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.

翻译：移动网络通过基站协调的先进复用策略，在授权频谱中提升了频谱效率。然而，在动态（非授权）频谱接入过程中，客户端受到的外部干扰会导致显著的性能下降。我们针对动态频谱接入引入了网络层析成像的概念，将客户端转化为频谱传感器，通过测量并利用它们的联合接入统计信息来解析干扰源。尽管这一方法前景可观，但直接执行此类层析成像会带来随所部署策略的复用阶数呈指数级膨胀的不可承受开销——而随着5G/6G技术的发展，复用阶数只会持续增长。为此，我们提出了一种名为NeTo-X的新型可扩展网络层析成像框架，它仅需线性开销即可估计客户端的联合接入统计信息，并构建干扰蓝图，从而为未来网络实现高效的动态频谱接入。NeTo-X的设计融合了智能算法：利用多信道多样性与干扰对客户端影响的空间局部性，仅从客户端间的成对测量值即可精准估计所需的干扰统计量。该框架在资源管理与干扰源定位应用中的优势得以展现，其性能显著优于基线方法，并在可扩展开销下近乎达到最优性能。