Skip to main content

Pulse Position Modulation (PPM)


Pulse Position Modulation (PPM) is a type of signal modulation in which M message bits are encoded by transmitting a single pulse within one of 2แดน possible time positions within a fixed time frame. This process is repeated every T seconds, resulting in a data rate of M/T bits per second.

Pulse Position Modulation Example

Bits per symbol: m = 3

Number of slots per symbol duration: M = 2^m = 2^3 = 8

Example symbol: 101 → decimal 5 → pulse sent in slot 5 (out of 0…7)

Symbol duration: T_s = 8 ms → each time slot: T_slot = T_s / M = 8 / 8 = 1 ms

Pulse for symbol 101 is transmitted between 5 ms and 6 ms.

Timeline representation of 3-bit PPM
Slot: 0   1   2   3   4   5   6   7
Time: 0-1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 ms
Pulse →                           █
    

PPM is a form of analog modulation where the position of each pulse is varied according to the amplitude of the sampled modulating signal, while the amplitude and width of the pulses remain constant. This means only the timing (position) of the pulse carries the information.

PPM is commonly used in optical and wireless communications, especially where multipath interference is minimal or needs to be reduced. Because the information is carried in timing, it's more robust in some noisy environments compared to other modulation schemes.

Although PPM can be used for analog signal modulation, it is also used in digital communications where each pulse position represents a symbol or bit pattern. However, it is not ideal for transmitting complex data files, as it is generally used for simple or low-data-rate signaling.

PPM waveform illustration
Fig: PPM Waveforms

Demodulation of PPM Signal

Demodulation circuit for PPM


The noise-corrupted PPM waveform is received by the PPM demodulator circuit. A pulse generator produces fixed-duration pulses from the incoming PPM signal and applies these pulses to the reset (R) input of an SR flip-flop.

Simultaneously, a reference pulse train of fixed frequency is extracted (or generated) from the PPM signal and applied to the set (S) input of the flip-flop.

As a result of these set and reset signals, the SR flip-flop generates a PWM (Pulse Width Modulated) waveform at its output. The width of each pulse corresponds to the time delay between the reference pulse and the received PPM pulse — effectively recovering the original signal information.


Pulse Position Modulation (PPM) of Sinusoidal Signal

We have a sinusoidal signal:

x(t) = A sin(2ฯ€ f t)

And we want to modulate this signal using Pulse Position Modulation (PPM).


Step-by-Step Example

1. Sinusoidal Signal

Let’s define a sinusoidal signal as:

x(t) = 5 sin(2ฯ€ ⋅ 1 ⋅ t)

This is a sine wave with:

  • Amplitude: A = 5
  • Frequency: f = 1 Hz

2. Pulse Position Modulation Concept

We’ll now encode information into the timing of pulses based on this sinusoidal signal.

If the amplitude is large (positive or negative), the pulse will be farther from a reference time (i.e., delayed). If it’s small, the pulse will be closer to the reference time.

3. Calculating the Pulse Positions

We compute the values of x(t) and the corresponding pulse positions:

Time Sample 1: t = 0

x(0) = 5 sin(0) = 0

Pulse at reference time t = 0.

Time Sample 2: t = 0.1

x(0.1) = 5 sin(0.2ฯ€) ≈ 2.939
Shift = 0.1 × 2.939 = 0.294
t = 0.1 + 0.294 = 0.394 seconds

Time Sample 3: t = 0.2

x(0.2) = 5 sin(0.4ฯ€) ≈ 4.7555
Shift = 0.1 × 4.7555 = 0.47555
t = 0.2 + 0.47555 = 0.67555 seconds

Time Sample 4: t = 0.3

x(0.3) = 5 sin(0.6ฯ€) ≈ 4.045
Shift = 0.1 × 4.045 = 0.4045
t = 0.3 + 0.4045 = 0.7045 seconds

Time Sample 5: t = 0.4

x(0.4) = 5 sin(0.8ฯ€) ≈ 4.7555
Shift = 0.1 × 4.7555 = 0.47555
t = 0.4 + 0.47555 = 0.87555 seconds

Time Sample 6: t = 0.5

x(0.5) = 5 sin(ฯ€) = 0
Pulse at reference time t = 0.5

4. Summary of Pulse Timing

Pulse positions based on sinusoidal amplitude:

Sample Time (t) Amplitude x(t) Pulse Position (tpulse)
0.0 0 0.0
0.1 2.939 0.394
0.2 4.7555 0.67555
0.3 4.045 0.7045
0.4 4.7555 0.87555
0.5 0 0.5

Effect of Noise on Pulse Position Modulation

Since in a PPM system the transmitted information is contained in the relative positions of the modulated pulses, the presence of additive noise affects the performance by falsifying the perceived pulse timing. Immunity to noise can be improved by making the pulse rise rapidly, reducing the time window for noise interference.

In theory, noise would have no effect if pulses were perfectly rectangular (requiring infinite bandwidth). In practice, finite rise times mean some degradation is inevitable. Similar to continuous-wave modulation, PPM performance can be evaluated using the output signal-to-noise ratio (SNR). The figure of merit compares output SNR to channel SNR, showing that performance improves with bandwidth but suffers a threshold effect when SNR drops too low.


Further Reading

  1. MATLAB Code for Pulse Position Modulation (PPM)

People are good at skipping over material they already know!

View Related Topics to







Contact Us

Name

Email *

Message *

Popular Posts

Constellation Diagrams of ASK, PSK, and FSK with MATLAB Code + Simulator

๐Ÿ“˜ Overview of Energy per Bit (Eb / N0) ๐Ÿงฎ Online Simulator for constellation diagrams of ASK, FSK, and PSK ๐Ÿงฎ Theory behind Constellation Diagrams of ASK, FSK, and PSK ๐Ÿงฎ MATLAB Codes for Constellation Diagrams of ASK, FSK, and PSK ๐Ÿ“š Further Reading ๐Ÿ“‚ Other Topics on Constellation Diagrams of ASK, PSK, and FSK ... ๐Ÿงฎ Simulator for constellation diagrams of m-ary PSK ๐Ÿงฎ Simulator for constellation diagrams of m-ary QAM BASK (Binary ASK) Modulation: Transmits one of two signals: 0 or -√Eb, where Eb​ is the energy per bit. These signals represent binary 0 and 1.    BFSK (Binary FSK) Modulation: Transmits one of two signals: +√Eb​ ( On the y-axis, the phase shift of 90 degrees with respect to the x-axis, which is also termed phase offset ) or √Eb (on x-axis), where Eb​ is the energy per bit. These signals represent binary 0 and 1.  BPSK (Binary PSK) Modulation: Transmits one of two signals...

Fading : Slow & Fast and Large & Small Scale Fading (with MATLAB Code + Simulator)

๐Ÿ“˜ Overview ๐Ÿ“˜ LARGE SCALE FADING ๐Ÿ“˜ SMALL SCALE FADING ๐Ÿ“˜ SLOW FADING ๐Ÿ“˜ FAST FADING ๐Ÿงฎ MATLAB Codes ๐Ÿ“š Further Reading LARGE SCALE FADING The term 'Large scale fading' is used to describe variations in received signal power over a long distance, usually just considering shadowing.  Assume that a transmitter (say, a cell tower) and a receiver  (say, your smartphone) are in constant communication. Take into account the fact that you are in a moving vehicle. An obstacle, such as a tall building, comes between your cell tower and your vehicle's line of sight (LOS) path. Then you'll notice a decline in the power of your received signal on the spectrogram. Large-scale fading is the term for this type of phenomenon. SMALL SCALE FADING  Small scale fading is a term that describes rapid fluctuations in the received signal power on a small time scale. This includes multipath propagation effects as well as movement-induced Doppler fr...

Online Simulator for ASK, FSK, and PSK

Try our new Digital Signal Processing Simulator!   Start Simulator for binary ASK Modulation Message Bits (e.g. 1,0,1,0) Carrier Frequency (Hz) Sampling Frequency (Hz) Run Simulation Simulator for binary FSK Modulation Input Bits (e.g. 1,0,1,0) Freq for '1' (Hz) Freq for '0' (Hz) Sampling Rate (Hz) Visualize FSK Signal Simulator for BPSK Modulation ...

Theoretical BER vs SNR for BPSK

Theoretical Bit Error Rate (BER) vs Signal-to-Noise Ratio (SNR) for BPSK in AWGN Channel Let’s simplify the explanation for the theoretical Bit Error Rate (BER) versus Signal-to-Noise Ratio (SNR) for Binary Phase Shift Keying (BPSK) in an Additive White Gaussian Noise (AWGN) channel. Key Points Fig. 1: Constellation Diagrams of BASK, BFSK, and BPSK [↗] BPSK Modulation Transmits one of two signals: +√Eb or −√Eb , where Eb is the energy per bit. These signals represent binary 0 and 1 . AWGN Channel The channel adds Gaussian noise with zero mean and variance N₀/2 (where N₀ is the noise power spectral density). Receiver Decision The receiver decides if the received signal is closer to +√Eb (for bit 0) or −√Eb (for bit 1) . Bit Error Rat...

What is - 3dB Frequency Response? Applications ...

๐Ÿ“˜ Overview & Theory ๐Ÿ“˜ Application of -3dB Frequency Response ๐Ÿงฎ MATLAB Codes ๐Ÿงฎ Online Digital Filter Simulator ๐Ÿ“š Further Reading Filters What is -3dB Frequency Response?   Remember, for most passband filters, the magnitude response typically remains close to the peak value within the passband, varying by no more than 3 dB. This is a standard characteristic in filter design. The term '-3dB frequency response' indicates that power has decreased to 50% of its maximum or that signal voltage has reduced to 0.707 of its peak value. Specifically, The -3dB comes from either 10 Log (0.5) {in the case of power} or 20 Log (0.707) {in the case of amplitude} . Viewing the signal in the frequency domain is helpful. In electronic amplifiers, the -3 dB limit is commonly used to define the passband. It shows whether the signal remains approximately flat across the passband. For example, in pulse shapi...

Pulse Shaping using Raised Cosine Filter (with MATLAB + Simulator)

  MATLAB Code for Raised Cosine Filter Pulse Shaping clc; clear; close all ; %% ===================================================== %% PARAMETERS %% ===================================================== N = 64; % Number of OFDM subcarriers cpLen = 16; % Cyclic prefix length modOrder = 4; % QPSK oversample = 8; % Oversampling factor span = 10; % RRC filter span in symbols rolloff = 0.25; % RRC roll-off factor %% ===================================================== %% Generate Baseband OFDM Symbols %% ===================================================== data = randi([0 modOrder-1], N, 1); % Random bits txSymbols = pskmod(data, modOrder, pi/4); % QPSK modulation % IFFT to get OFDM symbol tx_ofdm = ifft(txSymbols, N); % Add cyclic prefix tx_cp = [tx_ofdm(end-cpLen+1:end); tx_ofdm]; %% ===================================================== %% Oversample the Baseband Signal %% ===============================================...

Understanding the Q-function in BASK, BFSK, and BPSK

Understanding the Q-function in BASK, BFSK, and BPSK 1. Definition of the Q-function The Q-function is the tail probability of the standard normal distribution: Q(x) = (1 / √(2ฯ€)) ∫ x ∞ e -t²/2 dt What is Q(1)? Q(1) ≈ 0.1587 This means there is about a 15.87% chance that a Gaussian random variable exceeds 1 standard deviation above the mean. What is Q(2)? Q(2) ≈ 0.0228 This means there is only a 2.28% chance that a Gaussian value exceeds 2 standard deviations above the mean. Difference Between Q(1) and Q(2) Even though the argument changes from 1 to 2 (a small increase), the probability drops drastically: Q(1) = 0.1587 → errors fairly likely Q(2) = 0.0228 → errors much rarer This shows how fast the tail of the Gaussian distribution decays. It’s also why BER drops drama...

Theoretical BER vs SNR for binary ASK, FSK, and PSK with MATLAB Code + Simulator

๐Ÿ“˜ Overview & Theory ๐Ÿงฎ MATLAB Codes ๐Ÿ“š Further Reading Theoretical BER vs SNR for Amplitude Shift Keying (ASK) The theoretical Bit Error Rate (BER) for binary ASK depends on how binary bits are mapped to signal amplitudes. For typical cases: If bits are mapped to 1 and -1, the BER is: BER = Q(√(2 × SNR)) If bits are mapped to 0 and 1, the BER becomes: BER = Q(√(SNR / 2)) Where: Q(x) is the Q-function: Q(x) = 0.5 × erfc(x / √2) SNR : Signal-to-Noise Ratio N₀ : Noise Power Spectral Density Understanding the Q-Function and BER for ASK Bit '0' transmits noise only Bit '1' transmits signal (1 + noise) Receiver decision threshold is 0.5 BER is given by: P b = Q(0.5 / ฯƒ) , where ฯƒ = √(N₀ / 2) Using SNR = (0.5)² / N₀, we get: BER = Q(√(SNR / 2)) Theoretical BER vs ...