LTE & 5G

The 4G to 5G Training course prepares information technology and engineering professionals with and advanced level of knowledge about: The evolution of mobile communication networks. LTE-advanced features leading to 5G.

What is 5G?

5G is the next generation of wireless network technology that will fuel innovation and transform the way we live, work, and play.

How does 5G work?

5G networks can be built in different ways from multiple bands of wavelength spectrum: low-band, mid-band, and high-band.

High-band millimeter wave frequencies have greater bandwidth available to carry more data in dense urban areas but require cell sites to be in close proximity and have limited penetration in buildings. Mid-band balances speed and range, providing broader coverage than high-band. And it’s less impacted by buildings. However, much of its bandwidth is already in use, so there’s not a lot available for 5G growth. Low-band, like our powerful 600MHz spectrum, travels farther than other bands—over hundreds of square miles—and can pass through more obstacles, providing a better, more reliable signal both indoors and out.

5G vs 4G

What’s the difference between 5G and 4G LTE?

With 5G, high amounts of data can be transmitted more efficiently than 4G LTE. That means stronger network reliability, faster downloads, and support for more connected devices than ever before.

Modules Covered in the Course

  • Introduction to 5G Wireless Communication?
    > 5G Standard Development: Technology and Roadmap
  • Modulation, coding and waveform for 5G
    > Introduction to Modulations and Waveforms for 5G Networks
    > Faster-than-Nyquist Signaling for 5G Communication
    > Evolution of OFDM to Filter Bank Multicarrier (FBMC)
       – Principles and Comparisons
    > Filter Bank Multicarrier (FBMC) for Massive MIMO
    > Bandwidth-compressed Multicarrier Communication: Spectrally efficient frequency division multiplexing (SEFDM)
    > Non-orthogonal Multi-User Superposition and Shared Access
       – Downlink Non-orthogonal Multi-user Transmission
       – Uplink Non-orthogonal Multi-user Access
          – LDS-CDMA/OFDM
          – SCMA
          – MUSA
          – PDMA
    > Non-Orthogonal Multiple Access (NOMA)
       – NOMA Concept and Design
    > Major 5G Waveform Candidates
       – Major Multicarrier Modulation Candidates
          – CP-OFDM Modulation
          – Subcarrier Filtered MCM using Linear Convolution
          – Subcarrier Filtered MCM using Circular Convolution
          – Subband Filtered MCM
  • New spatial signal processing for 5G
    > Massive MIMO for 5G
       – Theory, Implementation and Prototyping
    > Millimeter-Wave MIMO Transceivers
       – Theory, Design and Implementation
    > 3D Propagation Channels
       – Modeling and Measurements
    > 3D-MIMO with Massive Antennas
       – Theory, Implementation and Testing
    > Orbital Angular Momentum-based Wireless Communications
       – Designs and Implementations
  • New spectrum opportunities for 5G
    > MillimeterWaves for 5G
       – Case Study: a mmWave Cellular PoC
    > 5G Millimeter-wave Communication Channel and Technology Overview
    > General Principles and Basic Algorithms for Full-duplex Transmission
    > Design and Implementation of Full-duplex Transceivers
  • New system-level enabling technologies for 5G
    > Cloud Radio Access Networks
       – Uplink Channel Estimation and Downlink Precoding
    > Energy-efficient Resource Allocation in 5G with Application to D2D
    > Ultra Dense Networks: General Introduction and Design Overview
    > Radio-resource Management and Optimization in 5G Networks
  • Reference design and 5G standard development
    > Full-duplex Radios in 5G: Fundamentals, Design and Prototyping

Case Studies

  • 5G SON
  • Capacity Maximization for Next Generation MIMO-OFDM, Massive MIMO Wireless Networks
  • Capacity optimization for MIMO-OFDM, 5G Massive MIMO, Cognitive Radio Networks
  • Role of Big Data in 5G Network
    > Optimization for Big Data in 5G Network
  • Role of Machine Learning in 5G Network
    > Optimization for Machine Learning, Support Vector Machines

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