Comparing Machine Learning Algorithms for Improving the Maintenance of LTE Networks Based on Alarms Analysis

Mobile network operators are facing many challenges to satisfy their subscribers in terms of quality of service and quality of experience provided. To achieve this goal, technological progress and scientific advances offer good opportunities for efficiency in the management of faults occurring in a mobile network. Machine learning techniques allow systems to learn from past experiences and can predict, solutions to be applied to correct the root cause of a failure. This paper evaluates machine learning techniques and identifies the decision tree as a learning model that provides the most optimal error rate in predicting outages that may occur in a mobile network. Three machine learning techniques are presented in this study and compared with regard to accuracy. This study demonstrates that the appropriate machine learning technique improves the accuracy of the model. By using the decision tree as a machine learning model, it was possible to predict solutions to network failures, with an error rate less than 2%. In addition, the use of Machine Learning makes it possible to eliminate steps in the network failure processing chain;resulting in reduced service disruption time and improved the network availability which is a key network performance index.

Investigation and Technological Comparison of 4G and 5G Networks

Mobile cellular data networks have allowed users to access the Internet whilst on the move. Many companies use this technology in their products. Examples of this would be Smart Meters in the home and Tesla cars having their “over the air updates”. Both of these two companies use the 4G and 5G technology. So this report will include a technical overview of the technology and protocols (LTE Advanced) used in 4G and 5G networks and how they provide services to the user and how data is transferred within the networks. And there are lots of different parts about the network architecture between the 4G and 5G systems. This report will talk about some different parts between these two systems and some challenges in them.

基于4G的机器人与云平台无线数据传输系统设计

将机器人采集到的温度、湿度和位置等数据信息传送至云平台服务器,便于云端数据分析、机器人数据共享以及人机信息交互等。本文应用第4代移动通信技术(the 4th generation mobile communication technology,4G)无线传输技术,设计的机器人与云平台无线数据传输系统主要包括硬件和软件实现2个部分,硬件方面机器人通过32位微控制器(STMicroelectronics32,STM32)嵌入式系统与4G模块组合,将传感器采集到的数据信息,通过Internet网络传输到云平台服务器中,软件部分运用C语言编程通过网络透传模式(transmission control protocol/user datagram protocol, TCP/UDP)实现无线通信过程,最终实验验证了机器人与云平台服务器之间的双向无线数据传输功能。

Performance Evaluation of Scheduling Algorithms for 4G (LTE)

Long Term Evolution (LTE) is designed to revolutionize mobile broadband technology with key considerations of higher data rate, improved power efficiency, low latency and better quality of service. This work analyzes the impact of resource scheduling algorithms on the performance of LTE (4G) and WCDMA (3G) networks. In this paper, a full illustration of LTE system is given together with different scheduling algorithms. Thereafter, 3G WCDMA and 4G LTE networks were simulated using Simulink simulator embedded in MATLAB and performance evaluations were carried out. The performance metrics used for the evaluations are average system throughput, packet delay, latency and allocation of fairness using Round Robin, Best CQI and Proportional fair Packet Scheduling Algorithms. The results of the evaluations on both networks were analysed. The results showed that 4G LTE network performs better than 3G WCDMA network in all the three scheduling algorithms used.

UEs Power Reduction Evolution with Adaptive Mechanism over LTE Wireless Networks

At present, the major drawback for mobile phones is the issue of power consumption. As one of the alternatives to decrease the power consumption of standard, power-hungry location-based services usually require the knowledge of how individual phone features consume power. A typical phone feature is that the applications related to multimedia streaming utilize more power while receiving, processing, and displaying the multimedia contents, thus contributing to the increased power consumption. There is a growing concern that current battery modules have limited capability in fulfilling the long-term energy need for the progress on the mobile phone because of increasing power consumption during multimedia streaming processes. Considering this, in this paper, we provide an offline meaning sleep-mode method to compute the minimum power consumption comparing with the power-on solution to save power by implementing energy rate adaptation(RA) mechanism based on mobile excess energy level purpose to save battery power use. Our simulation results show that our RA method preserves efficient power while achieving better throughput compared with the mechanism without rate adaptation(WRA).

6G技术中的空中接入网:进展与展望

空中接入网(aerial access network, AAN)具有抗毁性强、覆盖范围广、视距传播概率高的特点,与地面网络相结合能够缓解流量负载压力,提供全时空的连接与服务,可有效助力第六代移动通信网络(6th generation mobile networks, 6G)“万物智联”愿景的实现.因此,在分析AAN中的卫星通信和高空平台(high altitude platform station, HAPS)技术研究进展的基础上,研究了卫星通信在数据接入方面的应用以及HAPS在一体化网络架构中的作用,探究了卫星与HAPS两种技术对未来网络的影响以及进一步优化二者的潜在方法.最后,对卫星接入的数据碰撞问题和HAPS的缓存问题进行讨论分析,并从上述问题出发展望了卫星通信和HAPS技术的未来发展方向.

4G网络环境下移动终端安全威胁及防护研究

4G网络即第四代移动网络,提供了高速的移动通信速率和多平台接入能力,4G网络环境下,移动终端的安全防护变得更加复杂,移动终端安全问题日益凸显,本文介绍了4G网络特点、4G网络移动终端面临的新的威胁、提出相关的解决方法。

Next Decade of Telecommunications Artificial Intelligence

It has been an exciting journey since the mobile communications and artificial intelligence(AI)were conceived in 1983 and 1956.While both fields evolved independently and profoundly changed communications and computing industries,the rapid convergence of 5th generation mobile communication technology(5G)and AI is beginning to significantly transform the core communication infrastructure,network management,and vertical applications.The paper first outlined the individual roadmaps of mobile communications and AI in the early stage,with a concentration to review the era from 3rd generation mobile communication technology(3G)to 5G when AI and mobile communications started to converge.With regard to telecommunications AI,the progress of AI in the ecosystem of mobile communications was further introduced in detail,including network infrastructure,network operation and management,business operation and management,intelligent applications towards business supporting system(BSS)&operation supporting system(OSS)convergence,verticals and private networks,etc.Then the classifications of AI in telecommunication ecosystems were summarized along with its evolution paths specified by various international telecommunications standardization organizations.Towards the next decade,the prospective roadmap of telecommunications AI was forecasted.In line with 3rd generation partnership project(3GPP)and International Telecommunication Union Radiocommunication Sector(ITU-R)timeline of 5G&6th generation mobile communication technology(6G),the network intelligence following 3GPP and open radio access network(O-RAN)routes,experience and intent-based network management and operation,network AI signaling system,intelligent middle-office based BSS,intelligent customer experience management and policy control driven by BSS&OSS convergence,evolution from service level agreement(SLA)to experience level agreement(ELA),and intelligent private network for verticals were further explored.The paper is concluded with the vision that AI will reshape the future beyond 5G(B5G)/6G landscape,and we need pivot our research and development(R&D),standardizations,and ecosystem to fully take the unprecedented opportunities.