Explainable and Robust Millimeter Wave Beam Alignment for AI-Native 6G Networks

TL;DR

This paper presents an explainable and robust deep learning-based beam alignment system for millimeter-wave 6G networks, significantly reducing training overhead and improving interpretability and resilience against noise and outliers.

Contribution

It introduces a CNN-based beam alignment engine combined with Deep k-Nearest Neighbors for explainability and robustness, advancing AI-native 6G communication systems.

Findings

Reduces beam training overhead by 75%

Enhances outlier detection robustness by up to 5x

Maintains near-optimal spectral efficiency

Abstract

Integrated artificial intelligence (AI) and communication has been recognized as a key pillar of 6G and beyond networks. In line with AI-native 6G vision, explainability and robustness in AI-driven systems are critical for establishing trust and ensuring reliable performance in diverse and evolving environments. This paper addresses these challenges by developing a robust and explainable deep learning (DL)-based beam alignment engine (BAE) for millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems. The proposed convolutional neural network (CNN)-based BAE utilizes received signal strength indicator (RSSI) measurements over a set of wide beams to accurately predict the best narrow beam for each UE, significantly reducing the overhead associated with exhaustive codebook-based narrow beam sweeping for initial access (IA) and data transmission. To ensure transparency and resilience, the Deep k-Nearest Neighbors (DkNN) algorithm is employed to assess the internal representations of the network via nearest neighbor approach, providing human-interpretable explanations and confidence metrics for detecting out-of-distribution inputs. Experimental results demonstrate that the proposed DL-based BAE exhibits robustness to measurement noise, reduces beam training overhead by 75% compared to the exhaustive search while maintaining near-optimal performance in terms of spectral efficiency. Moreover, the proposed framework improves outlier detection robustness by up to 5x and offers clearer insights into beam prediction decisions compared to traditional softmax-based classifiers.