Pilot Allocation for Multi-Hop Over-the-Air Neural Inference under Imperfect CSI

TL;DR

This paper explores how CSI errors affect multi-hop over-the-air neural inference and proposes heuristic pilot allocation schemes to optimize classification accuracy under imperfect CSI conditions.

Contribution

It introduces five heuristic schemes for pilot allocation in multi-hop OTA neural inference to mitigate CSI errors and improve accuracy.

Findings

Balanced pilot allocation with sufficient power yields near-digital accuracy.

Trade-off exists between training overhead and classification performance.

Heuristic schemes outperform naive allocation strategies.

Abstract

A multi-hop amplify-and-forward (AF) relay network can emulate a fully connected (FC) neural network layer via over-the-air (OTA) computation. However, achieving high emulation accuracy requires accurate channel state information (CSI) across all links in the multi-hop network. In this work, we investigate the impact of CSI errors on classification performance. We propose five heuristic schemes for allocating the total channel training time (pilots) across hops and compare their effectiveness. Numerical results reveal a clear trade-off between channel training overhead and classification accuracy. In particular, with sufficient pilot power and balanced allocation of channel training resources, the system can achieve classification accuracy close to that of the digital baseline.