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.

This work revisits the classic robust adaptive beamforming that is widely adopted for interference suppression. A first-order method is proposed to solve the beamformers for large arrays. The method uses proximal gradient descent along with Nesterov’s acceleration. It has ${\mathcal{O}}\left( {{N^2}} \right)$ computational complexity per iteration where N is the array size. For sparse linearly constrained adaptive beamforming, the proposed method achieves performances comparable to the conjugate gradient method. For sparse robust adaptive beamforming with conic constraints, the proposed method is much more efficient than the standard interior point solver.

The millimeter-wave (mmW) communications is a key enabling technology in 5G to provide ultra-high throughput. Current mmW technologies rely on analog phased arrays to realize beamforming gain and overcome high path loss. However, due to a limited number of simultaneous beams that can be created with analog/hybrid phased antenna arrays, the overheads of beam training and beam scheduling become a bottleneck for emerging networks that need to support a large number of users and low latency applications. This paper introduces rainbow-link, a novel multiple access protocol, that can achieve low latency and massive connectivity by exploiting wide bandwidth at mmW frequencies and novel analog true-time-delay array architecture with frequency dependent beamforming capability. In the proposed design, the network infrastructure is equipped with the true-time-delay array to simultaneously steer different frequency resource blocks towards distinct directions covering the entire cell sector. Users or devices, equipped with a narrowband receiver and either a single antenna or small phased antenna array, connect to the network based on their angular positions by selecting frequency resources within their rainbow beam allocation. Rainbow-link is combined with a contention-based grant-free access to eliminate the explicit beam training and user scheduling. The proposed design and analysis show that rainbow-link grant-free access is a potential candidate for latency-critical use cases within massive connectivity. Our results show that, given less than 10−5probability of packet loss, a rainbow-link cell, over 1 GHz bandwidth using 64 element antenna array, attains sub-millisecond user-plane latency and Mbps user rates with an approximate 400m line-of-sight coverage and a density of up to 5 active single antenna users per second per m2.

Communication systems at millimeter-wave (mmW) and sub-terahertz frequencies are of increasing interest for future high-data rate networks. One critical challenge faced by phased array systems at these high frequencies is the efficiency of the initial beam alignment, typically using only phase-less power measurements due to high frequency oscillator phase noise. Traditional methods for beam alignment require exhaustive sweeps of all possible beam directions, thus scale communications overhead linearly with antenna array size. For better scaling with the large arrays required at high mmW bands, compressive sensing methods have been proposed as their overhead scales logarithmically with the array size. However, algorithms utilizing machine learning have shown more efficient and more accurate alignment when using real hardware due to array impairments. Additionally, few existing phase-less beam alignment algorithms have been tested over varied secondary path strength in multipath channels. In this work, we introduce a novel, machine learning based algorithm for beam alignment in multipath environments using only phase-less received power measurements. We consider the impacts of phased array sounding beam design and machine learning architectures on beam alignment performance and validate our findings experimentally using 60 GHz radios with 36-element phased arrays. Using experimental data in multipath channels, our proposed algorithm demonstrates an 88% reduction in beam alignment overhead compared to an exhaustive search and at least a 62% reduction in overhead compared to existing compressive methods.