Integrated Sensing and Communications for V2I Networks: Dynamic Predictive Beamforming for Extended Vehicle Targets
Integrated Sensing and Communications for V2I Networks: Dynamic Predictive Beamforming for Extended Vehicle Targets
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DOI：
10.1109/twc.2022.3219890
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发表时间：
2021-11
影响因子：
10.4
通讯作者：
Zhen Du;Fan Liu;W. Yuan;C. Masouros;Zenghui Zhang;G. Caire
中科院分区：
文献类型：
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作者：
Zhen Du;Fan Liu;W. Yuan;C. Masouros;Zenghui Zhang;G. Caire
我们调查的车辆到基础设施（V2I）通信的传感辅助波束形成，利用集成的传感和通信（ISAC）功能在路边单元（RSU）。RSU在毫米波上部署了一个大规模的多输入多输出（mMIMO）阵列。锐利的mMIMO波束和精细的距离分辨率意味着点目标假设是不切实际的，因为车辆的几何形状变得至关重要。因此，通信接收器（CR）可能永远不会位于波束中，即使当车辆被精确跟踪时。为了解决这个问题，我们认为扩展目标与两个新的计划。对于第一种方案，实时调整波束宽度以覆盖整个车辆，然后通过扩展卡尔曼滤波器根据分辨的散射体预测和跟踪CR的位置。提出了一种改进方案，将每个传输块分为两个阶段。第一级用于具有宽波束的ISAC。基于在第一阶段的感测结果，第二阶段专用于与双尖波束的通信，从而产生显著的通信改进。我们揭示了两个阶段之间的内在权衡在其持续时间，并制定了最佳的分配策略，最大限度地提高平均可实现的速率。最后，仿真验证了所提出的方案比国家的最先进的方法的优越性。
We investigate sensing-assisted beamforming for vehicle-to-infrastructure (V2I) communication by exploiting integrated sensing and communications (ISAC) functionalities at the roadside unit (RSU). The RSU deploys a massive multi-input-multi-output (mMIMO) array at mmWave. The pencil-sharp mMIMO beams and fine range-resolution implicate that the point-target assumption is impractical, as the vehicle’s geometry becomes essential. Therefore, the communication receiver (CR) may never lie in the beam, even when the vehicle is accurately tracked. To tackle this problem, we consider the extended target with two novel schemes. For the first scheme, the beamwidth is adjusted in real-time to cover the entire vehicle, followed by an extended Kalman filter to predict and track the position of CR according to resolved scatterers. An upgraded scheme is proposed by splitting each transmission block into two stages. The first stage is exploited for ISAC with a wide beam. Based on the sensed results at the first stage, the second stage is dedicated to communication with a pencil-sharp beam, yielding significant communication improvements. We reveal the inherent tradeoff between the two stages in terms of their durations, and develop an optimal allocation strategy that maximizes the average achievable rate. Finally, simulations verify the superiorities of proposed schemes over state-of-the-art methods.