5G系统中全维度MIMO天线架构的仰角波束形成教程

需积分: 10 72 浏览量更新于2024-07-17 收藏 1.01MB PDF 举报

"这篇文档是关于5G系统中全维度（Full Dimension, FD）MIMO架构的提升波束成形技术的教程。作者包括Qurrat-Ul-Ain Nadeem、Abla Kammoun和Mohamed-Slim Alouini等 IEEE会员。文章深入探讨了在无线通信网络中，尤其是5G技术背景下，3D波束成形的原理和应用，特别是利用二维平面的主动天线系统（Active Antenna System, AAS），实现对垂直（elevation）和水平（azimuth）两个维度的电子波束控制。" 5G系统中的全维度MIMO（Multiple-Input Multiple-Output）技术已经成为近年来无线通信行业的研究热点。这种技术的关键在于它能够在一个可行的基站（Base Station, BS）尺寸内放置大量天线元素，并且具备在垂直和水平两个方向上进行自适应电子波束控制的能力。与传统的MIMO技术相比，FD-MIMO能更有效地利用空间资源，提高数据传输速率，降低干扰，增强网络容量。文章作为一篇深度教程，不仅关注3GPP（3rd Generation Partnership Project）对FD-MIMO的标准化过程，还深入解析了该技术的理论基础。它可能涵盖了以下知识点： 1. **全维度波束成形**: 解释了如何通过调整天线阵列的相位来控制无线信号的方向，以实现对垂直维度的精确控制，从而优化覆盖范围和信号质量。 2. **2D主动天线系统（AAS）**: 描述了这种系统的设计和工作原理，包括天线元素的排列方式以及如何实现二维波束控制。 3. **波束赋形**: 介绍了如何利用FD-MIMO进行波束赋形，以提高信号能量的集中度，减少多路径传播造成的衰减，增强目标用户的接收信号强度。 4. **无线信道建模**: 分析了在3D空间中无线信道的特性，包括传播损耗、多径效应和用户间的干扰。 5. **性能评估**: 可能会讨论不同场景下FD-MIMO相对于传统MIMO的性能提升，如覆盖范围、吞吐量和能效等方面的比较。 6. **标准和实施**: 提及3GPP对FD-MIMO的标准化进程，这涉及到协议层面的考虑，如信令流程、资源分配策略等。 7. **实际应用挑战**: 可能会讨论在部署FD-MIMO时面临的硬件限制、成本问题以及复杂性挑战，以及解决这些问题的策略。 8. **未来研究方向**: 可能会展望FD-MIMO技术的未来发展，如更高维度的波束成形、动态波束管理以及与其他5G关键技术（如毫米波通信、网络切片）的融合。通过对这些知识点的深入理解和实践，工程师和研究人员可以更好地设计和优化5G网络，以满足高速、低延迟和高连接密度的通信需求。

recently, the authors in [72] have established a similar full

3D channel model to support the performance evaluation of

AAA-based wireless communication systems. The vertical

radiation pattern of an antenna port has been expressed as

a function of the individual element radiation pattern and the

array factor of the column of elements constituting the port.

The authors used the developed 3D SCM to study the effect of

mechanical downtilt, electrical downtilt and the combination

of two downtilts on the coverage and capacity performance

of different AAA conﬁgurations and showed that downtilt

optimization can introduce signiﬁcant gains in coverage and

capacity, when antenna ports have narrower vertical HPBWs.

There is still ongoing work in both industry and academia on

improving the 3D channel model and the estimates of different

elevation domain parameters [73]–[76]. An analysis of the

elevation domain parameters in the urban microcell scenario

with channel measurements at 2.3 GHz center frequency

can be found in [73]. A smaller ESD was observed for

higher Tx antenna height. Also, a signiﬁcant difference in

the ESDs between the line-of-sight (LOS) and the non-line-

of-sight (NLOS) propagation conditions was reported, with

the ESD following a negative exponential model with respect

to the distance for the former and a linear model for the

latter. The authors in [74] identiﬁed the limitations of the

existing 3D channel models in describing the cross-correlation

coefﬁcients of channel large-scale parameters, the distance

dependent properties of elevation domain parameters and the

inter-dependence between azimuth and elevation angles. They

used an outﬁeld measurement campaign to propose a reliable

3D stochastic channel model that addressed these limitations.

A log-normal distribution was proposed to ﬁt the probability

density function (PDF) of ESD, with a mean which decreased

with distance. A mixture of Von-Mises Fisher distributions

with a log-normally distributed concentration parameter was

used to model the interdependency between azimuth and eleva-

tion angles. A summary of the 3D channel model development

in the 3GPP can be found in [61].

Both the antenna port and the antenna element based ray-

tracing channel modeling approaches are outlined and com-

pared in section IV.

C. 3D Correlated SCMs

The compact structure of 2D AAAs deployed in FD-MIMO

systems to meet the form factor requirements often results

in small inter-element spacing between the antenna elements.

This increases the spatial correlation in the array. Recently,

measurement campaigns have also conﬁrmed the small values

of elevation angular spreads in realistic propagation environ-

ments, resulting in the elements to be highly correlated in

the vertical domain [71]. This dramatic increase in spatial

correlation makes it imperative to characterize it and take it

into account while evaluating the performance gains realizable

through elevation beamforming techniques.

Spatial correlation has been popularly known to deteriorate

the system performance. While this is always true for point-

to-point MIMO communications [77], [78], spatial correlation

can actually be beneﬁcial in multi-user massive MIMO set-

tings, where each user can experience high spatial correlation

within its channel vector, but the correlation matrices are

generally almost orthogonal for different users, resulting in

each user getting the full array gain proportional to the number

of antennas as shown in [79]. More discussion on this can be

found in section V-A.

The high correlation in FD-MIMO arrays can also reduce

the CSI feedback overhead incurred in the implementation of

elevation beamforming techniques. This is possible through

the design of elevation beamforming schemes using correlated

3D channel models that depend on the quasi-static spatial

channel covariance matrices of the users. A popular such

channel model for point-to-point MIMO system is the Kro-

necker model [80] and for MU-MISO system is the Rayleigh

correlated model [81]. The covariance matrices used to form

these models can be estimated using knowledge of the slowly-

varying large scale channel parameters instead of the small-

scale channel parameters that vary instantaneously. Note that

the 3D SCM discussed in the last subsection is a ray-tracing

model, which is one way to generate correlated FD-MIMO

channels. However, the explicit dependence of this channel

model on the number of propagation paths and associated

small-scale parameters like angles, powers and delays makes

the theoretical analysis of 3D beamforming generally in-

tractable. This has further motivated the characterization of

3D spatial correlation functions (SCF)s for FD-MIMO systems

that can be used to form these so-called correlated channel

models that depend only on the channel covariance matrices

and facilitate the design of 3D beamforming methods using

tools from RMT in the massive MIMO regime.

The initial SCFs proposed in literature were developed for

2D channels that ignore the elevation parameters in describing

the antenna patterns and propagation paths [82]–[88]. In [85],

approximate closed-form expressions for the spatial correla-

tion matrices were derived for clustered 2D MIMO channel

models, assuming a Laplacian azimuth angle of arrival (AoA)

distribution. The Kronecker channel model was shown to

provide a good ﬁt to the ray-tracing channel model. This is

encouraging for researchers interested in the use of the former

for the design of massive MIMO techniques.

The notion of spatial correlation in 3D propagation envi-

ronments has been addressed in some research works. An im-

portant contribution in this area appeared in [66]. The authors

developed closed-form expressions for the spatial correlation

and large system ergodic mutual information (MI) for a 3D

cross-polarized channel model, assuming the angles to be

distributed according to Von Mises distribution. The authors in

[89], showed that elevation plays a crucial role in determining

the SCF. The derivation was based on the spherical harmonic

expansion (SHE) of plane waves and assumed the distribution

of AoAs to be 3D Von Mises-Fisher.

In [65], closed-form expressions for the SCFs of several

omnidirectional antenna arrays utilizing a 3D MIMO channel

model were derived. These SCFs then formed the covariance

matrices that were used for the evaluation of channel capacity.

The derived results were expressed as a function of angular and

array parameters and used to study the impact of azimuth and

elevation angular spreads on the capacity. However, this work

assumed the angular distributions to be uniform. The uniform

剩余34页未读，继续阅读

arongxxf

粉丝: 3
资源: 6

5G系统中全维度MIMO天线架构的仰角波束形成教程

LTE系统级模拟仿真器说明书

beamforming.pdf

super-direction delay and sum beamforming python implement

elevation_map = [] for i in range(GRID_SIZE): row = [] for j in range(GRID_SIZE): elevation = random.uniform(MIN_ELEVATION, MAX_ELEVATION) row.append(elevation) elevation_map.append(row)根据这段代码生成elevation_map的图像

for i in np.arange(b[1]): for j in np.arange(b[1]): h[i][j]=get_elevation(map_data, i, j)这段中for循环速度特别慢，怎样提升速度

ROBOT-CENTRIC ELEVATION MAPPING WITH UNCERTAINTY ESTIMATES

android studio elevation 没效果

最新资源