Partial response (PR) signaling Model

Consider the generic baseband communication system model and its equivalent representation, shown in Figure 1, where the various blocks in the system are represented as filters. To have no ISI at the symbol sampling instants, the equivalent filter H(f) should satisfy Nyquist’s first criterion.

A generic communication system model and its equivalent representation
Figure 1: A generic communication system model and its equivalent representation

If the system is ideal and noiseless, it can be characterized by samples of the desired impulse response h(t). Let’s represent all the N non-zero sample values of the desired impulse response, taken at symbol sampling spacing T_{sym}, as {q_n}, for n=0,1,2,\cdots,N-1.

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The partial response signaling model, illustrated in Figure 2, is expressed as a cascaded combination of a tapped delay line filter with tap coefficients set to {q_n} and a filter with frequency response G(f). The filter Q(f) forces the desired sample values. On the other hand, the filter G(f) bandlimits the system response and at the same time it preserves the sample values from the filter Q(f) . The choice of filter coefficients for the filter Q(f) and the different choices for G(f) for satisfying Nyquist first criterion, result in different impulse response H(f), but renders identical sample values b_n in Figure 2 [1].

A generic partial response (PR) signaling model
Figure 2: A generic partial response (PR) signaling model

To have a system with minimum possible bandwidth, the filter G(f) is chosen as

equation 1 Partial response (PR) signaling Models - Nyquist filter

The inverse Fourier transform of G(f) results in a sinc pulse. The corresponding overall impulse response of the system h(t) is given by

equation 2 Partial response (PR) signaling Model impulse response

If the bandwidth can be relaxed, other ISI free pulse-shapers like raised cosine can be considered for the G(f) filter.

Given the nature of real world channels, it is not always desirable to satisfy Nyquist’s first criterion. For example, the channel in magnetic recording, exhibits spectral null at certain frequencies and therefore it defines the channel’s upper frequency limit. In such cases, it is very difficult to satisfy Nyquist first criterion. An alternative viable solution is to allow a controlled amount of ISI between the adjacent samples at the output of the equivalent filter Q(f) shown in Figure 2. This deliberate injection of controlled amount of ISI is called partial response (PR) signaling or correlative coding.

Partial Response Signaling Schemes

Several classes of PR signaling schemes and their corresponding transfer functions represented as Q(D) (where D is the delay operator) are shown in Table 1. The unit delay is equal to a delay of 1 symbol duration (T_{sym}) in a continuous time system.

Table 1: Partial response signaling schemes

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References

[1] Peter Kabal and Subbarayan Pasupathy, Partial-response signaling, IEEE Transactions on Communications, Vol. 23, No. 9, pp. 921-934, September 1975.↗

Topics in this chapter

Pulse Shaping, Matched Filtering and Partial Response Signaling
● Introduction
● Nyquist Criterion for zero ISI
● Discrete-time model for a system with pulse shaping and matched filtering
 □ Rectangular pulse shaping
 □ Sinc pulse shaping
 □ Raised-cosine pulse shaping
 □ Square-root raised-cosine pulse shaping
● Eye Diagram
● Implementing a Matched Filter system with SRRC filtering
 □ Plotting the eye diagram
 □ Performance simulation
● Partial Response Signaling Models
 □ Impulse response and frequency response of PR signaling schemes
● Precoding
 □ Implementing a modulo-M precoder
 □ Simulation and results

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3 thoughts on “Partial response (PR) signaling Model”

  1. Dear Mathuranathan,
    May I ask if the matlab code about PAM4 system with partial response signal is included in this book?
    Best regards,
    LY

    Reply
    • Hello LY,
      The book does not contain the simulation code for PAM4 with PR signal in a straight forward manner. I believe, the book has all the ingredients required to construct the aforementioned system. For example, the following sections in the book might be helpful

      Equivalent Complex baseband model for PAM modulation and demodulation (chapter 5)
      PR signal models (chapter 7)
      Impulse response and frequency response of PR signaling methods (chapter 7)
      Design and simulation of zero-forcing and MMSE equalizers (chapter 7)
      BPSK modulation with zero-forcing and MMSE equalizers – simulate BER vs. SNR curves over channels with ISI (chapter 8)

      Full table of contents available here
      https://www.gaussianwaves.com/wireless-communication-systems-in-matlab/

      Regards
      Mathuranathan

      Reply

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