This paper investigates two distinct types of block errors - undetected errors (confusions) and erasures - in additive white Gaussian noise (AWGN) channels with error-bounded block decoders operating in the finite blocklength (FBL) regime. While block error rate (BLER) is a common metric, it does not distinguish between confusions and erasures, which can have significantly different impacts in cross-layer protocol design, despite upper-layer protocols universally assuming physical (PHY) errors manifest as packet erasures rather than undetected corruptions - an assumption lacking rigorous PHY-layer validation. We present a systematic analysis of confusions and erasures under BLER-constrained maximum likelihood (ML) decoding. Through sphere-packing analysis, we provide analytical bounds for both block confusion and erasure probabilities, and derive the sensitivities of these bounds to blocklength and signal-to-noise ratio (SNR). To the best of our knowledge, this is the first study on this topic in the FBL regime. Our findings provide theoretical validation for the block erasure channel abstraction commonly assumed in medium access control (MAC) and network layer protocols, confirming that, for practical FBL codes, block confusions are negligible compared to block erasures, especially at large blocklengths and high SNR.

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.

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.

Smaller cells have been the most important contributor to throughput improvement since the birth of cellular networks. They are likely to evolve further in the shift to cell-free massive MIMO (CF mMIMO), where multiple closely placed access points (APs) collaborate to serve users. This scheme is particularly suitable for millimeter wave (mmWave) communication, which enables very high data rates with its large bandwidth, but encounters severe challenges of high path loss and blockage. The CF mMIMO network is a good countermeasure to these two challenges by utilizing overlapping signals from different APs and macro-diversity. In this work, we demonstrate that mmWave CF mMIMO network optimization is largely an AP-user assignment problem. To solve this large-scale, nondifferentiable problem, we propose an unsupervised machine learning (ML) approach, which looks for the optimal solution autonomously without labels. A customized graph neural network architecture tailored to the problem properties is proposed, which enables distributed optimization without a central unit, allows for a varying number of users, and hierarchical permutation-equivariance of APs and users. A teacher-student model is applied to prune the graph, where the teacher model uses a fully connected graph for maximum performance, and the student model uses a pruned graph to reproduce the teacher's behavior with less communication in fronthaul. Moreover, a special training method is designed, which relaxes the combinatorial problem to a continuous one. In this way, we can apply gradient-based neural network training. An entropy-inspired penalty is introduced to make the relaxed problem equivalent to the original one. The analytical augmented Lagrangian method is combined with ML for the constrained optimization. Simulation results show that the proposed approach outperforms baselines in both performance and computation time. In addition, with a properly pruned graph, the proposed approach performs inference in a distributed manner with sparse message passing between APs, realizing a low signaling overhead in fronthaul, and a performance close to the fully connected graph.

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.

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.

Physical layer deception (PLD) is a novel security mechanism that combines physical layer security (PLS) with deception technologies to actively defend against eavesdroppers. In this paper, we establish a novel semantic model for PLD that evaluates its performance in terms of semantic distortion. By analyzing semantic distortion at varying levels of knowledge on the receiver's part regarding the key, we derive the receiver's optimal decryption strategy, and consequently, the transmitter's optimal deception strategy. The proposed semantic model provides a more generic understanding of the PLD approach independent from coding or multiplexing schemes, and allows for efficient real-time adaptation to fading channels.

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.

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.

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.

Cell-free networks outperform cellular networks in many aspects, yet their efficiency is affected by imperfect channel state information (CSI). In order to address this issue, this work presents a robust resource allocation framework designed for the downlink of user-centric cell-free massive multi-input multi-output (CF-mMIMO) networks. This framework employs a sequential resource allocation strategy with a robust user scheduling algorithm designed to maximize the sum-rate of the network and two robust power allocation algorithms aimed at minimizing the mean square error, which are developed to mitigate the effects of imperfect CSI. An analysis of the proposed robust resource allocation problems is developed along with a study of their computational cost. Simulation results demonstrate the effectiveness of the proposed robust resource allocation algorithms, showing a performance improvement of up to 30% compared to existing techniques.

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.

Physical layer security (PLS) is a promising technology to secure wireless communications by exploiting the physical properties of the wireless channel. However, the passive nature of PLS creates a significant imbalance between the effort required by eavesdroppers and legitimate users to secure data. To address this imbalance, in this article, we propose a novel framework of physical layer deception (PLD), which combines PLS with deception technologies to actively counteract wiretapping attempts. Combining a two-stage encoder with randomized ciphering and non-orthogonal multiplexing, the PLD approach enables the wireless communication system to proactively counter eavesdroppers with deceptive messages. Relying solely on the superiority of the legitimate channel over the eavesdropping channel, the PLD framework can effectively protect the confidentiality of the transmitted messages, even against eavesdroppers who possess knowledge equivalent to that of the legitimate receiver. We prove the validity of the PLD framework with in-depth analyses and demonstrate its superiority over conventional PLS approaches with comprehensive numerical benchmarks.

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.

Active learning (AL) is a machine learning technique that aims to reduce annotation costs by selectively choosing the most informative samples for labeling. This process relies on acquisition functions, which can be broadly categorized into two types: representativity-based and uncertainty-based. Representativity-based functions focus on exploring the dataset, while uncertainty-based functions refine decision boundaries. This creates a trade-off known as the exploration-exploitation dilemma. To address this challenge, we propose a novel approach that alternates between these two types of acquisition functions. Our method employs an adaptive feedback-driven selection mechanism, an annealing-based approach, or a baseline random criterion to guide the alternation process. This strategy helps mitigate common AL issues, such as batch mode inefficiency and cold start problems. Our experiments demonstrate that the alternating approach enhances both the accuracy and robustness of the AL process. Additionally, we consider the balance between accuracy and energy consumption, contributing to the development of more sustainable AI systems. By evaluating our criterion across various models and datasets, we show its potential to reduce computational costs while maintaining or even improving accuracy. Notably, alternating between the BALD and BADGE acquisition functions yields particularly robust results.

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

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

Millimeter wave (mmWave) cell-free massive MIMO (CF mMIMO) is a promising solution for future wireless communications. However, its optimization is non-trivial due to the challenging channel characteristics. We show that mmWave CF mMIMO optimization is largely an assignment problem between access points (APs) and users due to the high path loss of mmWave channels, the limited output power of the amplifier, and the almost orthogonal channels between users given a large number of AP antennas. The combinatorial nature of the assignment problem, the requirement for scalability, and the distributed implementation of CF mMIMO make this problem difficult. In this work, we propose an unsupervised machine learning (ML) enabled solution. In particular, a graph neural network (GNN) customized for scalability and distributed implementation is introduced. Moreover, the customized GNN architecture is hierarchically permutation-equivariant (HPE), i.e., if the APs or users of an AP are permuted, the output assignment is automatically permuted in the same way. To address this combinatorial problem, we relax it to a continuous problem, and introduce an information entropy-inspired penalty term. The training objective is then formulated using the augmented Lagrangian method (ALM). The test results show that the realized sum-rate outperforms that of the generalized serial dictatorship (GSD) algorithm and is very close to an upper bound in a small network scenario, while the upper bound is impossible to obtain in a large network scenario.

This paper proposes a novel, resource-efficient approach to Visual Speech Recognition (VSR) leveraging speech representations produced by any trained Automatic Speech Recognition (ASR) model. Moving away from the resource-intensive trends prevalent in recent literature, our method distills knowledge from a trained Conformer-based ASR model, achieving competitive performance on standard VSR benchmarks with significantly less resource utilization. Using unlabeled audio-visual data only, our baseline model achieves a word error rate (WER) of 47.4% and 54.7% on the LRS2 and LRS3 test benchmarks, respectively. After fine-tuning the model with limited labeled data, the word error rate reduces to 35% (LRS2) and 45.7% (LRS3). Our model can be trained on a single consumer-grade GPU within a few days and is capable of performing real-time end-to-end VSR on dated hardware, suggesting a path towards more accessible and resource-efficient VSR methodologies.

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.

Federated Learning (FL) revolutionizes distributed machine learning, enabling clients to learn collaboratively while keeping data private. In contrast, Decentralized Federated Learning (DFL) offers direct communication between clients without a central server, improving fault tolerance and network efficiency, but communication overhead remains a challenge. To address this, we propose a new scheme called Low Huffman-coded Delta Quantization (LHDQ) which achieves a remarkable quantization rate of $\frac{5}{3}$ bits per parameter. We evaluate LHDQ within the DFL architecture under two proposed transmission protocols and compare it against conventional quantization schemes under various communication channel conditions. Despite a slight reduction in accuracy, LHDQ offers compelling advantages as alleviating communication bottlenecks, reducing transmitted bits, and accelerating training and convergence processes.

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.

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.

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.

The low-resolution reality of the hardware elements associated with massive mmWave antenna or reflector arrays is associated with the performance degradation of the wireless link when it is not properly controlled. In particular, the unintended angular radiations of the transmission or reflection arrays (e.g., transmission in non-intended directions) would invalidate the usual assumptions of information secrecy, even with perfect channel state information (CSI) knowledge at the transmitter, in the presence of low-resolution hardware. In this paper, we study a hybrid beamforming design for reconfigurable intelligent surface (RIS)-assisted multi-user multiple-input multiple-output (MU-MIMO) downlink (DL) communication, from the prospect of information secrecy maximization, wherein the array element phase rotations belong to the known discrete space. To address the NP-hard and non-convex nature of the problem at hand, we propose an iterative procedure by re-structuring the obtained discrete-domain problem into a tractable form which solves the problem numerically and guarantees the convergence to a stationary point. Further, we confirm the accuracy of the proposed optimization algorithm by an exhaustive search method based on graphical simulations. The minimal performance disparity that exists between the proposed algorithm and the considered digital beamforming (DBF) scheme as the upper bound validates the hybrid beamforming design. Moreover, the proposed work highlights the superiority of discrete-aware design over various existing baseline schemes, demonstrating the significant gains attainable by adopting discrete space design from the outset. Additionally, the proposed solution discusses the improvement in secrecy system performance by deploying RIS with an increased number of reflecting elements and thereby restricting the effect of eavesdroppers on secure communication.

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.

Active learning (AL) is a machine learning (ML) approach that entails carefully choosing the most informative samples for annotation during training, aiming to minimize annotation costs. AL has recently emerged as a promising approach in the context of green ML, as an energy-efficient learning method on top of being data-efficient. Nevertheless, given the significant cost of AL, it might lead one to question the effectiveness of this approach in reducing computational costs and promoting green ML. In this paper, we conduct a comparative analysis of both fundamental and advanced active learning methods against a random baseline selection, aiming to demonstrate the efficacy of active learning to reduce the cost of training. This study demonstrates that, with careful tuning of hyperparameters like query size and pool size, AL is able to reduce runtime while maintaining competitive accuracy for classification tasks.

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.

In this paper, we explore a novel approach of using a simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) to aid a multi-user (MU) multi-input multi-output (MIMO) system for simultaneous wireless information and power transfer (SWIPT) in the presence of statistical channel estimation errors (CEE). Our focus is on minimizing the power required for the SWIPT system through joint beamforming design at both the base station (BS) and STAR-RIS, while ensuring that the minimum rate and minimum energy harvesting requirements are met for information and energy receivers, respectively. Due to the non-convex and NP-hard nature of the problem, we utilize a minimum mean square error (MMSE) approach to simplify the problem and then use an alternating optimization framework to solve the beamforming design problems at the BS and STAR-RIS iteratively using general approximations. Simulation results show that the proposed algorithm provides a significant beamforming gain for STAR-RIS-aided SWIPT system over conventional RIS system while satisfying the given quality of service (QoS) constraints for SWIPT systems under the CEE.

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

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

The classification of Electrocardiograms (ECG) sig-nals is vital for diagnosing and monitoring cardiovascular diseases. Recently, there has been increasing interest in leveraging efficient machine learning models for ECG classification. Op-timizing computational resources in this domain is crucial for real-time analysis and reducing power consumption. This paper explores two approaches to enhance the efficiency of ECG classification on edge devices. Firstly, a smart lead-switching mechanism intelligently switches between a high-power model and a low- power model. Secondly, Apache Tensor Virtual Machine (TVM) is employed to optimize the implementation of the developed models on the Jetson Nano single-board computer. Experimental results demonstrate noticeable achievements, including up to 96% reduction in FLOPS and 95% reduction in inference time. Our results hold significant promise for improving ECG classification on edge devices, enabling early detection of abnormalities, and enhancing patient care.

In the era of big data, new learning techniques are emerging to solve the difficulties of data collection, storage, scalability, and privacy. To overcome these challenges, we propose a distributed learning system that merges the hybrid edge-cloud split-learning architecture with the semi-supervised learning scheme. The proposed system based on three semisupervised learning algorithms (FixMatch, Virtual Adversarial Training, and MeanTeacher) is compared to the supervised learning scheme and trained on different datasets and data distributions (IID and non-IID) and with a variable number of clients. The new system could efficiently utilize the local unlabeled samples on the client side and gave a performance encouragement that exceeds 30% in most cases even with small percentage of labelled data. Additionally, certain Split-SSL algorithms showed performance that was on par with or occasionally even better than more resource-intensive algorithms, although requiring less processing power and convergence time.

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.

Digital twin (DT) has emerged as a promising technology for improving resource allocation decisions in Internet of Vehicles (IoV) networks. In this paper, we consider an IoV network where mobile edge computing (MEC) servers are deployed at the roadside units (RSUs). The IoV network provides ubiquitous connections even in areas uncovered by RSUs with the assistance of unmanned aerial vehicles (UAVs) which can act as a relay between RSUs and task vehicles. A virtual representation of the IoV network is established in the aerial network as DT which captures the dynamics of the entities of the physical network in real-time in order to perform efficient resource allocation for delay-intolerant tasks. We investigate an intelligent delay-sensitive task offloading scheme for the dynamic vehicular environment which provides computation resources via local execution, vehicle-to-vehicle (V2V), and vehicle-to-roadside-unit (V2I) offloading modes based on the energy consumption of the system. Moreover, we also propose a multi-network deep reinforcement learning (DRL)-based resource allocation algorithm (RADiT) in the DT-assisted network for maximizing the utility of the IoV network while optimizing the task offloading strategy. Further, we compare the performance of the proposed algorithm with and without the presence of V2V computation mode. RADiT is further evaluated by comparing it with another benchmark DRL algorithm called soft actor-critic (SAC) and a non-DRL approach called greedy. Finally, simulations are performed to demonstrate that the utility of the proposed RADiT algorithm is higher under every condition compared to its respective conditions in SAC and greedy approach. Consequently, the proposed framework jointly improves energy efficiency and reduces the overall delay of the network. The proposed algorithm with UAV relay further increases the efficiency of the network by increasing the task completion rate.

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.

This work addresses the problem of 3D-localizing and enhancing the speech of one main speaker in noisy multi-speaker hospital environments using a multi-channel microphone array. In our model, we propose conducting speaker localization using a machine learning model based on convolutional recurrent neural networks (CRNN) followed by minimum variance distortionless response (MVDR) beamforming. In addition, to ensure that our speech enhancement module is adaptive when deployed in different environments, we trained a meta learning model. Firstly, in the localization step, an estimation of the direction of arrival (DOA) in the elevation and azimuth planes is executed. This is conducted in a 3D space with the presence of noise, reverberation, and up to two more speakers. Using estimated DOA, the MVDR beamformer then enhances the speech of the main speaker. In order to test our model, we adopted and simulated a real-world problem where the objective was to enhance the speech of a clinician in a noisy intensive care unit (ICU) with the presence of other speakers. Furthermore, in order to validate our model, we adopted a speech-to-text module to evaluate the word error rate. Moreover, we implemented our algorithm on hardware using commercially available components and tested it in a real environment. Results showed that our model outperforms other machine learning and non-machine learning algorithms. Finally, we used our trained Meta Learning model to show that our model can adapt to new environments while maintaining high performance after retraining with only a few-shot recordings.

Electrocardiography (ECG) is a standard diagnostic tool for evaluating the overall heart's electrical activity and is vital for detecting many cardiovascular diseases. Classifying ECG recordings using deep neural networks has been investigated in literature and has shown very good performance. However, this performance assumes that the training data is centralized, which is often not the case in real-life scenarios, where data resides in multiple places and only a small portion of it is labeled. Therefore, in this work, we propose an ECG classification system that focuses on preserving data privacy and enhancing overall system efficiency. We analyzed the complexity of previously proposed deep learning-based models and showed that the temporal convolutional network-based models (TCN) were the most efficient. Then, we built on the TCN models a modified split-learning (SL) system that achieves the same classification performance as the basic SL but reduces the communication overhead between the server and the client by 71.7% as well as reducing the computations at the client by 46.5% compared to the original SL system based on the TCN network. Finally, we implement semi-supervised learning in our system to enhance its classification performance by 9.1%-15.7%, when the training data consists only of 10% labeled data. We have tested our proposed system on a test IoT setup and it achieved satisfactory classification accuracy while being private and energy efficient for green-AI applications.

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.

Each year, pneumonia causes harm to millions of people worldwide. The chest radiograph remains the most important tool for diagnosing pneumonia and many other thoracic diseases. Many deep-learning approaches have been advocated in recent years, taking advantage of large public medical datasets and powerful computational resources to support radiologists in their examinations. However, the vast amount of data available is rarely utilized. In this work, we propose two novel attention-based mechanisms for fusing multimodal and multi-view data in a medical context, respectively. A unimodal multi-view classifier for chest X-rays and a multimodal classifier are developed, inter alia, operating these mechanisms to classify pneumonia using chest radiographs in combination with other clinically derived modalities. Our unimodal multi-view model outperforms other state-of-the-art approaches on the MIMIC-IV dataset in the AUROC metric by at least 9.2%, achieving an AUROC score of 81.5%. Our attention-based multimodal model, namely Radiopaths, achieved an AUROC score of 87.3%, further increasing the multi-view model's performance by 5.8%, while acquiring robustness to missing modalities and scalability.

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.

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.

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

In this paper, we investigate the robust resource allocation design for secure communication in an integrated sensing and communication (ISAC) system. A multi-antenna dual-functional radar-communication (DFRC) base station (BS) serves multiple single-antenna legitimate users and senses for targets simultaneously, where already identified targets are treated as potential single-antenna eavesdroppers. The DFRC BS scans a sector with a sequence of dedicated beams, and the ISAC system takes a snapshot of the environment during the transmission of each beam. Based on the sensing information, the DFRC BS can acquire the channel state information (CSI) of the potential eavesdroppers. Different from existing works that focused on the resource allocation design for a single snapshot, in this paper, we propose a novel optimization framework that jointly optimizes the communication and sensing resources over a sequence of snapshots with adjustable durations. Besides, artificial noise (AN) is exploited by the BS for joint sensing and physical layer security provisioning. To this end, we jointly optimize the duration of each snapshot, the beamforming vector, and the covariance matrix of the AN for maximization of the system sum secrecy rate over a sequence of snapshots while guaranteeing a minimum required average achievable rate and a maximum information leakage constraint for each legitimate user. The resource allocation algorithm design is formulated as a non-convex optimization problem, where we account for the imperfect CSI of both the legitimate users and the potential eavesdroppers. To make the problem tractable, we derive a bound for the uncertainty region of the potential eavesdroppers’ small-scale fading based on a safe approximation, which facilitates the development of a block coordinate descent-based iterative algorithm for obtaining an efficient suboptimal solution. Simulation results illustrate that the proposed scheme can significantly enhance the physical layer security of ISAC systems compared to three baseline schemes. Moreover, compared to the conventional multi-stage approach for ISAC system design, the proposed approach based on variable-length snapshots not only facilitates a highly-directional offline sensing beam design but also allows us to flexibly prioritize communication or sensing depending on the application scenario.

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.

Distributed machine learning systems train neural network models by utilizing the devices and resources in the network. One such system that was recently introduced is split learning. It trains a deep neural network collaboratively between the server and the client without sharing the raw data, ensuring private and secure training. Implementing such systems on the edge devices adds computation and communication overhead, which might not suit many edge devices, especially in IoT systems, where resources are limited. In this paper, we introduce a modified split learning system that includes an autoencoder and an adaptive threshold mechanism. The modified system has less communication and computation overhead compared to the original split learning system. The modified system was deployed on an IoT system and the results proved the advantages of the proposed mechanism. The communication overhead and computation overhead were reduced with negligible performance loss.

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 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

A fast multi-target detection technique-using a devised precoding in a wireless sensor network (WSN)-is proposed. The targets are detected by binary wireless sensors which only have one or zero logical outputs, indicating a target's presence or absence. The goal is to simultaneously decode all sensors' data in one time slot at the receiver node, where all sensors transmit data simultaneously on the same frequency band. For this to happen, a specific geometric deployment of the sensors is presented in the paper. The concept in this work might have similarities to the bijective mappings in the superposition modulation. Different channel gains and the additive white Gaussian noise are included in the model. The system model is described in detail and the communication scheme to fulfill a simultaneous decoding is mathematically formulated. Then, the parametric solutions to this general digital modulation problem are studied step by step through several theorems, and the optimal solutions that minimize the detection error are found. The combined effects of the noise and the non-perfect channel state information on the signal constellations are both analytically and numerically investigated. In addition, two other detection scenarios with a priori information about the targets are studied.

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 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.

Cyber-physical systems (CPS) are state-of-the-art communication environments that offer various applications with distinct requirements. However, security in CPS is a nonnegotiable concept, since without a proper security mechanism the applications of CPS may risk human lives, the privacy of individuals, and system operations. In this paper, we focus on PHY-layer security approaches in CPS to prevent passive eavesdropping attacks, and we propose an integration of physical layer operations to enhance security. Thanks to the McEliece cryptosystem, error injection is firstly applied to information bits, which are encoded with the forward error correction (FEC) schemes. Golay and Hamming codes are selected as FEC schemes to satisfy power and computational efficiency. Then obtained codewords are transmitted across reliable intermediate relays to the legitimate receiver. As a performance metric, the decoding frame error rate of the eavesdropper is analytically obtained for the fragmentary existence of significant noise between relays and Eve. The simulation results validate the analytical calculations, and the obtained results show that the number of low-quality channels and the selected FEC scheme affects the performance of the proposed model.

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.

This letter presents an adaptive learning framework for estimating structural parameters in stochastic state-based models (SSMs). SSMs are a useful modeling tool in systems biology and medicine. While models in these disciplines are traditionally hand-crafted, an automated generation based on experimental data becomes a topic of research interest. In particular, our goal is to classify measured processes using the generated models. An innovative likelihood-based adaptive learning approach capable of learning the structural parameters, i.e., the arc weights of SSMs from data and exploiting the reliability of detected inputs is presented in this letter. Its convergence behavior is analyzed and an expression for the error at steady state is derived. Simulations assess the performance of the proposed and existing algorithms for a gene regulatory network.

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.

This paper investigates the receiver positioning problem in a communication network, where the transmitters are distributed arbitrarily on the XY-plane with known positions. The purpose is to find the optimal solution for the position of the receiver such that the maximum distance between the receiver and the transmitters is minimized, while two constraints are fulfilled as follows: for any pair of transmitters, the delay caused by the difference in the distances between the transmitters and the receiver is less than a certain value, and the angle between the line-of-sight paths from each transmitter toward the receiver is within a tolerable range. The optimization problem is studied in its general form and the characteristics of the constraints and the objective function are analytically analyzed. According to the conducted analyses, an appropriate sub-optimal solution is proposed and an extensively reduced feasible set to find the optimum solution is presented. Although this optimum positioning approach might act merely as a performance enhancer-such as an energy optimizer or a delay minimizer-in conventional system designs, it is a crucial component for signal processing and constellation design in many multiplexing transmission schemes, as well as in the fast detection scenarios.

A novel distributed event-triggered communication for multi-agent systems is presented. Each agent predicts its future states via an artificial neural network, where the prediction is solely based on own past states. The approach is therefore scalable with the number of agents. A communication is triggered if the discrepancy between actual and predicted state exceeds a threshold. Numerical results show that this approach reduces the communication effort remarkably compared to existing methods.

Modern process automation and the industrial evolution heading towards Industry 4.0 require a huge variety of information to be fused in a Cyber-Physical System. Important for many applications is the spatial position of an arbitrary object given directly or indirectly in terms of data that has to be processed to obtain position information. Starting point for the idea of the technical reflection-based sound localization system presented in this paper is the biological role model of humans being able to learn how to localize sound sources. Compared to other forms of sound localization, this nature-inspired method has no need for high spatial and temporal accuracy or big microphone arrays. Possible applications for this system are indoor robot localization or object tracking.

The configuration and integration of automation devices into an existing communication infrastructure requires high efforts due to parameterization and commissioning processes. In addition, a linkage between device properties / values to logical identifiers used in process control code has to be adapted using a correct mapping of memory areas within the process memory acquired from the device. In order to exchange an automation device by a similar one, all steps above have to be adapted and checked until it can be used productively. To meet flexibility requirements of future automation scenarios, the integration and commissioning process of automation devices has to be made more flexible, less error-prone and less dependent of boundary conditions. This paper presents a concept for self-description of automation devices based on wireless communication using 802.15.1 (Bluetooth Low Energy) to facilitate commissioning processes of devices and assist engineers in process definition and device selection during development phase. A demonstrator is realized to validate this concept.

Cloud-Radio Access Networks (C-RANs) are known for their potential to accommodate the heavy processing required by the exponentially increasing data traffic. Thanks to virtualization techniques, the baseband processing in C-RANs, can now be performed on virtual machines (VMs) at the central unit (CU). This is a priceless tool for improving the processing efficiency, as it provides the opportunity for dynamic allocation and sharing of processing resources. In this work, we investigate the assignment of processing jobs and the allocation of resources among the virtual machines, while reducing the overall power consumption of the VMs. Furthermore, the developed model also accounts for communication overhead, which may incur due to package dependencies. The performance of the proposed technique is investigated with varying system constraints and the obtained results demonstrate the potential power savings made possible using the proposed method.

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.

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.

A set-membership affine projection algorithm is applied to estimate the communication channel between the wireless sensor nodes in a general form, where the channel is modeled as a complex matrix in the presence of additive white gaussian noise. An efficient hybrid model for the affine projection algorithm is briefly introduced and the problem of matrix invertibility in some cases of the affine projection algorithm is resolved by a new method which does not use any matrix inversion. Simulations show good performance of our proposed algorithm in terms of convergence speed and demonstrate reduced complexity.

Adaptive power allocation (PA) algorithms with different criteria for a cooperative multiple‐input multiple‐output network equipped with distributed space‐time coding are proposed and evaluated. Joint constrained optimisation algorithms to determine the PA parameters and the receive filter are proposed for each transmitted symbol in each link, as well as the channel coefficients matrix. Linear receive filter and maximum‐likelihood detection are considered with amplify‐and‐forward and decode‐and‐forward cooperation strategies. In these proposed algorithms, the elements in the PA matrices are optimised at the destination node and then transmitted back to the relay nodes via a feedback channel. The effects of the feedback errors are considered. Linear minimum mean square error expressions and the PA matrices depend on each other and are updated iteratively. Stochastic gradient algorithms are developed with reduced computational complexity. Simulation results show that the proposed algorithms obtain significant performance gains as compared with existing PA schemes.

In this paper, dual methods based on Lagrangian relaxation for multiuser multicarrier resource allocation problems are analyzed. Their application to non-convex resource allocation problems is based on results guaranteeing asymptotic optimality as the number of subcarriers tends to infinity. This work analyzes the workings and performance of dual methods for resource allocation problems with concave rate functions and a finite number of subcarriers. The core results are the convexity of resource allocation problems with subcarrier sharing and an upper bound on the number of subcarriers being shared. Based on these results, absolute and relative performance bounds are presented for dual methods when applied to the resource allocation problem without subcarrier sharing. The exemplary problems considered in this work are sum rate maximization with global and individual power budgets and sum power minimization with global and individual rate demands.

An adaptive distributed space-time coding (DSTC) scheme is proposed for two-hop cooperative MIMO networks. Linear minimum mean square error (MMSE) receive filters and adjustable matrices subject to a power constraint are considered with an amplify-and-forward (AF) cooperation strategy. In the proposed DSTC scheme, an adjustable matrix obtained by a feedback channel is employed to transform the space-time coded matrix at the relay node. Linear MMSE expressions of the adjustable code matrices based on the mean square error (MSE) and the maximum likelihood (ML) criteria are derived. The effects of the limited feedback and the feedback errors on the performance are considered. A stochastic gradient (SG) algorithm is also developed with reduced computational complexity. The simulation results show that the proposed algorithms obtain significant performance gains as compared to existing DSTC schemes.

An adaptive distributed space-time coding (DSTC) scheme is proposed for two-hop cooperative MIMO networks. Linear minimum mean square error (MMSE) receive filters and adjustable code matrices are considered subject to a power constraint with an amplify-and-forward (AF) cooperation strategy. In the proposed adaptive DSTC scheme, an adjustable code matrix obtained by a feedback channel is employed to transform the space-time coded matrix at the relay node. The effects of the limited feedback and the feedback errors are assessed. Linear MMSE expressions are devised to compute the parameters of the adjustable code matrix and the linear receive filters. Stochastic gradient (SG) and least-squares (LS) algorithms are also developed with reduced computational complexity. An upper bound on the pairwise error probability analysis is derived and indicates the advantage of employing the adjustable code matrices at the relay nodes. An alternative optimization algorithm for the adaptive DSTC scheme is also derived in order to eliminate the need for the feedback. The algorithm provides a fully distributed scheme for the adaptive DSTC at the relay node based on the minimization of the error probability. Simulation results show that the proposed algorithms obtain significant performance gains as compared to existing DSTC schemes.

In this paper, we consider a multihop wireless sensor network (WSN) with multiple relay nodes for each hop where the amplify-and-forward (AF) scheme is employed. We present a strategy to jointly design the linear receiver and the power allocation parameters via an alternating optimization approach that maximizes the sum-rate of the WSN. We derive constrained maximum sum-rate (MSR) expressions along with an algorithm to compute the linear receiver and the power allocation parameters with the optimal complex amplification coefficients for each relay node. Computer simulations show good performance of our proposed methods in terms of sum-rate compared to the method with equal power allocation.

Dual methods based on Lagrangian relaxation are the state of the art to solve multiuser multicarrier resource allocation problems. This applies to concave utility functions as well as to practical systems employing adaptive modulation, in which users' data rates can be described by step functions. We show that this discrete resource allocation problem can be formulated as an integer linear program belonging to the class of multiple-choice knapsack problems. As a knapsack problem with additional constraints, this problem is NP-hard, but facilitates approximation algorithms based on Lagrangian relaxation. We show that these dual methods can be described as rounding methods. As an immediate result, we conclude that prior claims of optimality, based on a vanishing duality gap, are insufficient. To answer the question of optimality of dual methods for discrete multicarrier resource allocation problems, we present bounds on the absolute integrality gap for three exemplary downlink resource allocation problems with different objectives when employing rounding methods. The obtained bounds are asymptotically optimal in the sense that the relative performance loss vanishes as the number of subcarriers tends to infinity. The exemplary problems considered in this work are sum rate maximization, sum power minimization and max-min fairness.

In this paper, we consider a class of models that describe parallel observations of a single source by many noisy sensors, lossy quantization at each sensor, and finally information fusion of the quantized data. Certain phenomena in biophysics and neural information processing, but also in detection networks and modern communications can be elucidated by these models. Mutual information is used as an analytical measure of information exchange. We characterize the optimum information fusion rule by maximum entropy of the corresponding output distribution. For discrete input distributions, this problem can be reduced to a generalized Knapsack problem, which is hard to solve in general. We suggest a heuristic that minimizes the decrease of entropy in each step, and show that for binary information fusion the true optimum is attained for dyadic distributions. The problem of finding optimum quantization rules is an essential part of the model and treated analogously. For input distributions with a density, optimality is achieved by determining appropriate quantization thresholds. Finally, by applying the data processing inequality, an upper bound for the mutual information of arbitrary stochastic pooling channels is found. This bound provides interesting insight into the resilience of parallel noisy information processing in biological systems.

In this work resource allocation is studied for orthogonal frequency division multiplexing (OFDM) unicasting by multiple base stations (BSs). In the considered scenario, each user is restricted to being covered by one BS, i.e., BS selection, while each subcarrier is assigned to at most one user, i.e., subcarrier assignment. Thus, interference does not exist among these BSs. We aim at maximizing the weighted sum of data rates subject to limited transmission power at each BS, while the minimum rates required by users are satisfied. BS selection and subcarrier assignment are considered jointly. A heuristic method is proposed. It has linear complexity in the number of users, subcarriers and BSs. Simulations demonstrate that the performance loss of the proposed method is relatively small.

We consider an information theoretic model that describes parallel reception of a common signal by many receivers. The signal is distorted by noise and independently decoded by each receiver. Receivers cooperate in the sense that the outcome is conveyed to a decision center, which makes the final decision on the received signal by a majority vote. A potential scenario which could be described by this model is a cluster of base stations jointly receiving signals from a mobile station and conjoining individual decodings. Similar principles apply for cognitive radio setups where a secondary user employs many weak subcarriers, subject to sudden drop out by occupation from the primary users. The whole system is modeled as a cascade of channels, and quality of information exchange is measured by mutual information. This allows for modeling and optimizing quantization and detection in a unifying approach. Numerical evaluations demonstrate that the increase of mutual information by using additional channels is only logarithmic. 4-QAM is investigated as a concrete example and system performance is numerically investigated. The model in this paper is motivated by one used for describing information exchange in biological neural networks, revealing so called stochastic resonance by adding noise to signals.

Putting bounding constraints on the input of a channel leads in many cases to a discrete capacity-achieving distribution with a finite support. Given a finite number of signaling points, we determine reduced subsets and the corresponding optimal probability measures to simplify the receiver design. The objective for the subset selection is to keep the channel quality high by maximizing mutual information and cutoff rate. Two approaches are introduced to obtain a capacity-achieving probability measure for the reduced subset. The first one is based on a preceded signaling point selection while the second one chooses the signaling points and corresponding probabilities simultaneously. Numerical results for both approaches show that using only a small number of signaling points achieves a very high mutual information compared to channels utilizing the full set of signaling points.

In adaptive orthogonal frequency division multiplexing (OFDM)systems, different powers and rates can be allocated to subcarriers optimally by water-filling. This strategy has been proved to improve the system performance significantly. However, a large signalling overhead is induced by water-filling to contain the group of presently employed mapping schemes. The faster the channel varies in time, the more frequently the large signalling overhead is needed. In this paper, to avoid such a problem, we propose that a constant rate and different powers are allocated to subcarriers while satisfying the constraints on the transmission rate and the average bit-error rate (BER). First, an approximate relationship between BER and signal-to-noise ratio (SNR) is employed. Then, based on the convex optimization framework, the transmission power is optimally distributed to the used subcarriers with an optimal constant rate. The subcarrier assignment can be determined by an upgraded bisection method, which can be also used for other resource allocation problems. Simulations demonstrate that the proposed resource allocation strategy has better performance than water-filling with the signalling overhead considered in fast time-varying environment.

This paper introduces a theoretical framework for subcarrier and power allocation algorithms in rate-adaptive OFDMA systems. The focal point is locating “capacity-achieving” waterlevels for a given allocation in order to minimize the distance to the boundary of the capacity region. We prove that it is possible to restrict the choice of waterlevels to an optimality polyhedron. This paper introduces weighted subcarrier allocations which have a natural correspondence to this polyhedron, and are therefore promising candidates for the above problem. Based on the introduced theory, a low-complexity algorithm is designed and shown to reliably locate capacity-achieving waterlevels.

This paper is concerned with dynamic resource allocation for heterogeneous downlink in orthogonal frequency division multiple access (OFDMA) systems. First, this paper formulates the resource allocation problem when one terminal may demand both non-real time (or best effort) transmission and real time transmission over the downlink simultaneously. We consider the constraint on the total transmission power of the base station for both types of services and the constraint on the rate requirement of each terminal for a real time transmission. To solve this problem, an efficient instantaneous per-symbol method with dual optimality is proposed based on the convex optimization framework. Compared to the exhaustive search, simulations show that the performance degradation of our method is limited to 0.002% of the optimal solution, when an OFDMA system consists of more than 16 subcarriers. Therefore, this paper illustrates that it is not computationally prohibitive to optimally solve the resource allocation problem for heterogeneous transmission over OFDMA downlink.

It has been shown that adaptive power and rate allocation for multiuser orthogonal frequency multiplexing (OFDM) improves the system performance significantly. In this paper, the resource allocation that aims at minimizing the total transmission power under certain data transmission constraints is considered. First, the power variation for single-user water-filling while changing the subcarrier assignment is derived. Based on this, a class of methods for multiuser resource allocation is proposed. The presented methods, consisting of tactical processes, can achieve a good balance of computational complexity and performance. Compared to previous works, simulations show that our methods have comparable or better performance and that the computing time for the proposed methods is approximately sublinearly increasing in the number of users K and the number of subcarriers N.

In this paper, we consider the dynamic power and rate allocation for the heterogeneous transmission over multiuser orthogonal frequency division multiplexing (OFDM) downlink, where users may require real time or non-real time transmission. The data rate for the real time transmission is lower bounded, while the total transmission power for both transmissions is limited to a fixed amount. Solutions to this problem must be computationally efficient in order to adapt to the fast time-varying channels in practice. To accelerate the resource allocation for the considered scenario, efficient approaches are given to update the power or rate variation while changing subcarrier assignments. By iteratively using these approaches, a resource allocation method is proposed to achieve a good balance between the performance and the complexity. Its complexity is linearly increasing in the number of subcarriers and the number of users. Simulation results demonstrate that our method has small performance loss compared to the dual optimum and achieves much better performance compared to previous works.

We introduce a model of a generalised multi-receiver radio network with quality-of-service (QoS) constraints. There are two key functions: (1) Ni is non-decreasing and homogeneous and gives i's QoS as function of its carrier-to-interference ratios at each of K receivers, (2) nu_ik is a semi-norm that gives the interference experienced by transmitter i at receiver k as function of the power vector. We utilise "norm" concepts and Banach's well-known fixed-point theorem to characterise the conditions under which a QoS vector is feasible, and the corresponding power-adjustment process converges. The critical power levels equal a_i/h_i where a_i is the QoS target, and h_i is the 'average' channel gain. hi=Ni(h_il,..., h_i,K) where h_ik is the channel gain from transmitter i to receiver k. If the interference experienced by each transmitter i at each receiver k is less than 1 when each power is set to the critical level (i.e., nu_ik(a_l/h_l, a_2/h_2,..., a_N/h_N)

In this paper, the saddle point behavior of mutual information is investigated for discrete channel models. We use the fact that mutual information is a convex function of the channel matrix, and a concave function of the input distribution. Interpreting transmission as a game, nature against the transmitter with payoff given by mutual information, equilibria are shown to exist for certain strategy sets of nature. The case that nature makes the channel useless with zero capacity is discussed in detail. If nature uses a singleton nonzero capacity strategy, a characterization of the capacity-achieving input distribution is derived. Relevant channel classes covered by this approach include the binary asymmetric and erasure channel with bounded error probabilities. Furthermore, for the symmetric n-symbol channel two classes of separation constraints are introduced and the according game equilibria are determined.

Resource management is the general topic of the present paper, particularly, we deal with capacity sharing for interference limited wireless networks by power control. Proportional reduction of the signal-to-interference ratio (SIR) requirements is suggested as the control mechanism to accommodate users in the case of overload. For this purpose, we carefully describe the geometrical structure and the asymptotic behavior of the set of feasible power vectors as a proportionality factor tends to its boundaries. In the case that there is no feasible power adjustment, the minimum proportional SIR reduction is determined under general power constraints. We conclude with developing a locally quadratic convergent algorithm for numerical computation of the optimum power assignment. The investigations provide both insight into the theoretical structure of optimum power allocation as well as a practical method for call admission control.

The present correspondence deals with the rate and power allocation problem in multiuser orthogonal frequency division multiple (OFDM) access systems. We first derive the solution of the single user OFDM power allocation problem explicitly for a class of general rate-power functions by means of directional derivatives. This solution is employed for both designing a new heuristic and obtaining bounds in a branch-and-bound algorithm for allocating power to subcarriers. The branch-and-bound algorithm is used for performance evaluation of our new and two known power allocation heuristics by computing the exact optimum, given the number of allocated subcarriers per user.

Discrete input distributions are capacity-achieving for a variety of noise distributions whenever the input is subject to peak power or other bounding constraints. In this paper, we consider additive noise with arbitrary absolutely-continuous distribution and ask the question what the optimal input distribution over a set of fixed signaling points would be. The capacity-achieving distribution is characterized by constant Kullback Leibler distance between the shifted noise distribution and a certain mixture hereof. As an application, the optimal input distribution for binary symmetric signaling over exponential noise channels is determined. It further follows that in certain symmetric cases the uniform distribution over all signaling points is capacity-achieving.

In this paper, derivatives of mutual information for a general linear Gaussian vector channel are considered. We consider two applications. First, it is shown how the corresponding gradient relates to the minimum mean squared error (MMSE) estimator and its error matrix. Secondly, we determine the directional derivative of mutual information and use this geometrically intuitive concept to characterize the capacity-achieving input distribution of the above channel subject to certain power constraints. The well-known water-filling solution is revisited and obtained as a special case. Also for shaping constraints on the maximum and the Euclidean norm of mean powers explicit solutions are derived. Moreover, uncorrected sum power constraints are considered. The optimum input can here always be achieved by linear precoding.

In this correspondence, we determine the optimal power allocation to antennas in a Gaussian vector channel subject to lscrp-norm constrained eigenvalues. Optimal solutions are characterized by using directional derivatives of the mutual information. As the central result, the optimal power assignment is obtained as the level crossing points of a set of simple monotone functions. The well-known water-filling principle for sum power constraints is retrieved as the limiting case p=1. A nested Newton type algorithm is given for finding numerical solutions

The optimal power allocation for Gaussian vector channels subject to sum power constraints is achieved by the well known water-filling principle. In this correspondence, we show that the discontinuous water filling solution is obtained as the limiting case of p-norm bounds on the power covariance matrix as p tends to one. Directional derivatives are the main vehicle leading to this result. An easy graphical representation of the solution is derived by the level crossing points of simple power functions, which in the limit p = 1 gives a nice dual view of the classical representation

When transmitting over multiple-input-multiple-output (MIMO) channels, in the case of total power constraints and complete channel state information (CSI) the optimum power distribution is obtained by water-filling the squared singular values of the channel matrix H. In this paper, we consider the case that nature behaves as an opponent to the optimum transmit strategy in choosing the channel as bad as possible. Interpreting the mutual information as payoff function for two players, the transmitter and a malicious nature, this approach may be seen as a two person zero sum game. We first analyze maximum points of the payoff function for a fixed channel matrix under general power restrictions and characterize such points via directional derivatives. Worst channel behavior must be separated from the zero channel where no transmission is possible at all. Loewner semi-ordering of nonnegative Hermitian matrices is employed to ensure minimum channel quality. It is shown that a Nash equilibrium exists for general power constraints. Concrete results are achieved for a limited total power budget and limiting the maximum available power for each subchannel

In this paper, we define the power region as the set of power allocations for K users such that everybody meets a minimum signal-to-interference ratio (SIR). The SIR is modeled in a multiuser CDMA system with fixed linear receiver and signature sequences. We show that the power region is convex in linear and logarithmic scale. It furthermore has a component-wise minimal element. Power constraints are included by the intersection with the set of all viable power adjustments. In this framework, we aim at minimizing the total expended power by minimizing a component-wise monotone functional. If the feasible power region is nonempty, the minimum is attained. Otherwise, as a solution to balance conflicting interests, we suggest the projection of the minimum point in the power region onto the set of viable power settings. Finally, with an appropriate utility function, the problem of minimizing the total expended power can be seen as finding the Nash bargaining solution, which sheds light on power assignment from a game theoretic point of view. Convexity and component-wise monotonicity are essential prerequisites for this result