The work presented in this thesis laid more emphasis on the performance of real time monitoring and availability of radio signals to a consumer in a communication network. The aim of this study is to investigate existing method of measuring network availability and to develop a method that can be applied by service provider as a quality measure for network operations in Nigeria. Attempts were made to have a real time monitoring of several base transceiver station (BTS) in Nigeria to ascertain the availability of signal to a subscriber. Most Companies rely on QOS-oriented data networks such that any form of interrupt can cause considerable economic loses, thus many of these industries have invested in securing their networks with redundancy and quality of service as well as demanding high network availability from their network operator. The objective of this monitoring was to have a common characteristics behavior for all the BTSs as it relates to performance in real time and subscriber access to the services paid for. In this study, reactive availability software M2000 mobile management system was used in gathering data from a ticketing system. From my result in table 4.1-4.12, it was observed that the statistics obtained for majority of the BTSs for the period of the study shows satisfactory level of network availability, thus, their availability values was greater than 98% but less than 99.999% of the desired value. further analysis from the table also reported that some BTSs performed below bar of meeting up to the desired value of 99.999% availability, however, their availability values fluctuated between 0% and 62.55%. Thus, the findings also reported the facts that power outages, location of base stations, poor maintenance and lack of security personnel were mostly responsible for lack of network availability. Similarly the findings reveal that the factors affecting network availability or access to network service across Nigeria are the same. This is clear violation of the policy that binds the service providers and the subscriber.

1.1 Background to the study
The global cellular communication network is one of electrical engineering’s crowning achievements, reliably connecting over half the planet’s population, virtually everywhere where people are. These networks – particularly in urban areas are in the midst of a paradigm shift as the number of base stations increases rapidly each year, nearly all by virtue of small base stations (pico and especially femto) being added to the existing network. This unprecedented escalation is due to intense consumer demand for faster data connectivity, the impossibility of meeting this demand by adding spectrum, and the increasing technical and financial viability of small base stations. By 2015, there will be perhaps 50 million base stations (Andrews, 2012) and some even predict that in the not-too-distant future, say 10-15 years out, the number of base stations may actually exceed the number of cell phone subscribers (Malladi, 2012), resulting in a cloud-like “data shower” where a mobile device may connect to multiple base stations, or at least frequently have a base station to itself (Jefferey, 2012). Base stations are typically envisioned as big, high-power towers or cell sites. And indeed, many are. Fundamentally though, a base station must do three things. First, it must be able to initiate and accommodate spontaneous requests for communication channels with mobile users in its “coverage area”. Second, it must provide a reliable backhaul connection into the core network. This connection often is but need not be wired, but if wireless (possibly to another wired-in BS) then it must not be in the same spectrum used for communication with the mobile users; else such a device should be Considered a “relay” Relays may be useful in some cases for coverage enhancement, but by reusing the same scarce “access” spectrum for backhaul, are inherently inferior to base stations. And third, base stations need to have a sustainable power source. Usually, this is a traditional wired power connection but it could in principle be solar, scavenging, wind-powered, fossil fuel generated (for example “mobile APs” in vehicles), or something else (Jeffrey, 2012). It may seem frivolous to define an ubiquitous technology that has existed for several decades. But it is important to recognize that traditional tower-mounted base stations – what we will call macro cells in this paper – are just a single type of base station, albeit the backbone that has enabled cellular success to date. However, in many important markets, adding further macro cells is not viable due to cost and the lack of available sites; for example many cities or neighborhood associations are simply not very cooperative about opening up new tower locations. The problem facing operators is not coverage – which is now nearly universal – but capacity. There are just too many mobile users demanding too much data. This will only worsen due to the continuing adoption of tablets, laptops with cellular connections, and smart phones along with their data-hungry applications. Adding base stations has been by far the most important factor historically for increasing capacity. When base stations are added, each user competes with an ever-smaller number of users for a BS’s bandwidth and backhaul connection: it may even have one or more BSs to itself. This is the only scalable way to meet the current “capacity crunch”. Note that Wi-Fi access points typically meet the above three criteria and are thus also BSs by our definition. Wi-Fi is rapidly integrating with the cellular network and roaming between cellular and Wi-Fi will become increasing transparent to end users. Smart phones and tablets have sophisticated user interfaces and high definition screens, expensive rechargeable batteries, substantial consumer software, and support multiple wireless standards. In short, there is no inescapable reason a BS needs to be more expensive than the phones they serve, once they have lower transmit power (the power amplifier cost is considerably higher in BSs, typically). Indeed, as of late 2012, an iPhone costs about 10x more than a typical Wi-Fi access point. Such trends will soon extend to femto cells and then Pico cells, in a dramatic reversal from a decade ago, when BSs cost about 1000x compared to the mobile devices they served (Jeffrey, 2012). A base transceiver station (BTS) is said to be available if it is in the ON state as a part of the operational policy and has enough energy to serve at least one user, i.e., has at least one unit of energy. The probability that a BS of tier k is available is denoted by k, which may be different for different tiers of BSs due to the differences in the capabilities of the energy harvesting modules and the load served (Dhillon et al, 2013).
Most companies rely on QOS-oriented data networks such that any form of interrupt can cause considerable economic losses (Mathias, 2004). As networks grow bigger and more complex, the factors influencing the network availability increased. Thus, many of these companies have invested in securing their networks with redundancy and quality of service as well as demanding high network availability from their network operator. For the network operator measuring and quantifying the network availability has become an important issue, not only to attract customers, but also as an indicator of the variation of quality in the network helping to organize maintenance and expansion of the network (Mathias, 2004). Service availability has become one of the most important aspects of service delivery in the highly competitive e business economy (Jihong, 2008). Moreover, service availability has dramatic impact on customer satisfaction and corporate reputation as customers are just a mouse click away from competitor’s offerings (Fisher, 2000). A lot of effort and improvement have been made to ensure high availability in each technology industry (Potegieter et al, 2005).Today most network operators include availability guarantees to some extent in their service level agreement (SLA) (Mathias, 2004).

1.2: Statement of the Problem
Network outages and service degradations are a fact of life in operating a mobile network. With the total number of mobile connections now exceeding the world's human population, this is hardly surprising. Sometimes, these incidents make it into the public domain. When operators suffer significant outages that impact a large number of subscribers, this information makes its way into the media, in no small part due to the rise of social media. When a network or service is down or delivering poor performance, many of today's consumers will turn to social networking sites to share their experiences and vent their frustration. And of course, there are also many smaller-scale outages and service degradations that impact fewer subscribers, or impact them less dramatically, with the result that they never make it in to the public domain (Patrick, 2016).
Equipment outages in cellular access networks result in degradation or complete service interruption. Such occurrences cause operator revenue losses and users dissatisfaction (Rao, 2006). After price and network coverage, outages are now considered the third most significant factor influencing subscribers churn (Patrick, 2016), what additionally contributes to the revenue losses. Mostly, mobile operator technical staff detects outages in real time, through reception of auto-mated failure logs sent to the Operations and Maintenance Center (OMC) (Kyriazakos, 2004). The Preprint submitted to Computer Communications January 19, 2016 importance of cell outages has been recognized by the 3rd Generation Partnership Project (3GPP). The 3GPP ongoing work in developing Technical Specification (TS) 32.541(Release 12), addresses mitigation of cell outages through automated self-healing process which is part of the next generation Self-Organizing Network (SON) concept (Markopoulou, 2008). Hence, the analysis presented in this paper can be useful for future development of cell outage detection and compensation algorithms which are currently topic of great interest (Li, et al, 2011). The Short-Time Cell Outages (STCO) phenomena affecting Base Stations (BSs) in a mobile cellular operator network as a short-time outage of all or some BS cells (sectors) that lasts up to 30 min in a day, thus still guaranteeing more than 98% of operation. It is a type of outage which cannot be detected directly through an operator network monitoring system. Although a complete characterization of STCOs has never been reported in the literature, such events are affecting the cellular network of every mobile operator. In particular, a statistical analysis of STCOs based on BSs measurements of a complete operator mobile network has been performed. The results shows that: (i) STCOs impact everyday life of an operator network, high load of cells corresponds to an increase in the number of STCOs and their duration, (iii) the impact of STCOs to single sectors and whole BSs is not negligible, (iv) most of STCOs are recorded in urban areas compared to rural ones, (v) the impact of STCOs on users is higher in rural areas compared to urban ones, and (vi) the STCOs are correlated with the transferred traffic rather than the outside air temperature. (Josip et al, 2016).
The research work described above have several limitations as described in the following:

(a)    The collection and measurement of data are generally very tedious and costly because large volume of data needs to be gathered and specialized expensive software also involved.

(b)   Monitoring a life network for the purpose of gathering data for this research work can be very demanding, it involves 24/7 monitoring of all the BTSs of the region under review with a software (M2000 mobile management system).

1.3. Justification of the Study
Network availability has become one of the most important aspects of service delivery in the highly competitive e business economy (Jihong, 2008). In recent years, a lot of effort and improvement have been made to ensure high availability in each technology industry (Potegieter et al., 2005).Today most network operators include availability guarantees to some extent in their service level agreement (SLA) (Mattias, 2004). However, the definition of network availability and the methods of collecting data vary, as there is no standard or praxis commonly used by the network operators (Mattias, 2004). To bridge this gap, it has become very important to have a well-defined process for defining and measuring network availability, because having a well-defined process serves the following purposes:

(a)          Maintaining customers service-level agreements
(b)         Attracting new customers
(c)          Providing statistics for the Network Operations division

With this background already established. The studies will be premeditated on the following questions identified:
(a)                How is Network Availability defined?
(b)               How can Network Availability be measured?
(c)                Why should Network Availability be measured?
(d)               What standards of Network Availability exist?
(d)               Are there any recommended values for Network Availability parameters?
(e)                How can Network Availability measurement be applied to radio access networks in Nigeria? In this work, we focus on a process that specifically define and measure access to network availability.

1.4. Objectives of the study
The overall aim of the study is to determine the performance of real time monitoring and network availability of a radio access signal.
The Specific Objectives of the Study are to:

(a).    investigate existing methods for measuring network availability of a Radio access network (RAN)
(b).   develop a method that can be applied by service provider as a quality measure for network operations in Nigeria.
(c).     determine causes of interruption, disturbances and frequent fault in the network.

1.5. Research Methods
The various methods that were adopted in the realization of this study are:-
(a)        Measurement of availability data with software called M2000 mobile management system. This software gives the performance statistics for the network. Its graphic user interface provides an overview of the network elements and the links connecting them. In this research work, a particular network provider was put into consideration i.e. (MTN network provider)
Below are the outlined steps for data collection.

i)              Monitor the alarms on the BTS every second to know when a site is down using the M2000 topology window
(i)           Escalate sites down to Field support engineer (FSE) and log a trouble tickets for the site. (The trouble ticket track the life cycle of the incident with basic information like the start time /date of the outage)
(ii)         Update the ticket and follow up with field support engineer (FSE) and power engineer assigned to the site, until the site is up and running again.
(iii)       Get a detailed root cause analysis of the fault and close the trouble ticket with the end time.
(iv)       Pull the files which are in CSV ASCII text file format (i.e. the fields are comma-delimited). The data will be analysed to know what information can be utilized for the measurement of availability.
(v)         The data will be measure for one year; however, this period will be taken in order to have a clear picture of the variation of the availability data and in order to have a clear analysis of the data measured.
(vi)       Estimation and analysis of the data collected will be carried out to know the start time, end time and the assigned time of the outage. These are necessary parameters for calculating

(viii)   Using Availability formula, defined as the percentage of time when a system is operational. This can be calculated using equation (1.1).In section 2 page 10.

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