End-to-End Hybrid Beamforming for mmWave Integrated Access and Backhaul with Active Sensing Strategy

The effectiveness of Millimeter Wave full-duplex (FD) Integrated Access and Backhaul (IAB) system relies on high-dimensional channel estimation with high computational complexity. To avoid high-overhead pilot training, we propose a novel low-complexity end-to-end (E2E) hybrid beamforming strategy for FD mmwave IAB systems using implicit channel state information (CSI). Particularly, the IAB node first dynamically senses spatial channels, where an sensing Transformer block is introduced to actively design the sensing vector. The active sensing strategy can effectively handle the sequential pilot observations with an arbitrary input length. Capitalizing on the implicit channel features extracted by the Transformer, a hybrid beamforming neural network (HBFnet) is further exploited to design the hybrid precoder/combiner of IAB node, thus efficiently mitigating SI while compensating channel fading. Simulation results demonstrate that the proposed scheme outperforms the benchmarks, especially with low pilot overheads.