Xiaoqing Tang

Unmanned aerial vehicle (UAV)-assisted data collection from battery-constrained Internet of Things (IoT) devices faces a fundamental trade-off between communication reliability and device energy depletion. This paper jointly optimizes UAV trajectory, user association, and radio resource management (RRM) to minimize the uplink weighted sum transmit powers, where each device’s weight is dynamically and inversely related to its residual energy, while treating UAV propulsion energy as a feasibility constraint. To circumvent the initialization trap of conventional alternating optimization (AO), we propose a two-layer non-alternating framework. The inner layer solves the per-slot RRM problem analytically via KKT conditions for a fixed UAV position, yielding analytical power allocation and a unique bandwidth solution, while user association is determined by an incremental greedy algorithm. The outer layer formulates trajectory planning (TP) as a Markov decision process (MDP), enabling single-pass trajectory synthesis without cross-layer iteration, thereby inherently avoiding initialization sensitivity. The framework supports the genetic algorithm (GA) and limited depth-first search (DFS) as trajectory solvers, with the deep Q-network (DQN) as a promising future extension, each offering distinct optimality–complexity trade-offs. Simulation results show that the proposed scheme consistently outperforms conventional iterative baselines across various network configurations.

This letter proposes an efficient discrete binary search (DBS) algorithm for source power minimization in time-switching simultaneous wireless information and power transfer (TS-SWIPT) multi-hop decode-and-forward (DF) relay networks, subject to an end-to-end rate constraint. By exploiting the problem’s inherent monotonicity, DBS directly searches over the practical discrete power set. It yields the globally optimal source power and TS ratios, eliminating iterative convex optimization and achieving linear complexity. Simulations verify that DBS achieves optimality while being orders of magnitude faster than interior-point methods (IPMs), and remains robust under a practical nonlinear energy harvesting (EH) model.

Multi-hop relaying can solve the problems of limited single-hop wireless communication distance, poor signal quality, or the inability to communicate directly by “relaying” data transmission through multiple intermediate nodes. It serves as the cornerstone for building large-scale, highly reliable, and self-adapting wireless networks, especially for the Internet of Things (IoT) and future 6G. This paper focuses on a decode-and-forward (DF) multi-hop relay network that employs simultaneous wireless information and power transfer (SWIPT) technology, with relays operating in a passive state. We first investigate the optimization of the power splitting (PS) ratio at each relay, given the source node transmit power, to maximize end-to-end network throughput. Subsequently, we jointly optimized the PS ratios and the source transmit power to minimize the source transmit power while satisfying the system’s minimum quality of service (QoS) requirement. Although both problems are non-convex, they can be reformulated as convex optimization problems. Closed-form optimal solutions are then derived based on the Karush–Kuhn–Tucker (KKT) conditions and a recursive method, respectively. Moreover, we find that the closed-form optimal solutions for the PS ratios corresponding to the two problems are identical. Through simulations, we validate that the performance of the two proposed schemes based on the closed-form solutions is optimal, while also demonstrating their extremely fast algorithm execution speeds, thereby proving the deployment value of the two proposed schemes in practical communication scenarios.

This letter presents a computationally efficient weighted sum power (WSP) minimization framework for underlaid device-to-device (D2D) networks. Our approach circumvents the high complexity of fractional programming (FP) inherent in energy efficiency (EE) and weighted sum EE (WSEE) maximization. The core innovation lies in decoupling the problem into two tractable components: (i) closed-form geometric optimal power allocation with O(1) time complexity, eliminating iterative computations; (ii) optimal link matching via the Kuhn-Munkres (KM) algorithm. Integrated with an energy-aware heuristic for dynamic weight adaptation, the proposed scheme achieves ∼50% average network lifetime extension compared to FP-based EE maximization while maintaining real-time feasibility.

This paper investigates a simultaneous wireless information and power transfer (SWIPT) decode-and-forward (DF) relay network, where a source node transmits data to a destination node through the assistance of multi-hop passive relays. We employ the time-switching (TS) protocol, enabling the relays to harvest energy from the received previous hop signal to support data forwarding. We first prove that the system throughput monotonically increases with the transmit power of the source node. Next, by employing logarithmic transformations, we convert the non-convex problem of obtaining optimal TS ratios at each relay to maximize the system throughput into a convex optimization problem. Comprehensively taking into account the convergence rate, computational complexity per iteration, and robustness, we selected the log barrier method—a type of interior point method—to address this convex optimization problem, along with providing a detailed implementation procedure. The simulation results validate the optimality of the proposed method and demonstrate its applicability to practical communication systems. For instance, the proposed scheme achieves 1437.3 bps throughput at 40 dBm maximum source power in a 2-relay network—278.6% higher than that of the scheme with TS ratio fixed at 0.75 (379.68 bps). On the other hand, it converges within a 1.36 ms computation time for 5 relays, 6 orders of magnitude faster than exhaustive search (1730 s).

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.

As the popularity of smartphones, wearable devices, intelligent vehicles, and countless other devices continues to rise, the surging demand for mobile data traffic has resulted in an increasingly crowded electromagnetic spectrum. Spectrum sharing serves as a solution to optimize the utilization of wireless communication channels, allowing various types of users to share the same frequency band securely. This paper investigates spectrum allocation and power control problems in overlay spectrum sharing, with a focus on promoting green communication. Maximizing weighted sum energy efficiency (WSEE) requires solving complex multiple-ratio fractional programming (FP) problems. In contrast, weighted sum power (WSP) minimization offers a more straightforward approach. Moreover, because WSP is directly related to users’ power consumption, we can dynamically adjust their weights to balance their residual energy. We prioritize WSP minimization over the more common WSEE maximization. This choice not only simplifies computation but also maintains users’ quality of service (QoS) requirements. The joint optimization for multiple primary users (PUs) and secondary users (SUs) can be decomposed into two components: a weighted bipartite matching problem and a series of convex resource allocation problems. Utilizing Newton’s method, our system-level simulation results show that the proposed scheme achieves optimal performance with minimal computational time. We explore strategies to accelerate the proposed scheme by refining the selection of initial values for Newton’s method.

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.

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.