Beamforming optimisation in energy harvesting cooperative full-duplex networks with self-energy recycling protocol

This study considers the problem of beamforming optimisation in an amplify-and-forward relaying cooperative network, in which the relay node harvests the energy from the radio-frequency signal. Based on the self-energy recycling relay protocol, the authors study the beamforming optimisation problem. The formulated problem aims to maximise the achievable rate subject to the available transmitted power at the relay node. The authors develop a semidefinite programming (SDP) relaxation method to solve the proposed problem. They also use SDP and the full search to solve the beamforming optimisation based on a time-switching relaying protocol as a benchmark. The simulation results are presented to verify that the self-energy recycling protocol achieves a significant rate gain compared with the timeswitching relaying protocol and the power-splitting relaying protocol

Transceiver design for wireless energy harvesting cooperative networks

In this thesis, the RF energy harvesting technique is studied in the cooperative wireless network, and different optimization studies are investigated. First, an energy-efficient optimization is considered in the cooperative system with the time switching relaying and power splitting relaying protocols. Then, a security issue in the cooperative network with energy harvesting is proposed, in which the optimization problem is designed to maximize the secrecy rate. We also consider the application of energy harvesting in the full-duplex relaying network with a self-energy recycling protocol. Finally, the energy harvesting is studied in the full-duplex cooperative cognitive radio network. The system performance of all studies is verified in the thesis with MATLAB simulation results

Beamforming optimization for full-duplex cooperative cognitive radio networks

This paper considers the problem of beamforming optimization in a cognitive cooperative energy harvesting network, in which the secondary transmitter (ST) harvests energy from the primary transmitter (PT) and relays the information for the primary user (PU) with amplify-and-forward (AF) relay protocol. When the channel of the primary system is affected with deep fading or shadowing effects, the ST can assist the primary information transmission. It is particularly useful to employ the energy harvesting protocol to avoid that the ST does not have enough energy to assist the PU. Based on the self-energy recycling relay protocol, we study the beamforming optimization problem. We develop a semidefinite programming (SDP) relaxation method to solve the proposed problem. We also use SDP and one-dimension (1-D) optimization to solve the beamforming optimization based on a time-switching relaying protocol (TSR) as a benchmark. The simulation results are presented to verify that the self-energy recycling protocol achieves a significant rate gain compared to the TSR protocol and the power-splitting relaying (PSR) protocol

Energy efficiency in energy harvesting cooperative networks with self-energy recycling

Cooperative communication has been identified as an important component in the 5G system. This paper considers a decode-and-forward (DF) relaying wireless cooperative network, in which the self-energy recycling relay is powered by radio-frequency (RF) signal from the source and its transmitted power from the loop-back channel. The harvested energy is used to support the relay transmissions. Based on a self-energy recycling relaying protocol, we study the optimization of energy efficiency in wireless cooperative networks. Although the formulated optimization problem is not convex, it can be re-constructed to a parametric problem in the convex form by using the non-linear fractional programming, to which closed form solutions can be found by using the Lagrange multiplier method. The simulation results are presented to verify the effectiveness of this solution proposed in this paper

Influences of tilted thin accretion disks on the optical appearance of hairy black holes in Horndeski gravity

Research on the optical appearance of black holes, both in general relativity and modified gravity, has been in full swing since the Event Horizon Telescope Collaboration announced photos of M87$^{*}$ and Sagittarius A$^{*}$. Nevertheless, limited attention has been given to the impact of tilted accretion disks on black hole images. This paper investigates the $230$ GHz images of non-rotating hairy black holes illuminated by tilted, thin accretion disks in Horndeski gravity with the aid of a ray tracing method. The results indicate that reducing the scalar hair parameter effectively diminishes image luminosity and extends both the critical curve and the inner shadow. This trend facilitates the differentiation between hairy black holes and Schwarzschild black holes. Furthermore, we observe that the inclination of the tilted accretion disk can mimic the observation angle, consequently affecting image brightness and the morphology of the inner shadow. In specific parameter spaces, the disk inclination has the ability to shift the position of the light spot in the images of hairy black holes. This finding may provide potential theoretical evidence for the formation of three flares at different positions in the Sagittarius A$^{*}$ image. Additionally, by examining the images of hairy black holes surrounded by two thin accretion disks, we report the obscuring effect of the accretion environment on the inner shadow of the black hole.Comment: 26 pages, 14 figure

Executives’ ESG cognition and enterprise green innovation: Evidence based on executives’ personal microblogs

Based on cognitive theory, we investigated the influence of executives’ ESG cognition on corporate green innovation using data from Chinese manufacturing listed companies from 2010 to 2019. The paper first constructs a metric of ESG cognition of company executives by presenting a quantitative analysis of data from their personal microblogs using textual analysis. The findings show that executive ESG perceptions significantly improve corporate green innovation. After addressing the endogeneity issue through a series of robustness tests, the findings of this paper still held true. Further research found that the enhancement effect of executive ESG perceptions on firms’ green innovation level was mainly found in the sample without heavy pollution and with lower financing constraints and a higher marketization process. This study makes an important contribution to the research on corporate green innovation based on the perspective of executive ESG cognition while also providing a theoretical basis and practical reference for corporate green innovation practices

SOT-MRAM-Enabled Probabilistic Binary Neural Networks for Noise-Tolerant and Fast Training

We report the use of spin-orbit torque (SOT) magnetoresistive random-access memory (MRAM) to implement a probabilistic binary neural network (PBNN) for resource-saving applications. The in-plane magnetized SOT (i-SOT) MRAM not only enables field-free magnetization switching with high endurance (> 10^11), but also hosts multiple stable probabilistic states with a low device-to-device variation (< 6.35%). Accordingly, the proposed PBNN outperforms other neural networks by achieving an 18* increase in training speed, while maintaining an accuracy above 97% under the write and read noise perturbations. Furthermore, by applying the binarization process with an additional SOT-MRAM dummy module, we demonstrate an on-chip MNIST inference performance close to the ideal baseline using our SOT-PBNN hardware