IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) e-ISSN: 2278-2834,p- ISSN: 2278-8735.Volume 10, Issue 6, Ver. I (Nov - Dec .2015), PP 31-35 www.iosrjournals.org DOI: 10.9790/2834-10613135 www.iosrjournals.org 31 | Page Litrature Survey of Traffic Analysis and Congestion Modeling In Mobile Network Aliyu Ozovehe 1 , Okpo U. Okereke 2 and Anene E. C 2 1 Trifield Technology Limited, Abuja, Nigeria 2 Electrical and Electronics Engineering Programme, Abubakar Tafawa Balewa University, Bauchi, Nigeria Abstract: Network congestion is one of the major problems of GSM service providers as the number of subscribers increase and new services are introduced. All the proposed techniques in literatures for controlling congestion are centered on two principles which are either to reject excessive traffic to prevent over-utilization of network resources or diverting excess load if overload occurs. These techniques do not specify how network resource can be provided to absorb rejected or diverted traffic so that revenue will not be lost during congestion and hence, they do not really address congestion during busy hour. Real-time traffic analysis is required to understand user traffic demand pattern on network resources for proper prediction of network congestion so that resources can be provided to take care of rejected or diverted traffic. However, available literature survey on mobile network congestion modeling showed that none of the existing literature: address congestion at the three basic elements of GSM network to characterize end-to-end connection; use busy hour traffic data to adequately dimension GSM network elements so that the network can cope with load B. Therefore, effective congestion control mechanism that can take these research gaps into consideration for proper forecasting and efficient dimension of the network resources to address busy hour congestion must be developed. This paper is a preliminary report on development of such accurate congestion prediction model through an ongoing research work using real live network data from one of the Service provider’s networks in Abuja, Nigeria as a case study. Key words: Busy hour, Key performance indicators, Network management system, Traffic congestion and Traffic load I. Introduction The performance of mobile network is one important issue that concerns Service provider and Telecommunication regulator with the main goal of keeping subscribers satisfy. In order to achieve the best performance, service providers have to monitor and optimize the network continuously to meet Regulator target metric for all the key performance indicators (KPI). A Network Management System (NMS) with an online database is responsible for the collection of data on the networks (Motorola, 2001). To measure the network congestion, traffic patterns of a normal day is required for profiling. Traffic profile can be done using hours of a whole day along the x-axis and traffic in Erlangs along the y-axis. The distribution of daily traffic in Erlangs varies with the time of day which could be high intensity - peak hours and festive periods; low intensity hours during a normal daily activities. There are three different traffic profiles (Fiche and Hebuterne, 2004):  Normal day corresponds to usual activities during a day - Load A  High load condition corresponds to special days in a year - Load B  Exceptional conditions corresponds to unexpected happenings –Load C It is important that the network resources can serve traffic of load A and very good if it can serve the traffic of load B. However, it is very difficult to meet the third condition, because this load corresponds to unpredictable situations like natural hazards. KPI that are used for traffic profile in mobile network are: busy hour Traffic (in Erlang), Call Setup Success Rate (CSSR), Handover Success Rate (HOSR), Stand-alone Dedicated Control Channel (SDCCH) congestion and Traffic channel (TCH) congestion, call drop rate (Adegoke et. al, 2008; Kollar, 2008) and traffic load. TCH congestion is used to measure the demand for services and channels utilization in the network. Call Setup Success Rate and Handover Success Rate are used to measure the impact of congestion in the two most important procedures during a call attempt and movement during the call. SDCCH and TCH congestion are used to locate where exactly congestion appears in terms of logical channels, as these channels are the ones most affected in a congestion situation (Huawei, 2008). Meeting the metric target set for these KPIs by the Nigerian Communications Commission (NCC) remained the greatest challenge to Service providers in Nigeria. The NCC target values for cell and BSC metric is shown in Table 1 (NCC, 2012).