DeepSIC: Deep Soft Interference Cancellation for Multiuser MIMO Detection

Nir Shlezinger, Rong Fu, Yonina C. Eldar

نتاج البحث: نشر في مجلةمقالةمراجعة النظراء

ملخص

Digital receivers are required to recover the transmitted symbols from their observed channel output. In multiuser multiple-input multiple-output (MIMO) setups, where multiple symbols are simultaneously transmitted, accurate symbol detection is challenging. A family of algorithms capable of reliably recovering multiple symbols is based on interference cancellation. However, these methods assume that the channel is linear, a model which does not reflect many relevant channels, as well as require accurate channel state information (CSI), which may not be available. In this work we propose a multiuser MIMO receiver which learns to jointly detect in a data-driven fashion, without assuming a specific channel model or requiring CSI. In particular, we propose a data-driven implementation of the iterative soft interference cancellation (SIC) algorithm which we refer to as DeepSIC. The resulting symbol detector is based on integrating dedicated machine-learning methods into the iterative SIC algorithm. DeepSIC learns to carry out joint detection from a limited set of training samples without requiring the channel to be linear and its parameters to be known. Our numerical evaluations demonstrate that for linear channels with full CSI, DeepSIC approaches the performance of iterative SIC, which is comparable to the optimal performance, and outperforms previously proposed learning-based MIMO receivers. Furthermore, in the presence of CSI uncertainty, DeepSIC significantly outperforms model-based approaches. Finally, we show that DeepSIC accurately detects symbols in non-linear channels, where conventional iterative SIC fails even when accurate CSI is available.

اللغة الأصليةإنجليزيّة أمريكيّة
رقم المقال9242305
الصفحات (من إلى)1349-1362
عدد الصفحات14
دوريةIEEE Transactions on Wireless Communications
مستوى الصوت20
رقم الإصدار2
تاريخ مبكر على الإنترنت28 أكتوبر 2020
المعرِّفات الرقمية للأشياء
حالة النشرنُشِر - 1 فبراير 2021

All Science Journal Classification (ASJC) codes

  • !!Computer Science Applications
  • !!Electrical and Electronic Engineering
  • !!Applied Mathematics

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