channel-capacity

Name: channel-capacity
Availability: InStock
Author: parcadei

Calculate channel capacity using mutual information and the Blahut-Arimoto algorithm.

3.9k|296|Updated Dec 23, 2025

Search Tags:#data transmission #information theory #channel capacity #mutual information #coding theory #communication systems

Ready to run

npx skills add https://github.com/parcadei/Continuous-Claude-v3 --skill channel-capacity-parcadei

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: channel-capacity
Source: https://github.com/parcadei/Continuous-Claude-v3/tree/main/.claude/skills/math/information-theory/channel-capacity
Command: npx skills add https://github.com/parcadei/Continuous-Claude-v3 --skill channel-capacity-parcadei

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scipy, sympy, z3, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides strategies and tools for solving problems related to channel capacity in information theory, enabling efficient data transmission analysis.

Core Features & Use Cases

Mutual Information Calculation: Compute mutual information using various formulas and Python libraries.
Channel Modeling: Understand and represent different channel models like BSC, BEC, and AWGN.
Capacity Computation: Apply algorithms like Blahut-Arimoto and utilize symbolic math for capacity determination.
Use Case: When analyzing the maximum reliable data rate of a noisy communication channel, use this skill to calculate its theoretical capacity.

Quick Start

Use the channel-capacity skill to calculate the capacity of a Binary Symmetric Channel with a crossover probability of 0.1.

Frequently Asked Questions about channel-capacity

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I calculate channel capacity for a Binary Symmetric Channel with a specific crossover probability?▼

Channel capacity for a Binary Symmetric Channel is calculated by applying mutual information formulas to the crossover probability, yielding the maximum reliable data rate for the communication system.

What is the Blahut-Arimoto algorithm used for in information theory?▼

The Blahut-Arimoto algorithm computes channel capacity iteratively by optimizing input distributions to maximize mutual information across communication channel models.

Can I compute mutual information for AWGN channels using Python?▼

You can compute mutual information for AWGN channels using Python with scipy and sympy, applying statistical modeling and symbolic math to evaluate the theoretical capacity limits.

Does this channel capacity calculation tool support Binary Erasure Channels?▼

Yes, this tool supports Binary Erasure Channels alongside Binary Symmetric and AWGN models, computing capacity by evaluating mutual information under specific channel parameters.

What Python dependencies do I need to calculate mutual information and channel capacity?▼

Calculating mutual information and channel capacity requires scipy, sympy, and z3, which provide the statistical functions and symbolic math capabilities necessary for accurate computation.