Supporting high mobility in millimeter wave (mmWave) systems enables a wide
range of important applications such as vehicular communications and wireless
virtual/augmented reality. Realizing this in practice, though, requires
overcoming several challenges. First, the use of narrow beams and the
sensitivity of mmWave signals to blockage greatly impact the coverage and
reliability of highly-mobile links. Second, highly-mobile users in dense mmWave
deployments need to frequently hand-off between base stations (BSs), which is
associated with critical control and latency overhead. Further, identifying the
optimal beamforming vectors in large antenna array mmWave systems requires
considerable training overhead, which significantly affects the efficiency of
these mobile systems. In this paper, a novel integrated machine learning and
coordinated beamforming solution is developed to overcome these challenges and
enable highly-mobile mmWave applications. In the proposed solution, a number of
distributed yet coordinating BSs simultaneously serve a mobile user. This user
ideally needs to transmit only one uplink training pilot sequence that will be
jointly received at the coordinating BSs using omni or quasi-omni beam
patterns. These received signals draw a defining signature not only for the
user location, but also for its interaction with the surrounding environment.
The developed solution then leverages a deep learning model that learns how to
use these signatures to predict the beamforming vectors at the BSs. This
renders a comprehensive solution that supports highly-mobile mmWave
applications with reliable coverage, low latency, and negligible training
overhead. Simulation results show that the proposed deep-learning coordinated
beamforming strategy approaches the achievable rate of the genie-aided solution
that knows the optimal beamforming vectors with no training overhead.

本文提出一种整合了机器学习和协调波束成形技术的解决方案，以克服毫米波系统中窄波束的使用以及高度移动用户在基站之间切换等挑战，并支持高度移动的毫米波应用，同时实现可靠的覆盖、低延迟和可忽略的训练开销。

高移动毫米波系统的深度学习协同波束成形

Deep Learning Coordinated Beamforming for Highly-Mobile Millimeter Wave  Systems

The focus of this paper is on multi-user MIMO transmissions for millimeter
wave systems with a hybrid precoding architecture at the base-station. To
enable multi-user transmissions, the base-station uses a cell-specific codebook
of beamforming vectors over an initial beam alignment phase. Each user uses a
user-specific codebook of beamforming vectors to learn the top-P (where P >= 1)
beam pairs in terms of the observed SNR in a single-user setting. The top-P
beam indices along with their SNRs are fed back from each user and the
base-station leverages this information to generate beam weights for
simultaneous transmissions. A typical method to generate the beam weights is to
use only the best beam for each user and either steer energy along this beam,
or to utilize this information to reduce multi-user interference. The other
beams are used as fall back options to address blockage or mobility. Such an
approach completely discards information learned about the channel condition(s)
even though each user feeds back this information. With this background, this
work develops an advanced directional precoding structure for simultaneous
transmissions at the cost of an additional marginal feedback overhead. This
construction relies on three main innovations: 1) Additional feedback to allow
the base-station to reconstruct a rank-P approximation of the channel matrix
between it and each user, 2) A zeroforcing structure that leverages this
information to combat multi-user interference by remaining agnostic of the
receiver beam knowledge in the precoder design, and 3) A hybrid precoding
architecture that allows both amplitude and phase control at low-complexity and
cost to allow the implementation of the zeroforcing structure. Numerical
studies show that the proposed scheme results in a significant sum rate
performance improvement over naive schemes even with a coarse initial beam
alignment codebook.

本文提出一种用于毫米波系统的混合预编码结构，通过额外的反馈开销来构建一个先进的定向预编码结构，旨在实现多用户的同时传输，并取得了显著的总速率表现提升。

基于信道重构的毫米波多用户 MIMO 系统混合预编码

Channel Reconstruction-Based Hybrid Precoding for Millimeter Wave  Multi-User MIMO Systems

Hybrid analog/digital precoding offers a compromise between hardware
complexity and system performance in millimeter wave (mmWave) systems. This
type of precoding allows mmWave systems to leverage large antenna array gains
that are necessary for sufficient link margin, while permitting low cost and
power consumption hardware. Most prior work has focused on hybrid precoding for
narrowband mmWave systems, with perfect or estimated channel knowledge at the
transmitter. MmWave systems, however, will likely operate on wideband channels
with frequency selectivity. Therefore, this paper considers wideband mmWave
systems with a limited feedback channel between the transmitter and receiver.
First, the optimal hybrid precoding design for a given RF codebook is derived.
This provides a benchmark for any other heuristic algorithm and gives useful
insights into codebook designs. Second, efficient hybrid analog/digital
codebooks are developed for spatial multiplexing in wideband mmWave systems.
Finally, a low-complexity yet near-optimal greedy frequency selective hybrid
precoding algorithm is proposed based on Gram-Schmidt orthogonalization.
Simulation results show that the developed hybrid codebooks and precoder
designs achieve very good performance compared with the unconstrained solutions
while requiring much less complexity.

本文研究混合模拟 / 数字预编码在毫米波宽带信道中的应用，提出了用于空间复用的高效混合模拟 / 数字编码本设计，并提出了基于 Gram-Schmidt 正交化的低复杂度但近似最优的贪婪式频率选择混合预编码算法。仿真结果表明，所提出的混合编码本和预编码设计与无约束解相比，在保证性能的同时，需要较少的复杂性。