Power Adjustment Algorithm Research Articles

Design of a reliable electrical photovoltaic generator based on power converters control

This work proposes a renewable energy generation system that operates with a similar behavior as a synchronous generator in terms of reliability in the power supply and inertial response properties against power changes and grid voltage disturbances. The main contributions of this work are: (1) the renewable energy variability managing by means of a battery energy storage strategy, whose charge and discharge are determined by a forecasted-based power adjustment algorithm, (2) the dynamical modeling of an inverter that includes physical inertia and its corresponding response to power and voltage variations, and (3) the synthesis of a nonlinear optimal control for the inertia-based inverter system where the reactive power injection is regulated. Simulation results for a photovoltaic-based generator are presented to demonstrate the effectiveness of the proposed methodologies.

Underwater Real-time Video Transmission via Wireless Optical Channels with Swarms of AUVs

Underwater wireless optical communication (UWOC) has received widespread attention recently due to its distinct advantages in terms of high bandwidth, low power consumption, low delay and confidentiality. However, unlike underwater acoustic communication systems, it suffers from constraints, such as limited communication range and misalignment in oceanic extreme environments. To perform long-distance high bandwidth transmission such as real-time video streaming, it requires relaying technology to perform reliable and robust transmission. Although pre-deploying a large number of sensor nodes is one of the possible solutions, this is impractical in response to emergency events, such as oil pipeline leakage and maritime search and rescue. To this end, in this paper, we propose to leverage a swarm of autonomous underwater vehicles (AUVs) to establish reliable underwater optical communication links in a rapidly changing marine environment for real-time video transmission. We first model the optical transmission channel, and then calculate both the optical angle and the reliable communicating range that satisfy a pre-defined bit error rate (BER). We then formulate the optimization problem based on two aligned solutions while targeting on minimizing both deployment energy consumption and time latency, in which the AUV's uncertainty issue caused by localization error and hovering instability is considered. Finally, we design an adaptive optical beam and power adjustment algorithm to enhance the robustness of the optical link further. Through extensive simulations, the results demonstrate that the proposed scheme is efficient and achieves reliable and robust communication.

Telematics Collaborative Resource Allocation Algorithm Based on Cloud Sidecar

This paper provides an in-depth study and analysis of a distributed allocation algorithm for collaborative resources for cloud-edge-vehicle-based Telematics. The approach starts from the emerging application of urban environmental monitoring based on vehicular networking, with an integrated design of data sensing detection and transmission, and collaborative monitoring of vehicle swarm intelligence based on urban air quality collection to avoid redundancy of information and communication overload. A hybrid routing method with minimal delay for reliable data transmission is proposed. The power adjustment algorithm divides the channel into 3 states. When the CBR is less than 0.5, the channel is in an idle state, and when the CBR is greater than 0.5 and less than 0.8, the channel is in an active state. The algorithm designs redundancy strategies based on coding mechanisms to improve the reliability of data transmission, combines coding mechanisms with routing design, incorporates routing switching ideas, and performs probability-based routing decisions to minimize the delay. In straight-line road sections, a fuzzy logic prediction-based vehicle adaptive connectivity clustering routing algorithm is proposed to reduce the communication overhead during vehicle collaboration and ensure high network connectivity; at intersections, a probability-based minimum delay routing decision algorithm is proposed to reduce the information transmission delay. Experiments show that the proposed method effectively improves the efficiency of data-aware collection and transmission, and increases the reliability of transmission. With the explosive growth of video services, the problem of intelligent transmission of DASH-based video streams has become another research hotspot in mobile edge networks. Based on the edge container cloud architecture of vehicular networking, the resource constraints of microservices when deployed in the edge cloud platform were analyzed, and a multi-objective optimization model for microservice resource scheduling was established with the comprehensive performance objectives of shortest microservice invocation distance, highest resource utilization of physical machine clusters, and ensuring load balancing as much as possible.

Joint optimization algorithm of user association and power control based on spatial traffic distribution

Abstract5G networks must reduce network overhead while meeting user needs. Based on the flow model, this paper jointly optimizes the user association and power control in cellular networks, aiming at achieving a reasonable compromise between user delay and network energy consumption. Unlike previous snapshot models, flow models focus on the spatial distribution of traffic demand instead of the specific location of users. We consider a nonuniformity of spatial traffic distribution and load coupling among base stations (BSs). Firstly, an iterative algorithm is proposed to solve the optimal user association strategy under certain power of BSs. Then, a greedy power adjustment and user association algorithm (GPA‐UA) is proposed to further optimize the objective function. The simulation results show that the proposed algorithm can reduce both user delay and network energy consumption in most cases, and the performance of the proposed algorithm is approximate to the Optimal scheme while GPA‐UA has lower complexity.

Power Adjustment Algorithm for Higher Throughput in Mobile Ad-Hoc Networks

In Mobile Ad Hoc Networks (MANETs), power control is necessary in order to reduce power consumption rates, avoid collisions within packets, increase spatial throughput of the system and to reduce contention among flows. The MANET nodes which are outside the transmission range are energy constraint and they consume more power for packet transmission. In this paper, we propose to develop a power adjustment algorithm to provide higher throughput and consume less power in the Mobile Ad hoc Networks. An optimal transmission power is calculated at the receiver based upon the data payload length and the interference amount. This power is given to the transmitter which increments or decrements the power with respect to the number of neighboring nodes. The adjusted power is retransmitted to the receiver so that the power level can be adjusted between the transmitter and receiver. Since the optimal transmission power is determined based upon the interference amount the possibility of collision among the nodes is reduced effectively. From our simulation results, we show that this algorithm provides higher throughput and lower energy consumption in ad hoc networks.

Distributed Stable Routing with Adaptive Power Control for Multi-Flow and Multi-Hop Mobile Cognitive Networks

In most existing routing algorithms for mobile ad hoc cognitive networks (MACNets), nodes are configured with a fixed even maximal transmission power. Links set up by such the algorithms often suffer from excessive co-channel interference, which significantly downgrades performance of MACNets because the link stability highly depends on not only node mobility but also co-channel interference. In this paper, we investigate how to improve route stability through jointly considering routing with adaptive power adjustment and mobility control. We first propose a set of power adjustment policies which dynamically sets up transmission power of cognitive nodes to enable the duration of links potentially interfered as long as possible. We, then, design a routing metric called integrated link stability (ILS) to quantitatively measure the link stability. This novel metric ILS considers both node mobility and channel interference. Finally, we propose a Joint Stable Routing and Adaptive Power Adjustment (J-SRAPA) algorithm for multi-flow and multi-hop MACNets, with the objective of maximizing network throughput. J-SRAPA dynamically adjusts the transmission power in a distributed way to mitigate co-channel interference and to improve channel utilization ratio accordingly, during both route setup and data transmission. NS2-based simulation results demonstrate that our J-SRAPA significantly outperforms related routing algorithms in terms of network throughput, end-to-end transmission delay, and packet loss ratio; and the higher channel interference degree MACNets experience, the more improvement our J-SRAPA will bring to the networks.

Distributed transmission power control for communication congestion control and awareness enhancement in VANETs.

The vehicular ad hoc network (VANET) has been identified as one of the most promising technologies for managing future intelligent transportation systems. This paper proposes a distributed transmission power adjustment algorithm for communication congestion control and awareness enhancement to address communication congestion problems that can arise in VANETs. The objective of the proposed algorithm is to provide maximum awareness of surrounding vehicles’ status while maintaining a communications channel load below the allowed threshold. The proposed algorithm accomplishes this by adjusting the transmission range of each vehicle in the network progressively and gradually, while monitoring the communications channel load of each vehicle. By changing the transmission range of a vehicle little by little according to the communications channel load of its neighboring vehicles, the algorithm finds the optimal transmission range that provides maximum awareness without resulting in communications congestion. In addition, the proposed algorithm appropriately controls the channel load in a fair manner without sacrificing awareness of specific vehicles in the congested situation. This allows nearby vehicles to obtain more peripheral information to help them stay away from potential hazards and maintain safety. The proposed algorithm was implemented in a simulation environment, and its performance was validated in various traffic scenarios. The simulation results show that the proposed algorithm can deal with communication congestion by controlling the transmission power fairly to a target threshold in various traffic situations.

Electric Vehicle Charging Scheme for a Park-and-Charge System Considering Battery Degradation Costs

This paper studies the electric vehicle (EV) charging scheduling problem of a park-and-charge system with the objective to minimize the EV battery charging degradation cost while satisfying the battery charging characteristic. First, we design the operating model of the system while taking the interests of both customers and parking garage into consideration. Subsequently, a battery degradation cost model is devised to capture the characteristic of battery performance degradation during the charging process. Taking into account the developed battery degradation cost model, EV charging scheduling problem is explored and a cost minimization problem is formulated. To make the problem tractable, we investigate the features of the problem and decompose the problem into two subproblems. A vacant charging resource allocation algorithm and a dynamic power adjustment algorithm are proposed to obtain the optimal solution of cost minimization. Several simulations based on realistic EV charging settings are conducted to evaluate the effectiveness and applicability of the proposed methods in discussed charging scenarios. Simulation results exhibit the superior performance of the proposed algorithms in achieving the most degradation cost reduction and the lowest peak power load compared with other benchmark solutions, which is beneficial for both customers and charging operators.

Fair Resource Allocation in Self-Organizing Heterogeneous Networks With Imperfect Connections

Interference management by controlling the transmission powers is crucial for unveiling the potential of heterogeneous networks. Power allocation has mostly been investigated using ideal communication links, where it is generally assumed that information can be relayed perfectly between base stations. The impact of delays and time-outs during communication is not well understood. We propose a model with nonideal links and a power adjustment algorithm for heterogeneous networks. Contrary to the power allocation approaches in the literature, channel state information is not required for power adjustment. The convergence properties of the proposed algorithm are investigated using tools from switched system theory and it is shown to converge to the max–min solution even with nonideal communication links. The simulation results are in agreement with the theoretical analysis.

A Power Allocation Algorithm For Multi-Tier Cellular Networks With Heterogeneous QoS and Imperfect Channel Considerations

Interference management via resource allocation under the quality of service (QoS) provisions is a crucial challenge for next-generation cellular networks. The task becomes more problematic due to emerging services and applications with non-homogeneous QoS requirements. The resource allocation problem has mostly been investigated using ideal communication links, where it is generally assumed that information can be relayed perfectly between base stations (BSs). The impact of delays and time-outs at communication links during coordination of BSs is not well understood. Furthermore, it is generally assumed that perfect channel state information is available at BSs which may not always be possible due to large channel feedback delays or estimation errors. In this paper, we propose a power adjustment algorithm for heterogeneous networks which considers varying QoS requirements of users. Contrary to the power allocation approaches in the literature, channel state information is not required for power adjustment. The performance of the algorithm is evaluated both analytically and numerically under a setup with non-ideal communication links. The theoretical analysis unveils that the proposed algorithm achieves optimum power allocation with respect to the QoS requirements of users. The numerical results are not only in agreement with theoretical analysis, but also exhibits significant improvement in terms of overall network performance.

Bio-Inspired Distributed Transmission Power Control Considering QoS Fairness in Wireless Body Area Sensor Networks.

Recently, the development of wireless body area sensor network (WBASN) has accelerated due to the rapid development of wireless technology. In the WBASN environment, many WBASNs coexist where communication ranges overlap with each other, resulting in the possibility of interference. Although nodes in a WBASN typically operate at a low power level, to avoid adversely affecting the human body, high transmission rates may be required to support some applications. In addition to this, since many varieties of applications exist in the WBASN environment, each prospective user may have different quality of service (QoS) requirements. Hence, the following issues should be considered in the WBASN environment: (1) interference between adjacent WBASNs, which influences the performance of a specific system, and (2) the degree of satisfaction on the QoS of each user, i.e., the required QoS such as user throughput should be considered to ensure that all users in the network are provided with a fair QoS satisfaction. Thus, in this paper, we propose a transmission power adjustment algorithm that addresses interference problems and guarantees QoS fairness between users. First, we use a new utility function to measure the degree of the satisfaction on the QoS for each user. Then, the transmission power of each sensor node is calculated using the Cucker–Smale model, and the QoS satisfaction of each user is synchronized dispersively. The results of simulations show that the proposed algorithm performs better than existing algorithms, with respect to QoS fairness and energy efficiency.

Cross-Layer Approach Based Energy Minimization for Wireless Sensor Networks

Prolonging the network lifetime is a crucial constraint in wireless sensor networks. Since data communication requires a more energy consumption than any other process, sensor nodes necessitate an energy efficient communication. Most related works generally consider the propagation path loss model to capture the channel effects for the energy consumption model and the routing metric choice. Nevertheless, this model does not incorporate the small-scale fading. Thus, it is inaccurate to evaluate link reliability. In this paper, we derive the analytical expression of the energy consumption by including the small-scale channel fading and its impact on energy efficiency. Then, we propose a cross-layer approach based on joint network and physical layers by including small and large scale fading. We improve the ad hoc on-demand distance vector protocol at the route discovery mechanism and the data transmission by considering both SNR and residual energy thresholds, and a power adjustment algorithm. Simulation results show that the proposed cross-layer approach significantly prolongs the network lifetime while guaranteeing a communication efficiency level.

A Distributed Coverage Adjustment Algorithm for Femtocell Networks

In two-tier femtocell networks, adjusting the transmission power values of femtocell base stations (BSs) such that indoor users receive high signal quality while limiting leakage to outdoor users is an important problem. This paper proposes a novel distributed and self-optimized power adjustment algorithm for two-tier femtocell networks, in which a BS adjusts the transmission power based on the signal quality of neighboring BSs. Achieving fairness among users with minimal information exchange is desired. There are no predefined target parameters used to adjust transmission power, contrary to existing approaches in the literature. The convergence properties of the algorithm under fixed and time-varying communication infrastructures are investigated. The performance is further verified by simulations and comparisons with other algorithms for the coverage problem.

Collaborative Topology Control for Many-to-One Communications in Wireless Sensor Networks

Topology control is relevant in wireless sensor network because of two reasons, namely minimal sensor coverage and power constraints. The former condition is typically satisfied by high-density deployment, whereas the latter mainly concerns with the control protocol design that is adaptable. Controlling communication topology is at the center of the efforts to optimize network performance while improving energy conservation. A dense topology often results in high interference and lower spatial reuse thus reduced capacity, while sparse topology is susceptible to network partitioning and sub-optimal path selection from the routing layer. Topology control has been extensively studied in both flat and hierarchical network by mean of power adjustment and clustering, respectively. Despite a common goal of making the topology less complex both techniques differ in their approach. While the focus of clustering is to form a connected backbone which consists of a minimum subset of nodes, i.e., dominating set, power adjustment focuses on minimizing energy consumption. Combining both approaches remains a relatively lesser explored area. We proposed a hybrid framework called collaborative topology control protocol, which combines dominating set-based clustering and transmission power adjustment. The protocol operates in two stages. During the first stage, a parameterized minimum virtual connected dominating set algorithm is executed to obtain clusters of various desirable properties. In the second stage, each cluster-head executes a distributed power adjustment algorithm. The simulation results show that the proposed topology control framework is capable of versatile performance in terms of transmission range/energy cost, the number of neighbors, edges, and hop distance. Moreover, the topology construction process uses the locally available information only with minimal communication overhead.

Performance analysis of a distributed power control algorithm for shared and split spectrum femtocell networks

In this paper, we study the performance of a distributed power adjustment algorithm for shared and split spectrum allocation setups. The theoretical analysis reveals that the convergence of the power control algorithm is guaranteed under different spectrum allocation schemes and the convergence rate is exponential. The performance analysis is also carried out via simulations which demonstrate the algorithm fairness under Jain’s and Atkinkons’ fairness indices.

A robust power control scheme for femtocell networks with probability constraint of channel gains

In this paper, a robust power control algorithm is proposed in two-tier femtocell network system in order to address the uncertain channel gains of the interference links. In the algorithm, an outage probability is used to guarantee the communication qualities of users. As the deterministic form of the outage probability constraint is not convex of power, a novel and simple method is utilized to solve the power allocation problem. The allocation solution can be determined based on the average channel gains which are engaged with a dynamic factor with average signal-to-interference-plus-noise ratio. In the scheme, a dynamic power adjustment algorithm is developed. The algorithm attempts to guarantee the outage probability requirement of the macrocell user, and to achieve the optimal power allocation of femtocell users. Apart from the appropriate resource for the network, an admission control algorithm is adopted to remove the femtocell user whose communication quality can not be ensured. Numerical results demonstrate the effectiveness of the robust power control algorithm and also explain the admission control algorithm.

LD-IMPSO Based Power Adjustment Algorithm for eICIC in QoS Constrained Hyper Dense HetNets

This paper studies the power adjustment based interference coordination algorithm for hyper-dense heterogeneous networks. Both the quality of service (QoS) requirements of the users and the practical serving cell selection rule are considered. Each user associates with the strongest cell in respect of the downlink received power and the association alters with the power adjustment of the cells. A time-saving power adjustment based interference coordination algorithm based on the combination of Lagrange duality (LD) and improved modified particle swarm optimization (IMPSO) is proposed to maximize the system throughput while guaranteeing the QoS requirements of the users. LD is used to optimize the initial transmit power of the small cells and accelerate the speed of finding the best solution while IMPSO is employed to tackle the NP-hard power adjustment problem caused by the alteration of the user association. Simulations show that, compared with the existing algorithms, the proposed algorithm can significantly reduce the run time while greatly improving the satisfied rate of the users and the throughput of the network.

Power and Load Coupling in Cellular Networks for Energy Optimization

We consider the problem of minimization of sum transmission energy in cellular networks where coupling occurs between cells due to mutual interference. The coupling relation is characterized by the signal-to-interference-and-noise-ratio (SINR) coupling model. Both cell load and transmission power, where cell load measures the average level of resource usage in the cell, interact via the coupling model. The coupling is implicitly characterized with load and power as the variables of interest using two equivalent equations, namely, non-linear load coupling equation (NLCE) and non-linear power coupling equation (NPCE), respectively. By analyzing the NLCE and NPCE, we prove that operating at full load is optimal in minimizing sum energy, and provide an iterative power adjustment algorithm to obtain the corresponding optimal power solution with guaranteed convergence, where in each iteration a standard bisection search is employed. To obtain the algorithmic result, we use the properties of the so-called standard interference function; the proof is non-standard because the NPCE cannot even be expressed as a closed-form expression with power as the implicit variable of interest. We present numerical results illustrating the theoretical findings for a real-life and large-scale cellular network, showing the advantage of our solution compared to the conventional solution of deploying uniform power for base stations.

An MBS-Assisted Femtocell Transmit Power Control Scheme with Mobile User QoS Guarantee in 2-Tier Heterogeneous Femtocell Networks

This study investigates how to adjust the transmit power of femto base station (FBS) to mitigate interference problems between the FBSs and mobile users (MUs) in the 2-tier heterogeneous femtocell networks. A common baseline of deploying the FBS to increase the indoor access bandwidth requires that the FBS operation will not affect outdoor MUs operation with their quality-of-service (QoS) requirements. To tackle this technical problem, an FBS transmit power adjustment (FTPA) algorithm is proposed to adjust the FBS transmit power (FTP) to avoid unwanted cochannel interference (CCI) with the neighboring MUs in downlink transmission. FTPA reduces the FTP to serve its femto users (FUs) according to the QoS requirements of the nearest neighboring MUs to the FBS so that the MU QoS requirement is guaranteed. Simulation results demonstrate that FTPA can achieve a low MU outage probability as well as serve FUs without violating the MU QoS requirements. Simulation results also reveal that FTPA has better performance on voice and video services which are the major trend of future multimedia communication in the NGN.

Forward-link performance of satellite CDMA with linear interference suppression and one-step power control

Wideband direct-sequence (DS)-code-division multiple-access (CDMA) is a strong candidate for both terrestrial and satellite components of UMTS. The forward-link capacity of a satellite DS-CDMA system with a conventional matched filter (MF) receiver is limited by interference from adjacent beams and possibly overlapping beams from multiple satellites. In this paper, we study the performance of the linear minimum mean squared error (MMSE) receiver for the satellite forward link. System constraints are long propagation delay, which prevents accurate closed-loop power control, and low on-board power consumption, which implies a low received bit energy to noise density ratio at the mobile receiver. We consider a one-step power adjustment algorithm which attempts to compensate for random shadowing and path loss, and compare the associated performance of the MMSE and MF receivers. Dual-satellite diversity is also considered. The effect of code rate on performance is studied through the use of punctured convolutional codes and the evaluation of random coding bounds. Our results indicate that linear MMSE interference suppression can improve the quality of service and increase system capacity significantly.