李 慧,彭 昱,韩昌彩,陈为刚.低复杂度高阶 APSK 解映射方法与实现结构[J].电子测量与仪器学报,2020,34(6):109-116
低复杂度高阶 APSK 解映射方法与实现结构
Low-complexity demapping algorithm and implementationarchitecture for high-order APSK
  
DOI:
中文关键词:  幅度相移键控  软解映射  现场可编程门阵列
英文关键词:amplitude phase shift keying (APSK)  soft demapping  field programmable gate array (FPGA)
基金项目:国家自然科学基金(61671324)资助项目
作者单位
李 慧 1.天津大学微电子学院 
彭 昱 1.天津大学微电子学院 
韩昌彩 1.天津大学微电子学院 
陈为刚 1.天津大学微电子学院 
AuthorInstitution
Li Hui 1.School of Microelectronics, Tianjin University 
Peng Yu 1.School of Microelectronics, Tianjin University 
Han Changcai 1.School of Microelectronics, Tianjin University 
Chen Weigang 1.School of Microelectronics, Tianjin University 
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中文摘要:
      针对高阶幅度相移键控(amplitude phase shift keying, APSK)解映射复杂度,不易硬件实现的问题,提出了一种低复杂度 的 APSK 解映射方案及电路实现结构。 具体而言,基于 Max-Log-MAP 算法,分析 APSK 星座图对称性并进行区域划分,对落到 每个区域的接收符号比特软信息计算进行化简,得到具有低计算量的解映射公式。 进一步,利用简化后每个比特软信息计算公 式的特点,设计了软信息计算电路结构并在现场可编程门阵列( field programmable gate array, FPGA)硬件平台上进行了性能测 试。 测试结果表明,信噪比为 14 dB 时,利用简化方法实现的 APSK 解映射电路可实现 10 -5 的误比特率(bit error rate,BER),与 传统解映射算法性能接近,且具有较低的硬件资源消耗。
英文摘要:
      Aiming at the problem of high-order amplitude phase shift keying ( APSK) demapping complexity and difficult hardware implementation, a low-complexity APSK demapping scheme and circuit implementation architecture are proposed. Specifically, the constellation map is divided into regions based on the analysis of symmetry. Then, based on the Max-Log-MAP algorithm, the bit soft information of the received symbols falling into each region is calculated and simplified, thereby obtaining a formula with a low calculation amount for calculating the soft information. Furthermore, using the characteristics of the simplified soft information calculation formula for each bit, the soft information calculation circuit architecture is designed and its performance is tested on the field programmable gate array (FPGA) hardware platform. Test results show that the APSK demapping circuit using the proposed simplified method can achieve a bit error rate (BER) of 10 -5 when the signal-to-noise ratio (SNR) is 14 dB, which is close to the performance of the traditional demapping algorithm and has lower hardware resource consumption.
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