Short name: SMART
Long name: Self-adaptive Machine Learning Approach for Real-time Tuning of IEEE 802.11 PHY and MAC layers
Company: INESC TEC
Call: F4Fp-08 (see call details)
Proposal number: F4Fp-08-M05
SUMMARY REMARKS & TESTBEDS
The worldwide demand for wireless access networks providing very high throughputs has been increasing exponentially, namely due to bandwidth-hungry applications such as high definition video streaming and augmented reality. In order to fulfil these requirements, the Wi-Fi standard was enriched and new parameters have been proposed for both physical (PHY) and media access control (MAC) layers, including channel bonding, a short guard interval (SGI), and advanced modulation and coding schemes (MCS). However, the high variability of the signal strength in the wireless radio channel, allied to the channel asymmetry, turns the selection of optimal configurations for these parameters a challenge. For runtime optimization, some algorithms have already been proposed. Still, they were designed considering legacy IEEE 802.11 releases, static scenarios, and usually a single parameter. Besides that, these parameters have their trade-offs that need to be properly managed. To help dealing with this, machine learning has been recently introduced in wireless networks, providing the intelligence that networks need in order to be smart and self-adaptive.