# Towards Sustainable Deep Learning for Wireless Fingerprinting Localization

arXiv: https://arxiv.org/abs/2201.09071 · HTML: https://mlsanity.com/paper/2201.09071/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 20 references that are in the map.
- First posted on arXiv 22 Jan 2022.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [AdaBatch: Adaptive Batch Sizes for Training Deep Neural Networks](https://mlsanity.com/paper/1712.02029/) (2017)
- [CSI-based Positioning in Massive MIMO systems using Convolutional Neural Networks](https://mlsanity.com/paper/1911.11523/) (2019)
- [On Deep Learning-based Massive MIMO Indoor User Localization](https://mlsanity.com/paper/1804.04826/) (2018)
- [CSI-Based Localization with CNNs Exploiting Phase Information](https://mlsanity.com/paper/2101.08983/) (2021)
- [Towards Practical Indoor Positioning Based on Massive MIMO Systems](https://mlsanity.com/paper/1905.11858/) (2019)
- [Improving CSI-based Massive MIMO Indoor Positioning using Convolutional Neural Network](https://mlsanity.com/paper/2102.03130/) (2021)
- [MaMIMO CSI-based positioning using CNNs: Peeking inside the black box](https://mlsanity.com/paper/2003.04581/) (2020)

## Built on by (most cited first, 3 of 3)

- [The Energy Cost of Artificial Intelligence Lifecycle in Communication Networks](https://mlsanity.com/paper/2408.00540/) (2024)
- [Resource-aware Deep Learning for Wireless Fingerprinting Localization](https://mlsanity.com/paper/2211.01759/) (2022)
- [Energy Efficient Deep Multi-Label ON/OFF Classification of Low Frequency Metered Home Appliances](https://mlsanity.com/paper/2307.09244/) (2023)

## Abstract (arXiv)

Location based services, already popular with end users, are now inevitably becoming part of new wireless infrastructures and emerging business processes. The increasingly popular Deep Learning (DL) artificial intelligence methods perform very well in wireless fingerprinting localization based on extensive indoor radio measurement data. However, with the increasing complexity these methods become computationally very intensive and energy hungry, both for their training and subsequent operation. Considering only mobile users, estimated to exceed 7.4billion by the end of 2025, and assuming that the networks serving these users will need to perform only one localization per user per hour on average, the machine learning models used for the calculation would need to perform 65*10^12 predictions per year. Add to this equation tens of billions of other connected devices and applications that rely heavily on more frequent location updates, and it becomes apparent that localization will contribute significantly to carbon emissions unless more energy-efficient models are developed and used. This motivated our work on a new DL-based architecture for indoor localization that is more energy efficient compared to related state-of-the-art approaches while showing only marginal performance degradation. A detailed performance evaluation shows that the proposed model producesonly 58 % of the carbon footprint while maintaining 98.7 % of the overall performance compared to state of the art model external to our group. Additionally, we elaborate on a methodology to calculate the complexity of the DL model and thus the CO2 footprint during its training and operation.