Deep Learning for Primary Sector Prediction in FR2 New Radio Systems

Millimeter wave communications technology is an essential component of the new radio (NR) standard for standalone 5G networks, i.e., frequency range 2 (FR2) bands. This technology provides contiguous bandwidth at the detriment of high path loss and blockage sensitivity. Hence beamforming architectures are leveraged to compensate for the channel impairments. However, beamforming introduces significant challenges in terms of initial beam access and beam adaptation requirements. Namely, the base station (BS) and mobile station (MS) are compelled to search the entire spatial directions to specify the pointing directions (optimum beamforming and combining vectors) that yield in the strongest impinged signal levels. This search process results in a high computational complexity, extended delay periods, high power consumption and energy inefficiency. Hence this paper proposes a novel sector (beam) prediction scheme that leverages the synergistic combination of convolutional neural networks (CNN) and long short-term memory (LSTM). The goal is to propose ultra-low access times for FR2-bsaed 5G networks, thus enhancing mmWave bands to operate independently as a standalone network without reliance on Frequency Range 1 (FR1) bands, e.g., dual-connectivity. The proposed scheme here predicts the primary sector with the highest popularity class at the BS, which is affiliated with the mostly used beamforming vector. This retrieves information about the sector locations with the highest MS traffic, thereby the BS can eliminate the spatial search over locations of scarce MS densities. Consequently, this process reduces the beam scanning search at the BS, while performing conventional search at the MS. The proposed scheme yields in reduced complexity and access times as compared to prominent existing methods.