How AI Enhances Beamforming

Beamforming will become increasingly important as the world enters the age of fifth-generation (5G) networks and multiple-input multiple-output (MIMO) technologies. Using artificial intelligence, engineers can future-proof this technology. What have they already accomplished? Where will they take research and development of AI and beamforming from there?

What Is Beamforming?

Beamforming is a radiofrequency management technique. Instead of using a broadcast antenna to spread a wireless signal in every direction, it uses multiple antennas close to each other to direct the signal toward a receiving device. This way, it improves the received signal-to-noise ratio (SNR), reducing transmission errors.

This radiofrequency management technique ensures consistent signal reception by directing and amplifying transmissions more efficiently. As advanced Wi-Fi systems and 5G network technologies proliferate, it is becoming increasingly common.

Why Use AI to Enhance Beamforming?

Beamforming is often deployed alongside MIMO technologies for optimal wireless communication system performance. Together, they improve received SNR, resulting in the best wireless communication system performance — a synergistic relationship.

Massive MIMO systems have highly focused transmit beams, enabling user-specific beamforming, meaning it is no longer limited to multiuser scenarios. Radiofrequency and classical signal processing methods do not scale well for these massive antenna arrays. A multimodal approach that leverages deep learning techniques is essential.

Ways AI Improves Beamforming

Professionals can use machine or deep learning algorithms to enhance beamforming techniques in multiple ways.

1. Provides Accurate Channel Predictions

Eigen beamforming improves MIMO system throughput. It ensures endpoints always receive an optimal signal by modifying amplitude and phase. However, it can only offer the best performance when it has the medium’s full information, which is virtually impossible to get outside of simulations since environmental conditions cause signal strength variations.

A deep-learning-based channel prediction technique can help telecommunication companies overcome the limitations of Eigen beamforming. Even with incomplete datasets, a well-trained algorithm can provide approximate predictions. A high-performing model with a low error rate can optimize transmission reception consistency.

2. Reduces Processing Power Consumption

Channel estimation overhead is the extra data and processing power necessary to determine a wireless medium’s characteristics. Conventional estimation optimization tools are inefficient and resource-intensive. Conversely, a deep learning model only needs historical data to make predictions, substantially reducing the overhead and optimizing power consumption.

3. Continuously Optimizes Beam Patterns

BeamPlanner is the world’s first network optimization tool for beamforming antenna systems. It uses AI to enable dynamic beam steering, adapting to changes in user traffic distribution to improve signal quality and minimize interference in real time.

This tool ensures targeted coverage at specific hot spots instead of low-traffic areas by continuously optimizing beam patterns. It provides beamforming recommendations based on terrain, user location, traffic, and obstacle data. Its three-dimensional ray tracing and pattern recognition capabilities allow for improved prediction accuracy.

Future AI-Driven Improvements

Since AI is relatively new, its potential applications are mainly unknown. Researchers are exploring possible use cases and integration strategies.

1. Enhances Channel Estimation

As data travels from its source to its destination, distortion is possible. To make it less likely, the medium’s properties must be known. This is where channel estimation comes in. However, if pilot sequences in adjacent cells interfere with each other — pilot contamination — performance degradation occurs. A multimodal estimation and pilot reduction solution is necessary.

One research group proposed a deep learning channel estimation and pilot reduction technique for MIMO systems. It uses an autoencoder — a neural network that efficiently encodes unlabeled input data. It can extract patterns and make forecasts by analyzing the statistical relationship of signals at different points in time.

2. Improves Bandwidth Efficiency

Bandwidth efficiency is the amount of data that can be transmitted over a given amount of bandwidth with minimal transmission errors. In hybrid beamforming, low SNR conditions significantly decrease bandwidth efficiency, so distinguishing a weak signal from noise is challenging.

AI is the ideal solution because massive MIMO systems’ signal variation and hardware complexity make alternatives too resource-intensive. Researchers discovered proximal policy optimization — a reinforcement learning architecture — fixes this issue by generating highly resourceful hybrid beams without affecting the beamforming parameters. The algorithm intelligently forms a beam pattern for transmission, improving bandwidth efficiency.

The Future of AI and Beamforming

Some AI subsets — namely neural networks and deep learning models — can be incredibly resource-intensive. Engineers must optimize their algorithms for machine learning tools to be a standard part of future state-of-the-art beamforming solutions.