Millimeter wave (mmWave) communication with large antenna arrays is a
promising technique to enable extremely high data rates due to the large
available bandwidth in mmWave frequency bands. In addition, given the knowledge
of an optimal directional beamforming vector, large antenna arrays have been
shown to overcome both the severe signal attenuation in mmWave as well as the
interference problem. However, fundamental limits on achievable learning rate
of an optimal beamforming vector remain.
This paper considers the problem of adaptive and sequential optimization of
the beamforming vectors during the initial access phase of communication. With
a single-path channel model, the problem is reduced to actively learning the
Angle-of-Arrival (AoA) of the signal sent from the user to the Base Station
(BS). Drawing on the recent results in the design of a hierarchical beamforming
codebook [1], sequential measurement dependent noisy search strategies [2], and
active learning from an imperfect labeler [3], an adaptive and sequential
alignment algorithm is proposed.
An upper bound on the expected search time of the proposed algorithm is
derived via Extrinsic Jensen-Shannon Divergence. which demonstrates that the
search time of the proposed algorithm asymptotically matches the performance of
the noiseless bisection search up to a constant factor. Furthermore, the upper
bound shows that the acquired AoA error probability decays exponentially fast
with the search time with an exponent that is a decreasing function of the
acquisition rate.
Numerically, the proposed algorithm is compared with prior work where a
significant improvement of the system communication rate is observed. Most
notably, in the relevant regime of low (-10dB to 5dB) raw SNR, this establishes
the first practically viable solution for initial access and, hence, the first
demonstration of stand-alone mmWave communication

该论文研究了大型天线阵列下的毫米波通信技术。通过自适应序列优化天线束成形向量，以改善信号衰减和干扰问题，提出了一种新的自适应序列对齐算法，其搜索时间与性能无噪声双分搜索相匹配。与之前的解决方案相比，该算法在低信噪比（-10dB 至 5dB）的情况下得到了明显的通信速度提升，完成了独立的毫米波通信的首次演示。

毫米波初始对齐的主动学习与 CSI 获取

Active Learning and CSI Acquisition for mmWave Initial Alignment

Millimeter wave (mmWave) technology is expected to be a major component of 5G
wireless networks. Ultra-wide bandwidths of mmWave signals and the possibility
of utilizing large number of antennas at the transmitter and the receiver allow
accurate identification of multipath components in temporal and angular
domains, making mmWave systems advantageous for localization applications. In
this paper, we analyze the performance of a two-step mmWave localization
approach that can utilize time-of-arrival, angle-of-arrival, and
angle-of-departure from multiple nodes in an urban environment with both
line-of-sight (LOS) and non-LOS (NLOS) links. Networks with/without
radio-environmental mapping (REM) are considered, where a network with REM is
able to localize nearby scatterers. Estimation of a UE location is challenging
due to large numbers of local optima in the likelihood function. To address
this problem, a gradient-assisted particle filter (GAPF) estimator is proposed
to accurately estimate a user equipment (UE) location as well as the locations
of nearby scatterers. Monte Carlo simulations show that the GAPF estimator
performance matches the Cramer-Rao bound (CRB). The estimator is also used to
create an REM. It is seen that significant localization gains can be achieved
by increasing beam directionality or by utilizing REM.

分析利用毫米波技术的二步式定位方法在城市环境下利用到达时间、到达角度和离开角度从多个节点获得的信号，以及利用具有无线电环境映射（REM）的网络定位附近散射体的性能，并提出梯度辅助粒子滤波（GAPF）估计器来解决 UE 位置估计中的问题，该估计器的性能与 Cramer-Rao bound（CRB）相匹配。