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Approximate Message Passing for Sparse Large MIMO Systems with Prior Information

机译:具有先验信息的稀疏大型MIMO系统的近似消息传递

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We consider large MIMO systems where only a few transmit antennas are active in every timeslot resulting in a sparse transmit vector. A typical scenario is a wireless sensor network where nodes transmit just sporadically to a single aggregation node. Having just a few receive antennas leads to spatial subsampling or compressive sampling. Based on the sparsity of the transmit signal, reconstruction algorithms known from compressed sensing can be applied. They can even exploit the discrete nature of transmit symbols used in digital communications. In this paper, the Bayesian approximate message passing algorithm is considered. We expand the algorithm by an individual prior component for each element, e.g. information from a decoder, and survey its influence on the recovery performance.
机译:我们考虑大型MIMO系统,其中每个时隙中只有几个发射天线处于活动状态,从而导致发射矢量稀疏。典型的场景是无线传感器网络,其中节点仅偶尔发送到单个聚合节点。只有几个接收天线会导致空间二次采样或压缩采样。基于发射信号的稀疏性,可以应用从压缩感测中已知的重建算法。他们甚至可以利用数字通信中使用的传输符号的离散特性。本文考虑了贝叶斯近似消息传递算法。我们针对每个元素通过单独的先验组件扩展算法,例如来自解码器的信息,并调查其对恢复性能的影响。

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