Massive MIMO systems can enhance spectral and energy efficiency, but they require accurate channel state information (CSI), which becomes costly as the number of antennas increases. While machine learning (ML) autoencoders show promise for CSI reconstruction and reducing feedback overhead, they introduce new challenges with standardization, interoperability, and backward compatibility. Also, the significant data collection needed for training makes real-world deployment difficult. To overcome these drawbacks, we propose an ML-based, decoder-only solution for compressed CSI. Our approach uses a standardized encoder for CSI compression on the user side and a site-specific generative decoder at the base station to refine the compressed CSI using environmental knowledge. We introduce two training schemes for the generative decoder: An end-to-end method and a two-stage method, both utilizing a goal-oriented loss function. Furthermore, we reduce the data collection overhead by using a site-specific digital twin to generate synthetic CSI data for training. Our simulations highlight the effectiveness of this solution across various feedback overhead regimes.

This paper addresses the problem of reliable task offloading in space-air-ground integrated network (SAGIN)-assisted edge computing systems, with the goal of maximising the ratio of tasks successfully offloaded and executed within quality-of-service (QoS) constraints. In the considered system, ground users offload computation tasks to a satellite-mounted edge server via unmanned aerial vehicles (UAVs) acting as relays. The formulated optimisation problem jointly considers task offloading portions and bandwidth allocations across ground-to-air and air-to-space links, subject to constraints on transmission rates, total bandwidth, energy budgets, and the satellite’s computational capacity. The resulting problem is non-linear, non-convex, and mixed-integer, making it challenging to solve with traditional optimisation techniques. To this end, we propose a deep reinforcement learning (DRL)-based solution to learn optimal offloading and resource allocation policies in dynamic environments. Furthermore, to enhance scalability and decentralised coordination, we develop a multi-agent DRL framework that enables cooperative decision-making across UAVs. Simulation results demonstrate that both the single-agent and multi-agent approaches achieve stable training performance, and the proposed method improves the reliable task offloading ratio by up to two times compared to benchmark schemes, while also achieving more efficient resource utilisation in complex SAGIN scenarios.

A parasitic reconfigurable antenna array is a low-power approach for beamforming using passive tunable elements. Prior work on reconfigurable antennas in communication theory is based on ideal radiation pattern abstractions. Beamforming with parasitic elements is inherently difficult because mutual coupling creates non-linearity in the beamforming gain objective. We develop a multi-port circuit-theoretic model of the hybrid array with parasitic elements and antennas with active RF chain validated through electromagnetic simulations with a dipole array. Based on this formulation, we derive the beamforming weight of the parasitic element using the theoretical beam pattern expression for the case of a single active antenna and multiple parasitic elements. The analysis shows that the parasitic beamforming is challenging because the weights are subject to coupled magnitude and phase constraints. To overcome this, a shift-of-origin transformation simplifies the optimization, leading to a closed-form expression for the parasitic reactance. The solution generalizes to arrays with multiple active and parasitic elements operating in multipath channels. The proposed hybrid architecture with parasitic elements outperforms conventional architectures in terms of energy efficiency.

This paper addresses the problem of reliable task offloading in space-air-ground integrated network (SAGIN) based edge computing systems. Specifically, we aim to maximise the successful task offloading ratio for ground users communicating with a satellite's edge server. In our network topology, end-to-end communications are facilitated by relay unmanned aerial vehicles (UAVs). The formulated problem jointly optimises task offloading portions and bandwidth allocations for both ground-to-air and air-to-space links, subject to quality-of-service (QoS) requirements, transmission rates, system bandwidth, and the computing capacity of the satellite's edge server. To solve the formulated complex non-linear, non-convex, and mixed-integer problem, we propose an efficient solution underpinned by a deep reinforcement learning (DRL). Simulation results demonstrate the effectiveness of the proposed method, which achieves stable training performance and an optimised reliable offloading ratio compared to benchmark schemes.

The rapid growth of edge computing has enabled low-latency and high-efficiency processing for a wide range of applications; however, it also leads to significant energy consumption and carbon emissions. In this context, this study investigates a CO2 emission minimisation problem in a digital twin-aided edge computing system, aiming to optimise task offloading decisions, transmit power, and processing rates of Internet of Things (IoT) devices. To address the formulated mixed-integer non-linear programming problem, we propose two solutions: an alternating optimisation method based on the successive convex approximation framework and a deep reinforcement learning (DRL) approach. Extensive simulations validate the effectiveness of the proposed solutions, demonstrating significant reductions in CO2 emissions, robust optimisation performance, and superior results compared to benchmark schemes. The findings highlight the feasibility of integrating advanced optimisation and artificial intelligence-driven techniques to achieve environmentally sustainable and high-performance edge computing systems, paving the way for greener technological innovation.

Deep learning (DL) techniques have demonstrated strong performance in compressing and reconstructing channel state information (CSI) while reducing feedback overhead in massive MIMO systems. A key challenge, however, is their reliance on extensive site-specific training data, whose real-world collection incurs significant overhead and limits scalability across deployment sites. To address this, we propose leveraging site-specific digital twins to assist the training of DL-based CSI compression models. The digital twin integrates an electromagnetic (EM) 3D model of the environment, a hardware model, and ray tracing to produce site-specific synthetic CSI data, allowing DL models to be trained without the need for extensive real-world measurements. We further develop a fidelity analysis framework that decomposes digital twin quality into four key aspects: 3D geometry, material properties, ray tracing, and hardware modeling. We explore how these factors influence the reliability of the data and model performance. To enhance the adaptability to real-world environments, we propose a refinement strategy that incorporates a limited amount of real-world data to fine-tune the DL model pre-trained on the digital twin dataset. Evaluation results show that models trained on site-specific digital twins outperform those trained on generic datasets, with the proposed refinement method effectively enhancing performance using limited real-world data. The simulations also highlight the importance of digital twin fidelity, especially in 3D geometry, ray tracing, and hardware modeling, for improving CSI reconstruction quality. This analysis framework offers valuable insights into the critical fidelity aspects, and facilitates more efficient digital twin development and deployment strategies for various wireless communication tasks.

The advancement of sophisticated communication technologies and robust computing systems has unlocked opportunities for new applications across various domains. While these applications promise enhanced convenience and improved living standards, they also raise a critical concern regarding the trade-off between convenience and environmental sustainability. This paper addresses this concern by investigating sustainable resource management, employing a digital twin approach to minimise CO2emissions in edge computing systems. Specifically, our aim is to reduce the amount of CO2emissions by optimising the allocation of computing and communication resources. This includes optimising transmit power, adjusting the clock speed for task processing, and making optimal decisions regarding task offloading. To tackle this complex optimisation problem, we employ an iteratively alternating optimisation algorithm. Through extensive simulations, we illustrate the efficacy of our proposed solution in not only mitigating CO2emissions but also optimising resource allocation, thereby contributing to both environmental sustainability and technological efficiency.

With the rapid development of communication applications, the integration of reconfigurable intelligent surface (RIS) and non-orthogonal multiple access (NOMA) techniques has emerged as a promising approach to enhance connectivity and data transmission rate in future wireless networks. To successfully deploy RIS-NOMA aided 6G network, an accurate channel estimation is a crucial task. Quantum machine learning (QML) is a novel approach showing potential computational advantages in various problems of 6G wireless communications. However, its application, particularly in channel estimation, remains largely theoretical rather than adopted in practice. We propose a hybrid quantum-classical neural network model based on convolutional neural network (CNN) and quantum long short-term memory (QLSTM) for channel estimation in RIS-aided 6G NOMA system. Our results show that the proposed CNN-QLSTM model has a better channel prediction compared to its classical counterpart with regard to root mean square error (RMSE) and mean absolute error (MAE).

The concept of sub-networks has been recently identified as an important component of 6G to enable high-demanding critical services with capabilities of hyper reliable low-latency communications (HRLLC) at the local area of the sub-network. In this paper, we formulate the power control problem for sub-networks as finding the transmit power vector, corresponding to the sub-networks, to minimize the sum interference-to-signal power ratios across all the sub-networks. We propose then a heuristic sequential iterative power allocation (SIPA) and an optimal power allocation based on gradient descent-based algorithm (GDPA). Extensive system simulations validated the proposed methods. In particular, the SIPA achieves significant performance gains over the fixed transmit power setting, with limited performance loss over the GDPA. In most cases, the proposed methods outperform the max-min power allocation with much lower feedback signaling overhead and complexity. Furthermore, to keep 99.9% of all the sub-network link instances to achieve reliability of 6 nines, the SIPA can save radio resources by about 27% compared to the fixed transmit power setting.

The convergence of advanced communication technologies and powerful computing architecture has unlocked a plethora of opportunities for Internet-of-Things applications. To fully realize this potential, a synergistic design encompassing sensing, computing, and communication is crucial. This article investigates these critical technologies to facilitate service-oriented systems by minimizing end-to-end latency and the number of deployed services at edge servers in mobile edge computing, all within the confines of stringent ultrareliable and low-latency communication requirements and system budget constraints. The addressed optimization problem takes into account variables, such as service placement strategies, task offloading portions, and bandwidth allocation. Simulation results validate the effectiveness of our solution and highlight the impact of key parameters on system performance.

Remote state estimation of large-scale distributed dynamic processes plays an important role in Industry 4.0 applications. In this paper, we focus on the transmission scheduling problem of a remote estimation system. First, we derive some structural properties of the optimal sensor scheduling policy over fading channels. Then, building on these theoretical guidelines, we develop a structure-enhanced deep reinforcement learning (DRL) framework for optimal scheduling of the system to achieve the minimum overall estimation mean-square error (MSE). In particular, we propose a structure-enhanced action selection method, which tends to select actions that obey the policy structure. This explores the action space more effectively and enhances the learning efficiency of DRL agents. Furthermore, we introduce a structure-enhanced loss function to add penalties to actions that do not follow the policy structure. The new loss function guides the DRL to converge to the optimal policy structure quickly. Our numerical experiments illustrate that the proposed structure-enhanced DRL algorithms can save the training time by 50% and reduce the remote estimation MSE by 10% to 25%, when compared to benchmark DRL algorithms. In addition, we show that the derived structural properties exist in a wide range of dynamic scheduling problems that go beyond remote state estimation.

Impedance-matching networks affect power transfer from the radio frequency (RF) chains to the antennas. Their design impacts the signal to noise ratio (SNR) and the achievable rate. In this paper, we maximize the information-theoretic achievable rate of a multiple-input-single-output (MISO) system with wideband matching constraints. Using a multiport circuit theory approach with frequency-selective scattering parameters, we propose a general framework for optimizing the MISO achievable rate that incorporates Bode-Fano wideband matching theory. We express the solution to the achievable rate optimization problem in terms of the optimized transmission coefficient and the Lagrangian parameters corresponding to the Bode-Fano inequality constraints. We apply this framework to a single electric Chu’s antenna and an array of dipole antennas. We compare the optimized achievable rate obtained numerically with other benchmarks like the ideal achievable rate computed by disregarding matching constraints and the achievable rate obtained by using sub-optimal matching strategies like conjugate matching and frequency-flat transmission. We also propose a practical methodology to approximate the achievable rate bound by using the optimal transmission coefficient to derive a physically realizable matching network through the ADS software.

In this article, we study the collocated and distributed deployment of intelligent reflecting surfaces (IRS) for a fixed total number of IRS elements to support enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) services inside a factory. We build a channel model that incorporates the line-of-sight (LoS) probability and power loss of each transmission path, and propose three metrics, namely, the expected received signal-to-noise ratio (SNR), expected finite-blocklength (FB) capacity, and expected outage probability, where the expectation is taken over the probability distributions of interior blockages and channel fading. The expected received SNR and expected FB capacity for extremely high blockage densities are derived in closed-form as functions of the amount and height of IRSs and the density, size, and penetration loss of blockages, which are verified by Monte Carlo simulations. Results show that deploying IRSs vertically higher leads to higher expected received SNR and expected FB capacity. By analysing the average/minimum/maximum of the three metrics versus the number of IRSs, we find that for high blockage densities, both eMBB and URLLC services benefit from distributed deployment; and for low blockage densities, URLLC services benefit from distributed deployment while eMBB services see limited difference between collocated and distributed deployment.

Conventional achievable rate analysis using Shannon's theory does not assume practical constraints imposed by Bode-Fano wideband matching theory. This leads to an achievable rate bound that cannot be attained by practical matching networks. In this paper, we generalize the information-theoretic achievable rate of a single-input-single-output (SISO) system by incorporating wideband matching constraints at the transmitter. We express the solution to the achievable rate optimization problem in terms of the optimized transmission coefficient and the Lagrangian parameters corresponding to the Bode-Fano inequality constraints. We also propose a practical strategy to design a physically realizable matching network through the ADS software which attains the achievable rate bound with near-optimality. In simulations, we apply this framework to a Chu's antenna and compare the achievable rate performance with the conventional conjugate matching strategy.

Recently, the advances of low-latency communication technologies and edge intelligence have enabled a wide range of task-oriented time-sensitive applications. This paper aims at designing adaptive service placement, task offloading, and bandwidth allocation for ultra-reliable and low-latency communication (URLLC)-aided edge networks. The main objective is to minimise both the total end-to-end (e2e) latency and number of installed services at edge servers. The optimal solutions are obtained by jointly optimising service placement decisions, task offloading portions and bandwidth allocation at dynamic timescales subject to network budgets and application requirements under uncertain environment. Selective simulation results are provided to validate the effectiveness of the proposed solution in term of reducing the latency as well as optimising service placement decisions.

This paper exploits a quantum-empowered machine learning algorithm to enhance computation learning speed. Under stochastic behaviours and quantum uncertainty, we examine the offloading problem to maximize the computational task processing efficiency, considering the computation latency, energy consumption, and quantum network adaptability. From the Markov decision process, the paper proposes a novel quantumempowered deep reinforcement learning (Qe-DRL) approach, combining quantum computing theory and machine learning to achieve exploration and exploitation trade-off via quantum parallelism significantly. Furthermore, we develop a modified Grover’s algorithm with exponential convergence speed to provide a searching strategy for transition quantum states probabilities. Simulation results establish the effectiveness of the proposed QeDRL algorithm and its superior computational learning speed.

In this paper, a communication and control codesign framework is presented based on survival time, i.e., the time that a closed-loop wireless control system can continue without an anticipated message. The goal is to ensure the stability of wireless control systems with minimal resource usage. A novel interface between the controller and the scheduler is proposed, where the key communication and control parameters are analyzed for co-design, and jointly optimized. The proposed co-design framework leverages link adaptation for the communications system and sampling period adaptation for the closed-loop control system to preserve more resources. Our numerical example on closed-loop velocity control demonstrates a pronounced reduction of resources needed for control stability in contrast to the separate design paradigm that requires ultrahigh link reliability. An additional 52% reduction in resource utilization is achieved by further adapting the key parameters when the system is in survival mode.

The concept of in-X subnetworks has been recently proposed to meet extreme communication requirements such as sub-millisecond latency and up to 9 nines reliability in 6thgeneration (6G) networks. On the other hand, many open challenges have already been recognized for this new concept, from air interface design to interference management in dense and dynamic scenarios. In this paper, we focus on subnetworks for industrial wireless control applications and propose an advanced frequency resource allocation scheme, denoted as sequential iterative subband allocation (SISA), which is designed to minimize the sum interference-to-signal ratio over all subnetwork links. Through extensive system level simulations, we evaluate the benefits of the proposed SISA scheme and compare it with the state-of-the-art. Numerical results show that SISA with interference weighting strongly outperforms a greedy distributed scheme, by reducing by half the frequency resources needed to enable 99.9% of all the subnetwork link instances to achieve reliability of 6 nines.

In this paper, we study joint communication and computation offloading (JCCO) for hierarchical edge-cloud systems with ultra-reliable and low latency communications (URLLC). We aim to minimize the end-to-end (e2e) latency of computational tasks among multiple industrial Internet of Things (IIoT) devices by jointly optimizing offloading probabilities, processing rates, user association policies and power control subject to their service delay and energy consumption requirements as well as queueing stability conditions. The formulated JCCO problem belongs to a difficult class of mixed-integer non-convex optimization problem, making it computationally intractable. In addition, a strong coupling between binary and continuous variables and the large size of hierarchical edge-cloud systems make the problem even more challenging to solve optimally. To address these challenges, we first decompose the original problem into two subproblems based on the unique structure of the underlying problem and leverage the alternating optimization (AO) approach to solve them in an iterative fashion by developing newly convex approximate functions. To speed up optimal user association searching, we incorporate a penalty function into the objective function to resolve uncertainties of a binary nature. Two sub-optimal designs for given user association policies based on channel conditions and random user associations are also investigated to serve as state-of-the-art benchmarks. Numerical results are provided to demonstrate the effectiveness of the proposed algorithms in terms of the e2e latency and convergence speed.

For future networks, it is highly demanding to satisfy a wide range of time-sensitive and computation-intensive services. This is a very challenging task, since it requires a combination of aspects from information, communication and computation in order to establish a digital representation of the real network environment. This paper introduces a fairness-aware latency minimisation (FALM) framework in the digital twin (DT) aided edge computing with ultra-reliable and low latency communications (URLLC), which jointly optimises various communication and computation parameters, namely, bandwidth allocation, transmission power, task offloading portions, and processing rate of user equipments (UEs) and edge servers (ESs). The formulated problem is highly complicated, due to non-convex constraints and strong coupling among optimisation variables. To deal with this problem, we develop both centralised and distributed optimisation approaches. In particular, we first resort to successive convex approximation (SCA) method to develop a low-complexity iterative algorithm and solve the problem in a centralised manner. Combining tools from SCA and alternating direction method of multipliers (ADMM), we develop an efficient distributed solution with parallel computation processing at ESs under global consensus in each iteration and strong theoretical performance guaranteed. Numerical results are provided to validate the proposed solutions in terms of convergence speed and overall latency as well as improving fairness among all UEs.

In the conventional sense, a passive intelligent reflecting surface (IRS) is perceived as an ideal phase shifter to the incident signal. It is assumed that the phase of the incident signal can be altered to any desired value without affecting its magnitude. In this paper, we question the veracity of this assumption which forms the basis for the communication model that is widely used in the scientific community. Although there exist rigorous electromagnetic (EM) based models to analyze and design metasurfaces, the same cannot be said about its successor, intelligent reflecting surface. Therefore, we attempt to present an EM-based model that accurately describes intelligent scattering by any arbitrary-shaped IRS. Our objective in this paper is to bridge the gap between the fundamental EM formulation for an IRS and the communication model that accurately captures its functioning. We use Method-of-Moments (MoM), a computational electromagnetic approach to quantify the intelligent scattering by an arbitrary-shaped IRS. The proposed theoretical model is then validated with computational EM simulation in Feko. We then adopt the general MoM-based model for a special case where each IRS element is a center-loaded wire. Closed-form expressions for pathloss and beamwidth are derived considering free space propagation. We show analytically and numerically, that the received power predicted by the conventional model vs. what is observed through computational EM simulations can differ by 6 dB. Furthermore, we demonstrate that the impact of optimizing an IRS using the conventional model, where each element is treated as an ideal passive phase shifter, can result in an additional$6-8$dB of power loss. As a final remark, we propose correction to the communication model that is currently used for IRS-aided networks when each IRS element is a center-loaded wire.

We study a joint communication and computation offloading (JCCO) for hierarchical edge-cloud systems with ultra-reliable and low latency communications (URLLC). We aim to minimize the worst-case end-to-end (e2e) latency of computational tasks among multiple industrial Internet of Things (IIoT) devices by jointly optimizing offloading probabilities, processing rates, user association policies and power control subject to their service delay and energy consumption requirements as well as queueing stability conditions. To tackle the problem, we first decompose the original problem into two subproblems and then leverage the alternating optimization (AO) approach to solve them in an iterative fashion by developing newly convex approximate functions. The numerical results are provided to demonstrate the effectiveness of the proposed algorithms in terms of the e2e latency and convergence speed.

For block fading channels with uncertainty in channel distribution knowledge, we propose and optimize a statistical measure as a way to surely assess reliability in finite-block communications regime. In particular, the confidence level in guaranteeing average block-error rate lower than a specific target is introduced and maximized to find the optimal blocklength, aiming to meet the strict requirements of ultra-reliable low latency communications (URLLC). In order to compute the confidence level, non-parametric learning algorithms are employed for channel modeling with a limited number of training samples. Bayesian optimization, i.e., the tool for black-box optimization, is applied to solve the problem in the absence of the closed form of the confidence level.

This paper studies communications service availability for industrial wireless control systems. We consider a motion controller with a continuous closed-loop control link to a group of actuator devices on a factory floor. The goal is to satisfy end-to-end latency for each packet and to guarantee that the communication service will not be un-available for longer than a survival time. We propose to decouple the scheduling operation between the normal and survival modes of operation, enabling a dual-mode ultra-reliable and low-latency communications (URLLC) scheduler. Scheduler strategies for the survival mode are presented, targeting link adaptation and signal to interference and noise ratio (SINR) estimation in presence of temporal and spatial channel correlation. Through numerical examples, we investigate the impact of channel correlation on the schedulers ability to target the required reliability for each mode. We further present our findings on system-level performance evaluation of such scheduling strategies by adopting a realistic system setup and channel model to obtain insights with high level of realism. Extensive simulation results are presented which demonstrate significant reduction in resource utilization with the proposed dual-mode scheduler when compared to single-mode URLLC scheduling. Specifically, our results demonstrate that the scheduler should target moderate packet error rate (PER) for normal mode of operation and very low PER for the survival mode; the latter guarantees service availability while the former saves radio resources.

In the framework of 5G-and-beyond Industry 4.0, jamming attacks for denial of service are a rising threat which can severely compromise the system performance. Therefore, in this paper we deal with the problem of jamming detection and mitigation in indoor factory deployments. We design two jamming detectors based on pseudo-random blanking of subcarriers with orthogonal frequency division multiplexing and consider jamming mitigation with frequency hopping and random scheduling of the user equipments. We then evaluate the performance of the system in terms of achievable block error rate (BLER) with ultra-reliable low-latency communications traffic and jamming missed detection probability. Simulations are performed considering a 3rd Generation Partnership Project spatial channel model for the factory floor with a jammer stationed outside the plant trying to disrupt the communication inside the factory. Numerical results show that jamming resiliency increases when using a distributed access point deployment and exploiting channel correlation among antennas for jamming detection, while frequency hopping is helpful in jamming mitigation only for strict BLER requirements.

We study an industry automation scenario where a central controller broadcasts critical messages to the wireless devices (e.g., sensors/actuators). We devise a stochastic geometry framework where the rate coverage probability of devices is modeled by taking into account the density of roaming blockages over the factory floor. To alleviate the loss in the coverage, we adopt a two-phase transmission policy, where in thebroadcast phase, the central controller broadcasts the messages intended for the devices in the network area. The devices in coverage in the broadcast phase act as decode-and-forward relays in therelay phase, so as to reinforce the signal strength at the devices in outage. The total downlink transmission time is, therefore, partitioned into two phases by a tunable factor. Finally, we study the optimal value of the partitioning factor with varying device densities, blockage densities, and file sizes, and we highlight that a longer transmission time should be allotted to the broadcast phase in the case of larger file sizes or lower transmit power of the controller.

This paper addresses the problem of minimising latency in computation offloading with digital twin (DT) wireless edge networks for industrial Internet-of-Things (IoT) environment via ultra-reliable and low latency communications (URLLC) links. The considered DT-aided edge networks provide a powerful computing framework to enable computation-intensive services, where the DT is used to model the computing capacity of edge servers and optimise the resource allocation of the entire system. The objective function is comprised of local processing latency, URLLC-based transmission latency and edge processing latency, subject to both communication and computation resources budgets. In this regard, the minimum latency is obtained by jointly optimising the transmit power, user association, offloading portions, the processing rate of users and edge servers. The formulated problem is highly complicated due to complex non-convex constraints and strong coupling variables. To deal with this computationally intractable problem, we propose an iterative algorithm which decomposes the original problem into three sub-problems and resolve this problem in the fashion of alternating optimisation approach combined with an inner convex approximation framework. Simulation results demonstrate the effectiveness of the proposed method in reducing the latency compared with other benchmark schemes.

Hybrid automatic repeat request (HARQ) is a key enabler for ultrareliable communications. This paper optimizes transmit power for the initial transmission and the subsequent retransmissions of HARQ with either incremental redundancy or Chase combining, aiming to minimize the expected energy consumption given the target outage probability and the target latency. The main challenge is due to the fact that the outage probability is a complicated function of the power variables which are nested in successive convolutions. The existing works mostly use a classic upper bound to approximate the outage probability by assuming unbounded transmit power, then convert the original problem to a geometric programming (GP) problem. In contrast, we propose a novel and much tighter upper bound by taking the practical power limit into consideration. The new bound and the resulting new GP method are further extended to a broader group of channel models with various fading, multiple antennas, and multiple receivers. As shown in simulations, the GP method based on the new bound significantly outperforms the existing strategies that either fix transmit power or optimize power by the classic bounding technique.

In this paper, we propose to use multiple mobile robots as relay terminals to assist the wireless connectivity between the base stations and industrial Internet-of-Things (IIoT) devices. Under the strict latency constraint via short blocklength, we propose an optimal resource allocation scheme to minimise the error probability at the IIoT devices. For fast deployment, we propose a deep neural network to optimise the positions of the mobile robots. Then, a joint blocklength and power allocation optimisation of the base stations and relay robots is considered. Due to non-convexity of such optimization problem, we propose a sub-problem with an effective iterative algorithm for solving the reliability maximisation. Representative numerical results are provided to demonstrate the advantages of our proposed scheme over the conventional approach.

Security attacks at the physical layer, in the form of radio jamming for denial of service, are an increasing threat in the Industry 4.0 scenarios. In this paper, we consider the problem of jamming detection in 5G-and-beyond communication systems and propose a defense mechanism based on pseudo-random blanking of subcarriers with orthogonal frequency division multiplexing (OFDM). We then design a detector by applying the generalized likelihood ratio test (GLRT) on those subcarriers. We finally evaluate the performance of the proposed technique against a smart jammer, which is pursuing one of the following objectives: maximize stealthiness, minimize spectral efficiency (SE) with mobile broadband (MBB) type of traffic, and maximize block error rate (BLER) with ultra-reliable low-latency communications (URLLC). Numerical results show that a smart jammer a) needs to compromise between missed detection (MD) probability and SE reduction with MBB and b) can achieve low detectability and high system performance degradation with URLLC only if it has sufficiently high power.

This work considers an Industry 4.0 scenario, where many heterogeneous sensors must communicate their data to a central factory management function. Unlike the general trend in the current literature, where wireless sensor networks (WSNs) are assumed to have homogeneous quality of service (QoS), we address the more challenging scenario where each sensor node has a different set of reliability and latency requirements to fulfill. Accordingly, a QoS-aware wireless sensor network (QAW) routing protocol is proposed to satisfy the different QoS requirements by each sensor node, while maximizing the network lifetime, thanks to a novel WSN hierarchical structure. The proposed QAW protocol accounts for the total energy of the nodes and their respective QoS requirements when establishing the routing structure. Periodical updates are proposed to guarantee an optimized network lifetime. Simulation experiments show that the proposed QAW protocol is able to enforce the desired QoS significantly better than the state-of-the-art approaches, while it rivals them in terms of energy consumption and preserves network lifetime. The presented results further confirm benefit of multi-hop WSN structure in improving network lifetime, when compared with single-hop sensor networks that minimize latency even when it is not strictly necessary.

This paper studies a industrial wireless control system where a motion controller has a continuous closed-loop control link to a group of actuator devices on a factory floor. The goal is to achieve service requirements of ultra-reliable and low-latency communications (URLLC) for such motion control system. We present our latest findings on group-based device-to-device (D2D) relaying for achieving spectrally efficient URLLC technology. Namely, we propose to group together the devices in close proximity and exploit intra-group D2D relaying to enforce reliable access. Next, we demonstrate the benefits of identifying the users with weak channel in each group and scheduling those users with on-demand relaying. We found that devices in momentary poor channel conditions are also the weakest link in the system-level reliability. Therefore, on-demand relaying for those devices can significantly improve reliability while maintaining a better utilization efficiency of network resources. The presented analysis leads to the conclusion that a future wireless control system for 5G and beyond shall include support for on-demand D2D relaying to achieve the best performance.

In this work, we consider a joint optimisation of real-time deployment and resource allocation scheme for UAV-aided relay systems in emergency scenarios such as disaster relief and public safety missions. In particular, to recover the network within a disaster area, we propose a fast K-means-based user clustering model and jointly optimal power and time transferring allocation which can be applied in the real system by using UAVs as flying base stations for real-time recovering and maintaining network connectivity during and after disasters. Under the stringent QoS constraints, we then provide centralised and distributed models to maximise the energy efficiency of the considered network. Numerical results are provided to illustrate the effectiveness of the proposed computational approaches in terms of network energy efficiency and execution time for solving the resource allocation problem in real-time scenarios. We demonstrate that our proposed algorithm outperforms other benchmark schemes.

This paper introduces a wireless communication protocol for industrial control systems that uses channel quality awareness to dynamically create network-device cooperation and assist the nodes in momentary poor channel conditions. To that point, channel state information is used to identify nodes with strong and weak channel conditions. We show that strong nodes in the network are best to be served in a single-hop transmission with transmission rate adapted to their instantaneous channel conditions. Meanwhile, the remainder of time-frequency resources is used to serve the nodes with weak channel condition using a two-hop transmission with cooperative communication among all the nodes to meet the target reliability in their communication with the controller. We formulate the achievable multi-user and multi-antenna diversity gain in the low-latency regime, and propose a new scheme for exploiting those on-demand, in favor of reliability and efficiency. The proposed transmission scheme is therefore dubbed adaptive network-device cooperation (ANDCoop), since it is able to adaptively allocate cooperation resources while enjoying the multi-user diversity gain of the network. We formulate the optimization problem of associating nodes to each group and dividing resources between the two groups. Numerical solutions show significant improvement in spectral efficiency and system reliability compared to the existing schemes in the literature. System design incorporating the proposed transmission strategy can thus reduce infrastructure cost for future private wireless networks.

Wireless motion control in the next generation of industrial control systems aims to provide the sensor/actuator devices on a factory floor with continuous closed-loop control updates from the controller entity, requiring communications with extremely low latency in the order of sub-ms and “cablelike” high reliability. This paper introduces a wireless communication protocol that uses channel state information (CSI) and cooperative communication among the devices to best utilize the radio resources and provide an ultra-reliable radio access. We propose to use CSI to identify devices with strong and weak channel conditions. We show that strong devices in the network are best to be served in a single-hop transmission with transmission rate adapted to their instantaneous channel conditions. Meanwhile, the remainder of time-frequency resources is used to serve the devices with weak channel condition, using a two-hop transmission with cooperative relaying. We formulate the optimization problem of partitioning time budget between the two groups and associating devices to each group. Numerical solution to the optimization problem and simulation results are provided. Thanks to combining multi-user diversity gain together with cooperative relaying, the proposed solution provides orders of magnitude improvement in system reliability, resulting in more than 10 dB signal to noise ratio (SNR) gain at 10-5system outage probability point, with respect to state-of-the-art protocols.

This paper proposes interference mitigation techniques for provisioning ultrareliable low-latency wireless communication in an industrial automation setting, where multiple transmissions from controllers to actuators interfere with each other. Channel fading and interference are key impairments in wireless communication. This paper leverages the recently proposed “Occupy CoW” protocol that efficiently exploits the broadcast opportunity and spatial diversity through a two-hop cooperative communication strategy among distributed receivers to combat deep fading, but points out that because this protocol avoids interference by frequency division orthogonal transmission, it is not scalable in terms of bandwidth required for achieving ultrareliability, when multiple controllers simultaneously communicate with multiple actuators (akin to the downlink of a multicell network). The main observation of this paper is that full frequency reuse in the first phase, together with successive decoding and cancellation of interference, can improve the performance of this strategy notably. We propose two protocols depending on whether interference cancellation or avoidance is implemented in the second phase, and show that both outperform Occupy CoW in terms of the required bandwidth and power for achieving ultrareliability at practical values of the transmit power.

We consider a downlink periodic wireless communications system, where multiple access points cooperatively transmit packets to a number of devices, e.g., actuators in an industrial control system. Each period consists of two phases: an uplink training phase and a downlink data transmission phase. Each actuator must successfully receive its unique packet within a single transmission phase; else, an outage is declared. Such an outage can be caused by two events: a transmission error due to transmission at a rate that the channel cannot actually support or time overflow, where the downlink data phase is too short, given the channel conditions to successfully communicate all the packets. We determine the closed-form expressions for the time overflow probability when there are just two field devices, as well as the transmission error probability for an arbitrary number of devices. In addition, we provide upper and lower bounds on the time overflow probability for an arbitrary number of devices. We propose a novel variable-rate transmission method that eliminates time overflow. Detailed system-level simulations are used to identify system design guidelines, such as the optimal amount of training time, as well as for benchmarking the proposed system design versus non-cooperative cellular, cooperative fixed-rate, and cooperative relaying.

Future wireless networks envision ultra-reliable, ultra-low latency communication with efficient use of the limited wireless channel resources. Closed-loop retransmission protocols, such as automatic repeat request (ARQ), where retransmission of a packet is enabled using a feedback channel has been adopted since early days of wireless telecommunication to achieve the required reliability. The performance of such protocols is strongly dependent on the feedback channel reliability. Focusing on the problem of feedback errors, a new method of acknowledging packet delivery to overcome unreliability of feedback channel is proposed in this paper. The proposed method is based on backwards composite acknowledgment of multiple packets and provides the transmitter with additional design parameters to configure ultra-reliable communication for a user depending on channel quality. Numerical analysis are presented showing orders of magnitude increase in reliability for the proposed method compared to traditional ARQ at the cost of a small increase in average experienced delay.

A reliable feedback channel is vital to report decoding acknowledgments in retransmission mechanisms such as the hybrid automatic repeat request (HARQ). While the feedback bits are known to be costly for the wireless link, a feedback message more informative than the conventional single-bit feedback can increase resource utilization efficiency. Considering the practical limitations for increasing feedback message size, this paper proposes a framework for the design of flexible-content multi-bit feedback. The proposed design is capable of efficiently indicating the faulty segments of a failed large-size data packet thanks to which the transmitter node can reduce the retransmission size to only include the initially failed segments of the packet. We study the effect of feedback size on retransmission efficiency through extensive link-level simulations over realistic channel models. Numerical result present significant savings in retransmission resources offered by the proposed flexible-content feedback design.

In this paper, we present a punctured scheduling scheme for efficient transmission of low latency communication (LLC) traffic, multiplexed on a downlink shared channel with enhanced mobile broadband traffic (eMBB). Puncturing allows to schedule eMBB traffic on all shared channel resources, without prior reservation of transmission resources for sporadically arriving LLC traffic. When LLC traffic arrives, it is immediately scheduled with a short transmission by puncturing part of the ongoing eMBB transmissions. To have this working efficiently, we propose recovery mechanisms for punctured eMBB transmissions, and a service-specific scheduling policy and link adaptation. Among others, we find that it is advantageous to include an element of eMBB-awareness for the scheduling decisions of the LLC transmissions (i.e. those that puncture ongoing eMBB transmissions), to primarily puncture eMBB transmission(s) that are transmitted with low modulation and coding scheme index. System level simulations are presented to demonstrate the benefits of the proposed solution.

In this paper we present our latest findings on dynamic user-centric scheduling for a flexible 5G radio design, capable of serving users with highly diverse QoS requirements. The benefits of being able to schedule users with different transmission time intervals (TTIs) are demonstrated, in combination with a user-centric multiplexing of control and data channels. The proposed solution overcomes some of the shortcomings of LTE-Advanced in terms of scheduling flexibility and performance. In general it is found that using short TTIs is advantageous at low to medium offered traffic loads for TCP download to faster overcome the slow start phase, while at higher offered traffic loads the best performance is achieved with longer TTIs. Using longer TTI sizes results in less control overhead (from scheduling grants), and therefore higher spectral efficiency. The presented analysis leads to the conclusion that a future 5G design shall include support for dynamic scheduling with different TTI sizes to achieve the best performance.

Besides coping with the increasing demand of broadband services, 5th Generation (5G) radio access technology is expected to support mission critical communication (MCC) services targeting very low latencies. In this paper, we investigate the feasibility of the usage of an early Hybrid Automatic Repeat reQuest (HARQ) feedback for reducing the latency of acknowledged transmissions without disregaring spectral efficiency. As enabler of such early feedback, a new technique for predicting the decoder outcome before decoding occurs, is proposed. This technique is intended to generate an early ACK/NACK, or an uncertain feedback in case a reliable prediction cannot be achieved. Simulation results show a very limited occurrence of false positives and false negatives, and uncertain feedback rates not exceeding 6% when adaptive modulation and coding (AMC) and a 10% Block Error Rate (BLER) target is assumed. A correct early ACK/NACK can be generated for around 90% of the transmissions or more.

It is generally recognized that efficient multiplexing of users with highly diverse requirements needs a flexible frame structure. For a new 5th generation mobile networks (5G) air interface, the hybrid automatic repeat request (HARQ) solution should also be revised to harvest all possible performance benefits that can be obtained with a flexible frame structure. In this paper, we outline a number of HARQ enhancements and design principles for a 5G wide area solution. This includes a flexible asynchronous HARQ scheme with options for dedicated per-link configurations of feedback timing and multi-bit richness. Among others, the proposed solution supports asymmetric link operation, network implementations with different fronthaul latencies, and multi-bit feedback to facilitate variable block length HARQ retransmissions for improved resource utilization. Numerical results are presented for different link configurations.

It is well recognized in the literature that the performance of the hybrid automatic repeat request (HARQ) transmission can be improved by adapting the transmission parameters through exploiting extra information from the receiver over the feedback channel. This introduces new degrees of freedom for a more flexible per-link optimized transmission. This paper proposes a framework considering the practical aspects of such feedback design and analyzes better resource utilization by retransmission techniques in the 5th generation mobile networks (5G) technology. We propose to generate a decoder state information (DSI) feedback at the receiver side and to enable using this information at the encoder side where retransmission parameters will be set to meet an optimal target block error rate. The savings in retransmission resources can then be utilized for transmission of other packets. The systemlevel simulation results show the attractive enhancement of ~8% average throughput gain by using only one extra feedback bit per HARQ process compared to conventional HARQ approach.

We analyze HARQ transmission over a fading channel with the help of a relay node. In the absence of the channel state information (CSI) at the transmitters, we establish the problem of maximizing the overall throughput of HARQ transmission by optimizing the variable transmission rate. We present a closed form of the throughput with respect to the transmission rates and use the well-known Dynamic Programming technique along with the necessary simplifications on the problem to find the optimal set of transmission rates for both the Source node and the Relay node in source-relay-destination scenario. We show that such an approach yields a significant increase in throughput compared to fixed-rate HARQ.

We analyze the average throughput and the outage probability for relay-based incremental redundancy HARQ transmission over block fading channel. We focus on the effects of having an error-free multi-bit feedback channel instead of the single-bit ACK/NACK feedback used in conventional HARQ. This multi-bit feedback message provides the cooperating nodes with outdated channel state information (CSI) so that they can adapt their transmission rate. We discuss a network with M relay nodes and assume adaptive transmission rate for all the cooperating nodes. We describe the adaptation problem as a Markov Decision Process (MDP) and employ the Dynamic Programming (DP) for optimization. The numerical results obtained in the case of one relay transmission, indicate that significant throughput gains can be obtained when compared to non-adaptive, i.e, fixed-rate HARQ. Moreover, we study the performance limits of the system model by finding an upper bound of the throughput for the cooperative HARQ channel which is applicable to both adaptive and non-adaptive models. Finally, we analyze the discretization issues and conclude that only a small number of feedback bits is required by the adaptive system to outperform the conventional single-bit HARQ.

In this paper we analyze the maximum achievable throughput of Hybrid ARQ (HARQ) protocol for transmission over cooperative block-fading channels. It is assumed that the instantaneous channel state information (CSI) is not available at the encoders and only its outdated version is available via the error-free feedback channel. The outdated CSI is then used to adapt the transmission rate of all cooperating nodes. Considering source-relay-destination scenario, we use Dynamic Programming (DP) to find the optimal rate adaptation policy for both the Source node and the Relay node. Such an approach yields a significant increase in throughput compared to non-adaptive fixed-rate HARQ. Moreover, the maximum achievable throughput is shown for the cooperative HARQ channel which is applicable to both adaptive and non-adaptive models.

This paper considers incremental redundancy hybrid ARQ (HARQ) transmission over independent block-fading channels. The transmitter, having no knowledge of the instantaneous channel state information (CSI) can or - allocate the transmission rate knowing the statistics of the channel, or - adapt the transmissions rates using the outdated CSI, i.e., the one experienced by the receiver in the past transmissions that resulted in a packet decoding failure. Aiming at throughput maximization problems under constraint on the outage probability, we show how to optimize the rate-adaptation and rate-allocation policies using dynamic programming framework. Numerical examples obtained in a Rayleigh-fading channel show that rate adaptation provides notable gains over a rate allocation and non-adaptive HARQ, and, for high SNR, only a few transmissions are necessary to approach closely the ergodic capacity.

This paper presents a new scheme for steganalysis of random LSB embedding, capable of applying to any kind of digital signal in both spatial and transform domains. The proposed scheme is based on defining a space whose elements relate to higher-order statistical properties of the signal and looking for special subsets, which we call closure of sets (CoS) in this space. We use this scheme for steganalysis of the LSB steganography in grayscale images, employing a vector of five accurate and monotone features. Experimental results show significantly higher accuracy of the proposed scheme, as compared to those reported in the literature, especially in low embedding rates applications.