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A Cross-Layer Analysis of Network Antifragility with RIS-assisted Links under Jamming Attacks

Mounir Bensalem, Thomas Röthig, Admela Jukan

TL;DR

This paper addresses antifragility in RIS-assisted wireless networks under jamming by developing a cross-layer model that integrates physical-layer RIS beamforming with detection, classification, and adaptive modulation/coding. The approach leverages both spatial and temporal orthogonality, AoA-based beamforming, and RS-coded adaptations to convert interference into throughput gains, with throughput potentially increasing by up to $5\times$ under certain regimes. Key contributions include a detailed jamming model (DRFM, PS, AS), a JM-aware path-delay estimation method, a MUSICC-based orthogonality framework, and an adaptive signaling strategy that yields antifragile performance in multi-hop RIS networks. The findings suggest that antifragility can be embedded into future networks, enabling cross-layer routing and scheduling that exploit deliberate or incidental interference as a resource, particularly in dense RIS deployments.

Abstract

Antifragility is an economics term defined as measure of (monetary) benefits gained from the adverse events and variability of the markets. This paper integrates for the first time the antifragility into the network based on communication links with Reconfigurable Intelligent Surface (RIS) affected by a jamming attack. We analyze whether antifragility can be achieved for several jamming models. Beyond the link-level gains, the results reveal how antifragile RIS-assisted links can be integrated into multi-hop systems to improve end-to-end network resilience, connectivity, and throughput under adversarial effects.

A Cross-Layer Analysis of Network Antifragility with RIS-assisted Links under Jamming Attacks

TL;DR

This paper addresses antifragility in RIS-assisted wireless networks under jamming by developing a cross-layer model that integrates physical-layer RIS beamforming with detection, classification, and adaptive modulation/coding. The approach leverages both spatial and temporal orthogonality, AoA-based beamforming, and RS-coded adaptations to convert interference into throughput gains, with throughput potentially increasing by up to under certain regimes. Key contributions include a detailed jamming model (DRFM, PS, AS), a JM-aware path-delay estimation method, a MUSICC-based orthogonality framework, and an adaptive signaling strategy that yields antifragile performance in multi-hop RIS networks. The findings suggest that antifragility can be embedded into future networks, enabling cross-layer routing and scheduling that exploit deliberate or incidental interference as a resource, particularly in dense RIS deployments.

Abstract

Antifragility is an economics term defined as measure of (monetary) benefits gained from the adverse events and variability of the markets. This paper integrates for the first time the antifragility into the network based on communication links with Reconfigurable Intelligent Surface (RIS) affected by a jamming attack. We analyze whether antifragility can be achieved for several jamming models. Beyond the link-level gains, the results reveal how antifragile RIS-assisted links can be integrated into multi-hop systems to improve end-to-end network resilience, connectivity, and throughput under adversarial effects.
Paper Structure (13 sections, 17 equations, 4 figures, 1 table)

This paper contains 13 sections, 17 equations, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Physical Layer Assumptions
  4. Source- and RIS-aware eavesdropping paths model
  5. Jammer model
  6. Antifragility Scheme Design
  7. Jamming detection and path delay estimation
  8. Orthogonality Approach
  9. Jamming classification
  10. Signal, modulation and code adaptation
  11. Antifragile gain
  12. Numerical Evaluation
  13. Conclusion

Figures (4)

  • Figure 1: Two hop network model with two jamming paths.
  • Figure 2: Throughput vs JSR for a fixed coding rate. Baseline SNR = 7 dB
  • Figure 3: Throughput vs JSR for optimal coding rate and varying RIS sizes.
  • Figure 4: Throughput vs JSR for optimal selected coding rate. Baseline SNR = 10 dB