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Channel feedback in FDD massive MIMO systems with multiple-antenna users
In this thesis, we consider the problem of Angle of Departure (AoD) based channel feedback in Frequency Division Duplex (FDD) massive Multiple- Input Multiple-Output (MIMO) systems with multiple antennas at the users. We consider the use of Zero-Forcing Block Diagonalization (BD) as the down- link p...
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| Format: | Thesis |
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AUC Knowledge Fountain
2019
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| Summary: | In this thesis, we consider the problem of Angle of Departure (AoD) based channel feedback in Frequency Division Duplex (FDD) massive Multiple- Input Multiple-Output (MIMO) systems with multiple antennas at the users. We consider the use of Zero-Forcing Block Diagonalization (BD) as the down- link precoding scheme. We consider two different cases; one in which the number of streams intended for a user equals the number of antennas at that user and the other case in which the number of streams is less than the number of user antennas. BD requires the feedback of the subspace spanned by the channel matrix at the user or a subspace of it in the case of having a smaller number of streams than the number of antennas at a specific user. Based on our channel model, we propose a channel feedback scheme that requires less feedback overhead compared to feeding back the whole channel matrix. Then, we quantify the rate gap between the rate of the system with perfect Channel State Information (CSI) at the massive MIMO Basestation (BS) and our proposed channel feedback scheme for a given number of feedback bits. Finally, we design feedback codebooks based on optimal subspace packing in the Grassmannian manifold. We show that our proposed codes achieve performance that is very close to the performance of the system with perfect CSI at the BS. We also propose a vector quantization scheme to quantize the channel matrix of the user when optimal power allocation across multiple streams is adopted. Sim- ulation results show that the vector quantization scheme combined with power optimization across the streams outperforms the subspace quantiza- tion scheme at the low SNR regime. However, the situation is reversed at high SNR levels and subspace quantization with uniform power allocation becomes better. |
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