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Feedback Quantization Strategies for Multiuser Diversity Systems

机译：多用户分集系统的反馈量化策略

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摘要

In a system utilizing multiuser diversity, regular feedback of channel-quality predictions to the base station is required for each user. Typically, the measure of channel quality must be quantized at each mobile station before it can be sent back. In this paper, we present two distributed scalar quantization schemes that optimize two different performance criteria: a) the minimization of the probability P e of incorrectly identifying the user with the best channel quality and b) maximization of the resulting throughput R. For a typical Rayleigh-fading system with 30 users per sector, numerical optimization results show that the Pe and R realized by the uniform quantization strategy with 16 quantization levels for each user can be achieved by only three quantization levels using the two proposed strategies. A practical approximation of the proposed schemes is studied and is shown to provide near-optimal performance for both performance criteria as the number of quantization levels becomes large
机译：在利用多用户分集的系统中，每个用户都需要定期向基站反馈信道质量预测。通常，必须在每个移动台上量化信道质量，然后才能将其发送回去。在本文中，我们提出了两种分布式标量量化方案，这些方案优化了两种不同的性能标准：a）最小化错误地识别具有最佳信道质量的用户的概率P e和b）最大化产生的吞吐量R。每个扇区有30个用户的瑞利衰落系统，数值优化结果表明，使用两个所提出的策略，只有三个量化级别才能实现通过每个用户具有16个量化级别的统一量化策略实现的Pe和R。研究了拟议方案的实际近似值，结果表明随着量化级别数量变大，这两种性能标准都能提供接近最佳的性能

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