MSc thesis: Channel prediction algorithms design in the presence of non-coherent phase jump from UE

Ericsson - Stockholm

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Our Exciting Opportunity


Background and Thesis Objectives

The “curse of mobility” for massive MIMO systems has been recently studied in both academic and internally in Ericsson, which targets on the capacity enhancement in the scenarios where UE is moving fast. It has been observed from both simulations and field tests that the throughput of the system degrades significantly in the TDD massive MIMO network. Channel aging serves as the main problem. Therefore, a reliable channel prediction algorithm has its significant practical meaning.


In academic, prior work focuses on techniques ranging from classical signal processing approach such as Kalman filter to various machine learning techniques such as generative learning. A popular line of thought for channel prediction is to model the channel dynamics based on auto-regressive models, which serves as the basis for classical channel prediction algorithms such as Kalman filter. However, a critical problem arises when consider practical UE antenna RF implementations, that a non-coherent phase occurs every time when UE switches from DL reception and to UL transmission. This non-coherent phase jump breaks the AR modeling on channel dynamics so that the direct application of prior art is foreseen to fail. Other prior works inspired by computer vision techniques focus on predicting channels as images based on generative learning.


The impact of non-coherent phase jump is not clear yet. In practice, the phase jump can be modelled as a scalar random variable. Therefore, its impact disappears when the sample covariance matrix of the MIMO channel serves as the interested feature, for example in the precoding/beamforming applications. Predicting the sample covariance or the eigenvectors associated with it requires further investigation in the massive MIMO system.

In the proposed study topic, this problem will be studied using either new exploration/invention of classic signal processing techniques or machine learning based algorithms.


Research Questions

This thesis project seeks answers to the following research questions:

  • How to design an algorithm that can conquer the negative impact of non-coherent phase jump on channel predictions?
  • Which channel feature should be used in the prediction so that beamforming gain can be achieved?
  • If the phase jump may be modeled as a scalar random variable (or a process) with different distributions, what performance can the designed algorithm achieve? Will there be any performance bound?
Examination Method

The thesis project aims to

  • propose new algorithms targeting the described problem in the massive MIMO systems,
  • implement the proposed algorithm in a MATLAB and/or Python environment and
  • extensively evaluate and compare its performance with benchmark algorithms.


The evaluation will be based on the novelty and performance of the proposed algorithm and the clarity of the thesis report.

The thesis report is expected to contain persuasive arguments and numerical results that clearly show that the proposed algorithm performs close or outperform benchmarking algorithms without unreasonable complexity increase of existing schemes.


What is available at Ericsson Research (ER)

At ER, there is an extensively used wireless network simulator, which will be used in the project. There is also related prior work/code/report on this problem. There also exists real world data set that can help algorithms training.


What´s in it for you?

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What happens once you apply?

To prepare yourself for next steps, please explore here: https://www.ericsson.com/en/careers/job-opportunities/hiring-process


Location: Kista, Sweden


Please submit your application in English as soon as possible - we are working continuously with candidate selection.

For specific questions please reach out to the responsible recruiter: Sylwia Kwiecien [email protected]


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Primary country and city: Sweden (SE) || || Stockholm || [[mfield2]]

Req ID: 617322

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