What is precoding? What does it do?

Precoding is a signal processing technique that modifies the phases and amplitudes of wireless signals to combat channel distortion and optimize the quality and reliability of data transmissions. It can support beamforming in multiple-input, multiple-output (MIMO) and massive MIMO antenna arrays.

This article reviews some of the benefits of precoding, looks at how it’s implemented, briefly compares analog, linear, and non-linear precoding, and closes by looking at hybrid precoding.

Precoding benefits include:

Signal optimization to compensate for channel impairments, like interference and fading.
Minimize interference from nearby signals.
Focus the energy on the receiver and maximize effective signal strength.
Improve the signal-to-noise ratio (SNR) and the signal-to-interference-plus-noise ratio (SINR) to enhance quality of service (QoS) and reliability.
Maximize channel capacity and sum-rate performance of MIMO and massive MIMO antenna systems.
Adapt to channel conditions, sometimes using dynamic real-time channel state information (CSI) to maintain optimal performance.

Precoding process flow

Precoding begins with CSI estimations. CSI is gathered by sending training sequences or pilot signals from the receiver to the transmitter (Figure 1).

Figure 1. Massive MIMO system with N users and M antennas showing the flow of the pilot signals to the precoding function. (Image: IEEE Access)

Next, the CSI estimates are used to compute the precoding matrix. There are several techniques used for precoding, including analog, linear, non-linear, and hybrid. Each involves trade-offs in terms of performance, cost, and complexity.

The precoding matrix is used to generate the precoded signal required for beamforming and data transmission. At the receiver, the process is reversed, and the precoding matrix is used to obtain the original data symbols.

Analog, linear, or non-linear?

Precoding can be implemented using analog or digital techniques. Digital techniques can be subdivided into linear and non-linear approaches.

In analog precoding, adjustable phase shifters are used to control the phase of the signal at each antenna element, creating the required radiation pattern. Analog precoding is simpler and lower in cost compared to digital precoding, but it is also less flexible.

Linear digital precoding is next in terms of complexity. With linear precoding, the relationship between the transmitted signal and the received signal is a linear transformation, making it computationally efficient. Examples of linear precoding include:

Maximum Ratio Transmission (MRT). MRT precoding exploits the channel’s spatial diversity to maximize the received signal power. It’s often used in noise-limited environments. However, it does not mitigate inter-user interference, which can significantly impact performance in environments with multiple active users and high levels of interference.

Zero-Forcing (ZF), on the other hand, is designed to eliminate inter-user interference. The precoding matrix is structured to eliminate interference at the receiver. While this technique effectively eliminates interference, it can also result in noise enhancement, which negatively impacts system performance.

Non-linear digital precoding can support higher data rates and better channel capacity, but at the cost of higher complexity. Unlike linear precoding, which applies simple matrix multiplication, non-linear precoding can involve iterative algorithms or complex mathematical functions to optimize signal transmission based on specific channel information.

Dirty Paper Coding (DPC) is a common example of non-linear precoding. To implement DPC, the transmitter must have complete knowledge of the interfering signal, allowing it to subtract the interference before encoding the data. As a result, it can present the receiver with an interference-free signal.

Hybrid precoding

Hybrid precoding combines analog and digital techniques. It’s particularly useful in massive MIMO systems, where many antennas are needed to form narrow beams. However, the high cost and power consumption of multiple RF chains make fully digital precoding impractical. In a typical implementation, the digital precoding precedes the analog processing (Figure 2):

A linear digital precoder uses the CSI to cancel interference and optimize power allocation.
The analog precoder then shapes the beam pattern using phase shifters.

Figure 2. Example of a hybrid precoding architecture for massive MIMO. (Image: MDPI electronics)

Summary

Precoding is an important operation in 5G communication systems. It can enhance channel capacity, minimize power consumption, and result in higher QoS. Depending on the specific cost and performance requirements, it can be implemented using analog, digital, or hybrid techniques.