# 电子工程代写|数字信号处理代写Digital Signal Processing代考|ECT474

## 电子工程代写|数字信号处理代写Digital Signal Processing代考|How to Measure the Utility of Data

Next-generation intelligent vehicles are required to intensively download/upload/exchange/distribute information to enable fundamental automotive applications and services. Therefore, investigating the actual “importance” of shared data to assess whether and which specific sensor information is worth transmitting (i.e., with the final goal of minimizing the network utilization and still deliver valuable information to the receivers) is an open research challenge. A fundamental role in this regard can be played by machine learning, ${ }^6$ which offers tools to perform a variety of operations, including the following:

• Learn Which Features Have Major Impact on Target Applications. Artificial Neural Networks (ANNs) can be trained in an unsupervised manner to extract features from input vectors of different types of signals and provide a more compact
• representation of the input data, which makes it possible to reduce the amount of data to be exchanged, thus saving transmission capacity and reducing the load. Generally, the reliability of the learning process increases with the number of relevant ANN entries in the input set [29].
• Detect Correlation Among Signals. By considering an input including several sources, a Generative Deep Neural Network (GDNN) [2] may reveal the presence of interdependencies among the readings of multiple sensors generated by vehicles in the same geographical area. The generative model can then be used to estimate the output samples from the input set. The accuracy of such predictions provides a way to measure the mutual information contained in different combinations of data.

## 电子工程代写|数字信号处理代写Digital Signal Processing代考|Knowledge Distributiona

While assessing the importance of different types of data plays a significant role in the efficient minimization of the network resource consumption, network utilization can be further optimized by a synergistic exploitation of multiple radio interfaces (with totally different propagation characteristics and features). More specifically, multi-connectivity (MC) [9] enables each vehicular and/or infrastructure node to integrate wireless technologies, including 3G, 4G-LTE, Wi-Fi, DSRC, mmWave, VLC, to support a variety of V2X services and benefit from the strengths of each radio technology, with the final goal of efficiently and reliably exchanging different types of data contents. Some relevant hybrid networking solutions include the following:

• Sclective Transmissions, in which data contents are transmitted through a single, dynamically selected radio interface. For instance, connected cars can maintain
• several signal paths to different infrastructures, operating at different frequencies, so that drops in one link can be overcome by switching data paths.
• Parallel Transmissions, in which data contents are duplicated and sent over different types of radios to add redundancy, making the message delivery more robust, but using more communication resources.
• Hierarchical Transmissions, in which a specific technology is used to provide a basic level of service, while different types of radios/paths are exploited to deliver supplemental information to improve the QoS of designated applications.
• However, how to implement efficient multi-connectivity systems on nextgeneration connected cars is still an open issue. In particular, among the challenges that need to be addressed, the definition of an intelligent network selection mechanism, driven by a distributed or centralized/cloud-assisted decision process, must be engineered, to allow high-quality $\mathrm{V} 2 \mathrm{X}$ applications to meet their requirements.

## 电子工程代写|数字信号处理代写Digital Signal Processing代考|How to Measure the Utility of Data

• 了解哪些功能对目标应用程序有重大影响。人工神经网络 (ANN) 可以以无监督的方式进行训练，以从不同类型信号的输入向量中提取特征，并提供更紧凑的
• 输入数据的表示，这使得可以减少要交换的数据量，从而节省传输容量并减轻负载。通常，学习过程的可靠性随着输入集中相关 ANN 条目的数量增加而增加 [29]。
• 检测信号之间的相关性。通过考虑包含多个来源的输入，生成深度神经网络 (GDNN) [2] 可以揭示同一地理区域中车辆生成的多个传感器的读数之间存在相互依赖性。然后可以使用生成模型从输入集中估计输出样本。这种预测的准确性提供了一种方法来衡量包含在不同数据组合中的互信息。

## 电子工程代写|数字信号处理代写Digital Signal Processing代考|Knowledge Distributiona

• 选择性传输，其中数据内容通过单个动态选择的无线电接口传输。例如，互联汽车可以维护
• 多个信号路径到不同的基础设施，以不同的频率运行，因此可以通过切换数据路径来克服一个链路中的丢包。
• 并行传输，其中数据内容被复制并通过不同类型的无线电发送以增加冗余，使消息传递更加稳健，但使用更多的通信资源。
• 分层传输，其中使用特定技术提供基本级别的服务，同时利用不同类型的无线电/路径来提供补充信息以提高指定应用程序的 QoS。
• 然而，如何在下一代联网汽车上实现高效的多连接系统仍然是一个悬而未决的问题。特别是，在需要解决的挑战中，必须设计由分布式或集中式/云辅助决策过程驱动的智能网络选择机制的定义，以允许高质量在2X应用程序以满足他们的要求。

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