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arXiv 2103.09444cs.ITeess.SPmath.IT
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mmWave V2X双选择多用户MIMO系统的混合预编码

Hybrid Precoding for mmWave V2X Doubly-Selective Multiuser MIMO Systems

Elyes Balti

摘要:

毫米波(mmWave)是为车联网(V2X)通信提供高数据率的实用解决方案。这使得未来的自动驾驶汽车能够与基站(BS)交换大数据,例如速度和位置,以提高高级驾驶辅助系统(ADAS)的感知能力。在这种情况下,我们考虑一个单小区多用户双选择系统,其中BS同时服务于多个车辆。为了实现这一要求,在混合架构中实现BS以支持多个空间流,而车辆具有仅模拟的结构。在这项工作中,我们开发了一种低复杂度的混合预编码算法,其中的混合预编码器在BS和模拟组合器在车辆的设计需要小的训练和反馈开销。我们提出了一个两阶段的混合预编码算法,其中第一阶段设计的模拟波束形成器在单用户的情况下,而第二阶段设计的多用户数字预编码器在BS。在第二阶段,我们推导出封闭形式的数字预编码器,如最大比传输(MRT),迫零(ZF)和最小均方误差(MMSE)作为第一个变量,而我们提出迭代数字预编码器作为第二个变量。用于两种变体的数字预编码器的设计需要从车辆发送到BS的有限反馈。我们参考随机矢量量化(RVQ)和波束控制码本来分别对变体I和II的反馈进行优化,因为完美的反馈需要长开销和大量训练。我们评估的数字和模拟码本的量化对完美的信道状态信息在发送端(CSIT)所产生的速率损失。

英文摘要:

Millimeter wave (mmWave) is a practical solution to provide high data rate for the vehicle-to-everything (V2X) communications. This enables the future autonomous vehicles to exchange big data with the base stations (BSs) such as the velocity and the location to improve the awareness of the advanced driving assistance system (ADAS). In this context, we consider a single-cell multiuser doubly-selective system wherein the BS simultaneously serves multiple vehicles. To accomplish this requirement, the BS is implemented in hybrid architecture to support multiple spatial streams while the vehicles have analog-only structures. In this work, we develop a low-complexity hybrid precoding algorithm wherein the design of the hybrid precoder at the BS and the analog combiner at the vehicles require small training and feedback overhead. We propose a two-stage hybrid precoding algorithm wherein the first stage designs the analog beamformers as in single user scenario while the second stage designs the multiuser digital precoder at the BS. In the second stage, we derive closed-form digital precoders such as Maximum Ratio Transmission (MRT), Zero-Forcing (ZF) and Minimum Mean Square Error (MMSE) as a first variant while we propose iterative digital precoder as a second variant. The design of the digital precoders for the two variants requires the limited feedback sent from the vehicles to BS. We refer to the random vector quantization (RVQ) and the beamsteering codebooks to quantize the feedbacks for variants I and II, respectively, since the perfect feedback requires long overhead and large training. We evaluate the rate loss incurred by the quantization of the digital and analog codebooks against the perfect channel state information at the transmitter (CSIT).

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