text: To manage and maintain large-scale cellular networks, operators need to know which sectors underperform at any given time. For this purpose, they use the so-called hot spot score, which is the result of a combination of multiple network measurements and reflects the instantaneous overall performance of individual sectors. While operators have a good understanding of the current performance of a network and its overall trend, forecasting the performance of each sector over time is a challenging task, as it is affected by both regular and non-regular events, triggered by human behavior and hardware failures. In this paper, we study the spatio-temporal patterns of the hot spot score and uncover its regularities. Based on our observations, we then explore the possibility to use recent measurements' history to predict future hot spots. To this end, we consider tree-based machine learning models, and study their performance as a function of time, amount of past data, and prediction horizon. Our results indicate that, compared to the best baseline, tree-based models can deliver up to 14 better forecasts for regular hot spots and 153 better forecasts for non-regular hot spots. The latter brings strong evidence that, for moderate horizons, forecasts can be made even for sectors exhibiting isolated, non-regular behavior. Overall, our work provides insight into the dynamics of cellular sectors and their predictability. It also paves the way for more proactive network operations with greater forecasting horizons.
  @cite_14: Significant geospatial and temporal correlations, in terms of traffic volume and application access, exist in cellular network usage as shown in recent studies on cellular network measurement. Such geospatial and temporal correlation patterns provide local optimization opportunities to cellular network operators for handling the explosive growth in the traffic volume observed in recent years. To the best of our knowledge, in this paper, we provide the first fine-grained joint characterization of the geospatial and temporal dynamics of application usage in a 3G cellular data network. Our analysis is based on two simultaneously collected traces from the radio access network (containing location records) and the core network (containing traffic records) of a tier-1 cellular network in the United States. To better understand the application usage in our data, we first cluster cell locations based on their application distributions and then study the geospatial and temporal dynamics of application usage across different geographical regions. The results of our measurement study present cellular network operators with fine-grained insights that can be leveraged to tune network parameter settings for better network performance and user experience.
  @cite_20: With a proliferation of new mobile data services, the complexity of wireless mobile networks is rapidly growing. While large amount of operational monitoring data such as performance measurement statistics is available, it is a great challenge to correlate such data effectively for real time performance analysis. Meantime, the dynamics of mobile applications and environments introduce another dimension of complexity for us to track the evolving system status. In this paper, we analyze the spatial and temporal correlations of Key Performance Indicators (KPIs) to track and interpret the operational status of wide-area cellular systems. We first correlate large number of raw measurements into limited number of KPIs. Further we exploit spatial and temporal correlations of these KPIs for cellular network management. We use large volume of field data collected from real cellular systems in our analysis. Experimental results demonstrate that it is promising to build a real-time data management and support system by effectively correlating KPIs.
  @cite_12: 
  @cite_11: In this work, we study and quantify the effects of hotspots in wireless cellular networks. Hotspots are caused when the bandwidth resources available at some location in the network are not enough to sustain the needs of the users, which are then blocked or dropped. A deeper understanding of hotspots can help in conducting more realistic simulations and enable improved network design. We identify some causes for the formation of hotspots and based on them, categorize hotspots into three different types: a) capacity based, b) delay based, and c) preferential mobility based. We show how these types have different effects on network performance. We also consider the effects of hotspots from various perspectives such as the number of hotspots, the placement of hotspots, etc. We also develop a fluid flow model and an analytical model to study hotspots. The fluid flow model is surprisingly simple yet effective in helping us understand hotspots and their properties. We also describe an analytical model in which we consider a cell as an M M B B queue. We use these models to substantiate some of the observations from the simulations.
summary: A few further research papers have been written in an attempt to understand how load evolves in cellular networks. For instance, Paul et al. @cite_12 analyzed the spatio-temporal behavior of network resource usage in a real 3G network. Interestingly, their results indicate that aggregate network usage exhibits periodic behavior, but individual sectors do not exhibit such properties (however, in @cite_20 , Jiang et al. do find such correlations in KPIs). The usage of individual applications in cellular data networks has been studied by @cite_14 . Finally, simulation has been also used to quantify hot spots in wireless cellular networks @cite_11 . None of the previous works considers real performance hot spots nor provides any forecasting methodology.