Transmission Power Control Research Articles

Battery-Level-Triggered Transmit Power Control for Energy Harvesting Communications

We consider a continuous-time energy harvesting (EH) communication system consisting of an EH transmitter with random lifetime and a receiver. We assume that neither the fading channel state nor the EH state is available to the transmitter and the transmitter can only observe its battery level, based on which it adjusts its transmit power. Specifically, we quantize the battery capacity of the transmitter into a number of levels, and whenever the energy in the battery hits a certain level, the EH transmitter updates its transmit power. Our main objective is to find the optimal battery-level-triggered (BLT) control policy to maximize the expected total throughput of the transmitter in its lifetime. We model the system as an extended two-dimensional stochastic fluid model (2D-SFM), and derive the Laplace-Stieltjes Transform (LST) matrices of the imbedded process on the decision time sequence, based on which, we formulate the power control problem as a Markov decision process (MDP). We obtain the BLT control policy and the maximum expected throughput by solving the 2D-SFM induced MDP. We evaluate the performance of the BLT policy by comparing it with the conventional uniform-in-time control policies, and results show that the proposed BLT power control policy provides significantly higher expected throughput under the same total energy consumption and the same average control frequency.

Formula omitted]: Adaptive control of data rate, transmission power, and connection interval in bluetooth low energy

This work investigates run-time parameter control for Bluetooth Low Energy (BLE), based on the latest Bluetooth specification 5 released in 2016. Compared to the previous version, Bluetooth specification 5 provides more options for data rate and transmission power and gives room for adaptive parameter control to improve BLE performance in time-varying wireless environments. To the best of our knowledge, this work is the first systematic study of this regime in the research community. We experimentally study how more parameter options in Bluetooth specification 5 impact BLE performance. Based on the preliminary study, we design AdaptaBLE that dynamically adjusts connection interval, data rate, and transmission power together to satisfy quality of service (QoS) requirements without wasting energy. Through extensive performance evaluation on off-the-shelf BLE chips, we show that AdaptaBLE provides stable performance regardless of wireless environments and reduces QoS failures by 5 times with similar energy consumption compared to the state-of-the-art parameter control scheme for BLE.

An adaptive packets hopping mechanism for transmission line monitoring systems with a long chain topology

To achieve the goals of efficient and safe operation of smart grids, a remote monitoring system for extra-high voltage (EHV) transmission line is required. In Taiwan, most of the towers of EHV transmission systems built by Taiwan Power Company (TPC) are located in remote areas and their line topology resembles a long chain. Therefore, the monitoring network designed for the EHV transmission lines cannot easily gain access to energy sources to power its communication nodes and may become fragmented due to any node failure. In view of the challenge, this study proposes a customized routing scheme for EHV transmission line monitoring, named transmission coverage adjustable multi-hop algorithm (TCAMA). TCAMA not only controls transmission power but also performs route selection to minimize power consumption. This algorithm also creates multiple redundant paths to meet the communication performance requirements. To verify the effectiveness of TCAMA, an Internet of Things-based monitoring system has adopted the proposed TCAMA to monitor an actual TPC EHV transmission system to examine its communication network performance. First, a gateway was developed, and a point-to-point transmission experiment was conducted to evaluate the transmission performance of the gateway with different transmission power and distances. Second, the results of the point-to-point experiment served as the transmission parameters in the network simulation, and 150 simulations were conducted. Then, the simulation results were analyzed to determine whether the data packet transmission performance met the communication requirements of a smart grid. Third, the performance of TCAMA was further examined using the distribution and topology of critical EHV transmission lines in Taiwan. Based on the field test results, TCAMA can prevent packet storms and can perform customized tasks using empirical measurements. In addition, TCAMA is capable of achieving 100% packet delivery while maintaining end-to-end communication delay much less than 2000 ms. The delay and energy consumption during the network transmission can also be reduced when TCAMA is employed.

Energy-Efficient Transmission Rate Selection and Power Control for Relay-Assisted Device-to-Device Communications Underlaying Cellular Networks

Device-to-device (D2D) communication underlaying cellular networks is seen as a promising technology for future communication systems, especially when relays are employed to extend the range of D2D links. However, the mutual interference between the spectrum-sharing D2D links and conventional cellular (CC) links is still the dominating performance limiting factor, which enforces those links to adopt higher transmission power. As energy saving is always one of the key concerns, we propose to minimize the total transmission energy consumption of a relay-assisted D2D link and a CC link sharing radio resources by jointly optimizing their transmission rate selection and power control (TRSPC). We formulate the optimization problem as a non-convex non-linear programming (NLP) problem and then develop a game theory based distributed approach to solve it. Simulation results demonstrate that our game theory based TRSPC approach can enormously decrease the total transmission energy consumption compared with existing works.

PI‐based and anti‐windup transmission power control for WirelessHART field devices

In the last couple of decades, the industry saw an increase in the number of wireless sensor networks (WSNs) in replacement of the old cabled technology due to lower deployment and maintenance costs. To keep this momentum, those industrial WSN (IWSN) must assure robustness, scalability, low power, and low maintenance, and the network protocol stack is an important piece of this puzzle. One great dilemma faced by IWSN protocols is how to sustain reliable radio links while avoiding draining too much energy from the battery. In this context, WirelessHART (WH) and ISA SP100.11a, for example require the hardware of the nodes to provide multiple power levels for transmission. Unfortunately, those protocols do not provide a standard procedure to choose between those levels. A simple but inefficient solution often employed simply chooses the highest level available. Seeking a better solution, this proposal presents a transmission power control (TPC) methodology that allows energy saving while sustaining the quality of the communication link employing a simple proportional–integral (PI) controller plus anti-windup. The proposed approach does not rely on special TPC-related packets, which is advantageous in comparison with other approaches from the literature by allowing this work to be fully compliant with existing IWSN protocols.

Design and Simulation of Spectrum Access and Power Management Protocol for Dynamic Access Networks

This work investigates the problem of managing the transmission power and assigning channels for multi-channel single-radio Cognitive Radio Ad-Hoc Networks (CRAHNs). The considered network consists of M primary users and N secondary users, where the secondary users can use the licensed channels opportunistically when they are not utilized by the primary users. The secondary users have the capability of sensing the licensed channels and determine their occupation status. They are also able to control their transmission power such that the transmitted data can be received with high quality-of-service with the lowest possible transmission power, and minimum interference among the secondary users. This also contributes in increasing the frequency spatial reuse of the licensed channels by the secondary users, when the channels are unoccupied, which increases the network throughput. This work proposes a channel assignment algorithm aims at assigning the unoccupied licensed channels among secondary users efficiently, and a transmission power control aims at tuning the transmission power used by the secondary users to maximize the network throughput. The results show an enhancement achieved by the proposed protocol when it is integrated to the considered network, which is seen through increasing the network throughput and decreasing in the access delay. In this context, the Network Simulator 2 (NS2) was used to verify our proposed protocol, which indicates a significant enhancement in network performance

Energy-Efficient Computation Offloading in Delay-Constrained Massive MIMO Enabled Edge Network Using Data Partitioning

We study a wireless edge-computing system which allows multiple users to simultaneously offload computation-intensive tasks to multiple massive-MIMO access points, each with a collocated multi-access edge computing (MEC) server. Massive-MIMO enables simultaneous uplink transmissions from all users, significantly shortening the data offloading time compared to sequential protocols, and makes the three phases of data offloading, computing, and downloading have comparable durations. Based on this three-phase structure, we formulate a novel problem to minimize a weighted sum of the energy consumption at both the users and the MEC server under a round-trip latency constraint, using a combination of data partitioning, transmit power control and CPU frequency scaling at both the user and server ends. We design a novel nested algorithm consisting of an inner primal-dual algorithm and an outer latency-aware descent algorithm to solve this problem efficiently. Optimized solutions show that for larger requests, more data is offloaded to the MECs to reduce local computation time in order to meet the latency constraint, despite higher energy cost of wireless transmissions. Massive-MIMO channel estimation errors under pilot contamination also causes more data to be offloaded to the MECs. Compared to binary offloading, partial offloading with data partitioning is superior and leads to significant reduction in the overall energy consumption.

On the Lifetime of Asynchronous Software-Defined Wireless Sensor Networks

In this article, we consider a software-defined wireless sensor network (WSN) architecture which conserves energy by applying asynchronous duty cycling. In asynchronous sensor networks, the overhearing adversely impacts the energy consumption of the nodes. Using a mathematical model, we compute the maximum lifetime of the network and accordingly propose a multichannel operation and transmit power control to reduce the effect of overhearing in asynchronous networks. Our comprehensive test results confirm that for the network with load-aware nonuniform sensor node deployment, the network lifetime is much higher compared to a uniform deployment. We also show that the use of data aggregation software-defined WSNs (SDWSNs) can improve the network lifetime as compared to the data gathering networks.

Outage-Optimal and Suboptimal Power Control for D2D Communications in SWIPT Cellular Networks With Local CSI

In this letter, we consider a downlink cellular network where a cellular user (CU) not only decodes information but also harvests energy. By reusing the spectrum resource of the CU, a device-to-device (D2D) transmitter sends its own signals to a D2D receiver. In order to protect the CU from the interference due to the D2D signals, we propose transmit power control schemes for the D2D transmission without increasing the outage probability of the CU. This is possible since the CU’s information loss due to the D2D signals can be compensated by the CU’s energy harvesting gain from the D2D signals. For asymptotic performance analysis, we provide a lower bound on the D2D outage probability. The numerical results show that the proposed schemes outperform the conventional scheme.

Adaptive Body Area Networks Using Kinematics and Biosignals

The increasing penetration of wearable and implantable devices necessitates energy-efficient and robust ways of connecting them to each other and to the cloud. However, the wireless channel around the human body poses unique challenges such as a high and variable path-loss caused by frequent changes in the relative node positions as well as the surrounding environment. An adaptive wireless body area network (WBAN) scheme is presented that reconfigures the network by learning from body kinematics and biosignals. It has very low overhead since these signals are already captured by the WBAN sensor nodes to support their basic functionality. Periodic channel fluctuations in activities like walking can be exploited by reusing accelerometer data and scheduling packet transmissions at optimal times. Network states can be predicted based on changes in observed biosignals to reconfigure the network parameters in real time. A realistic body channel emulator that evaluates the path-loss for everyday human activities was developed to assess the efficacy of the proposed techniques. Simulation results show up to 41% improvement in packet delivery ratio (PDR) and up to 27% reduction in power consumption by intelligent scheduling at lower transmission power levels. Moreover, experimental results on a custom test-bed demonstrate an average PDR increase of 20% and 18% when using our adaptive EMG- and heart-rate-based transmission power control methods, respectively. The channel emulator and simulation code is made publicly available at https://github.com/a-moin/wban-pathloss.

MEC-Assisted Immersive VR Video Streaming Over Terahertz Wireless Networks: A Deep Reinforcement Learning Approach

Immersive virtual reality (VR) video is becoming increasingly popular owing to its enhanced immersive experience. To enjoy ultrahigh resolution immersive VR video with wireless user equipments, such as head-mounted displays (HMDs), ultralow-latency viewport rendering, and data transmission are the core prerequisites, which could not be achieved without a huge bandwidth and superior processing capabilities. Besides, potentially very high energy consumption at the HMD may impede the rapid development of wireless panoramic VR video. Multiaccess edge computing (MEC) has emerged as a promising technology to reduce both the task processing latency and the energy consumption for HMD, while bandwidth-rich terahertz (THz) communication is expected to enable ultrahigh-speed wireless data transmission. In this article, we propose to minimize the long-term energy consumption of a THz wireless access-based MEC system for high quality immersive VR video services support by jointly optimizing the viewport rendering offloading and downlink transmit power control. Considering the time-varying nature of wireless channel conditions, we propose a deep reinforcement learning-based approach to learn the optimal viewport rendering offloading and transmit power control policies and an asynchronous advantage actor–critic (A3C)-based joint optimization algorithm is proposed. The simulation results demonstrate that the proposed algorithm converges fast under different learning rates, and outperforms existing algorithms in terms of minimized energy consumption and maximized reward.

Low-Cost Single-Switch Bidirectional Wireless Power Transceiver for Peer-to-Peer Charging

A single-switch bidirectional wireless power transceiver aiming at cost-effective and reliable peer-to-peer charging applications is proposed. The transceiver comprises a single-switch resonant power circuit, a frequency synchronization circuit, and a microcontroller. It can operate in either transmitter mode or receiver mode. As a transceiver, the required number of semiconductor devices is minimized, i.e., only single active switch is used. This makes it easy to implement and is relatively reliable and cost-effective. The bidirectional power flow and dc output regulation are achieved solely by adjusting the phase shift ratio of the control signal. To make the phase shift power control feasible and to secure wide operating range ZVS operation, a holistic design method is provided such that the ac voltage waveforms remain relatively constant and independent of the coupling and the phase shift ratio changes. The operating principles, steady-state and dynamic models, and design considerations are discussed. Simulation and experiments are performed on a prototype based on the design. The results validate the features of relatively constant ac voltage waveforms of the transceiver, the effectiveness of the time-domain model, the ZVS turn on and turn off operations over wide operating power range, phase shift regulated transmission power control, bidirectional power flow, and accurate output regulation.

Task offloading, load balancing, and resource allocation in MEC networks

To prolong the time duration of smart mobile devices (SMDs) or enable low-latency tasks, mobile edge computing (MEC) has emerged as a promising paradigm by offloading tasks to nearby MEC servers (MECSs). In this study the authors propose an optimisation problem to minimise the weighted sum of the total delay and energy consumption of all SMDs in a multi-MECS-multi-SMD network via multi-dimensional optimisation on offloading strategy making, load balancing, computation resource allocation and transmit power control. Since the problem is NP-hard, the authors decompose it into three subproblems to solve. First, they propose a low complexity heuristic algorithm to obtain the offloading strategies while guaranteeing load balancing between the multiple MECSs. Then they solve computation resource allocation subproblem using Lagrange dual decomposition. Finally, employing fractional programming, the authors transform the transmit power control subproblem into a convex programming problem where the closed-form solution is obtained. The proposed simulation results verify the convergence of the proposed iterative algorithms, and demonstrate that the proposed joint optimisation could achieve good performance in both delay and energy reduction.

Deep-Reinforcement-Learning-Based Energy-Efficient Resource Management for Social and Cognitive Internet of Things

Internet of Things (IoT) has attracted much interest due to its wide applications, such as smart city, manufacturing, transportation, and healthcare. Social and cognitive IoT is capable of exploiting social networking characteristics to optimize network performance. Considering the fact that the IoT devices have different Quality-of-Service (QoS) requirements [ranging from ultrareliable and low-latency communications (URLLCs) to minimum data rate], this article presents a QoS-driven social-aware-enhanced device-to-device (D2D) communication network model for social and cognitive IoT by utilizing social orientation information. We model the optimization problem as a multiagent reinforcement learning formulation, and a novel coordinated multiagent deep-reinforcement-learning-based resource management approach is proposed to optimize the joint radio block assignment and the transmission power control strategy. Meanwhile, the prioritized experience replay (PER) and the coordinated learning mechanisms are employed to enable communication links to work cooperatively in a distributed manner, which enhances the network performance and access success probability. The simulation results corroborate the superiority in the performance of the presented resource management approach, and it outperforms other existing approaches in terms of meeting the energy efficiency and the QoS requirements.

Dynamic power optimization for secondary wearable biosensors in e-healthcare leveraging cognitive WBSNs with imperfect spectrum sensing

The integration of cognitive radio with e-healthcare systems assisted by wireless body sensor networks (WBSNs) has been regarded as an enabling approach for a new generation of pervasive healthcare services, to provide differentiated quality of service requirements and avoid harmful electromagnetic interference to primary medical devices (PMDs) over the crowded radio spectrum. Due to the sharing spectrum bands with PMDs in e-healthcare scenario using cognitive WBSNs (CWBSNs), efficient transmit power control and optimization strategies for resource-constrained secondary wearable biosensors (SWBs) play a key role in controlling the inter-network interference and improving the energy efficiency. This paper investigates the problem of dynamic power optimization for SWBs in e-healthcare leveraging CWBSNs with practical limitations, e.g., imperfect spectrum sensing and quality of physiological data sampling. We develop a distributed optimization framework of dynamic power optimization via the theory of differential game, by jointly considering utility maximization and quality of physiological data sampling for every SWB, while satisfying the evolution law of energy consumption in SWB’s battery. With the non-cooperation and cooperation relations for all SWBs in mind, we transform the differential game model into two subproblems, namely, utility maximization problem and total utility maximization problem. Utilizing Bellman’s dynamic programming, we derive a non-cooperative optimal solution for power optimization as a Nash equilibrium point for the utility maximization problem posed by competitive scenario. By exploiting Pontryagin’s maximum principle, a cooperative optimal solution is obtained for the total utility maximization problem, wherein all SWBs fully cooperate to obtain the highest total utilities. Building upon the analytical results, the actual utility distributed to each SWB is compared between the non-cooperative and cooperative schemes. Extensive simulations show that the proposed optimization framework is indeed an efficient and practical solution for power control compared with the benchmark algorithm.

Joint 3D Maneuver and Power Adaptation for Secure UAV Communication With CoMP Reception

This paper studies a secrecy unmanned aerial vehicle (UAV) communication system with coordinated multi-point (CoMP) reception, in which one UAV sends confidential messages to a set of cooperative ground receivers (GRs), in the presence of several suspicious eavesdroppers. In particular, we consider two types of eavesdroppers that are non-colluding and colluding, respectively. Under this setup, we exploit the UAV's maneuver in three dimensional (3D) space together with transmit power adaptation for optimizing the secrecy communication performance. First, we consider the quasi-stationary UAV scenario, in which the UAV is placed at a fixed but optimizable location during the communication period. In this scenario, we jointly optimize the UAV's 3D placement and transmit power control to maximize the secrecy rate. Under both non-colluding and colluding eavesdroppers, we obtain the optimal solutions to the joint 3D placement and transmit power control problems in well structures. Next, we consider the mobile UAV scenario, in which the UAV has a mission to fly from an initial location to a final location during the communication period. In this scenario, we jointly optimize the UAV's 3D trajectory and transmit power allocation to maximize the average secrecy rate during the whole communication period. To deal with the difficult joint 3D trajectory and transmit power allocation problems, we present alternating-optimization-based approaches to obtain high-quality solutions. Finally, we provide numerical results to validate the performance of our proposed designs. It is shown that due to the consideration of CoMP reception, our proposed design with 3D maneuver significantly outperforms the conventional design with two dimensional (2D) (horizontal) maneuver only, by exploiting the additional degrees of freedom in altitudes. It is also shown that the non-colluding and colluding eavesdroppers lead to distinct 3D UAV maneuver behaviors, e.g., under colluding eavesdroppers, the UAV should fly farther apart from them (than that under the non-colluding ones) for avoiding their collaborative interception.

Different Energy Saving Schemes in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) are one of the very active research area. They have many applications like military, health care, environmental monitoring and industrial monitoring. The sensor nodes have limited energy source. Since in many cases the nodes are deployed in unreachable areas, hence recharging or replacing the battery of the sensor nodes is not an option. Therefore, one must employ techniques to conserve the energy by reducing the energy consumption by the nodes. In this paper, we discuss about the different energy saving schemes investigated by different research community in WSNs to reduce the energy consumption of the nodes and thereby improving the lifetime of the overall network. Energy saving protocols such as duty cycle, energy efficient routing, energy efficient medium access control (MAC), data aggregation, cross layer design and error control code (ECC) are discussed. Sleep/wake up method is adopted by the duty cycle approach to reduce the active time of the nodes and conserve their energy. The routing and MAC protocols use suitable energy efficient algorithms for saving energy. The data aggregation aims to save energy by reducing the number of transmissions. On the other side, cross layer approach looks for a cross layer optimization solution to improve the energy efficiency of the network. ECC reduces energy consumption by virtue of coding gain it offers which allows lower signal-to-noise ratio (SNR) to achieve the same bit error rate (BER) as an uncoded system. Some techniques such as use of directional antennas, topology control and transmission power control which have been widely investigated for other ad-hoc networks for energy conservation are also discussed in brief in this paper.

A behaviour-based approach to the assessment of cue utilisation: implications for situation assessment and performance

Although the utilisation of cues is widely recognised as essential for skilled situation assessment, it is a construct that is difficult to evaluate due to the idiosyncratic and nonconscious nature of cues. This raises risks for high consequence organisations where there is an expectation that operators are capable of accurate and timely situation assessment. The objective of this paper was to articulate the development and evaluation of novel, behaviour-based approach to the assessment of cue utilisation. Explorations that are based on this approach have yielded consistent outcomes in differentiating higher from lower performance across a range of operational domains, including transmission power control, paediatric intensive care, audiology, and aviation. Opportunities are proposed for further research to explain the inherent cognitive mechanisms involved and better model the performance of operators under a range of environmental conditions.

V2X offloading and resource allocation in SDN-assisted MEC-based vehicular networks

As an important application scenario of 5G, the vehicular network has a huge amount of computing data, which brings challenges to the scarce network resources. Mobile edge computing (MEC) sinks cloud services to the edge of network, which reduces the delay jitter caused by remote cloud computing. Software-defined networking (SDN) is an emerging network paradigm with the features of logic centralized control and programmability. In this paper, we construct an SDN-assisted MEC network architecture for the vehicular network. By introducing SDN controller, the efficiency and flexibility of vehicular network are improved, and the network state can be perceived from the global perspective. To further reduce the system overhead, the problem of vehicle to everything (V2X) offloading and resource allocation is proposed, where the optimal offloading decision, transmission power control, subchannels assignment, and computing resource allocation scheme are given. The optimization problem is transformed into three stages because of the heterogeneity of the offloaded tasks and the NP-hard property of the problem. Firstly, the analytic hierarchy process is used to select initial offloading node, then stateless Q-learning is adopted to allocate transmission power, subchannels and computing resources. In addition, the offloading decision is modeled as a potential game, and the Nash equilibrium is proved by the potential function construction. Finally, the numerical results show that the proposed mechanism can effectively reduce the system overhead and achieve better results compared with others' algorithms.

Energy Harvesting Powered Sensing in IoT: Timeliness Versus Distortion

We consider an Internet of Things (IoT)-based sensing system, in which an energy harvesting powered sensor observes the phenomenon of interest and transmits its observations to a remote monitor through a Gaussian channel. Based on the received signals, the monitor makes estimations of source signals with some distortion requirement. We measure the timeliness of the recovered signals using the Age of Information (AoI), which could be reduced by transmitting observations more frequently (i.e., with shorter intervals). We evaluate the recovery distortion with the mean-squared-error (MSE) metric, which would be reduced if a larger transmit power and a larger source coding rate were used. Since the energy harvested by the sensor is quite limited, however, the frequency and power of transmissions cannot be increased at the same time. Thus, we shall investigate the timeliness-distortion tradeoff of the system by minimizing the average weighted sum AoI and distortion over all possible transmit powers and transmission intervals. First, we explicitly present the optimal transmit powers for the performance limit achieving save-and-transmit policy and the easy-implementing fixed power transmission policy. Second, we investigate the offline optimization of the system and propose a backward water-filling-based power allocation scheme, as well as a genetic-based joint transmission scheduling and power control algorithm. Third, we formulate the online power control as a Markov decision process (MDP) and solve the problem with an iterative algorithm, which closely approaches the tradeoff limit of the system. We show that the optimal transmit power is a monotonic and bivalued function of current AoI and distortion. Finally, we present our results via simulations and extend the results on the save-and-transmit policy to fading sensing systems.