mmWave Beam Selection via AoA and SINR for Spatial Reuse
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Solution Overview
Problem
Millimeter-wave (mmWave) Wi-Fi communications face challenges due to higher free space path loss and inter-link interference caused by directional beam side-lobes, which limit spatial reuse and network throughput in dense deployments.
Innovation Solution
A network device identifies mmWave propagation paths and determines the estimated angle of arrival (AoA) for each path using power delay profiles from beam training frames, then selects a beam that maximizes the signal-to-interference and noise ratio (SINR) to mitigate interference and enhance spatial reuse.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If directional beams are used for mmWave communications, then data rates and channel bandwidths are improved, but inter-link interference from side-lobes increases
Solution Approach 1:
The patent applies local quality by differentiating between main-lobe and side-lobe regions. The network device determines spatial reuse parameters based on the angular position of neighboring devices relative to the main beam direction. Devices in the main-lobe region experience different interference characteristics compared to those in side-lobe regions, allowing for localized optimization of transmission parameters to manage interference while maintaining high data rates.
Solution Approach 2:
The patent implements dynamics by making beam selection and spatial reuse parameters adaptive rather than static. The network device dynamically determines spatial reuse parameters based on real-time measurements of beam training frames and power delay profiles, adjusting transmission characteristics according to the specific spatial arrangement and interference conditions of neighboring devices during operation.
2Productivity
If spatial reuse is increased in mmWave networks, then network throughput is improved, but interference management complexity increases
Solution Approach 1:
The patent applies preliminary action by performing beam training frame exchanges and power delay profile measurements before actual data transmission. The network device proactively determines spatial reuse parameters and identifies interference characteristics in advance, allowing for pre-computed beam selection and transmission parameter optimization that reduces the complexity of real-time interference management during active communication.
Solution Approach 2:
The patent implements feedback mechanisms where the network device uses received beam training frames and power delay profiles to determine spatial reuse parameters. This feedback loop allows the system to adapt transmission characteristics based on actual measured interference conditions, simplifying interference management by using measured data rather than complex predictions or assumptions.
3Reliability
If beamforming is used to compensate for free space path loss, then signal strength is improved, but side-lobe interference increases
Solution Approach 1:
The patent applies local quality by differentiating between main-lobe and side-lobe regions. The network device determines spatial reuse parameters based on the angular position of neighboring devices relative to the main beam direction. Devices in the main-lobe region experience different interference characteristics compared to those in side-lobe regions, allowing for localized optimization of transmission parameters to manage interference while maintaining high data rates.
Solution Approach 2:
The patent implements dynamics by making beam selection and spatial reuse parameters adaptive rather than static. The network device dynamically determines spatial reuse parameters based on real-time measurements of beam training frames and power delay profiles, adjusting transmission characteristics according to the specific spatial arrangement and interference conditions of neighboring devices during operation.
Data Source
AI summary
Examples described herein provide method and systems for high spatial reuse for mmWave Wi-Fi. Examples may include identifying, by a network device, a plurality of millimeter-wave (mmWave) propagation paths between the network device and a set of neighboring devices including a target neighboring device, based on power delay profiles (PDPs) of beam training frames received by the network device from each of the neighboring devices using a plurality of mmWave beams, and determining, by the network device for each of neighboring devices in the set, an estimated angle of arrival (AoA) of each identified mmWave propagation path between the network device and the neighboring device, based on the PDPs of the received beam training frames from the neighboring device. Examples may include selecting, by the network device, one of the mmWave beams that maximizes a signal to interference and noise ratio (SINR) along the estimated AoA of each identified mmWave propagation path between the network device and the target neighboring device, and communicating, by the network device, with the target neighboring device using the selected mmWave beam.