Upcoming MATLAB and Simulink Webinars

Spatial Multiplexing and Hybrid Beamforming for 5G Wireless Communications

Overview

Increasing demand for higher data rates and channel capacity drives the need to use the available spectrum more efficiently. As a result, 5G wireless systems will use millimeter wave (mmWave) frequency bands to take advantage of wider available bandwidth. In addition, 5G systems deploy large scale antenna arrays to mitigate severe propagation loss in the mmWave band. However, these configurations bring their unique technical challenges.

Spatial multiplexing can be used to create multiple subchannels in the scatterer rich environment so multiple data streams can be transmitted and recovered independently. This is achieved by applying a set of precoding and combining weights derived from the channel matrix. With large antenna arrays, it is not always practical due to cost and power budgets, to apply digital weights on each antenna element. Hybrid beamforming can be used to address this issue.

Highlights

In this webinar, we will show how to:

  • Develop an antenna array and visualizing the geometry, 2D and 3D directivity, and grating lobes
  • Import antenna patterns to increase model fidelity
  • Design spatial multiplexing system to increase channel capacity with MIMO operations
  • Partition beamforming function between the RF and digital domains in hybrid beamforming architectures
  • Design array architectures, generate RF phase shifts and digital complex weights, and evaluate the results

Please allow approximately 45 minutes to attend the presentation and Q&A session. We will be recording this webinar, so if you can't make it for the live broadcast, register and we will send you a link to watch it on-demand.

About the Presenter

Rick Gentile focuses on phased array, signal processing, and sensor fusion applications at MathWorks. Prior to joining MathWorks, Rick was a Radar Systems Engineer at MITRE and MIT Lincoln Laboratory, where he worked on the development of many large radar systems. Rick also was a DSP Applications Engineer at Analog Devices where he led embedded processor and system level architecture definitions for high performance signal processing systems. Rick co-authored the text “Embedded Media Processing”. He received a B.S. in Electrical and Computer Engineering from the University of Massachusetts, Amherst and an M.S. in Electrical and Computer Engineering from Northeastern University, where his focus areas of study included Microwave Engineering, Communications, and Signal Processing.

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