Ting Fang
ABSTRACT
A new block low-rank channel estimation algorithm is put forward for the problem that the complexity of the channel estimator matrix inverse computation for the traditional linear minimum mean square error (LMMSE) channel estimator in the massive MIMO system increases as the number of antenna increases. The block partitioning algorithm extracting the key information of the channel autocorrelation matrix taking the relevant bandwidth as a criterion and the low-rank estimation method by use of the frequency domain (and/or time-domain) correlation and singular value decomposition of the channel are employed. The channel autocorrelation matrix is divided into several blocks according to the block size calculated by the channel dependent bandwidth. In the inverse process, only the low frequency diagonal sub-arrays representing the main information amount of the channel are applied, while other sub-arrays that represent the channel high-frequency information have been ignored. Rank estimation, namely the determination of signal subspace dimension, should be considered with the compromise between the computational complexity and the estimation error. Under the slow fading channel with selective frequency, this algorithm compares the performance and computational complexity with the LMMSE and the block LMMSE estimation algorithm. The result shows that the algorithm reduces the computational complexity without loss of its performance.
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Index Terms
- A New Pilot-based Block Low-rank Channel Estimation Algorithm for Massive MIMO
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