## 5 5G FWA Field Trials
### Performance Evaluation of FWA System
#### 5.4.5 Network Quality Evaluation  
In network operation and maintenance centers, technology-centric metrics such as bandwidth, bit error rate, packet loss rate, jitter, throughput rate, transmission delay, and so on are usually used in terms of quality of service (\(QoS\)) to measure the network quality. However, it has been established that this approach did not give sufficient consideration to the actual experience (perceptions and feelings) of the user, making it insufficient for a thorough characterization of the service provided [352; 353]. This is due to its main focus on service quality improvement based on the application-level technical parameters without considering any type of human-related quality-influencing features. The \(QoS\) can be expressed as [354]  
\[QoS=\sum_{i=1}^{N}w_{i}\ f_{i}(x_{i}) \tag{8}\]  
where \(x_{i}\) denotes a network influencing factor, \(N\) represents the number of network influencing factors, \(w_{i}\) denotes their corresponding weight, and \(f_{i}(\cdot)\) is a mapping function. In addition, note that for given different users, the perceived quality level might be different for the same \(QoS\) level. Besides the system's technical features, other factors that are not captured by the \(QoS\) which have a considerable influence on the actual perceived user's experience are the context of use, delivering content, pricing of service, and human-specific characteristics [353]. The user-centric-based approach, quality of experience (\(QoE\)), offers a better means of user's quality perception measurement [354]. \(QoE\) signifies the perception of the end-user regarding the overall quality of a provided service, making it a very attractive alternative to the \(QoS\) method [353]. The relationship between both metrics can be defined using differential equation and non-linear equation, respectively, as [355; 356]  
\[\frac{\partial QoE}{\partial QoS} \sim-(QoE-\vartheta), \tag{9a}\] \[QoE =\sum_{i=0}^{N-1}a_{i}QoS_{i}^{k_{i}}, \tag{9b}\]  
where \(\vartheta\) denotes an estimated coefficient (i.e., for packet loss), \(a_{i}\) denote the constants, and \(k_{i}\) represent the exponents for \(N\) parameters. In addition, \(QoE\) can be integrated into network decision processes, resulting in \(QoE\)-_aware_ or \(QoE\)-_driven_ network. Furthermore, \(QoE\)-aware algorithms can significantly facilitate network function more efficiently. Therefore, the currently used criteria like Signal-to-Interference plus Noise Ratio (SINR) measurements can be replaced by \(QoE\). In this context, it can be a trigger factor of Radio Resource Management and Mobility Management mechanisms. Besides, when incorporated with SDN, video \(QoE\) metrics can be monitored to improve video streaming applications. Likewise, with \(QoE\) monitoring and control, _over-engineering_ could be prevented. For instance, it can help in recognizing when extra resource provision would not improve the perceived \(QoE\), resulting in better resource-efficient operation [353]. Furthermore, the \(QoE\) assessment can be based on both subjective and objective approaches. The quality evaluation in the former is typically accomplished by questionnaires and rating scales from the subscribers, making it not only a more direct but reliable technique of \(QoE\) evaluation. For instance, a subjective evaluation that is based on the Mean Opinion Score (MOS) can be used to quantify \(QoE\). As shown in Table 11, MOS represents the levels of experience that can be signified using a number in the range of 1 to 5. Nevertheless, it is costly, time-consuming, and inconvenient. Furthermore, the latter approach is based on predefined models for approximating estimation of the subjective approach without human intervention [354]. Moreover, note that the \(QoE\) evaluation model in which the features of a specific service type are exploited may not apply to other service types. Thus, there are concerted efforts on \(QoE\) algorithms that can address multiple service types.