Transmission Power Research Articles

Robust beamforming design for passive RIS‐assisted NOMA systems

AbstractA transmission power minimization problem for a reconfigurable intelligent surface (RIS) aided non‐orthogonal multiple access (NOMA) network has been studied here, which has assumed the channel state information is imperfect. The channel error in this paper has been modeled as two different error models, that is, bounded error model and statistical error model. First, a two‐step algorithm is proposed to decouple the decoding order and optimization variables under bounded error model. Then, for the statistical error model, the alternating optimization method is adopted to decouple optimization variables. Finally, the performance difference is compared between the proposed beamforming scheme and the non‐RIS scheme from two key parameters, the number of reflected elements and the error factor. Simulation results verify that passive RIS has the advantage on the system performance compared with the scheme without RIS.

Intelligent High-Awareness and Channel-Efficient Adaptive Beaconing Based on Density and Distribution for Vehicular Networks

In vehicle ad hoc networks (VANETs), a beacon is a periodic message sent to nearby vehicles containing essential details like the sender’s vehicle ID, location, speed, and direction. Maintaining the freshness of this information without causing network congestion requires adaptive beaconing to adjust to changes in mobility and network density. Our research, based on extensive simulation experiments, identifies specific parameter sets optimal for adapting beaconing rates to different scenarios. From this analysis, we introduce a novel scheme called high-awareness and channel-efficient adaptive beaconing (HACEAB), employing fuzzy logic to adapt to various environments and conditions. Initially, the protocol gauges network density using an adaptive threshold function, followed by estimating the node spatial distribution through the quadrat statistic method to discern uniform distribution or clustering. Utilizing these data, the protocol adjusts beaconing rates via appropriate input parameters for the fuzzy logic system. Remarkably, HACEAB represents the first beaconing scheme capable of simultaneously adjusting to changes in network density and spatial distribution. Furthermore, the protocol enhances performance by adapting transmission power to fluctuations in node density and distribution. NS-3 simulations validate the efficacy of these improvements.

Joint trajectory, transmission time and power optimization for multi-UAV data collecting system

Unmanned aerial vehicles (UAVs) have been generally applied in the field of communication due to their small size, flexible mobility, and convenient deployment. As a mobile base station, the UAV node can quickly establish a line-of-sight link with the ground node, thereby improving communication performance. In this paper, we study a multi-UAV assisted data collecting system. Specifically, in the case of limited system energy consumption, UAV flight energy consumption and ground node data transmission energy consumption are considered as an general limitation, and considering the channel interference between nodes, a multi-UAV assisted data collection model is studied. An non-convex problem that maximizes the minimum amount of data collected from ground nodes is further formulated. Since the original optimization problem is non-convex that difficult to solve directly, the problem is first decomposed into four sub-problems, and then the solution of each sub-problem is obtained by using successive convex approximation and block coordinate descent method. Finally, based on the solution of the four subproblems, an iterative algorithm for joint optimization of data transmission planning, transmission power, UAV trajectory and mission time is proposed. Simulation experiments show that the proposed algorithm can obtain more transmission data than the baseline algorithms.

Transmission power allocation in flow-guided nanocommunication networks

Flow-guided electromagnetic nanonetworks hold tremendous potential for transformative medical applications, enabling monitoring, information gathering, and data transmission within the human body. Operating in challenging environments with stringent computational and power constraints within human vascular systems, these nanonetworks face significant hurdles. Successful transmissions between in-body nanonodes and on-body nanorouters are infrequent, requiring novel approaches to enhance network throughput under such circumstances. Traditional flow-guided nanonetworks rely on nanonodes to transmit packets if they possess sufficient energy, irrespective of their proximity to the nanorouter. In this paper, we present an extended model for legacy flow-guided nanonetworks that offers substantial throughput improvements while reducing the required number of nanonodes compared to the baseline blind transmission approach. By allocating transmission energy to allow more than one transmission during a charging cycle, our proposed model significantly enhances network throughput, facilitating the deployment of nanocommunication-supported medical applications. For example, with only two transmissions, it is possible to increase throughput by around 46% with the same number of nanonodes or, equivalently, reduce the number of nanonodes by the same amount to achieve the same throughput.

Performance Analysis and Simulation of IRS-Aided Wireless Networks Communication

This paper introduces the novel IRS-based Optimal Relay Selection (ORS-IRS) method, aimed at analyzing the performance of wireless communication systems with an emphasis on symmetry. The ORS-IRS approach presents an innovative communication algorithm that seamlessly integrates Intelligent Reflecting Surfaces (IRS) with relay selection techniques. Through adaptive adjustments of reflection coefficients, IRS elements efficiently manipulate incoming signals, fostering symmetry in signal strength enhancement and latency reduction for improved signal delivery to the intended destination. This symmetrical optimization in channel capacity and transmission power ensures reliable data transmission with low latency, achieved through the seamless integration of IRS and relay selection techniques. In contrast, the Cell-Free Massive MIMO (CF-M-MIMO), with its decentralized architecture, excels in serving a larger user base and attaining remarkable capacity gains, showcasing a different dimension of symmetry. The Decode-and-Forward (DF) relaying approach demonstrates its potential in enhancing signal reliability across extended distances, contributing to the overall symmetry of the comparative analysis. This comprehensive evaluation provides valuable insights into selecting appropriate transmission strategies, particularly for applications that demand high capacity and reliability in the design of modern wireless communication systems with a symmetrical focus.

A DNN-orchestrated cognitive radio-based power control scheme for D2D communication

Device-to-device (D2D) communication demonstrates its efficacy across diverse domains, including military, surveillance, public safety, disaster management, content sharing, and local advertising, through its provision of proximity-based connections. Moreover, it elevates data rates, spectrum efficiency, and network throughput with efficient resource allocation while simultaneously reducing latency. However, it encounters challenges related to unregulated high-power consumption, leading to interference in D2D communication. Hence, in this paper, we proposed a deep neural network (DNN) and cognitive radio-driven efficient power control scheme for D2D communication. The proposed scheme operates in two distinct phases: the cognitive radio and the DNN-based phase. In this context, cognitive radio aids in selecting secondary users or cellular mobile equipment (CUE) through energy detection spectrum sensing. Subsequently, the DNN model is employed to optimize the transmission power of CUE in D2D communication. The effectiveness of the proposed scheme is assessed through various metrics, including loss, optimizers, learning rates, and sum rates.

Extrema-Triggered Conversion for Non-Stationary Signal Acquisition in Wireless Sensor Nodes

While wireless sensor node (WSNs) have proliferated with the rise of the Internet of Things (IoT), uniformly sampled analog–digital converters (ADCs) have traditionally reigned paramount in the signal processing pipeline. The large volume of data generated by uniformly sampled ADCs while capturing most real-world signals, which are highly non-stationary and sparse in information content, considerably strains the power budget of WSNs during data transmission. Given the pressing need for intelligent sampling, this work proposes an extrema pulse generator devised to trigger ADCs at significant signal extrema, thereby curbing the volume of data points collected and transmitted, and mitigating transmission power draw. After providing a comprehensive signal-theoretic rationale, we construct and experimentally validate these circuits on a system-on-chip field-programmable analog array in a 350 nm complementary metal-oxide-semiconductor (MOS) process. Operating within a power range of 4.3–12.3 µW (contingent on the input bandwidth requirements), the extrema pulse generator has proven to be capable of effectively sampling both synthetic and natural signals, achieving significant reductions in data volume and signal reconstruction error. Using a nonideality-resilient reconstruction algorithm, that we develop in this work, experimental comparisons between extrema and uniform sampling show that extrema sampling achieves an 18-fold lower normalized root mean square reconstruction error for a quadratic chirp signal, despite requiring 5-fold fewer sample points. Similar improvements in both the reconstruction error and effective sampling rate objectives are found experimentally for an electrocardiogram signal. Using both theoretical and experimental methods, this work demonstrates the potential of extrema-triggered systems for extending Pareto frontiers in modern, resource-constrained sensing scenarios.

Addressing Heterogeneity in Federated Learning with Client Selection via Submodular Optimization

Federated learning (FL) has been proposed as a privacy-preserving distributed learning paradigm, which differs from traditional distributed learning in two main aspects: the systems heterogeneity, meaning that clients participating in training have significant differences in systems performance including CPU frequency, dataset size, and transmission power, and the statistical heterogeneity, indicating that the data distribution among clients exhibits Non-Independent Identical Distribution. Therefore, the random selection of clients will significantly reduce the training efficiency of FL. In this article, we propose a client selection mechanism considering both systems and statistical heterogeneity, which aims to improve the time-to-accuracy performance by trading off the impact of systems performance differences and data distribution differences among the clients on training efficiency. First, client selection is formulated as a combinatorial optimization problem that jointly optimizes systems and statistical performance. Then, we generalize it to a submodular maximization problem with knapsack constraint, and propose the Iterative Greedy with Partial Enumeration (IGPE) algorithm to greedily select the suitable clients. Then, the approximation ratio of IGPE is analyzed theoretically. Extensive experiments verify that the time-to-accuracy performance of the IGPE algorithm outperforms other compared algorithms in a variety of heterogeneous environments.

Enhanced Gain Difference Power Allocation for NOMA-Based Visible Light Communications

With the escalating demand for high-data-rate wireless services, visible light communication (VLC) technology has emerged as a promising complement to traditional radio frequency wireless networks. To further enhance the achievable rate and error performance in non-orthogonal multiple access-based VLC downlinks, an efficient power allocation scheme named enhanced gain difference power allocation (EGDPA) is proposed for a multiple-input multiple-output VLC system. The power factors are determined by considering users’ channel gains and utilizing the residual allocation principle, which focuses on the remaining power available after allocating it to the previous users. In addition, the impacts of the user distribution and transmission power are investigated, and the performance metrics in terms of achievable data rate, energy efficiency, and bit error rate are also analytically presented. Simulation results demonstrate that energy efficiency can be significantly improved and the achievable data rate gain can be enhanced by at least 6.25% with the proposed EGDPA scheme as compared with other traditional methods, confirming its superiority and validity for efficient multi-user accessing.

Generation of transmission wave with low AM noise for sub-GHz CW-EPR spectrometer

This study describes a technique to clean amplitude modulation (AM) noise of RF transmission waves, which is used to observe the sub-GHz CW-EPR spectrum. An RF transmitter amplifier that has the function of cleaning AM noise has been developed. Cleaning of the AM noise was owing to saturation of the output at the amplifier. Three stages of the amplifiers in series could effectively suppress the AM noise to about –176 dBc/Hz and –183 dBc/Hz at offset frequency of 10 kHz and 100 kHz, respectively at the carrier frequency of 750 MHz and the output power of 29 dBm. Since phase modulation (PM) noise is suppressed by phase sensitive detection, the AM noise in the transmission is dominant cause of the noise in the sub-GHz CW-EPR absorption spectrum using a reflection bridge, which depends on the quality factor of the resonator and the power of the RF transmission. The additive phase modulation (PM) noise of this amplifier was –171 dBc/Hz at an offset frequency of 100 kHz, which indicated that the frequency modulation (FM) of the transmission wave was not distorted with this amplifier. Therefore, conventional CW-EPR spectrometers that typically require FM for automatic frequency control or automatic tunning control can use this technique to increase sensitivity.

Conventional band demultiplexer with high quality factor and transmission power based on four optimized shaped photonic crystal resonators

Abstract In this work, we propose a new filter design based on a ring resonator. This structure has 99% transmission ratio and high sensitivity to detect small refractive index variations of the order of 0.002. More specifically, the small size of this resonator gives the advantage to model a demultiplexer of size 463 µm2 based on four ports which operates in the conventional transmission band. Also, we show that our demultiplexer structure can reach more than 90% transmission ratio with an optical quality factor of about 3800, the spectral width is 0.72 nm and a crosstalk between −13.28 and −32.13. In our study, we emphasized the plane wave method to study the photonic band gap and FDTD to determine the transmission spectrum.

Phase shift control of dual active bridge DC‐DC converter based on feedforward compensation and transient optimization strategy

SummaryIn the context of the dual active bridge (DAB) bidirectional DC‐DC converter using the traditional dual‐phase‐shift (DPS) control method, a transient DC bias occurs during power co‐direction and commutation conversion. This bias negatively impacts the converter's transient performance and can lead to harsh effects, such as hard switching problems, resulting in power loss and excessive current stress. In a closed‐loop control system, poor transient response may destroy the stability of the converter. In this paper, the transient‐optimized‐dual‐phase‐shift (TODPS) control strategy is proposed to eliminate the transient DC bias and to maintain soft switching during transient states. It reduces power loss, eliminates excessive current stress, and allows for smooth transitions between single‐phase‐shift (SPS) and DPS control strategies without introducing DC bias. A closed‐loop control system based on TODPS control is established with feed‐forward compensation to adjust the primary‐side current and modify transmission power. This adjustment enhances the transient performance of the DAB converter. The proposed strategy is versatile and applicable to various power conversion scenarios involving DAB converters. It enables quick switching of the magnitude and direction of the primary‐side current during transient states, resulting in a smooth transient effect without noticeable overshoot or oscillation. The experiment results verify the effectiveness of the proposed transient optimization strategy.

Energy Efficiency in Wireless Body Area Network Using Various Radios

Abstract
 Wireless Body Sensor Networks (WBSNs) promote continuous and low-cost health monitoring for a better healthcare system. Sensors connected to a human body are normally battery powered devices with brief life times. IEEE 802.15.6 standard is the typical choice for low power ingestion and reliable short-range communication for WBSNs. In this paper, we have evaluated the performance of IEEE 802.15.6 standard for WBSNs. Our tests examined three radios while varying their transmission power and data rates. The results show that altering these parameters affected the number of packets delivered and overall system energy consumption. The effects are demonstrated using Omnet++ simulator and its Castalia extension for WBANs.

On Weighted Sum Rate of Multi-User Photon-Counting Multiple-Input Multiple-Output Visible Light Communication Systems under Poisson Shot Noise

Photon counting has been proven to possess excellent signal detection capabilities at low power levels and has extensive potential applications in sixth-generation (6G) communications. However, the inherent dependency between the signal and noise complicates system analysis, and optimizing achievable rates in photon-counting visible light communication (VLC) systems remains unresolved. This paper introduces a new method aimed at minimizing multi-user interference (MUI) through a zero-forcing (ZF) scheme and maximizing the weighted sum rate of the proposed downlink multi-user photon-counting multiple-input multiple-output (MU-PhC-MIMO) VLC system by solving an optimization problem. The key point lies in our utilization of the ZF approach to derive a reasonable asymptotic approximation expression for the weighted sum rate. Subsequently, we use variable substitution and methods like successive convex approximation (SCA) to iteratively convexify the non-convex optimization problem and maximize the weighted sum rate under the ZF form. Compared to other algorithms, this approach can save 2.5 dB of transmission power to achieve the same system-weighted sum rate and significantly outperforms the repetition coding scheme at sufficient transmission power.

Magnetic coupling resonant ultra‐high voltage electric vehicle wireless charging system based on parity‐time symmetry

SummaryIn recent years, the electric vehicle (EV) industry has developed rapidly, and research on wireless charging systems for the EV is very meaningful. This paper proposes a magnetic coupling resonant ultra‐high voltage EV wireless charging system based on parity‐time symmetry to meet the need of high transmission efficiency and high transmission power for the EV wireless charging system. Through the application of magnetic coupling resonance and parity‐time symmetry, the charging system can maintain constant transmission efficiency within a larger spatial range, which greatly reduces the high‐precision requirements for coil alignment. Through the proposed intelligent coil alignment system, the alignment degree of the power transmission coil, user experience, and energy transmission efficiency can be improved further, promoting the intelligence of the charging system. At the same time, an ultra‐high voltage booster module is added to the primary side of the system to provide high‐power input for the EV charging system. Simulation analysis and experiments verify the rationality and practicability of the charging system. Finally, the practical significance of the proposed system applied to power grid inspection work is discussed.

A Novel Framed Slotted Aloha Medium Access Control Protocol Based on Capture Effect in Vehicular Ad Hoc Networks

The capture effect is a frequently observed phenomenon in vehicular ad hoc networks (VANETs) communication. When conflicts arise during time slot access, failure to access does not necessarily occur; instead, successful access may still be achieved. The capture effect can enhance the likelihood of multiple access and improve communication efficiency. The security of VANETs communication is undoubtedly the primary concern. One crucial approach to enhance security involves the design of an efficient and reliable medium access control (MAC) protocol. Taking into account both aspects, we propose a novel framed slotted Aloha (FSA) MAC protocol model. Firstly, we derive the closed-form expression for the capture probability in the Rician fading channel in this paper. Subsequently, we analyze how the number of vehicles and time slots influence the success probability of vehicle access channels as well as examine the impact of the capture effect on this success probability. Then, under constraints regarding vehicle access channel success probability, we derive optimal values for slot numbers, access times, and transmission power while proposing a comprehensive implementation method to ensure high access channel success probabilities. We verify both theoretical derivations and proposed methods through simulation experiments.

On the Application of Quasi-Degradation to Network NOMA in Downlink CoMP Systems

The application of network non-orthogonal multiple access (N-NOMA) technique to coordinated multi-point (CoMP) systems has attracted significant attention due to its superior capability to improve connectivity and maintain reliable transmission for CoMP users simultaneously. Based on the concept of quasi-degraded channel for N-NOMA, this paper studies the precoding design for downlink N-NOMA scenarios with two base stations (BSs) equipped with multiple antennas. In specific, under quasi-degraded channels, simple linear precoding based N-NOMA can achieve the same minimal total transmission power as theoretically optimal but complicated dirty paper coding (DPC) scheme, when the users’ target rates and minimal transmission power of each BS are given. In this paper, the channel quasi-degradation (QD) condition is first rigorously derived for the scenario with single CoMP user and two NOMA users. The closed-form optimal precoders for N-NOMA under quasi-degraded channels are also provided. Then, based on QD condition, a novel hybrid N-NOMA (H-N-NOMA) scheme is proposed, which is a mixture of N-NOMA and conventional zero-forcing beamforming (ZFBF) scheme. Further, for the scenarios with more users, a low-complexity QD based user pairing (QDUP) algorithm is proposed, which is then combined with H-N-NOMA to make a novel scheme termed H-N-NOMA/QDUP into being. Numerical results are presented to reveal the impact factors on QD channels, and also demonstrate the superior performance of the proposed H-N-NOMA/QDUP scheme. It is shown that the proposed H-N-NOMA/QDUP scheme can effectively exploit the benefit of multi user diversity.

Research on relay protection technology of high temperature superconducting cable system

High-temperature superconducting cables have many advantages such as low line loss, large transmission capacity, small space occupation, and environmental friendliness, which provide a highly efficient, compact, reliable, and green way of power transmission for power grids, and have great potential for application in large-capacity power transmission and urban power grid expansion and reconstruction. However, high-temperature superconducting cables have a complex structure, which is quite different from traditional cable lines in terms of operating conditions and fault mechanisms, and the requirements for the secondary system are also more stringent. Relying on the project of China Power Grid, “Research and Demonstration Application of Superconducting Transmission Technology in Urban Distribution Networks”, the thesis carries out an in-depth research on relay protection technology adapted to the access of high-temperature superconducting cables.

A Comparative Study and Analysis of LoRaWAN Performance in NS3

Long Range Wide Area Network (LoRaWAN) is a widely adopted Internet of Things (IoT) protocol due to its high range and lower energy consumption. LoRaWAN utilizes Adaptive Data Rate (ADR) for efficient resource (e.g., spreading factor and transmission power) management. The ADR manages these two resource parameters on the network server side and end device side. This paper focuses on analyzing the ADR and Gaussian ADR performance of LoRaWAN. We have performed NS3 simulation under a static scenario by varying the antenna height. The simulation results showed that antenna height has a significant impact on the packet delivery ratio. Higher antenna height (e.g., 50 m) has shown an improved packet success ratio when compared with lower antenna height (e.g., 10 m) in static and mobility scenarios. Based on the results, it is suggested to use the antenna at higher allevation for successful packet delivery

An Artificial Intelligence Mechanism for the Prediction of Signal Strength in Drones to IoT Devices in Smart Cities

Abstract: Smart cities are comprised of intelligent objects that can collectively and automatically improve living standards, preserve lives, and function as a sustainable environment. Drones or Unmanned aerial vehicles (UAV), robotics, cognitive computing, and the Internet of Things (IoT) are mandatory to enhance the intellectual ability of smart cities by enhancing connectivity, energy efficiency, and Quality of Signal (QoS). Consequently, the integration of drones with IoT plays a crucial part in enabling a vast array of smart-city applications. Drones are undeniably the technology of the future. They glide in the air, keeping an eye on things in their metropolis. They do not need human control or operation. They capture data based on visuals and sounds by employing a variety of sensors, webcams, and mics, and then transfer it to a gateway for processing and retrieving information. Drones will let us gather vast volumes of data for processing, while also enhancing the intelligence of smart cities. The drone's signal is vulnerable to absorption, refraction, diffraction, and attenuation. Therefore, it is essential to predict the signal from the drone. This study presents an intelligent method using Deep Learning to predict the signal strength for enhancing network connectivity and delivering the desired QoS of IoTs and drone integration. This enables effective data transmission, boosts QoS for end-users/devices, and reduces data transmission power consumption.