# A Modular Web-Based Software Solution for Mobile Networks Planning, Operation and Optimization
## 2 Related Work  
Software solutions for wireless networks OAM are largely dominated by industry suppliers with proprietary solutions. While some solutions as Atoll allows the optimization of the network as well as its design, by providing access to both live network data and predictions based data [8], others tend to focus on the network planning stage as ASSERT which is a radio-planning tool that provides Radio Frequency (RF) coverage, capacity and neighbor planning for cellular networks [9]. Another example is provided by China Unicom, a large MNO, which proposed a platform particularly suited to handle large volumes of network traffic records [10]. However, such solutions, and others, tend to be aligned with the prospected evolution of wireless networks operations, in particular with the demand for network automation as a mean to increase network efficiency and reduce its cost. In [11], the authors discuss several principles to pursue network automation as developing open solutions or platforms, disrupting data and operational silos, and as using rule-based and Machine Learning (ML) algorithms for RAN operation.  
Open solutions or platforms that facilitate the gathering and abstraction of data from multiple sources and vendors break data silos, promote a consolidated access to information and empower the development of automated used cases. In [12], [PERSON] et al. developed a platform for mobile wireless networks, hinged on the open principle, by proposing a multi-layer architecture from data ingestion to the network application layer. The proposed solution includes access to multiple data sources but does not include the visualization of georeferenced data in maps, which is paramount for MNOs. [PERSON] et al. also presented the trend of multi-layer and modular architectures [13]. However, the proposed solution encompasses only one area of interest of MNOs, in this case, Base Station (BS) energy efficiency. [PERSON] et al. proposed a customized tool to solve the monitoring needs of CEM for MNOs [14]. Even though the proposed tool allows near real-time operation, it is highly focused on the network QoE analysis, not including other network performance areas as QoS. Also, it does not explore the network optimization stage of the network life cycle, as it is highly focused on the monitoring stage. A similar work was also developed by [PERSON] et al. in [1].  
Not surprisingly, monitoring networks nowadays implies at some point the need of processing and analysing large sets of data. Therefore, there is a demand in this field to incorporate big data technologies for that matter. We stress a work by [PERSON] and [PERSON], see [15], who proposed a monitoring framework based on big data technologies to manage mobile network infrastructures and services. The main goal was to improve QoS and network performance where big data technologies were used to process real time data sources such as BS measurements, user demands/claims, and other sensors. In [16], [PERSON] and [PERSON] discuss issues related to deployment of big data storage and analysis techniques in existing mobile network architectures.  
Concerning algorithms applied to RAN, the use of ML-based techniques continues to be a hot research topic. In [17], [PERSON] and [PERSON] produced an extensive overview on the use of ML for RAN automation, with objective application scenarios as predictive maintenance and capacity optimization. [PERSON] et al. developed a big data analytics platform for anomaly detection and root cause analysis in mobile networks, validating the proposed algorithms using real network data [18]. With respect to network performance optimization in RAN, [PERSON] et al. developed an optimal small cell placement algorithm, based on unsupervised learning, to optimize user coverage based on mobility patterns [19], while in [20], [PERSON] et al. presented a numerical optimization of coverage probability and user throughput using a novel user cell-association strategy.