Millimeter-Wave Beam Training Acceleration Through Low-Complexity Hybrid Transceivers

Abstract

Millimeter-wave (mm-wave) communication systems can provide much higher data rates than systems operating at lower frequencies, but achieving such rates over sufficiently large distances requires highly directional beamforming at both the transmitter and receiver. These antenna beams have to be aligned very precisely in order to obtain sufficient link margin. In this paper, we first propose a parallel-adaptive beam training protocol which significantly accelerates the link establishment between mm-wave devices by exploiting the ability of hybrid analog-digital beamforming antennas to scan multiple spatial sectors simultaneously. Second, we deal with practical constraints of mm-wave transceivers and design a novel greedy geometric algorithm to synthesize sector beam patterns featuring configurable beamwidth and multi-beam radiation as required by the proposed beam training protocol. These multi-beam patterns are then also used for concurrent data communication over multiple paths, in case several suitable directions are found during the beam training. Simulation results show that our algorithm is able to shape antenna patterns very close to those attained by a fullydigital beamforming architecture, yet requires lower complexity hardware compared with state-of-the-art solutions. Exploiting such multi-beam antenna patterns, our parallel beam training protocol can provide up to 82% effective rate increase and 70% search time decrease compared to existing sequential protocols. The acceleration of the beam training phase shifts the optimum balance between the search overhead and the achieved directivity gain so that the best performance is reached with a training load 30% to 60% lower than that of sequential beam training. Index Terms-Millimeter wave systems, hybrid beamforming, beam training, channel estimation, antenna patterns.

Deep Learning Coordinated Beamforming for Highly-Mobile Millimeter Wave Systems

“…Developing beamforming/channel estimation solutions to reduce this training overhead has attracted considerable research interest in the last few years [20]- [33]. This prior research has mainly focused on three directions: (i) beam training [20]- [23], (ii) compressive channel estimation [24]- [28], and (iii) location aided beamforming [29]- [33]. In beam training, the candidate beams at the transmitter and receiver are directly trained using exhaustive or adaptive search to select the ones that optimize the metric of interest, e.g., SNR.…”

“…In the second phase of the deep-learning coordinated beamforming approach, we simulate the uplink training by only calculating the omni-received sequence r omni k,n ∀k. We then use the machine learning model to predict the best RF beamforming vector f DL n for every BS n. Finally, the effective achievable rate is calculated using (23).…”

Tracking Position and Orientation Through Millimeter Wave Lens MIMO in 5G Systems

“…Similar to [64], the hybrid design solution for the proposed heuristic beamformer in (46) is obtained using sparsity constrained matrix reconstruction problem that can be solved using the orthogonal matching pursuit (OMP) type algorithms. Consequently, the baseband precoder is obtained by M MS nonzero rows of F BB,MS with M MS columns and F RF,MS is given by the corresponding columns of U MS for the ideal beam selection, and mapped to the closest 1-bit beamformer with {+1, −1} for 1-bit beam selection [65].…”

Fast Beam Training Architecture for Hybrid mmWave Transceivers

“…IEEE 802.11ad standardized a beam sector based scheme for beam training [22], whose main idea is to start with sectors of wide beams to do a coarse beam estimation and then shrink the beamwidth adaptively and successively to obtain a more refined beam. Most works considered sequential and single-directional scanning during the beam training [23]- [26], while, work in [27] leveraged the multi-directional scanning abilities of the hybrid transceivers to accelerate the beam training process. The drawback of such methods, however, is the need of successive feedback between the BS and the MS, which is difficult to achieve at the initial channel acquisition stage [3].…”