Transmission Power Control Research Articles

Rechargeable Multi-UAV Aided Seamless Coverage for QoS-Guaranteed IoT Networks

Due to their high flexibility, high maneuverability, and line-of-sight (LOS) predominant channel, unmanned aerial vehicles (UAVs) serving as flying base stations have received a lot of interest in emerging Internet of Things (IoT) networks. This article studies the energy-efficient cooperative strategy of rechargeable multi-UAVs for providing seamless coverage and long-term information services for IoT nodes. Considering the limited cruising duration of the UAV, multiple rechargeable UAVs are capable of constructing a closed chain for the sake of alternately supporting IoT nodes. Moreover, a joint IoT node assignment and UAV configuration optimization problem is proposed in order to maximize the energy efficiency of the system. Since the proposed problem is a mixed-integer nonconvex problem, we divide it into three subproblems, namely, node assignment scheduling, UAV trajectory planning, and transmit power control. By exploiting sequential convex optimization techniques, we reformulate the nonconvex subproblems into three convex optimization problems which can be solved within the polynomial time. A block coordinate descent-based iterative algorithm is proposed for solving these energy-efficiency oriented subproblems. Finally, the simulation results corroborate the effectiveness of our proposed method.

Receiver power allocation and transmitter power control analysis for multiple-receiver wireless power transfer systems

As different power has its own receivers, this paper analyzes and designs a multiple-receiver wireless power transfer (WPT) system systematically. The equivalent circuit model of the system is established to analyze the key parameters including transmitter power, receiver power, transmission efficiency, and each receiver power allocation. A control circuit is proposed to achieve the maximum transmission efficiency and transmitter power control and arbitrary receiver power allocation ratios for different receivers. Through the proposed control circuit, receivers with different loads can allocate appropriate power according to its power demand, the transmitter power and system efficiency do not vary with the change of the number of receivers. Finally, this control circuit is validated using a 130-kHz WPT system with three receivers whose power received is 3:10:12, and the overall system efficiency can reach as high as 55.5%.

Machine Learning-Based Multi-Layer Multi-Hop Transmission Scheme for Dense Networks

Multi-hop communication has attracted a lot of attention recently due to its ability to extend the coverage range and to overcome blockage. In this letter, we propose a machine learning-based selection approach that adaptively chooses the best forwarding scheme in hybrid multi-hop dense networks. The proposed transmission scheme employs a multi-layer selection where each layer represents one possible relaying case, namely amplify-and-forward, half-detection, or full-detection of the transmitted symbol, or even no-relaying. Moreover, the proposed system dynamically learns the proper forwarding scheme out of these layers for each involved relay to minimize the transmission error rate based on the relay location, and its residual energy. A heuristic approach is proposed for the forwarding scheme selection and transmission power control where a minimum threshold value for the transmission power of each relay node is derived in order to satisfy a target QoS requirement. The results are used for training a decision trees-based prediction model that achieves a remarkable accuracy beyond the 99% for both training and testing patterns.

Analysis of Data Rate and Transmission Power Hybrid Control in WSN IoT

The relationship between data rate and transmission power is analyzed in this paper. Moreover, an algorithmcombined with adaptive data rate (ADR) and transmission power control (TPC) is proposedin this paper, and it improves battery life significantly on IoT devices. In the premise that withoutprejudicing the accuracy of wireless transmission, the faster data rate and lower transmission powercan be used to reduce power consumption. The results of a longtime test show that the nodes withthe proposed algorithm not only achieve power saving when good transmitting environment, but alsoaccomplish stable communication in the harsh environment. In the best case, compared with the noalgorithmnodes, the nodes with the ADR and TPC algorithms can be reduced power consumptionup to 50%.

Secure Communication in Relay-Assisted Massive MIMO Downlink With Active Pilot Attacks

In this paper, the achievable secrecy rate of a relay-assisted massive multiple-input multiple-output (MIMO) downlink is investigated in the presence of a multi-antenna active/passive eavesdropper. The excess degrees-of-freedom offered by a massive MIMO base-station (BS) are exploited for sending artificial noise (AN) via random and null-space precoders. An active eavesdropper contaminates the uplink channel estimates by sending pilot sequences identical to those of the legitimate users/relay. This active pilot contamination makes the massive MIMO BS implicitly beamform the confidential signals toward the active eavesdropper during two-hop downlink transmissions. The achievable secrecy rates are derived by taking the detrimental effects of actively contaminated channel state information with estimation errors and spatially correlated fading at the multiple-antenna terminals into account. The secrecy rate loss incurred by active pilot attacks over passive eavesdropping is investigated, and the secrecy rate gap between random and null-space-based AN is compared. A novel transmit power control policy is designed to efficiently allocate transmit power at the BS/relay for payload data and AN sequences for maximizing the achievable secrecy rate. Our results reveal that active pilot contamination attacks significantly degrade the achievable secrecy rate in dual-hop transmissions, and the corresponding detrimental effects cannot be asymptotically mitigated in the infinite BS antenna regime.

Game Theory on Power Control in Wireless Sensor Networks Based on Successive Interference Cancellation

In infrastructure-less wireless sensor networks, with the requirements of saving energy and improving the network throughput, this paper proposes a non-cooperative game theory power control strategy based on CDMA wireless sensor networks. Based on (successive interference cancellation, SIC) of multi-packet reception theory in sink nodes, transmission power control scheme for sensor nodes is proposed. Specifically, this paper combines SIC algorithm and non-cooperative game theory, it discusses the Nash Equilibrium for the power control strategy, and it also proposes that as long as transmission power of sensor nodes meet transmission power threshold, it can satisfy non-cooperative game theory power control. Compared with other power control strategies in WSN, simulation results prove that the non-cooperative game theory power control scheme is effective; it also saves sensor nodes’ energy, obtains good performance for the whole network and greatly improves the network lifetime.

Adaptive Power Control for D2D Communications in Downlink SWIPT Networks With Partial CSI

In this letter, we consider the device-to-device (D2D) communication in downlink cellular networks, where the cellular user (CU) both decodes information and harvests the energy. Unlike the conventional D2D communications, we figure out that it is possible for the D2D pair to communicate without degrading the performance of the CU. Based on this observation, D2D transmit power control schemes are proposed for D2D rate maximization subject to maintaining the CU performance. In a decentralized way, the proposed power control schemes are performed at the D2D transmitter (DT) with instantaneous channel state information (CSI) of only the link between the D2D pair. For performance analysis, an asymptotic upper bound is provided on the D2D rates of the proposed schemes. Numerical results confirm the effectiveness of the proposed schemes.

Sealed-bid Auction based Energy Efficient Route Selection in Mobile Ad-Hoc Network

In MANET (Mobile Ad-hoc Network) is an unstructured network. The nodes are moving any directions and frequently change its locations. So the nodes facing an energy issues. There are many energy efficient routing techniques in MANET. An energy efficient routing mainly classified transmission power control, load balancing, power save and energy efficient designed algorithms or techniques. In this paper focused on sealed-bid Auction based energy efficient route selection in MANET. This technique give a best Packet Deliver ratio and power consumption during the transmission time.

Deep Learning Based Transmit Power Control in Underlaid Device-to-Device Communication

In this paper, a means of transmit power control for underlaid device-to-device (D2D) communication is proposed based on deep learning technology. In the proposed scheme, the transmit power of D2D user equipment (DUE) is autonomously learned via a deep neural network such that the weighted sum rate (WSR) of DUEs can be maximized by considering the interference from cellular user equipment. Unlike conventional transmit power control schemes in which complex optimization problems have to be solved in an iterative manner, which possibly requires long computation time, in our proposed scheme the transmit power can be determined with a relatively low computation time. Through simulations, we confirm that the proposed scheme achieves a sufficiently high WSR with a sufficiently low computation time.

Wireless Information and Power Transfer in Relay-Assisted Downlink Massive MIMO

The feasibility of utilizing a relay-assisted downlink massive multiple-input multiple-output (MIMO) to boost the performance of simultaneous wireless information and power transfer (SWIPT) is investigated. The key idea is to simultaneously transmit power and information toward the direct/relayed-users by leveraging the excess degrees-of-freedom offered by massive antenna arrays at the base-station (BS). The performance limits are established by deriving the harvested energies, achievable rates, and energy-rate trade-offs in the presence of imperfectly estimated/partial channel state information (CSI). These performance metrics are derived for a generalized SWIPT model, which facilitates analysis for both time-switching (TS) and power-splitting (PS) protocols. Two linear precoders based on zero-forcing and maximal ratio transmission are used at the massive MIMO BS, and a practically viable non-linear energy harvesting model is adopted for the direct/relayed-users. Joint detrimental implications of imperfect CSI at the BS, limited/statistical CSI at the users, and non-linear characteristics of energy harvesters are analytically quantified. In order to jointly guarantee user-fairness in terms of both energy and rate, a max-min optimal transmit power control policy is proposed. Then, the max-min fairness optimal energy-rate trade-offs are quantified for non-linear massive MIMO SWIPT. Our results reveal that the proposed relay-assisted downlink massive MIMO can be exploited to boost the achievable energy-rate trade-off by effectively reducing the end-to-end path-loss via shorter hop distances rendered by intermediate amplify-and-forward relays.

Analysis of the effectiveness of transmission power control as a location privacy technique

Location privacy is an increasing source of concern for wireless users since their positions can be unwittingly estimated by malicious eavesdroppers. Transmission power control (TPC) is one of the various obfuscation techniques mentioned in the literature with the potential to provide location privacy to wireless users. This technique consists of letting the user vary the mobile node’s transmission power in a way that only the nearest eavesdropper can overhear the mobile node’s signals. This variation reduces the number of eavesdroppers overhearing the mobile node’s transmissions, thus increasing the location error estimated by nearby eavesdroppers, thereby improving the mobile user’s location privacy. Although some works have highlighted the advantages of TPC as a location privacy technique, no previous work has studied its effectiveness considering wireless channel impairments, hardware limitations, and localization algorithms used by eavesdroppers. This paper analyzes the real value of using TPC as a location privacy technique through a probabilistic model that measures the ability of TPC to effectively reduce the number of overhearing eavesdroppers. The results presented in this work show that the effectiveness of TPC is considerably affected by wireless channel impairments as well as by eavesdroppers’ density. Moreover, since off-the-shelf 802.11 radios have limited transmission power levels, real test-bed experiments showed that mobile users cannot always adjust their transmission power to fulfill the required levels of TPC. This is particularly the case in densely deployed scenarios in which most of the time the required transmission power is below the radio’s minimum transmission level. These results demonstrate the limited location privacy capabilities of TPC in most real-life scenarios, thus disproving previous claims that place TPC as a solution for the location privacy problem.

Deep Learning Aided Transmit Power Estimation in Mobile Communication System

This letter investigates the problem of transmit power control in a mobile communication system. We propose a transmit power estimation scheme to maximize the overall system capacity where the transmit power control of the user is investigated using a recurrent neural network. This leads to a reduction in the need for generating a massive overhead with pilot signals in a dense cellular network. The gains obtained from the simulations are quantified in terms of the mean square error and overall system capacity. Our proposed scheme outperforms the conventional power control technique and other neural networks.

Adaptive Transmit Power Control Algorithm for Sensing-Based Semi-Persistent Scheduling in C-V2X Mode 4 Communication

For cellular-based vehicle-to-everything (C-V2X) communication, vital information about status and intention is periodically broadcasted by each vehicle using the cooperative awareness message (CAM) service. In C-V2X, the task of resource allocation can either be carried out in a centralized manner by the network, termed Mode 3, or by the vehicles themselves in a distributed manner without any core network support, termed Mode 4. Mode 4 scheduling is accomplished by employing sensing-based semi-persistent scheduling (SB-SPS), where the vehicles sense the medium and identify the best time-frequency resource combination for transmission of the CAM. Focusing on Mode 4 in this paper, we present a comprehensive analysis of the impact of variations in the transmit power of the vehicle on the performance of SB-SPS for C-V2X communications in various traffic scenarios through simulations. An adaptive-transmit power control (A-TPC) algorithm is presented to improve the quality of service for various large-scale traffic scenarios, where each vehicle uses real-time channel-sensing information to adjust the transmit power in order to avoid interference with neighbouring vehicles. The results demonstrate that our proposed algorithm outperforms the conventional TPC schemes.

Inter-Tenant Resource Sharing and Power Allocation in 5G Virtual Networks

Recently, the concept of network virtualization and network slicing attracted significant attention from both industry and academia as a key component of the evolving 5G architecture to allow the efficient entrance of vertical industries and tackle increased aggregate traffic by flexible network re-configurability. However, the potential price to be paid for facilitating network slicing in a multi-tenant virtual network is the underutilization of the scarce wireless network resources due to the different tenant requirements and the inherent dynamics of the traffic. A potential way to avoid such sacrifice of radio resources is to allow efficient inter-tenant resource sharing. To this end, this paper proposes a novel optimization framework for flexible inter-tenant resource sharing embedded with transmission power control to aggressively improve network capacity, the utilization of wireless access resources, user data rate as well as energy efficiency. More specifically, we define two novel resource sharing mechanisms called tight coupling and loose coupling, respectively, via mixed integer linear programming formulations. Furthermore, two resource and power joint allocation algorithms are designed to solve the optimization problem in polynomial time. Based on 3GPP network parameterization, a rigorous analysis via a wide set of numerical investigations reveal that significant gains in network throughput, individual user rate, and energy efficiency can be achieved compared with current baseline network slicing methods and constant power resource sharing algorithms.

Enhancing QoS in 5th generation Het-Net via synergistic TVWS spectrum sharing for distributed adaptive small cells

5G mobile network is expected to support an exploding traffic demand by densifying the network with heterogeneous deployment of multiple cells. Such dense network in LTE band results in spectrum scarcity and leads to high system interference. This brought in the necessity to incorporate Dynamic Spectrum Access (DSA) to additional bands for communication. The possibilities of utilizing TV White Spaces (TVWS) in cellular communication is recently considered by many researchers. However, considering the spatio-temporal variability and quality of available channels among these white spaces, there exists certain design challenges. In this paper, we present a solution to the spectrum shortage for 5G, by making the small cells in Heterogeneous Network (Het-Net) operate in TVWS. A channel detection model is developed, for identifying the available TVWS, by considering both channel sensing and geo-location database access. This improves the system accuracy. Further, a synergistic algorithm is developed, that assigns sub channels to mobile users by determining an optimum transmission power level for every mobile user on its allocated sub channel, such that it satisfies the required interference and minimum data rate constraints. The problem of jointly selecting the optimal sub channel as well as the transmission power level is formulated as a Mixed Integer Non-Linear Programming problem. The formulated problem is proved to be NP Hard, which motivated the utilization of a numerical heuristic approach to solve the problem. A Geometric WaterFilling algorithm with Sub Channel Peak Power and Multi-user Interference Constraint (GWSPI) is developed, that solves the problem in reduced time, while achieving optimal results. A detailed complexity analysis and extensive simulation analysis of the proposed method is executed to prove its efficiency compared to some standard approaches. The proposed approach shows improvement in terms of interference management and transmission power control, giving a near optimal result in real life scenario.

Joint Optimization of Scheduling and Power Control in Wireless Networks: Multi-Dimensional Modeling and Decomposition

The energy efficiency of future networks is becoming a significant and urgent issue, calling for greener network designs. However, the increasing complexity in network structure and resource space lead to growing problem scales and coupled resource dimensions, which bring great challenges in obtaining a joint solution in optimizing the energy efficiency. In this paper, we develop a multi-dimensional network model on the basis of tuple-links associated with transmission patterns (TPs) and formulate the optimization problem as a TP based scheduling problem which jointly solves transmission scheduling, routing, power control, radio, and channel assignment. In order to tackle the complexity issues, we propose a novel algorithm by exploiting the delay column generation technique to decompose the coupled problem into recursively solving a master problem for scheduling and a sub-problem for power allocation. Further, we theoretically prove that the performance gap between the proposed algorithm and the optimum is upper bounded by that for the sub-problem solution, where the latter is derived by solving a relaxed version of the sub-problem. Numerical results demonstrate the effectiveness of the multi-dimensional framework and the benefit of the proposed joint optimization in improving network energy efficiency.

Packet delivery ratio and energy consumption in multicast delay tolerant MANETs with power control

The packet delivery ratio and energy consumption serve as important performance metrics for delay tolerant mobile ad hoc networks (MANETs), where there are not continuous end-to-end paths between mobile nodes. Available these performance studies of MANETs mainly consider a unicast scenario, i.e., one source node has only one destination node, which cannot be applied to evaluate the performance of such networks under a multicast scenario, i.e, one source node has multiple destination nodes. Different from previous studies, the paper investigates the packet delivery ratio and energy consumption under multicast scenario with careful consideration of transmission power control issue for each node. To this end, we adopt a general two-hop relay RT-(f, d, τ, w) algorithm with redundancy factor f, multicast scale d, packet lifetime τ and power control parameter w for packet routing, where a packet at source node can be delivered to up to f different relay nodes, and each of d destination nodes may receive the packet from these relay nodes or the source node before the packet lifetime τ expires. In MANETs, each scenario has a fixed power control parameter w, which is the same for all nodes. Then we develop a Markov chain framework to depict the packet propagation process under the RT-(f, d, τ, w) algorithm. Based on this framework, we derive two analytical expressions for packet delivery ratio and energy consumption. Finally, simulation and numerical results are provided to validate our theoretical analysis and to explore how the network parameters affect the packet delivery ratio and energy consumption performance.

On the Lifetime of Compressive Sensing Based Energy Harvesting in Underwater Sensor Networks

Recently, there has been a growing interest in academia and industry on the development of underwater acoustic sensor networks (UASNs) for scientific, commercial, and military purposes. Severe underwater channel conditions and limited battery energy of underwater nodes pose great challenges to prolong UASNs lifetime. Compressive sensing (CS), energy harvesting (EH), and transmission power control (TPC) are three promising solutions to improve UASNs lifetime. This paper aims to quantitatively investigate the joint impact of CS, EH, and TPC methods on the lifetime of UASNs. A novel Mixed Integer Programming framework is developed to maximize the network lifetime by joint consideration of CS, EH, and TPC. The performance results show that the impact of CS on the network lifetime is higher than that of EH when both methods are combined with TPC. Moreover, when all three methods are combined, the network lifetime can be extended up to three times as compared to the case when all three methods are not utilized.

Link Scheduling in Wireless Networks With RF Energy Harvesting Nodes

In a wireless network, a scheduler ensures links are given frequent transmission opportunities. A short schedule means links transmit frequently and thus the resulting network capacity is high. In this paper, we consider a novel aim: deriving a link schedule that supports both data and energy links; the latter type of links are used to fulfill the energy requirement of radio frequency energy harvesting (EH) devices. This aim is significant as we envisage future wireless routers to have dual roles: forwarding data and charging devices. To this end, we present a mixed integer linear program to derive the shortest link schedule for both types of links; each with a data rate or energy requirement. We consider transmission power control and the physical interference model. In addition, we present a heuristic algorithm called linear programming approximation algorithm (LPAA) to derive the schedule for large-scale topologies. Our results show that the schedule length is affected by the number of links, energy requirement and energy conversion rate of EH nodes, transmit power, and signal-to-interference-noise ratio threshold. Lastly, our experimental results indicate that LPAA is capable of producing schedules that are near optimal.

Transmission Power Control Using Human Motion Classification for Reliable and Energy-Efficient Communication in WBAN

Transmission Power Control Using Human Motion Classification for Reliable and Energy-Efficient Communication in WBAN