No abstract

In order to improve the quality of the received signal and system spectral efficiency, accurate beamforming using a given antenna array is essential for multiple-input multiple-output (MIMO) systems. To obtain desired MIMO transmission performance, construction of codebooks which are composed of matching beamforming vectors to the array structure is important. To effectively cover different types of mobile traffic, the base station for 5G new radio employs antenna arrays in various sizes and shapes. Nevertheless, the codebooks adopted by the 3GPP standard so far are based on the uniform linear array and the uniform planar array, necessitating design techniques for a wider class of antenna arrays. In this paper, we propose codebook construction methods for the uniform circular array with parameters to flexibly set the initial phase and step size based on the channel characteristics of the user equipment (UE). When tested over the 3GPP spatial channel model, the proposed codebooks show a substantial amount of gain over the conventional codebooks in all UE locations within the cell.

Accurate beamforming under the constraint of limited-feedback for the channel state information (CSI) has always been a challenging task, despite its huge impact on the quality of multipleinput multiple-output (MIMO) transmission. The task is becoming especially important for millimeter-wave (mmWave) transmission which requires high-gain beams to overcome the severe pathloss experienced over the radio channel, since an inaccurate beam direction may cause a noticeable performance degradation. The signal blockage in the urban environment due to the mobile and human traffic can also degrade the beamforming performance, by generating blind spots for signal transmission as well as the CSI feedback. In this paper, a new way of transmitting accurate beams to highly mobile users with a substantially reduced amount of feedback overhead is proposed, by introducing a set of beam signatures that are composed of multiple beams along the trajectories of mobile users. Instead of forming a spot beam corresponding to the precoder matrix indicator (PMI) reported by the user equipment (UE), the base station (BS) utilizes the history of previous reports to determine an appropriate beam signature and transmit beams to predicted UE positions. The proactive decision for the next beam position is made with the aid of deep learning (DL) using the train data obtained from typical mobile movements for given road conditions, thus providing the adaptability to the channel environment with progressively improving accuracy. The set of beam signatures, which are called the beambook, includes the time dimension added to the conventional spatial dimension for beams to develop into a spatio-temporal codebook. The beambook produces enhanced and reliable beamforming over the mobile's trajectory, even when the CSI feedback interval is considerably longer than parameters supported by the current 5G new radio (NR) standard. It is demonstrated that the proposed beambook significantly outperforms the conventional codebooks based on the discrete Fourier transform (DFT) matrix and the vector quantization (VQ) in both beamforming accuracy and throughput performance.