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Machine Learning for Wireless Link Adaptation

Supervised and Reinforcement Learning Theory and Algorithms

Time: Thu 2021-05-20 13.00

Location: zoom link for online defense (English)

Subject area: Electrical Engineering

Doctoral student: Vidit Saxena , Teknisk informationsvetenskap

Opponent: Dr. Jakob Hoydis,

Supervisor: Professor Joakim Jaldén, Teknisk informationsvetenskap; Professor Mats Bengtsson, Signaler, sensorer och system; Dr. Hugo Tullberg, Ericsson AB


Wireless data communication is a complex phenomenon. Wireless links encounter random, time-varying, channel effects that are challenging to predict and compensate. Hence, to optimally utilize the channel, wireless links adapt the data transmission parameters in real time. This process, known as wireless link adaptation, can lead to large gains in link performance. Link adaptation is hence an integral part of state-of-the-art wireless deployments.

Existing link adaptation schemes use simple heuristics that match the data transmission rate to the estimated channel. These schemes have proven to be useful for the ubiquitous wireless services of voice telephony and mobile broadband. However, as wireless networks increase in complexity and also evolve to support new service types, these link adaptation schemes are rapidly becoming inadequate. The reason for this change is threefold: first, in several operating scenarios, simple heuristics-based link adaptation does not fully exploit the available channel. Second, the heuristics are typically tuned empirically for good performance, which incurs additional expense and can be error-prone. Finally, traditional link adaptation does not naturally extend to applications beyond the traditional wireless services, for example to industrial control or vehicular communications.

In this thesis, we address wireless link adaptation through machine learning. Our proposed solutions efficiently navigate the link parameter space by learning from the available information. These solutions thus improve the link performance compared to the state-of-the-art, for example by doubling the link throughput. Further, we advance link adaptation support for new wireless services by optimizing the link for complex performance objectives. Finally, we also introduce mechanisms that autonomously tune the link adaptation parameters with respect to the operating environment. Our schemes hence mitigate the dependence on empirical configurations adopted in current wireless networks.

This thesis is composed of six technical papers. Based on these papers, there are three key contributions of this thesis: a neural link adaptation model (Paper I, Paper II, and Paper III), link adaptation under packet error rate constraints (Paper IV  and Paper V), and efficient model-based link adaptation (Paper VI).

In this thesis, we emphasise the theoretical underpinnings of our proposed machine learning schemes for link adaptation. We approach this goal in three ways: First, we make theoretically reasoned choices for machine learning models and learning algorithms for link adaptation. Second, we extend these models for the specific problem formulations encountered in link adaptation. For this, we develop rigorous problem formulations that are analyzed using classical techniques. Third, we develop theoretical results for the real-time behaviour of the proposed schemes. These bounds extend the machine learning state-of-the-art in terms of performance bounds for stochastic online optimization. The contributions of this thesis hence go beyond the realm of wireless optimization, and extend to new developments applicable to broader machine learning problems.

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Last changed: May 20, 2021