Frequency-Asynchronous Multiuser Joint Channel-Parameter Estimation, CFO Compensation, and Channel Decoding

This paper investigates a channel-coded multiuser system operated with orthogonal frequency-division multiplexing (OFDM) and interleaved-division multiple access (IDMA). In general, there are many variations to multiuser systems. Our choice of the combination of OFDM and IDMA is motivated by its ability to achieve multiuser diversity gain in frequency-selective multiple-access channels. To realize OFDM-IDMA, however, two challenges must be addressed. The first challenge is the estimation of multiple channel parameters, particularly how to contain the estimation errors of the channel parameters of the multiple users, considering that the overall estimation errors may increase with the number of users because the estimations of their channel parameters are intertwined with each other. The second challenge is that the transmitters of the multiple users may be driven by different radio-frequency oscillators. The associated frequency asynchrony may cause multiple carrier frequency offsets (CFOs) at the receiver. Compared with a single-user receiver where the single CFO can be compensated away at the receiver prior to channel decoding, a particular difficulty for a multiuser receiver is that it is not possible to compensate away all the multiple CFOs simultaneously. To tackle the two challenges, we put forth an integrated framework to solve the problems of multiuser channel-parameter estimation, CFO compensation, and channel decoding jointly and iteratively. The framework employs the space-alternating generalized expectation-maximization (SAGE) algorithm to decompose the multiuser problem into multiple single-user problems and the expectation-conditional maximization (ECM) algorithm to tackle each of the single-user subproblems. Iterative executions of SAGE and ECM in the framework allow the two aforementioned challenges to be tackled in an optimal manner. Simulation results indicate that, compared with other approaches, our approach can achieve significant bit-error-rate (BER) and mean-square-error (MSE) improvements.