Skip to main content

Why do we require modulation in wireless communication?



Modulation : Why do we require modulation in wireless communication?



Wireless communication relies heavily on modulation techniques. Coaxial cable, twisted pair, and other types of wired communication are commonly used. Wired communication, on the other hand, is best for short-distance communication. An antenna is required for wireless communication to transfer signals. Now, if we want to send a baseband signal, we'll need a very large antenna with a range of many kilometers. Baseband signals are ones that typically contain a low or medium frequency message signal. It's also known as a message signal without modulation. Modulation is a technique for increasing the frequency of a message signal by the carrier frequency to a significantly higher frequency.
So, now I'll explain why modulation is necessary. The main two goals of modulation techniques are to reduce antenna height and to multiplex data (Multiplexing).


 Long-Distance Communication

Low-frequency base-band signals are not suitable for long-distance communication because they cannot generate a strong far field. The far field is the region where electromagnetic waves become plane waves and true wireless transmission occurs.

At low frequencies, the signal primarily creates a strong near field, which stores energy close to the antenna instead of radiating it efficiently into space. As a result, most of the energy remains near the source and does not propagate over long distances.

 

Reducing the height of antenna:

For short-range baseband communication, wired communication is sufficient. However, for long-distance communication, wireless is the best option. We know that the transmitter antenna emits a specific radiation pattern. The signal then travels through the earth's atmosphere until it reaches the receiver. We should be aware that antenna height is important for reliable transmission. The wavelength of the working frequency determines the antenna height. The antenna height should be

Ht = λ/4 and λ = c/f

Where, Ht = height of antenna

λ = wavelength of operating frequency

c = speed of light

f = operating frequency

We can also derive the following equations from the above equation:

As Ht = λ/4,

Therefore, Ht =c/4f; or Ht α 1/f

So, we can say that antenna’s height is inversely proportional to the operating frequency. If frequency is more, height of antenna should be smaller and vice-versa for reliable communication.

Example:

Let assume, operating frequency of a communication band is 20 KHz. Then the height of antenna, Ht, should be

Ht = c/4f = (3*10^8) / (4*20*10^3) = 3.75 kilometers

We can observe that the antenna height for reliable 20 KHz band communication should be 3.75 kilometers. As a result, we choose modulation, in which the lower frequency is shifted to the higher frequency. It is a method of enhancing transmission frequency while lowering antenna height.

On the other hand, if you use 100 MHz carrier signal then you only need a 0.75 meter antenna, which is perfectly practical for wireless communication (like FM radios, smartphones, etc.).


Multiplexing:

Multiplexing is another significant advantage of modulation techniques. The parallel data streams are multiplexed into a serial data stream. Simply put, modulation techniques allow us to deliver many simultaneous data streams from the transmitter to the receiver across a single channel. To your knowledge, we use the modulation technology to enable multiplexing in wired connection as well.

Noise Immunity and Signal Strength

Modulation schemes can be designed to make signals more resistant to noise, distortion, and interference.
For example, digital modulation techniques like QAM, PSK, or FSK improve bit error rate performance under noisy conditions.

Bandwidth Utilization

Modulation allows better utilization of available bandwidth, especially with advanced techniques like OFDM. It enables the transmission of high data rates in limited spectral resources.

Adaptability

Adaptive modulation helps wireless systems dynamically change their modulation scheme depending on channel conditions (e.g., switching from QPSK to 64-QAM in 4G/5G).

 

Further Reading

  1. Analog Modulation Techniques
  2. Digital Modulation Techniques  
  3. Importance of Modem in Telecommunication




Contact Us

Name

Email *

Message *

Popular Posts

CMOS Logic Gates: Inverter, NAND, and NOR

CMOS Logic Gates: Inverter, NAND, and NOR Complementary metal–oxide–semiconductor (CMOS) technology forms the backbone of modern digital IC design. Patented by Frank Wanlass in 1963 ( US patent 3,356,858 ), CMOS enables low-power, high-speed, and noise-immune logic circuits. CMOS gates, including inverters, NAND, and NOR, are widely used in both large-scale and small-scale integrated circuits due to their efficiency and versatility. CMOS Inverter A CMOS inverter consists of a complementary pair of PMOS and NMOS transistors. The PMOS transistor connects to the supply voltage V DD , while the NMOS connects to ground. The input voltage V IN controls both gates, and the output voltage V OUT is taken from the connected drains. CMOS inverters are highly energy-efficient and produce minimal heat due to their comple...

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) 📥 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 2026 - Question Paper Download PDF June 202...

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. ...

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 Flat Fading and Frequency-Selective Fading Effects of Multipath Components and the Nakagami-m Parameter 1. Flat Fading Flat fading occurs when the signal bandwidth is smaller than the channel's coherence bandwidth, meaning that all frequency components of the transmitted signal experience approximately the same fading. It is typically associated with narrowband signals, where the multipath delay spread is small relative to the symbol duration. ...

Decision Feedback Equalizer (DFE) in MATLAB

  MATLAB Code M = 4; % QPSK numSymbols = 10000; numTrainingSymbols = 5000; chtaps = [1 0.5*exp(1i*pi/6) 0.1*exp(-1i*pi/8)]; % Generate QPSK symbols data = randi([0 M-1], numSymbols, 1); tx = pskmod(data, M, pi/4); % DFE setup eq = comm.DecisionFeedbackEqualizer; eq.ReferenceTap = 1; eq.NumForwardTaps = 5; eq.NumFeedbackTaps = 3; % SNR sweep snrVec = 0:5:25; % 0,5,10,...25 dB berBefore = zeros(size(snrVec)); berAfter = zeros(size(snrVec)); for k = 1:length(snrVec) % Apply channel and AWGN rx = awgn(filter(chtaps,1,tx), snrVec(k), 'measured' ); % BER before equalization rxDataNoEq = pskdemod(rx, M, pi/4); [~, berBefore(k)] = biterr(data, rxDataNoEq); % Equalize [y,~,~] = eq(rx, tx(1:numTrainingSymbols)); % Phase correction phaseOffset = angle(mean(conj(tx(1:numTrainingSymbols)) .* y(1:numTrainingSymbols))); yCorrected = y * exp(-1i*phaseOffset); % BER after equalization rxDataEq = pskdemod(yCorrected, M, pi/4); [...

MUSIC Algorithm Explained (with MATLAB + Simulator)

Practical Implementation of the MUSIC Algorithm The focus is on how the algorithm works computationally , not just theory, and it explains the denominator (a H E n E n H a) mathematically and intuitively. 1. Introduction The MUSIC (Multiple Signal Classification) algorithm is a high-resolution method used in signal processing and array processing to estimate the Direction of Arrival (DOA) of signals received by a sensor array. Unlike classical beamforming methods, MUSIC uses eigenvector decomposition of the covariance matrix to separate the signal subspace and noise subspace , allowing it to achieve much higher angular resolution. In practical implementations, MUSIC works by: Simulating or collecting array signals Computing the covariance matrix Performing eigenvalue decomposition Separating signal and noise subspaces Scanning possible angles using a steering vector Constructing a pseudo-spectrum where peaks indicate signal directions 2. Signal Mo...

Linear vs Circular Convolution

Linear vs Circular Convolution Linear convolution vs circular convolution Linear convolution (what nature does) If: x[n] = transmitted signal h[n] = channel impulse response (multipath) Then the received signal is: y[n] = x[n] * h[n] This means: Past symbols spill into future symbols Causes inter-symbol interference (ISI) DFT of y[n] is not a simple product Circular convolution (what DFT assumes) The DFT assumes periodic signals : y[n] = x[n] ⊛ h[n], where indices wrap around modulo N Only under circular convolution does the DFT satisfy: Y[k] = X[k] · H[k] This is what enables one-tap frequency-domain equalization . What happens in the real channel (linear convolution) The channel performs linear convolution: y[n] = Σ l=0 Lh−1 h[l] x[n-l] But because: CP absorbs the “spillover” Receiver discards CP before DFT The useful part of the received symbol becomes: y[n] = Σ l=0 Lh−1 h[l] x[(n-l) mod N] Modulo inde...

Superheterodyne Receiver Online Simulator

Superheterodyne Receiver Simulator Simulation Controls Message Frequency (Hz): Carrier Frequency (Hz): Intermediate Frequency (Hz): Sampling Frequency (Hz): Simulation Duration (s): Run Simulation Signal Plots Numerical Values (first 10 samples) Superheterodyne Receiver Workflow The superheterodyne receiver converts a received RF signal to an intermediate frequency (IF) to simplify filtering and amplification, and finally recovers the original message signal. Steps: Input Message Signal: A low-frequency signal m(t) (e.g., sine wave). RF Modulation (AM): The message modulates the carrier with amplitude modulation: RF(t) = [1 + K_a * m(t)] * cos(2Ï€ f_c t) Mixing with Local Oscillator (LO): The RF signal is multiplied by a local oscillator LO(t) = cos(2Ï€ f_LO t) to produce: IF(t) = RF(t) * LO(t) Intermediate Frequency Filtering: A low-pass filter extracts t...