Rainbow-Link is a recently proposed millimeter-wave multiple access protocol tailored for IoT networks, utilizing a single-RF-chain base station and a true-time-delay (TTD) antenna array to generate a rainbow beam capable of simultaneously serving spatially distributed devices via frequency-domain multiple access. While prior work established the feasibility of this approach, it assumed perfect beam training had been performed in advance and left the initial access stage unaddressed. In this paper, we tackle this open challenge by leveraging the aliasing effect inherent in narrowband IoT receivers. We propose a wideband transmitted pilot structure and a corresponding receiver-side algorithm to enable efficient one-shot initial beam training. Simulation results show that the proposed pilot structure together with the algorithm can achieve sub-millisecond-level latency with reliable beam training accuracy.

Recently, a new multiple access protocol, called Rainbow-Link, has been proposed as a promising candidate to support various Internet of Things (IoT) networks. In Rainbow-Link, the base station (BS) is equipped with an analog true-time-delay (TTD) array that can create frequency-dependent rainbow beams in the millimeter-wave (mmW) band. The rainbow beams enable fast beam training and can support a large number of IoT devices at the same time thus reducing the latency in channel access. However, link establishment through initial access (IA) procedure using rainbow beams is not straightforward due to asymmetry in radio front-end and processing capabilities between IoT base station and terminals. Also, the superposition of multiple delayed signals introduced by the TTD array needs to be handled carefully in the synchronization process. In this paper, we propose a low latency Rainbow-Link IA procedure based on the new rainbow beam design suitable for narrowband IoT processing. We design joint beam training and timing synchronization algorithms for narrowband IoT transceivers that are robust to severe multipath delay spread introduced by the TTD array. We perform a comprehensive performance analysis under different system parameters including the number of antennas, the number of subcarriers, and the bandwidth ratio between BS and IoT terminals. Simulation results show that the proposed Rainbow-Link IA procedure can achieve millisecond-level latency with highly reliable synchronization accuracy.

Millimeter-wave (mmWave) systems require a large number of antennas, which makes the beam training challenging and time-consuming for conventional phased arrays. Recently, a true-time-delay (TTD) array-based beam training algorithm has been shown as an effective solution to overcome the training overhead in large arrays. In this paper, we present a custom-built over-the-air (OTA) testbed to study the effects of hardware impairments on the TTD-based beam training and verify its feasibility in a real system. We proposed an orthogonal matching pursuit (OMP) based reconstruction algorithm along with a phase calibration dictionary to combat nonidealities such as strong frequency selectivity and phase misalignment in the received raw IQ signal. Post-processing results showed that with the nonideality effects properly handled, the 3D TTD beam training algorithm can achieve high AOA estimation accuracy.