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A Scalable Architecture for Future Regenerative Satellite Payloads
This paper addresses the limitations of current satellite payload architectures, which are predominantly hardware-driven and lack the flexibility to adapt to increasing data demands and uneven traffic.
SAGE-HB: Swift Adaptation and Generalization in Massive MIMO Hybrid Beamforming
Deep learning (DL)-based solutions have emerged as promising candidates for beamforming in massive Multiple-Input Multiple-Output (mMIMO) systems.
Evolution of High Throughput Satellite Systems: Vision, Requirements, and Key Technologies
High throughput satellites (HTS), with their digital payload technology, are expected to play a key role as enablers of the upcoming 6G networks.
Learning Energy-Efficient Hardware Configurations for Massive MIMO Beamforming
First, we propose an energy model for different beamforming structures.
Flexible Unsupervised Learning for Massive MIMO Subarray Hybrid Beamforming
Hybrid beamforming is a promising technology to improve the energy efficiency of massive MIMO systems.
RSSI-Based Hybrid Beamforming Design with Deep Learning
Hybrid beamforming is a promising technology for 5G millimetre-wave communications.
