MIMO-OFDM系统中信道估计及信号检测算法的研究
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摘要
多输入多输出(MIMO)和正交频分复用(OFDM)是LTE的两大核心技术。多输入多输出(MIMO)技术利用各种分集技术带来的分集增益可以提高系统的信道容量、数据的传输速率以及系统的频谱利用率,这些都是在不增加系统带宽和发射功率的情况下取得的；正交频分复用(OFDM)技术是多载波调制技术的一种,其物理信道是由若干个并行的正交子信道组成,因此可有效地对抗频率选择性衰落,同时通过插入循环前缀(CP)可以有效消除由多径而引起的符号间干扰(ISI)。由于多输入多输出(MIMO)在提高系统容量和正交频分复用(OFDM)在对抗多径衰落方面的优势,基于两者结合的MIMO-OFDM系统已经引起了广泛的关注。
信道估计算法和信号检测算法是MIMO-OFDM系统的关键技术。其中信道估计算法对MIMO-OFDM系统接收端的相干解调和空时检测起着至关重要的作用,信道估计的准确性将影响系统的整体性能。目前对信道估计算法的研究有很强的理论与实用价值,虽然已有各种类型的信道估计算法提出,但是算法的准确性需要进一步地提高,同时为了算法能够顺利地应用于实际系统中,也要求算法的计算复杂度不能过高,需要在保证准确度的同时将算法的计算复杂度进一步地降低。MIMO-OFDM系统接收机中的信号检测算法性能好坏及其复杂度高低直接影响着整个通信系统的质量和发展前景。复杂度低的信号检测算法往往检测性能很差,而具有优异检测性能的算法往往伴随着偏高的复杂度。复杂度过高的算法实现又常常受限于当前硬件的处理能力,尤其是当天线的数目线性增加时,算法的复杂度往往呈现指数级的增加。因此研究既能拥有最优的信号检测性能且复杂度适中的信号检测算法对MIMO-OFDM系统的实现具有重要的意义。
论文以MIMO-OFDM系统中的信道估计算法和信号检测算法为研究内容,主要工作包括以下三部分：
1、研究信道估计算法,包括典型的数据辅助信道估计算法、盲信道估计算法以及结合前两者的半盲信道估计算法。总结和分析了现有的针对MIMO-OFDM系统的各类典型信道估计算法的优缺点,深入研究了现有的半盲信道估计算法,提出了一种基于空间交替广义期望最大化(SAGE)的半盲信道信道估计与信号检测联合算法。该算法首先将子帧划分为若干个OFDM子块,根据训练符号子块初始化子帧,然后在每一个子块上通过迭代不断更新信道状态信息及OFDM符号检测值。并通过相邻子块传递跟踪的信道状态估计信息来依次完成对所有子块的信号检测,最终完成对所有子帧的信号检测和整个信道状态信息的跟踪。通过仿真分析了所提出算法的性能,指出该算法和传统的信道估计算法相比具有良好的误码率性能,但是算法复杂度还是偏高。因为对子块的迭代更新检测和子块间的检测都用到了最大似然(ML)信号检测算法,其算法复杂度偏高,从而导致整个算法的复杂度较高。尤其是在采用了高阶调制和天线数目较多的MIMO-OFDM系统中,其算法复杂度呈现指数级的增加,因此降低算法中信号检测算法的复杂度成为优化该算法的关键。
2、研究信号检测算法。对现有的基于分层空时结构的MIMO系统的信号检测算法进行了研究,包括最优信号检测算法、次最优信号检测算法以及分层信号检测算法。详细分析了最优信号检测算法即最大似然(ML)信号检测算法在“搜素无序数据库中最小值”时存在的搜索复杂度高的问题,针对此问题提出了一种基于格洛弗量子搜索算法(Grover's Quantum search)的次最优信号检测算法。该算法通过设置基于双门限的判定函数,将未加整理的数据库搜索问题转换为判定问题以降低复杂度,同时执行Grover迭代可以将问题解的概率幅度进行放大,进而降低非解的概率幅度,从而在测量时以较大的概率得到问题解。通过仿真证明了该算法在保证检测准确度的同时可以大幅度降低最优信号检测算法的复杂度。在对次最优信号检测算法的研究中,分析了典型的基于球形译码(SD)的信号检测算法的原理,针对传统的球形译码(SD)信号检测算法的缺陷提出了一种有效的改进搜索半径的球形译码(SD)信号检测算法,仿真表明该算法在误码率性能上要优于传统的线性信号检测算法,且相比于传统的固定半径的球形译码(SD)信号检测算法具有较低的计算复杂度。
3、研究降低复杂度的信道估计和信号检测联合算法。针对第一部分提出的基于SAGE半盲信道估计与信号检测联合算法存在的复杂度高的问题,结合第二部分提出的基于格洛弗量子搜索算法(Grover's Quantum search)的次最优信号检测算法提出了一种改进的信道估计和信号检测联合算法。通过改变完全数据Z与观测数据Y的映射关系,将原算法的流程进行了改进,同时利用格洛弗量子搜索算法对最大似然(ML)信号检测算法的搜索过程进行了优化,有效降低了算法复杂度。通过仿真证明了降低复杂度后的联合算法仍然具有良好的算法性能。
MIMO and OFDM are two core technologies of the LTE. Under the premise that without increasing the system bandwidth and the transmitting power, MIMO can increase the channel capacity, data transfer rate and spectrum efficiency exponentially by using several diversity techniques fully. As a multi-carrier modulation technique, OFDM can oppose the frequency selective fading effectively by dividing a single physical channel into a number of parallel orthogonal subchannels, meanwhile, it can also eliminate the Inter Symbol Interference (ISI) caused by multipath effectively by inserting the Cyclic Prefix (CP). Because MIMO can boost the capacity, and OFDM can mitigate the detrimental effects due to multipath fading, the combination of MIMO and OFDM has caused widespread concern. Channel estimation algorithm and data detection algorithm are the two key technologies of the MIMO-OFDM system. Channel Estimation Algorithms play a vital role in coherent demodulation and space-time detection at the receiver of MIMO-OFDM system, which also have significant impact on overall system's performance. Currently, researches of channel estimation algorithm have high theoretical and practical value. Although many channel estimation algorithms have been proposed, the accuracy of them are still to be further improved. And in order to apply these algorithms to practical system successfully, it requires that the computational complexity of algorithms can not be too high. So it needs to reduce the computational complexity while ensuring the accuracy of the algorithms. The performance and complexity of the data detection algorithm in MIMO-OFDM system's receiver affect, directly, the entire communication system's quality and development prospects. Data detection algorithms with low complexity tend to have poor performance, and those with excellent performance are often with high complexity. Algorithm with too high complexity is often limited by current hardware processing capabilities. Especially, as the number of antenna increases linearly, the complexity of the algorithm increases exponentially. Therefore, the study which not only maintains optimal performance of the data detection algorithm but also has the moderate computational complexity has very important significance for the realization of MIMO-OFDM system.
This paper research on channel estimation algorithm and data detection algorithm in MIMO-OFDM system, mainly including the following three parts:
1、Research on channel estimation algorithms, including typical symbol assisted modulation channel estimation algorithm, blind channel estimation algorithm, and semi-blind channel estimation algorithm combined with the first two. In this part, this paper summarized and analyzed the advantages and disadvantages of the existing various types of typical channel estimation algorithms for MIMO-OFDM system, and studied the existing semi-blind channel estimation algorithm in depth, then proposed a joint algorithm based on the SAGE semi-blind channel estimation and data detection. First this algorithm divided a sub-frame of MIMO-OFDM system into some OFDM sub-blocks, and used the training symbols to initial estimation. in each sub-block We applied channel estimation of the previous sub-block to initial estimation in the current sub-block, in the current sub-block we updated channel estimate and OFDM data detection by iteration until converge. Then we could finish all the sub-blocks in turn and track channel state information. Through simulation, this part analyzed the performance of the proposed algorithm. The proposed algorithm had a better bit error rate performance compared to the traditional channel estimation algorithms, but the complexity of the algorithm was still high because of the use of ML data detection algorithm, which had the high complexity, in iterative update of each sub-block and detection between adjacent sub-blocks. Especially in the MIMO-OFDM system using higher order modulation and having large number of antennas, the complexity of the algorithm increased exponentially, thus, reducing the complexity of data detection algorithm was the key to optimize the proposed algorithm.
2、Research on data detection algorithm. The paper in this part studyed the existing data detection algorithms for V-BLAST system, including the optimal data detection algorithm, sub-optimal datda detection algorithm, and layered data detection algorithm. Then provided a detailed analysis of the complexity of the the optimal data detection algorithm (Maximum Likelihood algorithm) in "searching the minimal element from an unordered data set", and proposed a Grover's Quantum search based data detection algorithm to solve this problem. This algorithm transformed the search problem in an unsorted quantum database into a decision problem by establishment of two thresholds decision function, and increased the probability amplitude of solutions while reducing the probability amplitude of non-solutions by Grover's iterative process. It can get the solutions after measurement with a high probability. The simulation showed that the proposed data detection algorithm had a good Bit Error Rate performance, but with a significant reduction in complexity of the optimal data detection algorithm. In the research of sub-optimal datda detection algorithms, analyzed the principle of typical data detection algorithm based on sphere decoding, and proposed an effective data detection algorithm based on sphere decoding, which improved the search radius. The simulation showed that this algorithm had better performance in the BER than the traditional linear datda detection algorithms and lower complexity than traditional data detection algorithm based on sphere decoding with fixed radius.
3, Research on joint channel estimation algorithm and data algorithm with lower complexity. The paper in this part proposed an improved joint channel estimation algorithm and data detection algorithm combining with the Grover's Quantum search based data detection algorithm in part2to solve the problem of high complexity in joint algorithm based on the SAGE semi-blind channel estimation and data detection in part1. The algorithm improved the process of original algorithm by changing the many-to-one mapping between the complete data Z and the observed data Y. It used the Grover's Quantum search algorithm to optimize the search process of the ML data algorithm, which reduced the complexity of the algorithm effectively. The simulation showed that the proposed joint algorithm with lower complexity still had a considerable performance.
信道估计算法和信号检测算法是MIMO-OFDM系统的关键技术。其中信道估计算法对MIMO-OFDM系统接收端的相干解调和空时检测起着至关重要的作用,信道估计的准确性将影响系统的整体性能。目前对信道估计算法的研究有很强的理论与实用价值,虽然已有各种类型的信道估计算法提出,但是算法的准确性需要进一步地提高,同时为了算法能够顺利地应用于实际系统中,也要求算法的计算复杂度不能过高,需要在保证准确度的同时将算法的计算复杂度进一步地降低。MIMO-OFDM系统接收机中的信号检测算法性能好坏及其复杂度高低直接影响着整个通信系统的质量和发展前景。复杂度低的信号检测算法往往检测性能很差,而具有优异检测性能的算法往往伴随着偏高的复杂度。复杂度过高的算法实现又常常受限于当前硬件的处理能力,尤其是当天线的数目线性增加时,算法的复杂度往往呈现指数级的增加。因此研究既能拥有最优的信号检测性能且复杂度适中的信号检测算法对MIMO-OFDM系统的实现具有重要的意义。
论文以MIMO-OFDM系统中的信道估计算法和信号检测算法为研究内容,主要工作包括以下三部分：
1、研究信道估计算法,包括典型的数据辅助信道估计算法、盲信道估计算法以及结合前两者的半盲信道估计算法。总结和分析了现有的针对MIMO-OFDM系统的各类典型信道估计算法的优缺点,深入研究了现有的半盲信道估计算法,提出了一种基于空间交替广义期望最大化(SAGE)的半盲信道信道估计与信号检测联合算法。该算法首先将子帧划分为若干个OFDM子块,根据训练符号子块初始化子帧,然后在每一个子块上通过迭代不断更新信道状态信息及OFDM符号检测值。并通过相邻子块传递跟踪的信道状态估计信息来依次完成对所有子块的信号检测,最终完成对所有子帧的信号检测和整个信道状态信息的跟踪。通过仿真分析了所提出算法的性能,指出该算法和传统的信道估计算法相比具有良好的误码率性能,但是算法复杂度还是偏高。因为对子块的迭代更新检测和子块间的检测都用到了最大似然(ML)信号检测算法,其算法复杂度偏高,从而导致整个算法的复杂度较高。尤其是在采用了高阶调制和天线数目较多的MIMO-OFDM系统中,其算法复杂度呈现指数级的增加,因此降低算法中信号检测算法的复杂度成为优化该算法的关键。
2、研究信号检测算法。对现有的基于分层空时结构的MIMO系统的信号检测算法进行了研究,包括最优信号检测算法、次最优信号检测算法以及分层信号检测算法。详细分析了最优信号检测算法即最大似然(ML)信号检测算法在“搜素无序数据库中最小值”时存在的搜索复杂度高的问题,针对此问题提出了一种基于格洛弗量子搜索算法(Grover's Quantum search)的次最优信号检测算法。该算法通过设置基于双门限的判定函数,将未加整理的数据库搜索问题转换为判定问题以降低复杂度,同时执行Grover迭代可以将问题解的概率幅度进行放大,进而降低非解的概率幅度,从而在测量时以较大的概率得到问题解。通过仿真证明了该算法在保证检测准确度的同时可以大幅度降低最优信号检测算法的复杂度。在对次最优信号检测算法的研究中,分析了典型的基于球形译码(SD)的信号检测算法的原理,针对传统的球形译码(SD)信号检测算法的缺陷提出了一种有效的改进搜索半径的球形译码(SD)信号检测算法,仿真表明该算法在误码率性能上要优于传统的线性信号检测算法,且相比于传统的固定半径的球形译码(SD)信号检测算法具有较低的计算复杂度。
3、研究降低复杂度的信道估计和信号检测联合算法。针对第一部分提出的基于SAGE半盲信道估计与信号检测联合算法存在的复杂度高的问题,结合第二部分提出的基于格洛弗量子搜索算法(Grover's Quantum search)的次最优信号检测算法提出了一种改进的信道估计和信号检测联合算法。通过改变完全数据Z与观测数据Y的映射关系,将原算法的流程进行了改进,同时利用格洛弗量子搜索算法对最大似然(ML)信号检测算法的搜索过程进行了优化,有效降低了算法复杂度。通过仿真证明了降低复杂度后的联合算法仍然具有良好的算法性能。
MIMO and OFDM are two core technologies of the LTE. Under the premise that without increasing the system bandwidth and the transmitting power, MIMO can increase the channel capacity, data transfer rate and spectrum efficiency exponentially by using several diversity techniques fully. As a multi-carrier modulation technique, OFDM can oppose the frequency selective fading effectively by dividing a single physical channel into a number of parallel orthogonal subchannels, meanwhile, it can also eliminate the Inter Symbol Interference (ISI) caused by multipath effectively by inserting the Cyclic Prefix (CP). Because MIMO can boost the capacity, and OFDM can mitigate the detrimental effects due to multipath fading, the combination of MIMO and OFDM has caused widespread concern. Channel estimation algorithm and data detection algorithm are the two key technologies of the MIMO-OFDM system. Channel Estimation Algorithms play a vital role in coherent demodulation and space-time detection at the receiver of MIMO-OFDM system, which also have significant impact on overall system's performance. Currently, researches of channel estimation algorithm have high theoretical and practical value. Although many channel estimation algorithms have been proposed, the accuracy of them are still to be further improved. And in order to apply these algorithms to practical system successfully, it requires that the computational complexity of algorithms can not be too high. So it needs to reduce the computational complexity while ensuring the accuracy of the algorithms. The performance and complexity of the data detection algorithm in MIMO-OFDM system's receiver affect, directly, the entire communication system's quality and development prospects. Data detection algorithms with low complexity tend to have poor performance, and those with excellent performance are often with high complexity. Algorithm with too high complexity is often limited by current hardware processing capabilities. Especially, as the number of antenna increases linearly, the complexity of the algorithm increases exponentially. Therefore, the study which not only maintains optimal performance of the data detection algorithm but also has the moderate computational complexity has very important significance for the realization of MIMO-OFDM system.
This paper research on channel estimation algorithm and data detection algorithm in MIMO-OFDM system, mainly including the following three parts:
1、Research on channel estimation algorithms, including typical symbol assisted modulation channel estimation algorithm, blind channel estimation algorithm, and semi-blind channel estimation algorithm combined with the first two. In this part, this paper summarized and analyzed the advantages and disadvantages of the existing various types of typical channel estimation algorithms for MIMO-OFDM system, and studied the existing semi-blind channel estimation algorithm in depth, then proposed a joint algorithm based on the SAGE semi-blind channel estimation and data detection. First this algorithm divided a sub-frame of MIMO-OFDM system into some OFDM sub-blocks, and used the training symbols to initial estimation. in each sub-block We applied channel estimation of the previous sub-block to initial estimation in the current sub-block, in the current sub-block we updated channel estimate and OFDM data detection by iteration until converge. Then we could finish all the sub-blocks in turn and track channel state information. Through simulation, this part analyzed the performance of the proposed algorithm. The proposed algorithm had a better bit error rate performance compared to the traditional channel estimation algorithms, but the complexity of the algorithm was still high because of the use of ML data detection algorithm, which had the high complexity, in iterative update of each sub-block and detection between adjacent sub-blocks. Especially in the MIMO-OFDM system using higher order modulation and having large number of antennas, the complexity of the algorithm increased exponentially, thus, reducing the complexity of data detection algorithm was the key to optimize the proposed algorithm.
2、Research on data detection algorithm. The paper in this part studyed the existing data detection algorithms for V-BLAST system, including the optimal data detection algorithm, sub-optimal datda detection algorithm, and layered data detection algorithm. Then provided a detailed analysis of the complexity of the the optimal data detection algorithm (Maximum Likelihood algorithm) in "searching the minimal element from an unordered data set", and proposed a Grover's Quantum search based data detection algorithm to solve this problem. This algorithm transformed the search problem in an unsorted quantum database into a decision problem by establishment of two thresholds decision function, and increased the probability amplitude of solutions while reducing the probability amplitude of non-solutions by Grover's iterative process. It can get the solutions after measurement with a high probability. The simulation showed that the proposed data detection algorithm had a good Bit Error Rate performance, but with a significant reduction in complexity of the optimal data detection algorithm. In the research of sub-optimal datda detection algorithms, analyzed the principle of typical data detection algorithm based on sphere decoding, and proposed an effective data detection algorithm based on sphere decoding, which improved the search radius. The simulation showed that this algorithm had better performance in the BER than the traditional linear datda detection algorithms and lower complexity than traditional data detection algorithm based on sphere decoding with fixed radius.
3, Research on joint channel estimation algorithm and data algorithm with lower complexity. The paper in this part proposed an improved joint channel estimation algorithm and data detection algorithm combining with the Grover's Quantum search based data detection algorithm in part2to solve the problem of high complexity in joint algorithm based on the SAGE semi-blind channel estimation and data detection in part1. The algorithm improved the process of original algorithm by changing the many-to-one mapping between the complete data Z and the observed data Y. It used the Grover's Quantum search algorithm to optimize the search process of the ML data algorithm, which reduced the complexity of the algorithm effectively. The simulation showed that the proposed joint algorithm with lower complexity still had a considerable performance.
引文
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