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DPD (digital predistortion)

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Digital predistortion (DPD) is a signal processing technique used to compensate for nonlinearities in electronic systems, such as amplifiers, that can degrade signal quality. These nonlinearities can cause signal distortion, such as amplitude and phase distortion, that can lead to a variety of problems including reduced signal quality, increased power consumption, and interference with other signals. DPD works by applying a pre-distortion function to the input signal that compensates for the nonlinearities in the system, resulting in a cleaner and more accurate output signal.

In this article, we will discuss the fundamentals of DPD, its applications, and some of the key techniques used in DPD.

Fundamentals of DPD

The basic idea behind DPD is to pre-distort the input signal in such a way that the nonlinearities in the system cancel out the distortion introduced by the system. This is accomplished by modeling the nonlinearities in the system using a mathematical model, and then using this model to create a pre-distortion function that compensates for the nonlinearities.

The mathematical model used in DPD can take many forms, including polynomial models, lookup table models, and neural network models. The model is typically developed through a process called training, in which a known input signal is passed through the system, and the resulting output signal is measured and compared to the desired output signal. The difference between the measured and desired output signals is used to adjust the model parameters, so that the model becomes more accurate over time.

Once the model is developed, it is used to generate a pre-distortion function that is applied to the input signal. This pre-distortion function is designed to cancel out the distortion introduced by the system, so that the output signal is cleaner and more accurate.

Applications of DPD

DPD is used in a variety of applications, including wireless communication, satellite communication, and digital broadcasting. In each of these applications, DPD is used to compensate for the nonlinearities in the system, and to improve the quality and reliability of the signal.

In wireless communication, DPD is used to compensate for the nonlinearities in the power amplifiers used to transmit signals to mobile devices. These nonlinearities can cause distortion in the transmitted signal, which can lead to reduced signal quality and increased interference with other signals. By using DPD, the distortion can be reduced, resulting in a cleaner and more reliable signal.

In satellite communication, DPD is used to compensate for the nonlinearities in the transponders used to receive and transmit signals. These nonlinearities can cause distortion in the received signal, which can lead to reduced signal quality and increased interference with other signals. By using DPD, the distortion can be reduced, resulting in a cleaner and more reliable signal.

In digital broadcasting, DPD is used to compensate for the nonlinearities in the transmitters used to broadcast signals to television sets. These nonlinearities can cause distortion in the broadcast signal, which can lead to reduced signal quality and increased interference with other signals. By using DPD, the distortion can be reduced, resulting in a cleaner and more reliable signal.

Key Techniques used in DPD

There are several key techniques used in DPD, including:

  1. Baseband DPD: In baseband DPD, the pre-distortion function is generated at baseband, and then upconverted to the carrier frequency. This technique is often used in wireless communication systems, where the nonlinearities are typically found in the power amplifier.
  2. RF DPD: In RF DPD, the pre-distortion function is generated at the carrier frequency, and applied directly to the RF signal. This technique is often used in satellite communication systems, where the nonlinearities are typically found in the transponder.
  3. Memory effects: Memory effects are nonlinearities that depend on the history of the signal, rather than just the current input signal. These effects can be caused by factors such as temperature changes, aging of components, and variations in power supply voltage. Memory effects can be compensated for using DPD by using a model that takes into account the history of the signal.
  4. Adaptive DPD: In adaptive DPD, the model used to generate the pre-distortion function is continuously updated based on the measured input and output signals. This allows the DPD system to adapt to changes in the system, such as temperature changes and aging of components.
  5. Feedback DPD: In feedback DPD, the output signal is fed back to the DPD system, and used to adjust the pre-distortion function in real time. This technique can be particularly effective in compensating for memory effects.
  6. Nonlinear Least Squares (NLS) algorithm: The NLS algorithm is a commonly used algorithm for generating the pre-distortion function in DPD systems. This algorithm works by minimizing the difference between the measured output signal and the desired output signal, subject to constraints on the pre-distortion function.

Conclusion

DPD is a powerful signal processing technique that can be used to compensate for nonlinearities in electronic systems, and to improve the quality and reliability of signals in a variety of applications. By using mathematical models to generate pre-distortion functions that cancel out the distortion introduced by the system, DPD can help to reduce signal distortion, increase signal quality, and reduce interference with other signals. With the ongoing development of new technologies and applications, DPD is likely to continue to play a key role in improving the performance and reliability of electronic systems in the years to come.