Using AI to build your next-gen wireless system

MathWorks Australia

Thursday, 04 July, 2024

Using AI to build your next-gen wireless system

The wireless industry is experiencing an unprecedented surge in demand, with more than 7.1 billion human mobile users and a growing number of wireless machine-to-machine (M2M) connections. The central challenge engineers face when designing wireless systems and networks is their complexity.

Traditional predefined designs are inadequate or inflexible when handling system complexity and unadaptable when requirements and environments change. Founded on the principle of learning and adaptability, emerging AI-native technologies promise to address the complexity challenge.

The wireless standard organisation, 3GPP, has been vocal about AI’s significant role in the forthcoming 5G Advanced and 6G standards. They propose AI’s functionality for enhanced positioning, beam management, and channel state information (CSI) feedback. The Wireless Broadband Alliance (WBA) also touts AI for its ability to help wireless engineers in indoor positioning and beam management. The industry consensus is clear: engineers must integrate AI-native concepts to stay ahead in the next-gen wireless system race.

What are AI-native wireless systems, and why are they superior to traditional designs?

An AI-native wireless system inherently incorporates AI algorithms into its operational framework. AI-native systems offer three primary benefits for wireless engineers: better coverage, higher capacity and reliable robustness.

AI-native wireless systems are designed to learn from and adapt to their environment. This approach significantly differs from traditional designs based on more rigid, predefined models that have scalability limitations and often require costly, time-consuming signal processing resources.

Engineers designing AI-native systems need large real-world measured datasets. Most of this data is sourced from physical prototypes or by measuring real-world signals. However, most engineers use digital twins — representative virtual models that can be simulated — to augment data to train AI-native systems. Digital twins ensure that AI-native systems have sufficient data to handle adverse situations and efficiently manage system elements.

Designing and integrating an AI-native wireless system

Developing an AI-native wireless system is a complex process that involves creating a design workflow that includes gathering data, training and testing the model, and implementing and integrating the model into the wireless system.

1. Gathering and generating data

The first step in creating an AI-native wireless system involves data collection, by either acquiring over-the-air (OTA) signals or synthesising data from a digital twin. Synthetic data is especially useful, as it facilitates scalability testing, fault tolerance and anomaly detection while also aiding in environment modelling and system configuration optimisation. Maximising model efficiency requires that training data is representative of real-world scenarios the system will face. Engineers can use the collected data to perform training and validation of AI models, testing and simulation, and optimisation and performance tuning. With the data gathered, the next step involves simulation and modelling.

2. Training and testing the model

When training an AI model for a wireless system, it is essential to determine the quantity of system parameters, including bandwidth allocation, latency, signal strength, modulation and coding. Using these parameters and the comprehensive dataset obtained in the first step, the engineer selects and optimises machine learning algorithms for key system functions like autoencoders, channel estimation, channel feedback optimisation and resource allocation. During the training process, engineers must consider factors that affect real-time performance, including computational complexity, memory usage, and parallel processing on GPUs or clusters.

After an AI model is trained, the model is tested to ensure reliable performance in real-world systems. At this stage, the model’s performance is iteratively adapted to correct for biases, errors and inefficiencies. Once the adaptation is complete, the AI network should be pruned. Pruning involves converting the model to a fixed point and removing the neural network layers that do not contribute to the system’s overall behaviour. At this stage, the model is ready to be implemented in the wireless system.

3. Implementing the AI model

An AI model is only useful when it is implemented as part of a real-world system. The first step involves scaling and resource assessment. This involves evaluating the processing power, memory requirements and data throughput needed for the AI models to operate efficiently.

The second step is to use automatic code generation for deploying pretrained AI models on desktop or embedded targets using low-level code. This step automates the implementation process and reduces manual coding errors.

The final implementation step is the validation process that compares the performance of the implemented system to that of the original AI model. After engineers have identified and addressed discrepancies or performance issues, they are ready to perform model integration.

4. Integrating the model

The final step involves the integration of the implemented AI models within the overall wireless system. This phase ensures that the newly implemented AI solution works harmoniously with the rest of the legacy system. Before full-scale integration, engineers must ensure interoperability with existing system components by analysing the end-to-end system performance rather than individual algorithms and subsystems.

MATLAB can help engineers throughout all these stages of wireless system development. In MATLAB, they can perform tasks such as data generation, algorithm optimisation, code generation for implementation and model validation.

Challenges of using AI to design wireless systems

Integrating AI into wireless systems presents a variety of hurdles, including balancing conflicting performance metrics and ensuring superior performance relative to legacy systems. The goal is to achieve a balance that supports operational objectives by delivering high-quality overall performance.

Balancing performance metrics

In a typical design scenario, optimising a metric often compromises another, which makes it crucial to find an acceptable balance that meets the system’s overall goals. For example, increasing the network’s throughput may lead to higher power consumption and latency, necessitating a trade-off to maintain energy efficiency. Engineers can use modelling and simulation to explore various scenarios and configurations to balance the desired metrics. This predictive approach helps in making informed decisions and identifying optimal configurations without disrupting the actual system.

Ensuring superior performance

Transitioning from legacy wireless systems to AI-enhanced systems without disruption is challenging, but essential for achieving superior performance. AI models that continuously learn are key to this transition, as they enable the system to adapt to dynamic network conditions. Achieving superior performance requires training models using diverse, representative datasets.

One solution is to simulate the integrated system before full-scale deployment to ensure AI components will interoperate properly with legacy systems. Engineers use tools like MATLAB to facilitate interoperability testing and identify potential compatibility issues and performance bottlenecks.


The wireless industry is at a critical juncture. With the upcoming rollout of 5G Advanced and 6G standards, the next generation of wireless systems will deploy more AI-native technologies. Engineers tasked with designing modern wireless systems have realised that integrating AI is no longer optional; it is essential. By incorporating AI-native design principles, wireless engineers can develop systems and networks that meet today’s needs and are equipped to evolve with tomorrow’s wireless requirements and advancements.

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