UMUDIKE JOURNAL OF ENGINEERING AND TECHNOLOGY (UJET) VOL. 1, NO. 2, DECEMBER 2015 PAGE 61-66

Transcription

1 UMUDIKE JOURNAL OF ENGINEERING AND TECHNOLOGY (UJET) VOL. 1, NO. 2, DECEMBER 2015 PAGE PERFORMANCE EVALUATION OF QUALITY OF SERVICE OF CDMA20001X MOBILE RADIO NETWORK *Ifeagwu, E N., Obi, P.I. and Nwaorgu, O.A. Department of Electrical/Electronics Engineering Michael Okpara University of Agriculture, Umudike, Abia State. ABSTRACT This paper focused on the evaluation of quality of service of CDMA20001x network. In the experimentation the traffic data used in the performance evaluation of the quality of service of CDMA20001x network were obtained from the Operation and Maintenance Centre (OMC) of Visafone network located in Awka, the capital of Anambra State of Nigeria. These data were collected from to hours with total cumulative readings of 60mins per each hour. There is one reading per day consisting of 24hrs readings and seven readings per week consisting of 168hrs.The blocking probability and handover blocking probability models were used in determining the quality of service of the network. Results showed that the highest numbers of block calls occurs at 20:00 hour (7.00 to 8:00pm) and was obtained as the busy hour, which have the highest calls intensity. Results also showed that Monday have the highest attempt calls followed by Friday, Tuesday, Wednesday, Thursday, Saturday and Sunday. Result obtained from the blocking probability showed that the call blocking probability reduces as the number of transmission channels increases in the mobile network. Result also showed that the handover blocking probability increases as the number of offered traffic load in erlang increases with a given number of transmission channels Keywords: CDMA20001X, handover blocking probability, QoS, blocking probability 1. INTRODUCTION Recently in Nigeria there has been a rapid growth in the number of users of Global System of Mobile (GSM) communication, internet and other telecommunication networks with the emergence of various real-time applications. But, unfortunately, Nigerian subscribers are unsatisfied with the quality of service experienced during busy hours as a result of block calls or lost calls. Also, the Quality of Service (QoS) rendered by most of these network operators faces new challenges which are as a result of the inability to set up calls, call drops, occasional service outages, cross talks and network congestion (Roger, 2004).These challenges resulting to quality of service degradation makes the end users sometimes to find it very difficult getting to their family members, friends and business associates. The degradation in service is witnessed by over 105 millions GSM subscribers across the nation irrespective of the operator especially during festive periods. Block call as one of the Quality of Service (QoS) parameters is one of the fundamental cause of the unsatisfactory voice and data services witnessed in today s wireless communication. Corresponding Author: Ifeagwu Emmanuel, ifeagwuemma@yahoo.com Block call can result from network transmission handoff, interference, data traffic and weak signal (Rappaport, 2003). The high number of block calls experienced in mobile networks especially at busy hour has also lead to poor quality of service delivered in the mobile network. Block call is a factor of QoS experience during congestion. Therefore, to minimize block calls, there must be acceptable level between the capacity, a function of offered traffic and grade of service (Madhusmita, 2011). These block calls experience in mobile network should be reduced to a certain low level values (2% value) in line with recommended standard to ensure good QoS ( on/articles/62-302, pdf Pg 4-6). The major parameters used for effective network design and network planning are blocking probability and hand over blocking probability (Regis, 2002). Therefore, there is the need for a proper performance evaluation of the quality of service of Code Division Multiple Access (CDMA) 20001x mobile radio network. CDMA20001x is one of the main technologies for implementation of third Generation (3G) cellular systems (Prasad and Ojanpera, 2006)). In CDMA20001x interface, different users can simultaneously transmit at different data rates and date rates can even vary in time. The CDMA20001x physical layer has the flexibility of accommodating different service types simultaneously especially with respect to low and medium bit rates. CDMA20001x as one of the third Generation (3G) network uses pseudo-random code to identify different users on the network unlike the second Generation (2G) network such as GSM communication.gsm makes use of Time

2 Division Multiple Access (TDMA) as one of the multiplexing techniques (Guner, 1998). 2. MATERIALS AND METHODS 2.1 Materials Blocking Probability Consider the transmission channels in a cell as in Figure 1. The assumed total numbers of channels are shared into two segments, V channels used for voice calls and handover call, while g channels are used for handover calls only. Therefore, the total number of channels is V + g, where the total numbers of V channels are used for voice calls, handover calls and the numbers of g transmission channels are reserved for handover calls only. The blocking probabilities, Pb that represents the proportion that new voice channels V are busy is shown in equation (1). The blocking probability that all the servers (channels) in a system are busy is denoted by P (0) as shown in equation (2). When all the channels (servers) are busy, the network will not carry any further traffic and arriving subscriber s traffic (calls) are blocked. The blocking probability is a measure from the network or channels system point of view and is represented as (Verdone and Zanella, 2001): P b = (λ μ ) V P(0) (1) V! λ= average number of call arrival rate, = average holding time per call A = λ = offeredtrafficloadinerlang (A) P (0) = blocking probability that all the servers (channels) in a system are busy V = Voice channel But, P(0) = channel. 1 V 1 ( λ μ )k 1 k=0 k! + V+g ( λ Vμ )k 1 k=v (k 1)! (2) Where k = discrete number of occupied Inserting equation (2) into equation (1), we obtain equation (3) which shows the probability that the system is idle (Osahenvemwen, et al, 2011) P b = ( λ μ ) V V! V 1 ( λ μ )k 1 k=0 k! + V+g ( λ Vμ )k 1 k=v (k 1)! Substituting = λ, into equation (3): P b = (A) V v! A V 1 K V+g K=0 + (va) K! k 1 k=v (K 1)! The equation (4) was used to determine and predict the blocking probability of voice calls in a V channels in the CDMA20001x mobile network. (3) (4) Where Pb= blocking probabilities 0 V V g V + g Figure 1: Total channels in a cell system Handover Blocking Probability The handover blocking probability modelling put into consideration the mobility of subscribers form one cell to another. The probability that a call will fail during conversation or (experienced block call), while moving from one cell to another is referred to as handover blocking probability (phb).this implies, that there are no free channels in the destination cell. The handover blocking probability equation is given in equation (5). P hb = (λ μ ) V+g. P(0) (5) (V+g)! Inserting equation (2) into equation (5), we obtain equation (6) which shows the hand over blocking probability:

3 P hb = Where : λ ( λ μ ) V+g (V+g)! ( λ k V 1 μ ) 1 k=0 k! + V+g ( λ k Vμ ) 1 k=v (k 1)! is the total offered traffic load in erlang (A) A = λ, Substituting A into equation (6) p hb = A V+g (v+g)! A V 1 K V+g K=0 + (va) K! k 1 k=v (K 1)! Equation (7) is the handover call blocking probability that helped in determining the quality of service of the CDMA20001x network. (6) (7) Operation and Maintenance Centre (OMC) Counter Method In a network, the traffic data can be measured by three methods (Gunner, 1998).These methods include drive test, protocol analyzer and the Operation and Maintenance Centre (OMC) Counter methods. The traffic data used in the performance evaluation of the quality of service of the investigative CDMA20001x network were obtained from the Operation and Maintenance Centre (OMC) counter of the Visafone network located in Awka, the capital of Anambra State of Nigeria. The OMC network element is inbuilt inside the mobile communication network. The OMC run on UNIX operating system and other special software are incorporated into a counter situated at the Base Station Controller (BSC) and Mobile Station Controller (MSC) point. The BSC and MSC are connected to the OMC unit. The traffic data collected, are calls intensity from at the MSC point, which contains the following information; calls attempts (arrival calls), complete calls (carried calls), average holding time, blocks calls, date, number of installed circuit, number of functional or available circuit and time interval period. One of the functions of the OMC is to take measurement on network performance. Mobile Station 2.2. Method Air-interface Base Transceiver Station X.25 protocol Abis-interface The OMC is made by Ericson, with a model number of 350RB, a maximized capacity of 20,000 cells and 10,000 Transmitters. The maximized A-interface installed circuit Base numbers Station are 4096 circuits under Mobile consideration. This Controller OMC-counter is deployed because Switching has the ability to Centre A-interface Operation and Maintenance Centre Collection point Figure 2: The block diagram of the network layout for the investigation measure the entire network rather than a single Base Transceiver Station (BTS) or BSC. Therefore it gives a detail post-mortem analysis of the mobile communication network. The block diagram of the network layout for the investigation is shown in Figure 2. Readings were collected from to hours with total cumulative readings of 60mins per each hour. There was one reading per day consisting of 24hrs readings and seven readings per week consisting of 168hrs. Following the collection of data procedure, the average calls intensity expressed in terms of the average attempt calls,

4 CALLS INTENSITY CALLS INTENSITY IN BSC 8(12) ROUTE as well as the number of complete calls and average holding time for each A-interface route at MSC test point were presented for 24 hours daily as shown in Figure 3. The average calls intensity collected based on investigated network are presented Figure RESULTS AND DISCUSSION 3.1 Results The measured data from the base station controller of site 8(12) is shown in Figure 3.0 while Figure 4.0 shows the total call intensity for days of the week. In Figure 5.0, the Calls blocking probability versus traffic load in erlang with lower number of transmission channels (V) is shown while Figure shows handover Calls blocking probability versus traffic load in erlang with lower number of transmission channels (V+g). TIME(HOURS) Figure 3: The calls intensity in BSC 8(12) against time DAYS OF THE WEEK Figure 4: The average calls intensity in the base station controller for every day of the week

5 Blocking 0.14 Probability Develop blocking prob.traffic model V=7 V=8 V=9 V=10 V= Traffic load in erlang Figure 5: Calls blocking probability versus traffic load in erlang with lower number of transmission channels (V) Blocking Probability Handover blocking prob. V+g=7 V+g=8 V+g=9 V+g=10 V+g= Traffic load in erlang Figure 6: Handover Calls blocking probability versus traffic load in erlang with lower number of transmission channels (V+g). 3.2 DISCUSSION It was observed from Figure 3 of the result analysis, that there are two busy hours in mobile communication network in Nigeria. These hours are 11:00hour (10.00am to am) and 20:00 hour (7.00pm to 8.00pm). The 20:00 hour (7.00 to 8:00pm) is observed to have the highest busy hour. This 20:00hour observed busy hour is probably due to economic activities (social, vehicle traffic saturation, and capitalist driven economics etc). This busy hour (20:00hr) is the period that has the highest block calls, unanswered calls and offered traffic load in erlang. The value of the busy hour was used in determining the traffic load, capacity on the network and traffic performance indicators on the network. If the system capacity can handle the highest number of subscribers in busy hour then it can manage the rest. In Figure 4, Monday have the highest attempt calls followed by Friday, Tuesday, Wednesday, Thursday, Saturday and Sunday. This implies that when carrying out traffic analysis on traffic load estimation, Monday should probably be considered, for effective traffic analysis due to high number of attempt calls recorded, in the Busy Hour Traffic (BHT). If Mondays with higher number of attempt calls and traffic load could be managed within the mobile network, then other days could also be managed with less problems. Figure 5 and 6 results were obtained based on block call and handover block calls traffic model in equation 4 and equation 7 respectively. This traffic model is developed based on the unique future of the mobile communication network i.e. subscriber mobility on the network. In Figure 5 at any given offered traffic load (in erlang) (A), the call blocking probability reduces as the number of transmission channels increases in the mobile network. The handover blocking probability increases as the number of offered traffic load in erlang increases with a

6 given number of transmission channels (V+g) as presented in Figure 6. 4 CONCLUSION One of the major factors responsible for poor quality of service is the block calls experienced by the subscribers. These block calls experienced during the busy hour, were generated from the traffic congestion in the network A- interface routes of the mobile communication networks. It was observed from the data collected, that the highest numbers of block calls occurs at 20:00 hour (7.00 to 8:00pm) was obtained as the busy hour, which have the highest calls intensity. This 20:00 hour obtained was probably due to the social economic activities, vehicle traffic saturation, and capitalist driven economics etc. It was observed from the Key performance Indicators(KPIs) traffic parameters analysis that the network operator still need to improve on the network traffic performance to guarantee good quality of service. Also, 100 percent compliance with NCC recommended standard will ensure minimum block calls in mobile communication network. In addition, the effects and possible solution of unanswered calls in mobile communication network were presented. It was observed that high volume of unanswered calls, which include ostentatious calls, redial calls etc. contributed to the high Grade of Service (GOS) in the mobile communication network. However, from the experiment the numbers of unanswered calls overweigh the numbers of answered calls, leading to low revenue generation in mobile communication networks. [5] Rappaport, T.S. (2003). Wireless Communications: Principles and Practice, Second Edition, Prentice Hall of India private Limited, New Delhi, pp [6] Regis J. (2002). Broadband Telecommunication Handbook 2 nd Edition, McGraw Hill, Pg , [7] Roger, L. (2004).Telecommunication system Engineering, 4th Edition, Pg 7-9. [8] Verdone, R. and Zanella, Y. (2001). Evaluation of blocking probability in a mobile Radio system with directed Retry, IEEE Journal on selected Areas in communication, vol.19, pp [10] Traffic for mobile networks, (2002). Pg 4-6 REFERENCES [1] Guner, H. (1998).GSM Network. Ist Edition Artech House Boston, Pg [2] Madhusmita, P. and Saraju, P.P (2011).Traffic Analysis and Optimization of GSM network. International Journal of Computer Science Issues (IJCSI), V (3), Issue 5, Pg [3] Osahenvemwen, O.A. and Emagbetere, J.O. (2011). Sustainability of Mobile Communication Networks in Nigeria. Journal of Emerging Trends in Engineering and Applied Sciences (JETEAS) 5(7): [4] Prasad, R. and Ojanpera, T. (2006). An overview of CDMA evolution towards wideband CDMA, IEEEComm.Pg

7