In fifth-generation (5G) communications, millimeter wave (mmWave) is one of the key technologies to increase the data rate. To overcome this technology's poor propagation characteristics, it is necessary to employ a number of antennas and form narrow beams. It becomes crucial then, especially for initial access, to attain fine beam alignment between a next generation NodeB (gNB) and a user equipment (UE). The current 5G New Radio (NR) standard, however, adopts an exhaustive search-based beam sweeping, which causes time overhead of a half frame for initial beam establishment. In this paper, we propose a deep learning-based beam selection, which is compatible with the 5G NR standard. To select a mmWave beam, we exploit sub-6 GHz channel information. We introduce a deep neural network (DNN) structure and explain how we estimate a power delay profile (PDP) of a sub-6 GHz channel, which is used as an input of the DNN. We then validate its performance with real environment-based 3D ray-tracing simulations and over-the-air experiments with a mmWave prototype. Evaluation results confirm that, with support from the sub-6 GHz connection, the proposed beam selection reduces the beam sweeping overhead by up to 79.3 %.

Abstract-Massive multiple-input multiple-output (MIMO) is a promising approach for cellular communication due to its energy efficiency and high achievable data rate. These advantages, however, can be realized only when channel state information (CSI) is available at the transmitter. Since there are many antennas, CSI is too large to feed back without compression. To compress CSI, prior work has applied compressive sensing (CS) techniques and the fact that CSI can be sparsified. The adopted sparsifying bases fail, however, to reflect the spatial correlation and channel conditions or to be feasible in practice. In this paper, we propose a new sparsifying basis that reflects the long-term characteristics of the channel, and needs no change as long as the spatial correlation model does not change. We propose a new reconstruction algorithm for CS, and also suggest dimensionality reduction as a compression method. To feed back compressed CSI in practice, we propose a new codebook for the compressed channel quantization assuming no other-cell interference. Numerical results confirm that the proposed channel feedback mechanisms show better performance in point-to-point (single-user) and point-to-multi-point (multi-user) scenarios.Index Terms-MIMO system, multi-user system, channel feedback, compressed feedback.

Next generation wireless networks require massive uplink connections as well as high spectral efficiency. It is well known that, theoretically, it is not possible to achieve the sum capacity of multi-user communications with orthogonal multiple access. To meet the challenging requirements of next generation networks, researchers have explored non-orthogonal and overloaded transmission technologies-known as new radio multiple access (NR-MA) schemes-for fifth generation (5G) networks. In this article, we discuss the key features of the promising NR-MA schemes for the massive uplink connections. The candidate schemes of NR-MA can be characterized by multiple access signatures (MA-signatures), such as codebook, sequence, and interleaver/scrambler. At the receiver side, advanced multiuser detection (MUD) schemes are employed to extract each user's data from non-orthogonally superposed data according to MA-signatures. Through link-level simulations, we compare the performances of NR-MA candidates under the same conditions. We further evaluate the sum rate performances of the NR-MA schemes using a 3-dimensional (3D) ray tracing tool based system-level simulator by reflecting realistic environments. Lastly, we discuss the tips for system operations as well as call attention to the remaining technical challenges.