论文标题
具有低分辨率ADC的MIMO-OFDM系统中基于ANN的检测
ANN-Based Detection in MIMO-OFDM Systems with Low-Resolution ADCs
论文作者
论文摘要
在本文中，我们提出了一个多层人工神经网络（ANN），该网络（ANN）接受了Levenberg-Marquardt算法的培训，可用于通过多输入多输入的多输出正交频率多路复用（MIMO OFDM）进行信号检测，尤其是那些具有低分辨率模拟数字到数字化的转换器的系统。我们考虑了一种盲目检测方案，与经典算法相反，在不知道接收器（CSIR）的通道状态信息的情况下进行数据符号估计。拟议的基于ANN的检测器（ANND）的主要力量在于其多功能用途与任何调制方案，但盲目的结构没有变化。我们通过模拟与传统的接收器进行比较，即，最大可能性（ML），最小均方根误差（MMSE）和零效率（ZF），以符号错误率（SER）性能来进行比较。结果表明，ANND以较低的复杂性接近ML，在整个评估的信噪比（SNR）值范围内优于ZF，但是，MMSE在不同的SNR范围上也是如此。
In this paper, we propose a multi-layer artificial neural network (ANN) that is trained with the Levenberg-Marquardt algorithm for use in signal detection over multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems, particularly those with low-resolution analog-to-digital converters (LR-ADCs). We consider a blind detection scheme where data symbol estimation is carried out without knowing the channel state information at the receiver (CSIR)---in contrast to classical algorithms. The main power of the proposed ANN-based detector (ANND) lies in its versatile use with any modulation scheme, blindly, yet without a change in its structure. We compare by simulations this new receiver with conventional ones, namely, the maximum likelihood (ML), minimum mean square error (MMSE), and zero-forcing (ZF), in terms of symbol error rate (SER) performance. Results suggest that ANND approaches ML at much lower complexity, outperforms ZF over the entire range of assessed signal-to-noise ratio (SNR) values, and so does it also, though, with the MMSE over different SNR ranges.
