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Scalable Network Tomography for Dynamic Spectrum Access

Aadesh Madnaik, N. Cameron Matson, Karthikeyan Sundaresan

TL;DR

This work proposes 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.

Abstract

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.

Scalable Network Tomography for Dynamic Spectrum Access

TL;DR

This work proposes 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.

Abstract

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.
Paper Structure (29 sections, 18 equations, 11 figures, 1 table, 1 algorithm)

This paper contains 29 sections, 18 equations, 11 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. System Design
  4. Overview
  5. Direct Estimation Phase
  6. Channel Sampling
  7. Client Clustering
  8. Pairwise Probability Estimation
  9. Scaling to High Order Distributions
  10. Joint Probability Representation
  11. From Pairwise to HOD
  12. High Order Distribution Accuracy Analysis
  13. Blue-Printing Interference
  14. Leveraging NeTo-X in Practice
  15. Application in Resource Management
  16. ...and 14 more sections

Figures (11)

  • Figure 1: Illustration of hidden terminal interference
  • Figure 2: HOD prevents drop in performance with (a) increased interference, (b) larger number of antennas
  • Figure 3: System Design and Information Flow
  • Figure 4: Impact of hidden terminals across three channels.
  • Figure 5: Mean Squared Error between true and estimated HOD from pair-wise marginal inputs vs. latent variable size.
  • ...and 6 more figures