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In modern communication systems operating with Orthogonal Frequency-DivisionMultiplexing (OFDM), channel estimation requires minimal complexity withone-tap equalizers. However, this depends on cyclic prefixes, which must besufficiently large to cover the channel impulse response. Conversely, the useof cyclic prefix (CP) decreases the useful information that can be conveyed inan OFDM frame, thereby degrading the spectral efficiency of the system. In thiscontext, we study the impact of CPs on channel estimation with complex-valuedneural networks (CVNNs). We show that the phase-transmittance radial basisfunction neural network offers superior results, in terms of required energyper bit, compared to classical minimum mean-squared error and least squaresalgorithms in scenarios without CP.

CVNN-based Channel Estimation and Equalization in OFDM Systems Without Cyclic Prefix