Active intelligent reflecting surface (IRS) is a revolutionary technique for
the future 6G networks. The conventional far-field single-IRS-aided directional
modulation(DM) networks have only one (no direct path) or two (existing direct
path) degrees of freedom (DoFs). This means that there are only one or two
streams transmitted simultaneously from base station to user and will seriously
limit its rate gain achieved by IRS. How to create multiple DoFs more than two
for DM? In this paper, single large-scale IRS is divided to multiple small IRSs
and a novel multi-IRS-aided multi-stream DM network is proposed to achieve a
point-to-point multi-stream transmission by creating $K$ ($\geq3$) DoFs, where
multiple small IRSs are placed distributively via multiple unmanned aerial
vehicles (UAVs). The null-space projection, zero-forcing (ZF) and phase
alignment are adopted to design the transmit beamforming vector, receive
beamforming vector and phase shift matrix (PSM), respectively, called
NSP-ZF-PA. Here, $K$ PSMs and their corresponding beamforming vectors are
independently optimized. The weighted minimum mean-square error (WMMSE)
algorithm is involved in alternating iteration for the optimization variables
by introducing the power constraint on IRS, named WMMSE-PC, where the
majorization-minimization (MM) algorithm is used to solve the total PSM. To
achieve a lower computational complexity, a maximum trace method, called
Max-TR-SVD, is proposed by optimize the PSM of all IRSs. Numerical simulation
results has shown that the proposed NSP-ZF-PA performs much better than
Max-TR-SVD in terms of rate. In particular, the rate of NSP-ZF-PA with sixteen
small IRSs is about five times that of NSP-ZF-PA with combining all small IRSs
as a single large IRS. Thus, a dramatic rate enhancement may be achieved by
multiple distributed IRSs.

利用多个分布式智能反射面（IRS），提出了一种多反射面辅助多流方向调制（DM）网络，通过创建多个自由度实现点对点多流传输。在此基础上，采用了空间投影、零强迫和相位对齐等技术进行传输波束成形、接收波束成形和相位调整的设计，同时引入功率约束和最小均方误差优化算法，实现了对 IRS 的优化。通过数值模拟结果，证明了该方法在速率方面明显优于其他方法，尤其是利用多个小 IRS 的 NSP-ZF-PA 方法的速率相比将所有小 IRS 合并为一个大 IRS 的 NSP-ZF-PA 方法提高了约 5 倍，从而实现了显著的速率增强。

通过分布式多无人机辅助多智能反射面的多流方向调制网络传输

Multi-stream Transmission for Directional Modulation Network via  distributed Multi-UAV-aided Multi-IRS

In Integrated Sensing and Communication (ISAC) systems, matching the radar
targets with communication user equipments (UEs) is functional to several
communication tasks, such as proactive handover and beam prediction. In this
paper, we consider a radar-assisted communication system where a base station
(BS) is equipped with a multiple-input-multiple-output (MIMO) radar that has a
double aim: (i) associate vehicular radar targets to vehicular equipments (VEs)
in the communication beamspace and (ii) predict the beamforming vector for each
VE from radar data. The proposed target-to-user (T2U) association consists of
two stages. First, vehicular radar targets are detected from range-angle
images, and, for each, a beamforming vector is estimated. Then, the inferred
per-target beamforming vectors are matched with the ones utilized at the BS for
communication to perform target-to-user (T2U) association. Joint multi-target
detection and beam inference is obtained by modifying the you only look once
(YOLO) model, which is trained over simulated range-angle radar images.
Simulation results over different urban vehicular mobility scenarios show that
the proposed T2U method provides a probability of correct association that
increases with the size of the BS antenna array, highlighting the respective
increase of the separability of the VEs in the beamspace. Moreover, we show
that the modified YOLO architecture can effectively perform both beam
prediction and radar target detection, with similar performance in mean average
precision on the latter over different antenna array sizes.

本文研究了一种整合感知和通信系统中基于雷达的通信系统，其中基站配备 MIMO 雷达，通过目标用户关联和波束预测实现通信任务。提出了一种基于目标 - 用户关联的方法，通过修改 YOLO 模型实现多目标检测和波束推断，结果表明该方法在不同的移动场景下能够提供正确关联的概率，并展示了修改后的 YOLO 架构在雷达目标检测方面的效果。

基于深度学习的综合感知与通信系统中的目标与用户关联

Deep Learning-based Target-To-User Association in Integrated Sensing and  Communication Systems

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。