Antenna-First for IoT: Reversing the
Design Stack to Unlock Reliable Connectivity

field interactions drive radiation, and when current paths conflict, radiated fields can interfere or even cancel each other out. In theory, multiple antennas can provide constructive field combinations, but in practice—especially in compact devices—fields often interfere destructively due to their complex, unobservable interactions during normal operation.

For MIMO systems, performance gains hinge on low negative coupling and adequate isolation between antennas. Industry guidance, such as that from Keysight, emphasizes that insufficient spacing and high negative coupling can severely degrade MIMO effectiveness. In IoT devices, ideal spacing is rarely achievable, making an antenna-first design philosophy even more critical to avoid performance collapse.

Antenna-First in Practice: A Systems Engineering Discipline

Adopting an antenna-first methodology does not mean prioritizing RF at the expense of all else. Rather, it means recognizing RF integrity as a first-class architectural constraint—on par with thermal management, power integrity, and safety—because it is inseparable from real-world device performance.

• Design from the environment outward: IoT antennas rarely operate in free space; they function near hands, walls, racks, and other challenging environments. Early modeling and testing in representative environments is essential.
• Co-design antenna, ground, and enclosure: Antenna performance is a holistic system property, shaped by losses, coupling, and enclosure interactions. Radiation efficiency depends on the total electromagnetic design, and late-stage tuning cannot reliably compensate for earlier compromises.
• Design out self-interference: Strategies such as field alignment—shaping antenna structures so radiated fields add constructively—are essential for multi-antenna devices. The guiding principle is to ensure antennas cooperate rather than compete.
• Measure what matters: Over-the-air (OTA) testing, as formalized by CTIA, 3GPP, and ETSI, is indispensable for validating full-system performance in real radiated conditions. S-parameters alone are insufficient in modern, embedded designs.

Outcome Evidence: Structural, Not Incremental, Gains

The true measure of an antenna-first approach lies in outcomes. Two metrics are particularly revealing: antenna efficiency (the fraction of accepted power that is radiated) and TRP (total radiated output in real operation). Benchmarks consistently show that antenna-first implementations can achieve efficiency levels of ~80 percent, compared to ~40 percent in conventional designs—essentially doubling radiated power and eliminating avoidable losses. These improvements are not marginal but structural, demonstrating the value of reclaiming RF architecture early in the development process.

Why This Matters Now: FWA Scale and the IoT Reliability Gap

Two macro trends are intensifying the need for antenna-first design:

• Fixed Wireless Access (FWA) is scaling rapidly: Industry forecasts, such as those from Ericsson, project FWA connections to increase from approximately 185 million at the end of 2025 to around 350 million by the end of 2031. As FWA deployments scale, customer premises equipment (CPE) antenna systems become decisive for coverage, throughput, and installation economics.
• Connectivity inclusion remains a global challenge: According to the International Telecommunication Union (ITU), around 2.6 billion people—approximately 32% of the global population—remain offline as of 2024. Extending connectivity to these users often depends on edge-device performance in difficult propagation environments, not just on core network upgrades.

In both cases, edge-device RF quality is a leverage point that can dramatically influence cost, reliability, and the reach of connectivity solutions.

The Role of AI in Antenna-First Workflows

As IoT devices become more complex—integrating more radios, more antennas, and operating in more varied environments—the design space expands dramatically. With countless parameters, coupling pathways, and competing constraints, traditional design methods reach their limits. Artificial intelligence (AI) offers powerful tools for exploring and optimizing antenna configurations efficiently, especially when electromagnetic fields cannot be directly observed during normal operation.

However, while AI can accelerate design and provide valuable insights, it cannot replace the foundational physics or the need for early, environment-driven architectural decisions. The most successful workflows will blend deep domain expertise, rigorous measurement, and advanced computational methods to unlock the full potential of antenna-first IoT design.

Conclusion

The path to reliable, scalable, and inclusive IoT deployments runs through the edge—where antennas, not just algorithms or infrastructure, determine success. By elevating the antenna system to a first-class design constraint and integrating it early in the development process, organizations can achieve step-change improvements in performance, efficiency, and reliability. In an era of rapidly expanding connectivity demands and growing complexity, antenna-first is not just a technical recommendation—it is an imperative for the next generation of IoT innovation.