Multiple Model Framework for Noncoherent Detection in Communications

通信中的一个基本问题是检测在受到干扰、噪声和其它损伤的信道上传输的信息承载信号。本项目研究这个问题，特别关注的情况下，当传输信道的结构和噪声/干扰是未知的接收机。这种情况的一个典型实例是抑制由来自未知数量的利用相同频带的其他发射机的信号组成的干扰。这与几个实际问题有关：例如，第三代扩频（CDMA）系统的多用户检测，以及全球定位系统（GPS）或无线通信系统的抗干扰。总之，研究结果将有助于提高无线链路的质量，并使无线电频谱的使用更有效。 该项目对所研究的问题采取了系统的决策理论方法。 开发了一种新的范例，其中的关键思想是使用混合模型（a.k.a.“专家的混合”）针对接收到的数据。 讨论的具体主题包括：1）通过混合密度制定通信信号的一般模型/框架。2)混合模型参数估计的统计可靠方法的发展。 3)解调算法的设计，利用接收数据的混合模型公式，并可以与软判决可解码的GF（2）码集成。4)用于实际问题的算法设计，包括：抑制具有未知结构的干扰;可以在“噪声预白化”干扰抑制和多用户（联合）检测之间进行权衡的方法;与未知数量的"重要“路径的分集组合（例如，用于CDMA或UWB）;解调具有未知调制格式的信号;抑制非高斯噪声。

A fundamental problem in communications is that of detecting an information-carrying signal which has been transmitted over a channel subject to interference, noise and other impairments. This project studies this problem, with a special focus on the case when the structure of the transmission channel and the noise/interference is unknown to the receiver. One typical instance of this situation is suppression of interference that consists of signals from an unknown number of other transmitters who utilize the same frequency band. This is relevant to several practical problems: multiuser detection for the third generation spread spectrum (CDMA) system, and antijamming for the Global Positioning System (GPS) or wireless communications systems, for example. In summary, the outcome of the research will help improve the quality of wireless links and enable a more efficient use of the radio spectrum. The project takes a systematic, decision-theoretic approach to the problem under study. A new paradigm is developed, where the key idea is to use mixture models (a.k.a. ``mixture of experts'') for the received data. Specific topics addressed include: 1) Formulation of a general model/framework for communications signals via mixture densities. 2) Development of statistically sound methods for the mixture model parameter estimation. 3) Design of demodulation algorithms that utilize the mixture model formulation of the received data, and that can be integrated with soft-decision decodable GF(2) codes. 4) Algorithm design for practical problems including: suppression of interference with unknown structure; methods that can trade off between ``noise-prewhitening'' interference suppression and multiuser (joint) detection; diversity combination with an unknown number of ``significant'' paths (e.g., for CDMA or UWB); demodulation of signals with unknown modulation formats; suppression of non-Gaussian noise.