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What is CSI?

At the heart of almost every Wi-Fi sensing system is Channel State Information (CSI). As previously mentioned, CSI is the metric used to mitigate the effects of the transmission channel on the signal, and optimize aspects such as beamforming, power allocation, and modulation schemes. 1

Assuming a transmitter wants to transmit a baseband signal \( X \), as it travels through the channel, it gets affected by the physical environment. We represent this channel state as a complex scalar \( H = I + jQ \), where the real and imaginary parts encapsulate the physical distortion.

The receiver captures the distorted signal as \( Y \), which is a combination of the original signal, the channel’s effect, and additive noise \( N \). For a single point in time or a single sample, this leads to the equation: $$Y = H \cdot X + N$$

It can be inferred from the above equation that if we wish to recover the original signal \( X \) from the received signal \( Y \), we need to know the channel’s state \( H \). To acquire \( H \), the system performs channel estimation. This is the process of prepending known training symbols in the Wi-Fi preamble, known as Long Training Fields (LTFs). Because the receiver already knows the exact sequence of the transmitted LTF (\( X \)), it measures the received sequence (\( Y \)) and solves for \( H \). 1

Symbols are specific sequences of bits that are mapped to specific modulation states, allowing the connection to utilize both phase and amplitude to encode information. Thus, a symbol can represent multiple bits of information via higher-order modulation schemes such as Quadrature Amplitude Modulation (QAM). However, in noisy and interference-prone channels, lower-order modulation schemes such as Binary Phase Shift Keying (BPSK), which offer lower throughput but higher robustness, may be preferred. Rate adaptation based on CSI is one way systems mitigate channel degradation.

Wi-Fi uses Orthogonal Frequency Division Multiplexing (OFDM), which divides the frequency channel into multiple subcarriers. The three types of subcarriers are data, pilot, and guard/null subcarriers.

  • Data subcarriers transmit the actual user payload.
  • Pilot subcarriers transmit known reference symbols continuously during the payload to track and correct phase drift over time.
  • Guard/null subcarriers are set to zero (empty) to prevent interference between adjacent frequency channels and to prevent hardware leakage at the center frequency.

In terms of sensing, we make use of the channel estimates derived from the data and pilot subcarriers to extract meaningful features about the physical environment.

It should be noted that in MIMO systems, where the transmitter possesses \( M \) antennas and the receiver has \( N \) antennas, there will be an \( M \times N \) channel matrix. This means we get \( M \times N \times S \) complex CSI values, where \( S \) is the number of usable subcarriers. \( S \) depends on the Wi-Fi protocol and band in use. For example, while a 20 MHz 802.11n channel uses a 64-point FFT, the guard and null subcarriers are discarded, resulting in exactly 52 usable CSI subcarriers per antenna pair. 1

Since the IEEE 802.11ax amendment, OFDM was upgraded to OFDMA, which divides each frequency band into multiple resource units (RUs) to make multi-user frequency multiplexing possible. We, however, are mainly concerned with the concept of subcarriers, which remain the fundamental building blocks of both OFDM and OFDMA. 2

What can we do with CSI?

Raw Channel State Information is inherently noisy and difficult to interpret directly. To extract utility from the raw complex numbers, the system must process the data through feature extraction and subsequent denoising pipelines.

Feature Extraction

We can convert the raw complex pairs into two clear physical metrics: amplitude and phase.

  • Amplitude (Attenuation): Represents the signal power degradation caused by the channel (e.g., physical obstacles or distance). It is calculated as the magnitude of the complex number: $$|H| = \sqrt{I^2 + Q^2}$$
  • Phase (Shift): Represents the delay and multipath reflection introduced by the physical environment. It is calculated as the angle: $$\theta = \arctan\left(\frac{Q}{I}\right)$$

Alongside the raw CSI arrays, hardware drivers typically report macro-level metrics such as the Received Signal Strength Indicator (RSSI) and Signal-to-Noise Ratio (SNR). By feeding the raw CSI matrices and these supplementary features into various correlators, classifiers, and filtering algorithms, we can extract insights about the environment, such as human presence, activity recognition, and gesture recognition. 1 2

Summary

In this introductory chapter, we looked at:

  • The premise of Wi-Fi Sensing.
  • The optimal use cases for Wi-Fi Sensing.
  • CSI as the ideal metric to reconstruct the wave’s state and form a picture of the environment.
  • The structure of CSI, and what physical data we can construct from it.
  • The variation in CSI dimensions as we change the Wi-Fi protocol, bandwidth, and antenna configurations.

  1. Armenta-Garcia, J.A.; Gonzalez-Navarro, F.F.; Caro-Gutierrez, J.; Garcia-Reyes, C.I. Tools and Methods for Achieving Wi-Fi Sensing in Embedded Devices. Sensors 2025, 25, 6220. https://doi.org/10.3390/s25196220 ↩ ↩2 ↩3 ↩4

  2. Steven M. Hernandez and Eyuphan Bulut. 2023. WiFi Sensing on the Edge: Signal Processing Techniques and Challenges for Real-World Systems. Commun. Surveys Tuts. 25, 1 (Firstquarter 2023), 46–76. https://doi.org/10.1109/COMST.2022.3209144 ↩ ↩2