We discuss a new design methodology of polynomial neural networks (PNN) in the framework of genetic algorithm (GA). The PNN is based on the ideas of group method of data handling (GMDH). Each node in the network is very flexible and can carry out polynomial type mapping between input and output variables. But the performances of PNN depend strongly on the number of input variables available to the model, the number of input variables, and the type (order) of the polynomials to each node. In this paper, GA is implemented to better use the optimal inputs and the order of polynomial in each node of PNN. The appropriate inputs and order are evolved accordingly and are tuned gradually throughout the GA iterations. We employ a binary coding for encoding key factors of the PNN into the chromosomes. The chromosomes are made of three sub-chromosomes which represent the order, number of inputs, and input candidates for modeling. To construct model by using significant approximation and generalization, we introduce the fitness function with a weighting factor. Comparisons with other modeling methods and conventional PNN show that the proposed design method offers encouraging advantages and better performance.

A sampling frequency offset estimation scheme for MB-OFDM UWB systems is proposed based on technical specification and multi-band utilization of the MB-OFDM. An estimation scheme using simple weighting factor based on the received signal power of each sub-channel is also introduced to efficiently combine estimates obtained from all the sub-carriers and to improve the estimation performance.

This paper focuses on a multi-beam combining scheme for DS-CDMA systems, which has RAKE combiners in multiple overlapped beams, in order to increase the reverse link capacity of DS-CDMA. This scheme is a very attractive technique because the maximal ratio combining (MRC) is carried out in space and time domains. However, in a practical situation, since the terminals in own sector are not uniformly located, the interference levels in respective beams are different. Therefore, receivers at the base station do not achieve ideal combining. This paper proposes a multi-beam combining scheme for DS-CDMA systems using weighting factor based on interference level of each beam. A fast closed loop transmission power control (TPC) scheme for the multi-beam combining system is also proposed. It is confirmed by computer simulation that the proposed scheme has excellent performance in the reverse link even if terminals in own sector are not uniformly located.

Maximal-ratio combining (MRC), which maximizes the carrier to noise ratio (CNR) of the combined signal, generally requires envelope detection and multiplication having linear characteristic over a wide dynamic range to generate a weighting factor for each branch. In this paper, we propose a simplified two-branch diversity combining scheme without linear envelope detection. The proposed scheme, called "level comparison weighted combining (LCWC),"is simplified in a manner that its weighting factor for each branch is generated from hard-decision results of comparing signal envelopes between two branches. Performance of LCWC is evaluated by computer simulation and laboratory experiment, which shows that its diversity gain is almost identical to that of MRC in a Rayleigh fading channel.