Page 89 - AI for Good-Innovate for Impact

AI for Good-Innovate for Impact

occupancy, and is evaluated by buffer occupancy in the MAC layer. As the traffic increases,
CPU occupancy increases. Will that increase be linear? Well, that’s another research problem.
Understanding the CPU utilization enables us to assess Radio Unit (RU) occupancy levels and
allocate Physical Resource Blocks (PRBs) effectively. One example of this can be, suppose there 18 - HFCL
is gNB from where the data being sent to two UEs, UE1 and UE2, hence the total occupancy
level will be given by physical resource block required for the data transmission.

The study aims to leverage time-series data analysis techniques to optimize network resource
utilization, despite challenges posed by learning data and network utilization seasonality.
It seeks to determine if certain Radio Frequency (RF) chain links within 8T8R macro-Radio
units can be deactivated during low network demand without compromising service quality.
However, the time-series analysis of data presents challenges due to the presence of learning
data and network utilization seasonality. The dataset encompasses varying levels of network
activity over working days and weekends, necessitating a comprehensive analysis to discern
patterns and trends. Energy efficiency is highlighted, with an AI-based traffic prediction
system proposed to optimize power transmission, potentially increasing energy efficiency
to at least 50%. Key aspects include continuous CPU utilization monitoring, time-series data
analysis, understanding seasonal variations, developing optimization strategies, and evaluating
performance. Addressing these challenges aims to optimize network resource allocation,
enhancing the overall performance and reliability of 5G base station deployments.
To illustrate the importance of energy efficiency, consider the power requirements of an open
RAN-based MACRO RU. Each RF chain typically demands 46 dBm, equivalent to 40 Watts of
power. With 8 RF chains, the total power requirement reaches 320 Watts. However, this figure
only represents a fraction of the actual power consumption, as additional processing units like
antenna gain, power amplifiers, digital processing units, and beamforming logic significantly
contribute to the total power consumption. In a holistic view of the end-to-end solution, the
total power demand escalates to 1200 Watts.

This scenario yields an energy efficiency of merely 25%, indicating substantial wastage of
power. To address this inefficiency, leveraging an AI-based traffic prediction system becomes
imperative. Such a system can intelligently predict traffic patterns and optimize power
transmission accordingly, leading to a potential increase in energy efficiency to at least 50%.
This improvement underscores the critical role of AI-driven solutions in enhancing energy
efficiency and minimizing wastage in telecommunications infrastructure.

To address this problem, the following key aspects need to be considered:

1. CPU Utilization Monitoring: Continuous monitoring of base station CPU utilization to
assess operational workload and RU occupancy levels accurately.
2. Time-Series Data Analysis: Analyzing historical data to identify patterns and trends in
network utilization over working days and weekends.
3. Seasonal Variations: Understanding the impact of network utilization seasonality on
resource allocation decisions, considering fluctuations in demand throughout the week.
4. Optimization Strategies: Developing algorithms and models to determine the optimal
configuration of RF chain links based on observed network utilization patterns.
5. Performance Evaluation: Evaluating the effectiveness of optimization strategies in terms
of network efficiency, resource utilization, and service quality metrics.

73