MIMO Detection Algorithm For LTE-Advanced System

As a further enhancement tec hnology of LTE(Long Term Evolution), LTE-Advanced is a standard that to fully comply with the requirements of IMT-Advanced(International Mobile Telecommunication)and further enhance the competitive advantage of the LTE. MIMO(Multiple Input Multiple Output) technology has been enhanced for the LTE-Advanced system, the downlink can support eight layers and two code-word flows of transmission. However, each receive antenna will receive signals from all of other transmit antennas, and with the increasing of the antennas, the interference becomes more serious. That is a big challenge for MIMO detection. Therefore, how to recover the original transmission signal from each receiving antenna with a low complexity and good performance became a key issue.In this paper, consider the deficiency of the existing algorithms’, we propose an improved MIMO detection algorithm. Firstly, we briefly introduce the basic concept of the downlink and the whole signal processing of the downlink, among them, the layer mapping and precoding are discussed in detailed. Secondly, the traditional MIMO detection algorithms are investigated, analyzed, and simulated on the downlink platform for LTE- Advanced system. Combination of the theoretical analysis and the simulation results of the different detection algorithms shows that under the condition of the same SNR, compared with other algorithms, ML(Maximum Likelihood) detection algorithm provides the best performance for MIMO system. However, its computational complexity is highest, which results in difficulty to use in practice system. Finally, this paper proposes an improving algorithm on the basis of ML(Maximum Likelihood) algorithm, which mainly includes reducing the search space, taking plenty of possible transmit symbol set into account, as well as absorbing the SQRD(Sort QR Decomposition) algorithm into the analysis process. The simulation results and the analysis of complexity show that the performance of the improving algorithm is close to ML, while the complexity is well below the ML algorithm. It is especially suitable for the massive MIMO detection in the future 5G mobile communication systems.