A Gamma-distributed geometric constellation for ISAC, optimized with particle swarm optimization for joint sensing and communication metrics, matches neural network performance with fewer parameters and better compatibility with conventional hardware.
An algorithm for computing the capacity of arbitrary dis- crete memoryless channels
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cs.IT 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Two algorithms derived from Blahut-Arimoto optimality conditions recover the true channel parameters and optimal input distribution from output observations, while naive maximum-likelihood estimation fails.
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Gamma-Distributed Geometric Constellation for ISAC: Design and Analysis
A Gamma-distributed geometric constellation for ISAC, optimized with particle swarm optimization for joint sensing and communication metrics, matches neural network performance with fewer parameters and better compatibility with conventional hardware.
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Parameter Estimation of Mutual Information Maximized Channels
Two algorithms derived from Blahut-Arimoto optimality conditions recover the true channel parameters and optimal input distribution from output observations, while naive maximum-likelihood estimation fails.