Communications through a noisy channel

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SUMMARY

The discussion rigorously analyzes symbol transmission and decoding probabilities over a noisy binary channel using error probabilities \(\epsilon_0\) and \(\epsilon_1\). It establishes that the probability of correctly receiving the kth symbol is \(p(1-\epsilon_0) + (1-p)(1-\epsilon_1)\). For a 4-symbol sequence "1011", the correct reception probability is \((1-\epsilon_1)^3 (1-\epsilon_0)\). The decoding strategy using majority voting on three repeated symbols improves accuracy, with the probability of correctly decoding 0 exceeding the direct reception probability when \(0 < \epsilon_0 < \frac{1}{2}\). Bayesian inference is applied to compute posterior probabilities such as \(P(0 \text{ transmitted} | 101 \text{ received})\), incorporating prior probabilities and conditional error rates.

PREREQUISITES

  • Binary Symmetric Channel (BSC) error modeling
  • Conditional probability and Bayes' theorem
  • Majority decoding in error correction codes
  • Probability mass functions for discrete random variables

NEXT STEPS

  • Explore advanced error correction codes like Hamming and Reed-Solomon codes
  • Study channel capacity and Shannon's noisy channel coding theorem
  • Implement Bayesian decoding algorithms for noisy channels
  • Analyze performance metrics such as bit error rate (BER) and frame error rate (FER)

USEFUL FOR

Communication engineers, information theorists, and computer scientists working on digital communication systems, error correction coding, and probabilistic decoding methods will benefit from this discussion.

Kakashi
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Homework Statement
A source transmits a message through a noisy channel. Each symbol is 0 or with probability p and 1-p respectively and is received incorrectly with probability $$ \epsilon_{0} $$ and $$ \epsilon_{1} $$ respectively. Errors in different symbol transmissions are independent.

a) What is the probability that the kth symbol is received correctly?

b) What is the probability that the string of symbols 1011 is received correctly?

c) In an effort to improve reliability each symbol is transmitted three times and the received string is decoded by majority rule. In other words, 0 or 1 is transmitted as 000 or 111 respectively and its decoded at the receiver as 0 or 1 if and only if the received string contains atleast two 0's or 1s respectively. What is the probability that a 0 is correctly decoded?

d) For what values of $$ \epsilon_{0} $$ is there an improvement in the probability of correct decoding of a 0 when c) is used?

e) Suppose that c) is used. What is the probability that a symbol was 0 given that the received string is 101?
Relevant Equations
Bayes Rules
Total Probability Law
Independence
a) The kth symbol can be 0 or 1. If 0 or 1 are transmitted they can either be received correctly or incorrect.

P(kth symbol is transmitted correctly)=P(0 is transmitted and received correctly)+P(1 is transmitted and received correctly)=$$p(1-\epsilon_{0})+(1-p)(1-\epsilon_{1}) $$

b) P(1011 is received correctly|1011 is transmitted)=P(1011 is received correctly and transmitted)/P(1011 is transmitted)= $$ \frac{p(1-\epsilon_{1})^{3}(1-\epsilon_{0})}{p}=(1-\epsilon_{1})^{3}(1-\epsilon_{0}) $$

c) P(0 is correctly decoded| 0 is transmitted)=P(Three symbol string contains atleast two 0's| 0 is transmitted)=(P(000 and 0 is transmitted)+P(001 and 0 is transmitted)+P(010 and 0 is transmitted)+P(100 and 0 is transmitted))/P(0 is transmitted)=$$\frac{p(1-\epsilon_{0})^{3}+3p(1-\epsilon_{0})^{2}\epsilon_{0}}{p}=(1-\epsilon_{0})^{3}+3(1-\epsilon_{0})^{2}\epsilon_{0} $$

d) P(0 is correctly decoded| 0 is transmitted)>P(0 is received correctly|0 is transmitted)
$$ (1-\epsilon_{0})^{3}+3(1-\epsilon_{0})^{2}\epsilon_{0}>(1-\epsilon_{0}) $$
$$(1-\epsilon_{0})^{2}+3(1-\epsilon_{0})\epsilon_{0}>1 $$
$$ 0<\epsilon_{0}<\frac{1}{2} $$

e) P(0 is transmitted | 101 is received)=P(0 and 101 is received)/P(101 is received)=$$\frac{p\epsilon_{0}^{2}(1-\epsilon_{0})}{p\epsilon_{0}^{2}(1-\epsilon_{0})+(1-p)(1-\epsilon_{1})^2\epsilon_{1}} $$
P(0 and 101 is received)=P(101 is received| 0 is transmitted)P(0)=$$ p\epsilon_{0}^{2}(1-\epsilon_{0}) $$
101 can be trasmitted if the symbol is 0 or 1.
P(101)=P(101 is received and 0 is transmitted)+P(101 is received and 1 is transmitted)=$$ p\epsilon_{0}^{2}(1-\epsilon_{0})+(1-p)(1-\epsilon_{1})^2\epsilon_{1} $$
 

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