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Towards the Performance of ML and the Complexity of MMSE: A Hybrid Approach for Multiuser Detection

机译:机器学习的性能和MMSE的复杂性:一种用于多用户检测的混合方法

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摘要

In this paper, we consider a low-complexity multiuser detection for downlink of high speed packet access (HSPA) of universal mobile telecommunications system (UMTS). Instead of attempting to perform maximum likelihood (ML) detection of all users in multiple cells, which is impractical for battery powered mobile receiver, we utilize interference cancelled chips obtained from iterative linear minimum mean square error (LMMSE) estimation to perform a near ML detection in a reduced dimensional system. As a result, the worst case complexity of the detection process, achieved by the closest lattice point search (CLPS), is bounded to a controllable level irrespective of multipath spans. Furthermore, by exploiting the LMMSE estimate in tightening the hypersphere condition of the CLPS algorithm so called sphere decoding, we achieve significant improvement in search complexity. From simulations on realistic downlink communication scenario in HSPA systems, we show that the proposed method offers substantial performance gain over conventional receiver algorithms with reasonable complexity.
机译:在本文中,我们考虑了通用移动电信系统(UMTS)的高速分组接入(HSPA)下行链路的低复杂度多用户检测。我们没有尝试对多个小区中的所有用户执行最大似然(ML)检测,这对于电池供电的移动接收机来说是不切实际的,我们利用从迭代线性最小均方误差(LMMSE)估计获得的干扰消除芯片来执行接近ML检测在降维系统中。结果,与多径跨度无关,由最近的晶格点搜索(CLPS)实现的检测过程的最坏情况复杂度被限制在可控制的水平上。此外,通过利用LMMSE估计来加强CLPS算法(称为球面解码)的超球面条件,可以显着提高搜索复杂度。通过对HSPA系统中实际下行链路通信场景的仿真,我们表明,与具有合理复杂度的常规接收器算法相比,该方法可提供显着的性能提升。

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