As advanced radios integrate RF and digital technologies to a degree never seen before, RF and digital engineers need to understand how the RF front end affects system performance. Moreover, they must know how to partition designs between RF/analog and digital components to meet the performance and efficiency requirements of emerging 5G and IoT applications.
Today, RF engineering is system engineering. Developing agile or smart RF systems requires multiple design skills, including system architecture, DSP, RF, antenna, mixed signal, digital hardware, and embedded software. Most teams don’t have expertise in all of those areas. Even when they do, each specialist typically uses his or her favored tool. This makes system integration increasingly difficult, and pushes discovery of critical problems to the end of the development process when they’re most expensive to fix.
This challenge has different impacts at different stages of development. For example, researchers can’t effectively explore 5G hybrid beamforming techniques when they use different tools for digital and RF design. Advanced technology teams can’t prove their concepts in hardware prototypes when they have to rely on other teams for RTL implementation. And design teams are spending far too much time debugging highly integrated radio designs in the hardware lab or in the field.
Adapting to Incorporate New Tech, Design Environments
Emerging approaches are also adding fuel to the fire. Take, for example, technologies being developed for 5G, such as massive MIMO, mmWave, and the latest modulation schemes that require innovative combinations of new baseband technologies and RF architectures. Or, consider IoT devices that require power-efficient RF modules to add wireless connectivity. These technologies only deepen the need for highly integrated design environments and flexible connectivity to prototyping and test hardware.
These challenges have spurred advances in modeling and simulation software to deliver:
• Improved integration of RF, antenna, and digital modeling and simulation
• Faster simulation of complex RF architectures to facilitate rapid design exploration
• Connectivity to a range of SDR and RF test hardware to accelerate and lower the cost of prototyping and design verification
Modeling the Latest Wireless Systems
Design teams can use software like MATLAB and Simulink as a common platform to integrate highly accurate RF and antenna modeling with advanced DSP algorithm design and implementation. This enables more effective collaboration among RF, digital, and system engineers, allowing for faster development cycles and more thorough design verification.
Similarly, system engineers are able to quickly build reference designs and use system-level simulation to provide insight into component interactions. This exposes integration issues before building hardware, and enables more rigorous system verification much earlier in the development process.
Performing complete algorithm-to-antenna simulations is the first step in a Model-Based Design workflow. Simulations can eliminate many system-level and integration errors before building hardware. Within this workflow, engineers can automatically generate code from the models for hardware and software implementation of algorithms. As a result, algorithm designers can quickly develop hardware prototypes that create production quality IP implementations.
The models also provide a reusable testbench throughout the development process, saving time and ensuring consistency of testing. These combined capabilities enable faster design iterations and streamline verification. An upfront investment in modeling and code generation has been shown to reduce overall development time by 30% or more.
Today, leading-edge designers are using platforms like Model-Based Design to simulate the integration of RF front ends, allowing them to identify and fix issues earlier and at lower cost. As RF design expands to include emerging capabilities and complex applications, a more integrated and advanced approach is not only advised, but required for continued growth.
