j991222/MIMO_JCESD
A Comparative Study of Deep Learning and Iterative Algorithms for Joint Channel Estimation and Signal Detection in OFDM Systems
This project helps researchers and engineers working with wireless communication systems, specifically those using Orthogonal Frequency Division Multiplexing (OFDM). It provides tools to evaluate different methods for simultaneously estimating the communication channel and detecting signals. You input simulated or real-world OFDM signal data, and it outputs performance comparisons of deep learning and iterative algorithms for these critical tasks.
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Use this if you are a telecommunications researcher or engineer comparing the effectiveness of deep learning and traditional iterative algorithms for channel estimation and signal detection in OFDM systems.
Not ideal if you are looking for a general-purpose library for building new deep learning models from scratch, rather than comparing specific existing algorithms for OFDM.
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64
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10
Language
Python
License
MIT
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Last pushed
Jun 21, 2024
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