Skip to main content

ASCII to Binary using MATLAB and Vice Versa

 

MATLAB Script



Output

Recovered_ASCII =

    'Wireless Communication'

 


Code Explanation:

  • dec2bin(ASCII): Converts the text string into a matrix of binary characters.
  • bitget(..., 7:-1:1): Extracts individual bits. We use 7 bits because standard ASCII is 7-bit based.
  • y1_data: This vector stores the final Serial Bitstream used for transmission.
  • char(bin2dec(y3_data)): The inverse process used at the receiver to recover the original message.

Understanding the Logic

In digital wireless systems, information is transmitted as a series of 0s and 1s. This process involves Source Encoding, where characters (ASCII) are converted into binary format. This MATLAB script demonstrates Serialization—the process of converting parallel character data into a serial bitstream ready for modulation (like BPSK or QAM).


ASCII to Binary Reference Table

The following table shows how the first few characters of "Wireless" are converted into 7-bit binary sequences as used in the MATLAB script above.

Character Decimal (ASCII) Binary (7-bit Representation)
W 87 1010111
i 105 1101001
r 114 1110010
e 101 1100101

Note: While modern systems often use 8-bit (UTF-8), classical digital communication simulations frequently use 7-bit ASCII to save bandwidth.


5G & IoT Application

This serialization logic is fundamental for creating data packets in IoT sensors and 5G NR (New Radio) transport blocks.

Error Correction

Once in binary form, you can apply Hamming Codes or Convolutional Coding to protect the data from channel noise.


Frequently Asked Questions

Why does my binary conversion look different for different characters?

Standard ASCII uses 7 bits, but extended ASCII uses 8 bits. Ensure your bitget range matches your encoding standard.

Can this script handle special characters?

Yes, as long as they are within the ASCII/Unicode range supported by the dec2bin function in MATLAB.



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...