Skip to main content
Home Wireless Communication Modulation MATLAB Beamforming Project Ideas MIMO Computer Networks

Orthogonal Code Division Multiplexing (OCDM)

 

Code Division Multiplexing Access (CDMA) is the technique used to enable multiple signals from multiple users to share a single communication channel when CDM is utilized. Individual talks are encoded in a digital sequence, and each group of users is given a shared code. The users connected with a specific code can only access the data that is available on the shared channel.

Walsh-Hadamard codes are designed to provide efficient and orthogonal coding sequences, which allow multiple signals or users to share the same frequency band without interfering with each other

Example: H2  = [1, 1 ; 1, −1)] ; 

                H4  = [1, 1, 1, 1; 1, −1, 1, −1; 1, 1, −1, −1 ; 1, −1, −1, 1 ]

Each row in the above matrices represents a codeword

The dot product of any two distinct codewords is 0


Consider that there are four stations, w, x, y, and z, that have been assigned the codes cw, cx, cy, and cz and need to transmit data dw, dx, dy, and dz, respectively

The received data by the code of station y, which is dy

    data = (dw . cw+ dx . cx+ dy  . cy+ dz . cz  ) . cy

  = dw. cw. cy + dx. cx. cy+ dy. cy. cy+ dz. cz. cy

  = 0 + 0 + dy . 4  + 0 = 4dy

The user has received data from only station y while neglecting the other codes

People are good at skipping over material they already know!

View Related Topics to

Orthogonal Code Division Multiplexing (OCDM)







CATEGORY LIST :

  1. Modulation
  2. Signal Processing
  3. MATLAB
  4. Beamforming
  5. 5G
  6. Wireless
  7. Channel Impulse Response
  8. Fourier Transform
  9. MIMO - Multiple Input Multiple Output
  10. ASK FSK PSK
  11. Constellation Diagrams
  12. GATE-ESE-NET
  13. Programming
  14. Telecommunication
  15. Computer Networks
  16. Filters
  17. Fourier Series and Fourier Transform
  18. Millimeter wave
  19. Pulse Modulation
  20. Python
  21. BER vs SNR
  22. Equalizers
  23. Applications and Games
  24. Electronics Industry
  25. Frequency bands
  26. Gaussian Random Variable
  27. QAM
  28. Spectral density estimation
  29. Wireless Communication Q & A
  30. Channel Model
  31. Convolution
  32. Image Processing
  33. IoTs
  34. Singular Value Decomposition (SVD)
  35. UWB
  36. pskmod
  37. Antenna
  38. C Programming
  39. Channel Estimation
  40. Projects
  41. Q & A
  42. Rayleigh Fading
  43. Transform
  44. Alamouti's Scheme
  45. Fading
  46. Microwave
  47. News about 5G
  48. OFDM
  49. PAM
  50. PCM
  51. Python Matrix Operations
  52. SSC Exam
  53. Web Design
  54. WordPress
  55. Ionospheric Communication
  56. JavaScript
  57. MATLAB Simulink
  58. Mobile & Accessories
  59. Signal Processing for 5G
  60. Analog Circuits
  61. Cell Towers
  62. Computer
  63. Digital Circuits
  64. Fourier Series
  65. HomePage
  66. Information and Coding Theory
  67. Laplace Transform
  68. MySQL
  69. Node.js
  70. Search
  71. ShareLinkF
  72. Z Transform

Admin & Author: Salim

profile

  Website: www.salimwireless.com
  Interests: Signal Processing, Telecommunication, 5G Technology, Present & Future Wireless Technologies, Digital Signal Processing, Computer Networks, Millimeter Wave Band Channel, Web Development
  Seeking an opportunity in the Teaching or Electronics & Telecommunication domains.
  Possess M.Tech in Electronic Communication Systems.


Contact Us

Name

Email *

Message *

Popular Posts

Channel Estimation utilizing Decision Feedback Equalizer (DFE)

  Channel estimation using DFE is a similar process to a non-linear equalization process. In DFE (decision feed equalizer), equalization error bits/symbols between the feedforward tabs and feedback taps are calculated continuously. And equalizer's tap weights tap weights are updated correspondingly.  In plain language, the error between the received bits and known training bits is calculated, and tap weights are updated accordingly. The equalizer estimates the channel impulse response (CIR) .  Once we find the channel impulse response or channel information, we can easily retrieve the original message signal from the noisy data. In the communication process, the whole system is modeled as a linear time-invariant (LTI) system. And  y = h*x + n where, y = received signal            x = transmitted signal           n = additive white Gaussian noise [Read more about the Linear time-invariant (LTI) system and convolu...

MATLAB code for GMSK

  Copy the MATLAB code from here  % The code is developed by SalimWireless.com clc; clear; close all; % Parameters samples_per_bit = 36; bit_duration = 1; num_bits = 20; sample_interval = bit_duration / samples_per_bit; time_vector = 0:sample_interval:(num_bits * bit_duration); time_vector(end) = []; % Generate and modulate binary data binary_data = randi([0, 1], 1, num_bits); modulated_bits = 2 * binary_data - 1; upsampled_signal = kron(modulated_bits, ones(1, samples_per_bit)); figure; plot(time_vector, upsampled_signal); title('Message Signal'); % Apply Gaussian filter filtered_signal = conv(GMSK_gaussian_filter1(bit_duration, samples_per_bit), upsampled_signal); filtered_signal = [filtered_signal, filtered_signal(end)]; figure; plot(filtered_signal); title('Filtered Signal'); % Integration & GMSK modulation integrated_signal = cumsum(filtered_signal); gmsk_signal = exp(1i * integrated_signal); % Plotting the real and imaginary parts of the GMSK signal ...

BER vs SNR for M-ary QAM, M-ary PSK, QPSK, BPSK, ...

Modulation Constellation Diagrams BER vs. SNR BER vs SNR for M-QAM, M-PSK, QPSk, BPSK, ... 1. What is Bit Error Rate (BER)? The abbreviation BER stands for bit error rate, which indicates how many corrupted bits are received (after the demodulation process) compared to the total number of bits sent in a communication process. It is defined as,  In mathematics, BER = (number of bits received in error / total number of transmitted bits)  On the other hand, SNR refers to the signal-to-noise power ratio. For ease of calculation, we commonly convert it to dB or decibels.   2. What is Signal the signal-to-noise ratio (SNR)? SNR = signal power/noise power (SNR is a ratio of signal power to noise power) SNR (in dB) = 10*log(signal power / noise power) [base 10] For instance, the SNR for a given communication system is 3dB. So, SNR (in ratio) = 10^{SNR (in dB) / 10} = 2 Therefore, in this instance,...

MATLAB code for BER vs SNR for M-QAM, M-PSK, QPSk, BPSK, ...

Modulation Constellation Diagrams BER vs. SNR MATLAB code for BER vs SNR for M-QAM, M-PSK, QPSk, BPSK, ...   MATLAB Script for  BER vs. SNR for M-QAM, M-PSK, QPSk, BPSK %Written by Salim Wireless %Visit www.salimwireless.com for study materials on wireless communication %or, if you want to learn how to code in MATLAB clc; clear; close all; % Parameters num_symbols = 1e5; % Number of symbols snr_db = -20:2:20; % Range of SNR values in dB % PSK orders to be tested psk_orders = [2, 4, 8, 16, 32]; % QAM orders to be tested qam_orders = [4, 16, 64, 256]; % Initialize BER arrays ber_psk_results = zeros(length(psk_orders), length(snr_db)); ber_qam_results = zeros(length(qam_orders), length(snr_db)); % BER calculation for each PSK order and SNR value for i = 1:length(psk_orders) psk_order = psk_orders(i); for j = 1:length(snr_db) % Generate random symbols data_symbols = randi([0, psk...

Comparisons among ASK, PSK, and FSK | And the definitions of each

Modulation ASK, FSK & PSK Constellation MATLAB Simulink MATLAB Code Comparisons among ASK, PSK, and FSK    Comparisons among ASK, PSK, and FSK Comparison among ASK,  FSK, and PSK Performance Comparison: 1. Noise Sensitivity:    - ASK is the most sensitive to noise due to its reliance on amplitude variations.    - PSK is less sensitive to noise compared to ASK.    - FSK is relatively more robust against noise, making it suitable for noisy environments. 2. Bandwidth Efficiency:    - PSK is the most bandwidth-efficient, requiring less bandwidth than FSK for the same data rate.    - FSK requires wider bandwidth compared to PSK.    - ASK's bandwidth efficiency lies between FSK and PSK. Bandwidth Calculator for ASK, FSK, and PSK The baud rate represents the number of symbols transmitted per second Select Modulation Type: ASK...

Constellation Diagram of ASK in Detail

A binary bit '1' is assigned a power level of E b \sqrt{E_b}  (or energy E b E_b ), while a binary bit '0' is assigned zero power (or no energy). Energy per bit (Eb): For transmission of binary ‘1' We know that all periodic signals are power signals. Now we’ll find the energy of ASK for the transmission of binary ‘1’. **where Ac is the amplitude of the carrier signal fc is the carrier frequency in Hz To save transmitter energy, Eb should be small. For transmission of binary ‘0’ Constellation Diagram In constellation diagram the function whose energy is equal to 1 is said to be a normalized function. In the above figure the reference axes corresponds to normalized functions. High-order Amplitude Shift Keying (ASK) refers to using a large number of amplitude levels to represent digital data. For instance, in binary ASK (BASK), there are two amplitude levels, usually represented as 0 and 1. High-order ASK can have more than two amplitude leve...

Constellation Diagrams of ASK, PSK, and FSK

Modulation ASK, FSK & PSK Constellation 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: +√Eb​ or -√Eb (they differ by 180 degree phase shift), where Eb​ is the energy per bit. These signals represent binary 0 and 1.  This article will primarily discuss constellation diagrams, as well as what constellation diagrams tell us and the significance of constellation diagrams. Constellation diagrams can often demonstrate how the amplitude and phase of signals or symbols differ. These two characteristics lessen the interference between t...

Differences between Baseband and Passband Modulation Techniques

  1. Frequency Translation Baseband Modulation: The signal occupies the lower end of the frequency spectrum, close to DC (0 Hz). Noise at these frequencies (such as 1/f noise or flicker noise) can significantly impact the signal.  Passband Modulation: The signal is shifted to a higher frequency range by modulating it with a carrier frequency. This translation can help to avoid low-frequency noise and interference, which are often more prevalent and stronger in the baseband. 2. Bandpass Filtering Baseband Modulation: The filtering of baseband signals is often limited by the need to preserve the low-frequency components of the signal. This makes it difficult to filter out low-frequency noise effectively. Passband Modulation: The modulated signal can be passed through a bandpass filter centered around the carrier frequency. This filter can significantly attenuate out-of-band noise, reducing the overall noise power that affects the signal. It can also help to mitigate interfer...