108 research outputs found
Enabling Sphere Decoding for SCMA
In this paper, we propose a reduced-complexity optimal modified sphere
decoding (MSD) detection scheme for SCMA. As SCMA systems are characterized by
a number of resource elements (REs) that are less than the number of the
supported users, the channel matrix is rank-deficient, and sphere decoding (SD)
cannot be directly applied. Inspired by the Tikhonov regularization, we
formulate a new full-rank detection problem that it is equivalent to the
original rank-deficient detection problem for constellation points with
constant modulus and an important subset of non-constant modulus
constellations. By exploiting the SCMA structure, the computational complexity
of MSD is reduced compared with the conventional SD. We also employ list MSD to
facilitate channel coding. Simulation results demonstrate that in uncoded SCMA
systems the proposed MSD achieves the performance of the optimal maximum
likelihood (ML) detection. Additionally, the proposed MSD benefits from a lower
average complexity compared with MPA.Comment: Accepted for publication in IEEE Communications Letter
Sparse nonlinear optimization for signal processing and communications
This dissertation proposes three classes of new sparse nonlinear optimization algorithms for network echo cancellation (NEC), 3-D synthetic aperture radar (SAR) image reconstruction, and adaptive turbo equalization in multiple-input multiple-output (MIMO) underwater acoustic (UWA) communications, respectively.
For NEC, the proposed two proportionate affine projection sign algorithms (APSAs) utilize the sparse nature of the network impulse response (NIR). Benefiting from the characteristics of lā-norm optimization, affine projection, and proportionate matrix, the new algorithms are more robust to impulsive interferences and colored input than the conventional adaptive algorithms.
For 3-D SAR image reconstruction, the proposed two compressed sensing (CS) approaches exploit the sparse nature of the SAR holographic image. Combining CS with the range migration algorithms (RMAs), these approaches can decrease the load of data acquisition while recovering satisfactory 3-D SAR image through lā-norm optimization.
For MIMO UWA communications, a robust iterative channel estimation based minimum mean-square-error (MMSE) turbo equalizer is proposed for large MIMO detection. The MIMO channel estimation is performed jointly with the MMSE equalizer and the maximum a posteriori probability (MAP) decoder. The proposed MIMO detection scheme has been tested by experimental data and proved to be robust against tough MIMO channels. --Abstract, page iv
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