Coverage Optimization of LTE Networks Based on Antenna Tilt Adjusting Considering Network Load

In this paper,we investigate the coverage optimization for LTE networks considering the network load. The network coverage is defined as the number of served users of evolved Node B(eNB)which is determined by e NBs' antenna tilt angles(ATA). The coverage is optimized by optimizing the number of served users based on the Modified Particle Swarm Optimization(MPSO)algorithm. Simulation results show that both the number of served users by each e NB and the system throughput are significantly increased. As well,the average load and the bandwidth efficiency of the network are improved.

ZTE Launches the First PC-Based CPT for LTE Networks

22 October 2012, Shenzhen-ZTE Corporation announced it has launched the industry＇s first PC-based capacity planning tool （CPT） for LTE networks. The CPT uses an innovative concept to overcome limitations in capacity planning technology. It provides operators with a professional, systematic aid for building the highest-performance networks.

ZTE Cloud Radio Solution to Usher in New Era of High-Performance LTE Networks

ZTE showcased its innovative cloud radio solution for 4G network optimization at the Mobile Asia Expo in Shanghai.

ZTE Launches Innovative Energy-Saving Solution for LTE Networks

19 November, 2012, Shenzhen-ZTE Corporation, a publicly listed global provider of telecommunications equipment, network solutions, and mobile devices, announced the launch of its Energy Saving Solution for operator LTE networks. According to test results, a single site employing this solution can save up to 40 percent power.

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).

Elite Opposition Based Metaheuristic Framework for Load Balancing in LTE Network

In present scenario of wireless communications,Long Term Evolution(LTE)based network technology is evolved and provides consistent data delivery with high speed andminimal delay through mobile devices.The traffic management and effective utilization of network resources are the key factors of LTE models.Moreover,there are some major issues in LTE that are to be considered are effective load scheduling and traffic management.Through LTE is a depraved technology,it is been suffering from these issues.On addressing that,this paper develops an Elite Opposition based Spider Monkey Optimization Framework for Efficient Load Balancing(SMO-ELB).In this model,load computation of each mobile node is done with Bounding Theory based Load derivations and optimal cell selection for seamless communication is processed with Spider Monkey Optimization Algorithm.The simulation results show that the proposed model provides better results than exiting works in terms of efficiency,packet delivery ratio,Call Dropping Ratio(CDR)and Call Blocking Ratio(CBR).

ZTE is Top 3 Global LTE Network Infrastructure Vendor

ZTE Corporation,a leading global provider of telecommunications equipment and network solutions,has been named a Top 3 LTE Network Infrastructure Vendor by Gartner,the world’s leading information technology research

Design of a Neural Network Based Stable State Observer for Mimo Systems

MIMO (Multiple Input Multiple Output) is a key technology underpinning fourth generation or 4G networks. This technology allows 4G networks to increase throughput. However, the dynamics of the MIMO system are not under control due to the many uncertainties that destabilize the system. This work is therefore very relevant in the sense that an observer can be used to monitor the dynamics of such a system. This work presents a neuro-adaptive observer based on a radial basis function neural network for generic non-linear MIMO systems. Unlike most neuro-adaptive observers, the proposed observer uses a neural network that is non-linear in its parameters. It can therefore be applied to systems with high degrees of nonlinearity without any a priori knowledge of the system dynamics. Indeed, in addition to the fact that neural networks are very good nonlinear approximators, their adaptive behavior makes them powerful tools for observing the state without any a priori knowledge of the dynamics of the system. The learning rule of the neural network is an approach based on the modified backpropagation algorithm: A term has been added to guarantee the robustness of the observer. The proposed approach is not limited by a strong assumption. The stability of the neuro-adaptive observer is demonstrated by the direct Lyapunov method. Simulation results are presented in the context of MIMO signal transmission applied in LTE, to demonstrate the performance of our observer.