RIS-Assisted Survivable Backhaul Recovery in Small-Cell Systems

2026-05-26

AI Summary

This work addresses the vulnerability of wired backhaul links in dense small-cell networks to physical failures, which can cause service outages. To enhance network resilience, the study proposes the first integration of reconfigurable intelligent surfaces (RIS) into the backhaul architecture to establish wireless backup links. Upon primary link failure, disconnected traffic is rerouted by jointly optimizing the selection of neighboring base stations, RIS phase shifts, and precoding vectors to maximize recoverable throughput. The approach is particularly effective under antenna-constrained conditions, significantly improving coverage, signal quality, and overall network survivability. By employing an alternating optimization framework combined with quadratic transformation, the non-convex problem is decomposed into tractable convex subproblems. Simulations demonstrate that in high-traffic hotspot areas, network survivability increases from 58% to 72%, and even with only two antennas per base station, efficient disaster recovery is achievable under moderate traffic loads.

Abstract

The increasing densification of small-cell networks substantially expands cable-based backhaul infrastructure, creating heightened vulnerability to cable link failures. This paper proposes a reconfigurable intelligent surface (RIS)-assisted backup framework that exploits a key insight: during backhaul cable failures, base station (BS) radio components remain functional, enabling wireless backhaul traffic redistribution. Our framework maintains network connectivity by redistributing disconnected BS backhaul traffic to neighboring BSs through RIS-assisted wireless links. To maximize survivability across varying traffic conditions, we formulate a joint optimization problem that maximizes total resolvable backhaul traffic by jointly deciding BS selection, RIS phase shifts, and precoding vectors. The inherent non-convexity arising from coupling and quadratic fractional term is addressed through an alternating optimization algorithm that iteratively solves tractable convex subproblems via quadratic transformation. Comprehensive numerical evaluations demonstrate that the proposed RIS-enhanced framework significantly improves survivability from 58% to 72% under challenging high-intensity hotspot traffic conditions. Moreover, RIS provides the greatest gains for antenna-constrained systems by extending coverage to access more spare capacity of the distant BSs as well as enhancing the signal strength. Consequently, high survivability is achieved even with only two antennas per BS under moderate traffic intensity.

Problem

• backhaul failure
• network survivability
• small-cell networks
• wireless backhaul
• RIS-assisted communication

Innovation

• Reconfigurable Intelligent Surface (RIS)
• Wireless Backhaul
• Network Survivability
• Joint Optimization
• Alternating Optimization