In modern communication systems operating with Orthogonal Frequency-Division Multiplexing (OFDM), channel estimation requires minimal complexity with one-tap equalizers. However, this depends on cyclic prefixes, which must be sufficiently large to cover the channel impulse response. Conversely, the use of cyclic prefix (CP) decreases the useful information that can be conveyed in an OFDM frame, thereby degrading the spectral efficiency of the system. In this context, we study the impact of CPs on channel estimation with complex-valued neural networks (CVNNs). We show that the phase-transmittance radial basis function neural network offers superior results, in terms of required energy per bit, compared to classical minimum mean-squared error and least squares algorithms in scenarios without CP.

翻译：在现代采用正交频分复用（OFDM）技术的通信系统中，信道估计要求采用最小复杂度的单抽头均衡器。然而，这依赖于循环前缀（CP）必须足够长以覆盖信道脉冲响应。反之，循环前缀的使用会降低OFDM帧中可传输的有效信息量，从而影响系统的频谱效率。在此背景下，我们研究了循环前缀对基于复值神经网络（CVNN）的信道估计的影响。研究表明，在无循环前缀的情况下，相比经典的最小均方误差算法和最小二乘算法，相位透射径向基函数神经网络在每比特所需能量方面展现出更优的性能。