The wireless sector continues to evolve rapidly. GSMA estimates that mobile technologies and services generated 5% of global GDP in 2022, equating to US$5.2 trillion in economic value.
In parallel, more than 5.4 billion people subscribed to a mobile service, with 4.4 billion connected to the mobile internet. These numbers are impressive, but there is more to come. 5G networks promise exponential improvements in speed, reach, and capacity — and they are rolling out fast!
While the long-term outlook for mobile connectivity remains strong, several short-term challenges remain. Mobile network operators must evaluate how best to monetise 5G and weigh the implications of private network deployments, generative AI, non-terrestrial networks (NTNs), sustainability, Open RAN, and more.
Non-terrestrial networks use cases
NTN use cases include rural broadband, connectivity for aircraft and high-speed trains, network resilience, and global freight tracking. The technology behind NTNs is being developed by working groups within the 3rd Generation Partnership Project (3GPP), with key components defined in Releases 17 and 18 of the 5G specification.
The evolution of 5G systems is ongoing, with industry and research communities now focusing on 5G-Advanced (5G-A) and 6G. NTNs were integrated into 3GPP Release 17 and are expected to play a pivotal role in both 5G-A and 6G.
The primary focus of an NTN is to offer coverage in underserved areas. An essential aspect that sets 5G NTN apart from previous technologies is its seamless integration with existing terrestrial network infrastructure.
A key benefit of 5G NTNs is their ability to extend coverage to underserved areas. Unlike earlier technologies, they are designed for seamless integration with terrestrial infrastructure — opening up new opportunities:
- Public safety backup, providing critical communication in the event of terrestrial network outages caused by natural disasters or other emergencies.
- 3D coverage, enabling reliable communication through aerial platforms such as balloons or unmanned aerial vehicles (UAVs), and enhancing multidimensional coverage between terrestrial and non-terrestrial networks.
- Massive IoT, supporting global coverage and easing cross-border connectivity challenges while optimising power consumption and network resource use across terrestrial and NTN environments.
However, the mobility complexity of NTNs — spanning multiple satellites, ground stations, and orbital planes — along with new 5G NTN signalling for handovers and measurements, underscores the need to replicate these conditions in controlled lab environments. Testing devices under realistic and controlled conditions fosters test repeatability, which can accelerate test cycles.
Wi-Fi 7 use cases
Wi-Fi 7 promises enhanced performance, greater capacity, and lower latency across a broad range of use cases. From basic browsing to high-bandwidth activities like 8K streaming and virtual reality gaming, Wi-Fi 7 is designed to deliver a seamless user experience.
To support these improvements, the standard introduces features that enhance WLAN efficiency and coverage. For instance, multi-link operation (MLO) and multiple resource units (multi-RUs) expand the number of configurations that must be validated. Beyond physical-layer testing, engineers must emulate signalling between access points (APs) and stations (STAs) to assess how new Wi-Fi 7 features perform in real-world conditions.
6G use cases
Commercial deployment of 6G is expected by 2030, bringing with it transformative speeds, bandwidth, and ultra-low latency. The technology is expected to reshape industries including telecommunications, manufacturing, healthcare, transportation, and entertainment.
6G will connect physical, digital, and human environments through new spectrum bands, integrated AI capabilities, digital twins, and novel network architectures. These components will improve programmability and automation across various use cases.
FR3 channel emulation
The final 6G spectrum allocations remain unclear. Current discussions focus on three frequency ranges: sub-7 GHz (through refarming, new band allocation, and improved spectral efficiency), upper mid-band (7–24 GHz, also referred to as FR3), and sub-terahertz bands (roughly 90–300 GHz).
Performing end-to-end testing for the upper mid-band (FR3) in the lab is challenging but essential for evaluating emerging 6G technologies such as network sensing, extreme MIMO (xMIMO), and others. Engineers need AI-assisted radio channel emulation solutions to accelerate both development and validation. These solutions must meet the high-accuracy modelling demands of 6G system simulations, digital twins, and real-time RF channel emulation.
6G neural receiver performance verification
Channel estimation remains an essential receiver function in 6G systems. Several key technologies being explored for 6G introduce new challenges in channel estimation — challenges that research institutions and industry experts believe AI and machine learning (ML) will help address in future signal processing workflows.
A neural receiver replaces conventional signal processing blocks at the physical layer of a wireless communication system with trained ML models. This approach can improve link quality and increase throughput. However, verifying receiver performance still depends on precise channel estimation. Without a clear understanding of channel behaviour and the ability to compensate for real-time anomalies, 6G systems will consistently underperform.
Design engineers therefore need solutions that support training neural receivers using software-generated labelled data. Once the data is generated, they must validate how the neural network performs when integrated into a wireless system, ensuring that various channel conditions can be accurately emulated and accounted for within the system.
AI/ML and sensing in 6G
6G research engineers require access to tools that support integrated AI/ML-based signal processing models, as well as capabilities for verifying algorithm performance in over-the-air (OTA) test environments using real hardware. By integrating AI models into the testing process, engineers can optimize signal quality and dynamically adjust to real-time channel variations, ensuring that 6G networks meet performance benchmarks.
The process typically begins by training AI models — via supervised or unsupervised learning — to recognise and compensate for channel impairments such as signal fading and interference. Engineers then simulate the transmission and reception of signals under complex, real-world conditions. Once trained, the model can predict and correct signal degradation in real time. This process supports more efficient testing of advanced 6G technologies, including ultra-massive MIMO systems, within evolving architectural frameworks.
