Author: Raul Shahi

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.

Conclusion

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. 

1. Introduction

Hi everyone. I am Raul Shahi. I am currently pursuing a Master’s degree in Computer Engineering for IoT Systems from Nordhausen University of Applied Sciences, Nordhausen, Germany. For the past couple of months, I have undertaken the position of Research Assistant at my university, which involves researching and developing open-source software for IEEE 802.11 resource allocation.

For GSoC 2024, I will work on a novel Power Control algorithm to reduce interference and increase aggregate network throughput. This introductory blog post intends to summarize the necessary background to understand the details of the project.

2. Leveraging TPC performance for enhanced WiFi Performance

Regarding WiFi networks, ensuring everything runs smoothly is all about how we manage the available resources. As the concept of WiFi resource allocation has been covered by Prashiddha during his GSoC projects before, I would suggest the readers refer to his blog posts [3][4]. For IEEE 802.11 devices, deep diving into the system internals is required which can be overwhelming for developers as they would have to be familiar with kernel subsystems and Ansi C. This technical barrier makes it tough to adapt and innovate with new algorithms.

On top of that, many of the latest WiFi chips are becoming closed-source which means the open-source community can have a harder time developing new and advanced algorithms. However, chips like Mediatek, still share throughput information which can be utilized for the optimization of WiFi networks.

Active Transmit Power Control (TPC) is an exciting development area. By adjusting the transmit power for different devices, TPC algorithm extensions like Minstrel-Blues which is built on top of the Minstrel rate control algorithm are promising. They can reduce interference, boost overall network throughput, and improve how the network handles multiple devices in crowded spaces.

3. Motivation

To solve the challenges in WiFi resource allocation, the SupraCoNex project introduced the user-space WiFi Parameter Control API and an interface called Python-WiFi-Manger to enable the development of algorithms using Python. Developers can use these tools to create and test their algorithms on real WiFi chips. On top of the API, a remote socket extension has been realized to facilitate the remote management of access points. The API allows users to monitor transmission data and adjust rates and power on access points without technical knowledge of the kernel. 

I propose developing a new transmit power control algorithm using the WiFi-Manager for IEEE 802.11 ac chips, based on throughput estimates. This approach could potentially extend to Mediatek ax chips, paving the way for smarter, more efficient WiFi networks. However, the approach can be easily extended to any chips that expose the estimated throughput information and provide an interface to fine-tune power settings in real-time.

4. Methodology

The WiFi-Manager package in Python allows the user to monitor each transmitted frame while leveraging the options to control the rate and power for each connected access point. The first task will be to create a new Python package for the power controller and integrate it with WiFi-Manager. Since WiFi-Manager does not yet include throughput information, we will either add it to WiFi-Manager for broader use or directly into the power controller.

The power controller will track throughput at different power levels and average these observations using techniques like the Butterworth filter on EWMA. This can be the basis for determining the optimal transmission power. If throughput suddenly drops, the controller will have a fallback strategy, such as increasing power aggressively to restore the link quickly.

Like the Minstrel-HT [1] and Minstrel-Blues [2] algorithms, our power controller will have update and sample intervals. At the end of each update interval, it will adjust the power level based on recent observations. During sample intervals, it will explore different power levels to find the best settings.

The existing solutions from the Experimentation Python package from SupraConeX can be used to compare the proposed approach, evaluating performance based on rate selection, aggregation, throughput, and power levels.

5. Deliverables of The Project

The end goals of my GSoC 2024 project are as follows:

a. Implementation of a dynamic power control algorithm to adjust power levels
based on throughput information with the ready-to-run demo.

b. A detailed report documenting the research methodology, experimental setup,
results, analysis, and conclusions drawn from the study.

c. A study conducted using the Experimentation Python package that compares
the performance of the proposed approach with the existing solutions on
productive Freifunk Mesh and Access deployments.

6. What has been done until now?

a. Developed a foundational understanding of the importance of resource allocation in wireless transmission.

b. Familiarization with the ‘WiFi-Manager’ package as well as other packages from ‘SupraConeX’.

c. Remote access point setup for monitoring of data transmission and power, rate control.

7. What’s next?

a. Create a Python package for the power controller.

b. Preliminary performance comparison and evaluation of the developed algorithm.

8. Conclusion

I am incredibly grateful to be part of GSoC 2024 as we embark on this exciting journey. Developing and implementing a new power control algorithm with WiFi-Manager is just the beginning, and I am thrilled to contribute to innovations that could significantly improve WiFi performance. This project promises to push the boundaries of what’s possible in wireless communication, and I look forward to sharing our progress and successes with you. 

Please feel free to reach out and connect with me.

References

[1] Minstrel-HT Source Code in the Linux Kernel 

[2] A Measurement-Based Joint Power and Rate Controller for IEEE 802.11 Networks

[3] Minstrel TX Rate Control in User Space, Prashiddha, GSoC ‘22

[4] GSoC ‘22 and ‘23 blog posts from Prashiddha