transforming-ris-assisted-passive-beamforming

transforming-ris-assisted-passive-beamforming（RIS辅助被动波束成形）是一种新兴的无线通信技术，结合了反射智能表面（RIS）和被动波束成形技术。

在传统的无线通信系统中，基站通过主动控制天线的方向性来进行波束成形，以增强用户的接收信号质量。然而，这种主动技术需要高功耗和昂贵的射频链路设备。

RIS辅助被动波束成形是一种新兴的解决方案，通过使用布满被动元器件的反射智能表面，对入射信号进行调制和反射，实现对信号的波束成形。相比于传统的主动波束成形，RIS辅助被动波束成形具有低功耗和低成本的特点。

RIS由大量被动元器件（比如反射器和相移器）组成，并通过外界输入信号来调整这些元器件的相位和振幅。当入射信号与RIS相互作用时，RIS会对信号进行相位调制和反射，实现对信号的波束成形。这种方式可以显著增加信号的覆盖范围和传输距离，提高系统的容量和性能。

在RIS辅助被动波束成形中，RIS的位置、布局和调制参数的选择对系统性能起着重要作用。通过精确的设计和优化，可以实现对不同用户的个性化波束成形，提高整体的通信质量。

总之，transforming-ris-assisted-passive-beamforming是一种创新的无线通信技术，通过利用反射智能表面来实现被动波束成形，从而在无线通信系统中提供低功耗和低成本的解决方案，提高信号的覆盖范围和传输性能。

相关问题

python orion-ml

As an AI language model, I don't have personal preferences. However, I can provide information on Orion-ML.

Orion-ML is an open-source machine learning platform that allows developers to build, train, and deploy machine learning models. It provides a flexible and scalable architecture for building custom machine learning models using Python. Orion-ML includes a wide range of algorithms and tools for data preprocessing, model training, and evaluation.

Some of the key features of Orion-ML include:

1. Data preprocessing: Orion-ML provides tools for cleaning, transforming, and normalizing data before it is used to train a machine learning model.

2. Algorithm selection: Orion-ML supports a wide range of machine learning algorithms, including linear regression, decision trees, random forests, and neural networks.

3. Model training and evaluation: Orion-ML provides tools for training and evaluating machine learning models, including cross-validation and hyperparameter tuning.

4. Model deployment: Orion-ML allows developers to deploy machine learning models as RESTful APIs, making it easy to integrate them into other applications.

Overall, Orion-ML is a powerful and flexible machine learning platform that can help developers build and deploy machine learning models quickly and efficiently.

Auto-encoder

An autoencoder is a type of neural network that is used for unsupervised learning. It consists of two main parts: an encoder and a decoder. The encoder is responsible for transforming the input data into a more compact representation, while the decoder is responsible for transforming the compact representation back into the original input data.

Autoencoders are typically used for dimensionality reduction, data compression, and feature extraction. They can be trained on a large dataset of unlabeled data, and then used to extract meaningful features from new data.

Autoencoders have been used in a variety of applications, including image and speech recognition, anomaly detection, and data denoising. They are also commonly used in deep learning architectures, such as convolutional neural networks and recurrent neural networks.