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Posts

GSoC ‘23: Final Report on Joint Power and Rate Control in Userspace

less than 1 minute read

Published: September 25, 2023

The final GSoC ‘23 report presents the completed joint power and rate controller, inspired by Minstrel-Blues. The controller selects rates using a utility function balancing throughput and interference cost, while dynamically adjusting reference and sample power levels based on success probability thresholds. Experiments on TP-Link WDR4900 routers (ATH9K, UDP via iperf3) and a MacBook Pro station (TCP via Flent) showed consistent 10-25% throughput improvements over kernel Minstrel-HT — even in single-link setups — attributed to better aggregation decisions and dynamic power selection. Future work targets multi-AP spatial reuse experiments and an independent power controller for newer closed-source WiFi chips.

GSoC ‘23: Midterm Report on Joint Power and Rate Control in Userspace

less than 1 minute read

Published: July 26, 2023

The midterm report covers two workstreams. First, a passive variant of py-minstrel-ht was developed to validate that user space rate selection matches the kernel Minstrel-HT — running alongside the kernel algorithm and comparing MRR chain decisions. Multiple bugs were identified and fixed, bringing error rates below 0.5% across all MRR positions. Second, the proposed joint power and rate controller is introduced: it uses a utility function that trades off throughput against interference cost to select rates, and tracks safe and optimal power levels per rate through cyclic power sampling.

GSoC ‘23: Joint Power and Rate Control in Userspace for Freifunk OpenWrt Mesh & Access Networks

less than 1 minute read

Published: May 28, 2023

This post introduces the GSoC ‘23 project on joint power and rate control in user space. While rate control algorithms like Minstrel-HT optimize transmission rates, they typically use a fixed high power level that causes unnecessary interference in dense networks. The project extends the existing py-minstrel-ht package with a power tuning module that finds the lowest power level still delivering peak throughput. Three modes are planned: fixed power, power ceiling, and maximum throughput. The post provides background on the WPCA API that enables joint rate and power setting from user space.

Final Report on Minstrel TX Rate Control in User space – GSoC ‘22

less than 1 minute read

Published: September 12, 2022

The final GSoC ‘22 report details all contributions to the user space Minstrel HT. New kernel functions were ported including dynamic retransmission calculation, sudden-death detection, and spatial stream reduction. The codebase was restructured into separate minstrel and sample modules, significantly reducing computation time. A rate-setting experiment framework was built to validate rate control behavior on real hardware. Additionally, a user space implementation of the Sample Rate algorithm (Bicket, 2005) was developed as a standalone Python package. All initial goals were met and exceeded.

Update on Minstrel TX Rate Control in User space – GSoC ‘22

less than 1 minute read

Published: July 20, 2022

The midterm report covers three main additions to the user space Minstrel HT. First, two new estimators were implemented: the Butterworth filter (now used by the kernel) and an Exponentially Discounted Averaging filter for research. Second, the output was redesigned to match the kernel debug format, producing human-readable rate statistics tables and CSV files for offline analysis. Third, a configuration module was added to let users tune filter parameters and rate control properties. The post concludes with a first analysis comparing the three filters on real WiFi hardware.

Minstrel TX Rate Control in User space – GSoC ‘22

less than 1 minute read

Published: June 13, 2022

This introductory post for GSoC ‘22 covers the motivation and plan for implementing Minstrel HT — Linux’s default WiFi rate control algorithm — in user space for OpenWRT access points. Development in kernel space is restricted (no floating-point, high crash risk, limited debugging), making a Python-based user space variant valuable for research and experimentation. The post introduces the WiFi-Manager package and outlines the planned deliverables: a fully functional user space Minstrel HT with documentation, demo scripts, and performance comparisons against the kernel variant.

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publications

Multi-Armed Bandits in IEEE 802.11ac: Efficient Algorithms and Testbed Experiments

Published in IEEE International Workshop Technical Committee on Communications Quality and Reliability (CQR), Seattle, WA, USA, 2024

Evaluates multi-armed bandit algorithms for rate adaptation in IEEE 802.11ac networks through real testbed experiments.

Recommended citation: Martin Le, Bile Peng, Sankalp Prakash Pawar, Peter Auer, Prashiddha Dhoj Thapa, et al. "Multi-Armed Bandits in IEEE 802.11ac: Efficient Algorithms and Testbed Experiments." IEEE CQR 2024.

Towards Infrastructure-assisted WiFi Rate Adaptation for Converged Networks with Morpheus

Published in 17th ACM Workshop on Mobility in the Evolving Internet Architecture (MobiArch), Washington, D.C., USA, 2024

Proposes Morpheus, an infrastructure-assisted WiFi rate adaptation approach for converged WiFi and cellular networks.

Recommended citation: Prashiddha Dhoj Thapa, Arne Kappen, and Julius Schulz-Zander. "Towards Infrastructure-assisted WiFi Rate Adaptation for Converged Networks with Morpheus." Proceedings of MobiArch 2024.

ORCA - Open-source Resource Control API for real WiFi networks

Published in 18th ACM Workshop on Wireless Network Testbeds, Experimental evaluation & Characterization (WiNTECH), Washington, D.C., USA — Proceedings of ACM MobiCom 2024, 2024

Presents ORCA, an open-source API for resource control in real WiFi networks, demonstrated on a 40+ AP testbed.

Recommended citation: Sankalp Prakash Pawar, Prashiddha Dhoj Thapa, Jonas Jelonek et al. "ORCA - Open-source Resource Control API for real WiFi networks." Proceedings of WiNTECH 2024 (ACM MobiCom).

Interface on demand: Towards AI native Control interfaces for 6G

Published in Workshop on Next-Gen Networks through LLMs, Action Models, and Multi-Agent Systems (IEEE ICC 2025), Montreal, Canada, 2025

Explores AI-native control interfaces for 6G networks using LLMs, action models, and multi-agent systems.

Recommended citation: Abhishek Dandekar, Prashiddha Dhoj Thapa, Ashrafur Rahman, and Julius Schulz-Zander. "Interface on demand: Towards AI native Control interfaces for 6G." Proceedings of the IEEE ICC 2025.