Large language models (LLMs) concentrate substantial knowledge in specialized domains due to extensive pretraining and instruction tuning, and they are now central to commercial and scientific practice. Yet access is usually limited to costly, rate-limited interfaces, which motivates methods that can extract targeted domain knowledge with minimal querying effort. A further challenge is that the target domain may be unknown in advance, so naive or generic prompts waste queries and fail to expose the underlying concepts and relations that structure the domain. In this work, we introduce a query-efficient approach for domain-specific knowledge stealing from black-box language models. Rather than issuing random questions or generic templates, our framework performs self-directed exploration that lets the model find the direction and mine domain knowledge by itself. Starting from a small and diverse seed, it discovers salient domain entities and induces their relations through structured question families that elicit definitional, functional, and compositional information. A feedback-driven controller analyzes the errors and uncertainty of the extracted surrogate model and uses this signal to refine subsequent queries, all without relying on prior domain knowledge or external resources. We evaluate the method in two expert-centric settings, medicine and finance, and observe consistently better performance while requiring significantly fewer queries.

In this paper, we study a multi-source wireless power transfer (MS-WPT) enabled Internet of Nano Things (IoNT), where massive nanonodes wirelessly transmit packets to the same destination via clustered data collection and multi-hop relaying with the aid of nanonodes. A fairness-aware reliability-oriented design is provided aiming at minimizing the maximum transmission error probability among all the nanonodes. In particular, we formulate a joint resource allocation problem that optimizes MS-WPT dynamic transmit power and the blocklength for both WPT and wireless information transfer (WIT) phases. However, the problem is non-convex and intractable due to the mutual effects of multi-source, the nonlinear EH model, the complex finite blocklength (FBL) reliability model, and the infinite optimization variables regarding time-varying MS-WPT power. To tackle these difficulties, we first characterize the optimal frame structure for MS-WPT and prove that an equivalent optimal performance can be achieved by limited WPT decisions corresponding to a finite number of sub-slots. Following this frame structure reconstruction, an optimization problem with finite number of variables is formulated, nevertheless, remaining nonconvex. To cope with it, variable substitution, nonconvex relationship decoupling, relax variable introduction and successive convex approximation (SCA) are utilized, to further transfer the problem into local convex ones. A sub-optimal solution is finally achieved by the proposed iteration-based algorithm. Via numerical simulation, it is validated that a significant performance improvement is achieved by reasonable joint resource allocation while maintaining an appropriate compromise among massive nanonodes.

In this work, we study a fluid antenna multiple access (FAMA) system, where a base station (BS) with multiple fluid antennas is responsible for the communication service supply to multiple users also equipped with fluid antennas. We concentrate on the optimal joint antenna configuration and resource allocation design, where the transmit power control is jointly optimized with the antenna configuration including BS antenna assignment and port selection at all activated fluid antennas. The large number of discrete variables needed for antenna configuration makes the joint optimization very challenging. To address these challenges without loss of optimality, we develop in this work a novel methodology for globally optimal FAMA designs. We first focus on FAMA throughput maximization while taking user fairness into account and accordingly formulate a mixed-integer nonlinear problem. To facilitate the optimal design, we characterize the optimal power control with given antenna configuration, which enables us to build up a system of equations and inequalities (SEI) tailored for examining the achievability of any throughput level. A fixpoint-based approach is subsequently proposed for effectively inferring the solvability of established SEI, as well as the throughput achievability. Leveraging the proposed fixpoint-based inference approach, we develop an efficient iterative algorithm for the optimal antenna configuration filtering, where all nonoptimal configuration candidates are efficiently filtered and removed via fixpoint inspections. The optimal power control associated with the optimal antenna configuration finalizes the globally optimal FAMA design. Afterwards, we extend the whole design methodology to a scenario requesting energy efficiency maximization, achieving globally optimal energy-efficient FAMA design. Finally, the obtained FAMA solutions are examined via numerical simulations, verifying the global optimality and spotlighting the high benefits of considering joint antenna configuration and power control in FAMA.

The technological landscape is rapidly evolving toward large-scale systems. Networks supporting massive connectivity through numerous Internet of Things (IoT) devices are at the forefront of this advancement. In this paper, we examine Wireless Power Transfer (WPT)-enabled networks, where a server requires to collect data from these IoT devices to compute a task with massive Ultra-Reliable and Low-Latency Communication (mURLLC) services. We focus on information freshness, using Age-of-Information (AoI) as the key performance metric. Specifically, we aim to minimize the maximum AoI among IoT devices by optimizing the scheduling policy. Our analytical findings demonstrate the convexity of the problem, enabling efficient solutions. We introduce the concept of AoI-oriented cluster capacity and analyze the relationship between the number of supported devices and network AoI performance. Numerical simulations validate our proposed approach's effectiveness in enhancing AoI performance, highlighting its potential for guiding the design of future IoT systems requiring mURLLC services.

In this paper, we consider a latency-sensitive wireless network and aim at minimizing the overall transmission latency via an optimal cross-layer design. In particular, we assume a packet divided into multiple subpackets is supposed to be routed from a source node to a destination node through a wirelessly connected multi-device network. Each activated routing link is assigned a dedicated subcarrier, allowing simultaneous transmission and reception. Taking into account the routing ability at the network layer and the finite blocklength (FBL) effects at the physical layer, via an error propagation method, we first derive out the average transmission latency for completing a data forwarding task under buffer limit at each routing device, while retransmissions are scheduled against transmission failures. Afterwards, we formulate an average transmission latency minimization problem via jointly optimizing the routing path at the network layer and the blocklength allocation at the physical layer. To optimally address the cross-layer mixed-integer nonlinear problem, we characterize the optimal blocklength design for given routing path as an equation system, which is efficiently solved via iterative fixpoint checks. The performed characterization enables a filtering criterion for efficiently evaluating the performance bound of any routing path with respect to a threshold, based on which we propose an efficient algorithm for the optimal routing path filtering, together with a low-complexity iterative routing algorithm for suboptimal routing design. The global optimal joint solution is obtained as the filtered optimal path, combined with the correspondingly optimized blocklength solution. Finally, we numerically validate the effectiveness and optimality of our proposed solution, as well as the necessity of cross-layer design for latency minimization.

The complexity of online applications is rapidly increasing, bringing more sophisticated performance anomalies in today's cloud datacenter. To fully understand application behaviors, we should obtain both inter-service communication data via RPC-level tracing and intra-service execution traces via application-level tracing to precisely reason about event causality. However, the average time overhead of existing intra-service tracing schemes on the traced applications is generally about 5-10%, possibly reaching 18% in the worst case. To realize practical intra-service tracing in shared and stressed datacenters, one must achieve extreme tracing efficiency with an overhead at the per-mille level.

This paper studies a wireless powered communication network (WPCN), where a passive user first harvests energy from a wireless power transfer (WPT) base station (BS), and then transmits data to a targeted receiver. To maximize the system throughput under finite BS energy budget and latency constraint, we formulate a joint dynamic WPT power, energy harvesting (EH) duration and wireless information transfer (WIT) duration optimization problem. To ensure the practicality of the design, a realistic nonlinear EH model is considered, making the problem nonconvex, while the infinite number of variables associated to WPT power control makes it more intractable. To address these issues and achieve the optimal solution, we first analytically characterize the structure of the optimal WPT policy for maximizing the harvested energy, following which we prove that the optimal WPT power control can be reduced to a constant-power policy without loss of optimality. Specifically, the optimal WPT power is characterized as a piecewise function determined by the EH duration and BS energy budget. We further reveal the tradeoff between WIT duration and SNR under given WIT energy budget, and prove that the system throughput increases monotonically with WIT duration despite reduced transmit power. These insights, on optimal WPT and WIT solution properties, allow us to equivalently transform the original non-convex problem into a single-variable optimization problem. The globally optimal solution can be efficiently obtained via one-dimensional exhaustive search. Simulation results validate the effectiveness of the proposed optimal WPCN design.

This work investigates a fluid antenna multiple access (FAMA) system, in which a base station (BS) with multiple fluid antennas serves multiple users, each also equipped with fluid antennas. With the objective of fairness-aware throughput maximization, we propose an optimal joint antenna configuration and resource allocation design, incorporating transmit power control alongside BS antenna assignment and port selection for all active fluid antennas. The antenna assignment and port selection introduce numerous discrete variables, resulting in a mixed-integer nonlinear problem, thus significantly complicating the joint optimization. To address these challenges without compromising optimality, we develop a novel methodology for globally optimal FAMA design. Specifically, we first characterize the optimal power control with a given antenna configuration, which enables the formulation of a system of equations and inequalities (SEI) to assess the achievability of any throughput level. A fixpoint-based inference approach is then developed to determine SEI solvability, facilitating the iterative filtering of nonoptimal configurations. The globally optimal FAMA design is finally achieved by through optimal power control associated with the best antenna configuration. Finally, numerical results validate the global optimality and the high benefits of our proposed design.

The utilization of sensing signals from multiple anchors for three-dimensional (3D) spatial localization represents one of the commonly employed wireless localization techniques for unmanned aerial vehicles (UAVs) in global navigation satellite system (GNSS)-denied environments, which has been extensively investigated. However, this methodology typically necessitates more than three anchors with distinct spatial distribution characteristics, coupled with precise alignment between the UAV local coordinate system and the global reference frame. These stringent requirements are often challenging to meet in practical operational scenarios. In this work, we explore an observable UAV 3D self positioning and orientation alignment of local coordinate system supported by only one angle of arrival (AoA) anchor, with significantly reduced implementation cost and complexity. We first proved the observability of designed positioning system with a static anchor which is the new theoretical limit supporting observable UAV positioning on minimal anchor number, and can significantly reduce the requirement for anchor number in practical positioning applications. Then, we develop an efficient two-layer iterative algorithm for the estimation problem which provides real-time positioning estimation with extremely low computing cost. Finally, numerical results confirm that the proposed scheme has high positioning accuracy and strong robustness to measurement noise.

In this paper, we study efficient trajectory and user assignment design for an unmanned aerial vehicle (UAV)-aided covert transmission against cooperative detection from multiple wardens, which is still an open issue in the literature. Starting with analysis on basic principles of cooperative detection, we derive the closed form expression of covertness metric under cooperative detection. Then a joint design of trajectory and user assignment is formulated to maximize the minimum throughput. Although the problem is highly nonconvex with infinite variables, we adopt the optimal successive-hover-and-fly (SHF) structure to reformulated the problem and reduce the complexity without loss of optimality. Then, an efficient algorithm is developed based on a convex approximation to obtain a high-quality solution. Finally, simulations verify the necessity of considering cooperative detection and the performance advantages of proposed design.

In this paper, we focus on a satellite-ground multi-casting scenario assisted by a free space optical (FSO) link, where multiple ground devices are requesting the same data packet from a satellite via the FSO link. Due to the extremely long link distance in satellite-ground communication, the coverage of an optical beam has been considerably enlarged. We aim at deploying the corresponding coverage benefits of the optimal beam in provisioning multicasting services to ground devices. At first, we characterize the achievable multicasting capacity for considered satellite-ground communication. Assuming the deployment of an optical beam can be switched between an activation mode and an idle mode, we formulate a multicasting throughput maximization problem under a maximum average power limit for the optical signal emission, via jointly optimizing the optical beam deployment and the activation slot scheduling. Both optical power bias and beam pointing direction will be optimized in the optical beam deployment design. For optimally solving the formulated nonconvex problem, we perform a two-fold problem reformulation and successfully convert the nonconvex problem to a convex one. The convex problem reformulation allows us to equivalently and optimally tackle the original problem via convex optimization tools. At last, in comparison with two benchmarks, we verify the optimality of our proposed design and illustrate the performance benefits of allowing idle operation mode and performing beam pointing design.

In this paper, we consider a multi-hop full-duplex (FD) relaying system that supports low-latency communication, and aim to explore the potential of FD technology in suppressing transmission latency. Specifically, we begin with a two-hop relaying system, where a source node is expected to transmit a large message to the destination node via a relaying node operating in FD mode. We assume the large message is equally divided into multiple smaller packets, while the whole transmission is operated in a packet-by-packet manner and retransmissions are scheduled against decoding failures. Notably, we have for the first time characterized the expected transmission latency while taking into account the finite blocklength (FBL) impact on transmission reliability. Through a proposed error probability propagation policy, we have recursively derived the expected number of transmissions required to successfully conveying the entire message via FD relaying system. An optimization problem is then formulated to minimize the expected transmission latency by jointly optimizing packet division, blocklength allocation, and transmit power control. To deal with the inherent nonconvexity of the problem, we reformulate it using variable substitution and subsequently construct a tight convex approximation based on an arbitrary feasible point. This facilitates an iterative algorithm that progressively refines the solution until convergence to a suboptimal point. The whole approach for latency characterization and minimization is then extended to the multi-hop relaying scenario. Finally, simulation results validate the convergence behaviours of our proposed algorithms and highlight the latency benefits of our solution compared to both half-duplex relaying and full-duplex relaying without optimal power control.

Semantic communication has achieved great progress in improving efficiency for completing tasks successfully, instead of directly transmitting bits. However, substantial challenges remain in real-time intelligent communication, which demands stringent low latency and rapid understanding of massive data. In this paper, we propose a latency-driven design for promoting real-time multi-task multi-access semantic communications. More specifically, we investigate a deep learning-based framework for multi-access scenarios, where multiple users with individual latency requirements continuously request real-time semantic updates from an edge server. Two typical image-based semantic tasks, i.e., image classification and object detection, are considered as representative multi-task example. Furthermore, since the low-latency requirements in real-time systems force the application of finite blocklength (FBL) codes to be a significant consideration, we take into account the effects of FBL on transmission reliability. To adapt to the low-latency demands, we adopt a parameter-sharing strategy for multi-task computer vision (CV) applications and design an adaptive mixed-precision compression module for effective feature compression. The design target is to maximize the minimum weighted task success probability among all users via jointly optimizing feature extraction, mixed-precision quantization bit selection, transmit power allocation and semantic decoding. To facilitate the overall joint optimization, we propose an approach for efficient optimal decision-making on joint quantization bit selection and power allocation, which is integrated into deep learning process for adaptive feature extraction. Simulation results verify the promising performance of our proposed latency-driven design for real-time multi-task CV applications, as well as the superior benefits of our proposed efficient optimal resource allocation for real-time communication scheduling.

Unmanned aerial vehicle (UAV)-enabled communication is recognized as a promising technique in Internet of Vehicles (IoV) to address the issue of ineffective transmission of road condition data and driving instructions caused by obstruction and random fading of ground channels. However, the inherently open channel characteristics in UAV to ground links brings new secure and covert problem in vehicular IoV which largely limit the applications of UAV in IoV. To resolve the issue, this work studies a UAV-enabled covert autonomous vehicular communication network where a UAV is deployed as a relay aided by a jammer to assist the data transmission from the base station to an autonomous vehicle without being detected by a warden whose exact location is unknown. For network performance boosting with transmission covertness consideration, an upload throughput maximization problem is formulated by jointly designing UAV trajectory and resource allocation under a more generally joint covertness constraints. To solve the complicated and highly non-convex problem which contains a large number of variables, we first analyze the detection performance and derives the closed-form expressions of the warden’s minimal detection error probability considering the warden’s location uncertainty. Then, the characterizations on the convexity of minimal detection error probability and the optimal transmission rate are provided, which helps in simplifying original problem and developing an efficient iterative algorithm to solve this problem based on a proposed novel convex approximation method. Simulations are offered to demonstrate the superior convergence, throughput, computation time, and covertness performance of proposed scheme in UAV-enabled vehicular network.

In this paper, we focus on an unmanned aerial vehicle (UAV)-assisted covert communication scenario, and introduce an optimal joint resource allocation and continuous UAV trajectory design. We aim at maximizing the information throughput between UAV and a ground user, while protecting the transmission behavior from being detected by a warden. Due to the continuity of UAV trajectory in both time and space, the formulated problem has infinitely large number of variables to be optimized, i.e., being not only cutting-edge, but also very challenging to be coped with. To address this issue, we provide an artificial potential field (APF)-based approach, with which a closed-form optimal solution is for the first time obtained for considered UAV-assisted covert communication. In particular, first based on investigation on the covertness constraint, the maximal transmit power is characterized as a closed-form binary decision function with respect to UAV position. Following the characterization, we then transform the joint optimization problem to one of pure UAV trajectory design. Subsequently, via conducting an APF to covert transmission rate between the UAV and the user, the trajectory design problem is completely equivalent to a mechanical problem, i.e., a density-variable rope shape design problem in the APF, based on mechanical equivalence technique. Such mechanical problem can be optimally solved. Specifically, the force field in the conducted APF corresponding to a covert communication is actually twisted due to the presence of the warden, for which we reorganize a brand new mechanical analysis process accordingly, including reanalyzing the direction of the force field and updating the force balance expression. Then, according to the minimum total potential energy principle, the closed-form solution of the optimal rope shape is constructed following the equilibrium analysis. In addition, acknowledging that the lowest potential point of APF changes with the covert requirement, we also discuss all the three cases for optimal trajectory distinguishing in hovering behavior of the UAV.

In this paper, we focus on an unmanned aerial vehicle (UAV)-assisted covert communication scenario, and introduce an optimal joint resource allocation and continuous UAV trajectory design. Our goal is to maximize the information throughput between UAV and a ground user, while protecting the transmission behavior from being detected by a warden. To tackle the formulated non-convex continuous trajectory design problem, we provide an artificial potential field (APF)-based approach, with which a closed-form optimal solution is for the first time obtained for considered UAV-assisted covert communication. In particular, by characterizing the covertness constraint and decoupling the original joint problem, we then convert the resulting pure trajectory design into a mechanical problem in the APF, which can be optimally solved based on mechanical equivalence technique. Specifically, the force field in the conducted APF corresponding to covert transmission rate is actually twisted due to the presence of the warden, for which we reorganize a brand new mechanical analysis process accordingly, including reanalyzing the direction of the force field and updating the force balance expression. Then, according to the minimum total potential energy principle, the closed-form solution of the optimal rope shape is constructed following the equilibrium analysis. Finally, we also verify our proposed algorithm and confirm the optimality of the obtained solution via simulations.

This paper explores the potential of employing two-hop full-duplex (FD) relaying systems to alleviate transmission latency. The approach involves dividing a message into smaller packets and transmitting them sequentially with possible retransmissions. Notably, we characterize the expected transmission latency of multiple packet transmissions for the first time. By introducing a novel error probability propagation method, the expected number of time slots needed for successfully transmitting all packets is recursively derived. The article tackles the minimization of transmission latency by jointly considering packet division, blocklength per packet, and power allocation. To cope with the complex nonconvex nature of this optimization problem, a subproblem is extracted, and a reformulation utilizing variable substitution is proposed. Furthermore, a tight convex approximation at any feasible point is developed to facilitate the design of an iterative algorithm to gradually converge towards a suboptimal solution. Simulation results validate the efficacy of the proposed solution, demonstrating its convergence and latency advantages over both half-duplex (HD) and FD relaying systems lacking power control.

In this paper, we consider an CSMA/CA network supporting multi-node transmissions. To meet the timeliness and reliability, the communications are operated with finite blocklength (FBL) codes and truncated hybrid automatic repeat request (HARQ) scheme. We characterize the timeliness of packets utilized for decision-making via Age upon Decisions (AuD) in such unsaturated CSMA/CA wireless networks under truncated HARQ protocol. In particular, we develop an equivalent unsaturated Markov transfer model according to the considered network and calculate the value of transmission probability and collision probability, respectively. Then, we introduce a method to calculate the average AuD with Bernoulli decision process and obtain a closed-form expression following these characterizations. Via simulations, the performance of the considered network is evaluated and we conclude a series of design guidelines.

In this paper, we study a multi-source wireless power transfer (MS-WPT) enabled Internet of Nano Things (IoNT) supporting multi-hop ultra-reliable low-latency communications (URLLC), i.e., nanosensors wirelessly transmit short packets to the same destination in a multi-hop collecting-then-relaying manner. For such MS-WPT enabled nanoscale relaying network, we for the first time characterize the fairness-aware reliability and propose a joint blocklength and dynamic MS-WPT power allocation design for maximum transmission error probability minimization. However, the mutual effects between multi-source, the infinite MS-WPT schemes, the nonlinear EH model, and the complex finite blocklength (FBL) reliability model make the problem nonconvex and intractable. To tackle these difficulties, we first characterize the optimal frame structure for MS-WPT and prove that an equivalent optimal performance can be achieved by limited WPT decisions corresponding to a finite number of sub-slots. Following that, an optimization problem with finite number of variables is formulated, nevertheless, remaining nonconvex. To cope with it, variable substitution, nonconvex relationship decoupling, relax variable introduction as well as successive convex approximation (SCA) are utilized to further reformulate the problem into local convex ones. A sub-optimal solution is finally achieved by the proposed iteration-based algorithm. Via numerical simulation, it is validated that a significant performance improvement is achieved by our proposed design.

In this paper, we focus on an ultra-reliable low latency communication (URLLC) scenario and investigate the multi-access services with individual latency constraints. More specifically, the wireless communications between the access point and multiple users are requested to be accomplished while satisfying different maximum allowed delays. Taking the finite blocklength (FBL) impacts into account, we model the individual latency constraints as diverse blocklength consumption limits for users and concentrate on a blocklength allocation problem minimizing the overall decoding error probability. Aiming at achieving the optimal blocklength design in an extremely efficient manner, we start with characterizing the optimal solution features and find out that the error probability derivatives in the optimal solution follow a stepwisely increasing manner. As a result, we are enabled to alternatively determine the derivative step levels for optimally solving the problem. Subsequently, an efficient algorithm is proposed for the optimal step level design and for recovering the optimal blocklength solution. The solution optimality is then verified via both theoretical discussions and numerical evaluations. In addition, our proposed analytical solution based on step level design has also been numerically confirmed with an extremely lower complexity, in comparison with the conventional convex optimization approach.

In this paper, we focus on an ultra-reliable low latency communication (URLLC) scenario, where the access point (AP) is supposed to support latency-critical communication via a non-orthogonal multiple access (NOMA) scheme. Moreover, we allow the device with the stronger channel acting as a relay for cooperatively enhancing the transmission reliability for the other device. Based on the considered NOMA-assisted cooperative scheme, we characterize out the maximum error probability between two devices as the objective to be minimized. Together with an energy constraint for the whole transmission period, we formulate a problem jointly optimizing the blocklength assigned to two phases, i.e., the NOMA phase and the cooperative phase, and power resources allocated in each transmission attempt. To address this non-convex problem, we reformulate the problem by introducing auxiliary variables and construct a tight convex approximation at any feasible local point, based on which we further propose an efficient algorithm for iteratively improving the local point until a convergence to a sub-optimum. Via numerical results, we validate the convergence of the proposed iterative algorithm and confirm the reliability advantages of NOMA-assisted cooperative scheme, compared with multiple benchmarks.

In this paper, towards enabling massive connectivity in the next generation ultra-reliable low latency communication (URLLC) Internet-of-Things (IoT) network, we investigate a scalable clustered network, where the user scheduling at the access point (AP) is completely replaced by the cooperation scheduling among clustered IoT users, in order to alleviate the overload at AP. In particular, while serving the clustered network, the AP simply broadcasts out all data for the whole network. Each clustered user attempts to decode the broadcast signal. Afterwards, cooperation retransmissions will be scheduled among users for compensating the overall transmission reliability. Considering limited energy and blocklength resources, we start with the cooperation case based on a cluster head and aim at fairly minimizing the maximum error probability among all users, while the resource allocation and cooperation scheduling are jointly designed. To deal with the inherent nonconvexity, we construct a tight convex approximation for the problem based on an arbitrary feasible point, which enables an iterative algorithm for constantly improving the solution until a convergence to a suboptimal. Next, to further exploit the high cooperation flexibility in clustered URLLC network, we extend the whole design to the case allowing arbitrary cooperation among users, i.e., the case without cluster head. Finally, simulation results validate the convergence of our proposed algorithms and highlight the reliability benefits over benchmarks. The impact of cluster head selection and the high cooperation flexibility of the case without cluster head are also illustrated.

In this paper, we study covert communications in an unmanned aerial vehicle (UAV)-enabled network, where a UAV transmits information to multiple ground users (GUs) without being detected by a hidden detector. Considering fairness issue, we aim at maximizing the minimum throughput among GUs by jointly optimizing the UAV’s trajectory, transmit power and power allocation coefficient, under UAV mobility and covertness constraints. On the one hand, according to the covertness constraint, the maximal transmit power is characterized as a close form expression of UAV’s position. On the other hand, the optimal UAV trajectory structure is characterized as a successive-hover-and-fly (SHF) structure. Following the two fundamental characterizations, we first transform the original problem to a joint trajectory and power allocation design one and then it is reformulated to another one addressing only a limited number of hovering points, corresponding hovering durations, turning points and allocation coefficient. Although being still non-convex, the new problem is efficiently solved via applying the sequential convex programming (SCP) method. Namely, by introducing a series of tight concave function in each iteration, we can solve a series of convex problems iteratively to make the trajectory converge to a high-quality solution. Numerical results confirm the convergence of our approach and show the high performance comparing with benchmark.

To support abundant mission-critical applications, the next-generation ultra-reliable low latency communication (URLLC) is expected to meet more stringent requirements. In this work, to promote the advancement of URLLC, we target at exploring the fundamental trade-offs in finite blocklength (FBL) regime. Taking the short blocklength impacts into account, we integrate broadcasting into time-division multiple access (TDMA) strategy and adopt a hybrid broadcasting-TDMA (BC-TDMA) strategy for the multiple access URLLC services. Within hybrid BC-TDMA, user grouping has been implemented, such that grouped users can be served over the shared large blocklength and thus get rid of the performance hindrance from short blocklengths. We formulate a problem for fairly minimizing the error probability for all users via optimizing the user grouping decision together with the joint power and blocklength allocation. For given grouping, we characterize four necessary optimality conditions for the joint resource allocation and accordingly construct the optimal closed-form resource allocation solution. The analytical characterizations have also enabled two criteria for efficiently filtering out the optimal grouping in an iterative manner. Finally, via simulations, we examine our proposed algorithms for both obtaining optimal resource allocation and filtering the optimal grouping. The extremely low complexity and significant reliability advantages of our proposed hybrid BC-TDMA solution are also highlighted in comparison to benchmarks.

In this paper, we investigate an unmanned aerial vehicle (UAV)-enabled covert communication network with multiple ground users (GUs), a warden, and a UAV. The UAV carries directional antenna to communicate with GUs covertly, i.e., without being exposed to the warden. Focusing on task to improve the throughput of all GUs, we provide a joint user assignment and trajectory design aiming to maximize the throughput of GUs with worst condition. In particular, we consider the impact of directional antenna pattern on covertness performance, and derive the expression for maximum allowed transmit power satisfying the covertness and maximum transmit power requirement based on the modified antenna pattern. Following the characterization, the non-convex joint design problem is formulated. For making the highly non-convex problem analysable, we adopt two lemmas to construct the concave approximation function for the throughput which enables us to build up a convex problem. Accordingly, an effectively iterative algorithm is putting forward to settle the problem. Finally, numerical results confirm that our scheme with directional antenna outperforms the benchmarks.

Integrated Sensing, Communication, and Computation (ISCC) multi-functional networks represent a new paradigm in wireless communications, enabling comprehensive environmental perception, data processing, and communication. However, realizing the full potential of these networks requires addressing cooperative gain-a challenge given the competitive nature of the tasks associated with the various functionalities. This paper investigates the concept of semantic communication as a potential pathway towards achieving this cooperative gain. Despite the considerable body of research in semantic communications, the area of semantic reliability remains relatively unexplored, and characterization of semantic reliability within ISCC networks is particularly limited. In this study, we focus on the performance of semantic reliability within the ISCC framework. We formulate a joint resource allocation problem aimed at maximizing semantic reliability, thereby addressing the trade-off between different functionalities with limited resources. This approach transforms the traditionally competitive objectives into a cooperative framework from the perspective of semantic communications. Our analytical findings validate the efficacy of this approach, highlighting the benefits of focusing on semantic communication over traditional data communication in ISCC networks.

Data security is one of the key concerns in the next generation of ultra-reliable and low-latency networks, especially with machine-type communications. In this work, we propose a novel metric, leakage-failure probability, to represent the reliable-secure performance of the considered system. We discover that the system performance can be enhanced by counter-intuitively trading the reliability for security, i.e., allocating less blocklength in the short-packet transmission. In order to solve the corresponding blocklength allocation problem, we propose a novel optimization framework, for which a lower-bounded approximation of the decoding error probability in the finite blocklength regime is provided. Based on that, we reformulate the optimization problem into a convex one and propose an iterative searching method. We show the efficiency and the convergence of such a method analytically. Furthermore, we discuss the extendability of the proposed framework with an example of the effective secure throughput as the metric. Via numerical results, we verify the performance of the optimization problem and demonstrate the reliability-security tradeoff under various setups.

In this paper, we study an unmanned aerial vehicle (UAV)-enabled covert communication network in which a UAV communicates to multiple ground users (GUs) without being detected by a ground detector. Considering the fairness, we aim at a joint UAV trajectory and transmit power design to maximize the minimum throughput among all GUs under constraints including mobility and covertness. By characterizing the covertness constraint, the joint design problem is transformed to a pure trajectory design which is still non-convex and with infinite number of variables. To address the problem, we adopt the optimal successive-hover-and-fly (SHF) trajectory structure to reformulate it to a new one with limited number of variables, and efficiently solve the reformulated problem via introducing a set of tight convex approximations to the problem and applying the successive convex approximation (SCA) method. Simulation results show the high performance with respect to covert communication throughput and the low complexity of proposed design in comparison to the benchmark.

How to provide information security while fulfilling ultra reliability and low-latency requirements is one of the major concerns for enabling the next generation of ultra-reliable and low-latency communications service (xURLLC), specially in machine-type communications. In this work, we investigate the reliability-security tradeoff by defining the leakage-failure probability, a metric that jointly characterizes both reliability and security performances for short-packet transmissions. We discover that the system performance can be enhanced, counter-intuitively, by allocating fewer resources for the transmission with finite blocklength (FBL) codes. In order to solve the corresponding optimization problem for the joint resource allocation, we propose an optimization framework, that leverages lower-bounded approximations for the decoding error probability in the FBL regime. We characterize the convexity of the reformulated problem and establish an efficient iterative searching method, the convergence of which is guaranteed. To show the extendability of the framework, we further discuss the blocklength allocation schemes with practical requirements of reliable-secure performance, as well as the transmissions with the statistical channel state information (CSI). Numerical results verify the accuracy of the proposed approach and demonstrate the reliability-security tradeoff under various setups.

In this paper, we consider a unmanned aerial vehicle (UAV) aided integrated sensing and communications (ISAC) network with moving ground users in constant-velocity trajectory. A global optimal trajectory design scheme is proposed including a continuous analytic solution as well as optimization condition, which is theoretically different from numerical schemes obtaining a discrete piece-wise solution with approximate optimality. However, it is challenging to maximize the performance over entire infinite time slots in moving-user scenarios. By projecting the trajectory onto a user-relative coordinate frame, we reduce the performance to a location-determined function, which is in physical equivalence to an artificial potential field (APF). Accordingly, the optimization problem is reformulated to the shape determination problem of a density-varying catenary in the APF. By performing force analysis, we describe the topology of the catenary via a second-order differential equation determined by a boundary, i.e., any three combination of the location, orientation and turning curvature at arbitrary waypoints. Equivalently, the representation of the continuous solution is minimized in ultra-low-dimension parameter space and offers a flexible and lightning-speed design practice. Through complexity analysis, we find that the complexity of the proposed analytic scheme is significantly lower than the traditional discrete schemes. Further, we also prove that the global optimality, existence and uniqueness of the solution holds under a condition of a strong applicability to general sensing and communications (S&C) services. In simulation, the equivalence is confirmed and the results show global optimality, low-complexity and the high flexibility under avoidance, crossing and G-force limit.

We study a multi-source wireless power transfer (WPT) enabled network supporting multi-sensor transmissions. Activated by the energy harvesting from multiple WPT sources, the sensors transmit short packets to a destination with finite blocklength (FBL) codes. This work for the first time characterizes the FBL reliability for such multi-source WPT enabled network and accordingly provides reliability-oriented resource allocation designs, while a practical nonlinear EH model (including the effects of mutual interference among multiple RF signals) is considered. For the scenario with a fixed frame structure, we aim to maximize the FBL reliability via optimally allocating the transmit power among the multiple WPT sources. In particular, we investigate the relationship between the overall error probability and the transmit power of multiple WPT sources, based on which a power allocation problem is formulated. To solve the formulated non-convex problem, we first introduce auxiliary variables to make the problem analytically tractable, based on which an iterative algorithm is proposed while applying successive convex approximation (SCA) technique to the non-convex components of the problem. Then, we extend our design into a dynamic frame structure scenario, i.e., the blocklength allocated for WPT phase and short-packet transmission phase are adjustable, which introduces more flexibility and new challenges. In particular, we provide a joint power and blocklength allocation design maximizing the overall reliability under total power and blocklength constraints. A problem with high-dimension variables is formulated, which suffers from the complex and non-convex relationship among system reliability, multiple source power and blocklength. To tackle the difficulties, auxiliary variables introduction, multiple variable substitutions along with SCA technique utilization are exploited to reformulate and efficiently solve the problem. Finally, through numerical results, we validate our analytical model and evaluate the system performance, where a set of guidelines for practical system design are concluded.

We consider an unmanned aerial vehicle (UAV) assisting data collection from multiple sensor nodes (SNs). We provide a completion time minimization design via jointly deciding the UAV trajectory and the SN assignment scheme. In particular, we first characterize the fundamental features of the joint optimal solution to the formulated problem. On the one hand, the optimal UAV trajectory is proved following a successive-hover-fly (SHF) structure. Namely, in an optimal solution, the UAV successively visits multiple hovering points and performs hovering with designated duration, while the maximum speed is achieved during the whole flying period between each two hovering points. On the other hand, the optimal SN assignment is characterized to follow a segment-based scheme. Based on the two characterizations, we are motivated to implement SHF structure with turning points in trajectory design and reasonably assume each segment in SHF structure having constant SN assignment. Afterwards, we relax the binary constraints for SN assignments and establish a convex approximation for the reformulated problem, which enables an iterative algorithm. A suboptimal joint solution is obtained via iteratively optimizing the completion time. A realization strategy is also provided for the relaxed solution while assuring the completion of data collection tasks. Finally, the proposed solution is validated and evaluated through numerical results. Both a low complexity and an accurate task completion guarantee of our proposed solution are observed in comparison with the benchmarks.

To support ultra-reliable and low-latency services for mission-critical applications, transmissions are usually carried via short blocklength codes, i.e., in the so-called finite blocklength (FBL) regime. Different from the infinite blocklength regime where transmissions are assumed to be arbitrarily reliable at the Shannon’s capacity, the reliability and capacity performances of an FBL transmission are impacted by the coding blocklength. The relationship among reliability, coding rate, blocklength and channel quality has recently been characterized in the literature, considering the FBL performance model. In this paper, we follow this model, and prove the joint convexity of the FBL error probability with respect to blocklength and transmit power within a region of interest, as a key enabler for designing systems to achieve globally optimal performance levels. Moreover, we apply the joint convexity to general use cases and efficiently solve the joint optimization problem in the setting with multiple users. We also extend the applicability of the proposed approach by proving that the joint convexity still holds in fading channels, as well as in relaying networks. Via simulations, we validate our analytical results and demonstrate the advantage of leveraging the joint convexity compared to other commonly-applied approaches.

Enabling ultra-reliable and low-latency communication services while providing massive connectivity is one of the major goals to be accomplished in future wireless communication networks. In this paper, we investigate the performance of a hybrid multi-access scheme in the finite blocklength (FBL) regime that combines the advantages of both non-orthogonal multiple access (NOMA) and time-division multiple access (TDMA) schemes. Two latency-sensitive application scenarios are studied, distinguished by whether the queuing behaviour has an influence on the transmission performance or not. In particular, for the latency-critical case with one-shot transmission, we aim at a certain physical-layer quality-of-service (QoS) performance, namely the optimization of the reliability. And for the case in which queuing behaviour plays a role, we focus on the link-layer QoS performance and provide a design that maximizes the effective capacity. For both designs, we leverage the characterizations in the FBL regime to provide the optimal framework by jointly allocating the blocklength and transmit power of each user. In particular, for the reliability-oriented design, the original problem is decomposed and the joint convexity of sub-problems is shown via a variable substitution method. For the effective-capacity-oriented design, we exploit the method of Lagrange multipliers to formulate a solvable dual problem with strong duality to the original problem. Via simulations, we validate our analytical results of convexity/concavity and show the advantage of our proposed approaches compared to other existing schemes.

For unmanned aerial vehicle (UAV)-assisted wireless networks, the continuous trajectory designs generally suffer from infinite number of variables of the continuous UAV trajectory. In this paper, to avoid unexpected trajectory approximation and overcome the difficulty in obtaining an accurate continuous trajectory, we aim at characterizing an analytical optimal solution for jointly designing the resource allocation and continuous UAV trajectory. We focus on a scenario with UAV at a fixed altitude being deployed to assist the wireless communication with a ground user. With limited energy accessible for the wireless transmissions, we construct a throughput maximization problem for jointly optimizing the continuous transmit power and the UAV's continuous trajectory. Via duality analysis, we obtain the features of the optimal power control and successfully convert the dual problem to a series of pure trajectory design problems, which can be optimally addressed based on a mechanical equivalence approach. Afterwards, we accordingly propose an algorithm for optimally solving the dual problem, from which the optimal joint solution can be analytically constructed in a closed form. Finally, we also verify our proposed algorithm and confirm the optimality of the obtained solution via simulations.

In this paper, we consider an unmanned aerial vehicle (UAV)-enabled multi-user network with nonlinear wireless power transfer (WPT), where multiple user sensors are distributed on the ground. Acting as an energy source, the UAV operates at a fixed height and transfers energy to the multiple sensor nodes (SNs) via wireless signals. For more efficient energy harvesting (EH), an antenna array has been installed on UAV with a structure of three dimensional (3D) uniform linear array (ULA), which enables the UAV to perform analog beamforming for power concentration. Taking into account the UAV energy consumption and considering a practical nonlinear EH model, we characterize the UAV energy efficiency particularly for WPT task and subsequently formulate an efficiency maximization problem, in which the analog beamforming and UAV trajectory planning are jointly determined together with the transmit power control scheme. To deal with the nonconvex joint optimization problem, we first propose a cosine-based approximation for the complicated 3D ULA antenna pattern, in which a convex property is proved. Combining with the proved convexity in nonlinear EH model, through a series of mathematical analysis, we construct a convex subproblem based on any feasible point, solving which guarantees an improvement of the energy efficiency. Afterwards, an iterative algorithm is proposed for iteratively addressing the joint design until a convergence to a suboptimal solution. Via simulations, we verify the convergence and performance advantages of our proposed iterative solution. Among the solution, different beamforming preferences regarding the beam coverage enlargement and power concentration are also observed with respect to different antenna array scales.

In this paper, we study a full-duplex (FD) relaying network operating with finite blocklength (FBL) codes. Based on Polyanskiy’s FBL model, we characterize the FBL reliability of the relaying network under both decode-and-forward (DF) and amplify-and-forward (AF) relaying schemes. Based on the characterisation, we provide reliability-optimal designs via optimal power allocation for both schemes in a single-carrier scenario. In particular, we prove that under the FD DF relaying scheme the (tightly approximated) overall error probability is convex in the transmit power at the relay. In addition, we show that minimizing the overall error probability of the FD AF relaying is equivalent to maximizing the overall signal to interference plus noise ratio (SINR), which is further proved to be pseudo-concave. Then, the designs for a single-carrier scenario are further extended to a multi-carrier scenario with a joint power and data allocation among carriers. In particular, for either the FD DF or FD AF relaying scheme, a joint optimization problem is reformulated to a single problem maximizing the reliability via finding and achieving the optimal SINRs, while auxiliary variables are introduced in FD AF relaying to facilitate the reformulation. Based on mathematical analysis, we respectively construct convex approximations and subsequently propose iterative algorithms, with which the error probability is reduced iteratively until an eventual convergence to an efficient suboptimal value. Hence, a corresponding suboptimal data and power allocation solution can be constructed for the multi-carrier scenario. Via numerical analysis, we validate our analytical model and the proposed allocation algorithms. The FD DF and FD AF relaying schemes are compared with direct transmission in both single-carrier and multi-carrier scenarios, and the benefits of applying FD relaying schemes and joint optimization among multiple carriers are observed.

In this work, we study a downlink multi-user network, where a single access point (AP) is supposed to accomplish data transmissions to all users under low latency constraints. To more effectively cope with the multiple access demand, we consider a hybrid strategy for the multi-user downlink service in finite blocklength (FBL) regime, which combines broadcasting with time-division multiple access (TDMA). In the hybrid strategy, the users are first clustered into different groups. Different groups are served in a TDMA manner with dedicated time slots, while users within each group are served together via a broadcasting signal from the AP. By taking into account the fairness of transmission reliability among all users, we formulate a problem minimizing the maximum error probability among users via jointly determining the user grouping and allocating blocklength among all groups. To address the complicated non-convex problem, we first characterize the optimal blocklength allocation under each given grouping decision, which leads to an optimal closed-form allocation solution via solving an equation system. Based on the characterized features, we are enabled to efficiently distill out the optimal grouping from all possible groupings, which forms the efficient optimal solution for the optimal joint design. Afterwards, aiming at a complexity reduction, we further propose a low-complexity iterative solution, in which the grouping is iteratively improved via the introduced operations until a convergence to a suboptimum. Finally, via simulations, we validate the proposed solutions and reveal the close optimality of the iterative solution. In addition, the hybrid strategy has shown a significant reliability advantage in comparison to pure broadcasting or TDMA, and this performance advantage becomes further enlarged in case of more users.

In this paper, we focus on a relaying network working with a decode-and-forward (DF) principle. A source reports latency-critical information updates to the destination with the help of the relay under periodic request, while this two-hop transmission is operating with finite blocklength (FBL) codes. To evaluate the data freshness at destination, we characterize the average age-of-information (AoI) of the two-hop relaying. Based on the characterization, we consider a problem minimizing the average AoI by jointly optimizing the blocklengths allocated to both hops. To address this non-convex problem, we construct a tight convex approximation for the average AoI at a feasible local point (values of the two blocklengths). Then, we propose an efficient algorithm which iteratively applies the convex approximation, solves the approximated convex problem and updates the local point until a convergence to a suboptimum. Via numerical results, we validate the convergence of the proposed iterative algorithm and confirm the high performance and high efficiency of the proposed solution. The performance advantage of relaying in improving the data freshness is also shown in comparison to direct transmission.

In this work, we study an energy harvesting (EH)-enabled low-latency communication network where a full-duplex server continuously performs wireless power transfer (WPT) to a half-duplex sensor. The sensor is designed to operate periodically in each updating round, during which the sensor firstly harvests energy via the WPT process, then collects measurement data and wirelessly transmits an update to the server based on the harvested energy. We assume that no energy can be reserved at the end of each round, due to the deployed capacitor-structured energy container. Leveraging the recent characterization on the error probability in the finite blocklength (FBL) regime, we derive the average Age-of-Information (AoI) in the considered network and construct a problem minimizing the average AoI via optimizing the duration of the updating round. The convexity of the optimization problem is shown, following which an efficient optimal solution is provided. At last, via Monte Carlo simulations, the convexity of the problem can also be visualised, and the average AoI performance of the network is evaluated.

Unmanned aerial vehicles (UAVs), also known as drones, have already been widely implemented in wireless networks for promoting network performance and enabling new services. To efficiently explore the diversity introduced by the mobility of UAV, many efforts have been made in the design of the UAV trajectory under various wireless scenarios. However, the continuity of a UAV trajectory in both time and topology forces researchers to approximate the UAV trajectory by a discrete model, which always results in a sub-optimal solution. To tackle the difficulty and obtain the optimal trajectory, in this work we introduce an artificial potential field (APF) to reformulate the objective in trajectory design, with which the UAV trajectory problem can be completely equivalent to a mechanical problem. In such mechanical problem, the UAV trajectory is represented by an extremely soft and thin rope with variable density carrying UAV speed information, and the original objective of optimizing the system performance is transformed to minimizing the overall artificial potential energy on the rope. As a result, the rope in the optimal solution stays in a state of equilibrium and the UAV trajectory can be equivalently optimized by designing the shape of a rope under the APF via mechanical principles. We provide a case study to describe in detail the problem equivalence, i.e., taking a single-user network as an example in which the throughput between UAV and the user is considered as the objective performance. In particular, the optimal trajectory of a UAV is constructed based on mechanical principles, while the global optimality is also rigorously proved and further confirmed via simulations. Moreover, we also highlight that the novel strategy of constructing equivalent mechanical problem has the possibilities to be extended to various UAV trajectory problems under different scenarios with different performance optimization objectives.

In this work, we investigate an unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) network with multiple ground sensor nodes (SNs). A UAV is operated at a fixed altitude with a directional antenna array and is designed to wirelessly transfer energy to the SNs. We consider a non-linear energy harvesting (EH) model and a directional antenna structure of uniform linear array (ULA) where we apply an analog directional beamforming scheme. Taking the fairness issue into account, we consider a problem aiming at maximizing the minimum harvested energy among all SNs during a fixed time period by jointly optimizing the UAV trajectory and the orientation of the directional antenna on the UAV. However, the complex antenna pattern expression of analog directional beamforming and the implicit non-linear function in the EH model introduce significant difficulties in handling the non-convex problem of the joint design. To tackle these difficulties, we propose and adopt a modified approximate antenna pattern model, i.e., a modified cosine antenna pattern, and reformulate the original problem via quantizing the UAV trajectory in the time domain. Later, by employing a convex property in the EH model and a proved lemma, we successfully construct a tight convex approximation for the reformulated problem, based on which the problem can be solved via a proposed iterative algorithm and the objective converges to an efficient suboptimal solution. Finally, we provide numerical results to confirm the convergence of the proposed algorithm, examine the approximation error and evaluate the system performance. The results show the performance advantage of the directional antenna in UAV-enabled WPT networks than the omni-directional antenna case, and illustrate how the directional antenna of the UAV overcomes its coverage limitation

Abstract Deep nets have done well with early adopters, but the future will soon depend on crossing the chasm. The goal of this paper is to make deep nets more accessible to a broader audience including people with little or no programming skills, and people with little interest in training new models. A github is provided with simple implementations of image classification, optical character recognition, sentiment analysis, named entity recognition, question answering (QA/SQuAD), machine translation, speech to text (SST), and speech recognition (STT). The emphasis is on instant gratification. Non-programmers should be able to install these programs and use them in 15 minutes or less (per program). Programs are short (10–100 lines each) and readable by users with modest programming skills. Much of the complexity is hidden behind abstractions such as pipelines and auto classes, and pretrained models and datasets provided by hubs: PaddleHub, PaddleNLP, HuggingFaceHub, and Fairseq. Hubs have different priorities than research. Research is training models from corpora and fine-tuning them for tasks. Users are already overwhelmed with an embarrassment of riches (13k models and 1k datasets). Do they want more? We believe the broader market is more interested in inference (how to run pretrained models on novel inputs) and less interested in training (how to create even more models).

In this paper, we study an unmanned aerial vehicle (UAV)-enabled multiuser wireless power transfer (WPT) network, where a UAV is responsible for providing wireless energy for a set of ground devices (GDs) deployed in an area. We focus on the design of UAV trajectory subject to the maximum flight speed limit, in order to maximize the minimum harvested energy among GDs over a particular charging duration. Different from prior works that considered simplified linear energy harvesting models, this paper for the first time takes into account the realistic nonlinear energy harvesting model for the UAV trajectory design. However, the formulated trajectory design problem is highly non-convex and has infinite number of variables, thus making it be challenging to be solved optimally. To tackle this difficulty, we adopt the following three-step approach to obtain an efficient solution. First, we rigorously characterize that the optimal trajectory follows a new successive-hover-and-fly (SHF) structure, where the UAV hovers at a certain set of points for efficiently transferring energy, and flies among these hovering points with the maximum speed following certain arcs (not necessarily straight lines). Next, based on this SHF structure, we transform the original problem to a new one for finding a set of turning point variables during the maximum-speed flight, at which the UAV changes the flight direction without hovering. Finally, we use the techniques of convex approximation to solve the transformed problem. According to the convexity of the nonlinear energy harvesting model, we iteratively solve a series of convex optimization problems to update the UAV trajectory towards a high-quality solution. Numerical results show the convergence of the proposed approach, and validate its performance gain over conventional designs.

In this work, we study an unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) system with multiple ground users. We aim at solving the non-convex UAV trajectory design problem which maximizes the minimal received energy among all users by determining the UAV's flying path under given UAV speed constraints. To solve such intractable problem, we propose a genetic algorithm (GA) based successive hover-and-fly (SHF) scheme that iteratively searches the optimal hovering points and optimizes the corresponding hovering time. Moreover, we extend the study to scenarios with no-fly zones, for which an improved GA based method with a penalizing strategy is proposed accordingly. Numerical results confirm the performance advantage of the proposed GA based algorithm in comparison to the benchmark algorithms in prior works under a wide range of system parameters.

In this paper, we study an unmanned aerial vehicle (UAV)-enabled wireless power transfer network, where a UAV flies at a constant altitude in the sky to provide wireless energy supply for a set of ground nodes with a linear topology. Our objective is to maximize the minimum received energy among all ground nodes by optimizing the UAV's one-dimensional (1D) trajectory, subject to the maximum UAV flying speed constraint. Different from previous works that only provided heuristic and locally optimal solutions, this paper is the first to present the globally optimal 1D UAV trajectory solution to the considered min-energy maximization problem. Toward this end, we first show that for any given speed-constrained UAV trajectory, we can always construct a maximum-speed trajectory and a speed-free trajectory, such that their combination can achieve the same received energy at all these ground nodes. Next, we transform the UAV-speed-constrained trajectory design problem into an equivalent UAV-speed-free problem, which is then optimally solved via the Lagrange dual method. The optimal 1D UAV trajectory solution follows the so-called successive hover-and-fly structure, i.e., the UAV successively hovers at a finite number of hovering points each for an optimized hovering duration, and flies among these hovering points at the maximum speed. Building upon the optimal UAV trajectory structure, we further present a low-complexity UAV trajectory design by first transforming the original problem into an equivalent non-convex problem with only the UAV hovering locations and durations as optimization variables and then updating the trajectory via the successive convex approximation technique. Our analysis shows that the low-complexity design is guaranteed to converge to a suboptimal solution at a significantly lower complexity irrespective of the geographical network size. Numerical results show that the proposed low-complexity design actually achieves the same performance as the proposed optimal solution, and both of them outperform the benchmark algorithms in prior works under different scenarios.