True-time-delay (TTD) arrays can implement frequency-dependent rainbow beams and enable fast beam alignment in wideband millimeter-wave (mmWave) systems. In this paper, we consider 3D rainbow beam training with planar TTD arrays. We first design a planar TTD-array-based codebook that can realize a partial or complete scan of the entire 3D sphere using a single Orthogonal Frequency Division Multiplexing (OFDM) symbol. We demonstrate that a rainbow-beam codebook with a proper configuration of delays and phase shifts, and a sufficient number of subcarriers, can simultaneously probe all angular directions in the desired coverage sector with the required beamforming gain. Leveraging this full coverage codebook, we propose two frequency-domain power-based 3D beam training algorithms for fast estimation of the azimuth and elevation angles corresponding to the dominant propagation direction. The proposed algorithms take into account practical hardware-imposed delay range constraints. We numerically evaluate the performance of the proposed algorithms in mmWave channels as a function of the TTD codebook and system parameters. We show that the proposed 3D beam training offers considerable overhead reduction relative to the state-of-the-art methods with planar phased antenna arrays (PAA).

Hybrid arrays enable millimeter-wave (mmW) base stations and users to steer multiple beams simultaneously. However, in dense mmW networks with small inter-site distances and many users, a large number of serving beams can lead to significant inter- and intra-cell interference that prevents data-hungry users from satisfying their rate requirements. In this work, we address this problem by designing a linear multi-step optimization framework for user association and beam scheduling with interference management. In the first step, the framework aims to maximize the number of users with fully satisfied rate requirements by scheduling a minimal number of non-interfering beams. In the second step, any remaining non-interfering beams are distributed among other users to maximize the number of them with at least partially satisfied requirements. Hybrid precoders and combiners are then designed to remove any excess sidelobe interference among the scheduled beams. Finally, power allocation is optimized on a network level to boost the data rates of the partially satisfied users. Given that the framework includes NP-hard optimization problems, we propose an algorithm that attains a sub-optimal solution in polynomial-time. The proposed framework is numerically evaluated in realistic mmW channels and the results reveal its advantages over the baseline user association schemes.

When conventional phase shifter based arrays are used in millimeter-wave systems, the angle of departure (AoD) and angle of arrival (AoA) estimates are obtained through high-overhead exhaustive beam sweeping (EBS). Recently, true-time-delay arrays re-emerged as a promising architecture for fast angle estimation. In this work, we develop an algorithm for joint AoD and AoA estimation using only one Orthogonal Frequency Division Multiplexing (OFDM) symbol and frequency-dependent beams that can be synthesized by fully digital and true-time-delay arrays. We compare the developed algorithm with wideband single-carrier based EBS in terms of the misalignment probability and required training overhead. Numerical simulations in millimeter-wave channels reveal the advantages of the proposed algorithm.

In millimeter-wave (mmW) networks, beam training enables the base station and user to estimate the dominant angle of departure (AoD) and angle of arrival (AoA) and align their directional beams. With conventional phased arrays, the training requires an exhaustive beam sweeping (EBS), which imposes a large overhead. Alternative array architectures, including digital and true-time-delay arrays, enable simultaneous probing of different angular directions using different frequency components of the signal, which can speed up the training. In this work, we propose a beam training procedure based on frequency-dependent angle probing that uses a single Orthogonal Frequency-Division Multiplexing (OFDM) symbol to jointly estimate the AoD and AoA. We describe the design of the required training codebooks and frequency-domain power-based algorithm. Further, assuming an error-free array at the base station, we analyze how practical hardware impairments in the user's array affect the receive beamforming gain and misalignment probability in beam training. The proposed beam training is evaluated in realistic mmW channels and compared with existing beam training approaches in terms of the misalignment probability, angle estimation accuracy, required overhead, and computational complexity. The results indicate that the proposed algorithm requires a single OFDM symbol and low complexity to achieve a performance comparable to that of the EBS with arrays based on phase shifters.

The best beam steering directions are estimated through beam training, which is one of the most important and challenging tasks in millimeter-wave and sub-terahertz communications. Novel array architectures and signal processing techniques are required to avoid prohibitive beam training overhead associated with large antenna arrays and narrow beams. In this work, we leverage recent developments in true-time-delay (TTD) arrays with large delay-bandwidth products to accelerate beam training using frequency-dependent probing beams. We propose and study two TTD architecture candidates, including analog and hybrid analog-digital arrays, that can facilitate beam training with only one wideband pilot. We also propose a suitable algorithm that requires a single pilot to achieve high-accuracy estimation of angle of arrival. The proposed array architectures are compared in terms of beam training requirements and performance, robustness to practical hardware impairments, and power consumption. The findings suggest that the analog and hybrid TTD arrays achieve a sub-degree beam alignment precision with 66% and 25% lower power consumption than a fully digital array, respectively. Our results yield important design trade-offs among the basic system parameters, power consumption, and accuracy of angle of arrival estimation in fast TTD beam training.