AI 中文总结
针对传统CIM在高传输速率下信息分配受限的问题，提出CGIM方法，通过分组扩频码实现并行检测，在相同速率下比传统CIM获得更优的误码率性能。
AI 中文摘要
高效利用扩频资源对提升扩频通信的传输效率至关重要。码索引调制（CIM）通过扩频码索引传输额外信息，无需额外射频链。现有CIM方案中固定数量的码索引分支，在目标速率提升时限制了码索引域与调制符号域之间的信息分配。本文提出码域分组索引调制（CGIM），将正交扩频码库划分为多个组，将每个组内一个正交幅度调制（QAM）符号的同相和正交分量映射到两个独立的码索引上。分组解扩支持并行检测，开发了分组级最大似然（ML）检测和低复杂度贪心检测（GD），二者在所述条件下均能实现联合ML判决。推导了基于成对错误概率（PEP）的误码率（BER）分析，适用于瑞利和Nakagami-m衰落，以加性高斯白噪声（AWGN）为基准。数值结果表明，在相同传输速率下，CGIM比传统CIM基准方案实现了更优的BER性能，原因在于码索引域与调制符号域之间的信息比特分布更均衡。
英文摘要
Efficient exploitation of spreading resources is important for improving the transmission efficiency of spread-spectrum communications. Code index modulation (CIM) conveys additional information through spreading-code indices without requiring extra radio-frequency chains. The fixed number of code-index branches in existing CIM schemes limits information allocation between the code-index and modulation-symbol domains as the target rate increases. This paper proposes code-domain grouped index modulation (CGIM), which partitions an orthogonal spreading-code bank into multiple groups and maps the in-phase and quadrature components of one quadrature amplitude modulation (QAM) symbol in each group onto two independent code indices. Group-wise despreading enables parallel detection. Group-wise maximum-likelihood (ML) and low-complexity greedy detection (GD) are developed, with both achieving joint ML decisions under the stated conditions. A pairwise error probability (PEP)-based bit error rate (BER) analysis is derived for Rayleigh and Nakagami-$m$ fading, with additive white Gaussian noise (AWGN) as a benchmark. Numerical results show that CGIM achieves better BER performance than traditional CIM benchmark schemes at the same transmission rate, owing to a more balanced distribution of information bits between the code-index and modulation-symbol domains.
