Progress Update on GSoC 2024 Project: Throughput-based Dynamic WiFi Power Control for Real Networks

Hello everyone! With the first evaluation of GSoC 2024 approaching, I am sharing this blog post to give a comprehensive update on the progress of the Throughput-based dynamic Power Control Approach. Please check out my initial blog post for more information in case you need to become more familiar with WiFi power control. 

New Developments

1. Basic Python Package Creation

A basic Python package for the power control algorithm was successfully created. Let’s refer to this package as ‘power_controller’. The power controller includes a main class and exposes two primary functions: ‘configure’ and ‘run’.

The ‘configure’ function is designed to facilitate the setup of the Transmit Power Control (TPC). It allows for manual activation of the Transmit Power Control (TPC) mode. It initializes the modes of allowed power and parses the incoming control parameters. These parsed parameters are then returned to the ‘run’ function to execute the main logic.

The ‘run’ function implements the core logic of the power controller. This function is responsible for dynamically adjusting the transmission power at configurable intervals. It supports various power selections ensuring the power control algorithm can adapt to different network conditions in real time.

2. Integration with Python-WiFi-Manager

The power_controller has been connected with the existing Python-WiFi-Manager package from SupraCoNex. The WiFi-Manager package provides an interface to monitor each transmitted frame and control the transmission rate and power.

We utilize this interface by linking the power_control package and exposing the  ‘configure’ and ‘run’ functions for the WiFi-manager package for further power control. 

3. Development of Power Controller Logic

The ‘run’ command now includes a simple yet effective power controller logic. Key features of this logic are:

a) Configurable Update Interval: A customizable update interval has been added, allowing the system to adjust transmission power at defined intervals.

b) Dynamic Power Selection: At each update interval, a random supported power level is set as the transmission power which helps in dynamically exploring different power settings.

4. Power Setting Modes

Talking about dynamic power selection, various modes of power setting are introduced. These modes define how the transmission power is selected during each update interval:

  1. Highest: Always selects the highest supported power level.
  2. Lowest: Always selects the lowest supported power level.
  3. Random: Selects a random power level from the supported range.
  4. Round Robin: Cycles through the supported power levels in a round-robin manner.
  5. Fixed: Uses a predefined, fixed power level for all intervals.

5. Experimentation and Observation

A settings file was created to run the power controller with different power modes using the Experimentation Python package from SupraCoNex. The observations from the trace files were promising

  • Power Control Verification: It was confirmed that the power levels were being dynamically controlled as per the logic defined.
  • Rate Adaptation: The packet transmission rate was observed to change and adapt in response to the varying power levels, demonstrating the algorithm’s effectiveness in real-time conditions.

Next Steps

The next phase of the project will focus on advancement of the power_controller as well as data analysis and visualization to gain deeper insights into the performance of the developed power control algorithm. We will also advance the power_controller package.

Here is the outline:

1. Advancement of Power Controller

The major step would be extracting real-time throughput information for the power controller. Another step will be to implement the strategy to lower transmit power when the transmission is optimal. If in case transmission fails in this low power transmission, we stratetigize a method to fallback to higher transmission again.

2. Data Extraction and Plotting

  • Data Collection: Write a Python script to extract data on rate-time and power-time relationships from the trace files generated during the experimentation phase.
  • Visualization Setup: Utilization of visualization libraries such as ‘’matplotlib’ or ‘seaborn’ to create plots that can illustrate:
    1. Rate vs Power Selection Over Time: Plot how the selected transmission rate varies with different power levels over time.
    2. Throughput Analysis: Visualize throughput variations based on the chosen power settings and rate adaptations.
    3. Frame Aggregation: Analyze frame aggregation patterns and efficiencies under different power control strategies.

3. Analysis and Insights

Graphical Analysis: Interpret the plotted graphs to derive insights into:

a. Optimal Power Settings: Identify trends where certain power levels consistently lead to higher transmission rates and better throughput.

b. Impact of Rate Adaption: Understand how dynamic rate adjustments influence overall network performance.

c. Efficiency of Frame Aggregation: Evaluate the effectiveness of frame aggregation techniques in optimizing data transmission.

Based on this analysis, we can further iterate the power control algorithm to fine-tune parameters and enhance performance.


With the successful creation of the Python package, integration with SupraCoNex tools, and promising initial experiments, the second half of the coding period will be focused on power control in a real changing environment through data analysis and visualization. Please feel free to reach out to me if you would like to know more about the ongoing project. Thank you for reading. 

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