Role of an Equalizer in Channel Estimation

Equalizers in Wireless Communication

Why Equalizers Are Needed

Wireless channels distort signals due to:

• Multipath propagation → Inter-Symbol Interference (ISI)
• Frequency-selective fading → some frequencies attenuated more
• Noise → Additive White Gaussian Noise (AWGN)

Received signal model:

r(t) = s(t) * h(t) + n(t)

• s(t): transmitted signal
• h(t): channel impulse response
• n(t): noise

Goal of the equalizer: Recover s(t) from r(t) by compensating for the channel h(t).

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Mathematical Model of a Simple Wireless Equalizer

r[n] = Σ (h[k] * s[n-k]) + n[n],   k = 0..L-1

The equalizer applies a filter w[m] to estimate s[n]:

ŝ[n] = Σ (w[m] * r[n-m]),   m = 0..M-1

Goal: Minimize Mean Square Error (MSE):

min_w E[ |s[n] - ŝ[n]|² ]

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Types of Equalizers

Linear Equalizer

• Simple FIR filter w[m]
• Zero-Forcing (ZF) equalizer: W_ZF = H⁻¹
• Disadvantage: amplifies noise in weak channel frequencies

Minimum Mean Square Error (MMSE) Equalizer

• Minimizes MSE considering noise

W_MMSE = (Hᴴ H + σ_n² I)⁻¹ Hᴴ

Decision Feedback Equalizer (DFE)

• Uses previous detected symbols to cancel ISI
• Combines feedforward and feedback filters

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Frequency-Domain View

If the channel is frequency-selective:

R(f) = H(f) S(f) + N(f)

Frequency-domain equalization:

Ŝ(f) = W(f) * R(f)

This is similar to audio equalizers: shape the frequency response to recover the original signal.

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Simple Example: 2-Tap Channel

Channel: h[0] = 1, h[1] = 0.5

r[n] = s[n] + 0.5 s[n-1] + n[n]

Linear equalizer coefficients w[0], w[1] chosen such that:

ŝ[n] = w[0] r[n] + w[1] r[n-1] ≈ s[n]

Solution via MSE minimization approximately recovers s[n].

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Summary

• Equalizers undo channel distortion.
• Crucial for multipath channels and frequency-selective fading.
• Can be time-domain (FIR/IIR) or frequency-domain (FFT-based).
• Trade-off between ISI reduction and noise enhancement (ZF vs MMSE).
• Often combined with adaptive algorithms (LMS, RLS) in time-varying channels.
• Wireless channels distort signals → equalizers restore them.
• Discrete-time model: ŝ[n] = Σ w[m] r[n-m]
• Linear equalizer: direct FIR filter
• MMSE equalizer: balances noise and ISI
• Frequency-domain equalizer: multiplies by 1/H(f)
• DFE: cancels ISI using past decisions

 

In general wireless communication systems are modeled as linear time-invariant (LTI) systems. The received signal is considered the convolution of a transmitted signal and channel input response (CIR) in the time domain. In the frequency domain, we observe a slight frequency shift. To retrieve the original signal at the receiver side, we need to go through the 'deconvolution' process. There the no standard process named 'deconvolution' in the case of wireless communication. The equalization process does the same job.

The function of an Equalizer

The channel estimate is followed by the equalizer's operation. A signal processing procedure known as equalization decreases inter-symbol interference, or ISI. Equalization is the reversal of distortion that a signal experiences during channel transmission. Since equalization is an inverse channel filter, we can say that.

When we transmit a signal from the transmitter side, it reaches at receiver with different time delays. So, a shift frequency shift occurs. The main function of an equalizer is to estimate the original signal from known pilot bits.

with the help of an equalizer, we can calculate the channel impulse response from the received bits/symbols and training bits.

Further Reading

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