In the sublinear sparsity limit the ML estimator achieves vanishing squared error below a noise threshold that coincides with the converse bound for constant-amplitude signals, proving asymptotic optimality of separable Bayesian estimators.
Or thogonal frequency division multiplexing with index modulation
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New 2D index modulation schemes integrating SM, STBC, and CIM for LPWAN outperform benchmarks in data rate and energy efficiency.
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Direct and Converse Theorems in Estimating Signals with Sublinear Sparsity
In the sublinear sparsity limit the ML estimator achieves vanishing squared error below a noise threshold that coincides with the converse bound for constant-amplitude signals, proving asymptotic optimality of separable Bayesian estimators.
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Generalized Two-Dimensional Index Modulation in the Code-Spatial Domain for LPWAN
New 2D index modulation schemes integrating SM, STBC, and CIM for LPWAN outperform benchmarks in data rate and energy efficiency.