Research On Channel Estimation Algorithm And Data Detection Algorithm In MIMO-OFDM System

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.