Limited Bandwidth Constraints Research Articles

Ultra-wide band and high efficient Polarization converting metasurface employing Bell-shaped unit cell for RCS reduction

Polarization is a dominant parameter regarding communication systems in recent times. The standard polarization techniques can be used only to a certain extent because of their huge size and limited bandwidth constraints. A design of bell-shaped polarization converting metasurface (PCMS) is presented in this paper which is highly efficient for Radar cross section (RCS reduction) and also operates in ultra-wideband. The designed PCMS attains a polarization conversion ratio of more than 90% by demonstrating the transformation of the linear polarization wave to its orthogonal counterpart. The PCMS is arranged in a triangular chessboard configuration to reduce the RCS. RCS reduction is obtained by achieving destructive interference, accomplished by two unit cells. The two unit cells, PCMS and its mirror will have a phase difference of 180 ± 37°. The phase cancellation is obtained by rotating and arranging the proposed PCMS at 90°, 180° and 270°. RCS reduction is obtained in a frequency range from 4 GHz to 24.8 GHz with 144% FBW. This PCMS is verified experimentally. A good agreement is found between the simulation and the experimental results.

A Knowledge Base Driven Task-Oriented Image Semantic Communication Scheme

With the development of artificial intelligence and computer hardware, semantic communication has been attracting great interest. As an emerging communication paradigm, semantic communication can reduce the requirement for channel bandwidth by extracting semantic information. This is an effective method that can be applied to image acquisition of unmanned aerial vehicles, which can transmit high-data-volume images within the constraints of limited available bandwidth. However, the existing semantic communication schemes fail to adequately incorporate the guidance of task requirements into the semantic communication process and are difficult to adapt to the dynamic changes of tasks. A task-oriented image semantic communication scheme driven by knowledge base is proposed, aiming at achieving high compression ratio and high quality image reconstruction, and effectively solving the bandwidth limitation. This scheme segments the input image into several semantic information unit under the guidance of task requirements by Yolo-World and Segment Anything Model. The assigned bandwidth for each unit is according to the task relevance scores, which enables high-quality transmission of task-related information with lower communication overheads. An improved metric weighted learned perceptual image patch similarity (LPIPS) is proposed to evaluate the transmission accuracy of the novel scheme. Experimental results show that our scheme achieves a notable performance improvement on weighted LPIPS while the same compression ratio compared with traditional image compression schemes. Our scheme has a higher target capture ratio than traditional image compression schemes under the task of target detection.

High-speed super-resolution structured illumination microscopy with a large field-of-view

Structured illumination microscopy (SIM) has been extensively employed for observing subcellular structures and dynamics. However, achieving high-speed super-resolution SIM with a large field of view (FOV) remains challenging due to the trade-offs among spatial resolution, imaging speed and FOV under limited bandwidth constraints. Here, we report a novel SIM technique to address this issue. By utilizing a high-speed camera and a rolling image reconstruction strategy to accelerate super-resolution image acquisition, as well as using a deep resolution enhancement to further improve spatial resolution, this SIM technique achieves imaging with a spatial resolution of 94 nm, a FOV of 102 × 102 µm2, and an imaging speed of 1333 frames per second. The exceptional imaging performance of this proposed SIM technique is experimentally demonstrated through the successful recording of the Brownian motion of fluorescent microspheres and the photobleaching of fluorescently labeled microtubules. This work offers a potential tool for the high-throughput observation of high-speed subcellular dynamics, which would bring significant applications in biomedical research.

Task offloading with enhanced Deep Q-Networks for efficient industrial intelligent video analysis in edge–cloud collaboration

With the rapid development of 5G and IoT technologies, industrial video surveillance systems face increasingly significant challenges in processing large-scale data. Traditional cloud-centric video analysis methods often lead to processing delays and inefficiencies in resource allocation due to centralized data processing. In this context, the emergence of edge computing, especially in edge–cloud collaborative intelligent video analysis, offers an innovative solution. Within this architecture, task offloading strategies focus on intelligent computation task distribution to minimize latency and optimize resource utilization. However, existing methods frequently overlook key application-specific metrics in video analysis, such as accuracy and timeliness. This study introduces a novel Edge–Cloud Collaborative Industrial Video Intelligent Analysis Architecture (ECIVA) based on the GRU-Enhanced Deep Q-Network (GEDQN). This architecture integrates the strengths of Gated Recurrent Units (GRU) for processing sequential data with the efficiency of Deep Q-Networks in optimizing task-offloading decisions. By incorporating a token bucket mechanism, the architecture optimally manages the task offloading rate within the constraints of limited network bandwidth, thus ensuring high accuracy and real-time responsiveness in video analysis. Empirical evaluation with offshore drilling platform video data shows that the architecture achieves 87.5% mean Average Precision (mAP) at a 0.5 IoU threshold and increases system throughput by 39.2% over traditional cloud-centric methods.

Enhancing high-speed networks using RGB-based WLAN through Ro-FSO integration in the 5 GHz band

Abstract The limited bandwidth constraints imposed by conventional wireless carriers pose a significant hurdle when it comes to the delivery of high-speed broadband services. In response to this challenge, Radio over Free Space Optics (Ro-FSO) has emerged as a viable and innovative solution, seamlessly amalgamating wireless and optical systems. This integration proves particularly invaluable in sensitive environments, such as hospitals, where the risk of electromagnetic interference disrupting critical medical equipment is a real concern. Ro-FSO offers a disruption-free avenue for high-speed data transmission, positioning it as the ideal choice for broadband services, including Wireless Local Area Networks (WLANs). Within the scope of this study, we introduce a high-speed Ro-FSO link, showcasing the capability to concurrently transmit three independent channels with Red, Green and Blue (RGB) laser respectively, each supporting a robust 1 Gbps data rate. These data streams are skilfully up-converted to the 5 GHz RF bands, encompassing transmission distances of 650 m across the FSO channel. Our numerical simulation findings underscore the successful transmission of all the channels using wavelength division multiplexing (WDM), seamlessly meeting the prescribed Bit Error Rate (BER) and eye pattern criteria, solidifying the Ro-FSO’s standing as a promising solution for high-speed broadband delivery.

Event-Triggered Communication Network With Limited-Bandwidth Constraint for Multi-Agent Reinforcement Learning.

Communicating agents with each other in a distributed manner and behaving as a group are essential in multi-agent reinforcement learning. However, real-world multi-agent systems suffer from restrictions on limited bandwidth communication. If the bandwidth is fully occupied, some agents are not able to send messages promptly to others, causing decision delay and impairing cooperative effects. Recent related work has started to address the problem but still fails in maximally reducing the consumption of communication resources. In this article, we propose an event-triggered communication network (ETCNet) to enhance communication efficiency in multi-agent systems by communicating only when necessary. For different task requirements, two paradigms of the ETCNet framework, event-triggered sending network (ETSNet) and event-triggered receiving network (ETRNet), are proposed for learning efficient sending and receiving protocols, respectively. Leveraging the information theory, the limited bandwidth is translated to the penalty threshold of an event-triggered strategy, which determines whether an agent at each step participates in communication or not. Then, the design of the event-triggered strategy is formulated as a constrained Markov decision problem and reinforcement learning finds the feasible and optimal communication protocol that satisfies the limited bandwidth constraint. Experiments on typical multi-agent tasks demonstrate that ETCNet outperforms other methods in reducing bandwidth occupancy and still preserves the cooperative performance of multi-agent systems at the most.

Multi-Objective Optimisation-based Robust H∞ Controller Design Approach for a Multi-Level DC-DC Voltage Regulator

In case an analytical approach to the selection of any weighting function is not possible, the selection process is usually a random and time-consuming process. In robust H∞ control theory, the selection of scalar, time, or frequency-dependent weighting functions is the main issue to shape the amplitude-frequency characteristic curve of the feedback controller. Therefore, we propose a robust H∞ control approach which utilises the multi-objective grey wolf optimisation algorithm (MOGWO) to obtain the optimal performance weighting functions in the presence of right half-plane zeros and limited bandwidth constraints. A trade-off design flowchart is proposed, providing Pareto optimal solutions to choose the optimal configuration of the robust feedback controller. The control method is structured by combining the robust H∞ optimal technique and the multi-objective algorithm. The effectiveness of the approach is compared with the non-convex single-objective heuristic solutions like the multi-verse optimisation algorithm (MVO), whale optimisation algorithm (WOA), and grey wolf optimisation algorithm (GWO). The focus of this design is to track and stabilise the output voltage of the DC-DC converter in the presence of external disturbances and parameter uncertainties. The optimised controllers are implemented using a digital signal processor (DSP) on a 200 W interleaved boost converter. The simulation results and experimental findings show that the proposed control method provides supreme disturbance rejection along with maintaining the stability of the system.

Survey of Reinforcement-Learning-Based MAC Protocols for Wireless Ad Hoc Networks with a MAC Reference Model.

In this paper, we conduct a survey of the literature about reinforcement learning (RL)-based medium access control (MAC) protocols. As the scale of the wireless ad hoc network (WANET) increases, traditional MAC solutions are becoming obsolete. Dynamic topology, resource allocation, interference management, limited bandwidth and energy constraint are crucial problems needing resolution for designing modern WANET architectures. In order for future MAC protocols to overcome the current limitations in frequently changing WANETs, more intelligence need to be deployed to maintain efficient communications. After introducing some classic RL schemes, we investigate the existing state-of-the-art MAC protocols and related solutions for WANETs according to the MAC reference model and discuss how each proposed protocol works and the challenging issues on the related MAC model components. Finally, this paper discusses future research directions on how RL can be used to enable MAC protocols for high performance.

Compressed Deepfake Detection using Spatio-Temporal Approach with Model Pruning

Deepfake is a deep learning technology that replaces source person face photos with the target person face photos in a movie to create a video of the target by executing the actions performed by source person. Due to the limited storage capacity and network bandwidth constraints, compressed media is now commonly employed in social networks. The goal of this research study is to determine whether or not a given compressed video is a deepfake. To do this, both the spatial and temporal components of the video should be considered, and the findings will be integrated by using an appropriate fusion approach. Hence, the deep learning model used in the spatial approach is pruned using the Network Pruning technique to achieve a better performance. The combined prediction of the spatial and temporal approaches indicates whether the given video is deepfake or not.

Opportunity Based Energy Efficient Routing Algorithm for Underwater Wireless Sensor Network for Submarine Detection

The submarine detection is the most significant research area of Under Water Acoustic (UWA) environment with extensive application in commercial and navy domains. The environmental complexity and variable nature of the UWA makes Underwater Wireless Sensor Network (UWSN) to exhibit fluidity, sparse deployment, time unpredictability, frequency selectivity, limited accessible bandwidth and energy constraints pose problems in the underwater positioning technology. Thus, an adaptable, scalable, and highly efficient UWA is required for the submarine routing systems. The depth-based routing has received lots of interest as it is capable of delivering effective operation without requiring full-dimensional position information of nodes. However, it has issues of vacant regions and detouring forwards. To delineate the aforementioned problems, this paper proposes an Opportunity-based Distance Vector Routing (ODVR) technique. The distance vectors, which have lowest hop counts in the direction of sink for underwater sensor nodes are determined by ODVR through a query method. Depending on the distance vectors, a dynamic routing is created to manage the packet forwarding. In the opportunistic forwarding, the ODVR has a minimal signaling cost and minimum energy consumption with the potential of eliminating the long detours issues. The outcomes of simulations demonstrate that the ODVR outperforms the conventional routing algorithms.

A Generic Online Nonparametric Monitoring and Sampling Strategy for High-Dimensional Heterogeneous Processes

With the rapid advancement of in-process measurements and sensor technology driven by zero-defect manufacturing applications, high-dimensional heterogeneous processes that continuously collect distinct physical characteristics frequently appear in modern industries. Such large-volume high-dimensional data place a heavy demand on data collection, transmission, and analysis in practice. Thus, practitioners often need to decide which informative data streams to observe given the resource constraints at each data acquisition time, which poses significant challenges for multivariate statistical process control and quality improvement. In this article, we propose a generic online nonparametric monitoring and sampling scheme to quickly detect mean shifts occurring in heterogeneous processes when only partial observations are available at each acquisition time. Our innovative idea is to seamlessly integrate the Thompson sampling (TS) algorithm with a quantile-based nonparametric cumulative sum (CUSUM) procedure to construct local statistics of all data streams based on the partially observed data. Furthermore, we develop a global monitoring scheme of using the sum of top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${r}$ </tex-math></inline-formula> local statistics, which can quickly detect a wide range of possible mean shifts. Tailored to monitoring the heterogeneous data streams, the proposed method balances between exploration that searches unobserved data streams for possible mean shifts and exploitation that focuses on highly suspicious data streams for quick shift detection. Both simulations and a case study are comprehensively conducted to evaluate the performance and demonstrate the superiority of the proposed method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the critical challenge of online process monitoring by considering the cost-effectiveness and resource constraints in practice (e.g., limited number of sensors, limited transmission bandwidth or energy constraint, and limited processing time). Unlike the existing methodologies which rely on the restrictive assumptions (e.g., normally distributed, exchangeable data streams) or require historical full observations of all data streams to be available offline for training, this paper proposes a novel monitoring and sampling strategy that allows the practitioners to cost-effectively monitor high-dimensional heterogeneous data streams that contain distinct physical characteristics and follow different distributions. To implement the proposed methodology, it is necessary: (i) to identify sample quantiles for each data stream based on historical in-control data offline; (ii) to determine which data streams to observe at each acquisition time based on the resource constraints; and (iii) to automatically screen out the suspicious data streams to form the global monitoring statistic. Experimental results through simulations and a case study have shown that the proposed method has much better performance than the existing methods in reducing detection delay and effectively dealing with heterogeneous data streams.

Global Cooperative Output Regulation of Linear Multiagent Systems With Limited Bandwidth

This article investigates the global cooperative output regulation problem of continuous-time linear multiagent systems (MASs), under the communication constraint of limited bandwidth. With a novel scaling function strategy, a distributed protocol based on the sampled and quantized data is designed to solve this problem. Compared with the existing works on quantized control of MASs, not only the boundedness assumption on initial conditions is removed, but also the communication graph is not restricted to be balanced or strongly connected. Moreover, the proposed quantized cooperative output regulation protocol does not rely on any global information of the communication graph. A simulation example is finally presented to illustrate the effectiveness of the proposed quantized cooperative output regulation protocol.

C4ISR Service Deployment Based on an Improved Quantum Evolutionary Algorithm

To overcome the limited communication bandwidth in the deployment of command, control, communications, computers, intelligence, surveillance, and reconnaissance (C4ISR) services, a service deployment platform was extended from the cloud to a terminal platform based on the concept of fog computing. Additionally, computing and memory resources on the terminal platform were used to alleviate the communication pressure due to the flow of combat information. A corresponding C4ISR service deployment model was constructed, and the quantum evolutionary algorithm, the shortest path algorithm, and the pairwise exchange method were combined to create a two-level search algorithm. This method can plan the operational information flow path and service deployment simultaneously, thereby achieving the optimal deployment of services even under the constraints of limited communication bandwidth.

Rate Constrained Discrete-time Maximum Principle

Abstract Actuators in real-world systems operate under limited bandwidth and saturation constraints. Mathematically, these limitations manifest as certain types of constraints being imposed on the sequence of control action. While saturation constraints have been studied extensively, bandwidth limitations in the actuators have received very little attention in the control community. While an attempt has been made in the direction of incorporating frequency constraints at the synthesis stage, rate constraints on the control at the design stage has not been studied extensively in the regime of discrete-time. This article tries to fill in that gap by incorporating rate constraints on the control trajectories. We propose a new discrete-time Pontryagin maximum principle (PMP) with rate constraints being imposed on the control and derive a set of first order necessary conditions for optimality. As an illustration, the linear quadratic (LQ) optimal control problem under rate constraints has been considered.

Multi-Hop Multi-Task Partial Computation Offloading in Collaborative Edge Computing

Collaborative edge computing (CEC) is a recent popular paradigm where different edge devices collaborate by sharing data and computation resources. One of the fundamental issues in CEC is to make task offloading decision. However, it is a challenging problem to solve as tasks can be offloaded to a device at multi-hop distance leading to conflicting network flows due to limited bandwidth constraint. There are some works on multi-hop computation offloading problem in the literature. However, existing works have not jointly considered multi-hop partial computation offloading and network flow scheduling that can cause network congestion and inefficient performance in terms of completion time. This article formulates the joint multi-task partial computation offloading and network flow scheduling problem to minimize the average completion time of all tasks. The formulated problem optimizes several dependent decision variables including partial offloading ratio, remote offloading device, start time of tasks, routing path, and start time of network flows. The problem is formulated as an MINLP optimization problem and shown to be NP-hard. We propose a joint partial offloading and flow scheduling heuristic (JPOFH) that decides partial offloading ratio by considering both waiting times at the devices and start time of network flows. We also do the relaxation of formulated MINLP problem to an LP problem using McCormick envelope to give a lower bound solution. Performance comparison done using simulation shows that JPOFH leads to up to 32 percent improvement in average completion time compared to benchmark solutions which do not make a joint decision.

Scene-level two-pass video rate controller for H.265/HEVC standard

A scene-level two-pass video rate controller for high efficiency video coding (HEVC) standard is proposed in this paper. The proposed rate controller is suitable for many pre-recorded applications in which encoded videos are transferred later on through a communication channel with limited bandwidth and buffering constraint. This rate controller operates in two passes. In the first pass, the video sequence is compressed and relevant information is collected. In the second pass, an optimal base quantization parameter (QP) for each video scene is calculated by simplifying the λ-domain rate-distortion model. Then, using the optimal base QP and the known QP cascading technique, a QP is computed for each picture in the scene. In this way, the quality of the compressed video is kept relatively constant during a scene and thus it provides a high-level perceptual quality for the compressed video. The base QP may be slightly modified during the second pass encoding by a fuzzy controller according to the second pass encoding results and the buffer status. The proposed rate controller has been implemented on H.265/HEVC standard reference software. Experimental results show that in comparison with the constant QP encoding and three known rate control algorithms the proposed algorithm provides a high-level quality for tested video sequences while the required buffering delay is low and the available bandwidth is fully used.

Adaptive hop-by-hop cone vector-based forwarding protocol for underwater wireless sensor networks

In the recent past, a significant increase has been observed in the use of underwater wireless sensor networks for aquatic applications. However, underwater wireless sensor networks face several challenges including large propagation delays, high mobility, limited bandwidth, three-dimensional deployments, expensive manufacturing, and energy constraints. It is crucial for underwater wireless sensor networks to mitigate all these limitations primarily caused by the harsh underwater environment. To address some of the pertinent challenges, adaptive hop-by-hop cone vector-based forwarding routing protocol is proposed in this article which is based on the adaptive hop-by-hop vector-based forwarding. The novelty of adaptive hop-by-hop cone vector-based forwarding includes increasing the transmission reliability in sparse sensor regions by changing the base angle of the cone according to the network structure. The number of duplicate packets and end-to-end delay is also reduced because of the reduced base angle and a smart selection criterion for the potential forwarder node. The proposed routing protocol adaptively tunes the height and opening of the cone based on the network structure to effectively improve the performance of the network. Conclusively, this approach significantly reduces energy tax, end-to-end delay, and packet delivery ratio.

QoE-Driven Coupled Uplink and Downlink Rate Adaptation for 360-Degree Video Live Streaming

360-degree video provides an immersive 360-degree viewing experience and has been widely used in many areas. The 360-degree video live streaming systems involve capturing, compression, uplink (camera to video server) and downlink (video server to user) transmissions. However, few studies have jointly investigated such complex systems, especially the rate adaptation for the coupled uplink and downlink in the 360-degree video streaming under limited bandwidth constraints. In this letter, we propose a quality of experience (QoE)-driven 360-degree video live streaming system, in which a video server performs rate adaptation based on the uplink and downlink bandwidths and information concerning each user's real-time field-of-view (FOV). We formulate it as a nonlinear integer programming problem and propose an algorithm, which combines the Karush-Kuhn-Tucker (KKT) condition and branch and bound method, to solve it. The numerical results show that the proposed optimization model can improve users' QoE significantly in comparison with other baseline schemes.

Robust distributed stochastic secondary control of microgrids with system and communication noises

This study proposes an advanced robust stochastic cooperative frequency/voltage restoration control considering the effects of both communication/system noises based on Kalman-filtering theory for secondary control of islanded microgrids. Compared with existing ideal and noise-resilient distributed methods, the proposed robust stochastic distributed secondary control method can achieve mean-square synchronisation for voltage and frequency restoration of distributed generations (DGs) along with the real power sharing accuracy through a sparse communication network, even though both the system measurement and communication channels are susceptible to noise interferences and limited band-width constraints. To this end, the graph theory, stochastic theory, and Kalman-filtering theory are employed to estimate the states of each DG based on the information of noisy measurement, and then the estimated signals are sent to the neighbour DGs via a noisy communication environment. The main advantages of the proposed method are its ability to provide the quality of the estimate, low complexity, low computational burden, and working even though both the system and communication channels are susceptible to noise interferences and limited band-width constraints. Finally, the simulation results in MATLAB/SimPowerSystems Toolbox show that the proposed method has better, robust, resilient, acceptable, and desirable performance compared with the recent and effective methods.

Nonlinear Mean-Square Power Sharing Control for AC Microgrids Under Distributed Event Detection

This article proposes a nonlinear distributed cooperative control scheme that can regulate the power output to achieve efficient utilization of renewable energy in ac microgirds, which ensures mean-square autonomous proportional power sharing over a nonlinear microgird system via a sparse cyber network subject to noisy disturbance and limited bandwidth constraints. The cyber networks are exposed to noisy disturbances and limited bandwidth constraints that terribly reduce the stability and quality of the whole system. To eliminate the adverse effects of noisy disturbances and limited bandwidth constraints, we propose a robust distributed control strategy designed by using partial feedback linearization for the dynamical nonlinear model of a microgrid system. Moreover, a distributed event detection mechanism with noise-dependent threshold is adopted to update the control signals with the consideration of unnecessary data communication reduction. Through adopting stochastic stability theory and Lyapunov function, the stability and convergence analysis of the proposed dynamic distributed event-detection conditions considering noise interferences is derived. As a result, the suggested method decreases the sensitivity of the system to failures and increases its reliability. Finally, a modified IEEE 34-bus test system in MATLAB/Simulink is utilized to verify the effectiveness of the proposed controller design scheme.