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arXiv 0705.3344cs.ITmath.IT
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动态环境下的多用户检测第一部分:用户识别和数据检测

Multiuser detection in a dynamic environment Part I: User identification and data detection

Ezio Biglieri, Marco Lops

摘要:

在随机接入通信系统中,活跃用户数随时间变化,对接收机的性能有很大影响。因此,不仅要查明所传送的信息,而且要查明其数目的技术在这些系统中起着核心作用。可以在多用户检测(MUD)中找到这些技术的应用的示例。在典型的MUD分析中,接收器基于这样的假设,即活跃用户的数量是恒定的并且在接收器处是已知的,并且与有权访问系统的用户的最大数量一致。这种假设通常过于悲观,因为许多用户可能在任何给定时间都处于不活动状态,并且在用户数量大于实际用户数量的假设下进行检测可能会损害性能。本文的主要目标是介绍一种通用的方法来识别活跃用户和估计他们的参数和数据在随机接入系统中,用户不断进入和离开系统的问题。我们提倡使用的工具是随机集理论:应用这一点,我们得出最佳的接收机在环境中的发射机的集合包括一个未知数量的元素。此外,我们可以推导出贝叶斯滤波器方程,该方程描述了未知用户参数的后验概率密度随时间的演化,并使用该密度来推导最佳检测器。在本文中,我们限制自己的身份识别和数据检测,而在一个配套文件中,我们将研究更复杂的问题,估计用户的参数。

英文摘要:

In random-access communication systems, the number of active users varies with time, and has considerable bearing on receiver's performance. Thus, techniques aimed at identifying not only the information transmitted, but also that number, play a central role in those systems. An example of application of these techniques can be found in multiuser detection (MUD). In typical MUD analyses, receivers are based on the assumption that the number of active users is constant and known at the receiver, and coincides with the maximum number of users entitled to access the system. This assumption is often overly pessimistic, since many users might be inactive at any given time, and detection under the assumption of a number of users larger than the real one may impair performance. The main goal of this paper is to introduce a general approach to the problem of identifying active users and estimating their parameters and data in a random-access system where users are continuously entering and leaving the system. The tool whose use we advocate is Random-Set Theory: applying this, we derive optimum receivers in an environment where the set of transmitters comprises an unknown number of elements. In addition, we can derive Bayesian-filter equations which describe the evolution with time of the a posteriori probability density of the unknown user parameters, and use this density to derive optimum detectors. In this paper we restrict ourselves to interferer identification and data detection, while in a companion paper we shall examine the more complex problem of estimating users' parameters.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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