Transmission Power Levels Research Articles

Adaptive transmission power level algorithm according to random deployment of nodes in WSN

Wireless sensor nodes are generally deployed randomly in hostile, harsh and inaccessible environments. For this reason, the sensor nodes are supposed to operate over long periods of time without human intervention in order to extend the life of the network as much as possible, and also, it is not possible to restore the nodes or change their positions after their deployment, but by changing the transmitting power level and redeploying a new nodes above the deployment Previously, the network performance improves and we guarantee that the deployed nodes are not lost, and we also guarantee the operation of the network as a whole. The researcher has developed an algorithm "Adaptive transmission power level according to random deployment (ATPLRD)", where the presented algorithm includes determining the power levels relative to random deployment and identifying possible paths in the network in order to reach high interconnection between nodes to achieve the least number of published nodes at the lowest energy levels for the nodes, and also determines the most important nodes in the network whose exit or failure leads to the collapse of the network, and determining The boundary nodes of the network, as well as the weakest coverage areas, which represent gaps in the network, and from it determines the number of nodes needed to deploy within these gaps as few as possible. The results of the study showed that the imposed algorithm is effective in all of the above, and we focus in this research on adaptively determining the transmission energy levels of the nodes and reducing the number of deployed nodes that make the network work effectively and improving the quality of deployment by deploying additional nodes within the Reigon of Interest. The results showed achieving the least number of deployed nodes at the lowest transmission power level and achieving high interconnection between nodes. An overall energy consumption improvement of 31.25% was achieved.

Games in Normal and Satisfaction Form for Efficient Transmission Power Allocation Under Dual 5G Wireless Multiple Access Paradigm

In this paper, to exploit the challenges and potential offered by the simultaneous use of non-orthogonal multiple access (NOMA) and orthogonal frequency division multiple access (OFDMA) transmission options in future 5G wireless systems, we aim at the proper modeling and transformation of the uplink power allocation problem. In particular, in this setting, each user has two degrees of freedom in the decision making process, namely its overall transmission power level, and the corresponding power investment to the OFDMA and/or NOMA based transmissions. The resulting multi-variable power allocation problem is treated and solved under three different perspectives, namely: 1) Games in Normal Form and Nash Equilibrium (NE); 2) Optimization techniques targeting system social welfare through a centralized optimal solution; and 3) Games in Satisfaction Form and Efficient Satisfaction Equilibrium (ESE). Based on these approaches, different solutions and stable operation points are identified and their properties are analyzed. An in depth evaluation and comparison of the various obtained outcomes is achieved, via modeling and simulations. The focus is placed on the impact and the interplay of the NOMA specific features, including the potential over-exploitation of the available bandwidth, the fairness in accessing it, and the interference treatment. It is also shown that, using the satisfaction form games for the users to converge to the ESE, provides an efficient and promising user-centric modeling approach to the power allocation problem, as the system adapts to the users’ application needs, while at the same time eliminates a significant amount of interference.

Joint User Scheduling and Resource Allocation for Millimeter Wave Systems Relying on Adaptive-Resolution ADCs

Millimeter wave (mmWave) communication systems using adaptive-resolution analog-to-digital converters (RADCs) have recently drawn considerable interests from the research community as benefit of their high energy efficiency and low implementation cost. In this paper, we focus on the mmWave uplink using RADCs and investigate the joint user scheduling and resource allocation problem. Specifically, we seek to maximize the system throughput of the scheduled users by jointly optimizing their transmit power level and hybrid combiners as well as the number of quantization bits, subject to practical constraints. By relying on fractional programming (FP) techniques, we first covert this problem into a form amenable to optimization and exploit the specific structures in its solutions with the aid of the so-called Ky Fan <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> -norm.Then, the resultant optimization problem is solved using a penalty block successive concave approximation (P-BSCA) algorithm. Our numerical results reveal that the proposed algorithm substantially enhances the throughput of the scheduled users compared to the state-of-the-art benchmark schemes and provides more flexible and efficient resource allocation control.

Efficient configuration for multi-objective QoS optimization in wireless sensor network

Wireless sensor network (WSN) is a self-organizing network consisting of many sensor nodes situated in the monitoring area. One of the most important challenges of WSNs is to configure the network in a way to improve the quality-of-service parameters, and thus enhance the network performance and reduce energy consumption. Energy consumption increases due to several reasons such as unsuccessful delivery of packets to the receiver, re-transmission of packets, delay in packet delivery to the sink, low received signal strength, inadequate link quality, noise level, etc. Each sensor network consists of different configuration parameters including the distance between receiver and transmitter, packet arrival time, packet size, maximum queue size, maximum number of transmissions, transmission power level, etc. All the mentioned parameters will affect several indicators such as latency, received signal strength, link quality, successful/fail packet delivery, noise level, and the number of re-transmissions. Finding the relationship between these two sets of parameters and optimizing them reduce energy consumption in the network and increase network lifetime. To achieve this, an efficient and new method based on support vector regression and genetic algorithm called MSOG is presented in this paper to model and optimize wireless sensor network indicators (such as delay, received signal strength, packet arrival time, successful packet delivery). The simulation results show that MSOG has better outcomes in optimizing network parameters and indicators in comparison with a similar algorithm.

Transmit Power Pool Design for Grant-Free NOMA-IoT Networks via Deep Reinforcement Learning

Grant-free non-orthogonal multiple access (GF-NOMA) is a potential multiple access framework for short-packet internet-of-things (IoT) networks to enhance connectivity. However, the resource allocation problem in GF-NOMA is challenging due to the absence of closed-loop power control. We design a prototype of transmit power pool (PP) to provide open-loop power control. IoT users acquire their transmit power in advance from this prototype PP solely according to their communication distances. Firstly, a multi-agent deep Q-network (DQN) aided GF-NOMA algorithm is proposed to determine the optimal transmit power levels for the prototype PP. More specifically, each IoT user acts as an agent and learns a policy by interacting with the wireless environment that guides them to select optimal actions. Secondly, to prevent the Q-learning model overestimation problem, double DQN (DDQN) based GF-NOMA algorithm is proposed. Numerical results confirm that the DDQN based algorithm finds out the optimal transmit power levels that form the PP. Comparing with the conventional online learning approach, the proposed algorithm with the prototype PP converges faster under changing environments due to limiting the action space based on previous learning. The considered GF-NOMA system outperforms the networks with fixed transmission power, namely all the users have the same transmit power and the traditional GF with orthogonal multiple access techniques, in terms of throughput.

On Optimizing WiFi RSSI and Channel Assignment using Genetic Algorithm for WiFi Tuning

In this work, we proposed a genetic algorithm-based Wi-Fi-tuning platform that could facilitate the network administrators to cope with co-channel interference triggered by other wireless sources. Generally, with a well-designed WLAN, signal interference from adjacent areas is usually minimized. Unfortunately, when other wireless sources are introduced into the WLAN system, co-channel interference is inevitable. Interference usually causes degradation and/or disruption of network services. Resolving this issue becomes even more complicated when the interfering signals come from access points owned by other ISPs and are not accessible by the network administrators. This paper proposed a Wi-Fi tuning platform that allowed automatic reconfiguration of WLAN settings by finding the best settings for channel assignment and transmission power level. When signal interference is detected, the platform attempts to find heuristic solutions for wireless settings based on a genetic algorithm. From our experiments, we could see that our proposed algorithm could regenerate WLAN settings that provided stronger signal levels, higher coverage ranges while reducing interference levels in the deployment area. With the proposed platform, troubleshooting became less complicated, requiring less cost and time. With the help of the Wi-Fi tuning platform, the network administrators could promptly react to the incidence leading to the enhancement of availability, reliability, and consistency of the WLAN system.

Balancing Awareness and Congestion in Vehicular Networks Using Variable Transmission Power

Vehicular ad Hoc networks (VANETs) support a variety of applications ranging from critical safety applications to “infotainment” or “comfort” applications. In North America, 75 MHz of the spectrum in the 5.9 GHz band has been allocated for vehicular communication. Safety applications rely on event-driven “alert” messages as well as the periodic broadcast of Basic Safety Messages (BSMs) containing critical information, e.g., position, speed, and heading from participating vehicles. The limited channel capacity and high message rates needed to ensure an adequate level of awareness make the reliable delivery of BSMs a challenging problem for VANETs. In this paper, we propose a decentralized congestion control algorithm that uses variable transmission power levels to reduce the channel busy ratio while maintaining a high level of awareness for nearby vehicles. The simulation results indicate that the proposed approach is able to achieve a suitable balance between awareness and bandwidth usage.

Aging and Delay Analysis Based on Lyapunov Optimization and Martingale Theory

The age of information (AoI) is a key performance metric for data freshness in real-time systems, measuring the time elapsed between status updates received at a remote destination. This paper addresses the analysis of both the AoI and the delay for a system demanding timely status updates, consisting of an internet of things (IoT) community, and a multi-core edge computing node (EN). The IoT devices perform channel contention according to a slotted aloha non-orthogonal multiple access scheme with two transmission power levels to access the EN. The objective of the paper is the dynamic selection of both the EN computation capabilities and the suitable access probability for the two power levels, aiming at maximizing the time-average system utility, while guaranteeing the queues stability. The device end-to-end delay is analyzed by resorting to the martingale theoretical approximation under the condition that the Lyapunov optimization framework is performed on a time slot basis, hence leading to a novel instantaneous mixed integer problem formulation. Furthermore, the generalized benders decomposition method is employed to provide a suboptimal solution for the formulated problem. Finally, the accuracy of the proposed framework is validated by comparing the obtained analytical predictions with simulation results.

A game-theoretic joint optimal pricing and resource allocation for Mobile Edge Computing in NOMA-based 5G networks and beyond

Mobile Edge Computing is a new computing paradigm that offers cloud computation capabilities at the edge of the mobile network. Due to the proximity to Mobile Users (MUs), the edge cloud can be accessed with low latency by resource-limited mobile devices with the aim of extending their capabilities through computation offloading. Despite its unique features, the efficiency of offloading is highly influenced by the purchasing power of MUs as well as the competition arisen to access both limited communication and computation resources. However, since the MUs’ demands for these resources change dynamically, the pricing approaches should be capable of adapting in real-world scenarios. To address the aforementioned challenges, we propose a game-based distributed scheme to jointly and dynamically allocate and price resources required for proper offloading in a two-tier NOMA-based mobile system. To this end, we consider the incentives of both the edge provider and MUs as well as conflicts arising from their interactions. Under the proposed scheme, MUs should pay not only based on the given resources but also based on their transmission power level as a punishment for their produced interference. We formulate the interactions among the monopolist edge provider and MUs using the Stackelberg game. Further, the power allocation in NOMA and the competition of MUs over computation resources are modeled using game theory and shown admitting the potential and the weakly coupled constraint game, respectively. Furthermore, we prove the existence and uniqueness of the equilibrium of both corresponding games and calculate it for both the edge provider and MUs. Simulation results demonstrate the efficiency of the proposed approach in terms of the utility of MUs and the edge provider, average allocated communication and computation resources, optimal pricing, and the number of rejected MUs under different purchasing power.

A joint power and bandwidth allocation method based on deep reinforcement learning for V2V communications in 5G

Vehicular communications have recently attracted great interest due to their potential to improve the intelligence of the transportation system. When maintaining the high reliability and low latency in the vehicle-to-vehicle (V2V) links as well as large capacity in the vehicle-to-infrastructure (V2I) links, it is essential to flexibility allocate the radio resource to satisfy the different requirements in the V2V communication. This paper proposes a new radio resources allocation system for V2V communications based on the proximal strategy optimization method. In this radio resources allocation framework, a vehicle or V2V link that is designed as an agent. And through interacting with the environment, it can learn the optimal policy based on the strategy gradient and make the decision to select the optimal sub-band and the transmitted power level. Because the proposed method can output continuous actions and multi-dimensional actions, it greatly reduces the implementation complexity of large-scale communication scenarios. The simulation results indicate that the allocation method proposed in this paper can meet the latency constraints and the requested capacity of V2V links under the premise of minimizing the interference to vehicle-to-infrastructure communications.

Subsystem of sample processing of active and reactive power of transformer substation

Purpose. Apply statistical analysis to the volumetric samples of electricity consumption parameters to reduce the amount of network node capacity that will be stored. Methodology. Using the statistical data methods processing in the graphical programming system LabVIEW. Findings. Electricity is one of the energy types, the generation volume, transportation, distribution and use of which is constantly growing. At all these stages, the control of the change rate parameters of electrical energy – power occurs. Power is characterized by certain parameters that are subject to continuous monitoring. The change capacity in the conditions of industrial enterprises has a complex character with certain stochastic components. The need to record detailed information causes an increase in the data amount to be stored. As a result, there is a processing data problem on electric energy volumes and electric power parameters with reduction data volume and informativeness preservation. Based on the statistical normal distribution concept, in the graphical programming environment the data processing subsystem on sampling intervals of active and reactive powers of low voltage section the step-down substation is constructed. Using the indicators of the characteristics of the normal distribution of active and reactive power, the analysis of their semi-daily samples was performed. Selected intervals at which the indicators of normal distribution differ significantly, which allowed to form conclusions about the presence of modes close to idling Originality. It is established that due to the difficulty covering long time periods under the fixing condition data on electric power parameters, there is a dilemma regarding the resulting information amount and its detailing, to avoid information loss, a procedure based on the law of normal data sampling is the resulting data to be stored, with the possibility recording significant deviations in terms of excess and sample asymmetry. Practical value. Applying the proposed method in the monitoring system for long-term monitoring electrical power parameters, it is possible to reduce significantly the amount of data during the transmission basic information, power level and range, and, if necessary, use additional information about changes in observation intervals expressed through excess and asymmetry.

A Reinforcement Learning Approach for Interference Management in Heterogeneous Wireless Networks

&lt;span lang="EN-US"&gt;Due to the increased demand for scarce wireless bandwidth, it has become insufficient to serve the network user equipment using macrocell base stations only. Network densification through the addition of low power nodes (picocell) to conventional high power nodes addresses the bandwidth dearth issue, but unfortunately introduces unwanted interference into the network which causes a reduction in throughput. This paper developed a reinforcement learning model that assisted in coordinating interference in a heterogeneous network comprising macro-cell and pico-cell base stations. The learning mechanism was derived based on Q-learning, which consisted of agent, state, action, and reward. The base station was modeled as the agent, while the state represented the condition of the user equipment in terms of Signal to Interference Plus Noise Ratio. The action was represented by the transmission power level and the reward was given in terms of throughput. Simulation results showed that the proposed Q-learning scheme improved the performances of average user equipment throughput in the network. In particular, &lt;/span&gt;&lt;span lang="EN-US"&gt;multi-agent systems with a normal learning rate increased the throughput of associated user equipment by a whooping 212.5% compared to a macrocell-only scheme.&lt;/span&gt;

Transmission power assignment in network-coding-based-multicast-wireless-sensor networks

The advantages of both transmission-power assignment and network-coding-based multicast in wireless sensor networks, in reducing the complexity (medium access control and routing), saving energy, and prolonging network lifetime, are well known. To improve the network efficiency, this paper proposes a Memetic ant colony algorithm that combines the transmission power assignment with the network-coding-based-multicast routing. The proposed algorithm stores the solution in a memory and maintains diversity among the population list to accelerate finding the solutions in the dynamic networks. In the proposed algorithm, energy consumption for receiving, the irregular shape of transmission domain, the discrete transmitting power levels of sensors, and dynamical changes in the network features are of concern. The results of the assessments run through simulations indicate that the proposed algorithm could obtain the optimal or near-optimal satisfactory transmission power levels and multicast routes. In addition, the runtime of proposed algorithm is reduced greatly compared to existing methods.

Fundamental Analysis of Vehicular Light Communications and the Mitigation of Sunlight Noise

Intelligent transport systems (ITS) rely upon the connectivity, cooperation and automation of vehicles aimed at the improvement of safety and efficiency of the transport system. Connectivity, which is a key component for the practical implementation of vehicular light communications (VeLC) systems in ITS, must be carefully studied prior to design and implementation. In this paper, we carry out a performance evaluation study on the use of different vehicle taillights (TLs) as the transmitters in a VeLC system. We show that, the transmission coverage field of view and the link span depend on TLs illumination patterns and the transmit power levels, respectively, which fail to meet the typical communication distances in vehicular environments. This paper proposes an infrared-based VeLC system to meet the transmission range in daytimes under Sunlight noise. We show that, at the forward error correction bit error rate limit of 3.8 ×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> , the communication distances of the proposed link are 63, 72, and > 89 m compared with 4.5, 5.4 and 6.3 m for BMW's vehicle TL at data rates of 10, 6, and 2 Mbps, respectively.

Uplink Power Control Framework Based on Reinforcement Learning for 5G Networks

In this work, we propose an uplink power control (PC) framework compliant with the technical specifications of the fifth generation (5G) wireless networks. We apply the fundamentals of reinforcement learning (RL) to develop a power control algorithm able to learn a strategy that enhances the total data rate on the uplink channel and mitigates the neighbor cell interference. The base station (BS) uses a set of commands to specify by how much the user equipment (UE) transmit power should change. After implementing such commands, each UE reports a set of information to its serving BS, and this, in turn, predicts the next commands to achieve a suitable UE transmit power level. The BS converts the UE reports into rewards according to a predefined cost function, which impacts the longterm behavior of the UE transmit power. The simulation results indicate a near-optimum performance of the proposed framework in terms of total transmit power, total data rate, and network energy efficiency. It provides a self-exploratory power control strategy that overcomes soft dropping power control (SDPC) with similar signaling levels.

Maximum lifetime convergecast tree in wireless sensor networks

We study the problem of building a maximum lifetime data collection tree for periodic convergecast applications in wireless sensor networks. We experimentally observe that if two nodes transmit the same number of data packets, the amount of energy consumption of the nodes is approximately the same even if the payload lengths of the transmitted packets are different. This is because the major energy consumption during a packet transmission arises from radio start-up and medium access control overhead. Our formulated lifetime maximization problem captures the energy expenditure due to message transmissions/ receptions in terms of the number of data packets transmitted/ received, in contrast to prior works, which consider the number of data units (amount of sensor data generated by a node) transmitted/ received. Variable transmission power levels of the radio and accounting for the sensor energy consumption are other factors that make our problem formulation different from those in prior work. We prove that this problem is NP-complete by reducing the set cover problem to it and propose an algorithm to solve it. The performance of the proposed algorithm is experimentally evaluated using Jain’s fairness index as a metric by implementing it on an actual testbed consisting of 20 sensor nodes and compared with those of the widely used shortest path tree and random data collection tree algorithms. The energy consumption of different nodes under the proposed algorithm are shown to be more balanced than under the shortest path tree and random data collection tree algorithms. Also, the performance of the proposed algorithm in large networks is studied through simulations and is compared with those of the state-of-the-art RaSMaLai algorithm, directed acyclic graph algorithm and clustering based OPTIC algorithm, and the shortest path tree, minimum spanning tree, and random tree based data collection schemes. Our simulations show that the proposed algorithm provides a significantly higher network lifetime compared to all the other considered data collection approaches.

MBSO Algorithm For Handling Energy-Throughput Trade-Off In Cognitive Radio Networks

Abstract Cognitive Radio network (CRN) depends on opportunistic spectrum access and spectrum sensing for improving the wireless networks’ spectrum efficiency. Since throughput maximization can result in high-energy consumption, the spectrum sensing technique should address the energy-throughput trade-off. The spectrum sensing time has to be determined by the considering the residual battery energies of each secondary user (SU). The primary user (PU) interference degrades the throughput of the entire network. Hence, the transmit power level should be determined by considering the PU interference and SU battery energy. This paper proposes the multi-objective brain storm optimization (MBSO) algorithm for handling energy-throughput trade-off in CRN. In this work, the sensing time is adaptively determined based on the residual battery energy of SUs, and the transmit power is determined based on the energy level of the PU signal and the residual battery energy of the SUs. A multi-objective optimization problem is formulated in order to maximize the throughput and minimize the packet error rate (PER) and is solved by applying the MBSO algorithm. Experimental results show that MBSO attains improved throughput, higher residual energy with lesser PER. The spectrum sensing performance is enhanced with higher probability of detection.

Fractional Frequency Reuse in Random Hybrid FD/HD Small Cell Networks With Fractional Power Control

Small cells are regarded as a promising solution for improving the coverage and spectral efficiency of the fifth generation (5G) cellular networks. Small cell networks utilize low transmission power levels and thus, are appropriate for deploying full-duplex (FD) technology. In this paper, a hybrid full/half duplex (FD/HD) small cell network exploiting fractional frequency reuse (FFR) and fractional power control (FPC) for interference mitigation is considered. Theoretical expressions for the coverage probability and the average sum-rate in uplink and downlink directions are derived for systems that make use of conventional frequency reuse, hard FFR, and soft FFR. The impact of various system parameters, such as power control parameter, small base station (SBS) density, and self-interference cancellation (SIC) parameters on system performance are investigated. The numerical experiments show that SBSs tend to operate in FD mode for large values of SBS density and SIC level. Also, interestingly, the optimal sum-rate is achieved when all SBSs operate either in FD or in HD mode. Finally, it is observed that exploiting FFR techniques in FD networks making use of FPC, significantly improves downlink coverage probability.

Power Optimization for Spectrum Sharing in Vehicular Networks

The main goal of vehicular communication is to provide a more safe and efficient vehicular operation. The challenge in a Vehicle-to-Everything (V2X) network is to provide reliable connectivity for the Vehicle-to-Vehicle (V2V) links and high data rate connectivity for the Vehicle-to-Infrastructure (V2I) links at the same time. This requirement leads to spectrum sharing in vehicular communication. As the vehicular systems increases, the transmit power levels increases in the environment which in turn causes harmful effects on the atmosphere. The objective of this paper is to analyze the graph-based spectrum sharing algorithms that are available for vehicular communication and to develop a power optimization algorithm based on Hidden Markov Model (HMM) and to incorporate it into these algorithms in such a way to achieve better sum capacity for the V2I links along with a guaranteed reliability for the V2V links.

Low‐cost localization technique for heterogeneous wireless sensor networks

SummaryIn heterogeneous wireless sensor networks (HWSN), the probability‐based node discovery method may result in inaccuracy and time consumption. Also, the adaptive adjustment of transmission range based on overhead and connectivity may result in additional overhead and time. Hence, in this paper, the author proposes to design a low‐cost localization technique using Artificial Bee Colony (ABC) optimization algorithm for HWSN. In this technique, ABC optimization algorithm is applied for deploying the anchor nodes for coverage. Then, the connectivity between the nodes is ensured with minimum transmission power. Since this method involves minimum transmission power level (TPL) and the only minimum set of anchor nodes with strong coverage is involved, it has low‐cost utilizing lower power consumption. By simulation result, it has been shown that the proposed method reduces power consumption leading to low cost.