Joint DOA-Frequency Offset Estimation and Data Detection in Uplink MIMO-OFDM Networks with SDMA Techniques

This paper presents an antenna-array-assisted approach to jointly estimate nominal directions of arrival (DOAs) and frequency offsets, and detect data in uplink multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) wireless networks. To achieve this, two MUltiple SIgnal Classification (MUSIC)-based algorithms are first addressed to jointly estimate the nominal DOAs and frequency offsets of the incoming rays. The first algorithm is an extension of the classic two-dimensional (2-D) MUSIC, which, however, calls for enormous amount of computations. To alleviate the computational overhead, the second algorithm estimates the nominal DOAs and frequency offsets in a space-frequency-space (SFS) tree structure, in which two S-MUSICs and one F-MUSIC are invoked alternatively to estimate the nominal DOAs and the frequency offsets, respectively. In between every other MUSIC, a spatial beamforming process and a temporal filtering process are employed to decouple the uplink signals from different transmitters, thus enhancing the estimation accuracy. Thereafter, based on the estimated nominal DOAs and frequency offsets, a data detection procedure is also addressed. Simulations show that both algorithms can provide satisfactory performance while the SFS MUSIC calls for substantially lower computational complexity.