Design of an Interconnection Architecture and Sizing of Two (2) EPC Core Networks: The Case of Orange-Guinea

With the arrival of the 4G and 5G,the telecommunications networks have experienced a large expansion of these networks.That enabled the integration of many services and adequate flow,thus enabling the operators to respond to the growing demand of users.This rapid evolution has given the operators to adapt,their methods to the new technologies that increase.This complexity becomes more important,when these networks include several technologies to access different from the heterogeneous network like in the 4G network.The dimensional new challenges tell the application and the considerable increase in demand for services and the compatibility with existing networks,the management of mobility intercellular of users and it offers a better quality of services.Thus,the proposed solution to meet these new requirements is the sizing of the EPC(Evolved Packet Core)core network to support the 5G access network.For the case of Orange Guinea,this involves setting up an architecture for interconnecting the core networks of Sonfonia and Camayenne.The objectives of our work are of two orders:(1)to propose these solutions and recommendations for the heart network EPC sizing and the deployment to be adopted;(2)supply and architectural interconnection in the heart network EPC and an existing heart network.In our work,the model of traffic in communication that we use to calculate the traffic generated with each technology has link in the network of the heart.

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.

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).

ZTE Contributed to Launch of 4G Network of BASE Company in Belgium

4 November 2013,Shenzhen,China-ZTE Corporation,a publicly-listed global provider of telecommunications equip ment,network solutions and mobile devices,and BASE Company,the Belgian subsidiary of Dutch mobile operator KPN,have launched BASE Company＇s 4G services in Belgium.

Link Budget and Networking Analysis of FuTURE 4G TDD Trial System

The FuTURE 4G Time Division Duplex (TDD) trial system uses 3.5 GHz carrier frequency and several crucial technologies including broadband Multiple Input Multiple Output (MIMO) and Orthogonal Frequency Division Multiplexing (OFDM). These technologies challenge the link budget and networking analysis of the FuTURE 4G TDD trial network. This paper analyzes the practical 3.5 GHz propagation model and the link budget of Radio Frequency (RF) parameters of the trial system. Moreover,it introduces networking analysis and network planning of the trial system,which combines the field test results of the MIMO system. The FuTURE 4G TDD trial system and its trial network have been accomplished with successful checkup. The trial system fulfills all the requirements with two Access Points (AP) and three Mobile Terminals (MT),which supports multi-user,mobility,a high peak rate of 100 Mb/s,High-Definition TV (HDTV),high-speed data download,and Voice over IP (VoIP) services.

Dynamic Resource Allocation in LTE Radio Access Network Using Machine Learning Techniques

Current LTE networks are experiencing significant growth in the number of users worldwide. The use of data services for online browsing, e-learning, online meetings and initiatives such as smart cities means that subscribers stay connected for long periods, thereby saturating a number of signalling resources. One of such resources is the Radio Resource Connected (RRC) parameter, which is allocated to eNodeBs with the aim of limiting the number of connected simultaneously in the network. The fixed allocation of this parameter means that, depending on the traffic at different times of the day and the geographical position, some eNodeBs are saturated with RRC resources (overused) while others have unused RRC resources. However, as these resources are limited, there is the problem of their underutilization (non-optimal utilization of resources at the eNodeB level) due to static allocation (manual configuration of resources). The objective of this paper is to design an efficient machine learning model that will take as input some key performance indices (KPIs) like traffic data, RRC, simultaneous users, etc., for each eNodeB per hour and per day and accurately predict the number of needed RRC resources that will be dynamically allocated to them in order to avoid traffic and financial losses to the mobile network operator. To reach this target, three machine learning algorithms have been studied namely: linear regression, convolutional neural networks and long short-term memory (LSTM) to train three models and evaluate them. The model trained with the LSTM algorithm gave the best performance with 97% accuracy and was therefore implemented in the proposed solution for RRC resource allocation. An interconnection architecture is also proposed to embed the proposed solution into the Operation and maintenance network of a mobile network operator. In this way, the proposed solution can contribute to developing and expanding the concept of Self Organizing Network (SON) used in 4G and 5G networks.

A Genetic Algorithm for Overall Designing and Planning of a Long Term Evolution Advanced Network

In the mobile radio industry, planning is a fundamental step for the deployment and commissioning of a Telecom network. The proposed models are based on the technology and the focussed architecture. In this context, we introduce a comprehensive single-lens model for a fourth generation mobile network, Long Term Evolution Advanced Network (4G/LTE-A) technology which includes three sub assignments: cells in the core network. In the resolution, we propose an adaptation of the Genetic Evolutionary Algorithm for a global resolution. This is a combinatorial optimization problem that is considered as difficult. The use of this adaptive method does not necessarily lead to optimal solutions with the aim of reducing the convergence time towards a feasible solution.

Signal Detection Algorithm Design Based on Stochastic Resonance Technology Under Low Signal-to-Noise Ratio

In the current 4th generation(4G)communication network,the base station with the same frequency transmission makes a serious interference among adjacent cells,and information transmission is susceptible to interference such as channel multipath fading and occlusion effect.Detecting effectively spectrum signal under low signal-to-noise ratio(SNR),directly affects the whole performance of the wireless communication network system.This paper designs an energy signal detection algorithm based on stochastic resonance technology which transforms noise's signal energy into useful signal energy,and improves output SNR.The energy signal detection algorithm realizes the function of providing effective detection of signal under low SNR,and promotes the performance of the whole communication system.