Abstract
Millimeter-wave (mmWave) communication with a large bandwidth can result in a significantly improved data rate in wireless communications. To overcome high path-loss in the mmWave frequency band, beamforming technology is necessary. Especially, there has been widespread interest in development of hybrid beamforming (HB) technologies, in view of reducing cost and power consumption in massive multiple input multiple output (MIMO) systems. Some of existing researches on HB algorithms assumed perfect channel state information (CSI) and the others used beam training process in case of assuming imperfect CSI. When beam training process is used, enough beam training has to be conducted to achieve sufficient system performance in massive MIMO systems, which results in significant training overhead. Thus, it is necessary to reduce beam training complexity. Compared to state-of-the-art technology, we propose a multi-user HB system using codebooks based on a deep neural network (DNN) in this paper. In our proposed scheme, beam codewords for the base station (BS) and all users can be inferred using limited beam training in cases when the channel state information (CSI) is unknown. In order to apply the proposed scheme to situations where the CSI is unknown, reference radio frequency (RF) beamformers were introduced. Also, the proposed DNN structure is designed considering introduced reference RF beamformers. By using the proposed DNN with reference RF beamformers, the proposed system can inferred optimal beam codewords with limited beam training. Results obtained from simulations indicate that the proposed scheme can achieve almost the same performance as a conventional scheme with less beam training complexity. We also show that the performances achieved by the proposed scheme are gradually increased as training epoch is increased, eventually converging to a steady-state value.
Highlights
Millimeter-wave communication is a technology that shows considerable promise in that it allows a significantly increased data rate using a large bandwidth in wireless communications [1]
A hybrid beamformer at the base station (BS) is composed of a baseband beamformer, FBB = [fBB,1 · · · fBB,K ] ∈ CNRF ×Ns, and a radio frequency (RF) beamformer, FRF = [fRF,1 · · · fRF,K ] ∈ CNt ×NRF, where NRF is the number of RF chains at BS, Ns is the number of data streams, and K is the number of users served, respectively
The performances of the proposed deep neural network (DNN)-based hybrid beamforming (HB) were compared with those of fullydigital beamforming with perfect channel state information (CSI), using the unconstrained block diagonalization algorithm described in [25] and random beam selection choosing the beam codewords of the BS and all users randomly
Summary
Millimeter-wave (mmWave) communication is a technology that shows considerable promise in that it allows a significantly increased data rate using a large bandwidth in wireless communications [1]. Cho: Multi-User HB System Based on DNN in mmWave Communication achieve almost the same system performance as fully-digital beamforming system. We propose a DNN-based HB system for a multi-user environment in mmWave communication, which can achieve a sub-optimal rate performance with low beam training complexity when the CSI is unknown. Because of using imperfect CSI in the proposed DNN based HB system, we introduce reference RF beamformer which receives pilot signals transmitted from users into beam training procedure. By using the proposed DNN for HB trained by supervised learning, beam codewords of the BS and users can be inferred simultaneously based only on signals received at the reference RF beamformer using limited beam training This allows HB system to be designed with significantly lower beam training complexity than conventional scheme. I is identity matrix and N (m, C) represents a complex Gaussian random vector with mean m and covariance C
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