BandWeave: Enhanced Channel Estimation in MIMO Networks with Multi-Band Fusion

High-throughput wireless communication relies on high-resolution channel estimates to accurately compensate for the distortions imposed by the underlying wireless multipath propagation. However, due to the frequency-dependent super-position of delayed propagation paths, certain components may undergo destructive interference at specific frequencies, thus obscuring their contribution. This leads to a partial reconstruction of the channel which ultimately degrades communication performance. As such, we propose BANDW EAVE, a new general-purpose multi-band channel frequency response (CFR) fusion approach that significantly improves channel estimation accuracy by leveraging complementary multipath information across multiple frequency bands. BANDW EAVE enables adaptive, robust and efficient CFR fusion through a three-phase progressive learning algorithm. We experimentally evaluated BANDW EAVE with an IEEE 802.11ac multi-user (MU)-multiple-input multiple-output (MIMO) testbed and a 60 GHz single-user (SU)-MIMO system in three propagation environments with different fading conditions. Experimental results show that BANDW EAVE channel estimation outperforms single-band estimation by achieving more than 16% of throughput improvement and up to 10.2% bit error rate (BER) reduction. BANDW EAVE achieves a gain of more than 7.5× in communication performance compared to state of the art (SOTA) band-merging approaches. Measurements on Jetson Nano, Jetson Orin Nano and Raspberry Pi 5 show that BANDW EAVE reduces inference time and energy consumption by up to 20× and 18×, respectively, compared to SOTA methods. A video demonstration of BANDW EAVE is available.