Transmission Power Control Research Articles

Computation Efficiency Maximization and QoE-Provisioning in UAV-Enabled MEC Communication Systems

Since the traditional mobile edge computing (MEC) server is fixed at the edge of the network, it is likely to cause severe propagation loss between the edge server and users. However, ground users can be served when the MEC server is integrated into the unmanned aerial vehicle (UAV) base station (BS). Nevertheless, there are three main factors, including the limited system radio resources, the finite UAV energy and severe interference between the uplink and downlink, that make it challenging for the system to guarantee quality-of-experience of users in the downlink while ensuring the minimum amount of offloading data for users in the uplink. Therefore, this paper investigates computation-efficiency maximization. We jointly optimize computing scheduling, UAV 3D trajectory, bandwidth allocation and transmission power control to maximize the amount of offloaded data, minimize UAV energy consumption, and simultaneously guarantee the QoE of users in the downlink. Due to the nonconvex and nonconcave objective function and the coupling between variables, a multi-stage alternative optimization algorithm is proposed to solve the problem. The simulation results have demonstrated that the computation-efficiency obtained by the proposed scheme is higher than that of other benchmark schemes and meets the QoE demands of users under insufficient resources.

Neural-Network-Based Power Control Prediction for Solar-Powered Energy Harvesting Communications

In this article, we design neural network (NN)-based transmit power control prediction for solar-powered energy harvesting (EH) communications under single-user (SU) and multiuser (MU) scenarios with real solar data. Although the directional water filling (DWF) is known as the optimal scheme for the SU case, it necessitates the full (past and future) channel state information (CSI) and energy state information (ESI) in realizing the optimal solution over a time period. To conquer this impracticality, an SU-GreenPCNet, which only requires the past short-term CSI and ESI for predicting the SU transmit power, is proposed and trained with the historical solar data. For the MU case, two iterative algorithms, namely, weighted-sum minimum mean-square error (WMMSE) and iterative DWF (IDWF), are investigated to tackle the original nonconvex power control problem when the full state knowledge is assumed to be known in advance. The solutions with the historical solar data are then served as benchmarks for designing two MU-GreenPCNets. As an extension of the SU-GreenPCNet, a centralized scheme is proposed at the central controller for jointly determining the MU transmit power values based on the past short-term state knowledge of all users. A distributed scheme is further investigated to reduce the signaling overhead, in which each transmitter merely utilizes the past short-term CSI, ESI, and MU interference (MUI) knowledge associated with its user pair. The simulation results show that the proposed power control prediction schemes can pragmatically achieve satisfied sum rate performance in both SU and MU scenarios, as compared with the benchmark schemes.

Joint Location, Bandwidth and Power Optimization for THz-Enabled UAV Communications

In this letter, the problem of unmanned aerial vehicle (UAV) deployment, power allocation, and bandwidth allocation is investigated for a UAV-assisted wireless system operating at terahertz (THz) frequencies. In the studied model, one UAV can service ground users using the THz frequency band. However, the highly uncertain THz channel will introduce new challenges to the UAV location, user power, and bandwidth allocation optimization problems. Therefore, it is necessary to design a novel framework to deploy UAVs in the THz wireless systems. This problem is formally posed as an optimization problem whose goal is to minimize the total delays of the uplink and downlink transmissions between the UAV and the ground users by jointly optimizing the deployment of the UAV, the transmit power and the bandwidth of each user. The communication delay is crucial for emergency communications. To tackle this nonconvex delay minimization problem, an alternating algorithm is proposed while iteratively solving three subproblems: location optimization subproblem, power control subproblem, and bandwidth allocation subproblem. Simulation results show that the proposed algorithm can reduce the transmission delay by up to 59.3%, 49.8% and 75.5% respectively compared to baseline algorithms that optimize only UAV location, bandwidth allocation or transmit power control.

SBT (Sense Before Transmit) Based LTE Licenced Assisted Access for 5\xa0GHz Unlicensed Spectrum

Utilization of unlicensed spectrum under licensed assisted access ensuring fair co-existence with Wi-Fi networks is a good solution to address immense usage of mobile data. Radio communication operation of LTE in unlicensed frequency band is referred as LTE-unlicensed (LTE-U) or LTE-licensed assisted access. In this paper, we consider a HGNW in which coverage area of Wireless-Fidelity (Wi-Fi)’s Access Point is integrated within the LTE-U small base station’s cellular network coverage area. To overcome the disadvantages of existing LTE-U technics like carrier sense adaptive transmission and listen before talk, we proposed a new methodology i.e., sense before transmit in this paper by adopting a transmit power control mechanisms using reciprocity theorem based on the channel state information to assign the secondary carriers in the uplink as well as in the downlink directions in the unlicensed spectrum to carry the traffic. In our proposal, LTE-U users are allowed to use the unlicensed spectrum provided that the interference produced at Wi-Fi users due to LTE-U activities is remained below a certain threshold. We evaluated the performance of proposed network model in terms of outage probability and achievable throughputs.

Power Control for Body Area Networks: Accurate Channel Prediction by Lightweight Deep Learning

Recent advances in the Internet of Things (IoT) are reforming the health care industry by providing higher communication efficiency, lower costs, and higher mobility. Among the many IoT applications, wireless body area networks (BANs) are a remarkable solution caring for a rapidly growing aged population. Predictive transmit power control schemes improve BAN communications’ reliability and energy efficiency through long-term optimal radio resources allocation that supports consistent pervasive healthcare services. Here, we propose LSTM-based neural network (NN) prediction methods that provide long-term accurate channel gain prediction of up to 2 s over nonstationary BAN on-body channels. An incremental learning scheme, which enables the LSTM predictor to operate online, is also developed for dynamic scenarios. Our main contribution is a lightweight NN predictor, “LiteLSTM,” that has a compact structure and higher computational efficiency than other variants. We show that LiteLSTM remains functional under an incremental learning scheme, with only marginal performance degradation when implemented on hand-held devices. For optimal power allocation, we develop an interquartile range (IQR)-based power control for our channel prediction. When extensively tested using empirical channel measurements at different sampling rates, our proposed methods outperform the existing state-of-the-art methods in terms of prediction accuracy, power consumption, level crossing rate (LCR), and outage probability and duration.

Learning to Charge RF-Energy Harvesting Devices in WiFi Networks

In this paper, we consider a solar-powered Access Point (AP) that is tasked with supporting both non-energy harvesting or legacy data users such as laptops, and devices with Radio Frequency (RF)-energy harvesting and sensing capabilities. We propose two solutions that enable the AP to manage its harvested energy via transmit power control and also ensure devices perform sensing tasks frequently. Advantageously, our solutions are suitable for current wireless networks and do not require perfect channel gain information or non-causal energy arrival at devices. The first solution uses a deep Q-network (DQN) whilst the second solution uses Model Predictive Control (MPC) to control the AP's transmit power. Our results show that our DQN and MPC solutions improve energy efficiency and user satisfaction by respectively 16% to 35%, and 10% to 42% as compared to competing algorithms.

Spectrum Sharing Incentive for Legitimate Wireless Information Surveillance

In this paper, spectrum sharing incentive for legitimate wireless information surveillance at the physical layer is investigated. The concept behind this is that authorized government agencies want to eavesdrop the communication between a suspicious source-destination pair, and stimulate the help of another source-destination pair (referred to as secondary users) looking for transmission opportunities. We thus propose to let the secondary receiver act as a monitor to eavesdrop the suspicious communication and let the secondary transmitter act as a friendly jammer to improve the eavesdropping performance, such that they are granted to share the spectrum of the suspicious users, as a reward. To guarantee the eavesdropping performance, the successful eavesdropping probability is required to be larger than that with passive eavesdropping. The problems of jointly optimizing the transmit power of the secondary transmitter and the successive interference cancellation (SIC) decoding order at the secondary receiver to maximize the average secondary transmission rate and minimize the secondary transmission outage probability are investigated. Two scenarios are considered for the availability of the channel state information (CSI). The first scenario assumes perfect CSI, and the optimal strategies for the transmit power control and the SIC decoding ordering are derived. The second scenario assumes partial CSI, where the instantaneous CSI of the secondary transmission link and the statistical CSI of the links related to the suspicious communication are available, and the optimal strategies for maximizing an approximate average secondary transmission rate and minimizing an approximate secondary transmission outage probability are proposed. Extensive simulation results validate the effectiveness of the proposed strategies.

Game theory based adaptive transmit power control algorithm for IoT wireless sensor networks

Objectives: To design a transmission power control (TPC) algorithm for the Internet of Things (IoT) based Wireless Sensor Networks (WSN) such that the energy cost is reduced and the expected throughput is increased. Method: In this study, a Game theory based adaptive transmit power control (GT-ATPC) algorithm for IoT-WSN is proposed. In this algorithm, for each sender, the initial transmission power level (TPL) is determined from the difference of Received Signal Strength (RSS) measurements at the base station (BS). The TPL is adaptively adjusted by means of Game theory. For each sender, the expected throughput, energy cost and connectivity values are estimated from which a utility function is derived. Findings: By simulation results, it has been shown that GT-ATPC attains 37% higher throughput with 57% reduced energy cost and 30% lesser overhead when compared with GTS-PM approach. Novelty: The TPL of a node is adjusted based on the estimated values of utility function such that the throughput and connectivity is high and energy cost is less. Keywords: Internet of Things (IoT); wireless sensor network (WSN); transmission power; Energy cost; game theory; utility function

Joint Mobility, Communication and Computation Optimization for UAVs in Air-Ground Cooperative Networks

Unmanned aerial vehicles (UAVs) play a significant role in various 5G or Beyond-5G (B5G)-enabled Internet-of-Things (IoT) applications. However, the UAV performance in an air-ground cooperative network is significantly affected by its mobility and air-to-ground (A2G) communication and computation behaviors. In this paper, a UAV-oriented computation offloading system is investigated, where the UAV desires to complete its onboard computation demands with the assistance of a ground edge-computing infrastructure, i.e., a road-side unit (RSU). The objective is to maximize the energy efficiency of the UAV. Specifically, a non-convex constrained optimal control problem is formulated to optimize the overall energy efficiency of UAV by jointly considering the coupled effects of UAV's longitudinal mobility, A2G communication, and computation dynamics. To address the coupled complexity and non-convexity of the original problem, a primal decomposition approach is developed to transform the problem into a convex subproblem and a primary problem, and then a closed-form optimal transmission power control is derived by solving the subproblem, which is dependent on mobility information. By embedding the closed-form optimal power control into the primary problem, a gradient projection-based iterative algorithm is proposed to obtain a joint optimal solution for both the longitudinal acceleration control and the power control, the feasibility and convergence of which is also theoretically proven. Extensive simulations have been conducted to validate the effectiveness of the proposed method in terms of constraint satisfaction and convergence speed, and comparative results also demonstrate that it can outperform other benchmark methods in terms of global energy efficiency.

Joint Optimization of BS Clustering and Power Control for NOMA-Enabled CoMP Transmission in Dense Cellular Networks

Non-orthogonal multiple access (NOMA)-enabled coordinated multipoint (CoMP) transmission has great potential in balancing spectrum utilization and interference mitigation in dense cellular networks. However, NOMA reforms the spectrum sharing policy of CoMP transmission due to introducing additional interference among coordinated base stations (BSs), which deteriorates the CoMP transmission rate. In this paper, we investigate the joint optimization of BS clustering and power control for NOMA-enabled CoMP transmission in dense cellular networks to maximize system sum-rate. We first characterize the impact of interference among coordinated BSs on CoMP transmission rate and find that the BS clustering is dependent on NOMA user grouping and restricted by the NOMA decoding condition. We then derive a tight lower bound on the system sum-rate and exploit it to design a joint BS clustering and power control scheme. Specifically, a power control algorithm is designed by a penalty convex-concave procedure to satisfy the NOMA decoding condition and users’ rate requirements. A BS clustering algorithm based on successive convex approximation is designed to iteratively update the BS clustering and NOMA user grouping to increase system sum-rate. Finally, two algorithms are alternately performed until all users successfully access and the system sum-rate converges. Simulation results show that the proposed scheme can efficiently alleviate interference among coordinated BSs to improve system sum-rate and spectrum efficiency of NOMA-enabled CoMP transmission even under high user density.

Dynamic Aerial Base Station Placement for Minimum-Delay Communications

Queuing delay is of essential importance in the Internet-of-Things scenarios where the buffer sizes of devices are limited. The existing cross-layer research contributions aiming at minimizing the queuing delay usually rely on either transmit power control or dynamic spectrum allocation. Bearing in mind that the transmission throughput is dependent on the distance between the transmitter and the receiver, in this context we exploit the agility of the unmanned-aerial-vehicle (UAV)-mounted base stations (BSs) for proactively adjusting the aerial BS (ABS)’s placement in accordance with wireless teletraffic dynamics. Specifically, we formulate a minimum-delay ABS placement problem for UAV-enabled networks, subject to realistic constraints on the ABS’s battery life and velocity. Its solutions are technically realized under three different assumptions in regard to the wireless teletraffic dynamics. The backward induction technique is invoked for both the scenario where the full knowledge of the wireless teletraffic dynamics is available, and for the case where only their statistical knowledge is available. In contrast, a reinforcement learning aided approach is invoked for the case when neither the exact number of arriving packets nor that of their statistical knowledge is available. The numerical results demonstrate that our proposed algorithms are capable of improving the system’s performance compared to the benchmark schemes in terms of both the average delay and of the buffer overflow probability.

Transmission Power Control for Anchor-Assisted Localization in Wireless Sensor Networks

Energy-efficiency and localization play significant roles in Wireless Sensor Networks. Localization provides geographical information about the sensors deployed in a certain geographical region. Single mobile anchor-assisted localization is considered as an energy-efficient approach where numerous GPS-equipped sensors are removed to reduce the consumption of energy in the network. In recent years, various path planning mechanisms for mobile anchor have been proposed with single transmission power assigned to the anchor. Preservation of power is critical while performing mobile beacon-assisted localization so as to increase the lifetime. Providing the ability to dynamically adjust transmission power for the anchor node does not only reduce the energy consumption but also improves the overall performance of the localization. In this article, we propose a new trajectory, Anchor-power, that provides transmission power control for the mobile beacon. Anchor-power efficiently adjusts the transmission power of the beacon node for the reliable transmission of beacon messages. Experimental results reveal that our proposed approach enables unknown sensors to locate themselves with better accuracy in comparison with the existing models. Anchor-power improves performance in terms of accuracy and energy efficiency along with the localization success ratio and localization time.

Achievable Rate Characterization of NOMA-Aided Cell-Free Massive MIMO With Imperfect Successive Interference Cancellation

This paper investigates the throughput improvement of cell-free massive multiple-input multiple-output (MIMO) systems by non-orthogonal multiple access (NOMA) for future cellular networks under stochastic access point and user locations. In this context, the node locations are modeled with Poisson point processes. The time division duplexing mode is employed, and uplink channels are estimated locally using uplink pilots. Furthermore, unique pilot sequences are used between NOMA clusters, while pilot reuse occurs within each cluster to strike a balance between the training overhead and the number of clusters. Matched-filter-based precoding is utilized for downlink transmission. The aggregate received signal is analytically characterized by deriving the moment generating function and approximations via moment matching. Then, the asymptotic achievable rates of the NOMA users are derived, thereby quantifying the adverse impact of error propagation owing to imperfect successive interference cancellation. Special scenarios with prior downlink channel state information and log-distance power control are also considered. We show that NOMA greatly increases the achievable average rate, especially under low path loss exponents and dense networks, while user fairness may be boosted by the adoption of a log-distance transmit power control scheme with proper parameter selection (i.e. lower values for the power control parameter).

Continuous power regulation in wireless power transfer system with a new magnetic field superposition transmitter

The research of transmission power is always an essential and hot topic in the wireless power transmission (WPT). To control transmission power efficiently, a continuous power regulation in WPT for small and medium power appliances is proposed. This paper designs a new transmitter with two coils nested inside each other. The transmission power is adjusted by the phase difference of the current between the two coils. Firstly, analyse the transmission power theoretically. The formulae for transmission power and phase difference is obtained. Secondly, graph the function of the formula. Maxwell is used to simulate the magnetic field strength. Finally, build an experimental platform and verify the power regulation effect. The incandescent light bulb is used to regulate in the range of 0.26 W–12 W continuously. In a word, the simulation and experiment results show that the proposed system can regulate the power transmission continuously under the phase-shifting principle.

A machine learning-based dynamic link power control in wearable sensing devices

The main research challenges on developing Wireless Body Area Networks (WBAN) are related to the quality of the communication link and energy consumption. This article combines a Transmission Power Control (TPC) with a packet scheduler mechanism to reduce packet loss and minimize energy waste and radio interferences during on-body communications. The proposed solution takes advantage of Neural Networks and Fuzzy Inference Systems for modelling nonlinear dynamical systems to describe the on-body channel as a function of the operating environment, the relative position of the user’’ arm, and the body posture. Simulations show improved system communication reliability (10% less data packet loss than communications carried out at 0 dBm) at the latency expense. Thus, this mechanism shows that if packets are transmitted uniquely at instants in which the radio channel quality is favourable for communications, these inherently unreliable links can become reliable.

Optimal trajectory and downlink power control for multi-type UAV aerial base stations

Unmanned Aerial Vehicles (UAVs) enabled Aerial Base Stations (UABSs) have been studied widely in future communications. However, there are a series of challenges such as interference management, trajectory design and resource allocation in the scenarios of multi-UAV networks. Besides, different performances among UABSs increase complexity and bring many challenges. In this paper, the joint downlink transmission power control and trajectory design problem in multi-type UABSs communication network is investigated. In order to satisfy the signal to interference plus noise power ratio of users, each UABS needs to adjust its position and transmission power. Based on the interactions among multiple communication links, a non-cooperative Mean-Field-Type Game (MFTG) is proposed to model the joint optimization problem. Then, a Nash equilibrium solution is solved by two steps: first, the users in the given area are clustered to get the initial deployment of the UABSs; second, the Mean-Field Q (MFQ)-learning algorithm is proposed to solve the discrete MFTG problem. Finally, the effectiveness of the approach is verified through the simulations, which simplifies the solution process and effectively reduces the energy consumption of each UABS.

Energy Efficiency of Uplink Cell-Free Massive MIMO With Transmit Power Control in Measured Propagation Channel

Cell-free massive MIMO (CF-mMIMO) provides wireless connectivity for a large number of user equipments (UEs) using access points (APs) distributed across a wide area with high spectral efficiency (SE). The energy efficiency (EE) of the uplink is determined by (i) the transmit power control (TPC) algorithms, (ii) the numbers, configurations, and locations of the APs and the UEs, and (iii) the propagation channels between the APs and the UEs. This paper investigates all three aspects, based on extensive (~30,000 possible AP locations and 128 possible UE locations) channel measurement data at 3.5 GHz. We compare three different TPC algorithms, namely maximization of transmit power (max-power), maximization of minimum SE (max-min SE), and maximization of minimum EE (max-min EE) while guaranteeing a target SE. We also compare various antenna arrangements including fully-distributed and semi-distributed systems, where APs can be located on a regular grid or randomly, and the UEs can be placed in clusters or far apart. Overall, we show that the max-min EE TPC is highly effective in improving the uplink EE, especially when no UE within a set of served UEs is in a bad channel condition and when the BS antennas are fully-distributed.

ATOMIC: Adaptive Transmission Power and Message Interval Control for C-V2X Mode 4

To support the emerging vehicle-to-everything (V2X) communication for autonomous vehicles and smart transportation services, 3rd Generation Partnership Project (3GPP) has recently introduced cellular V2X (C-V2X) standards. In C-V2X, radio resources can be managed not only centrally by the cellular network base station, but also in a completely distributed manner ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Mode 4</i> ) without any cellular support. However, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Mode 4</i> may suffer significant collisions and interference in a dense environment since each vehicle selects its own resources to transmit V2X messages autonomously without adapting appropriately to vehicle density. To address this challenge, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ATOMIC</i> , an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Adaptive Transmission pOwer and Message Interval Control scheme for C-V2X Mode 4</i> , in which each vehicle utilizes real-time channel sensing and neighbor information to reduce channel contention for improved reliability and latency. Through analysis and extensive simulations, we show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ATOMIC</i> outperforms the standard <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Mode 4</i> in both urban and highway scenarios especially in highly dense environments.

Deep Learning-Based Power Control Scheme With Partial Channel Information in Overlay Device-to-Device Communication Systems

In the overlay device-to-device (D2D) communication systems, transmit power control is critical to better manage interference, so that the sum rate is maximized. Such power control for sum-rate optimization is NP-hard, which is typically tackled by iterative algorithms such as weighted minimum mean square error (WMMSE) method. However, the iterative power control schemes inherently incur high complexity and excessive latency. To overcome the limitations, we propose a deep learning-based power control scheme with reduced complexity and latency, where partial and outdated channel state information (CSI) is considered. Using a deep neural network (DNN)-based approach, we formulate an optimization problem to maximize the spectral efficiency under the constraints of user fairness and energy efficiency, where the DNN-based method is based on unsupervised learning with no label data generation process. In addition, a CSI reporting method based on the channel-to-interference power ratio is proposed for partial CSI feedback, which considerably reduces the feedback overhead. Through simulations, we show the results of the spectral efficiency, energy efficiency, and fairness performance for various topographical sizes and channel correlation coefficients. Also, it is shown that the proposed scheme achieves better spectral efficiency and energy efficiency than the WMMSE scheme even when it uses a small amount of CSI feedback.

Deep Learning–Based Energy Beamforming With Transmit Power Control in Wireless Powered Communication Networks

In this paper, we propose deep learning–based energy beamforming in a multi-antennae wireless powered communication network (WPCN). We consider a WPCN where a hybrid access point (HAP) equipped with multiple antennae broadcasts an energy-bearing signal to wireless devices using energy beamforming. We investigate the joint optimization of the time allocation for wireless energy transfer (WET) and wireless information transfer (WIT) with the design for energy beams while minimizing the transmit power at the HAP for efficient use of its available resources. However, this is a non-convex problem, and it is numerically intractable to solve it. In the literature, the traditional approach to solving this problem is based on an iterative algorithm that incurs high computational and time complexity, which is not feasible for real-time applications. We study and analyze a deep neural network (DNN)-based scheme and propose a faster and more efficient approach for the fair approximation of a near-optimal solution to this problem. To train the proposed DNN, we acquire training data samples from a sequential parametric convex approximation (SPCA)-based iterative algorithm. Instead of acquiring data samples and training the DNN, which is highly complex, we use offline training for the DNN to provide a faster solution to the real-time resource allocation optimization problem. Through the simulation results, we show the proposed DNN scheme provides a fair approximation of the traditional SPCA method with low computational and time complexity.