In typical signal combining scenarios, the combining weights are estimated using the criterion of maximum average signal-to-noise ratio (SNR) or maximum combined output power (COP). Eigen-based algorithms are very important and popular in signal combining. The conventional SNR EIGEN or COP EIGEN may not necessarily be effective in terms of performance or system complexity. The main contribution of this study is the introduction of the combined signal autocorrelation coefficient as a newer objective function to signal combining. The corresponding eigen-based combining algorithm AC EIGEN and its modified algorithm MAC EIGEN are also derived. Proposed algorithms have the same simple system structure as the COP EIGEN, which can successfully avoid estimating the noise correlation matrix. Simulation results indicate that the AC EIGEN and the MAC EIGEN have good combining performance for signals with white Gaussian noise when the SNR of the signals is low.Considering the system complexity of the SNR EIGEN, and the COP EIGEN being biased for non-uniform noise variance signals, the proposed algorithms are attractive.

In the signal combining system of deep space network, the estimation error of time-delay between signals will reduce the effectiveness. The time-delay alignment technique based on combined output signal as the reference (CC-SUMPLE algorithm) makes use of the mutual information offered by multi-antenna and improves the alignment performance. However, it only takes ordinary cross-correlation into consideration rather than the cyclostationary of digital communication signal during calculating time-delay in the iterative process. As to this problem, this paper proposes multi-antenna signal time-delay alignment algorithm based on cyclostationary of communication signal (MCCC-SUMPLE algorithm) which reconstructs the combined reference signal and takes advantage of multi-cycle frequencies. The simulation results show that the proposed algorithm will improve the estimation accuracy and time-delay alignment performance compared with CC-SUMPLE algorithm.

This paper deals with maximum-likelihood (ML) detection of symbol sequence in the absence of synchronization information. A novel iterative scheme is proposed to obtain the ML estimates of the symbols without an estimation of synchronization parameters. Instead of the optimal sampling point recovery and explicit carrier phase compensation, the detection of symbols employs the direct calculation of the matched filter output, eliminating the need for a separate synchronizer. The detection problem is treated as ML estimation from incomplete data, which is solved by means of an iterative scheme based on the expectation-maximization algorithm. The proposed scheme is compared with conventional non-data-aided and iterative ML synchronizers. Accordingly, the simulation results indicate that the proposed detector enables improvements on both the bit error rate and convergence property.