Deep Learning Integration in Index Modulation for Enhanced Wireless Communication Systems

Index modulation (IM) has emerged as a promising technique for improving spectral efficiency and hardware efficiency in next-generation communication systems by embedding information into signal indices such as antennas, subcarriers, or time slots. Representative schemes such as spatial modulation (SM) effectively mitigate inter-channel interference while reducing RF chain complexity in MIMO systems. With the rapid development of intelligent communication paradigms, deep learning (DL) has demonstrated strong potential in enhancing IM-based system performance, particularly in signal detection, channel estimation, and joint transceiver optimization. Compared to conventional model-driven approaches, DL-based methods can better adapt to nonlinear impairments, imperfect channel knowledge, and practical hardware constraints. Topic editors: Zhaopeng Xu, Hao Zhao, Shravan Kumar Bandari Source: https://www.frontiersin.org/research-topics/79693/deep-learning-integration-in-index-modulation-for-enhanced-wireless-communication-systems