Skip to main content

Sender, Source & Channel Coding, Channel, Receiver in wireless communication - step by step



Mechanism of wireless communication - step by step:

Example: 

 

 Original Analog Message signal

 
 Sampled Message Signal (Digitalized Signal)
 
Quantized Message Signal (Mapped to Finite Signal Levels)


Encoded Signal (after Source Coding)

 
 
Then we perform channel coding to enable error correction during transmission. Then, we apply modulation and transmit the signal through a wireless medium. After receiving the signal, we first demodulate it, then apply channel decoding followed by source decoding, and finally retrieve the original message signal. 
In our case, the source message signal is analog, not digital. However, the process discussed here is applicable to Pulse Code Modulation (PCM) signal. For analog signal transmission, we simply modulate the signal with a higher frequency and then transmit it. On the receiver side, we apply the demodulation process to the received signal and retrieve the original analog message signal. 
 
 
 
 





Modern Wireless Communication Process:

 


 

Fig: Process of wireless communication

 

In the above figures, a typical wireless communication system is illustrated. The original message signal—such as digitized computer data (a bit stream of 0s and 1s)—is first sampled and then quantized. After quantization, the signal undergoes source coding, where it is efficiently encoded into binary form. To transmit this signal over a wireless medium, the binary bits are modulated using an appropriate modulation scheme.

At the receiver end, the signal is first demodulated, followed by source decoding and any additional processing needed to reconstruct the original message signal (e.g., audio). Channel coding, which is typically applied after source coding, enables error detection and correction to combat impairments like attenuation and multi-path fading introduced during transmission.

 

Wireless communication is a method of communication in which the transmitter and receiver communicate over the air or free space. Between the transmitter and the receiver, there is no wiring for wireless communication. The communication path, which is air or free space in this case, is referred to as a channel. The electrical signal is converted by the transmitter as '0' and '1'. The electric signal then transmits via the channel (air or free space) after a successful modulation procedure. The signal is then received by the receiver. It is practically difficult to recover the same signal that the transmitter sends. Due to attenuation or distortion, the signal becomes corrupted while travelling across the channel. A wireless communication system's fundamentals are as follows.

The following is a list of the various elements involved in the wireless communication process

1.Sender
2.Message
3.Encoding (source & channel coding)
4.Channel
5.Receiver
6.Decoding
7.Acknowlegement / Feedback



Sender:


Here, in communication process sender is who sends messages, files, audio, etc. to indented receiver. Here, sender send his message from smartphones, PCs, etc. using specific application.


Digitization of Message Signal in Communication Process (sampling + quantization):

In general, message signal's source is analogue in nature. Now, the analogue signal is turned into a digital signal (or, the original analogue signal is changed into '0' or'1' bits) by sampling and then quantization). Quantization helps to map the signal into finite levels. We convert analog signals to digital signals using the analog to digital converter (ADC).

There are also some exceptional cases where the source signal is not analog. The acquired images by radar, for example, are not analog signals because the image is a digital signal. After that, we process it and deliver it to the receivers on earth.


Source coding / encoding:


We are aware that the original message file is huge in size. Imagine how much memory is required to store a one-hour voice recording. It's likely that a few GB of memory is required. When we convert it to digital by just sampling at the very beginning of transmission procedure, it still requires a large number of memories to store. On the other hand, we always prefer to transmit a compressed signal over an uncompressed huge file if possible. So, we compress it. We use coding, also known as source coding, to compress the digitalized message signal. Source coding can reduce the size of a message signal. The message signal could be text, audio, or voice, for example. Text, voice, and audio messages can all benefit from source coding. For sending, original message without compressing it, it will take longer and result in more bit errors due to the larger file size. Popular examples of source coding are Huffman coding, LZW coding, etc.


Channel Coding:


After source coding, channel coding allows us to code the compressed message signal with various types of coding, such as forward error correcting (FEC) coding, so that we can recover the required message signal at the receiver terminal even if some bits are lost or distorted. Another illustration is the use of the CRC or cyclic coding technique in OFDM 4G-LTE communication to retrieve the original signal or measure the channel's status.


 

Simulation Results:

1. Suppose we are sending a text message signal 'Wireless'
 

Explore This Simulation

Explore Signal Processing Simulations

# Wireless channel are more prone to bit error than wired channels

Digital communication and its application and pictures


Next Page>>


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...

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); [...

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. ...

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...

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. ...

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...