A novel power-domain non-orthogonal multiple access (NOMA) scheme with high-dimensional modulation is proposed. Signals for two users, each of which selected from a high-dimensional modulation constellation matrix, ar...
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A novel power-domain non-orthogonal multiple access (NOMA) scheme with high-dimensional modulation is proposed. Signals for two users, each of which selected from a high-dimensional modulation constellation matrix, are superimposed on the same time-frequency resource for transmissions. While inter-user interference is treated as noise at the receiver of the far user, successive interference cancellation is used at the receiver of the near user. By analyzing the upper bounds of the detection errors, the power allocation factor is derived, which depends only on the relative power gain of the two users, i.e., the ratio between the squared of the two channel gains, but not on the operating signal-to-noise ratio. This nice feature allows us to perform user pairing easily for a system with more than two users. The optimal user pairing strategy that minimizes the total power consumption is analytically derived. Simulation results show that our proposed design outperforms some benchmark scheme.
We consider the distributional connection between the lossy compressed representation of a high-dimensional signal X using a random spherical code and the observation of X under an additive white Gaussian noise (AWGN)...
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We consider the distributional connection between the lossy compressed representation of a high-dimensional signal X using a random spherical code and the observation of X under an additive white Gaussian noise (AWGN). We show that the Wasserstein distance between a bitrate-R compressed version of X and its observation under an AWGN-channel of signa-to-noise ratio 2(2R) - 1 is bounded in the problem dimension. We utilize this fact to connect the risk of an estimator based on the compressed version of X to the risk attained by the same estimator when fed the AWGN-corrupted version of X. We demonstrate the usefulness of this connection by deriving various novel results for inference problems under compression constraints, including minimax estimation, sparse regression, compressed sensing, and universality of linear estimation in remote source coding.
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