M. Yusuf Fadila
Digital Telecommunication Network Program, PoliteknikNegeri Malang
Jalan Soekarno Hatta No. 9, Jatimulyo, Malang City, East Java 65141.
Abstract: This paper investigates the key determinants affecting the availability of wireless communication systems, a crucial parameter in ensuring system reliability and continuous service delivery. Using a literature review methodology, the study explores both internal and external factors that influence availability. Internally, elements such as network topology design, hardware/software reliability (measured via MTBF and MTTR), and collaboration between service providers significantly impact system performance. Externally, environmental conditions, geographical challenges, socioeconomic disparities, and policy frameworks are found to play major roles. Additionally, the issue of unbalanced base station availability due to poor radio port selection is addressed. The study emphasizes that while internal factors can be optimized through technical improvements and cooperation, external factors require strategic planning and preventive actions. The findings serve as a foundation for designing more resilient and efficient wireless communication networks.
Keywords: Wireless communication, system availability, MTBF, MTTR, network topology, signal interference, socioeconomic factors, environmental conditions, unbalanced availability, broadband infrastructure.
INTRODUCTION
Wireless communication systems have revolutionized communication connectivity, enabling efficient communication in various domains, including telecommunications and the Internet. They support remote control systems and are crucial to modern society, driving innovation and transforming individual interactions in an increasingly interconnected world (Dewangan & Singh, 2024). One important parameter in assessing the reliability of communication systems is availability. High availability indicates that the system is capable of operating with minimal disruption. However, various factors can affect availability, ranging from signal interference, hardware errors, extreme weather conditions, to maintenance management. Therefore, it is necessary to conduct a literature study to understand the main factors that affect the availability of wireless communication systems in order to improve network performance and efficiency in the future.
According to Roger L. Freeman, In modern communication systems, availability is an important indicator in assessing how reliable a system, network, or terminal is in performing its operational functions consistently. Availibility is defined as the average long-term time in which a system is in an adequate service condition to perform the tasks for which it has been designed. In general, this availability is expressed in decimal or percentage form, for example 99.9%.
Technically, device availability can be calculated using the parameters Mean Time Between Failure (MTBF) and Mean Time To Repair (MTTR), with the formula:
\[ A = \frac{\text{MTBF}}{\text{MTBF} + \text{MTTR}} \].
However, this approach only covers hardware failures and their recovery times, not taking into account interference such as signal dimming (fading) that is common in wireless communications, particularly in line-of-sight microwave links.
For larger coverage, system availability can also be described as:
\[ A = \frac{\text{uptime}}{\text{uptime} + \text{downtime}} \]
The above formula reflects the ratio of the time the system is functional versus the total operating and interruption time. For example, if the system has 10,000 hours of uptime and 10 hours of downtime, then the availability rate is 99.9%.
In contrast, unavailability reflects the condition when the system fails to provide optimal service, for example, due to network interference or degraded bit error rate (BER) performance for at least 10 seconds. The value of unavailability is derived from availability, i.e:
U = 1 - A.
In practice, both one-way (e.g. from west to east) and two-way (two-way communication) availability have strict targets. To illustrate, a two-way communication system with an unavailability target of 0.02% requires each direction to have an unavailability rate of only about 0.01% which is equivalent to about 53 minutes of interruption per year for one-way transmission. Therefore, an in-depth understanding of the factors that affect the availability of wireless communication systems is very important, especially in designing reliable and efficient networks.
METHODS
This research uses the library research method, which is an approach carried out by examining, identifying, and analyzing relevant scientific literature or references to gain an in-depth understanding of the factors that affect the availability of wireless communication.
This method is descriptive qualitative in nature, where the data collected comes from various written sources such as scientific journals, textbooks, international standards (such as Institute of Electrical and Electronics Engineer sIEEE documents), as well as technical reports from institutions or telecommunication device manufacturers.
RESULTS AND DISCUSSION
Internal and External Factors Affecting the Availability of Wireless Telecommunications
The availability of wireless telecommunications is influenced by various internal and external factors. Internally, the mean time to failure (MTBF) and mean repair time (MTTR) of links, network topology, cooperation between operators, optimization of hardware and software (Zou et al., 2007). Externally, geographical and socio-economic factors, as well as environmental conditions, have a significant impact on availability. Understanding these factors is important to improve service quality and ensure optimized access and high availability.
Internal Factors Affecting Wireless Telecommunications Availability
In terms of internal factors, the design and redundancy of the network topology plays a very important role in determining the level of service availability. Higher redundancy, realized through the addition of links connecting network switches or other alternate paths, inherently increases network availability as it provides a backup path in the event of a failure in the primary component. However, this increased redundancy has a direct consequence on increased investment costs, both for the purchase of additional hardware and for its implementation and maintenance. In simple network topologies, availability calculations and computations are still relatively easy to perform analytically. This allows network operators to accurately predict availability without the need for complex tools, resulting in lower computational resources and costs.
The challenge arises when dealing with more complex network topologies with a large number of nodes and links. In these scenarios, it is difficult to derive analytical expressions for network availability. This complexity not only makes it difficult to determine the exact availability calculation, but also makes it difficult to reduce the cost and resources required for analysis and design optimization. To overcome these limitations, specialized tools such as the Network Availability Simulator (NAS) have been developed. NAS is an event-driven simulator designed to address the complexity of massive networks. It works by randomly generating failure and repair events based on given link availability parameters. Furthermore, NAS will check the network connectivity after each event to determine its availability. By performing a large number of simulations (or more), NAS can provide accurate and statistically valid estimates of network availability. This approach allows operators to optimize topology design and manage costs more effectively, even for very large and dense networks with more than 100 nodes.
In addition, the collaboration factor of network providers is also important in the context of internal availability factors, the effect of optimized collaboration between telecommunications service providers has a significant influence on the context of internal availability on the efficiency of wireless communications. An optimized and efficient multihoming handover facilitated by frameworks such as 802.21 IMH, can improve access by allowing users to connect over the best available network. this leads to a maximum probability of availability efficiency. This will also have a positive impact on the network provider.
Hardware and software reliability, on the other hand, play a crucial role in the context of internal availability factors. The availability of network elements is directly determined by two key metrics: mean time to failure (MTBF) and mean time to repair (MTTR). MTBF refers to the expected mean time between two consecutive failures of a system or component. The higher the MTBF value, the longer a network element can operate without failing, which directly contributes to increased availability. In contrast, MTTR is the average time required to repair a network element after a failure and return it to full operational condition. A low MTTR value is critical as it minimizes the duration of down time and, therefore, increases overall service availability. Ensuring high reliability of these components, both hardware and software, deserves serious attention to maintain optimal service availability. In other words, investing in high-quality components that have high MTBF and implementing efficient recovery procedures to achieve low MTTR are fundamental strategies to achieve the desired level of network availability and fulfill service level agreements (SLAs) with consumers.
External Factors Affecting Wireless Telecommunications Availability
Geophysical and environmental conditions play an essential role in affecting network availability, especially in dynamic configurations such as laser communication (lasercom) systems. Geographical features and weather conditions can significantly change the configuration of these networks, which in turn has a direct impact on the level of availability. For example, in lasercom systems, optical paths can be affected by atmospheric factors such as fog, rain, or air turbulence, which can substantially reduce signal quality or even cause connection drops. This underscores that availability depends not only on the reliability of internal hardware and software components, but also on the complex interaction between the network infrastructure and its physical environment.
Furthermore, geophysical variables also affect broadband deployment costs. For example, difficult terrain, such as mountains or swampy areas, require more complex construction techniques and stronger materials, increasing the cost of installing cable or fiber optic infrastructure. Similarly, areas with a high risk of natural disasters (e.g., earthquakes, floods) may require more resilient and expensive network designs to ensure resilience and continuous service availability. Therefore, in network design and implementation, careful consideration of geophysical and environmental conditions is imperative to accurately estimate costs, identify potential vulnerabilities, and design robust solutions to ensure optimal service availability in the long term (Flamm, 2006).
In the context of social economy, broadband availability is not only determined by technical or geographical factors, but also significantly influenced by socioeconomic variables. One of the main aspects is the income and wealth of a region or population. In areas with higher levels of income and wealth, there tends to be greater demand for high-quality broadband services. This demand attracts service providers to invest in broadband infrastructure in these areas, due to the potential return on investment. In other words, local economic prosperity creates incentives for service providers to expand coverage and improve the quality of broadband services. In contrast, in low-income areas, this incentive is reduced, which often leads to underinvestment and, ultimately, gaps in broadband availability.
Market size also plays a crucial role. Large markets, which are typically characterized by high population density (e.g., in large cities), offer a broad base of potential customers. This allows service providers to achieve economies of scale, where the cost of building infrastructure per subscriber is lower. Thus, large markets are more attractive for service providers to undertake extensive broadband deployment, leading to higher availability. Conversely, in rural areas or small markets with dispersed populations, the cost per subscriber to build the same infrastructure can be very high, hindering broadband deployment and availability despite the need.
Another highly influential aspect is policy at the state or regional level. These policies have the power to significantly accelerate or hinder broadband deployment. Supportive policies, such as subsidizing infrastructure development in underserved areas, tax incentives for telecommunications companies investing in broadband, or simplifying the licensing process for network construction, can be strong catalysts for increased availability. These policies can reduce the financial risk for providers and speed up the implementation time. Conversely, less supportive policies, such as burdensome regulations, slow bureaucracy in issuing permits, or lack of financial support programs, can be a significant barrier, even in areas with market potential. Therefore, a proactive and supportive policy framework for broadband investment is essential to ensure broad and equitable availability.
Overall, an analysis of broadband availability would not be complete without considering the socioeconomic dimension. Factors such as consumers' economic capabilities, market size and density, and government policy interventions all interact to shape the landscape of broadband availability in different regions. An in-depth understanding of these interactions is essential for formulating effective strategies to address the digital divide and ensure more equitable broadband access (Flamm, 2006). Also noteworthy in wireless systems is that a significant challenge affecting overall system availability and capacity is the phenomenon of overlapping coverage between base stations and radio port selection mechanisms by user devices. Coverage overlap occurs when signals from multiple base stations can be received by user devices in an area. While this overlap may intuitively seem beneficial as it offers potential redundancy and connection options, it can lead to unbalanced availability issues.
Unbalanced availability arises when, despite multiple connection options, the device's selection of radio ports is not always optimal or efficient. For example, a simple port selection algorithm may only select the base station with the strongest signal without considering the capacity or traffic load of that station. As a result, some base stations may become overloaded while others remain underutilized, even though both are within range of the device.
This unbalanced availability condition directly affects the overall system capacity. When one base station is overloaded, the quality of service (grade of service) for users connected to it will degrade, characterized by delays, packet loss, or even disconnection. At the same time, the available capacity in underutilized base stations cannot be effectively used to balance the load, so the total capacity of the wireless system is not optimally achieved. This problem becomes more complex in dynamic environments where users move between cells or when channel fading conditions vary.
Therefore, the management of coverage overlap and optimization of radio port selection algorithms become critical in wireless systems to ensure balanced availability and efficient use of system capacity. More sophisticated approaches, such as capacity-based load balancing or adaptive cell selection algorithms, can help mitigate the impact of unbalanced availability and ultimately improve the quality of service and capacity that wireless networks can deliver.
CONCLUSIONS
Based on the above discussion, it can be concluded that internal influences can be optimized by the human resources involved or the parties involved in the wireless telecommunications system. The use of software and collaboration between the parties involved can minimize the decrease in the efficiency of network availability and also maximize the efficiency of network availability. Meanwhile, external factors are more difficult to control and have a wider range of variables, therefore preventive action is a more effective solution to optimize external factors in the context of availability. therefore it is important for related parties to calculate and plan carefully by taking into account internal and external factors to achieve the maximum level of availability.
LIST OF REFERENCES
Dewangan, B., & Singh, A. (2024). Wireless communication system (pp. 110–126).
Roger L. Freeman. Reference manual for telecommunications engineering, (1985)
Zou, W., Janic, M., Kooij, R. E., & Kuipers, F. A. (2007). On the availability of networks.
Flamm, K. (2006). Diagnosing the Disconnected: Where and Why is Broadband Access Unavailable in the U.S.? Social Science Research Network
Folstad, E. L., & Helvik, B. E. (2009). Managing availability in wireless inter domain access. International Conference on Ultra Modern Telecommunications, 1–6.
Mayer, R. C. (2006). Calculating Availability for a Time -Varying Multi -Path Network.
Freedman, A., Gil, A., & Giladi, R. (2002). The Impact of Unbalanced Availability on the Grade of Service of Wireless Systems. Wireless Personal Communications, 20(1), 21–40.
William, T., & Zayyad, M. A. (2023). A review of Factors Affecting Mobile Networks Deployment. African Muiltidisplinary Journal, 12(3), 90–100.
Rathnayake, U., Ott, M., & Seneviratne, A. (2009). A DBN approach for network availability prediction. Modeling Analysis and Simulation of Wireless and Mobile Systems, 181–187.
Verdugo, E., Luini, L., Riva, C., da Silva Mello, L., Resteghini, L., D’Acierno, A., Lombardi, R., Zheng, Y., & Nebuloni, R. (2024). Availability of Optical and mmWave Terrestrial Links at Low and Mid Latitudes. 1–6
Patani, R. H., Agrawal, A. K., & Konduri, S. (2012). Systems and methods of providing high availability of telecommunications systems and devices.
Luo, W., & Bodanese, E. (2011). A network resource availability model for IEEE802.11a/b-based WLAN carrying different service types. Eurasip Journal on Wireless Communications and Networking, 2011(1), 103.
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