毫米波通信系统QPSK调制器设计

为解决毫米波通信系统中数据速率和频谱资源紧张的难题,采用直接数字频率合成(DDS)和锁相环(PLL)技术,基于改进的π/4-QPSK调制方式,以现场可编程门阵列（FPGA）为控制单元,设计了一种用于毫米波通信系统的QPSK调制器。重点介绍了应用FPGA实现Gold码的编码过程,并给出了Gold编码Modelsim仿真结果。测试结果表明,该毫米波调制器工作稳定,QPSK调制信号中心频率30 GHz,数据速率3 Gb/s,输出功率大于4 dBm,相位噪声优于-100 dBc/Hz@10 kHz,可用于实际工程。     

模拟正交调制误差数字校正方法

模拟正交调制可有效降低对FPGA等数字器件的工作频率要求,特别适合于高等级大规模高速FPGA器件受到限制的星载设备及军用设备研制场合。但由于器件指标限制及电路的不一致性,模拟正交调制存在较大的电路误差,而通过电路设计和调试将误差减小的工程实现难度较大。提出一种数字校正方法,通过在基带信号引入2个校正因子,可有效校正模拟正交调制的电路误差,完全满足正交不平衡度小于1°、幅度不平衡度小于0.2dB的指标需求。     

基于FPGA的QPSK调制解调器

研讨基于FPGA的0PSK调制解调器的设计。采用硬件描述语言在Altera公司QuartusII开发环境下对其功能进行了仿真验证。     

Raptor码在卫星通信中抗干扰性能研究

为了提高卫星通信抗干扰性能,在对卫星通信的信道特性分析基础上,提出一种将Raptor码作为纠删码应用于卫星通信系统的方案.文中采用了LDPC码+LT码串行级联编码来实现一个码率为0.95的Raptor码,并对在载波噪声干扰信道中传输的Raptor码的译码算法进行了研究,通过系统仿真,搭建一种基于最优化度分布Raptor码的卫星通信系统模型,并对其抗干扰的性能进行分析及研究.     

A new optimized least-square sparse channel estimation scheme for underwater acoustic communication

In underwater acoustic (UWA) communication, orthogonal frequency division multiplexing (OFDM) is a promising technology that is highly essential to get channel state information meant for channel estimation (CE). Nevertheless, higher complexity, slower convergence, and poor performance, which degrade the performance estimation, are the limitations of the traditional CE methodologies. Thus, by amalgamating the least square (LS)-CE algorithm along with polynomial interpolated black widow optimization (PI-BWO) model, an optimized least square sparse (OLSS) CE algorithm has been proposed to intend for a UWA-OFDM communication system. Formerly, by utilizing the 2's complement shift left turbo encoding (2CSL-TE) methodology, the input signal is encoded. After that, the modulated encoded signal is provided for inverse fast Fourier transform (IFFT) operations; subsequently, they are transferred over the UWA channel toward the receiver OFDM. By employing the OLSS methodology, the received OFDM signal's interference-free region is utilized for sparse CE at the receiver. Regarding symbol error rate (SER), bit error rate (BER), mean square error (MSE), and peak signal-to-noise ratio (PSNR), the proposed model's experiential outcome is evaluated and analogized with the other prevailing methodologies. When analogized with the conventional models, the proposed estimation methodologies achieved better performance.