| 摘要: |
| 目前在单向双跳多输入多输出（MIMO）中继系统中，基于嵌套张量模型的接收算法主要采用单步交替最小二乘（ALS）和KRF（Khatri-Rao Factorization）算法。在时变信道且实时性要求较高场景下，计算复杂度高是制约其应用的主要因素。为此，在对单向双跳MIMO中继系统建模基础上，提出了基于嵌套张量模型的双步组合接收算法。该算法通过对接收的数据张量进行重建，将符号估计和信道估计分离，充分利用ALS和KRF的算法优势，有效降低了计算复杂度。同时，对算法的可辨识性进行了分析。仿真结果表明，该算法保持了与传统嵌套PARAFAC的最小二乘（Nested PARAFAC ALS）算法的相同估计性能，在源天线个数变化时，计算复杂度降低了80%以上；在中继天线个数变化时，计算复杂度降低了50%以上。 |
| 关键词: 多输入多输出 中继系统 半盲接收 嵌套张量模型 |
| DOI：10.3969/j.issn.1001-893x.2017.08.006 |
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| 基金项目: |
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| An assembled receiver algorithm based on nested tensor model in MIMO relay communication systems |
| WANG Rui,RUI Guosheng,ZHANG Yang |
| (Electronic Information Engineering Department,Naval Aeronautical Engineering Institute,Yantai 264001,China) |
| Abstract: |
| The alternating least squares(ALS) algorithm and Khatri-Rao factorization(KRF) algorithm,which are one-step algorithms,are mainly adopted by receiver algorithms based on nested tensor in multiple-input multiple-output(MIMO) relay communication systems.The high computational cost is a dominant factor restricting their application under time-varying channel and real-time conditions. For this reason,an assembled receiver algorithm based on nested tensor model is proposed by building one-way and two-hop MIMO relay communication system. The processes of symbol estimation and channel estimation are separated by reconstructing received signal,and the computation complexity is reduced effectively. Moreover,identifiability conditions of the algorithm are analyzed. Simulation results show that the computation complexity of the algorithm is decreased more than 80% and 50% with different source antennas and relay antennas,while the algorithm has the same estimation performance with nested PARAFAC ALS(NP-ALS) algorithm. |
| Key words: multiple-input multiple-output(MIMO) relay system semi-blind receiver nested tensor model |
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