Abstract
The emergence of 6G wireless networks presents a transformative leap in telecommunication technology, promising unprecedented data rates, ultra-reliable low-latency communications, and seamless integration of a multitude of connected devices. The dynamic and complex requirements of 6G networks necessitate advanced adaptive beamforming (ABF) solutions to ensure high-quality communication links. This thesis explores innovative machine learning (ML) techniques, particularly leveraging neural networks (NNs), to enhance the efficiency, adaptability, and scalability of ABF processes, addressing critical challenges that traditional deterministic algorithms struggle to resolve in dynamic environments. The contributions of this work span four main pillars, each targeting a distinct aspect of ABF for next-generation wireless networks. The first pillar focuses on using deep learning for efficient beamforming weight calculation in uniform linear arrays (ULAs). Traditional deterministic methods suc ...
The emergence of 6G wireless networks presents a transformative leap in telecommunication technology, promising unprecedented data rates, ultra-reliable low-latency communications, and seamless integration of a multitude of connected devices. The dynamic and complex requirements of 6G networks necessitate advanced adaptive beamforming (ABF) solutions to ensure high-quality communication links. This thesis explores innovative machine learning (ML) techniques, particularly leveraging neural networks (NNs), to enhance the efficiency, adaptability, and scalability of ABF processes, addressing critical challenges that traditional deterministic algorithms struggle to resolve in dynamic environments. The contributions of this work span four main pillars, each targeting a distinct aspect of ABF for next-generation wireless networks. The first pillar focuses on using deep learning for efficient beamforming weight calculation in uniform linear arrays (ULAs). Traditional deterministic methods such as the minimum variance distortionless response (MVDR) and null-steering beamforming (NSB) techniques provide accurate results but suffer from high computational complexity and limited adaptability to rapidly changing scenarios. This work introduces a recurrent neural network (RNN) architecture, specifically a gated recurrent unit (GRU) model, trained using large datasets generated by an NSB algorithm. This NN-based solution demonstrates comparable accuracy to deterministic methods while significantly reducing response time and enhancing scalability through parallelization. Comparative analysis reveals that the GRU model offers a mean main lobe and null-placement error of 0.43 and 0.076 degrees, respectively, for realistic 16-element ULAs, proving its potential as a robust and efficient alternative to traditional beamforming techniques. Building upon this, the second pillar extends the proposed methods to the more complex scenario of three-dimensional (3D) beamforming using uniform planar arrays (UPAs). This work explores the design and tuning of NNs for 3D ABF, focusing on achieving high accuracy while maintaining manageable computational requirements. Hyperparameter optimization techniques are applied to identify optimal network configurations, and two novel techniques are introduced: a method for efficient null identification and a self-improvement approach for continuous NN fine-tuning. This self-improving method identifies and addresses underperforming cases, enhancing model performance with less data. The proposed LSTM-based beamformer achieves a mean main lobe and null-placement error of 0.25 and 0.23 degrees, respectively, demonstrating the feasibility of NN-based beamforming for complex 3D scenarios. The third pillar addresses sidelobe suppression, a critical aspect of ABF for minimizing interference and enhancing signal quality. By iteratively placing nulls towards the directions of high sidelobes, this work develops a recursive sidelobe damping method and tunes its parameters for a balance between sidelobe level (SLL) reduction and response time. The resulting NNs, trained using this method, achieve a mean maximum SLL of -20.7 dB on an 8×8 UPA while offering response times that are more than 8000 times faster than the iterative approach used for training. The fourth and final pillar shifts the focus to proactive beamforming through DoA prediction. Proactive beamforming anticipates the future DoA of incoming signals based on historical observations, reducing beamforming latency and improving system adaptability. This work proposes an NN-based predictive model trained on realistic movement paths simulated using OpenStreetMap data for urban environments. Transformer neural networks (TNNs) and recurrent architectures are employed to forecast future DoAs, demonstrating superior accuracy, with mean prediction errors of 1.36 degrees for line-of-sight (LoS) and 3.01 degrees for non-line-of-sight (NLoS) scenarios. The proposed approach is validated through simulations of moving users, showcasing its ability to maintain optimal signal strength and SIR levels during user movement. Collectively, this thesis highlights the transformative potential of ML-based approaches in ABF for 6G networks. By addressing key challenges such as latency reduction, scalability, 3D beamforming complexity, sidelobe suppression, and proactive DoA prediction, this work contributes to the development of next-generation wireless systems capable of meeting the demanding requirements of 6G. Through novel NN architectures and data-driven optimization methods, it paves the way for more intelligent, adaptable, and efficient beamforming solutions that push the boundaries of wireless communication performance.
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