CIF: Small: Exploiting Interference via Data-Dependent Precoding

在无线通信中，管理干扰是已经研究了多年的关键问题。当一个蜂窝塔或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，以至于无法被各个接收器分开。最常见的方法是以试图完全消除这种多用户干扰的方式对信号进行编码，通常通过将每个信号分配给"正交"信道，例如，在不同时间、在不同频带上或使用不同传输波束发送信号。这个项目的目标是利用观察到的并非所有的干扰都是"坏的"。“当信号被限制在有限的字母表中时，信号就不需要完全按照它被发送的形式被接收;只需要接收器正确地解码信号字母表中的哪个符号被发送。虽然多用户干扰不可避免地使波形失真，但是发射机可以以干扰不阻止（并且实际上可以增强）接收机正确解码其相应符号的能力的方式对波形进行编码。 这种方法的关键优点是，它使发射机能够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向每个接收机的期望信号添加额外的功率。这种方法可以显著节省能源，这可能会对无线系统的性能产生革命性的影响，并以当前所需能耗的一小部分实现无线网络基础设施和物联网设备的更广泛部署。多天线实现已经成为当今WiFi和蜂窝通信网络中的标准，并且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。该提议集中于最近出现的新的符号级预编码范例，其中不仅利用信道状态信息，而且还利用要发送的符号的知识。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试图消除干扰，但符号级预编码（SLP）利用有用或"相长"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，并且使得无线系统能够以显著更少的功率操作，并且因此使得相长干扰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，以及在具有受限射频前端的情况下算法的操作（例如，低分辨率量化、每天线功率限制）、物理层安全、大型网络中的用户选择、性能理论分析、广播以外的网络设置等。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响力审查标准进行评估，被认为值得支持。

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.