaranteeing the minimum EH requirement for each EH node. First, we derive an analytical expression for the transmit power via dual decomposition and propose a suboptimal transmit power control algorithm, which is implemented in an iterative manner with low complexity. The simulation results confirm that the proposed scheme outperforms the conventional distributed schemes by more than 10% in terms of the average secrecy rate and outage probability and can also considerably reduce the computation time compared with the optimal scheme.