In this paper, we study the three-dimensional (3D) path planning for a cellular-connected unmanned aerial vehicle (UAV) to minimize its flying distance from given initial to final locations, while ensuring a target link quality in terms of the expected signal-to-interference-plus-noise ratio (SINR) at the UAV receiver with each of its associated ground base stations (GBSs) during the flight. To exploit the location-dependent and spatially varying channel as well as interference over the 3D space, we propose a new radio map based path planning framework for the UAV. Specifically, we consider the channel gain map of each GBS that provides its large-scale channel gains with uniformly sampled locations on a 3D grid, which are due to static and large-size obstacles (e.g., buildings) and thus assumed to be time-invariant. Based on the channel gain maps of GBSs as well as their loading factors, we then construct an SINR map that depicts the expected SINR levels over the sampled 3D locations. By leveraging the obtained SINR map, we proceed to derive the optimal UAV path by solving an equivalent shortest path problem (SPP) in graph theory. We further propose a grid quantization approach where the grid points in the SINR map are more coarsely sampled by exploiting the spatial channel/interference correlation over neighboring grids. Then, we solve an approximate SPP over the reduced-size SINR map (graph) with reduced complexity. Numerical results show that the proposed solution can effectively minimize the flying distance/time of the UAV subject to its communication quality constraint, and a flexible trade-off between performance and complexity can be achieved by adjusting the grid quantization ratio in the SINR map. Moreover, the proposed solution significantly outperforms various benchmark schemes without fully exploiting the channel/interference spatial distribution in the network.
This paper proposes a new handover (HO) Algorithm for LTE high-speed rail networks aiming to reduce time latency during handover and enhance the smoothness of handover in high-speed railway networks. In railway networks, travel environment including the trace and location information of the train and position of the target eNodeB can be precognitive, which can be utilized to receive a fast handover scheme to simplify the system design and increase handover performance. The simulation results show that the proposed scheme has better performance than typical A3 event based handover algorithm in high-speed rail environment.
In this letter, we consider the use of an unmanned aerial vehicle (UAV) as a mobile relay to assist the communication between two ground users without a direct link. We propose a novel store-then-amplify-and-forward (SAF) relaying protocol for the UAV to exploit its mobility jointly with the low-complexity AF relaying. Specifically, the received signal from the source is first stored in a buffer at the UAV, then amplified and forwarded to the destination when the UAV flies closer to the destination. With this new SAF protocol, we aim to maximize the throughput of the UAV-enabled relaying system by jointly optimizing the source/UAV transmit power and the UAV trajectory, as well as the time-slot pairing for each data packet received and forwarded by the UAV. As this problem is a non-convex mixed integer optimization problem that is difficult to solve, we propose an efficient algorithm for obtaining a suboptimal solution for it by applying the techniques of Hungary algorithm, alternating optimization and successive convex approximation. Numerical results show that the proposed mobile SAF relaying outperforms the conventional AF relaying without signal storing.
In wireless sensor networks (WSNs), utilizing the unmanned aerial vehicle (UAV) as a mobile data collector for the ground sensor nodes (SNs) is an energy-efficient technique to prolong the network lifetime. Specifically, since the UAV can sequentially move close to each of the SNs when collecting data from them and thus reduce the link distance for saving the SNs' transmission energy. In this letter, considering a general fading channel model for the SN-UAV links, we jointly optimize the SNs' wake-up schedule and UAV's trajectory to minimize the maximum energy consumption of all SNs, while ensuring that the required amount of data is collected reliably from each SN. We formulate our design as a mixed-integer non-convex optimization problem. By applying the successive convex optimization technique, an efficient iterative algorithm is proposed to find a sub-optimal solution. Numerical results show that the proposed scheme achieves significant network energy saving as compared to benchmark schemes.
Wireless powered communication network (WPCN) is a new networking paradigm where the battery of wireless communication devices can be remotely replenished by means of microwave wireless power transfer (WPT) technology. WPCN eliminates the need of frequent manual battery replacement/recharging, and thus significantly improves the performance over conventional battery-powered communication networks in many aspects, such as higher throughput, longer device lifetime, and lower network operating cost. However, the design and future application of WPCN is essentially challenged by the low WPT efficiency over long distance and the complex nature of joint wireless information and power transfer within the same network. In this article, we provide an overview of the key networking structures and performance enhancing techniques to build an efficient WPCN. Besides, we point out new and challenging future research directions for WPCN.
Nonradiative resonances demonstrate revolutionary technological potential in capturing incident light, enhancing localized electric field intensity, manipulating far-field scattering, and controlling nonlinear effects at the micro/nanoscale. Here, we systematically elucidate the low-loss field localization achieved through the interference of electric dipoles in nonradiative anapole modes, while bound states in the continuum (BICs), as idealized nonradiative resonant modes, exhibit perfect field localization capability. These two distinct resonant modes, though based on different physical mechanisms—symmetry-protected mode interference versus topological eigenstate confinement—jointly overcome the performance limitations of conventional radiative systems, offering a disruptive platform for light-field manipulation in ultrasensitive optical sensing. With an aim toward practical biosensing applications, these nonradiative metasurfaces with exceptional light–matter coupling characteristics can be integrated with functionalized colloidal gold and monoclonal tag antibodies, thereby constructing a hybrid immunosensing platform capable of highly selective and label-free detection of free prostate-specific antigen (f-PSA) in complex environmental matrices. Experimental results reveal a pronounced concentration-dependent response toward f-PSA, with an impressive limit of detection down to 10 pg · mL −1 . Notably, increasing the concentration of target antibodies markedly enhances the capture efficiency of f-PSA by accelerating molecular binding kinetics and promoting saturation of recognition site occupancy. These advances pave the way for next-generation biosensing platforms that combine nanophotonic field enhancement with molecular recognition for clinical diagnostics and environmental monitoring.
In this paper, we study the trajectory design for a cellular-connected unmanned aerial vehicle (UAV) with given initial and final locations, while communicating with the ground base stations (GBSs) along its flight. We consider delay-limited communications between the UAV and its associated GBSs, where a given signal-to-noise ratio (SNR) target needs to be satisfied at the receiver. However, in practice, due to various factors such as quality-of-service (QoS) requirement, GBSs' availability and UAV mobility constraints, the SNR target may not be met at certain time periods during the flight, each termed as an outage duration. In this paper, we aim to optimize the UAV trajectory to minimize its mission completion time, subject to a constraint on the maximum tolerable outage duration in its flight. To tackle this non-convex problem, we first transform it into a more tractable form and thereby reveal some useful properties of the optimal trajectory solution. Based on these properties, we then further simplify the problem and propose efficient algorithms to check the feasibility of the problem as well as to obtain its optimal and high-quality suboptimal solutions, by leveraging graph theory and convex optimization techniques. Numerical results show that our proposed trajectory designs outperform the conventional method based on dynamic programming, in terms of both performance and complexity.
Next-generation wireless networks will rely on mmWave/sub-THz spectrum and extremely large antenna arrays (ELAAs). This will push their operation into the near field where far-field beam management degrades and beam training becomes more costly and must be done more frequently. Because ELAA training and data transmission consume energy and training trades off with service time, we pose a cross-layer control problem that couples PHY-layer beam management with MAC-layer service under delay-sensitive traffic. The controller decides when to retrain and how aggressively to train (pilot count and sparsity) while allocating transmit power, explicitly balancing pilot overhead, data-phase rate, and energy to reduce the queueing delay of MAC-layer frames/packets to be transmitted. We model the problem as a partially observable Markov decision process and solve it with deep reinforcement learning. In simulations with a realistic near-field channel and varying mobility and traffic load, the learned policy outperforms strong 5G-NR--style baselines at a comparable energy: it achieves 85.5% higher throughput than DFT sweeping and reduces the overflow rate by 78%. These results indicate a practical path to overhead-aware, traffic-adaptive near-field beam management with implications for emerging low-latency, high-rate next-generation applications such as digital twin, spatial computing, and immersive communication.
This paper presents power-efficient transmission schemes for the multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) block-fading channel under the assumption that the channel during each fading block is known perfectly at the receiver, but is unavailable at the transmitter. Based on the well-known vertical Bell Labs layered space-time (V-BLAST) architecture that employs independent encoding for each transmit antenna and successive decoding at the receiver, this paper presents a per-antenna-based power and rate feedback scheme, termed the "closed-loop" V- BLAST, for which the receiver jointly optimizes the power and rate assignments for all transmit antennas, and then returns them to the transmitter via a low-rate feedback channel. The power and rate optimization minimizes the total transmit power for support of an aggregate transmission rate during each fading block. Convex optimization techniques are used to design efficient algorithms for optimal power and rate allocation. The proposed algorithms are also modified to incorporate practical system constraints on feedback complexity and on modulation and coding. Furthermore, this paper shows that the per-antenna-based power and rate control can be readily modified to combine with the conventional linear MIMO transmit preceding technique as an efficient and capacity-approaching partial-channel-feedback scheme. Simulation results show that the closed-loop V-BLAST is able to approach closely the MIMO-OFDM channel capacity assuming availability of perfect channel knowledge at both the transmitter and the receiver.
This article provides an overview of the state-of-art results on communication resource allocation over space, time, and frequency for emerging cognitive radio (CR) wireless networks. Focusing on the interference-power/interference-temperature (IT) constraint approach for CRs to protect primary radio transmissions, many new and challenging problems regarding the design of CR systems are formulated, and some of the corresponding solutions are shown to be obtainable by restructuring some classic results known for traditional (non-CR) wireless networks. It is demonstrated that convex optimization plays an essential role in solving these problems, in a both rigorous and efficient way. Promising research directions on interference management for CR and other related multiuser communication systems are discussed.
Magnetron, as a kind of high-power highefficiency and low-cost microwave generator, has been widely improved and used since 1930s. For industrial utility, magnetron with CW output power of 75 kW operating in 915 MHz is widely used. Therefore, one type of water load with tolerance of 100 kW is developed for the convenience of high power test. Three type of materials, ceramic, mica, Teflon are considered to be used as microwave window of water load. According to simulation result, its VSWR is optimized to be less than 1.08, 1.25 and 1.5 within a bandwidth of 100 MHz for ceramic, mica and Teflon respectively.
This paper studies the fading multiple-access channel (MAC) with additive Gaussian noise and multiple transmit and receive antennas. It is assumed that the receiver has the perfect channel state information (CSI) while the mobile transmitters have no such knowledge. This paper presents a transmission scheme where the receiver jointly optimizes the signal covariance matrices for all transmitters, based on the channel distribution information (CDI) and the rate demand of each individual user, and then feeds them back to all transmitters. Under this scheme the achievable multi-user power region that is composed of all transmit power-tuples with which a given set of "ergodic" rates is achievable for all transmitters, is characterized by using convex optimization techniques. It is observed from simulation results that the transmit optimization based on the CDI can provide substantial power savings in the fading MAC
Time serves as the foundation of modern society and will continue to grow in value in the future world. Unlike previous research papers, authors delve into various time sources, ranging from atomic time and GPS time to quartz time. Specifically, we explore the time uncertainty associated with the four major Global Navigation Satellite Systems. Additionally, we provide a summary of eight metrics used to evaluate time accuracy. In existing time synchronization simulations provide partial usages. However, our research introduces a comprehensive and precise time synchronization simulation named P-TimeSync, leading to a better understanding of time synchronization in distributed environments. It is a state-of-the-art simulation tool for time because (1) it can simulate atomic clocks and quartz clocks with user-defined software clock algorithms, (2) the simulation provides nanosecond-level precision time across different network propagation paths and distances, (3) the tool offers a visualization platform with classic algorithms for distributed time synchronization, such as Cristian's algorithm and Berkeley algorithm, and (4) the simulation includes three time-sync attack functions, including distributed denial-of-service (DDoS) attack, IP spoofer, and router hijacker. The simulation easily allows for the redefinition of configurations and functions, supporting advanced research and development. The simulation tool could be downloaded via the website: https://github.com/rui5097/purdue_timesync
This paper investigates power allocation design for a wireless powered communication system, where one user harvests radio frequency (RF) energy from an energy access point (EAP) that can be used immediately to power its information transmission to a data access point (DAP). The channels from the EAP to the user and from the user to the DAP are assumed to be time-varying over two separate bands but a priori known. Our objective is to maximize the achievable rate at the DAP by jointly optimizing the power allocation at both links, subject to the sum power constraint at the EAP and the energy causality constraint at the user. We study the structural properties of the optimal power allocation, based on which we propose an efficient algorithm to solve the problem. It is shown that by optimizing energy transmission at the EAP, higher rate can be achieved than a conventional energy harvesting system where the user randomly harvests energy from the environment with the same sum power.
