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Found 6 papers, 2 papers with code
Lightweight Deep Learning Based Channel Estimation for Extremely Large-Scale Massive MIMO Systems
Extremely large-scale massive multiple-input multiple-output (XL-MIMO) systems introduce the much higher channel dimensionality and incur the additional near-field propagation effect, aggravating the computation load and the difficulty to acquire the prior knowledge for channel estimation.
Edge Semantic Cognitive Intelligence for 6G Networks: Novel Theoretical Models, Enabling Framework, and Typical Applications
Edge intelligence is anticipated to underlay the pathway to connected intelligence for 6G networks, but the organic confluence of edge computing and artificial intelligence still needs to be carefully treated.
Deep Multi-Stage CSI Acquisition for Reconfigurable Intelligent Surface Aided MIMO Systems
This article aims to reduce huge pilot overhead when estimating the reconfigurable intelligent surface (RIS) relayed wireless channel.
Acquisition of Channel State Information for mmWave Massive MIMO: Traditional and Machine Learning-based Approaches
The accuracy of channel state information (CSI) acquisition directly affects the performance of millimeter wave (mmWave) communications.
Framework on Deep Learning Based Joint Hybrid Processing for mmWave Massive MIMO Systems
For millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, hybrid processing architecture is essential to significantly reduce the complexity and cost but is quite challenging to be jointly optimized over the transmitter and receiver.
Reinforcement Learning Based Cooperative Coded Caching under Dynamic Popularities in Ultra-Dense Networks
For ultra-dense networks with wireless backhaul, caching strategy at small base stations (SBSs), usually with limited storage, is critical to meet massive high data rate requests.
