Improve GSM Network Call Drop by RF Optimization (original) (raw)
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All GSM Service Provider uses KPI (Key Performance Indicator) to monitor their quality of service (QOS) Performance. Nowadays major issue is call block in GSM. So if optimization major issue is call block in GSM. So RF optimization and drive test is tool to find reason of call drop. To improve the the performance of the service provide. In this research paper some practical cases and solutions are adopted to reduce the call and increase the customer and profit of the service. Major parameters Rxlevel, Handover failuer, Rxquality, C/A worst, etc. Drive test tool Ascom TEMS 16.3.1 and analyser mapinfo 9.3 used to perform drive test and analyse log files recoded in TEMS to find problem and give the si\olution of call drop. In addtion the RF drive test simulation results is attached which can clearly shows that call drop id reduced and imrovement in the parameter
Analysis of Call Drop Problem in 3G Networks by Using KPI Report
ABSTRTACT This paper present, a basic Approach to radio network planning that provides effective solution in terms of coverage and quality. The objective of this study, which is coverage driven is to find the minimum number of sites required providing sufficient coverage. large no. of BTS present in the network which we check their individual & mutual performance on the basis of different parameters. In this 3G KPI parameter for GSM are used on the basis of telecom software .There are few example of parameters like total Attempted Calls, Total Dropped Calls, Total Blocked Calls etc.by improving these parameters, we improve the quality of network.
RF Optimization for call setup and analysis of GSM network using agilent tools
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The Call Set up Success Rate, successful handovers, and maintaining the quality of call are most important features used by all mobile operators. So, it is extremely necessary and mandatory to identify possible means to measure these parameters and eliminate the existing problems in a GSM network. Therefore the different operators can use various type of optimization process. RF optimization is used to make proposals on how operators can optimize radio resources as well as provide the required of service quality to the subscribers. Since call drop and call failure are the major problems in the GSM networks. So this RF optimization technique helps to solve this problem.
Study And Implementation Of Drive Test For Development Of GSM Network
Even though mobile radio systems deliver more and more performance data there is always a need to measure the performance of the network in the field. These measurements can either be part of the deploying new network sites inorder to meet coverage, capacity and quality requirements, optimization of the network, benchmarking of performance, trouble shooting, or to verify the performance after an upgrade or reconfiguration of the network. These measurements are performed through Drive Test and RF survey. Drive testing is principally applied in both the planning and optimization stage of network development. The paper focuses on the procedure of drive test and its importance in network planning.
Abstract—All GSM operators use Key Performance Indicators
All GSM operators use Key Performance Indicators (KPIs) to judge their network performance and evaluate the Quality of Service (QoS) regarding end user perspective. All the events being occurred over air interface are triggering different counters in the Base Station Controller (BSC). The KPIs are derived with the help of these counters using different formulations. In this paper, a well established real GSM radio frequency (RF) network performance evaluation is presented on the basis of four major KPIs i.e., Call set up success rate (CSSR), Call Drop Rate (CDR), Handover Success Rate (HSR) and Radio traffic channel (TCH) congestion rate. It has been focused to analyze the live network performance; irrespective of the discussions and modeling available in the literature. Different issues, findings, trials and improvements have been summarized and observations/recommendations have been listed to correlate the practical aspects of RF optimization, which affect the performance, and QoS of an operational cellular network.
— The Call Set up Success Rate is one of the most important Key performance Indicators (KPIs) used by all mobile operators. However there is no standard measurement possible for this parameter. Therefore the different operators can measure it differently. How to optimize the BTS coverage area successfully along with better service is the real challenge .In this paper the main motive is to identify the causes of call setup failures in a GSM service test area and necessitate steps to increase the call success rate using RF optimization. RF Optimization is a very important process in any service provider's operating lifecycle which is a critical set of activities in the life cycle of any GSM wireless network. RF Optimization involves drive testing, post processing, data analysis, recommendations and action steps. Optimization will be continuous and iterative process of improving network quality. By successful optimization, the Quality of Service, reliability and availability of RF Coverage area is highly improved.
Performance Assessment of the Quality of Service of a GSM Network in Kaduna State Nigeria
1st International Conference on Engineering and Applied Natural Sciences, 2022
As the number of mobile subscribers exponentially increase, one of the key goals of Mobile Network Operators (MNOs) nowadays is to keep its subscribers satisfied with good Quality of Service (QoS) in order to increase the Quality of Experience (QoE) for the end user. In order to achieve the best performance, MNOs need to be continuously monitored in order to for them to optimize their network continuously. On this premise, this research work utilizes the statistical data of call traffic from the Base Station Controller (BSC_KDBH14) in Kaduna State, which takes into account the KPI obtained from 48 BTSs. The evaluation of the KPI measurements taken from the MNO showed that 2% of the Call Setup Success Rate (CSSR) failed to be within Nigerian Communications Commission (NCC's) benchmark (≥98%), and as a result, 2% of the dataset generated for the Call Setup Failure Rate (CSFR) failed to be within NCC's benchmark (≤2%). The Call Completion Rate (CCR) had all the datasets within NCCs benchmark (≥96%), but unfortunately, the Call Drop Rate had 15% of its BTSs Key Performance Indicators (KPIs) outside the NCC's benchmark (≤2%). To this end, this work highlighted the BTSs that needed close attention in order to optimize the network.
QUALITY OF SERVICE PARAMETERS EVALUATION IN CELLULAR NETWORKS
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Quality of Service refers to the evaluation of the overall performance of a service, such as a telephony or computer network particularly the performance seen by the users of the network. QoS is mostly observed from the subscriber's side. This includes aspects such as the mobile signal strength available to users and other call avaibility measurements. QoS is normally calculated during signal transmission called KPIs that is connected to the subscriber's happiness whilst using mobile services. QoS parameters are monitored through RF analysis by Drive Test. Some of these QoS parameters related to call and data are discussed in this paper. These parameters directly or indirectly represents the quality of service provided by the network operators in context of Nepal. This analysis of parameters has helped us identify problems like call performance, slow servicing, least ease of use in a service test area for different service providers in the country.
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The cellular communication forms the backbone of the present society. The GSM network for its deployment and maintenance costs a major revenue. Efficient deployment and utilization form the key factor of the invested infrastructure. In this paper, the Call Detail Record (CDR) of the real-time data set is analyzed to find the traffic intense region over a spanning area. The k-means clustering algorithm is used and it is enhanced with the help of the elbow method. The anomalies in the CDR dataset are the abnormal behavior of the users in the coverage area. The dataset is subjected to regressive optimization and the anomalies are removed to find the usage characteristics and behavior in the coverage area. The dataset is fed to the Bayesian Generalized linear model to predict the usage of the coverage area, which is proposed as a novelty in this paper, and in future, this data can be very crucial for the network service provider to reconfigure the bandwidth of the network in the signal traffic intense areas. Efficient bandwidth allocation results in the organized load balancing in the network which on a prolonged time frame will improve the Quality Of Service (QoS) in the network.