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Found 9 papers, 0 papers with code
Sherman-Morrison Regularization for ELAA Iterative Linear Precoding
The design of iterative linear precoding is recently challenged by extremely large aperture array (ELAA) systems, where conventional preconditioning techniques could hardly improve the channel condition.
Robot Subset Selection for Swarm Lifetime Maximization in Computation Offloading with Correlated Data Sources
When the number of subgraphs is maximized, the proposed subset selection approach is shown to be optimum in the AWGN channel.
Constellation-Oriented Perturbation for Scalable-Complexity MIMO Nonlinear Precoding
The basic concept of COP is to apply vector perturbation (VP) in the constellation domain instead of symbol domain; as often used in conventional techniques.
Power Allocation for FDMA-URLLC Downlink with Random Channel Assignment
With imperfect CSIT, the proposed approach can still provide remarkable user capacity at limited cost of transmit-power efficiency.
Network-ELAA Beamforming and Coverage Analysis for eMBB/URLLC in Spatially Non-Stationary Rician Channels
Finally, it is shown that the network-ELAA can offer significant coverage extension (50% or more in most of cases) when comparing with the single-AP scenario.
Massive-MIMO MF Beamforming with or without Grouped STBC for Ultra-Reliable Single-Shot Transmission Using Aged CSIT
In addition, a combinatorial approach of the MF beamforming and grouped space-time block code (G-STBC) is proposed to further mitigate the detrimental impact of the CSIT uncertainty.
End-to-End Learning for Uplink MU-SIMO Joint Transmitter and Non-Coherent Receiver Design in Fading Channels
In this paper, a novel end-to-end learning approach, namely JTRD-Net, is proposed for uplink multiuser single-input multiple-output (MU-SIMO) joint transmitter and non-coherent receiver design (JTRD) in fading channels.
On Deep Learning Solutions for Joint Transmitter and Noncoherent Receiver Design in MU-MIMO Systems
Otherwise, it is called non-systematic waveform, where no artificial design is involved.
A Modular Neural Network Based Deep Learning Approach for MIMO Signal Detection
Motivated by this finding, we propose a novel modular neural network based approach, termed MNNet, where the whole network is formed by a set of pre-defined ANN modules.
