Top Wireless and Engineering Trends for 2026 from MathWorks: 

1. Hybrid NTN-TN Networks Take Shape in 2026: Non-terrestrial networks (NTNs) integrated with terrestrial networks (TNs) will create hybrid wireless ecosystems that expand global coverage and provide uninterrupted connectivity. 
2. Channel Modeling Gets a GenAI Upgrade: Wireless engineers will use generative AI to build more accurate, context-aware channel models for better wireless system design and management. 
3. Agentic AI and Model Context Protocol Redefine Engineering Workflows: Agentic AI systems, enhanced by standardized Model Context Protocols, will autonomously execute tasks while enabling collaboration across tools and teams.   
4. AI Enhances Performance of Complex Embedded Systems: AI will transform embedded systems using efficient local models that drive edge decisions and power advanced applications like virtual sensing.
5. AI-based ROM Redefines Simulation and Design Processes: AI-powered Reduced Order Modeling (ROM) will make real-time, high-fidelity simulations possible, accelerating design optimization and predictive analysis.

Trend #1: Hybrid NTN-TN Networks Take Shape in 2026
Non-Terrestrial networks (NTNs) are entering a new phase of deployment, with real-world implementations now complementing terrestrial network (TN) infrastructure. The 3GPP Release 17 standard provides a baseline for NTN-TN interoperability, specifying reliability and latency parameters, while Release 18 expands support for NTN-IoT and higher frequency bands—critical for scalable, high-throughput architectures. For wireless engineers, this shift introduces new design and integration challenges across direct-to-cell connectivity and network coordination. 

NTNs are not replacing TNs—they’re augmenting them, forming a hybrid ecosystem that will define the next generation of global wireless connectivity. A major technical focus for wireless engineers is to ensure reliable transitions between satellite and terrestrial links. Interoperability between NTNs and TNs is paramount, as handover management and resource coordination will ultimately determine the success of the overall system design. For the RF community, the emergence of NTN-TN networks also signals a growing need for flexible, multi-band transceivers and robust channel modeling across variable propagation environments.

Trend #2: Channel Modeling Gets a GenAI Upgrade
Wireless engineers are now exploring the use of large language models (LLMs) within their workflows and designs. Researchers are considering how to use LLMs to enable context-aware decision-making and simplify complex wireless environment management. 

One critical process that stands to benefit from LLM integration is channel modeling. Though originally considered a supplementary feature with scalability limitations, accurate channel modeling has become an essential process for multi-user Multiple Input Multiple Output (MIMO) and beamformed systems. GenAI will allow engineers to produce more representative and actionable channel models by exploring complex scenarios that were previously unattainable. 

While LLMs may not be ready to directly control physical layer functions like beam steering yet, they can inform higher-level decisions that guide RF behavior. Early deployments will be constrained by power and compute limitations, but ongoing research into lightweight GenAI models and AI-native architectures is paving the way for scalable, edge-ready implementations. For wireless system designers, this evolution signals a growing need to align physical-layer performance with AI-driven orchestration and decision-making.