KANsformer for Scalable Beamforming

TL;DR

This paper introduces KANsformer, an unsupervised deep learning model combining transformers and Kolmogorov-Arnold networks to enable scalable, real-time beamforming in mobile communications, outperforming existing methods.

Contribution

The paper presents a novel KANsformer model that integrates transformer and KAN for scalable, adaptive beamforming with superior performance over benchmarks.

Findings

Outperforms existing deep learning beamforming approaches.

Demonstrates strong generalization and transfer learning capabilities.

Provides real-time, near-optimal inference for varying numbers of users.

Abstract

This paper proposes an unsupervised deep-learning (DL) approach by integrating transformer and Kolmogorov-Arnold networks (KAN) termed KANsformer to realize scalable beamforming for mobile communication systems. Specifically, we consider a classic multi-input-single-output energy efficiency maximization problem subject to the total power budget. The proposed KANsformer first extracts hidden features via a multi-head self-attention mechanism and then reads out the desired beamforming design via KAN. Numerical results are provided to evaluate the KANsformer in terms of generalization performance, transfer learning and ablation experiment. Overall, the KANsformer outperforms existing benchmark DL approaches, and is adaptable to the change in the number of mobile users with real-time and near-optimal inference.