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Network Infrastructure Magazine | Tuesday, September 07, 2021
Machine learning is already being utilized in telecommunications, particularly in the core network, and its importance is projected to increase shortly.
FREMONT, CA: Wireless networking offers numerous uses for big data. One is artificial intelligence for 5G network management, which is expected to play a critical role in 6G networks.
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The data sets pertaining to wireless communication range from low-level physical layer measurements to social network analysis. Due to the stochastic nature of wireless communication, data collecting has always been critical. For instance, channel sounding has a long history of validating theoretical channel models in real-world settings and establishing model parameters.
Increased use of signal processing techniques and advancements in machine learning has shifted attention to higher-layer analysis.
Data Sets for Wireless Communication
A. Completed one-off experiments
To ascertain the features of wireless networks, experiments with a set period have been conducted. They have been conducted in laboratories, places with features considered representative of a broad range of settings, and locations expected to be unusual. For instance, consider general urban regions and industry. Universities or government entities produce most data sets. Typically, data sets derived from one-time trials focus on bottom layer (PHY) data.
B. Ongoing attempts at data collection
Continuous data collection data sets typically focus on higher layer (or application) data. An approach is to do data collection using an open time window. These efforts generally are not based on specially installed gear but instead rely on data generated by the public. They give software tools that enable anyone interested in contributing data points to the data collection using their gear. Numerous developed countries have agencies dedicated to this, although individual efforts are also (commercial and non-commercial). A recent white paper defines, illustrates and discusses the issues associated with crowd-sourced measurements.
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Machine Learning Applications in Wireless Networks
Due to future heterogeneity and complexity, the traditional model-based approach to the construction and operation of wireless networks will become impractical, away from model parameterization, machine learning, and even continuous optimization for Self-Organizing Networks (SON). The data-driven design will complement the mathematical model-based design. This data-driven method allows network operators to use machine learning techniques in many network locations: Machine learning can be utilized for power control, spectrum management, backhaul, cache, and resource management.
Depending on the application, training data is either generated individually and parameterized by configuration or collected online. The former is used for lower-layer issues, whereas the latter is used for higher-layer issues.
Aside from applications that directly interact with the network, machine learning can be utilized for predictive analytics. Machine learning models can forecast network metrics based on the data's intrinsic structure (e.g., data rate, latency, or reliability). The quality of these forecasts relies on data density, time, and length fluctuation (primarily due to user behavior) (mainly caused by network topology and geography). Technically, this is an under sampling problem where the data set determines the under sampling rate. In general, a perfect Nyquist-Shannon reconstruction is impossible. Machine learning approaches may perform better in certain circumstances because they can implicitly use the underlying data structure.
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