Two-Way Training for Discriminatory Channel Estimation in Wireless MIMO Systems
Two-Way Training for Discriminatory Channel Estimation in Wireless MIMO Systems
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DOI：
10.1109/tsp.2013.2245124
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发表时间：
2011-06
影响因子：
5.4
通讯作者：
Chao-Wei Huang;Tsung-Hui Chang;Xiangyun Zhou;Y. Hong
中科院分区：
文献类型：
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作者：
Chao-Wei Huang;Tsung-Hui Chang;Xiangyun Zhou;Y. Hong
研究了在多输入多输出(MIMO)无线系统中，如何使用双向训练来有效区分合法接收器(LR)和非授权接收器(UR)的信道估计性能。该工作改进了Chang提出的使用多阶段反馈和再训练的原始区分信道估计(DCE)方案。物理层保密性的研究大多是在信息论的框架下，直接集中在数据传输阶段，而DCE的研究主要集中在训练阶段，目的是提供一种实用的信号处理技术来区分LR和UR的信道估计性能(从而区分有效的接收信号质量)。DCE设计的一个关键特征是在训练信号中插入人工噪声(AN)以降低UR下的信道估计性能。要做到这一点，必须根据发射机对LR的信道的了解，将其放置在精心选择的子空间中，以便将其对LR的影响降至最低。在本文中，我们采用了双向训练的思想，允许发射机和LR都发送训练信号，以便于两端的信道估计。同时考虑了互易信道和非互易信道，并针对每种情况提出了一种双向DCE方案。为了便于数学处理，我们假设所有终端都采用线性最小均方误差准则进行信道估计。基于所有终端的信道估计的均方误差(MSE)，我们建立并解决了一个最优化问题，其中训练信号和AN之间的最优功率分配是通过最小化LR的信道估计的MSE来实现的，约束在UR处可达到的MSE。数值结果表明，所提出的DCE方案可以有效地区分信道估计，从而区分LR和UR下的数据检测性能。
This work examines the use of two-way training to efficiently discriminate the channel estimation performances at a legitimate receiver (LR) and an unauthorized receiver (UR) in a multiple-input multiple-output (MIMO) wireless system. This work improves upon the original discriminatory channel estimation (DCE) scheme proposed by Chang where multiple stages of feedback and retraining were used. While most studies on physical layer secrecy are under the information-theoretic framework and focus directly on the data transmission phase, studies on DCE focus on the training phase and aim to provide a practical signal processing technique to discriminate between the channel estimation performances (and, thus, the effective received signal qualities) at LR and UR. A key feature of DCE designs is the insertion of artificial noise (AN) in the training signal to degrade the channel estimation performance at UR. To do so, AN must be placed in a carefully chosen subspace, based on the transmitter's knowledge of LR's channel, in order to minimize its effect on LR. In this paper, we adopt the idea of two-way training that allows both the transmitter and LR to send training signals to facilitate channel estimation at both ends. Both reciprocal and nonreciprocal channels are considered and a two-way DCE scheme is proposed for each scenario. For mathematical tractability, we assume that all terminals employ the linear minimum mean square error criterion for channel estimation. Based on the mean square error (MSE) of the channel estimates at all terminals, we formulate and solve an optimization problem where the optimal power allocation between the training signal and AN is found by minimizing the MSE of LR's channel estimate subject to a constraint on the MSE achievable at UR. Numerical results show that the proposed DCE schemes can effectively discriminate between the channel estimation and, hence, the data detection performances at LR and UR.