pith:LBAF7AKM
Geometry-Aware Multi-Armed Bandits for Antenna Beam Selection on Spheres, Tori, $\SO(3)$, and Reconfigurable Intelligent Surfaces
Intrinsic Matérn kernels on spheres, tori and discrete tori cut cumulative regret in mmWave beam selection by 25 to 45 percent versus standard codebook methods.
arxiv:2605.13005 v1 · 2026-05-13 · eess.SP
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Claims
On four static 3GPP-style mmWave benchmarks, intrinsic-kernel GP-UCB reduces cumulative regret by 25–45% vs. codebook UCB1/Thompson and by 10–33% vs. Euclidean-ambient GP-UCB on the toroidal arm spaces; AdaptiveGP-v2 is statistically indistinguishable from the hand-tuned fixed-window oracle at every speed.
That the intrinsic Matérn kernel from Borovitskiy et al. accurately captures the reward landscape on these manifolds and that per-sample marginal likelihood selection of window size W remains stable and unbiased under the non-stationarity induced by Doppler.
A Kronecker-factorized intrinsic Matérn kernel renders GP-UCB tractable on RIS spaces with up to 10^90 configurations while an online marginal-likelihood adaptive window controller matches hand-tuned performance across speeds without per-deployment calibration.
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| First computed | 2026-05-18T03:09:00.333303Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
58405f814c528be7f74ebd05dc2247a30ef91a783bee9d8f0a397b01b3cb4c26
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/LBAF7AKMKKF6P52OXUC5YISHUM \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 58405f814c528be7f74ebd05dc2247a30ef91a783bee9d8f0a397b01b3cb4c26
Canonical record JSON
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