An iterative scheme using geometric programming for power allocation and generalized eigenvalue problems for receiver design maximizes the min-rate in cell-free Massive MIMO under fronthaul quantization constraints, with optimality shown via uplink-downlink duality.
Bashar, Cell-free Massive MIMO and Millimeter W ave Channel Modelling for 5G and Beyond
3 Pith papers cite this work. Polarity classification is still indexing.
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The authors formulate and solve an energy efficiency maximization problem for limited-backhaul cell-free Massive MIMO using quantized MRC signals, Bussgang-modeled quantization errors, and successive convex approximation under power, backhaul, and rate constraints.
Low-complexity massive MIMO scheduling via DFT of correlation functions and correlation-matrix-based precoding maintains near-optimal throughput with major complexity reduction under the COST 2100 model, even without CSIT.
citing papers explorer
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Max-Min Rate of Cell-Free Massive MIMO Uplink with Optimal Uniform Quantization
An iterative scheme using geometric programming for power allocation and generalized eigenvalue problems for receiver design maximizes the min-rate in cell-free Massive MIMO under fronthaul quantization constraints, with optimality shown via uplink-downlink duality.
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On the Energy Efficiency of Limited-Backhaul Cell-Free Massive MIMO
The authors formulate and solve an energy efficiency maximization problem for limited-backhaul cell-free Massive MIMO using quantized MRC signals, Bussgang-modeled quantization errors, and successive convex approximation under power, backhaul, and rate constraints.
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Evaluation of Low Complexity Massive MIMO Techniques Under Realistic Channel Conditions
Low-complexity massive MIMO scheduling via DFT of correlation functions and correlation-matrix-based precoding maintains near-optimal throughput with major complexity reduction under the COST 2100 model, even without CSIT.