Deep Hypernetwork-based Robust Localization in Millimeter-Wave Networks

Wireless localization and sensing are increasingly important capabilities when the networks are evolving towards the $6^{t h}$ generation era. While the physics-inspired geometrical models are known to perform well in line-of-sight (LoS) dominant scenarios, harnessing the power of artificial intelligence (AI) to improve robustness, efficiency, and performance in more complex propagation scenarios is an intriguing prospect. To this end, the hypernetwork (HN) is an emerging neural network (NN) architecture, where one model is used to parameterize the weights of the other, promising dynamic weight adaptation among other performance improvements. In this work, we propose the concept of Hypernetwork Localization (HypLoc) - a hybrid HN-based architecture for localization in beamforming millimeter-wave (mmWave) networks, while combining angle-of-arrival (AoA), time-of-flight (ToF), and received power (RP) as representative measurements. Considering a realistic urban vehicular environment, we first demonstrate the baseline effectiveness of HypLoc with a fixed and known gNodeB (gNB) deployment scenario. We then also study a scenario where the factory pre-training covers multiple different gNB deployment constellations and show that the proposed HypLoc clearly outperforms the traditional NNs. Finally, we also show that the HypLoc adapts faster and requires less training data when adapting to a previously unseen deployment scenario. Overall, the proposed approach facilitates efficient factory pre-training when operating under multiple different gNB deployment options.