Distributed Multi-View Vision-Only RSSI Estimation

TL;DR

MulViT-TF is a vision-only, multi-view Transformer framework that improves RSSI estimation accuracy and coverage without auxiliary sensors, reducing overhead and latency.

Contribution

This work introduces MulViT-TF, a novel multi-view Transformer-based approach for RSSI estimation that overcomes hardware and auxiliary input limitations.

Findings

Achieves up to 26.3% RMSE reduction in indoor scenes.

Improves 3dB error coverage by up to 13.8 percentage points.

Uses fewer FLOPs and parameters than single-view baselines.

Abstract

Received Signal Strength Indicator (RSSI) estimation is essential for wireless link management, yet conventional feedback-based approaches incur uplink overhead, suffer from measurement instability, and are subject to inherent feedback loop latency, rendering proactive adaptation infeasible. Although vision-based approaches have been explored, existing methods remain limited by hardware dependency or auxiliary inputs, and lack the spatial diversity needed to resolve camera-side NLoS conditions. To address these limitations, we propose MulViT-TF, a vision-only RSSI estimation framework that exploits distributed multi-view observations through Transformer-based fusion, achieving complementary spatial coverage without any auxiliary sensing inputs. Experimental results across two distinct indoor scenes demonstrate that MulViT-TF achieves RMSE reductions of up to 26.3% and improves the 3dB error coverage by up to 13.8 percentage points over the best-performing single-view baseline, while using fewer FLOPs and parameters.