Invest in AI/ML Technologies to Lead Next-Generation Wireless Networks
The integration of Machine Learning (ML) and Artificial Intelligence (AI) into the 802.11 Task Group for a comprehensive study marks a significant step towards advancing next-generation wireless networks. The study, focusing on the potential use cases of Artificial Intelligence in wireless networks, draws substantial inspiration from the progress made by 3GPP. This initiative highlights the crucial role that AI and ML will play in the future of wireless technology.
In the typical innovation cycle, companies often begin their research and development well before the finalization of standards. They advocate for the integration of their technologies into these standards later on. Understanding the technologies currently being explored by industry leaders and their potential for standardization is essential. This foresight allows companies to align their R&D efforts with emerging trends and secure a competitive advantage. The landscape of technical innovations and early investments is vital to gaining an edge over competitors in the rapidly evolving wireless networks sector.
To gain insights into these emerging trends, we performed a comprehensive landscape analysis of the technical innovations discussed and secured in the recent technical report. This analysis allowed us to identify and curate a list of AI/ML-driven innovations that have the potential to become standards in WLAN.
AI/ML Technologies to Lead Next-Generation Wireless Networks
The table below lists potential key technologies, the pioneering companies, and their respective patent numbers. Notably, some of these technologies have been addressed in the technical report, while others remain less visible, buried within numerous patents.
| AI/ML Research Areas | Discussed in AIML TIG Technical ReportDraft (Source) | Researching Companies |
| ML Model Sharing | ✔ | Interdigital – WO2024102975A1, TW202322654A, KR2024074781AQualcomm – WO2024072549A1, WO2024039482A1Huawei – CN117499981A |
| Beamforming Training | ✔ | ETRI – KR2508071B1, KR2491353B1, KR2388198B1 |
| Bandwidth Allocation | ✔ | Intel – US10887796B2 |
| MAC Randomization Detection | ✔ | Fortinet – US20230006967A1 |
| Channel Access | ✔ | Huawei – CN117580185A, CN117279113A |
| CSI Optimization | ✔ | Interdigital – WO2023081376A9 |
| WLAN Sensing/Positioning | ✘ | Mitsubishi Electric – US11902811B2Univ Hunan – CN114928882AZhongda Testing Hunan Co Ltd – CN114126042AUniv Beijing Technology – CN112653991AUniv Nanjing Technology – CN112533136BChina Mobile Communications – CN112333652BGuangdong University Of Technology – CN111263295BTianjin University – CN110830939B, CN109151727BUniv Zhejiang Gongshang – CN110740417AUniv Chongqing Posts & Telecom – CN108416419B |
| Load Balancing/AP Steering | ✘ | Fortinet – US11882467B2 |
| Multi-Link Channel Coordination | ✘ | Charter Communications – US20230232479A1 |
| AP Mobility Detection | ✘ | Hewlett Packard Enterprises – US11297593B2 |
| Automatic Channel Allocation | ✘ | Rohde & Schwarz – EP4290915A1 |
| MAC Layer Optimization | ✘ | Huawei – CN116939716A State Grid Of China – CN111917715A |
| AI-Assisted Channel Aggregation | ✘ | Huawei – CN116938412A |
| AI-assisted Rate Self-adaptation | ✘ | Huawei – CN116939715A |
| WLAN Network Planning | ✘ | State Grid Of China – CN117835257A |
| Network Coverage Improvement | ✘ | Shenzhen Peizhe Microelectronics – CN116546609A |
| Power Saving Optimization | ✘ | Univ China Electronic Sci & Technology – CN111278161B |
Initial Trends
Noteworthy companies such as Huawei, Interdigital, and Qualcomm have already shifted their research focus towards Artificial Intelligence in Next-Generation Wireless Networks. Our analysis reveals:
- Approximately 40% of patent filings focus on ML Model Sharing and Channel Access Optimization.
- Huawei, Intel, and Qualcomm are leading with notable contributions in AI-driven innovations, emphasizing increased performance and efficiency.
Prominent areas include ML Model Sharing, Channel Access Optimization, Beam Forming Training, and CSI Feedback Optimization, as extensively covered in the 802.11 technical report. Investments by industry leaders in these areas highlight their perceived importance and potential. Monitoring other technologies under industry focus remains crucial for evaluating their potential.
Industry Impact of AI/ML in Next-Generation Wireless Networks
The integration of AI and Machine Learning (ML) in next-generation wireless networks is poised to revolutionize Wi-Fi standards, with Wi-Fi 8 on the horizon. Industry leaders must synchronize their R&D efforts with the right AI/ML technologies and vigilantly monitor competitor innovations to maintain competitiveness. Building a robust AI/ML patent portfolio now will secure a significant edge in developing Wi-Fi 8 and beyond.
Staying ahead of innovations in AI/ML in next-generation wireless networks allows companies to enhance WLAN performance, drive new use cases, and sustain leadership in the rapidly evolving wireless technology landscape. Investing in these technologies today is crucial for companies aiming to influence future standards and dominate the next-generation wireless networks domain.
Conclusion
Advancements in machine learning/artificial intelligence in next-generation wireless networks not only signify shifting technological paradigms but also highlight emerging market dynamics. Companies that invest in AI/ML technologies today will lead the next wave of wireless innovation, much like how Tulip Innovation’s strategic initiatives have set future trends in the battery industry.
However, the landscape of next-generation wireless networks is still unclear, requiring multiple strategies to understand how to lead effectively. With GreyB’s authority in this domain, we can help you plan way ahead of your competitors to understand the technologies that will become standards.
Fill out the form to invest in the nascent yet highly rewarding technologies and dominate the next-generation wireless networks domain.
Authored By: Aman Kumar, Prior-Art Team
