Construction of tunable radial basis function networks using orthogonal forward selection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Particle swarm optimization aided orthogonal forward regression for unified data modeling
IEEE Transactions on Evolutionary Computation
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In this contribution a range of coded modulation (CM)-assisted radial basis function (RBF)-based turbo equalization (TEQ) schemes are investigated when communicating over dispersive Rayleigh-fading channels. Specifically, 16 quadrature amplitude modulation-based trellis coded modulation (TCM), turbo TCM (TTCM), bit-interleaved coded modulation (BICM), and iteratively decoded BICM (BICM-ID) are evaluated in the context of an RBF-based TEQ scheme and a reduced-complexity RBF based in-phase/quadrature-phase (I/Q) TEQ scheme. The least mean square (LMS) algorithm was employed for channel estimation, where the initial estimation step-size used was 0.05, which was reduced to 0.01 for the second and the subsequent TEQ iterations. The achievable coding gain of the various CM schemes was significantly increased, when employing the proposed RBF-TEQ or RBF-I/Q-TEQ rather than the conventional noniterative decision feedback equalizer (DFE). Explicitly, the reduced-complexity RBF-I/Q-TEQ-CM achieved a similar performance to the full-complexity RBF-TEQ-CM, while attaining a significant complexity reduction. The best overall performer was the RBF-I/Q-TEQ-TTCM scheme, requiring only 1.88 dB higher signal-to-noise ratio at BER=10-5, than the identical throughput 3 b/symbol uncoded 8 PSK scheme communicating over an additive white Gaussian noise channel. The coding gain of the scheme was 16.78 dB.