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Low-Resolution 5G Beamforming Codebook Design

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Beamforming enables control of the beam pattern to focus energy to/from a specific direction. In this example, we want to focus energy on the user located at 60°, while avoiding the user located at 120°.

Short Description

Massive multi-antenna (MIMO) technology is a key component for fifth-generation (5G) wireless communication systems. By using a large number of antenna elements, the transmitted signals combine constructively or destructively to create a specific beam pattern. A different beam pattern can be created by modifying the signals transmitted by the individual antenna elements, providing the system with the capability to “beamform,” that is, to control the direction and shape of the beam pattern. Thanks to beamforming, the transmitter can focus energy on the receiver, which significantly improves the communication data-rates. The same idea applies not only for transmission of data, but also for reception: By adequately combining the signals received by multiple antennas, one can recover signals coming from a specific direction.

In practice, one is interested in finding the signals that, given a specific transmitter-receiver location and a message to send, will form an appropriate beam pattern. This is a complex problem that can be tackled in several ways. For example, one can try to find a solution in real time (online), but then this solution needs to be found within a very tight time window. Or alternatively, one can compute several potential solutions offline to form a fixed “codebook,” and then at run time, one only needs to choose the most adequate candidate solution.

In this project, we will investigate the latter approach, with a special emphasis on beamforming codebooks that can be represented with few bits of resolution. To do so, we will start by exploring and learning previously proposed codebook methods, including the one outlined by the 3GPP 5G New Radio (NR) technical specification. This first part will be of theoretical nature, although it will also have a simulation component in order to verify our understanding of the existing methods. Then, we will combine the knowledge acquired from this first step with “Finite-Alphabet Baseband Processing” [1], a novel beamforming paradigm that uses low-resolution numbers to reduce power consumption and circuit complexity of beamforming circuitry. The goal is to determine if a low-resolution codebook method is advantageous over existing online methods. This performance evaluation will be done theoretically and based on simulations—if time permits, we will also investigate the consequences in terms of power consumption and hardware complexity.

[1] O. Castañeda, S. Jacobsson, G. Durisi, T. Goldstein, and C. Studer, "Finite-Alphabet MMSE Equalization for All-Digital Massive MU-MIMO mmWave Communication," IEEE Journal on Selected Areas in Communications, vol. 38, no. 9, pp. 2128-2141, Sep. 2020

Status: Available

Looking for 1-2 Semester/Master students
Contact: Oscar Castañeda


Communication Systems (or a similar course)
VLSI I (optional)


30% Literature research
70% System development


Christoph Studer

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Detailed Task Description


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