@article{MTMT:2928592,
	title = {Coded modulation assisted radial basis function aided turbo equalization for dispersive Rayleigh-fading channels},
	url = {https://m2.mtmt.hu/api/publication/2928592},
	author = {Ng, SX and Yee, MS and Hanzó, Lajos},
	doi = {10.1109/TWC.2004.837410},
	journal-iso = {IEEE T WIREL COMMUN},
	journal = {IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS},
	volume = {3},
	unique-id = {2928592},
	issn = {1536-1276},
	abstract = {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.},
	year = {2004},
	eissn = {1558-2248},
	pages = {2198-2206}
}

@article{MTMT:2928618,
	title = {Least bit error rate adaptive nonlinear equalisers for binary signalling},
	url = {https://m2.mtmt.hu/api/publication/2928618},
	author = {Chen, S and Mulgrew, B and Hanzó, Lajos},
	doi = {10.1049/ip-com:20030284},
	journal-iso = {IEE P-COMMUN},
	journal = {IEE PROCEEDINGS-COMMUNICATIONS},
	volume = {150},
	unique-id = {2928618},
	issn = {1350-2425},
	abstract = {The paper considers the problem of constructing adaptive minimum bit error rate (MBER) neural network equalisers for binary signalling. Motivated from a kernel density estimation of the bit error rate (BER) as a smooth function of training data, a stochastic gradient algorithm called the least bit error rate (LBER) is developed for adaptive nonlinear equalisers. This LBER algorithm is applied to adaptive training of a radial basis function (RBF) equaliser in a channel intersymbol interference (ISI) plus co-channel interference setting. A simulation study shows that the proposed algorithm has good convergence speed, and a small-size RBF equaliser trained by the LBER can closely approximate the performance of the optimal Bayesian equaliser. The results also demonstrate that the standard adaptive algorithm, the least mean square (LMS), performs poorly for neural network equalisers because the minimum mean square error (MMSE) is clearly suboptimal in the equalisation setting.},
	year = {2003},
	eissn = {1359-7019},
	pages = {29-36}
}