Recent advancements in Large Reasoning Models (LRMs), such as OpenAI's o1/o3 and DeepSeek-R1, have demonstrated remarkable performance in specialized reasoning tasks through human-like deliberative thinking and long chain-of-thought reasoning. However, our systematic evaluation across various model families (DeepSeek, Qwen, and LLaMA) and scales (7B to 32B) reveals that acquiring these deliberative reasoning capabilities significantly reduces the foundational capabilities of LRMs, including notable declines in helpfulness and harmlessness, alongside substantially increased inference costs. Importantly, we demonstrate that adaptive reasoning---employing modes like Zero-Thinking, Less-Thinking, and Summary-Thinking---can effectively alleviate these drawbacks. Our empirical insights underline the critical need for developing more versatile LRMs capable of dynamically allocating inference-time compute according to specific task characteristics.

Jiahe Guo, Xiangran Guo, Yulin Hu, Zimo Long, Xingyu Sui, Xuda Zhi, Yongbo Huang, Hao He, Weixiang Zhao, Yanyan Zhao, Bing Qin. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Emotional Support Conversation requires not only affective expression but also grounded instrumental support to provide trustworthy guidance.However, existing ESC systems and benchmarks largely focus on affective support in text-only settings, overlooking how external tools can enable factual grounding and reduce hallucination in multi-turn emotional support.We introduce TEA-Bench, the first interactive benchmark for evaluating tool-augmented agents in ESC, featuring realistic emotional scenarios, an MCP-style tool environment, and process-level metrics that jointly assess the quality and factual grounding of emotional support.Experiments on nine LLMs show that tool augmentation generally improves emotional support quality and reduces hallucination, but the gains are strongly capacity-dependent: stronger models use tools more selectively and effectively, while weaker models benefit only marginally.We further release TEA-Dialog, a dataset of toolenhanced ESC dialogues, and find that supervised fine-tuning improves in-distribution support but generalizes poorly.Our results underscore the importance of tool use in building reliable emotional support agents. 1

Abstract Machine Unlearning (MU) has emerged as a promising technique for aligning large language models (LLMs) with safety requirements to steer them forgetting specific harmful contents. Despite the significant progress in previous studies, we argue that the current evaluation criteria, which solely focus on safety evaluation, are actually impractical and biased , leading to concerns about the true effectiveness of MU techniques. To address this, we propose to comprehensively evaluate LLMs after MU from three aspects: safety, over-safety, and general utility. Specifically, a novel benchmark M u B ench with 18 related datasets is first constructed, where the safety is measured with both vanilla harmful inputs and 10 types of jailbreak attacks. Furthermore, we examine whether MU introduces side effects, focusing on over-safety and utility-loss. Extensive experiments are performed on 3 popular LLMs with 7 recent MU methods. The results highlight a challenging trilemma in safety alignment without side effects, indicating that there is still considerable room for further exploration. M u B ench serves as a comprehensive benchmark, fostering future research on MU for safety alignment of LLMs.

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.

Visible Light Positioning (VLP) has emerged as a promising indoor localization technology owing to its high accuracy, low power consumption, and lighting compatibility. The Received Signal Strength (RSS)-based multi-anchor VLP pre-serves these advantages while having drawn considerable research attention due to its simple implementation and high reliability. However, its large-scale deployment encounters challenges at every stage: inefficient anchor calibration, limited model-based ranging performance, and robustness reduction from undetected gross errors. To address these issues, we propose a VLP and inertial navigation system fusion framework comprising an anchor position estimation module, a distance estimation module, and a fusion positioning module. For anchor calibration, a LiDAR-inertial odometry-based calibration scheme enhanced by a twolayer optimization strategy is introduced, which provides prior knowledge of anchor positions for the whole system. To improve the performance of the RSS-based ranging method, a hybrid Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)-Bidirectional Long Short-Term Memory (Bi-LSTM) network with an embedded distance quality assessor is developed, achieving over 50% higher accuracy than model-based baselines. It delivers precise distance estimates and their validity labels for measurement updates in subsequent fusion positioning. Additionally, a two-stage error detection mechanism filters low quality observations by combining network-generated usability labels with prior-state estimates. The system consistently attains decimeter-level positioning across various trajectories, meeting the needs of diverse Internet of Things applications.

Federated learning (FL) has been considered as a promising way to train distributed wireless systems in a privacy-preserving manner. However, the significant communications overheads caused by uploading local parameters and the potential unreliability of wireless links emerged as one of the bottlenecks of FL. To address this challenge, this paper investigates a reconfigurable intelligent surface (RIS)-assisted sparse FL network, where the RIS is utilized for wireless transmission reliability enhancement, and the sparsification operation is used to reduce the communications overheads. Considering that the wireless transmissions of the FL uploads are carried by finite blocklength (FBL) codes, wefor the first timeinvestigate the convergence of sparse FL while taking into account both the FBL decoding errors and FL sparsification errors. Following such a model, a novel joint learning and communication design framework is provided. In particular, an optimization problem is formulated to minimize the impacts of the above errors on the convergence via jointly determining the coding rate, transmit power, and RIS phase shift. To tackle the formulated non-convex problem, a block coordinate descent (BCD)-based algorithm is proposed, which decomposes the problem into two sub-ones and solves them alternately. On the one hand, for the resource allocation sub-problem, we derive a closed-form expression of optimal coding rate with respect to power that drastically reduces the optimization problem dimension, and shows the convexity of the resulting power allocation problem. For the RIS phase shift design sub-problem, on the other hand, a trust-region based linear approximation is used, along with problem transformations and tight successive convex approximations, to derive a highly effective iterative algorithm based on the closed-form expression for each variable. The entire proposed iterative algorithm converges efficiently to a suboptimal solution. Then, we extend the proposed algorithm to the imperfect channel state information (CSI) scenarios by using second-order Taylor approximation. Numerical results demonstrate that the proposed design significantly improves the FL performance in comparison to benchmark schemes.

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.

The extreme requirements for high reliability and low latency in the upcoming Sixth Generation (6G) wireless networks are challenging the design of multi-hop wireless transport networks. Inspired by the advent of the virtualization concept in the wireless networks design andopennessparadigm as fostered by the Open-Radio Access Network (O-RAN) Alliance, we target a revolutionary resource allocation scheme to improve the overall transmission efficiency. In this paper, we investigate the problem of automatic repeat request (ARQ) in multi-hop decode-and-forward (DF) relaying in the finite blocklength (FBL) regime, and propose a dynamic scheme of multi-hop hybrid ARQ (HARQ), which maximizes the end-to-end (E2E) communication reliability in the wireless transport network.We also propose an integer dynamic programming (DP) algorithm to efficiently solve the optimal Dynamic Multi-Hop HARQ (DMH-HARQ) strategy. Constrained within a certain time frame to accomplish E2E transmission, our proposed approach is proven to outperform the conventional listening-based cooperative ARQ, as well as any static HARQ strategy, regarding the E2E reliability. It is applicable without dependence on special delay constraint, and is particularly competitive for long-distance transport network with many hops.

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.

Distributed edge learning (DL) is considered a cornerstone of intelligence enablers, since it allows for collaborative training without the necessity for local clients to share raw data with other parties, thereby preserving privacy and security. Integrating DL into the 6G networks requires a coexistence design with existing services such as high-bandwidth (HB) traffic like eMBB. Current designs in the literature mainly focus on communication round-wise designs that assume a rigid resource allocation throughout each communication round (CR). However, rigid resource allocation within a CR is a highly inefficient and inaccurate representation of the system’s realistic behavior, especially when CR duration far exceeds the channel coherence time due to large model size or limited resources. This is due to the heterogeneous nature of the system, as clients inherently may need to access the network at different time instants. This work zooms into one arbitrary CR, and demonstrates the importance of considering a time-dependent design for sharing the resource pool with HB traffic. We first formulate a time-slot-wise optimization problem to minimize the consumed time by DL within the CR while constrained by a DL energy budget. Due to its intractability, a session-based optimization problem is formulated assuming a CR lasts less than a large-scale coherence time. Some scheduling properties of such multi-server joint communication scheduling and resource allocation framework have been established. An iterative algorithm has been designed to solve such non-convex and non-block-separable-constrained problems. Simulation results confirm the importance of the efficient and accurate integration design proposed in this work.

Weixiang Zhao, Yulin Hu, Yang Deng, Jiahe Guo, Xingyu Sui, Xinyang Han, An Zhang, Yanyan Zhao, Bing Qin, Tat-Seng Chua, Ting Liu. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Weixiang Zhao, Yulin Hu, Yang Deng, Tongtong Wu, Wenxuan Zhang, Jiahe Guo, An Zhang, Yanyan Zhao, Bing Qin, Tat-Seng Chua, Ting Liu. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Weixiang Zhao, Jiahe Guo, Yulin Hu, Yang Deng, An Zhang, Xingyu Sui, Xinyang Han, Yanyan Zhao, Bing Qin, Tat-Seng Chua, Ting Liu. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

This paper investigates a multi-user MIMO down-link network operating in the finite blocklength (FBL) regime. We propose an efficient power allocation scheme that balances overall system performance and user fairness. Specifically, we design a power allocation scheme to maximize the overall FBL throughput of the system. To address the non-convexity of the formulated problem, we employ a successive convex approximation (SCA) -based approach, transforming the non-convex problem into a series of convex subproblems to obtain the optimal power allocation. Subsequently, we introduce a fairness-oriented power allocation scheme that maximizes the minimum user FBL throughput. By combining these two approaches, we develop a unified power allocation scheme that effectively balances overall system performance and user fairness. Simulation results demonstrate that the proposed scheme efficiently addresses the trade-off between system-wide FBL performance and user fairness, providing a flexible solution for diverse application scenarios.

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.

In dense user scenarios, WiFi networks adopting IEEE 802.11n/ac standards often suffer from significant throughput degradation due to increased contention and frequent collisions. Unmanned aerial vehicles (UAVs), with their high mobility, on-demand deployment, and strong line-of-sight communication capabilities, provide a promising solution as supplementary communication infrastructure to offload users from overloaded WiFi access points. This paper investigates a multi-UAV assisted WiFi network architecture, aiming to maximize total network throughput while minimizing the number of deployed UAVs through the joint optimization of user association, UAV coordinates, and power allocation. To address the formulated NP-hard multi-objective optimization problem with dynamic dimensionality, we propose NSGA-II-HLA—a hybrid evolutionary algorithm that integrates a modified non-dominated sorting genetic algorithm II (NSGA-II) for global exploration with a distance-based heuristic for refined user association. Extensive simulation results demonstrate that the proposed approach significantly enhances network throughput, reduces UAV deployment cost, and achieves balanced performance across heterogeneous access domains.

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.

Distributed learning (DL) is considered a cornerstone of intelligence enabler, since it allows for collaborative training without the necessity for local clients to share raw data with other parties, thereby preserving privacy and security. Integrating DL into the 6G networks requires coexistence design with existing services such as high-bandwidth (HB) traffic like eMBB. Current designs in the literature mainly focus on communication round (CR)-wise designs that assume a fixed resource allocation during each CR. However, fixed resource allocation within a CR is a highly inefficient and inaccurate representation of the system's realistic behavior. This is due to the heterogeneous nature of the system, where clients inherently need to access the network at different times. This work zooms into one arbitrary communication round and demonstrates the importance of considering a time-dependent resource-sharing design with HB traffic. We propose a time-dependent optimization problem for minimizing the consumed time and energy by DL within the CR. Due to its intractability, a session-based optimization problem has been proposed assuming a large-scale coherence time. An iterative algorithm has been designed to solve such problems and simulation results confirm the importance of such efficient and accurate integration design.

Multilingual reasoning remains a significant challenge for large language models (LLMs), with performance disproportionately favoring high-resource languages. Drawing inspiration from cognitive neuroscience, which suggests that human reasoning functions largely independently of language processing, we hypothesize that LLMs similarly encode reasoning and language as separable components that can be disentangled to enhance multilingual reasoning. To evaluate this, we perform a causal intervention by ablating language-specific representations at inference time. Experiments on 10 open-weight LLMs spanning 11 typologically diverse languages show that this language-specific ablation consistently boosts multilingual reasoning performance. Layer-wise analyses further confirm that language and reasoning representations can be effectively disentangled throughout the model, yielding improved multilingual reasoning capabilities, while preserving top-layer language features remains essential for maintaining linguistic fidelity. Compared to post-training methods such as supervised fine-tuning or reinforcement learning, our training-free language-reasoning disentanglement achieves comparable or superior results with minimal computational overhead. These findings shed light on the internal mechanisms underlying multilingual reasoning in LLMs and suggest a lightweight and interpretable strategy for improving cross-lingual generalization.

Personalized alignment is essential for enabling large language models (LLMs) to engage effectively in user-centric dialogue. While recent prompt-based and offline optimization methods offer preliminary solutions, they fall short in cold-start scenarios and long-term personalization due to their inherently static and shallow designs. In this work, we introduce the Reinforcement Learning for Personalized Alignment (RLPA) framework, in which an LLM interacts with a simulated user model to iteratively infer and refine user profiles through dialogue. The training process is guided by a dual-level reward structure: the Profile Reward encourages accurate construction of user representations, while the Response Reward incentivizes generation of responses consistent with the inferred profile. We instantiate RLPA by fine-tuning Qwen-2.5-3B-Instruct, resulting in Qwen-RLPA, which achieves state-of-the-art performance in personalized dialogue. Empirical evaluations demonstrate that Qwen-RLPA consistently outperforms prompting and offline fine-tuning baselines, and even surpasses advanced commercial models such as Claude-3.5 and GPT-4o. Further analysis highlights Qwen-RLPA's robustness in reconciling conflicting user preferences, sustaining long-term personalization and delivering more efficient inference compared to recent reasoning-focused LLMs. These results emphasize the potential of dynamic profile inference as a more effective paradigm for building personalized dialogue systems.

In this paper, we study an industrial Internet of Thing (IIoT) network supporting massive ultra-reliable and low-latency communications, where each user has strict timeliness requirements. We propose an optimal framework to maximize the effective throughput via jointly choosing the uplink transmission blocklength for multiple users. To address the formulated non-convex problem, we first characterize the quasi-concavity of the effective throughput to users’ blocklength. Then, following the characterization, the problem is reformulated to a quasi-convex one. Utilizing Dinkelbach’s transformation, an efficient algorithm is developed to obtain the optimal solution. Finally, through simulations, we confirm our analytical model and the superiority of the proposed design in comparison to benchmarks.

The evolution towards 6G communication technology heightens the demand for data freshness. Consequently, Age of Information (AoI), a key metric quantifying data freshness, has raised significant attention from academia and industry. This paper investigates a Multi-access Edge Computing (MEC) network with multiple servers designed to support mission-critical, low-latency computational services. We characterize the transmission reliability with FBL codes in the communication phase. Using extreme value theory, we analyze the occurrence of extreme queue length violations during the computation time phase. Based on the characterizations, we develop an optimal framework incorporating server selection and scheduling strategies for minimizing the average AoI. Via numerical simulations, we validate our algorithm’s effectiveness in enhancing AoI performance, demonstrate how varying parameters affect system performance, and illustrate the potential of our method for guiding future MEC system designs.

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 article, we consider an Industrial Internet of Things (IIoT) network operating under a carrier sense multiple access with collision avoidance (CSMA/CA) protocol. Latency-sensitive packets generated at multiple stations randomly are transmitted to a destination for decision making. To meet the strict timeliness constraint, the transmissions are carried by finite blocklength (FBL) codes, while the truncated hybrid automatic repeat request (HARQ) scheme is exploited to improve the reliability. For such unsaturated CSMA/CA networks, for the first time, we characterize the timeliness of packets utilized for decision-making via the age upon decisions (AuD) metric which emphasizes the information freshness at decision moments in comparison to age of information (AoI). To explicitly quantify the AuD performance, we develop an equivalent and tractable unsaturated Markov transfer model for the considered network and investigate the transmission probability and collision probability, respectively. Subsequently, the probability density functions of interarrival time and service time of the successfully transmitted packets are derived. We further derive a closed-form expression for the average AuD under a geometric decision process accordingly. Based on these characterizations, we aim at improving average AuD by jointly allocating the blocklength and transmit power. The formulated nonconvex problem is decomposed into subproblems, and we prove its joint convexity across all feasible intervals. Via simulations, we evaluate the performance of the considered network and conclude a series of design guidelines.

Enabling Mobile edge computing (MEC) services with massive connectivity and low energy consumption is crucial for future Internet of Things (IoT) infrastructures. In this article, we investigate an IoT network, where MEC is adopted as the computing framework for complicated IoT services while nonorthogonal multiple access (NOMA) is introduced to enable the interdependent data input offloading from multiple IoT devices to an edge server. The MEC service frame consists of a communication phase and a computation phase, where the latter phase requires the complete data offloaded in the former one to complete a specific task. To minimize the weighted sum energy consumption of both users and the edge server, a joint resource allocation original problem is formulated, which is unfortunately nonconvex. To tackle the difficulty, we first decompose the problem into subproblems, and characterize the structure of optimal solution to the subproblems. Following the characterization, the original problem is reformulated into a tractable one. We then develop a Branch-Reduce-and-Bound (BRB) based algorithm to obtain the optimal solution. Additionally, to further investigate the MEC scenario with the offloading of multiple users, we apply the state-of-the-art hybrid NOMA (H-NOMA) scheme to evaluate its benefits to multiuser MEC. We rigorously prove that, with interdependent user data inputs, pure NOMA (P-NOMA) is not only a special case but also an optimal case of H-NOMA. Via simulation, the analytical findings and the proposed algorithm are validated and evaluated.

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 consider a multi-access mobile edge computing (MEC) network with multiple sensors and one MEC server in industrial Internet of Things networks, where the MEC server provides a joint computation service (in the computation phase) for a set of sub-tasks offloaded by different sensors (in the communication phase). Due to the requirements of low latency and ultra reliability, we utilize finite blocklength information theory to characterize the reliability of the communication phase and exploit extreme value theory to investigate the delay violation probability in the computation phase. Following these characterizations, we derive the average end-to-end error probability of the entire service and provide two average end-to-end reliability-optimal design frameworks considering fixed frames structure and dynamic frames structure, in both of which the goal is to minimize the average end-to-end error probability by optimally allocating the total time length to each frame, as well as allocating each frame length to the communication phase and the computation phase. For the fixed frames structure, the original problem is decomposed, and the joint convexity of the decomposed sub-problems is rigorously proved, and the optimal solutions are obtained by the proposed optimal time allocation algorithm. Moreover, for the dynamic frames structure, we reformulate the optimization problem by introducing an average time constraint. By exploiting Lagrange multipliers, we transform the reformulated optimization problem into a dual problem with strong duality, the solutions of which can be obtained by the proposed time allocation algorithm. Via simulations, we validate the proven convexity and the approximation in our analytical model and evaluate the performance for both fixed frames length structure and dynamic frames length structure.

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.

Intelligent Omni-Surface (IOS) has attracted considerable attention for its advantages of high energy efficiency, which are similar to those of Reconfigurable Intelligent Surface (RIS), while also being able to overcome the limited scope of RIS services. In this paper, we provide a security energy efficiency (SEE) maximization design for IOS and artificial noise (AN) assisted cell-free massive MIMO (CF-mMIMO) networks, via jointly optimizing the transmission beamforming and AN covariance matrix of the AP, the reflection and transmission phase-shift matrices of the IOS, and the reflection-transmission power ratio of the IOS. To handle the formulated problem with non-convexity and high complexity, we first decouple it into two sub-problems. Then, we design low-complexity algorithms for each sub-problem i.e., an AP transmission beamforming and AN noise covariance matrix joint optimization algorithm based on the SSNCG-ALM, and an IOS reflection and transmission phase-shift matrix joint optimization algorithm based on the RPM-TR. Finally, a SEE maximization iterative algorithm based on block coordinate descent and successive convex approximation is established. The simulation results demonstrate that the proposed design significantly enhances the SEE of CF-mMIMO networks.

Weixiang Zhao, Shilong Wang, Yulin Hu, Yanyan Zhao, Bing Qin, Xuanyu Zhang, Qing Yang, Dongliang Xu, Wanxiang Che. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

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.

Autonomous driving technology has become increasingly important in recent years, with the potential to revolutionize transportation systems and improve road safety. Vision-based methods have long been used in this field, but the major challenges in object detection are efficiency and occlusion. To address this challenge, anchor-free collaborative detection has been proposed as a promising solution. Despite its potential, there has been limited research on this approach. This study proposes an efficient vision-based multi-view object detection and localization method that leverages anchor-free collaborative detection to improve the accuracy of pedestrian detection. The method first generates feature maps to extract the head and foot of pedestrians and then applies spatial aggregation to fuse information from different views. Additionally, the study examines the efficiency of different convolutional neural network architectures for the feature map extraction model and identifies ResNet18 and ResNet34 as the most efficient models for the task. The proposed method has the potential to significantly improve the accuracy of pedestrian detection and localization in autonomous driving scenarios, which is critical for ensuring safety. Overall, this work contributes to the development of vision-based methods for autonomous driving and has significant implications for the future of transportation technology.

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.

This article investigates the influence of limited information on the efficacy of traffic signal control algorithms for supporting Intelligent Transportation Systems. Our project has collected several classic and trending intelligent traffic signal control schemes and evaluated algorithmic performance under constrained data conditions. In these simulating scenarios, access to certain traffic data is restricted. The findings reveal that although information scarcity generally degrades algorithm performance, algorithms that perform well with comprehensive data maintain their superiority even in data-limited environments. This underscores the necessity of designing resilient algorithms capable of adapting to varying levels of information availability, offering valuable insights for developing robust traffic control systems.

We consider a reconfigurable intelligent surface (RIS)-aided wireless network supporting local gradient upload for federated learning (FL). For the first time, the impact of wireless uploads with finite blocklength (FBL) on FL performance is investigated and provides a corresponding performance enhancement design. More specifically, we characterize the im-pact of wireless transmissions/uploads on the convergence and the optimality gap of FL, and formulate a resource allocation problem to minimize such impact accordingly. To tackle the formulated non-convex problem, we first conduct a convex approximation to the problem, then propose a block coordinate descent (BCD) based algorithm alternately optimizing the power allocation and RIS phase shifts via addressing two sub-problems. Specifically, we prove the convexity for the pure power allocation sub-problem, while for RIS phase design one, a closed-form expression of the optimal solution is derived by applying the path-following (PF) method. Numerical results demonstrate that the proposed design significantly improves the FL performance compared to baseline schemes.

A new search method named two-dimensional greedy search (TDGS) is proposed to progressively determine each element within an exponent matrix, so as to generate quasi-cyclic (QC) low-density parity-check (LDPC) codes without 4-cycles and 6-cycles. By applying four different two-dimensional scanning orders to the TDGS, a design framework is formulated to unify several existing and novel explicit constructions for short (3,$L$)-regular QC-LDPC codes without 4-cycles and 6-cycles. Through equivalence analysis between the exponent matrices greedily found by the TDGS and directly defined by explicit constructions, novel girth properties of (and connections between) these existing explicit constructions are also revealed.

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.

The strict block error rate (BLER) requirement under the time-varying nature of wireless channels in Ultra-reliable low-latency communication (URLLC) systems pose sig-nificant challenges for link adaptation (LA). To tackle these challenges, we propose a novel LA method that adaptively selects the modulation and coding scheme (MCS) without requiring perfect channel knowledge which is unrealistic to obtain in URLLC. The goal is to maximize the coding rate while ensuring strict BLER constraints in URLLC systems. To achieve this, we utilize the Deep Q-Network (DQN) algorithm to select the MCS dynamically. Furthermore, we enhance the MCS selection process by using the Outer Loop Link Adaptation algorithm for transmission reliability improvement. Given the nature of URLLC, the samples of ACK and NACK are highly imbalanced, which can cause issues in the training process. To address it, we propose a novel training mechanism that improves the performance of DQN model and convergence speed during the training stage. Through extensive simulations, we demonstrate that our proposed algorithm outperforms existing methods regarding coding rate and imposing strict BLER constraints.

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.

Unmanned aerial vehicles (UAVs) are being used extensively in various fields. UAVs provide various services to users, including monitoring, logistics, and sensing, because of their flexible deployment and dynamic reconfigurability. However, UAV networks have become more susceptible to malicious threats because of their multiconnectivity and openness. A great effort has been made to develop an effective intrusion detection system (IDS) based on machine-learning approaches for UAVs. Unfortunately, existing methods were unable to identify real time and zero-day attacks for UAV networks. This is due to that existing methods have still used obsolete data sets and past knowledge-based detection. Also, the shortcomings of standalone IDS render them unsuitable for defending UAV networks from potential security risks. Further, the lack of precise identification for compromised UAV nodes in UAV networks poses a critical security gap, risking the entire network’s integrity with the compromise of a single node. Therefore, in this work, we propose an autonomous collaborative IDS (UAV-CIDS) with a feedforward convolutional neural network (FFCNN), which accurately identifies zero-day with high accuracy. The proposed solution takes into account encoded Wi-Fi traffic logs of three popular UAVs types: 1) DBPower UDI; 2) parrot Bebop; and 3) DJI spark. Evaluation results indicate that our FFCNN model has produced outstanding results based on the UAVIDS data set with 98.23% accuracy compared to existing models. After the detection of attacks, their mitigation is equally significant. In addition, we also design and implement real-time incident response handling against cyber-attacks on UAV Networks. The incident response handling will assist in minimizing the effects of a security breach, remediate vulnerabilities and systematically secure the entire UAV networks.

With the growing popularization of the Internet of Vehicles (IoVs), the combination of satellite navigation system and IoVs is also in a state of continuous improvement. In this article, we present a heterogeneous signcryption scheme with group equality test for IoVs (HSC-GET), which avoids the adversaries existing in the insecure channels to intercept, alter or delete messages from satellite to vehicles. The satellite is arranged in an identity-based cryptographic (IBC) system to ensure safe and fast transmission of instruction, while the vehicles are arranged in certificateless cryptosystem (CLC) to concern the security of the equipment. In addition, the group granularity authorization is integrated to ensure the cloud server can only execute the equality test on ciphertext generated by the same group of vehicles. Through rigorous performance and security analyses, we observe that our proposed construction reduces the equality test overhead by about 63.96%, 81.23%, 80.84%, and 54.98% in comparison to other competitive protocols. Furthermore, the confidentiality, integrity and authenticity of messages are guaranteed.

To meet the needs of high energy efficiency (EE) and various heterogeneous services for 6G, in this article, we probe into the EE of reconfigurable intelligent surfaces (RISs) subsurface (SSF) architecture-aided cell-free Internet of Things (CF-IoT) networks. Specifically, we jointly optimize the base station (BS)-RIS-IoT device (ID) joint associations, the RIS’s phase shift matrix (PSM), and the BS’s transmit power to enhance CF-IoT’s EE. The elevated complexity (NP-hard) and nonconvexity of the formulated problem pose significant challenges, making the solution highly difficult and intricate. To handle this challenging problem, we first develop an alternating optimization framework based on block coordinate descent, which can decouple the original problem into several subproblems. We then carefully design the corresponding low-complexity algorithm for each subproblem to solve it. Moreover, the proposed joint optimization framework serves as a versatile solution applicable to a wide range of scenarios aiming to maximize EE with the assistance of RISs. Simulations confirm that deploying RISs in CF-IoT scenarios is beneficial for improving the EE of the system, and the SSF architecture can further enhance the EE of the system.

For unmanned aerial vehicles (UAVs)-assisted post-disaster emergency communication networks, UAVs serves as relay nodes of air-based backup network to support transmission of rescue messages to emergency communication vehicles (ECVs), while ECVs provide on-site ground communication and connectivity to the command center (CC) of the rescue operation. Existing works seldom emphasize communication security such as authenticity of communicating parties and integrity of message content. In this connection, authentication and key agreement (AKA) protocols are promising solutions for achieving communication security. However, the traditional approaches to endpoint security and entity authentication of principals may not be practical in emergency situations, in which network equipment and security modules are exposed to an open and untrusted physical environment. Besides, there is a lack of attention to the study of privacy impacts resulted from the physical loss of UAVs. More importantly, cyber attacks and excessive overhead may deteriorate AKA availability. Motivated by above challenges, we propose an AKA protocol, namely AKAEC, which is based on three-factor (i.e. smart card, biometrics, and password) and physically unclonable function (PUF) for protecting UAVs-assisted emergency communication. Specifically, AKAEC includes ECV-to-UAV (E2U) and UAV-to-UAV (U2U), where the former achieves secure emergency communication between ECV and UAV, while the latter realizes secure emergency communication between UAV and UAV. We then provide a formal security proof under the Real-Or-Random (ROR) model and formal security verification by AVISPA. This is followed by a security analysis to show that AKAEC meets the security goals defined for emergency situations. Finally, the performance of AKAEC is evaluated from communication overhead and computational overhead.

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 investigate a reconfigurable intelligent surface (RIS)-assisted mobile edge computing (MEC) network aiming to maximize the energy efficiency in the finite blocklength (FBL) regime under both coding length and maximum decoding error rate constraints. We first analyze the single user equipment (UE) case and propose a three-step alternating optimization algorithm to solve the problem. Extending the system model, we subsequently investigate a network with multiple UEs, in which non-orthogonal multiple access (NOMA) transmission is adopted. In this more general setting, we also conduct a convergence analysis. Furthermore, we introduce a UE-grouping scheme for hybrid NOMA-TDMA transmission and develop a dynamic CPU frequency allocation algorithm at the mobile edge computing (MEC) server. Numerical results show that the proposed algorithms solve the problem efficiently. Via numerical results, we also identify the impact of various parameters (e.g., coding blocklength, the number of RIS elements, computational resources, number of UEs) on the energy efficiency. Furthermore, with the numerical results, we verify the validity of UE grouping method and demonstrate that the proposed dynamic CPU frequency allocation can enhance the performance substantially.

Elliptic positioning system offers a precise alternative to global navigation satellite system (GNSS). However, ranging measurements upon a single UAV only delineate the location estimates to a spherical region. Data infusion from inertial measurement units (IMUs) may refine these estimates, while its fidelity is undermined by IMUs’ lack of self alignment to a specified reference frame. In this paper, we explore the minimal number of assisted UAVs or anchors for absolute positioning, i.e., location and alignment in a fixed frame, during complete GNSS outages. We first prove that the observability establishes under a UAV in a 3-D trajectory or a pair of static anchors. The two numbers are new theoretical limit, significantly lower than the three anchors in 2-D or four in 3-D scenarios required by traditional theorems. We propose a sequential scheme for the multi-parameter estimation problem ensuring rapid convergence. An iterative solution is derived, flexible to UAV and anchor-based configurations, that provides instant location updates free of computational overhead. Thereafter, we also circumvent NLOS effects by employing inverse estimation of range. Accordingly, we devise a tiered positioning framework that commences with a location-unknown UAV to first cooperate with LOS anchors, and then extend the service to UE via a single NLOS link outside anchors’ coverage. In the experiments, the proposed scheme reaches (10−2)° orientation alignment and centimeter-level accuracy in NLOS scenarios, which attains the Cramer-Rao lower bound (CRLB) accuracy. Moreover, the accuracy notably exceeds the noise level of ranging measurements at high sampling rate, and also shows robustness against local clock drifting.

In this paper, we study a multi-access mobile edge computing (MEC) network in the industrial Internet-of-Things (IoT) scenario, which aims at providing a joint computation service for a group of sub-tasks offloaded from multiple user equipments (UEs). The whole MEC service, including a communication phase and a computation phase, is required to satisfy both a low latency and a high reliability requirement. We derive the end-to-end reliability (error probability) of the whole MEC service and provide corresponding reliability-optimal design frameworks, where both the perfect channel state information (CSI) and outdated CSI scenarios are considered. In particular, we characterize the low-latency communication behavior with the consideration of the finite blocklength (FBL) impact, and exploit the extreme value theory to study the delay violation probability in the computation phase. Following the characterizations, in the perfect CSI scenario, a design framework minimizing the instantaneous end-to-end error probability is provided, i.e., via optimally choosing the time length for each user’s offloading and the time length for the computation phase. We rigorously prove the convexity of the problem, investigate the relationships among the variables in the optimal solution, based on which a low-complexity method is proposed achieving the optimal solution. In addition, for the scenario with only the outdated CSI, after deriving the expected end-to-end error probability conditioned on the outdated CSI value, a corresponding optimal time allocation design is provided as well, where the convexity of the formulated problem is characterized and the optimal solution is obtained. Via simulations, we validate our analytical model and evaluate the network performance under the design.

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 federated learning (FL), local workers learn a global model collaboratively using their local data by communicating trained models to a central server for privacy concerns. Due to its local nature, FL is typically subject to various heterogeneities, including system and statistical heterogeneity. To address these concerns, Federated Proximal (FedProx) has been considered a promising FL paradigm to provide more stable learning convergence in the presence of computation stragglers and statistical heterogeneity. However, in wireless networks with unreliable communication channels, the errors of packet transmissions should be considered, introducing additional heterogeneity. For the first time, we rigorously prove the convergence of FedProx in the presence of transmission packet errors in heterogeneous networks. In addition, we propose a joint client selection and resource allocation strategy that maximizes the number of effective participating users for convergence acceleration. The method is combined with a random weight mechanism to reduce the statistical bias caused by the client selection strategy. An efficient low-complexity algorithm for solving the optimization problem is developed. The proposed method achieves faster convergence and requires fewer communication rounds to attain accuracy than existing state-of-the-art client selection methods.

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.

As a convenient means of transportation, unmanned aerial vehicles (UAVs) can provide consumers and merchants with safe, efficient, and contactless instant delivery services. However, online orders are often concurrent, dynamic, and with time constraints in an instant delivery system. Therefore, it's important to optimally schedule the delivery sequence upon the UAVsystem. In this paper, we design a real-time UAV delivery scheduling model considering dynamic online orders. Through our instant delivery management system, the urban delivery network can be updated in real time according to order information. Considering the practical situation, customers with spatial diversity may produce orders with common order requirements, so we consider the overlap of UAV routes in the scheduling process. An order merging algorithm (OMA) is then proposed to improve the delivery efficiency (the ratio of the number of completed orders to the times UAVs visit stores) of UAVs. With the help of proposed system-level decision-making method, our system can meet real-time concurrent order demands across urban delivery networks. To evaluate the performance of system, we further carry out simulation experiments to verify the effectiveness of our scheme. Results show that the proposed UAVscheduling scheme improves the efficiency of UAV instant delivery.

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.

As a potential solution to the growing conflict between the increasing demand for transportation and the limited capacity of transportation infrastructure, Intelligent Transportation Systems have gained considerable attention for their effectiveness in improving the efficiency of existing transportation infrastructure and enhancing traffic safety. Among various research areas, traffic flow prediction is a vital application, and researchers have devoted a lot of effort to designing accurate and fast algorithms. Currently, to satisfy various performance requirements, hybrid prediction methods that can take advantage of different sub-modules are beginning to emerge and show advantages in prediction accuracy and timeliness over other prediction algorithms that rely solely on machine learning. In this paper, we introduce a novel high precise traffic flow prediction method by utilizing the Fourier analysis (FA)-assisted denoising. Briefly, three sub-modules are introduced. Singular Spectrum Analysis (SSA) module is able to filter the noise of the original data, FA module is applied to extract periodic features of the traffic flow, and Long Short-Term Neural Networks (LSTM) is utilized to predict the future trend of time series residuals. We conducted simulation experiments. The corresponding test results demonstrate a substantial improvement in the accuracy compared to pure sub-models and other machine learning methods.

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.

We consider an update-and-decide IoT-based wireless network, where information packets generated from dual sources are co-stored in the transmitter's buffer, while decisions are made at the destination. Two practical assumptions about the communications between the transmitter and destination are taken into account: the communications are operating with finite blocklength (FBL) codes, and truncated hybrid automatic repeat request (HARQ) schemes are exploited to improve the FBL reliability, i.e., the number of allowed rounds of (re)transmissions is finite. For the first time, this paper characterizes the timeliness of status updates, namely age upon decisions (AuD) (which highlights the timeliness of the information at decisions in comparison to the concept of age of information), for such truncated HARQ-assisted wireless network. First, we characterize the inter-arrival time between two adjacent successfully transmitted packets, while taking into consideration the preemption policy and the randomness of the number of preempted packets from the same source. In particular, the probability density function, statistical performance of such inter-arrival time are derived. Following these characterizations, we propose a new approach to determine the average AuD and obtain a closed-form expression accordingly. Via simulations, we evaluate the performance and conclude a set of guidelines for designs on the considered network.

How to effectively improve the efficiency of vehicle traffic in the road system will play an essential role in improving the operational efficiency of the traffic system while eliminating the energy consumption and environmental pollution problems caused in particular, and this is also a key concern in the field of intelligent transportation systems. Timely and accurate traffic flow prediction is regarded as the key to solve the above problems because it can effectively improve the efficiency of traffic flow management. Many prediction methods have been proposed and among them, Machine Learning (ML)-based forecasting methods have gradually become mainstream in recent years because of their inherent ability to learn and predict nonlinear features in traffic information. However, we notice that most of the existing ML-based traffic prediction methods were designed relying fully on historical data while ignoring the structure and the impacts of the whole road network. Therefore, in this paper, we proposed a mixed method to take both historical data and road networks into consideration. Based on the real-world dataset, we conducted simulation experiments. The corresponding test results demonstrate a substantial improvement in the prediction accuracy of our method compared to conventional ML-based methods.

Mobile edge computing (MEC) based heterogeneous vehicular networks (HetVNets) can interwork between IEEE 802.11p-based vehicular networks and cellular-assisted vehicular networks for vehicle-to-everything (V2X) communications. It is an attractive technology for supporting low latency applications for vehicles. However, in the practical system without precise prior knowledge of the dynamic wireless environment, solving joint vehicle-to-edge (V2E) association and resource allocation problem is a challenge. In this paper, first, we use stochastic geometry to model a real scenario. Specifically, the intersection area is modeled as two perpendicular streets, the spatial distribution of vehicle nodes on each street is modeled as an independent one-dimensional (1D) homogeneous Poisson Point Process (PPP), the spatial distribution of different types of edge nodes is modeled as different and independent PPPs. We consider the service time during which a vehicle node with different types of network interfaces gets a service from an edge node. Then, a deep reinforcement learning (DRL) based method is proposed to solve the uplink-and-downlink V2E association problem minimizing the service time while ensuring the computation resource allocation constraints. Simulation results illustrate the better performance of our solution than that of other traditional methods.

Reconfigurable intelligent surfaces (RISs) are considered as an effective means to improve both the spectral efficiency and coverage in wireless systems. By properly setting the phase shift matrix, RIS can enhance the propagation environment. In this paper, we investigate an RIS aided mobile edge computing (MEC) network in the finite blocklength (FBL) regime with the goal to maximize the energy efficiency under both coding length and maximum decoding error rate constraints. We first investigate the single user equipment (UE) scenario and propose a three-step optimization algorithm. To extend the system model, we further investigate the two-UE scenario where non-orthogonal multiple access (NOMA) transmission is adopted. A revised three-step optimization algorithm is demonstrated to address the problem. Numerical results verify that the proposed three-step optimization algorithms can solve the problems in both scenarios efficiently. In particular, the numerical results show that loosening the FBL constraint can improve the performance and a larger CPU frequency at the MEC server leads to an improved energy efficiency. It is also noted that adjusting the RIS phase shift matrix can enhance the signal-to-noise ratio (SNR)/signal-to-interference-plus-noise ratio (SINR) at the BS and improve the decoding error rate under both scenarios.

The unmanned aerial vehicles (UAVs) have been widely used in civilian environments, due to its high flexibility, low cost and ease of deployment. In this paper, an UAV-enabled mobile edge computing (MEC) system is studied, in which the UAV serves as an aerial mobile base station to provide services for a group of ground user equipments (UEs) with computation task requests. We jointly optimize the time allocation, resource allocation and the UAV flying trajectory to minimize the time required for the UAV to complete the task, subject to the constraints of different kinds of resources, energy and velocity. Due to the non-convexity of the formulated problem, we first transform it to a feasibility check problem and then divide it onto three convex optimization subproblems. By using the block coordinate descent method and the successive convex approximate (SCA) method, we propose an efficient iterative algorithm to solve the three subproblems alternately with ensured convergence. Extensive simulation results show that the proposed joint optimization algorithm reduces the task completion time compared with other schemes.

Owing to the unique advantages of low cost and controllability, reconfigurable intelligent surface (RIS) is a promising candidate to address the blockage issue in millimeter wave (mmWave) communication systems, consequently has captured widespread attention in recent years. However, the joint active beamforming and passive beamforming design is an arduous task due to the high computational complexity and the dynamic changes of wireless environment. In this paper, we consider a RIS-assisted multi-user multiple-input single-output (MU-MISO) mmWave system and aim to develop a deep reinforcement learning (DRL) based algorithm to jointly design active hybrid beamformer at the base station (BS) side and passive beamformer at the RIS side. By employing an advanced soft actor-critic (SAC) algorithm, we propose a maximum entropy based DRL algorithm, which can explore more stochastic policies than deterministic policy, to design active analog precoder and passive beamformer simultaneously. Then, the digital precoder is determined by minimum mean square error (MMSE) method. The experimental results demonstrate that our proposed SAC algorithm can achieve better performance compared with conventional optimization algorithm and DRL algorithm.

Reconfigurable intelligent surface (RIS) is considered as an extraordinarily promising technology to solve the blockage problem of millimeter wave (mmWave) communications owing to its capable of establishing a reconfigurable wireless propagation. In this paper, we focus on a RIS-assisted mmWave communication network consisting of multiple base stations (BSs) serving a set of user equipments (UEs). Considering the BS-RIS-UE association problem which determines that the RIS should assist which BS and UEs, we joint optimize BS-RIS-UE association and passive beamforming at RIS to maximize the sum-rate of the system. To solve this intractable non-convex problem, we propose a soft actor-critic (SAC) deep reinforcement learning (DRL)-based joint beamforming and BS-RIS-UE association design algorithm, which can learn the best policy by interacting with the environment using less prior information and avoid falling into the local optimal solution by incorporating with the maximization of policy information entropy. The simulation results demonstrate that the proposed SAC-DRL algorithm can achieve significant performance gains compared with benchmark schemes.

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.

Accurate crowd flow prediction has gained increasing importance for the development of social Internet-of-Things (IoT) systems. In this article, we provide an efficient crowd flow prediction for social IoT systems in urban space based on the mobile network big data. In particular, the usage detail records (UDRs) are used in the prediction. The feasibility of using UDRs in the prediction is first analyzed. Then, a graph data model is exploited to record and represent the mobile behavior of users. In particular, we propose to apply the heterogeneous information network (HIN) representing the UDR data and characterize the users’ behavior through the embedding methods of HIN. Moreover, an attention-based spatiotemporal graph convolution network with embedded vectors (EA-STGCN) is proposed for the final prediction. Through experimental evaluation, the advantages of the proposed model are shown in comparison to benchmarks.

For the ultra-reliable low-latency communication (URLLC), the existing joint design algorithms of channel training and data transmission are not applicable due to the stringent reliability requirement and limited blocklength. To address this issue, we develop a low-complexity joint design framework for MISO communication based on the finite blocklength code (FBC). Specifically, an approximate bound of the packet error probability (PEP) is first derived and validated by practical modulation and coding schemes. It reveals the inherent tension between reliability, latency, and information bit number. Then, we formulate the joint design into a nonconvex optimization problem with the objective to maximize the information bit number. By exploiting the monotonicity of the PEP approximate bound, we provide closed-form solutions of power and blocklength allocation. Thereby, we develop a low-complexity algorithm to support the URLLC services aiming at the information bit number maximization. Furthermore, we investigate the joint designs to optimize the reliability, latency, and total energy, respectively, to fully meet the diverse demands of URLLC services. Finally, numerical results are provided to validate the proposed joint designs. The results show the outage capacity-based design severely underestimates the required wireless resources.

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.

We consider a mobile edge computing (MEC) network supporting low-latency, critical offloading workloads. The task offloading from the user to the server is operated under a truncated Hybrid Automatic Repeat reQuest (HARQ) process, i.e., we consider finite retransmission attempts. Both the HARQ type-I and type-II schemes are studied. For each scheme, we first characterize the total error probability and the total energy cost, while the impact of finite blocklength (FBL) on the stochastic retransmission behavior is considered. Following the characterizations, we are interested in optimal frameworks for each considered HARQ type, where the number of potential retransmission attempts is optimized together with the duration of each transmission, while the CPU frequency at the edge node is adjusted via voltage scaling. The objective is to minimize the total energy cost with error probability threshold. We show that the resulting stochastic optimization problems can be solved by means of convex optimization. We furthermore demonstrate that sharp minima exist among the energy consumption, underlying the importance of near-optimal parameter choice in the studied scenarios. Our results underline the importance of trading off communication and computational characteristics in delay-critical MEC setups with FBL codes.

In mobile edge computing (MEC) networks, by offloading tasks (partially or completely) to the MEC server, it becomes possible to complete computation-intensive and latency-critical applications without communicating with the cloud center, resulting in dramatic reduction both in latency and energy consumption. Performance improvements depend on the offloading decisions at the user equipments (UEs) and computational resource allocation at the MEC server. In this paper, we aim to optimize the UE offloading data ratios and MEC computational resource allocation under delay constraints with the goal to minimize the global energy consumption. Both conventional optimization method and learning-based approach are studied. Simulation results are provided to compare the performances of different schemes.

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.

In this paper, we study the robust secure unmanned aerial vehicle (UAV) communication system, where a UAV with full-duplex (FD) capability simultaneously receives the information signal from a ground unit (GU) and transmits jamming signal to degrade the wiretap capability of potential multiple ground eavesdroppers (Eves). With the consideration of estimation error of Eves' locations, we aim to maximize the average worst secrecy rate inside a certain flight period of the UAV by jointly optimizing the transmit power of the GU and UAV as well as the trajectory of the UAV. The resulting problem is intractable due to its non-convex nature and strongly coupled variables. Furthermore, the estimation error of Eves' locations results in an infinite number of constraints, which makes the problem even more difficult. To tackle this difficulty, we first propose an iterative algorithm based on the Schur complement lemma and successive inner approximation method to efficiently solve the problem suboptimally under the estimated Eves' locations. Then, in order to cope with the of Eves' location errors, we develop a cutting-set method, which solves the problem by alternating between optimal power-trajectory design and worst-case Eves' locations analysis. Via simulation, we show the improvement of the proposed algorithm compared to other benchmark algorithms under high FD self-interference cancellation levels.

In sixth-generation (6G) communication networks, ultrahigh-data rate and reliability are greatly vital for massive user connections and network sensors, such as Internet of Things (IoT) devices. Simultaneous wireless information and power transfer (SWIPT) has been evolved as an efficient strategy to enhance the reliability of wireless communication systems through prolonging the battery lifetime by harvesting energy from the received radio-frequency (RF) signals. Furthermore, cooperative relay sensors in IoT networks can extend the network coverage. In this article, we consider a massive multiple-input–multiple-output (MIMO) two-way relaying system, where the relay node splits the received RF signals into two power streams, one for information decoding (ID) and the other for energy harvesting (EH). Two classical and linear relay precodings, i.e., zero-forcing reception/zero-forcing transmission (ZFR/ZFT) and maximum-ratio combining/maximum-ratio transmission (MRC/MRT), are adopted to satisfy the requirements of high rate in this relay system. Different from prior work, the SWIPT technique and large-scale fading effects of MIMO channels are taken into account for deriving the asymptotic sum-rates of four prevalent power scaling cases when the number of relay antennas grows to infinity. Finally, the analytical results are evaluated by the presented simulation and numerical results.

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

Time delay estimation (TDE) is of great importance in reconnaissance and passive localization. Though TDE could leverage compressive sensing (CS) kernel to enjoy the advantage of sub-Nyquist samples, the estimation accuracy and robustness are inclined to be reduced by jamming or noise. To address the issue, this letter proposes a scheme of defensive compressive time delay estimation (DCTDE) with a novel sensing kernel optimization method and a defense augmentation strategy for the kernel. Inspired by the information bottleneck (IB) theory, we deduce a new analytic principle to enhance the estimation capability of the kernel, which focuses on the fidelity to the non-compressive scheme and the relevant information provided for TDE task.The strategy adopts an adversarial idea for the anti-spoofing design of kernel. Via simulations, the proposed scheme shows promising estimation accuracy, robustness, and high adaptability in both Gaussian noise and jamming environments, and more competitiveness than the Nyquist scheme.

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.

Taking into consideration of vehicle mobility and fairness, in this paper we provide a cooperative offloading algorithm maximizing the energy efficiency for a vehicular edge computing network, while guaranteeing the shortest delay of the worst-case vehicle. In particular, through exploiting the geometrical feature of the unidirectional road, we formulate a mixed integer convex problem by jointly designing the offloading selection and allocating the computation resource simultaneously. The optimization is carried out by a proposed two-step optimization algorithm: We first optimize the server selection to obtain the minimized achievable delay, and subsequently optimize jointly the selection and resource allocation to maximize the energy efficiency while maintaining the optimal achievable delay. Via simulations, we show the advantage of proposed algorithms and evaluate the system performance.

This work is related to ultra-reliable and low latency communication (URLLC) in Internet-of-Thing (IoT) networks. In particular, we consider a multi-device IoT network performing blind retransmissions on shared radio resources. We characterize the reliability and goodput performances of such network in the finite blocklength regime. In addition, following the characterization we provide two designs minimizing the error probability and maximizing the network goodput (under reliability constraints), respectively. In particular, the optimal solution is obtained for the reliability-oriented design. In addition, an efficient solution is proposed for the second design maximizing the goodput, which provides a performance tightly close to the one obtained via exhaustive search. Through simulation, we validate our analytical model and evaluate the system performance.

We consider a relay-assisted non-orthogonal multiple access (NOMA) network consisting of a source (S), an energy-constrained relay (R), and two users, where a hybrid power-splitting (PS) and time-splitting (TS) scheme is applied at R for energy harvesting. We provide optimal transmission designs for such a network by jointly optimizing the transmit power of S, the TS and PS ratios, the power allocation ratios at S and R, and the user ordering (indicating which user should apply the successive interference cancellation). In particular, when full channel state information at the transmitters (CSIT) is available, our design can minimize the energy consumption while ensuring that two users correctly receive the desired information. When only partial CSIT is available, our design can minimize the system outage probability. We analytically show that the joint optimal solution for a given TS ratio can be derived in a closed form for both CSIT cases. The optimal TS ratio can be found either in a closed form or using a bisection method for the full CSIT case, while it can be found using a one-dimension search for the partial CSIT case. Finally, numerical results are provided to validate the analytical results and to evaluate the performance advantage of the hybrid PS/TS scheme with the proposed optimal 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.

We consider a low-latency communication network operating with finite blocklength (FBL) codes. During the transmission, the minimum mean squared error (MMSE) channel estimation is assumed to be applied to obtain the instantaneous but imperfect Channel State Information (CSI) for the rate selection. We aim at optimizing the FBL throughput of the system under given reliability constraints. First, we provide an optimal frame structure design by optimally allocating the total frame length for MMSE training of channel estimation and data transmission. In addition, we further improve the FBL throughput considering channel dynamics which optimally selects the coding rate per frame. Combining the frame structure and the coding rate selection, a joint optimization problem is studied and solved by a sub-optimal algorithm. In the simulation study, we validate the proposed analytical model and evaluate the FBL throughput of the proposed solution in comparison to benchmark schemes.

The partial offloading technologies in the cooperative mobile edge computing (MEC) networks are considered as promising solutions to enable the emerging latency-sensitive and compute-intensive applications. In this paper, we characterize the performance model of an energy-aware MEC networks with multiple servers cooperatively computing a set of interdependent tasks. To minimize the total delay of the whole process of the set of tasks, an optimal offloading design is provided under given energy constraints. In particular, we provide an optimal solution to the offloading problem, which makes a 3-dimensional decision (matrix) representing at which time instant to offload which task to which server. Via simulation, we investigate the performance of the proposed design for the tasks with different interdependency structures. In particular, the impacts of the number of MEC servers, CPU frequencies and in the proposed algorithm on the system performance are studied. In addition, the tradeoff between the delay performance and computation complexity is addressed.

This paper considers simultaneous wireless information and power transfer (SWIPT) mechanisms in a relaying-assisted ultra-reliable low latency communication network operating with finite blocklength codes. The reliability of the network is maximized by the optimal selection of SWIPT parameters under both a power splitting (PS) protocol and a time switching (TS) protocol. In addition, we propose a protocol to improve the reliability performance by introducing a tradeoff between the PS and TS protocols. To further improve the reliability, a joint design is provided, which aligns the optimal selection of SWIPT parameters together with a blocklength allocation between two relaying hops. Via simulations, we validate our analytical model and show that the proposed algorithm achieves the same performance as that obtained with exhaustive search. In addition, we evaluate the considered network, and characterize the impact of blocklength, transmit power, and packet size on the reliability of the considered SWIPT-enabled relaying network. Finally, the performance advantages of the proposed protocol (in comparison with the PS and TS protocols) and the proposed joint designs are investigated.

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.

In this paper, we consider the constructions of type-II quasi-cyclic (QC) low-density parity-check (LDPC) codes with girth eight from a Sidon sequence. We first derive the necessary and sufficient conditions guaranteeing a girth-eight type-II QC-LDPC code. By combining these conditions with the concept of the Sidon sequence, three classes of type-II QC-LDPC codes are subsequently proposed with girth eight. To the best of our knowledge, the second and the third classes we proposed are the first series of systematic and algebraic constructions for the girth-eight type-II QC-LDPC codes that have rates being greater than a half and, at the same time, have distance upper bounds being not limited to 12. Via simulations, we show the promising performance of the proposed type-II QC-LDPC codes with girth eight. In addition, a set of general bounds and explicit/random constructions without using a Sidon sequence are also presented for the type-II QC-LDPC codes with girth six or eight. We observe that the proposed codes perform better than or equally well as the random QC-LDPC codes from PEG and quadr.-congr. methods, while the novel codes possess both highly structured parity-check matrices and very flexible choices in circulant size.

In this work, we consider type-II quasi-cyclic LDPC codes with girth eight from a Sidon sequence. We first derive the necessary and sufficient conditions guaranteeing girth-eight type-II QC-LDPC codes. By combining these conditions and the concept of a Sidon sequence, two classes of type-II codes are subsequently proposed with girth up to eight. We discuss the distance upper bounds of the two classes of codes and show that the second class provides a larger distance upper bound. In particular, to the best of our knowledge, the second class we proposed yields the first algebraic construction for girth-eight type-II codes with rates larger than a half and distance upper bounds exceeding twelve. Via simulations, we show that the girth-eight type-II codes from the second class significantly outperform the existing CDF-based girth-eight type-II codes, and that they perform better than or almost identically to the randomly generated girth-eight quadr. congr. codes.

In this work, motivated by emerging low-latency applications, we consider an amplify-and-forward relaying network operating with finite blocklength (FBL) codes subject to delay quality of service (QoS) constraints. Hence, we address both transmission delay (via FBL codes) and queueing delay (via delay QoS requirements). We first derive the QoS-constrained throughput of the network. Subsequently, we state a resource allocation problem aiming at allocating the power between the source and the relay to maximize the throughput. The convexity of the problem is proved and the optimal power allocation policy is provided. Via simulations, we confirm the accurateness of our analytical model. In addition, we provide interesting insights on the system behavior by characterizing the impact of the error probability, the QoS-exponent and coding blocklength on the throughput performance.

In this paper, we consider a downlink multiuser network operating with finite blocklength codes under statistical quality of service (QoS) constraints. An optimal power allocation algorithm is studied to maximize the normalized sum throughput under QoS constraints. We first determine the finite blocklength (FBL) throughput formulations and subsequently state optimization problems. We show the convexity of the power allocation problem under certain conditions and propose an optimal algorithm to solve the problem. Via numerical analysis, we demonstrate the performance improvements with the optimal power allocation. In addition, we provide interesting insights on the system behavior by characterizing the impact of the error probability, the QoS-exponent and blocklength on the performance.

In this paper, we consider a downlink multiuser network operating with finite blocklength (FBL) codes under statistical quality of service (QoS) constraints. Optimal power allocation algorithms are studied to maximize the normalized sum throughput under QoS constraints, while considering different types of data arrivals, namely, constant-rate, Markov, and Markov-modulated Poisson arrivals. We first determine the FBL throughput formulations and subsequently state optimization problems. We show the convexity of the power allocation problem under certain conditions and propose optimal algorithms (for scenarios with different data arrivals). In addition, the FBL performance of equal power allocation and a sub-optimal power allocation algorithm is discussed. Via numerical analysis, we demonstrate the performance improvements with the optimal power allocation. In addition, we provide interesting insights on the system behavior by characterizing the impact of the error probability, the QoS exponent, the blocklength, the number of users, and the source burstiness on the performance.

This paper considers simultaneous wireless information and power transfer (SWIPT) mechanisms in a relaying network with finite blocklength (FBL) codes. The reliability of the network is optimized under both a power splitting (PS) protocol and a proposed joint PS and time switching (TS) protocol. Under both protocols, we first determine the overall error probability formulation of the SWIPT-enabled two-hop transmission in the FBL regime. Subsequently, we state and solve optimization problems minimizing the overall error probability. Via numerical analysis, we show the appropriateness of our analytical model and demonstrate the performance advantage of the proposed protocol in comparison to TS and PS protocols. In addition, we provide interesting insights on the system behavior by characterizing the impact of the blocklength, transmit power and packet size on the reliability performance.

In this work, we focus on a low-latency multiuser broadcast network operating in the finite blocklength regime and employing a non-orthogonal multiple-access (NOMA) scheme. By letting the user with the stronger channel from the source act as a relay, we propose two relay-assisted transmission schemes, namely relaying and NOMA-relay. We study the finite blocklength performance of the proposed schemes in comparison with the NOMA scheme. Both the average performance of and fairness between users are considered. Our results show that the NOMA scheme is not preferred in the low-latency scenario in comparison to the proposed schemes. In particular, the relaying scheme generally provides the best fairness between users, while the NOMA-relay scheme is able to achieve a higher average throughput by setting the packet size relatively aggressively.

The proliferation of wireless communications has lead to a high interest to establish this technology in industrial settings. The main arguments in favor of wireless are reduced costs in deployment and maintenance, as well as increased flexibility. In contrast to home and office environments, industrial settings include mission-critical machine-to-machine applications, demanding stringent requirements for reliability and latency in the area of 1–10−9 PDR and 1ms, respectively. One way to achieve both is cooperative Automatic Repeat reQuest (ARQ), which leverages spatial diversity. This paper presents a wireless multi-user Time Division Multiple Access system with cooperative ARQ for mission-critical communication. We evaluate two design options analytically, using an outage-capacity model, to investigate whether the relaying of messages should be performed centrally at a multi-antenna AP with perfect Channel State Information (CSI) or decentrally at simultaneously transmitting stations with average CSI. Results indicate that both options are able to achieve the targeted communication guarantees when a certain degree of diversity is implemented, showing a stable system performance even with an increasing number of stations.

In this paper, the blocklength-limited performance of a relaying system is studied, where channels are assumed to experience quasi-static Rayleigh fading while at the same time only the average channel state information (CSI) is available at the source. Both the physical-layer performance (blocklength-limited throughput) and the link-layer performance (effective capacity) of the relaying system are investigated. We propose a simple system operation by introducing a factor based on which we weight the average CSI and let the source determine the coding rate accordingly. We show that both the blocklength-limited throughput and the effective capacity are quasi-concave in the weight factor. Through numerical analysis, we investigate the relaying performance with average CSI while considering perfect CSI scenario and direct transmission as comparison schemes. We observe that relaying is more efficient than direct transmission in the finite blocklength regime. Moreover, this performance advantage of relaying under the average CSI scenario is more significant than under the perfect CSI scenario. Finally, the speed of convergence (between the blocklength-limited performance and the performance with infinite blocklengths) in relaying system is faster in comparison to the direct transmission under both the average CSI scenario and the perfect CSI scenario.

This paper considers a multiple decode-and-forward (DF) relay assisted network in the Long Term Evolution Advanced (LTE-A) system. Multiple relay nodes (RNs) joint transmission is applied to enhance the fairness of capacity for edge user (UE). But a number of RNs activated in the same time-frequency block group (TFBG) will let inter-cell interference (ICI) be stronger, and will reduce the system average capacity. In order to reconcile this contradiction between average capacity and fairness, this paper proposes an utility function (UF) which takes "RN-UE's demand" and "RNs' efficiency" into account. Besides, a low complexity greedy multi-RN selection (GMRS) scheme based on the UF (GMRS-UF) also be proposed. The simulation results show that GMRS has low complexity but outstanding performance. Compared with previous RN selection strategy, GMRS-UF could reduce ICI by discarding in efficient RNs, thereby promoting the average system throughput. And GMRS-UF can achieve any compromise between average capacity and fairness.