Quantization-Aware Precoding For Mu-Mimo With Limited-Capacity Fronthaul
Author:
Affiliation:
1. KTH Royal Institute of Technology,School of Electrical Engineering and Computer Science,Stockholm,Sweden
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9745891/9746004/09747196.pdf?arnumber=9747196
Reference27 articles.
1. Distortion-aware linear pre-coding for massive MIMO downlink systems with non-linear power amplifiers;aghdam;unpublished paper,2020
2. Uplink Performance of Wideband Massive MIMO With One-Bit ADCs
3. Quantized Massive MU-MIMO-OFDM Uplink
4. Quantized Precoding for Massive MU-MIMO
5. Linear Precoding With Low-Resolution DACs for Massive MU-MIMO-OFDM Downlink
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1. Constrained Deep Reinforcement Learning for Fronthaul Compression Optimization;2024 IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN);2024-05-05
2. Learning-Based Latency-Constrained Fronthaul Compression Optimization in C-RAN;2023 IEEE 28th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD);2023-11-06
3. Optimized Precoding for MU-MIMO With Fronthaul Quantization;IEEE Transactions on Wireless Communications;2023-11
4. Performance of Quantized Massive MIMO With Fronthaul Rate Constraint Over Quasi-Static Channels;IEEE Access;2023
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