What is the system level simulation approach for predicting the EVM of a complete radio transmitter?
System-Level EVM Simulation
System-level simulation is the primary design verification tool for modern wireless transmitters. It predicts the transmitted signal quality before hardware is built, enabling design optimization and trade-off analysis.
| Parameter | Free Space | Urban | Indoor |
|---|---|---|---|
| Path Loss Model | Friis (1/r²) | Okumura-Hata | IEEE 802.11 |
| Fading Margin | 0 dB | 10-30 dB | 5-15 dB |
| Multipath | None | Severe | Moderate-severe |
| Typical Range | Line of sight | 1-30 km | 10-100 m |
| Shadow Fading (σ) | 0 dB | 6-12 dB | 3-8 dB |
Margin Allocation
When evaluating the system level simulation approach for predicting the evm of a complete radio transmitter?, engineers must account for the specific requirements of their target application. The optimal choice depends on the frequency range, power level, environmental conditions, and cost constraints of the overall system design.
Propagation Modeling
When evaluating the system level simulation approach for predicting the evm of a complete radio transmitter?, engineers must account for the specific requirements of their target application. The optimal choice depends on the frequency range, power level, environmental conditions, and cost constraints of the overall system design.
Fade Mitigation
When evaluating the system level simulation approach for predicting the evm of a complete radio transmitter?, engineers must account for the specific requirements of their target application. The optimal choice depends on the frequency range, power level, environmental conditions, and cost constraints of the overall system design.
- Performance verification: confirm specifications against the application requirements before finalizing the design
- Environmental factors: temperature range, humidity, and vibration affect long-term reliability and parameter drift
- Cost vs. performance: evaluate whether the application demands premium components or standard commercial grades
Interference Analysis
When evaluating the system level simulation approach for predicting the evm of a complete radio transmitter?, engineers must account for the specific requirements of their target application. The optimal choice depends on the frequency range, power level, environmental conditions, and cost constraints of the overall system design.
Frequently Asked Questions
How accurate is the simulation?
Accuracy depends on the component models. With well-characterized component data (measured AM-AM/AM-PM curves, measured I/Q imbalance, measured phase noise PSD): the simulated EVM typically matches the measured EVM within 0.5-1% EVM (absolute). Without measured data (using datasheet specifications): accuracy is 1-3% EVM. The largest source of prediction error is usually the PA model (particularly memory effects at wide bandwidths) and the phase noise model (the close-in phase noise shaping significantly affects the EVM).
What simulation bandwidth is needed?
The simulation must cover at least 3-5x the signal bandwidth to capture the spectral regrowth from PA nonlinearity. For a 100 MHz 5G NR signal: simulate with at least 300-500 MHz bandwidth. The sampling rate must be at least 2x the simulation bandwidth (Nyquist): 600 MHz - 1 GHz sample rate. This means: for a 100 MHz 5G signal with 10 ms simulation time: 6-10 million complex samples. The simulation is computationally intensive but manageable on modern workstations.
Can I include DPD in the simulation?
Yes. The DPD algorithm (memory polynomial or generalized memory polynomial) is implemented in the digital domain of the simulation, before the DAC model. The simulation flow: generate the ideal signal, apply DPD, convert through the DAC model, pass through the analog chain and PA model, and measure EVM at the output. This allows: optimizing the DPD model order and memory depth, evaluating the DPD performance with different PA operating points, and predicting the ACLR (adjacent channel leakage ratio) improvement from DPD. The DPD model is trained on the simulated PA input/output data, replicating the real-time DPD adaptation process.