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

2025-01-23

AI Summary

Addressing stability, low overhead, and interpretability challenges in millimeter-wave (mmWave) beam alignment for AI-native 6G networks, this paper proposes an interpretable and robust beam alignment engine. Methodologically, it introduces Deep k-Nearest Neighbors (DkNN) to mmWave communications for the first time, integrating Received Signal Strength Indicator (RSSI)-based wide-beam measurements with a lightweight CNN to predict optimal narrow beams—enabling end-to-end training without requiring explicit channel state information. Key contributions include: (i) DkNN simultaneously enabling prediction interpretability and out-of-distribution anomaly detection; (ii) joint optimization of beam codebook and network architecture to drastically reduce training overhead. Experiments demonstrate a 75% reduction in beam training overhead, spectral efficiency reaching 98% of the optimal scheme, fivefold improvement in anomaly detection robustness, stable performance under high noise and weak-signal conditions, and human-interpretable decision rationales.

Technology Category

• Machine Learning: Hardware-aware ML
• Search and Optimization: Learning to Search
• Intelligent Robots: State Estimation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• 6G Network
• Millimeter Wave Alignment
• AI Explainability and Robustness

Innovation

• Deep Learning
• Beam Alignment
• Explainable AI