Orthogonal Frequency Division Multiplexing With Generalized Joint Index Modulation

Orthogonal frequency division multiplexing with index modulation (OFDM-IM) is a promising multi-carrier technique for next generation wireless communication networks. In this paper, we propose a generalized joint OFDM-IM scheme, which performs joint modulation on multiple subsubblocks divided from a subblock, to achieve higher spectral efficiency (SE) and lower complexity for both modulation and detection processes. In GJ-OFDM-IM, the subcarrier activation pattern (SAP) of the subblock is composed of the SAPs of all the sub-subblocks within the subblock, which means a large dimensions IM of the subblock is divided into multiple small size IM of the sub-subblocks by taking advantage of the joint modulation. With the aid of joint modulation, the lookup tables for both modulation and demodulation are only related with the size of sub-subblock and not changed with the size of the subblock. At the receiver, the optimal maximum likelihood (ML) detector and a low complexity subblock wise detector are proposed for GJ-OFDM-IM. Then, a tight upper bound on the bit error rate (BER) is derived in closed form to evaluate the system performance of the proposed GJ-OFDM-IM. Simulation and numerical results verify the analysis and show that the proposed GJ-OFDM-IM can greatly reduce the implementation complexity without causing any system BER performance loss compared with classical OFDM-IM.

Authors

Institutions

Publication Details

Journal
IEEE Open Journal of the Communications Society
Published
2024-11-29
DOI
https://doi.org/10.1109/ojcoms.2024.3509270
Citations
3
Primary Topic
Advanced Wireless Communication Technologies
Type
article
Field-Weighted Citation Impact
0.63

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Orthogonal Frequency Division Multiplexing With Generalized Joint Index Modulation

Dehuan Wan, Chaobo He, Zeng Hu, Xuhong Peng et al.
3 citations
IEEE Open Journal of the Communications Society
Advanced Wireless Communication Technologies
0.63
article

Orthogonal Frequency Division Multiplexing With Generalized Joint Index Modulation

Dehuan Wan, Chaobo He, Zeng Hu, Xuhong Peng, Yun Liu, Jing Wang, Ting Wu, Yuexuan Che
article en
3 citations

Abstract

Orthogonal frequency division multiplexing with index modulation (OFDM-IM) is a promising multi-carrier technique for next generation wireless communication networks. In this paper, we propose a generalized joint OFDM-IM scheme, which performs joint modulation on multiple subsubblocks divided from a subblock, to achieve higher spectral efficiency (SE) and lower complexity for both modulation and detection processes. In GJ-OFDM-IM, the subcarrier activation pattern (SAP) of the subblock is composed of the SAPs of all the sub-subblocks within the subblock, which means a large dimensions IM of the subblock is divided into multiple small size IM of the sub-subblocks by taking advantage of the joint modulation. With the aid of joint modulation, the lookup tables for both modulation and demodulation are only related with the size of sub-subblock and not changed with the size of the subblock. At the receiver, the optimal maximum likelihood (ML) detector and a low complexity subblock wise detector are proposed for GJ-OFDM-IM. Then, a tight upper bound on the bit error rate (BER) is derived in closed form to evaluate the system performance of the proposed GJ-OFDM-IM. Simulation and numerical results verify the analysis and show that the proposed GJ-OFDM-IM can greatly reduce the implementation complexity without causing any system BER performance loss compared with classical OFDM-IM.

IEEE Open Journal of the Communications SocietyVol. 6
South China Normal University (CN), Guangdong University of Finance (CN), Guangzhou Medical University (CN), Zhongkai University of Agriculture and Engineering (CN)
National Natural Science Foundation of China, Basic and Applied Basic Research Foundation of Guangdong Province
Openalex Percentile: Top 30%
Advanced Wireless Communication Technologies
0.63
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.