AoD-Adaptive Channel Feedback for FDD Massive MIMO Systems With Multiple-Antenna Users

Abstract

In this paper, we propose an efficient feedback scheme for an angle of departure (AoD) based channel estimation in frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems with multiple antennas at the users. The channel feedback scheme is based on zero-forcing block diagonalization (BD) and it is proposed for two distinct design cases; in case I, the number of streams intended for a user equals the number of antennas at that user; in case II, the number of streams is less than the number of receive antennas. Case I is applicable in scenarios where high data rate requirements are needed as it transmits data symbols over all of the available degrees of freedom of the system. Diversely, case II is applicable when reliability is a priority in the system as it uses the additional receive antennas at the user to achieve spatial diversity to enhance the link performance. The proposed scheme is analyzed for the two cases by quantifying the downlink rate gap from the case of perfect channel state information (CSI). Moreover, we design structured feedback codebooks based on optimal subspace packing in the Grassmannian manifold and show that these codes achieve close performance to the perfect CSI case. Additionally, a vector quantization scheme is proposed to quantize the user's channel matrix when optimal power allocation across multiple streams is adopted in the low signal-to-noise ratio (SNR) region. The feedback codebooks are based on optimal line packing in the Grassmannian manifold, where every vector of the user's channel matrix is quantized and sent to the BaseStation. The results demonstrate a fundamental trade-off between vector quantization, with power optimization across the data streams, and subspace quantization. Specifically, vector quantization codebooks outperform subspace-based codebooks in the low SNR region, while the situation is reversed in the high SNR region. INDEX TERMSMassive MIMO, FDD, multiple antenna users, block diagonalization, singular values and singular vectors, channel feedback, subspace codebooks, water-filling. Nomenclature AoD angle of departure BD zero-forcing block diagonalization BS BaseStation CS compressive sensing CSI channel state information DFT discrete Fourier transform FDD frequency division duplex i.i.d. independently and identically distributed LSTM long-short-term memory MIMO multiple-input multiple-output mmWave millimeter-wave MUSIC multiple signal classification RVQ random vector quantization

Utilizing Attitude Information for Efficient Multi-User Millimeter-Wave Communications

“…Conventional BD algorithms [27], [28] require the BS to have full CSI, which might lead to a lower efficiency when the wireless channel changes rapidly. Since mmWave channel shows the spatial-domain sparsity and geometric properties, it is sufficient for achieving BD of the composite multi-user channel with only AoD message associated with every UE [29], [30]. Thus, we explain how BD with subspace projection based AoD (SP-AoD-BD) works as follows.…”

“…From the perspective of SVD decomposition, the essence of the receiver's compensation is to align the column space with the wireless channel column space after rotation. We note that the receiver design in (29) has the same column space as C j U a . We can choose W ′ j = αC j U a as the final combining matrix solution, where…”