Scalable Network Tomography for Dynamic Spectrum Access
arxiv(2024)
摘要
Mobile networks have increased spectral efficiency through advanced
multiplexing strategies that are coordinated by base stations (BS) in licensed
spectrum. However, external interference on clients leads to significant
performance degradation during dynamic (unlicensed) spectrum access (DSA). We
introduce the notion of network tomography for DSA, whereby clients are
transformed into spectrum sensors, whose joint access statistics are measured
and used to account for interfering sources. Albeit promising, performing such
tomography naively incurs an impractical overhead that scales exponentially
with the multiplexing order of the strategies deployed – which will only
continue to grow with 5G/6G technologies.
To this end, we propose a novel, scalable network tomography framework called
NeTo-X that estimates joint client access statistics with just linear overhead,
and forms a blue-print of the interference, thus enabling efficient DSA for
future networks. NeTo-X's design incorporates intelligent algorithms that
leverage multi-channel diversity and the spatial locality of interference
impact on clients to accurately estimate the desired interference statistics
from just pair-wise measurements of its clients. The merits of its framework
are showcased in the context of resource management and jammer localization
applications, where its performance significantly outperforms baseline
approaches and closely approximates optimal performance at a scalable overhead.
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