CIF: Small: Exploiting Interference via Data-Dependent Precoding

在无线通信中，干扰管理是一个研究多年的关键问题。当手机信号塔或WiFi接入点同时向多个用户发送信号时，它必须确保这些信号不会相互混淆，以至于无法被单个接收器分离。最常见的方法是以一种试图完全消除这种多用户干扰的方式对信号进行编码，通常是通过将每个信号分配到一个“正交”信道，例如，在不同的时间、不同的频带或使用不同的发射波束发送信号。这个项目的目标是利用并不是所有的干扰都是“坏的”这一观察结果。当信号限于一个有限的字母时，没有必要完全按照发送的形式接收信号；只需要接收机正确地解码信号字母表中的哪个符号被传输。虽然多用户干扰不可避免地会扭曲波形，但发射器可以以这样一种方式对波形进行编码，即干扰不会阻止（实际上可以增强）接收器正确解码各自符号的能力。这种方法的主要优点是，它使发射器使用更少的功率来获得相同的性能，因为干扰本质上是为了给每个接收器所需的信号增加额外的功率。这种方法可以节省大量能源，对无线系统的性能产生革命性的影响，并使无线网络基础设施和物联网设备的部署范围更广，而当前所需能耗的一小部分。多天线实现已经成为当今WiFi和蜂窝通信网络的标准，是实现下一代系统所需的大吞吐量和高可靠性的关键技术之一。本文提出了一种新的符号级预编码范式，该范式不仅利用了信道状态信息，而且还利用了要传输的符号的知识。这种方法为优化提供了一个强大的额外维度，可以显著提高性能。虽然大多数预编码方法都试图消除干扰，但符号级预编码（SLP）利用有用的或“建设性”的干扰，并将其重新用作所需信号的能量。这增加了信号检测的鲁棒性，使无线系统能够以更低的功耗运行，从而使建设性干扰SLP成为低成本和高可靠性应用的有希望的候选者。该项目旨在研究降低SLP复杂性的方法，算法在受限射频前端场景下的操作（例如，低分辨率量化，每天线功率约束），物理层安全性，大型网络中的用户选择，性能的理论分析，除广播之外的网络设置，该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，认为值得支持。

In wireless communications, managing interference is a key problem that has been studied for many years. When a cell tower or a WiFi access point sends signals simultaneously to several users, it has to ensure that these signals do not get so mixed up with each other that they can't be separated by the individual receivers. The most common approach is to encode the signals in a way that attempts to completely eliminate this multi-user interference, usually by assigning each signal to an "orthogonal" channel, e.g., sending the signals at different times, on different frequency bands, or using different transmission beams. The goal of this project is to exploit the observation that not all interference is "bad." When the signals are confined to a limited alphabet, it is unnecessary that the signal be received in exactly the form it was transmitted; it is only necessary that the receiver properly decode which symbol from the alphabet of signals was transmitted. While multi-user interference inevitably distorts the waveform, the transmitter can encode the waveform in such a way that the interference does not prevent (and in fact can enhance) the ability of the receivers to correctly decode their respective symbols. The key advantage of this approach is that it enables the transmitter to use much less power to get the same performance, since the interference essentially serves the purpose of adding extra power to the desired signals for each receiver. The significant energy savings that can result from this approach could have a revolutionary effect on the performance of wireless systems, and enable a much wider deployment of wireless network infrastructure and IoT devices at a fraction of the currently required energy consumption. Multi-antenna implementations have become standard in today’s WiFi and cellular communication networks, and are one of the key technologies for achieving the large throughputs and high reliability required by next-generation systems. This proposal is focused on a new symbol-level precoding paradigm that has recently emerged in which not only the channel state information is exploited, but also knowledge of the symbols to be transmitted. This approach provides a powerful extra dimension for optimization that can yield dramatic improvements in performance. While most precoding methods try to eliminate interference, symbol-level precoding (SLP) exploits useful or "constructive" interference and repurposes it as energy for the desired signals. This increases the robustness of the signal detection and enables wireless systems to operate with significantly less power, and thus makes constructive interference SLP a promising candidate for low-cost and high-reliability applications. This project seeks to study methods for reducing the complexity of SLP, operation of the algorithms in scenarios with constrained radio-frequency front ends (e.g., low-resolution quantization, per-antenna power constraints), physical layer security, user selection in large networks, theoretical analyses of performance, network settings other than broadcast, etc.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.