While the long-term future of mobile connectivity is bright, there are still short-term challenges to face.
The wireless industry evolves constantly; GSMA estimates that mobile technologies and services generated 5% of the global GDP in 2022, which equates to $5.2 trillion of 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 much more to come. 5G networks promise exponential speed, reach, and capacity improvements. And they are rolling out fast!
While the long-term future of mobile connectivity is bright, there are still short-term challenges to face. Mobile network operators must grapple with how best to monetize 5G and consider the pros and cons of private network deployments, generative artificial intelligence (AI), non-terrestrial networks (NTN), sustainability, Open RAN, and more.
This blog highlights some of the latest wireless industry challenges and use cases in response to:
NTNs enable new and exciting use cases, including rural broadband, aircraft and high-speed train connectivity, network resilience, and global freight tracking. The technology behind NTN is guided by working groups within the 3rd Generation Partnership Project (3GPP), with elements defined in Release 17 and Release 18 of the 5G specification. The definition of 5G systems progresses continuously, and the scientific and industrial communities are already addressing their evolution with 5G-Advanced (5G-A) and 6G. NTN has been successfully integrated into 3GPP Rel. 17 and is expected to play a pivotal role in 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. This integration unlocks the following new opportunities and use cases:
However, the complexity of mobility scenarios, encompassing multiple satellites and base stations, sophisticated link channel models spanning multiple orbital planes, and the new 5G NTN signaling for handovers and measurements, underscores the importance of replicating these conditions in a controlled lab setting. Testing devices under realistic and controlled conditions fosters testing repeatability, which can accelerate test cycles.
NTN use cases:
The IEEE 802.11be standard, or Wi-Fi 7, builds on the foundation laid by its predecessor, Wi-Fi 6 / 6E, to deliver even faster and more reliable connectivity. With Wi-Fi 7, users can expect enhanced performance, increased capacity, and reduced latency. Wi-Fi 7 is expected to provide a seamless experience for various applications, from basic browsing to bandwidth-intensive activities such as virtual reality gaming and 8K video streaming.
To achieve this goal, the standard introduces several new features for improving WLAN efficiency, capacity, and coverage. Features such as multi-link operation (MLO) and multiple resource units (multi-RUs) increase the number of configurations and test scenarios needed to validate a device thoroughly. In addition to physical-layer testing, test engineers must emulate signaling to verify interactions between access points (APs) and stations (STAs) and the performance of Wi-Fi 7 new features under real-world conditions.
Wi-Fi 7 use cases:
6G is expected to be commercially available by 2030, revolutionizing connectivity with lightning-fast speeds, unprecedented bandwidths, and ultra-low latencies. It will transform various sectors, including telecommunications, manufacturing, healthcare, transportation, and entertainment.
6G aims to connect the physical, digital, and human worlds through various means, including new spectrum utilization, AI integration into networks and devices, digital twins, and new network architectures. These elements enhance network programmability and automation across various 6G use cases.
The spectrum that will be available for 6G is unclear. Three frequency ranges are under discussion, including the upper mid-band (sometimes called mid-band or FR3) from 7 to 24 GHz and sub-terahertz bands from roughly 90 to 300 GHz. The third range maximizes spectrum below 7 GHz through refarming, new band allocation, and increased spectral efficiency.
Performing end-to-end testing for the upper mid-band (FR3) in the lab is challenging but essential to evaluate new 6G technologies such as network sensing, extreme MIMO (xMIMO), and more. Engineers need AI-assisted radio channel emulation solutions to accelerate the development and evaluation of complex 6G technologies. The test solutions must address accurate modeling needs in 6G system simulations, digital twins, and real-time RF channel emulation.
Channel estimation continues to be an essential receiver functionality in 6G systems. Several key technologies envisioned for 6G impose new channel estimation challenges that research institutes and industry experts expect AI /ML will overcome for signal processing tasks.
A neural receiver replaces signal processing blocks for the physical layer of a wireless communications system with trained machine learning models, increasing link quality and impact throughput. Verifying receiver functionality in 6G systems requires accurate channel estimation. If engineers do not understand channel behavior and fail to compensate for real-time anomalies, 6G performance will fall consistently short of expectations.
Design engineers need a solution to train neural receivers using software-generated labeled data. After generating the data, they must validate the neural network’s performance when integrated into a wireless system so they can emulate and integrate different channel conditions into the system.
Real-world and realistic simulated channel data are pivotal in 6G applications such as integrated sensing and communication. 6G research engineers need tools using integrated AI/ML signal processing models and performance verification of the algorithms with a real hardware connection in an over-the-air (OTA) system. 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 begins by training AI models to recognize and adjust for channel impairments, such as signal fading and interference, using supervised or unsupervised learning techniques. Engineers then simulate the transmission and reception of signals under complex, real-world conditions. Once engineers have trained the model, it can help predict and correct real-time signal issues. This process enables efficient testing of ultra-massive MIMO systems and other advanced technologies in 6G architecture.
In this blog, we explored some of the latest testing challenges for the wireless communications sector. Watch Keysight and industry subject matter experts explain these challenges in greater detail and demonstrate how to overcome them here.
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