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首页深度学习驱动的ISAC系统中车辆目标-用户关联优化
在现代通信系统中,集成传感和通信(ISAC)的概念正在崭露头角,它融合了雷达和无线通信的功能,旨在提高效率和频谱利用率。本文的核心研究是"基于深度学习的目标-用户关联",着重于在这样的系统中实现雷达目标与通信用户设备(UEs)的有效匹配。这种技术在ISAC中的应用可以支持诸如主动切换和波束预测等关键通信任务。 研究者提出了一种雷达辅助通信系统,其中基站(BS)配置了具有多输入多输出(MIMO)能力的雷达,其主要目标有两个:一是实现车载雷达目标与车载通信设备(VEs)在波束空间中的精确关联;二是利用雷达数据预测每个VE的波束形成矢量。这个目标-用户(T2U)关联过程分为两个步骤:首先,通过深度学习模型(如改进的YOLO模型)处理距离-角度雷达图像,进行车辆目标的多目标检测,并同时估算目标对应的波束形成向量。这一阶段的关键在于对复杂场景下的目标检测能力,尤其是在雷达图像中。 第二个阶段是将推断出的目标波束形成向量与BS用于通信的波束形成向量进行匹配,以实现T2U关联。这种匹配策略旨在确保即使在高密度车辆环境中,也能准确识别出最适合通信的VE。随着BS天线阵列尺寸的增加,系统的性能通常会提升,因为更大的阵列提供更好的方向性,使得VE在波束空间中的区分度增强,从而提高了关联的准确性。 研究结果表明,优化后的YOLO架构不仅能够有效进行波束预测,而且在不同大小的天线阵列情况下,目标检测的平均精度保持稳定,这显示了深度学习在这一领域的强大适应性和鲁棒性。本文的工作对于推动ISAC系统的发展,提升通信系统的智能感知和自适应能力具有重要意义,有助于构建更高效、智能化的无线通信环境。
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(a)
(b)
Fig. 1: ISAC system with co-located mmWave radar sensor and
communication base station. (a) provides a front view representation
of the system, showing analog communication beams on the vertical
direction at the BS, and highlighting the radar slant-range plane used
for radar imaging. (b) shows a top view of the system, depicting
analog communication beams on the horizontal direction at the BS.
simulation software Remcom Wireless InSite [29] and
Remcom WaveFarer [30]—used, respectively, for high-
fidelity wireless channel and radar channel simulations.
The paper is organized as follows: Section II presents the
reference communication system, channel model, and radar
signal models; Section IV introduces the proposed beamspace
Target-to-User association method relying on DL-based radar
target detection and beam prediction; Section V details the
simulation framework and shows the achieved results, while
Section VI reports the conclusions.
Notation
Matrices are denoted by bold upper-case letters, while
lower-case letters describe column vectors. A
T
, A
∗
, A
H
and
∥A∥ indicate, respectively, the matrix transpose, conjugate,
conjugate transpose and Frobenius norm. A Gaussian multi-
variate circularly complex random variable a is denoted With
a ∼ CN(µ, C) with mean µ and covariance C. R and C
represent the sets of real and complex numbers, respectively.
II. SYSTEM MODEL
Let us consider the downlink ISAC system depicted in
Fig. 1, where a BS is equipped with a mmWave MIMO
Fig. 2: Hybrid MIMO sub-connected Tx. architecture.
radar for sensing and a hybrid sub-connected MIMO array
for communication. Fig. 1 depicts the ISAC system over
two different views, highlighting the radar placement and the
analog communication beams at the BS. In the following, we
detail the model of the communication system and of the radar
system.
A. Communication system model
We consider a hybrid mmWave MIMO system where the BS
is equipped with a planar antenna array with N
T
= N
h
T
×N
v
T
antenna elements (along horizontal and vertical directions,
respectively) and N
RF
T
RF chains and is using N
S
data
streams to communicate with K VEs. Each VE is equipped
with a planar antenna array with N
R
= N
h
R
× N
v
R
antenna
elements and N
RF
R
RF chains. A sub-connected hybrid MIMO
configuration is considered, where the antennas are grouped
into sub-arrays of N
B
T
= N
T
/N
RF
T
and N
B
R
= N
R
/N
RF
R
antenna elements at the BS and at the VEs, respectively.
The transmitted N
S
complex-valued symbols are such that
s ∈ C
N
S
×1
∼ CN (0, I
N
S
/N
S
), and they are spatially
precoded using the cascade of digital and analog precoders
F = F
RF
F
BB
, where F
BB
∈ C
N
RF
T
×N
S
is the base-band
digital precoder and F
RF
∈ C
N
T
×N
RF
T
is the RF analog
counterpart, obtaining the time-discrete transmitted signal
x = F s. (1)
We assume analog precoding F
RF
implemented with phased
shifters. Hence, its elements are constrained to have the
same norm, i.e., [F
(i)
RF
F
(i),H
RF
]
k,k
= 1/N
T
. Moreover, the Tx
total power constraint is enforced by designing F
BB
such
that ∥F
RF
F
BB
∥
2
= N
S
[31]. In the sub-connected hybrid
configuration, depicted in Fig. 2, the analog precoding matrix
F
RF
is block-diagonal
F
RF
=
f
(1)
RF
0 ··· 0
0 f
(2)
RF
··· 0
.
.
.
.
.
.
.
.
.
.
.
.
0 ··· 0 f
(N
RF
T
)
RF
, (2)
where 0 ∈ C
N
B
T
×1
is a vector with zero-elements and f
(n)
RF
∈
C
N
B
T
×1
, n = 1, . . . , N
RF
T
denotes the precoding vector for
the n-th Tx sub-array.
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