调制编码技术分析与应用

通信对数据业务的需求越来越高,现代战争又越来越依赖高效、可靠的通信保障,实现恶劣环境下的高速率可靠通信是重点应用研究的方向。文中对比分析了目前几种流行的调制技术,分析了这些调制编码技术的特点,并结合通信应用,分析了OFDM调制与LDPC编码的应用价值。     

无人机OFDM数据链路中信道估计算法研究

针对无人机信道的特点,以及采用正交频分复用技术进行无人机数据链路传输的优点.在对OFDM关键技术信道估计进行研究时,要确保接收端能够准确的检测出由于无线信道变化造成的码间干扰,准确的解调出原信号.根据系统需要设计了梳状导频和块状导频联合的导频插入方式,该方式可以在数据传输和信道估计性能上做到折中,并对常用的信道估计算法进行分析研究提出了改进的最小均方误差信道估计算法,最后建立频率选择性信道模型对不同的信道估计算法进行仿真,分析仿真结果可以看出该导频插入方式和信道估计算法具有较低的复杂度,而且又具有良好的信道估计性能,适合在实际系统中应用.     

MIMO-OFDM系统中的自适应比特分配

正交频分复用(OFDM)与多天线(MIMO)技术的结合可以有效提高系统的性能。为了研究自适应比特分配对MIMO-OFDM系统性能的影响,首先介绍了MIMO-OFDM的系统结构和MIMO信道的奇异值分解(SVD)过程。将多载波系统中的Hughes-Hartogs自适应分配算法应用于天线数为2×2的MIMO-OFDM系统中。仿真结果表明,采用Hughes-Har-togs算法的MIMO-OFDM系统的数据速率达到甚至超过了单天线OFDM(SISO-OFDM)系统的2倍,但SVD分解也带来了巨大的运算量,提出了在MIMO-OFDM系统中使用自适应分配算法的策略。     

基于效用公平的多用户下行OFDM系统机会调度

基于对单信道系统中效用公平机会调度的研究,提出了一种多用户下行OFDM多载波系统中的调度方案.该方案充分利用信道的时变和频率选择特性,由参数更新和调度决策两个模块组成:参数更新模块根据调度结果更新控制参数;调度决策模块根据所有用户的信道信息和参数更新模块输出的公平性控制参数决定该时隙每个用户使用哪些子载波.仿真结果表明,参数选取合适时,所提的简单调度方案能够在保证效用公平性的前提下,达到比复杂的Max-Min子裁波分配算法更好的系统性能.     

电力线通信技术的应用研究

分析了电力线通信技术(PLC)的特点和技术要素,详细论述了广东工业实训中心大楼的PLC解决方案.最后通过对实际应用效果的总结,指出了电力线通信在我国的应用前景.     

Least squares decoding in binomial frequency division multiplexing

This paper proposes a method that can reduce the complexity of a system matrix by analyzing the characteristics of a pseudoinverse matrix to receive a binomial frequency division multiplexing (BFDM) signal and decode it using the least squares (LS) method. The system matrix of BFDM can be expressed as a band matrix, and as this matrix contains many zeros, its amount of calculation when generating a transmission signal is quite small. The LS solution can be obtained by multiplying the received signal by the pseudoinverse matrix of the system matrix. The singular value decomposition of the system matrix indicates that the pseudoinverse matrix is a band matrix. The signal-to-interference ratio is obtained from their eigenvalues. Meanwhile, entries that do not contribute to signal generation are erased to enhance calculation efficiency. We decode the received signal using the pseudoinverse matrix and the removed pseudoinverse matrix to obtain the bit error rate performance and to analyze the difference.     

Bi-directional LSTM based channel estimation in 5G massive MIMO OFDM systems over TDL-C model with Rayleigh fading distribution

In this work, a deep learning (DL)-based massive multiple-input multiple-output (mMIMO) orthogonal frequency division multiplexing (OFDM) system is investigated over the tapped delay line type C (TDL-C) model with a Rayleigh fading distribution at frequencies ranging from 0.5 to 100 GHz. The proposed bi-directional long short-term memory (Bi-LSTM) channel state information (CSI) estimator uses online learning during training and offline learning during the practical implementation phase. The design of the estimator takes into account situations in which prior knowledge of channel statistics is limited and targets excellent performance, even with limited pilot symbols (PS). Three separate loss functions (mean square logarithmic error [MSLE], Huber, and Kullback–Leibler Distance [KLD]) are assessed in three classification layers. The symbol error rate (SER) and outage probability performance of the proposed estimator are evaluated using a number of optimization techniques, such as stochastic gradient descent (SGD), momentum, and the adaptive gradient (AdaGrad) algorithm. The Bi-LSTM-based CSI estimator is trained considering a specific number of PS. It can be readily seen that by incorporating a cyclic prefix (CP), the system becomes more resilient to channel impairments, resulting in a lower SER. Simulations show that the SGD optimization approach and Huber loss function-trained Bi-LSTM-based CSI estimator have the lowest SER and very high estimation accuracy. By using deep neural networks (DNNs), the Bi-LSTM method for CSI estimation achieves a superior channel capacity (in bps/Hz) at 10 dB than long short-term memory (LSTM) and other conventional CSI estimators, such as minimum mean square error (MMSE) and least squares (LS). The simulation results validate the analytical results in the study.     

电力线信道模型下OFDM通信系统的Simulink仿真研究

电力线通信技术是具有良好发展前景的一种宽带接入方式,但是噪声干扰严重,通信环境恶劣,正交频分复用是解决电力线通信问题的有效方法。文章运用Simulink建立了基于时间流的电力线通信信道模型和OFDM基带通信模型。通过对电力线信道模型下的采用OFDM的通信系统模型进行仿真验证,结果表明可以通过适当的编码和调制方式来提高系统的可靠性,并定性地分析了编码和误码率、调制方式和误码率之间的关系。     

Linearly time-varying channel estimation and training power allocation for OFDM/MIMO systems using superimposed training

We address the problem of estimating the linearly time-varying (LTV) channel of orthogonal frequency division multiplexing (OFDM)/multiple-input multiple-output (MIMO) systems using superimposed training (ST).The LTV channel is modeled by truncated discrete Fourier bases.Based on this model,a two-step approach is adopted to estimate the LTV channel over multiple OFDM symbols.We also present performance analysis of the channel estimation and derive a closed-form expression for the channel estimation variance...     

基于FPGA的OFDM发射机设计

OFDM技术已经成为未来无线通信的关键技术,其发射机必须具有强大的计算能力,因此使用FPGA来完成OFDM发射机设计已成大势所趋。对整个OFDM系统参数和帧结构进行全面设计的基础上,用FPGA芯片具体实现。采用数字时钟管理器,乒乓RAM等方式提高系统运行的可靠性,并选用最优的IP核参数来减少硬件资源的占用。测试结果表明该方案占用芯片资源较少,速度较快,可靠性较高,具有广阔的应用前景。