DC-offset and IQ-imbalance estimation in the MIMO system

Multiple Input Multiple Output (MIMO) systems are one of the advanced modern technology in the field of the cellular mobile telecommunications. This is due to the fact that its using gives the possibility of increasing the information rate or noise performance of the system. Estimation of the Raleigh Fading channel and the signal impairments caused by the Direct-Conversion Receiver (DCR) with spatial multiplexing MIMO are considered in the paper. The amplitude-phase imbalance, DC-drift and residual frequency shift are meant under the distortions. The matrix of the channel complex factors is assumed the constant during the transmission and receiving information interval. The problem is solved in the presence not only the Additive Noise with the unknown distribution but also the Phase Noise, which is rarely included in the analysis. The estimation is executed by the least square method with the polynomial approximation of the channel in the sliding time window. The method was chosen because of its simplicity. During the first stage of the unknown parameters estimation the algorithm uses the training sequence. Then it switches to the detected information symbols. Soft decision finding is realized with the Zero Forcing method. Hard decision of the each received symbol is determined by the minimum of the residual norm square, which coincides with the maximum-likelihood method under the white Gaussian noise. The estimation of the unknown parameters algorithm efficiency is approved by the computational experiment with the different dimensions of the channel matrix. 2, 4 and 8 transmitting and receiving antennas are considered in the work. Dependence of the root mean square error on the signal-noise ratio of amplitude-phase imbalance, DC-drift and channel factors estimation was chosen as the accuracy criteria. Noise immunity curves of the uncoded QAM-4, -16, -64 after the work of the proposal algorithm are also included. The experiment showed the strong influence of the phase noise to the receiving quality with the growth of the transmitting and receiving antennas. Phase noise model identification is necessary in order to the drawback elimination.