SUSTAINABILITY OF MOBILE COMMUNICATION NETWORKS IN NIGERIA

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1 Journal of Emerging Trends in Engineering and Applied Sciences (JETEAS) 5(7): 95-1 Journal Scholarlink of Emerging Research Trends Institute in Engineering Journals, 214 and (ISSN: Applied ) Sciences (JETEAS) 5(7): 95-1 (ISSN: ) jeteas.scholarlinkresearch.com SUSTAINABILITY OF MOBILE COMMUNICATION NETWORKS IN NIGERIA 1 Osahenvemwen O.A. and 2 Emagbetere J.O. 1 Department of Electrical and Electronic Engineering Faculty of Engineering and Technology. Ambrose Alli, University, Ekpoma, Edo State, Nigeria. 2 Department of Electrical and Electronic Engineering Faculty of Engineering. University of Benin, Benin City, Edo State. Corresponding Author: Osahenvemwen O.A Abstract This paper presents sustainability of mobile communication networks in Nigeria due to incessant block calls experienced by the subscribers during busy hour. The study is focused on develop blocking probability traffic model, handover blocking probability traffic model, analysis on traffic usage based on available and installed number of transmission channels. Traffic data were collected from Globacom (GLO) network 18 MHz in Nigeria for period of two years (1st March 21 to 29 th February 212). Subscriber s usage was analyzed based on offered traffic, carried traffic, blocking traffic, available and installed number of transmission channels in this investigation. The queue theory was used to develop blocking probability traffic model, fashioned along side with handover blocking probability traffic model. Validation were done by Statistical tools such as standard error of estimate, correlation coefficient, coefficient of determination were used to compared the developed blocking probability and Erlang B blocking probability model. The result obtained from Standard Error of Estimate is.3, used to determine the error, correlation coefficient is , used to determine the relationship and coefficient of determination obtained is , used to determine the variation between developed and Erlang B blocking probability traffic model. Keywords: blocking probability, grade of service, transmission channels and offered traffic INTRODUCTION When Nigeria gained her independence in 196, there were only 18,724 functional telephone lines for an estimated population of 45 million people, with a "teledensity" ratio of.4 telephones per 1 people. During the thirty years of military rule, there was very little or no recommendable progress in telecommunications sector. According to the International Telecommunication Union, Nigeria at 1996 teledensity ratio was a mere.36. It gradually rose slightly to.4 by 1999; according to the Nigeria Communication Commission (NCC). Nigeria's teledensity is a far cry from the African average of Even the NCC admits that Nigeria has limited telephone lines for many years, and the waiting list is estimated to be over 1 million people, who have applied to the incumbent monopoly, Nigeria Telecommunication (NITEL) established in However, with the liberalization of the telecommunication industry in 21, the story changed dramatically. The teledensity ratio had tripled within just one year of GSM operation in the country (http//: en.wikipedia.org/wiki/ telecommunication_in Nigeria). So Nigerians must have breathe a sigh of relief when Econet (now Airtel) and MTN Nigeria launched their GSM mobile services in 21. The GSM has contributed positively in boosting economic activities in Nigeria. It has also improved the standard of living in Nigerians by creative jobs and human capital development (http// mobile_in_nigeria.html). During this era, there were competition for subscribers between the mobile operators and mobile operators became flexuous between themselves, which resorted to "price wars" to win subscribers. Subscribers, on the other hand, have more choice than ever regarding which GSM operator to use with enormous attractive service packages, and subscribers are able to access their caller at first dial. Seven years after the start of GSM era in Nigeria, it has gradually shifting toward subscribers experiencing calls congestion (block calls) and dropped calls due to high number of subscribers in the networks. High number of block calls are experienced during the busy hour in mobile communication network in Nigeria (Osahenvemwen et al., 211). The NCC has challenged the mobile operators to sustain the quality of service offered to Nigerian subscribers, but this sustainability is taking a slow pace ( This study is made to address these high levels of block calls and dropped calls experienced in Nigeria mobile communication networks (GSM). Over the years, the issues of management of limited transmission channels in cellular networks have pose a great concern to network operators. Various techniques have been developed to increase the number of subscriber s using the limited transmission channels. The frequency spectrum and transmission channels are in scare resources, which are referred to as trunking management in mobile communication networks. The concept of trunking allows a large number of subscribers to share relatively small number of available transmission channels by providing 95

2 access to each users. In a trunked-radio system each user is allocated a channel on a per call basis, and on termination of call, the previously occupied channel is returned to the pool of available channels. However, the possible way to utilize the available limited resources is by effectively deploying different techniques, developed by different researches, such as cell splitting, sectoring, frequency reuse, that will enhances overall network performance. Also, different multiplexing access technique were deployed in mobile communication networks, example are CDMA, TDMA, FDMA and OFDMA (Shmldon, 29; Madhusmita et al, 211; Rappaport, 23). Once the number of subscribers in network exceeds the maximum network capacity, some subscribers will not receive response from the networks, which is referred to as block calls. Also, as the number of subscriber s increases in a network with given number of transmission channels, the network experience high blocked calls expressed in blocking probability. Blocking probability is aspect of Quality of Service (QOS) which is expressed as Grade of Service (GOS) this aspect is referred to as teletaffic Engineering (Eric et al, 29; http//www en,wikipedia-or/wiki/grade-of-service). the future traffic demand and transmission channel capacity of network. The word traffic is used to determine usage, traffic is define as the amount of data or voice call over a circuit or channel in a particular period of time (Anuj et al, 212; Junqiang et al., 27). Teletraffic theory is used to determine the relationship between offered traffic loads (in erlang), number of transmission channels (capacity) and Grade of service expressed in blocking probability(http//www en,wikipedia-or/wiki/grade-of-service; Boulmalf et al.,29). Traffic engineering is a method used to optimize the performance of a telecommunications network by dynamically analyzing, predicting and regulating the behavior of data or voice call transmitted over that network (Anuj et al, 212). Traffic Load Flow Characteristic Traffic is generally referred to as the usage of mobile communication equipment, such as timeslots, trunks, routes, or channels in a cell (network) (Roger, 24). In Fig 1, the traffic load can be characterized into the following categories using traffic flow; ( communication/28/; Balint et al., 29). Offered traffic Traffic engineering is also known as teletraffic Carried traffic (served traffic) engineering and traffic management. The task of Block teletraffic Engineering is to design networks as low cost as possible with a predefined grade of service based on traffic (loss traffic or rejected traffic Offered traffic System Carried traffic Source: ( communication/28) Fig. 1: Traffic flow chart The relationship between offered traffic and carried traffic is given as; Carried traffic (C t ) = offered traffic (1- Blocking probability) (1) Also, offered traffic = carried traffic + block traffic (2) In general, Grade of Service (GOS) is measured by: carried traffic, offered traffic, and blocked traffic or lost traffic. The proportion of lost calls is the measure of GOS. Overflow or block traffic or loss traffic refusal. GOS is calculated using the Erlang-B formula, as a function of the number of channels required for the offered traffic load in erlang. The GOS is expressed in blocking probability used to determine the trade-off between the offered traffic in erlang and number of transmission channel utilization. Traffic can be measure in either Erlang or Centium-Call Second (CCS).The relationship between Erlang and Centium-Call Second (CCS) is shown in Equation 4. The CCS is used in North America (Verdone et al., 21; Boulmalf et al.,29). 1 Erlang = 1 call hour=36 call seconds = 36ccs (4) GOS = Number of lost calls / Number of offered calls (3) one hour of continuous use of one channel = 1 Erlang 1 Erlang = 1 hour (6 minutes) of traffic In data communications, an 1 E = 64 kbps of data The cellular circuit groups GOS acceptable value =.2. In telephone, 1 Erlang = 6 mins = 1 x 36 call seconds I.e. at busy period, 2 users out of 1 will encounter a call 96

3 Average Holding Time Average Holding Time (AHT) is the total time of all calls in a specified period divided by the number of calls in that period. The measurement of AHT should be taken on a 15 minutes or 6 minutes basis (ITU recommendation). The AHT will be calculated as follows; The acceptable AHT level is less or equal to 21 seconds in business environment (wwwcisco.com/en/us /docs/ios/solutions/docs/voip-solution/ta_isd.pdf, Popoola et al., 29; Junqiang et la., 27). Offered Traffic in Erlang Offered traffic: It is the traffic carried if no calls were rejected due to lack of capacity or the total number of calls carried through the maximum network capacity. The offered traffic can be determined from these parameters as follows (Madhusmita et al., 211; ITU-D, 26; Sanjay, 21; Jahangir et al., 2). (5) Offered traffic (A) = x µ (6) = average number of call arrival rate µ= average holding time per call system platform from Globacom (GLO) network 18 MHz in Nigeria for period of two years (1st March 21 to 29 th February 212). Subscriber s usage was analyzed based on offered traffic, carried traffic, blocking traffic, available and installed number of transmission channels in this investigation. Markov chain analysis based on queue theory of continuous time and discrete space was used to develop blocking probability traffic model, fashioned along side with handover blocking probability traffic model. The blocking probability traffic models were simulated using MATLAB (version ) program and statistical tools, such as standard error of estimate, correlation coefficient and coefficient of determination were used to validated the developed blocking probability traffic model with Erlang B blocking probability traffic model. Evaluations of Blocking Probability Blocking probability is evaluated based on the average data obtained for various routes (transmission channels). The average data obtained are as following; BSC:8(12), Route ID, with total number of subscribers of in 1 hour, total number of transmission channels (C) is Average Holding Time (AHT) =28.21 minutes. Recall Equation (9) Note: The observation interval time is taking into consideration (E.g. 15min. or 6 min). (7) Equ. 9 used to compute offered traffic load in erlang, considering the time in hour. (8) (9) (1) Traffic intensity: is defined as the traffic measured in term of occupancy of the channel (server) in the mobile network (Thiagarajan, 26). (11) The calculated offered traffic in erlang is , the given number of channels 1126 and the corresponding blocking probability is.197 determined from Erlang B traffic model (using Erlang B traffic model, Table). Also, the evaluation of blocking probability can be further be explain as follows; transmission channels are required to support 1 subscribers with a Grade of Service (GOS) of 2%, if the average traffic per subscriber is.25erlang. 2.5 Erl at 2% GOS is equal to 7 channels, using Erlang B traffic model shown in Table 1. Table 1 Erlang B traffic model Alternatively it is given as: Traffic intensity = traffic volume / time interval which is a measure of demand Also, offered traffic and carried traffic are expressed in blocking probability as followed; (12) (13) METHODOLOGY Traffic data were collected through the Operation and Maintenance Centre (OMC) network element, made by Alcatel, Model 1353RA, which runs on UNIX operating A Mathematical blocking probability traffic model was developed to predict the blocking probability for voice calls using queuing theory, which is based on Markov 97

4 chain analysis of continuous time and discrete space. This queuing model is characterized by the Poisson arrival process, independent exponential service times and independence between the arrival process and the service times. The developed Mathematical blocking probability is given in Equation 15, while Equation 16 presents the handover blocking probability traffic model; (15) The following parameters used in blocking probability in Equ.15 are defined below; A=offered traffic, K is discrete number of occupied channels. The total numbers of channels are shared into two segments, V number of channels used for voice calls and handover call, while g number of channels is used for handover calls only. The handover blocking probability is presented as (16) RESULTS AND DISCUSSION In Fig 2 shown the comparison between the available number of transmission channels and the installed number of transmission obtained from average traffic data such are attempt calls, successful calls, average holding time, and.2 GOS. Fig 3: plot of number of attempt calls in the circuits and average installed and available number of circuit or channels. The developed analytical (mathematical) blocking probability model in Equation 15 is simulated using MATLAB program, based on certain high number of transmission channels and offered traffic load in erlang. It is observed that blocking probability does not exhibits linear characteristic and also, at given (constant) number of transmission channels the blocking probability increases as the offered traffic load in erlang increases shown in Fig 4. In addition, it is observed that blocking probability is a function of the total number of transmission channels capacity and the offered traffic load in erlang shown in Fig 5. Fig 2: plot of comparison between available and installed number of circuit in each A- interface channels The average calls attempts were presented for 2 interface routes, the average available number of channels and the average installed numbers of channels are shown in Fig 3. Fig 4: plot of blocking probability against traffic load in erlang at a given number of channels 98

5 Blocking Probability Number of Channels (V) 2 develop blocking prob.traffic model 2 4 traffic load in erlang Fig 5: plot of blocking probability against traffic load in erlang and number of channels. Fig 5 shows the relationship between blocking probability, numbers of transmission channels and offered traffic load in erlang. In Fig 6, shows the comparison between Erlang B blocking probability and the developed blocking probability using the same set of traffic data obtained from the mobile network. It is observed that both Erlang B and developed blocking probability has significant pattern, it has slight variation which is based on the number of transmission channels reserved for handover calls. However, the developed traffic model was designed alongside with handover blocking probability based on mobility of subscribers, moving from one cell to another cell (referred to as handover process) aimed at minimal the total block call in the system. Statistical tools such as Standard Error of Estimate used to determine the error with a value of.3 and correlation coefficient used to determine the relationship with a value of.999 obtained. B locking P robability developed BPTM at v=1 erlangb at V= offered traffic load in erlang Fig 6: plot of comparison between developed and erlang B blocking probability against offered traffic load in erlang 6 It is observed that increase in offered traffic load in erlang, also increase blocking probability value, at a given number of transmission channels. Despite different techniques deployed in mobile communication networks, such as cluster size, frequency reuse, bandwidth, cell sectorization, etc used to increase the capacities of the mobile networks. Oftentimes the subscribers still witness block calls especially in the peak hour in Nigeria (Osahenvemwen et al, 211). The blocking probability traffic models in Equation 15 & 16 help to manage the block calls experienced and keep them within the specified range in mobile networks. Handover blocking probability traffic model, was deployed to compensate for the movement of subscribers from one cell to another. Therefore, these traffic models are mobility friendly compared with famous Erlang B blocking probability traffic model, that does not considered in and out flow of the subscribers in mobile networks. The optimal aim is to reduce the total block calls in the mobile network, which are expressed in blocking probability and increase in the system capacity (transmission channels) in proportion with increase in subscribers demand. CONCLUSION The incessant block calls experienced in mobile communication network is generally unacceptable by subscribers and it has generated into poor quality of service in mobile networks. Therefore this study is focused on sustainability of mobile communication networks in Nigeria, with consideration on developed blocking probability traffic model, handover blocking probability traffic model, analysis between the available and installed number of transmission channels. In this study the evaluation of traffic flow characteristics, such as offered traffic load in erlang, carried traffic, blocked traffic were considered. Traffic data were collected from Globacom (GLO) network 18 MHz in Nigeria for period of two years. Subscriber s usage was analyzed based on obtained traffic data. Mathematic blocking probability traffic model and handover blocking probability traffic model were developed using queuing theory, which is based on Markov chain analysis of continuous time and discrete space. The comparison between the developed blocking probability and Erlang B blocking probability traffic model, were carried out using the following statistical tools, such as standard error of estimate, correlation coefficient, coefficient of determination were deployed to determine the error, relationship and variation. Also, the unique nature of this blocking probability traffic model is associated with handover blocking probability traffic mode used to compensate for movement of subscribers in the networks. REFERENCES 1 years of GSM mobile in Nigeria (21, March,4). Retrieved from http// /8/1_years_gsm_mobile_in_nigeria.html, Analysis for Voice over IP-Cisco system.(28, March,14). Retrieved from http//www Cisco.com/en/US /docs/ios/solutions-docs/voip-solutions/ta_isdpdtraffic, 99

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