Millimeter wave (mmWave) has been recognized as one of key technologies for
5G and beyond networks due to its potential to enhance channel bandwidth and
network capacity. The use of mmWave for various applications including
vehicular communications has been extensively discussed. However, applying
mmWave to vehicular communications faces challenges of high mobility nodes and
narrow coverage along the mmWave beams. Due to high mobility in dense networks,
overlapping beams can cause strong interference which leads to performance
degradation. As a remedy, beam switching capability in mmWave can be utilized.
Then, frequent beam switching and cell change become inevitable to manage
interference, which increase computational and signalling complexity. In order
to deal with the complexity in interference control, we develop a new strategy
called Multi-Agent Context Learning (MACOL), which utilizes Contextual Bandit
to manage interference while allocating mmWave beams to serve vehicles in the
network. Our approach demonstrates that by leveraging knowledge of neighbouring
beam status, the machine learning agent can identify and avoid potential
interfering transmissions to other ongoing transmissions. Furthermore, we show
that even under heavy traffic loads, our proposed MACOL strategy is able to
maintain low interference levels at around 10%.

利用毫米波、5G 和车辆通信研究具有潜力的提高信道带宽和网络容量，通过开发一种名为多主体背景学习（MACOL）的新策略，利用环境上下文信息来控制干扰并分配毫米波束为网络中的车辆服务，成功实现了在重载交通负荷条件下将干扰水平保持在约 10% 左右。

多智能体上下文学习策略下的毫米波车联网干扰感知波束分配

Multi-Agent Context Learning Strategy for Interference-Aware Beam  Allocation in mmWave Vehicular Communications

This paper investigates a new spectrum sharing scenario between unmanned
aerial vehicle (UAV) and terrestrial wireless communication systems. We
consider that a cognitive/secondary UAV transmitter communicates with a ground
secondary receiver (SR), in the presence of a number of primary terrestrial
communication links that operate over the same frequency band. We exploit the
UAV's controllable mobility via trajectory design, to improve the cognitive UAV
communication performance while controlling the co-channel interference at each
of the primary receivers (PRs). In particular, we maximize the average
achievable rate from the UAV to the SR over a finite mission/communication
period by jointly optimizing the UAV trajectory and transmit power allocation,
subject to constraints on the UAV's maximum speed, initial/final locations, and
average transmit power, as well as a set of interference temperature (IT)
constraints imposed at each of the PRs for protecting their communications.
However, the joint trajectory and power optimization problem is non-convex and
thus difficult to be solved optimally. To tackle this problem, we propose an
efficient algorithm that ensures to obtain a locally optimal solution by
applying the techniques of alternating optimization and successive convex
approximation (SCA). Numerical results show that our proposed joint UAV
trajectory and power control scheme significantly enhances the achievable rate
of the cognitive UAV communication system, as compared to benchmark schemes.

本研究探讨了一种新的无人机和地面无线通信系统之间的频谱共享场景，并利用航迹设计优化 UAV 通信性能，控制主要接收器的同频干扰，以及保护其通信所需的干扰温度约束。最后采用交替优化和连续凸逼近（SCA）技术解决非凸问题，数值结果表明，我们的方案比基准方案显著提高了认知 UAV 通信系统的可达速率。

利用联合轨迹和功率控制实现认知型无人机通信

Cognitive UAV Communication via Joint Trajectory and Power Control

This paper considers the problem of interference control through the use of
second-order statistics in massive MIMO multi-cell networks. We consider both
the cases of co-located massive arrays and large-scale distributed antenna
settings. We are interested in characterizing the low-rankness of users'
channel covariance matrices, as such a property can be exploited towards
improved channel estimation (so-called pilot decontamination) as well as
interference rejection via spatial filtering. In previous work, it was shown
that massive MIMO channel covariance matrices exhibit a useful finite rank
property that can be modeled via the angular spread of multipath at a MIMO
uniform linear array. This paper extends this result to more general settings
including certain non-uniform arrays, and more surprisingly, to two dimensional
distributed large scale arrays. In particular our model exhibits the dependence
of the signal subspace's richness on the scattering radius around the user
terminal, through a closed form expression. The applications of the
low-rankness covariance property to channel estimation's denoising and
low-complexity interference filtering are highlighted.

本文研究在巨量 MIMO 多小区网络中通过使用二阶统计量来控制干扰的问题，研究了共位巨型阵列和大规模分布式天线设置的情况，并着重研究了用户信道协方差矩阵的低秩特性，以期通过改进通道估计（所谓的导频净化）和空间滤波来抑制干扰。