Reliable Communication Services Research Articles

A framework for automated reconstruction of communication towers from terrestrial laser scanning point clouds

ABSTRACT Communication towers provide reliable wireless communication services and play a vital role in modern society. This paper presents an automatic 3D reconstruction framework for communication towers based on point clouds. The antenna is reconstructed by a model-driven method, which constructs an energy function including the data and smoothing terms to evaluate the model accuracy. The parameters of the antenna model are obtained by minimizing energy function with hybrid Genetic Algorithm (GA) and Simulated Annealing (SA). And the predefined model is transformed using these parameters to achieve antenna 3D reconstruction. For tower body reconstruction, the point cloud of the tower body is finely extracted with the region growing followed by piecewise fitting to obtain the model. The performance of the proposed method is evaluated on point clouds from six scenes. The average root-mean-square error (RMSE) of antenna reconstruction is 1.3 cm. The average precision, recall, and F1-score values of the tower body point extraction are 99.9%, 93.5%, and 96.6%, respectively. The average RMSE of tower body reconstruction is 1.2 cm. The experimental results indicate that the method can effectively reconstruct the antenna and tower body with point clouds, providing a feasible framework for the 3D reconstruction of communication towers.

Optimal Beamwidth for Maximizing Uplink Coverage Probability in Quasi Earth-Fixed LEO Satellite Communication System

Satellite communication is proposed to fulfill the ubiquitous coverage for next-generation wireless networks. Considering the propagation delay and path loss, low-earth orbit (LEO) satellites are widely adopted. However, since the beam boresight directions become close in quasi-earth-fixed cells (QEFC) scenarios at low elevation angles, the interference increases and causes low communication quality. This paper introduces the optimal beamwidth maximizing uplink coverage probability scheme for quasi-earth-fixed cells in LEO satellite communication systems. The proposed scheme dynamically adjusts the beamwidth to achieve max uplink coverage probability at different elevation angles. The simulation results show that the proposed scheme matches the exhaustive search method in different scenarios and target signal-to-interference-plus-noise ratios. Furthermore, the proposed scheme significantly mitigates interference and improves the uplink coverage probability. Compared with the 3GPP setting, the proposed scheme improves the uplink coverage probability by 0.93 at time 100 s, and compared with the three-color frequency reuse, the proposed scheme improves the uplink coverage probability by 0.195 at time 100 s. The dynamic beamwidth and boresight direction adjustments enable the satellite to maintain seamless and reliable communication services across a wide range of operating conditions, ultimately realizing the goal of ubiquitous communications in the QEFC LEO satellite system.

Enhanced FRER Mechanism in Time-Sensitive Networking for Reliable Edge Computing.

Time-Sensitive Networking (TSN) and edge computing are promising networking technologies for the future of the Industrial Internet. TSN provides a reliable and deterministic low-latency communication service for edge computing. The Frame Replication and Elimination for Reliability (FRER) mechanism is important for improving the network reliability of TSN. It achieves high reliability by transmitting identical frames in parallel on two disjoint paths, while eliminating duplicated frames at the destination node. However, there are two problems with the FRER mechanism. One problem is that it does not consider the path reliability, and the other one is that it is difficult to find two completely disjoint path pairs in some cases. To solve the above problems, this paper proposes a method to find edge-disjoint path pairs considering path reliability for FRER in TSN. The method includes two parts: one is building a reliability model for paths, and the other one is computing a working path and a redundant path with the Edge-Disjoint Path Pairs Selection (EDPPS) algorithm. Theoretical and simulation results show that the proposed method effectively improves path reliability while reducing the delay jitter of frames. Compared with the traditional FRER mechanism, the proposed method reduces delay jitter by 15.6% when the network load is 0.9.

A Two-Phase Approach for Reliability-Redundancy Optimization of a Communication Satellite

The development and launch of communication satellite projects pose significant challenges and costs. The expenses can range from several hundred million dollars, contingent on factors such as mission objectives, satellite system size and complexity including the launch vehicle, and ground infrastructure. Satellites must be designed to withstand harsh conditions in space, such as the extreme temperatures, radiation, and other hazards, while delivering reliable communication services to its users. However, once a satellite is launched, physical maintenance interventions become infeasible in the event of technical problems. Thus, reliability is a critical aspect for these expensive systems. This study aims to minimize the cost of a high-tech communication satellite by addressing design considerations that meet customer reliability requirements without exceeding power and redundant equipment limits. To achieve this goal, we propose an integer non-linear programming model in this research. To solve the satellite design problem, we adopt a two-stage solution approach. Conventional industrial practices in satellite design often involve iterative attempts to determine the redundancy level of onboard units based on customer reliability requirements. These processes rely heavily on the experience of design engineers who evaluate a limited number of alternatives to determine the number of redundant units, resulting in sub-optimal outcomes. In contrast, our proposed approach systematically handles the problem and yields optimal results. Our findings demonstrate that the proposed two-phase approach can achieve optimal redundancy levels within seconds.

Stochastic modeling of enhancing energy efficiency for HAP in HAP-LEO systems using Markov regenerative process

The sixth-generation (6G) communication systems heavily rely on space-air-terrestrial networks (SAT), which integrate high-altitude platforms (HAPs) and low Earth orbit (LEO) satellites. LEO satellites play a crucial role in ensuring global coverage and supporting a large number of users, while HAPs are strategically deployed to cover regions with high demand, providing a more reliable communication service to ground users. The principal aim of the proposed model is to explore techniques for energy conservation and efficient energy harvesting within the multi-layered system for HAP. In this energy harvesting scenario, it is assumed that HAPs will store the energy units generated through solar power. Moreover, the presented model incorporates two energy-saving states: standby and sleep states, during which HAPs consume minimal or zero energy. To analyze the described multi-layer system, a stochastic model is formulated using Markov regenerative process (MRGP), with consideration for the mean sojourn times of energy-saving states following a non-exponential distribution. The numerical results vividly demonstrate the significant contribution of energy-saving states in enhancing the energy efficiency of the HAP system. Also, trade-offs have been presented to analyze the impact of energy harvesting and energy saving in the proposed model. These findings are visually presented in graphs, and to validate the proposed model, simulation results have been carried out.

A novel low-latency scheduling approach of TSN for multi-link rate networking

Time Sensitive Networks (TSN), as an important representative of deterministic networks, provide low-latency and highly reliable communication services for the growing network applications that have strict requirements. Cyclic Queuing and Forwarding (CQF) is a well-known mechanism proposed by IEEE 802.1Qch for low-latency flow control of time-sensitive networks. It achieves bounded end-to-end delay and jitter transmission through a set of queues without complicated queue gating. However, most of the current work overlooks the widespread existence of multi-link rate networks in LANs and WANs, and the single-cycle CQF is unable to adjust different link rates, resulting in low bandwidth utilization and high latency. In this paper, we propose a novel scheduling approach named Multi-Cycle CQF (MCCQF) to solve the transmission problem in multi-link rate networks, aiming to reduce deterministic end-to-end delay and improve link bandwidth utilization. In addition, we formulate the scheduling constraints, being of guiding significance for designing the transmission of multi-link-rate networks, and we design an online scheduling algorithm based on it. We compare the proposed scheme with the single-cycle CQF online scheduling algorithm in hierarchical multi-link-rate networking scenarios, and the evaluation shows that our algorithm achieves better end-to-end ultra-low latency (38.9% reduction) with a smaller schedulability gap compared with single-cycle CQF. And we also improved the schedulability based on MCCQF by utilizing internal offset.

Smart sensing enabled dynamic spectrum management for cognitive radio networks

Cognitive Radio Networks (CRNs) have ushered in a transformative era in wireless communication, reshaping the landscape of radio spectrum utilization and management. At the core of CRNs lies the pivotal capability to sense the radio frequency spectrum dynamically and adapt transmission parameters to preemptively address interference and optimize spectrum utilization. This article addresses the escalating challenges associated with Quality of Service (QoS) management in CRNs, exacerbated by their dynamic nature, especially in scenarios characterized by high mobility. Concurrently, the article underscores the critical significance of energy efficiency, given its direct implications on network operational costs and sustainability. To effectively navigate the intricate interplay between QoS and energy management in CRNs, we propose a Smart Sensing Enabled Dynamic Spectrum Management scheme (SSDSM). Within the SSDSM framework, cognitive user energy undergoes intelligent sensing, while QoS is governed through dynamic spectrum management. The proposed scheme optimizes service response time by refining fuzzy-based controllers and curtails energy consumption through periodic sensing triggered by predefined rules. Operationalizing within a centralized paradigm, the entire network is overseen by a central controlling node, tasked with formulating an optimal channel list using the SSDSM scheme and allocating it to cognitive users. The efficacy of the proposed scheme is evaluated and validated through rigorous testing using MATLAB. Results reveal tangible enhancements in system efficiency, encompassing maximized throughput, reduced handoff ratio, and minimized service response delay. This research contributes to the ongoing discourse on advancing the performance metrics of cognitive radio networks in the pursuit of reliable and sustainable wireless communication services.

The Application of 5G Wireless Communication in Maritime Environment

Recently, 5G wireless communication have been rapidly adopted for maritime applications. As a result, significant progress has been made in the ocean digitalization. 5G features with low latency, high reliability, and a large bandwidth, which satisfies the requirements of maritime applications. Due to significant differences between land and marine radio propagation environments, the maritime communication encounters a large challenge for providing maritime users with reliable data communication service. In order to solve the abovementioned challenge, we designed a maritime 5G terminal and tested it on an unmanned surface vehicle (USV) in the Lanyue Lake of Zhejiang Ocean University. The test data were collected, and the corresponding data analysis are presented, which includes signal strength, signal reception quality, upload and download data rate. The received signal level (RSL) and transmission data rate obtained by the maritime 5G terminals demonstrate great potential of meeting the communication requirements of the maritime users. It is also can be found that the RSL and the data transmission rate of the maritime 5G can be used for the remote control of the unmanned ships, with the signal strength at sea up to -89.45 dBm, and maximum rate up to 226 Mbps.

Resource Allocation and Offloading Strategy for UAV-Assisted LEO Satellite Edge Computing

In emergency situations, such as earthquakes, landslides and other natural disasters, the terrestrial communications infrastructure is severely disrupted and unable to provide services to terrestrial IoT devices. However, tasks in emergency scenarios often require high levels of computing power and energy supply that cannot be processed quickly enough by devices locally and require computational offloading. In addition, offloading tasks to server-equipped edge base stations may not always be feasible due to the lack of infrastructure or distance. Since Low Orbit Satellites (LEO) have abundant computing resources, and Unmanned Aerial Vehicles (UAVs) have flexible deployment, offloading tasks to LEO satellite edge servers via UAVs becomes straightforward, which provides computing services to ground-based devices. Therefore, this paper investigates the computational tasks and resource allocation in a UAV-assisted multi-layer LEO satellite network, taking into account satellite computing resources and device task volumes. In order to minimise the weighted sum of energy consumption and delay in the system, the problem is formulated as a constrained optimisation problem, which is then transformed into a Markov Decision Problem (MDP). We propose a UAV-assisted airspace integration network architecture, and a Deep Deterministic Policy Gradient and Long short-term memory (DDPG-LSTM)-based task offloading and resource allocation algorithm to solve the problem. Simulation results demonstrate that the solution outperforms the baseline approach and that our framework and algorithm have the potential to provide reliable communication services in emergency situations.

Research on Location Selection Model of Base Station based on Improved Genetic Algorithm

With the increase in the number of users accessing the 5G network in the future, how to choose the location of 5G base stations to ensure effective network coverage of the service area, so as to provide reliable communication and transmission services, is a key issue to be considered. By establishing an improved genetic algorithm model, the site planning problem is solved. According to the circular coverage area of the base station, the coverage requirement and the minimum cost optimization goal are completed. First, denoising according to business volume, and finally get 35,915 weak coverage points. The sum of the cost of setting up macro base stations and micro base stations is the optimization goal, and the coverage rate of the weak coverage point is more than 90%, is set as the constraint condition. At the same time, the lethal factor is set in the selection process, and the individuals who do not meet the threshold conditions of the original base station are deleted. The improved operator is adopted in the crossover link and mutation link, which speeds up the running speed of the algorithm and ensures the population diversity and the accuracy of the results. Finally, 689 macro base stations and 269 micro base stations are established, with a coverage rate of 93.51% and an optimal cost solution of 7159. By visualizing the results on the coordinate map, it can be clearly seen that most of the weak coverage points have been covered, which shows that the improved genetic algorithm shows fast convergence speed and good optimal value, and verifies the effectiveness of the model.

Multichannel S-ALOHA-Enabled Autonomous Self-Healing in Industrial IoT Networks

For industrial Internet of Things network operators, undesired and abrupt network failure is a critical problem to be resolved quickly. In order to provide reliable communication services to devices in faulty cells, in this article, we propose a distributed autonomous self-healing mechanism that allows a random-access-based instantaneous communication to the neighbor cells. The design of the self-healing mechanism is challenged by the diverse device locations and the different available number of channels provided by the neighbor cells due to their intracell traffic load. By estimating the number of devices communicating with each neighbor cell in an online manner, our proposed mechanism can control the channel access probability of each cell to maximize throughput. In addition, the devices can reselect their serving cells in a distributed manner while realizing maximum but fair throughput among devices. Through extensive simulations, we show that our proposed mechanism can provide effective performance for autonomous self-healing.

Multi-UAV Enabled Aerial-Ground Integrated Networks: A Stochastic Geometry Analysis

Multiple unmanned aerial vehicles (UAVs) can function as aerial base stations to provide flexible and reliable communication services for massive ground devices (GDs). It is quite a challenging task to analyze such multi-UAV networks when considering practical mutually exclusive relationships among UAVs. Based on the tools of stochastic geometry, we in this paper develop a theoretical framework for modeling and analyzing aerial networks with UAVs following Matérn hard-core point process (MHCPP). As the tractable probability generating functional (PGFL) of repulsive point processes is unavailable, we employ an approximate approach based on the Poisson point process to analyze the cumulative interference and the signal-to-interference ratio (SIR) of a typical GD. By considering both line-of-sight (LOS) and none-line-of-sight (NLOS) communications, we obtain the approximation expressions of the network coverage probability and average rate. Finally, extensive simulation results are presented to validate the efficiency and accuracy of our proposed framework.

Reinforcement-Learning Based Handover Optimization for Cellular UAVs Connectivity

The demand for services provided by Unmanned Aerial Vehicles (UAVs) is increasing pervasively across several sectors including potential public safety, economic, and delivery services. As the number of applications using UAVs grows rapidly, more and more powerful, quality of service, and power efficient computing units are necessary. Recently, cellular technology draws more attention to connectivity that can ensure reliable and flexible communications services for UAVs. In cellular technology, flying with a high speed and altitude is subject to several key challenges, such as frequent handovers (HOs), high interference levels, connectivity coverage holes, etc. Additional HOs may lead to “ping-pong” between the UAVs and the serving cells resulting in a decrease of the quality of service and energy consumption. In order to optimize the number of HOs, we develop in this paper a Q-learning-based algorithm. While existing works focus on adjusting the number of HOs in a static network topology, we take into account the impact of cells deployment for three different simulation scenarios (Rural, Semi-rural and Urban areas). We also consider the impact of the decision distance, where the drone has the choice to make a switching decision on the number of HOs. Our results show that a Q-learningbased algorithm allows to significantly reduce the average number of HOs compared to a baseline case where the drone always selects the cell with the highest received signal. Moreover, we also propose which hyper-parameters have the largest impact on the number of HOs in the three tested environments, i.e. Rural, Semi-rural, or Urban.

Evolution Toward 6G Multi-Band Wireless Networks: A Resource Management Perspective

In this article, we first present the vision, key performance indicators, key enabling techniques (KETs), and services of 6G wireless networks. Then, we highlight a series of general resource management (RM) challenges as well as unique RM challenges corresponding to each KET. The unique RM challenges in 6G necessitate the transformation of existing optimization-based solutions to artificial intelligence/machine learning-empowered solutions. In the sequel, we formulate a joint network selection and subchannel allocation problem for 6G multi-band network that provides both further enhanced mobile broadband (FeMBB) and extreme ultra reliable low latency communication (eURLLC) services to the terrestrial and aerial users. Our solution highlights the efficacy of multi-band network and demonstrates the robustness of dueling deep Q-network in obtaining efficient RM solution with faster convergence rate compared to deep Q-network and double deep Q-network algorithms.

Chinese Deep Space Stations: A brief review [Antenna Applications Corner

To meet the rapidly growing needs of current and future deep space exploration missions, China has developed three deep space stations: Jiamusi and Kashi, (China), and Argentina (South America), for providing continuous telemetry, tracking, and command (TT&C), reliable communication, and precise navigation services. Integrated with state-of-the-art technologies, these three deep space stations are not only powerful for transmission but also extremely sensitive for receiving. Many remarkable innovations and achievements were made during the construction process, including the beam waveguide (BWG), adjustable subreflector, high-power transmitter, cryogenic low-noise amplifier (LNA), antenna-arraying technique, digital baseband receiver, and interferometry. This article briefly reviews the system architecture, general performance, critical system design issues, and contributions made by these Chinese deep space stations (CDSSs) during Chang’e missions.

Design and verification of the multinode and multisystem relay communication system for the Tianwen-1 Mars Probe

<p indent="0mm">As an important part of China’s first Mars exploration mission, the Tianwen-1 relay communication system is responsible for providing efficient and reliable communication services between the lander-rover and the orbiter and for supporting the completion of the patrol and exploration mission on the Martian surface. Considering the large elliptical relay orbit of the Mars exploration mission, the XHF/UHF dual-band relay communication scheme of the Tianwen-1 probe is introduced in this paper. Moreover, the design, technical mechanism, and verification approach of the critical parts of the relay communication system are given. Finally, the results of the on-orbit flight data are analyzed. The flight verification results show that the newly developed relay communication system of the Tianwen-1 probe successfully achieves the exploration mission objectives. Furthermore, its design, implementation, and application provide technical reference for the subsequent development of China’s deep-space relay communication system.

CLARQ: A Dynamic ARQ Solution for Ultra-High Closed-Loop Reliability

Emerging wireless control applications demand for extremely high closed-loop reliability under strict latency constraints, which the conventional Automatic Repeat reQuest (ARQ) solutions with static schedules fail to provide. To overcome this issue and enable data-link layer error control for ultra reliable low-latency communication (URLLC) services, we propose a novel protocol: the Closed-Loop ARQ (CLARQ), which forces to accomplish an information exchange round within a fixed loop-back latency, and dynamically re-allocates the remaining resource between uplink and downlink slots upon the result of last uplink transmission. The proposed method guarantees to meet the latency requirement, while delivering high communication reliability and power efficiency. It can be efficiently offline optimized by means of dynamic programming techniques, and is capable of real-time deployment with a low-cost implementation based on look-up tables. Numerical evaluations have verified that CLARQ outperforms baselines with significantly improved closed-loop reliability and reduced energy consumption. Especially, over a Rayleigh channel with 0dB mean SNR, it is able to provide a closed-loop error rate below 1e-7 within 10ms loop-back latency, which makes our proposal competitive for practical URLLC applications in future 5G-and-beyond networks.

Indoor performance analysis of URLLC service for industrial IoT

The ultra reliable low latency communication (URLLC) service consists of a great opportunity for mobile network operators (MNOs) for the industrial internet-of-things (IIoT) use cases. The URLLC service deployment requires careful considerations, and 100% service availability needs to be the ultimate goal across the factory area. However, it is critical to model the service availability for capacity analysis and observe performance tests for the URLLC service. In this article, the possible deployment scenarios for the URLLC service that can be provided in a factory environment are examined. Besides, a service availability model for URLLC is described. Afterwards, performance testing for realistic deployment scenarios that are based on time division duplex (TDD) is carried out. The results reveal that the framing structure of TDD has a direct impact on the capacity of URLLC, so that framing needs to be selected as a result of a careful site planning process.

Distributed Optimization for Mobile Robots under Mobile Edge Computing Environment

Driven by the development of the Internet industry, mobile robots (MRs) technology has become increasingly mature and widely used in all walks of life. Since MRs are densely distributed in the network system, how to establish a reliable communication architecture to achieve good cooperation and resource sharing between MRs has become a research hotspot. In this respect, mobile edge computing (MEC) technology and millimeter wave (mmW) technology can provide powerful support. This paper proposes a mmW communication network architecture for distributed MRs in MEC environment. The mmW base station provides reliable communication services for MRs under the coverage of information cloud (IC). We design a joint resource and power allocation strategy aimed at minimizing network energy consumption. First, we use the Lyapunov optimization technique to transform the original infinite horizon Markov decision process (MDP) problem. Then, a semidistributed algorithm is introduced to solve the distributed optimization problem in the mmW network. By improving the autonomous decision-making ability of the mmW base station, the signaling overheads caused by information interaction are reduced, and information leakage is effectively avoided. Finally, the global optimal solution is obtained. Simulation results demonstrate the superiority of the proposed strategy.

Potential technologies and applications based on deep learning in the 6G networks

The fifth generation (5G) networks provide high speed, low latency, and reliable communication services, and have been making great differences in our daily life. However, it might be difficult to meet the growing demands of Internet of Things (IoT) devices based on the existing technologies in the future, so the concept of sixth generation (6G) networks were raised to improve the existing 5G networks and promote the development of intelligent applications furthermore. Therefore, we introduce the characteristics, challenges, and potential technologies of 6G networks, and find that deep learning is potential to overcome these challenges with the help of big data and cloud computing. In addition, Deep learning approaches and potential applications in 6G networks are also discussed. We summarize the important viewpoints and studies, and provide some new ideas for developing 6G networks.