Integrating AI into Wireless Communication Systems
Mobile wireless technology is evolving from 5G to 5G-Advanced and 6G. These technologies are enabling new industrial applications and societal trends, such as autonomous vehicles, smart factories, and virtual medicine. The result is an increase in the complexity of wireless systems design and in expectations for network quality, reliability, and flexibility.
The advances in technology have increased wireless systems and network complexities by widening the range of parameters that need to be introduced, continuously monitored, and tuned to ensure quality of the overall systems.
- More antennas with the emergence of massive MIMO (multiple-input multiple-output)
- More spectral frequencies, such as mmWave
- Varying channel conditions depending on location
- Increasing numbers of users and user density
- Growing numbers of use cases, from machine-to-machine and human-to-machine to human-to-human uses as well as directed communication
To deliver on the promise of these new wireless technologies, engineers must find ways to optimize these systems and configure their parameters. But the complexity involved in solving these design challenges is testing the limits of the human mind. Traditional rules-based mathematical methods will no longer be sufficient.
It is time to think beyond traditional methods and consider using artificial intelligence (AI) techniques.
AI excels at solving problems that involve multiple dimensions and complex dynamics. By using AI models to perform key functions in a wireless network, you can:
- Improve wireless system efficiency.
- Reduce computational complexity and resource usage.
- Continually compensate for environmental changes (from solar flares to overheating amplifiers).
- Account for varying channel conditions.
There are many design challenges that can be solved with AI:
- Digital predistortion using deep learning to compensate for environmental changes affecting power amplifier operations
- Beam selection using deep learning to reduce computational complexity and resource overhead
- Beam selection using a deep Q-network (DQN) reinforcement learning agent to reduce complexity of beam search
- Detect log likelihood ratios (LLRs) using deep learning to reduce computational complexity
AI can also help you meet increasing needs for localization:
- 3D indoor positioning using deep learning to account for location changes
What else can you model with AI?
- Spectrum sensing using deep learning to support different spectral frequencies
- Modulation classification using deep learning to account for varying channel conditions
- Autoencoders using deep learning to improve reliability
- Channel state information (CSI) feedback with autoencoder neural networks to compress downlink CSI sent over a wireless channel
- Detect WLAN router impersonation using deep learning to improve security
This ebook will walk through the development of an AI-based 5G channel estimation model and show how the AI model can improve overall network performance.
AI Made Easier with MATLAB
With MATLAB®, you can create AI-based solutions even if you don’t have experience with machine learning or deep learning. MATLAB makes it easy to integrate AI-based system design into your workflow.
MATLAB supports an iterative design, test, and deployment process that enables you to continuously improve your AI models, integrate them into your system for testing and validation, and deploy them onto production networks.
Data cleansing and preparation
Model design and tuning
Simulation & Test
Integration with complex systems
System verification and validation
Edge, cloud, and desktop
With MATLAB, you can:
- Prepare Data
- Capture signals over the air using supported hardware to create data for training AI models.
- Generate standard-specific data/waveforms as well as custom waveforms for various technologies such as 5G, LTE, WLAN, Bluetooth, and various satellite communications standards such as DVB, CCSDS, and GPS using the Wireless Waveform Generator app.
- Augment signal space by adding RF impairments and channel models to your generated signals so that your data set is realistic and robust.
- Apply your domain expertise to label signals and add human intelligence to data collected from wireless systems using the Signal Labeler app.
- Create AI Models
- Apply reusable and streamlined training, simulation, and testing workflows to various wireless applications using the Deep Network Designer and Experiment Manager apps.
- Add custom layers to your deep learning designs.
- Simulate and Test
- Simulate an end-to-end wireless system that includes AI models.
- Quickly assess the effects of an AI model on system behavior and iterate to improve your design.
- Validate and tune your AI model and system using over-the-air signals.
- Deploy Your Models
- Automatically generate code for specific target hardware.
- Deploy to embedded hardware or to the cloud.