A dual limb attention based deep learning network for multipath classification of millimetre wave signals in intelligent transportation system

The presence of line-of-sight (LOS) and non-line-of-sight conditions in an intelligent transportation system (ITS) is critical, as it directly impacts the localisation and overall system performance. Distinguishing LOS, first-order and higher-order multipaths (HOMP) is hindered by the dense multipath (MP) propagation. The computational resource requirement and limited accuracy associated with traditional iterative methods have stimulated the use of deep learning (DL). This work proposes a novel, lightweight, dual limb attention (DLA) based DL network termed as “MOVENetx64”, for robust identification of MPs in ITS. DLA mechanism stacked with convolutional neural networks and long-short-term memory layers forms the prominent feature extractor, which enables feeding raw, unprocessed data into the DL pipeline. A novel alternating loss strategy is designed to nullify the effect of the extreme imbalance associated with the target output classes. Ray-tracing-based vehicular datasets for City, Suburban, and Highway are generated. A combination of time, received power, angular characteristics, and phase spectrum forms the input feature-set. The proposed MOVENetx64 showcases MP classification accuracy of (98.71%,99.41%,96.44%), and achieves the least HOMP prediction error of (0.6%,0.17%,2.28%) on the datasets. These results validate that the proposed MOVENetx64 is scalable and computationally efficient for distinguishing multipaths efficiently and enabling reliable vehicular communication in ITS.