多小区MIMO网络的自适应联合非线性收发处理

Liang Sun, M. Lei
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引用次数: 7

摘要

研究了具有多天线用户的下行协调多点多输入多输出(MIMO)系统的信号处理算法。提出了一种基于零强迫(ZF)准则的自适应联合非线性收发处理算法。在该算法中,首先在多个基站上应用分块连续ZF预编码来预消部分多用户干扰。然后应用非线性Tomlinson-Harashima预编码进一步减少其他用户的数据流与同一用户的其他数据流之间的干扰,同时对每个用户进行线性均衡和模运算。首先,本文提出的联合非线性收发处理算法有效地将多用户MIMO信道分解为并行独立的单用户MIMO信道。与[1]的方法不同的是,我们的算法允许每个用户的子信道数为任意数,不超过该用户的等效信道的秩,然后针对给定的子信道数集合和给定的所有用户的功率分配,推导出发送和接收处理矩阵的封闭表达式,以优化每个用户的输出信噪比。随后,提出了一种根据用户信道矩阵的衰落来调整子信道数量的自适应方法(称为自适应算法)。仿真结果表明,该算法比文献中的非自适应非线性预处理算法[1]和已知的块对角化技术等处理方法具有更好的吞吐量性能。
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Adaptive joint nonlinear transmit-receive processing for multi-cell MIMO networks
This paper considers signal processing algorithms for the downlink coordinated multi-point multiple-input multiple-output (MIMO) systems with multiple-antenna users. A novel adaptive joint nonlinear transmit-receive processing algorithm is proposed based on zero-forcing (ZF) criterion. In this algorithm, a block successive ZF precoding is first applied at the multiple base stations to pre-cancel partial multiuser interference. Then nonlinear Tomlinson-Harashima precoding is applied to further reduce interference between the data streams of other users and the other data streams of the same user, whereas linear equalization and modulo operation are applied at each user. We first show that the proposed joint nonlinear transmit-receive processing algorithm effectively decomposes the multiuser MIMO channel into parallel independent single-user MIMO channels. Different from the method in [1], our proposed algorithm allows the number of sub-channels of each user to be arbitrary number no more than the rank of that user's equivalent channel, and then for the given set of the numbers of sub-channels and the given power allocation of all users, closed-form expressions of the transmit and receive processing matrices are derived to optimize the output signal to interference plus noise ratio of each user. Subsequently, an adaptive method is proposed to adapt the number of sub-channels according to the fading of the users' channel matrices (termed as adaptive algorithm). Simulation results show that the proposed algorithm achieves much better throughput performance than the processing methods in the literature, such as the non-adaptive nonlinear preprocessing algorithm in [1] and the known block diagonalization techniques.
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