Radio-frequency (RF)-powered backscattering communication (BC) has many advantages, such as low-power consumption, battery-free, small size, low cost, full sealing, manual maintenance-free, and easy mass deployment. However, the weak RF energy harvested and the short BC distance have become the two critical obstacles in the real-world implementation of this potential technology. In this article, we propose a high-sensitivity RF energy harvesting (RFEH) and long-distance BC method, and for the first time, we demonstrate the RF-powered self-sustainable long-distance BC device named RFEH LoRa. In order to improve RFEH sensitivity, an ultralow-power (ULP) consumption voltage monitoring and startup control circuit topology is designed to reduce the power consumption to 245 nanowatt (nW) during the accumulation of RF energy. While in order to increase the BC distance, a direct digital frequency synthesis technology is used to generate a linear frequency modulation square wave sequence conforming to the LoRa specifications based on the ULP microcontroller unit, and the square wave is used to drive an RF switch to achieve long-distance BC of hundreds of meters. Test results show that the instantaneous BC current is as low as 130 microampere ( μA), and the standby current is only 52 nanoampere (nA). RFEH LoRa is implemented with commercial off-the-shelf components. It can self-startup by harvesting RF energy as low as -22.5 dBm, and send its sensing data to the receiver of 381-m away.

Battery free Internet of Things (BF IoT), which is realized by harvesting ambient energy to power the IoT devices, has many advantages such as low power, low cost, free-maintenance, easy deployment, and environmental protection. As the two key enabling technologies of BF IoT, the integration of energy harvesting, management and backscatter communication (BackCom) is expected to meet practical applications. However, several fundamental issues need to be addressed to fully develop the potential of such integration. In this paper, we propose an ultra-low power energy harvesting and management scheme that effectively reduces the startup and quiescent power consumption. In the case of extremely limited energy harvested, the Long Range (LoRa) BackCom digital waveform generation algorithm with ultra-low power consumption and low complexity is studied, which can support low-power and low-cost micro-controllers (MCUs). Then, a radio frequency (RF) front-end scheme that is compatible with RF energy harvesting (RFEH), BackCom, and On-Off-Keying (OOK) receiving is proposed. On this basis, we design and implement a fully functional BF LoRa Tag, which integrates tag antennas, LoRa BackCom, low-power OOK receiving, multi-parameter real-time sensing, RFEH and management, at a cost of only $6. Our test results show that the BF LoRa Tag can be self-powered by harvesting the RF energy as low as –19 dBm, with the cold-start power as low as 280 nW, the quiescent power as low as 49 nW, the OOK receiving power consumption as low as 1.74 μW, the LoRa BackCom power consumption of only 251 μW, and the communication distance up to 445 meters. Compared with the prototypes in existing literature, the BF LoRa Tag not only has lower power consumption, wider coverage, and the lowest cost, but also has complete system functions. Finally, we discuss the reciprocity between stations and receivers, as well as the BF LoRa cellular communication network, which provides useful references for the practical application and large-scale deployment of BF IoT in the future.

With the increase in the scale of demand side management (DSM) activities, the load characteristic of power system at the load reduction and recovery times will experience significant changes due to DSM actions and hence affect the dynamic performance of power systems. This paper proposes a methodology to assess the effect of DSM actions on power system network performance - small signal stability of transmission network. A case study of shifting of different type of load from one hour to the other is tested and investigated to what extent the impact of these DSM activities can have on grid performance. A generic 295 bus distribution network and the IEEE standard 9-bus transmission network are used to demonstrate this effect. In the proposed methodology, different types of load sectors (domestic, commercial, industrial) with corresponding mix of load categories (small and large induction motors, constant power, constant impedance and constant current load) in each of them are modelled in DigSILENT/PowerFactory based on typical structure and composition of demand in distribution network. The paper demonstrates and quantifies the impact of the DSM actions on transmission network performance and establish the extent to which DSM can contribute, or otherwise, to power system stability. It will pave the way to development of optimal DSM that will ensure that not only total demand is managed but also the way how it should be managed in order not to endanger network performance in case of disturbances.

Device-to-device (D2D) technology enables direct communication between adjacent devices within cellular networks. Due to its high data rate, low latency, and performance improvement in spectrum and energy efficiency, it has been widely investigated and applied as a critical technology in 5G New Radio (NR). Cooperative D2D communication can achieve a win-win situation between cellular users (CUs) and D2D users (DUs) through cooperative relaying techniques. In addition to conventional overlay and underlay D2D communications, it has attracted extensive attention from academic and industrial circles in the past decade. This paper delves into optimizing joint spectrum allocation, power control, and link-matching between multiple CUs and DUs for cooperative D2D communications. Weighted sum energy efficiency (WSEE) is used as the performance metric to address the challenges of green communication and sustainable development. This mixed-integer fractional programming (MIFP) problem can be decomposed into: 1. a classic weighted bipartite graph matching; 2. a series of nonconvex spectrum allocation and power control problems between potentially matched cellular and D2D link pairs. To address this issue, we propose a hybrid centralized-distributed scheme based on deep reinforcement learning (DRL) and the Kuhn-Munkres (KM) algorithm. Leveraging the former, the CUs and DUs autonomously optimize spectrum allocation and power control by only utilizing local information. Then, the base station (BS) determines the link matching utilizing the latter. Simulation results reveal that it achieves more than 96% WSEE of the optimal scheme and 98% WSEE of the centralized DRL-based scheme. It significantly enhances the network convergence speed with low centralized computational overheads. In addition, we also propose and utilize cooperative link sets for corresponding D2D links to accelerate the proposed scheme and reduce signaling exchange further: an average of about 85% WSEE of the optimal scheme is achieved, while more than 50% of signaling and distributed computing overheads are reduced.

Wireless temperature measurement, compared with traditional temperature measurement methods, can not only offer lower costs and higher convenience but also measure the temperature of the target object in real time and continuously. However, current mainstream-based wireless temperature measurement schemes still face issues, such as low-accuracy, high-power consumption and limited data transmission distance. To this end, this article proposes a novel ultrahigh frequency (UHF) battery-free wireless temperature sensing system. The system utilizes the radio frequency (RF) electromagnetic wave signals in the surrounding environment for energy supply, employs a self-compensating temperature sensor to accurately measure the temperature, and achieves data communication by modulating the electromagnetic wave signals through backscattering. This system is characterized by its low-power consumption, long working distance, broad temperature measurement scope and high accuracy. The chip is fabricated in Huahong 0.18× m 1P6M BCD technology, with a tag chip area of 0.097 mm2. Operating at the frequency of 915 MHz, the RF energy harvesting sensitivity is −17 dBm, and the effective distance of RF energy harvesting can be up to 10 m at the equivalent isotropically radiated power (EIRP) of 36 dBm. With a two-point calibration, the measurement covers a range from 0°C to 100°C with a mean error of +0.47/- 0.79.°C The average power consumption of the chip is as low as 0.97×W.

This paper considers device-to-device (D2D) communications overlaying cellular networks. Combined with cooperative relay technology, a D2D user can serve as a relay for a cellular link and gain the opportunity to access the corresponding cellular spectrum. Considering mobile terminals’ limited battery capacity and motivated by green communication, we propose a resource scheduling scheme, which comprises joint power and spectrum allocation, mode selection, and link matching. Unlike previous works, to balance energy utilization and extend the network lifetime, this scheme sets a price for each node and minimizes the system cost during each frame instead of energy efficiency (EE). Specifically, we investigate the power and spectrum allocation problem to characterize the minimum cost of matched links. We prove it to be nonconvex and obtain the optimal solution by the graphic method and Newton’s method with a small number of iterations. We also discuss the energy pricing strategy. In order to reduce communication and computation overhead, we propose a heuristic pricing strategy that involves no iteration and supposes nodes’ energy prices are inversely proportional to their residual energy. Through simulations under various cases, we verify that the proposed scheme significantly improves network lifetime and suits real-time operations while guaranteeing the quality of service (QoS) requirement. Moreover, the simulation results demonstrate that the proposed scheme performs the same as the maximum EE scheme if the energy prices are equal at each node.