Skip to main content

Frequency Modulation (FM) & Demodulation


Principles of Frequency Modulation (FM)

Theoretical Framework:

Frequency modulation (FM) is a non-linear angle modulation technique wherein the instantaneous frequency of a high-frequency carrier wave, defined by \( c(t) = \cos(2\pi f_c t) \), is varied in linear proportion to the instantaneous amplitude of the baseband modulating signal \( m(t) \). In contrast to amplitude modulation (AM), the envelope of the FM carrier remains invariant, while the information is encoded within the temporal variations of the carrier's phase. The analytical representation of the FM signal is given by:

\( S(t) = A_c \cos\left[ 2\pi f_c t + 2\pi K_f \int_{0}^{t} m(\tau) d\tau \right] \)

Where the parameters are defined as follows:

  • \( A_c \): The constant peak amplitude of the carrier.
  • \( f_c \): The unmodulated quiescent carrier frequency.
  • \( K_f \): The frequency sensitivity constant (Hz/V), which dictates the magnitude of frequency deviation per unit of input voltage.
  • \( m(t) \): The modulating baseband signal.
  • \( \int m(\tau) d\tau \): The integral of the message signal, representing the accumulated phase deviation over time.

This mathematical formulation implies that the instantaneous frequency, \( f_i(t) = f_c + K_f m(t) \), is directly proportional to the message signal. Consequently, the carrier frequency \( f_c \) undergoes a dynamic shift determined by the amplitude of \( m(t) \), resulting in the characteristic frequency-modulated waveform.

System Architecture

m(t) ∫ dt Phase Mod Carrier Oscillator s(t)

Fig 1: Functional Block Diagram of Frequency Modulation

Modulation Index (\( \beta \)) and Deviation Ratio

The modulation index, denoted as \( \beta \), is a dimensionless parameter that characterizes the depth of the frequency deviation relative to the modulating bandwidth:

\( \beta = \frac{\Delta f}{f_m} \)

  • \(\Delta f\): Peak frequency deviation, defined as \( K_f A_m \).
  • \(f_m\): The maximum frequency component of the message signal \( m(t) \).
  • \(K_f\): The frequency sensitivity of the modulator.
  • \(A_m\): Peak amplitude of the modulating signal.

The magnitude of \( \beta \) categorizes the signal into two distinct operational modes:

  • \(\beta \ll 1\) (typically \(\beta < 0.3\)): Narrowband FM (NBFM), which exhibits spectral characteristics analogous to AM.
  • \(\beta > 1\): Wideband FM (WBFM), offering enhanced signal-to-noise ratio (SNR) and noise immunity at the expense of increased bandwidth.

Spectral Analysis in the Frequency Domain:

The Fourier expansion of an FM signal reveals a theoretically infinite series of sidebands, separated from the carrier \( \omega_c \) by integer multiples of the modulating frequency \( \omega_m \).

\( S(j\omega) = \sum_{n=-\infty}^{\infty} J_n(\beta) \cdot \delta(\omega - \omega_c - n \omega_m) \)

Component Definition:

  • \(J_n(\beta)\): The \(n^{th}\)-order Bessel function of the first kind, which determines the amplitude of each sideband.
  • \(\delta(\cdot)\): The Dirac delta function, representing discrete spectral lines.
  • \(\omega_c\) and \(\omega_m\): The angular frequencies of the carrier and message, respectively.

Bandwidth Requirements: Carson’s Rule

While the FM spectrum is infinite, the majority of the power is concentrated within a finite bandwidth. According to Carson’s Rule, the transmission bandwidth required to capture approximately 98% of the signal power is:

BW \(\approx 2 (\Delta f + f_m)\)

In the NBFM regime (\(\beta \ll 1\)), the bandwidth converges to \(2f_m\). Conversely, WBFM (\(\beta > 1\)) requires significantly more bandwidth to accommodate the higher-order sidebands necessary for high-fidelity transmission.

Frequency Demodulation Architectures

FM Input × Phase Detector Loop Filter m(t) Output VCO

Fig 2: FM Demodulation via Phase-Locked Loop (PLL) Synthesis

The depicted schematic illustrates the demodulation of an FM signal using a Phase-Locked Loop (PLL). The PLL functions as a closed-loop feedback control system that synchronizes the phase of a local voltage-controlled oscillator with the phase of the incoming FM carrier, thereby tracking the instantaneous frequency deviations.

  • Input Signal Dynamics: The received signal \( s(t) \) possesses an instantaneous phase that contains the integrated message signal. The system seeks to extract \( m(t) \) from this phase information.

  • Phase Detector (PD): This stage functions as a multiplier or comparator, generating an error signal proportional to the phase discrepancy between the input and the VCO feedback.

  • Loop Filter (LF): A low-pass filter that determines the loop dynamics and stability, suppressing high-frequency components and noise to produce a quasi-DC control voltage.

  • Voltage-Controlled Oscillator (VCO): The VCO's oscillation frequency is modulated by the filtered error voltage. To maintain phase lock, the VCO must mirror the frequency shifts of the input signal.

Signal Recovery: Under locked conditions, the control voltage required to drive the VCO in synchronization with the input frequency is directly proportional to the original modulating signal \( m(t) \), effectively achieving baseband reconstruction.



Contact Us

Name

Email *

Message *

Popular Posts

LDPC Encoding and Decoding Techniques

Low Density Parity Check (LDPC) Guide Comprehensive analysis of linear error-correcting block codes, Tanner graphs, and 5G-NR implementations. 📘 Overview 🧮 Encoding 🧩 Decoding 📚 Resources Theory Encoding Tech Tanner Graph 5G Encoding Decoding 'LDPC' is the abbreviation for 'low density parity check'. LDPC code H matrix contains very few amount of 1's and mostly zeroes. LDPC codes are error correcting code. Using LDPC codes, channel capacities that are close to the theoretical Shannon limit can be achieved. Low density parity check (LDPC) codes are linear error-correcting block code suitable for error correction in a large block sizes transmi...

Flat vs Frequency Selective Online Simulator

Flat vs Frequency Selective Online Simulator Channel Type Without Fading Flat Fading Multipaths Nakagami m SNR(dB) Run Simulation Input Signal Signal After Fading Constellation Diagram BER vs SNR Explore Advanced Flat vs Frequency-Selective Fading Simulator Want to see these equations in action? Visualize it. Launch Simulator Tool Interactive Rayleigh Fading Simulator Want to see Rayleigh fading in action? Visualize it. Launch Simulator Tool Return to DSP Simulations Main Page →

Online Simulator for ASK, FSK, and PSK Signal Generation

Interactive Digital Signal Processing (DSP) Tutorial and Simulator for ASK, FSK, and BPSK modulation techniques. Try our new Digital Signal Processing Simulator!   •   Interactive ASK, FSK, and BPSK tools updated for 2025. Start Now Digital Modulation Visualizer: ASK, FSK, & BPSK Simulator Learn and visualize binary modulation techniques (ASK, FSK, BPSK) in real-time with adjustable carrier and sampling parameters. Perfect for DSP students and engineers. 📡 ASK Simulator đŸ“ļ FSK Simulator 🎚️ BPSK Simulator 📚 More Topics ASK Modulator FSK Modulator BPSK Modulator Demodulation More Topics 1. ASK (Ampli...

Design of CMOS Flip-Flops (SR, D, JK)

Design of CMOS Flip-Flops (SR, D, JK) A flip-flop or latch is a circuit with two stable states, used to store state information. It is the basic storage element in sequential logic and a fundamental building block in digital electronics systems, including computers and communication devices. Flip-flops and latches act as data storage elements for states, pulse counting, and synchronization of variably-timed input signals to a reference clock. Flip-flops can be transparent/opaque (latches) or clocked (synchronous, edge-triggered). Latches are level-sensitive, while flip-flops are edge-sensitive. In sequential logic, the output depends on current inputs and previous states. Fig.1 shows a sequential circuit combining a combinational block and a memory element. ...

Gaussian minimum shift keying (GMSK)

📘 Overview & Theory 🧮 Simulator for GMSK 🧮 MSK and GMSK: Understanding the Relationship 🧮 MATLAB Code for GMSK 📚 Simulation Results for GMSK 📚 Q & A and Summary 📚 Further Reading Dive into the fascinating world of GMSK modulation, where continuous phase modulation and spectral efficiency come together for robust communication systems! Core Process of GMSK Modulation Phase Accumulation (Integration of Filtered Signal) After applying Gaussian filtering to the Non-Return-to-Zero (NRZ) signal, we integrate the smoothed signal to produce a continuous phase signal. For GMSK, the modulation index is $h=0.5$, meaning a bit '1' results in a phase shift of $\pi/2$: θ(t) = 2Ī€h ∫ 0 t m filtered (Ī„) dĪ„ This integration is crucial for avoiding abrupt phase transitions, ensuring smooth and continuous phase changes. Phase Mo...

UGC NET Electronic Science Previous Year Question Papers with Solutions

Download Papers and Solutions Exam Pattern Preparation Tips FAQs More Home / Engineering & Other Exams / UGC NET 2026 PYQ 📊 Exam Highlights: Electronic Science (88) Feature Details Junior Research Fellowship (JRF) ₹37,000 + HRA per month Eligibility M.Sc/M.Tech in Electronics (55%) Validity of Certificate JRF (3 Years) | Lectureship (Lifetime) đŸ“Ĩ Download UGC NET Electronics PDFs Complete collection of previous year question papers, answer keys and explanations for Subject Code 88. Start Downloading 📂 View All Question Papers June 2025 - Question Paper Download PDF June 2025 - Sol...

Q-function in BER vs SNR Calculation (with Simulation)

Q-function in BER vs. SNR Calculation In digital communications and signal processing, the Q-function plays a significant role in predicting system reliability. It allows engineers to quantify the probability that Gaussian noise will exceed a specific threshold, causing a bit error. What is the Q-function? The Q-function is a mathematical function representing the tail probability of the standard normal (Gaussian) distribution. It is the complementary cumulative distribution function (CCDF) of a standard Gaussian distribution. Q(x) = (1 / √(2Ī€)) ∫ₓ∞ e^(-t² / 2) dt The Role of the Q-function in BER vs. SNR The Q-function is the standard tool for calculating BER in systems like BPSK or QPSK over AWGN (Additive White Gaussian Noise) channels. For BPSK: In BPSK, we transmit +√E b (bit 1) and -√E b (bit 0). The decision boundary is set at 0 . If -√E b was sent, an error occurs if noise r > √...