Centrala begrepp
Machine learning-based approaches can decouple beam selection between the user equipment (UE) and the base station (BS) in mmWave vehicular systems, reducing the overhead of beam pair selection while maintaining comparable performance to joint beam pair selection at the BS.
Sammanfattning
The paper proposes three scenarios for beam selection in mmWave vehicular systems:
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Coupled Beam Selection with Location (CBSwL): The BS determines the beam pairs for both the BS and the UE based on the UE's location information.
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Decoupled Beam Selection with Location (DBSwL): The BS and the UE independently select their own beams based on the UE's location information.
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Decoupled Beam Selection without Location (DBSwoL): The BS selects beams to cover the region of interest, while the UE selects its beams based on its own location information.
The authors develop machine learning-based algorithms for each scenario and evaluate their performance in terms of throughput ratio and misalignment probability using realistic ray-traced channel samples in an urban street environment.
The key findings are:
- Decoupling beam selection with location information (DBSwL) performs comparably to the coupled scenario (CBSwL), with only a minor throughput ratio decrease of less than 5%.
- Disaggregating the UE's location information from the BS (DBSwoL) leads to up to 22% throughput ratio decrease, but the proposed clustering-based beam selection algorithm gradually recovers the performance loss.
- The decoupled scenarios have a higher misalignment probability compared to the coupled scenario, but this does not significantly impact the throughput ratio, as there are other suboptimal beam pairs that still yield high throughput.
The results demonstrate the feasibility of decoupling beam selection between the UE and BS using machine learning, which can reduce the overhead of beam pair selection in dynamic mmWave vehicular environments.
Statistik
The average rate for the beam pair (wi, fj) is calculated as:
Ri,j = (1/K) * sum(log2(1 + |yi,j[k]|^2 / σ^2)), where k = 0 to K-1 subcarriers.
The throughput ratio is defined as:
RT = max(wi,fj)∈S Ri,j / max(wi,fj)∈B Ri,j
Citat
"Decoupling beam selection with location information (DBSwL) performs comparably to the coupled scenario (CBSwL), with only a minor throughput ratio decrease of less than 5%."
"Disaggregating the UE's location information from the BS (DBSwoL) leads to up to 22% throughput ratio decrease, but the proposed clustering-based beam selection algorithm gradually recovers the performance loss."