System design needs to rise above ìC-levelî

5Ken describes how it is now possible to choose from a wider palette than one containing just traditional software and EDA tools – and how this is especially helpful for design problems such as optimizing high-speed mixed-signal designs.

The inability to observe these system-level interactions, combined with prohibitively slow simulation times in circuit-level tools, limits the number of design variations that can be explored within the project time constraints. As a result, engineers are likely to make suboptimal tradeoffs in performance, area, and power that might lead to poor design quality, increased development time, and higher costs. Model-Based Design offers a faster, more cost-effective approach by supporting system-level integration of analog and digital components in a single multidomain environment, as shown in Figure 4.

Figure 4: Workflow for Model-Based Design that facilitates multidomain modeling of advanced wireless devices, including analog, digital, and software components.
(Click graphic to zoom)


Figure 5 shows the use of MATLAB and Simulink to model a wireless communication system based on the IEEE 802.16-2004 OFDM physical link. This model includes the baseband and RF sections of the transmitter and baseband section of the receiver. The transmitter, which is primarily a high-power amplifier, can be designed with a high-level behavioral model of a DPD amplifier using a Simulink model.

Figure 5: Using MATLAB and Simulink to model a wireless communication system based on the IEEE 802.16-2004 OFDM physical link.
(Click graphic to zoom by 1.9x)

These systems use complex digital waveforms with high data rate and spectral efficiency, so the peak-to-average power ratio (PAPR) requirement is high. In general, the power amplifier in a transmitter design must be operated in a relatively linear mode of operation to meet the spectral emission specifications of the standard. Because of the high PAPR, the amplifier in these transmitters must be backed off considerably from the peak efficiency operating point. To recover the efficiency while still meeting spectral emission specifications, the transmitter incorporates digital predistortion (DPD) of the power amplifier.

While the DPD algorithm can compensate for the nonlinear effects of the amplifier, the model has the ability to evaluate key system-level metrics, including error vector magnitude (EVM), adjacent channel leakage ratio (ACLR), and bit-error rate (BER), and their impact on the overall system performance. Designers can optimize the RF amplifier performance within the overall wireless communication system all prior to prototyping or implementation. (See Figure 6.) And designers can interface the model to measurement equipment in the test lab to further verify the efficacy of the DPD algorithm using live measured data from the actual power amplifier.

Figure 6: Behavioral model for RF power amplifier and DPD algorithm. This model enables system designers to explore, optimize, and test the algorithm design under the full range of operating conditions before building a hardware prototype.
(Click graphic to zoom by 1.9x)

Interfacing to hardware and software development tools

To maintain a continuous verification workflow, multiple interfaces are available to connect system-level models and algorithms developed in MATLAB and Simulink to digital and analog hardware simulators from major EDA vendors, as well as popular embedded software IDEs and real-time operating systems. These interfaces support cosimulation and additional automation for the code-generation process.


Some of the most pressing system-level problems are difficult to address with traditional software and EDA. Unlike C-based ESL tools, Model-Based Design tools address these issues by connecting concept generation to implementation through automatic code generation, and enable engineers to solve complex design problems such as optimizing high-speed mixed-signal designs. With interfaces to many EDA and embedded development tools, Model-Based Design with MATLAB and Simulink creates a unified workflow that alleviates the limitations of dissimilar tools and workflow gaps.

Ken Karnofsky is the Senior Strategist for Signal Processing Applications at The MathWorks. Through his 20 years of experience, first with BBN Technologies, then with The MathWorks, Ken has been involved in development and marketing of software for signal processing and data analysis technologies. Ken holds a degree in Systems Engineering from the University of Pennsylvania. He can be reached at


The MathWorks