Strategic Optimization and Application Analysis of Orthogonal Frequency Division Multiplexing Index Modulation Systems

Haochen Liao

doi:10.54097/hset.v68i.12046

Authors

Haochen Liao

DOI:

https://doi.org/10.54097/hset.v68i.12046

Keywords:

OFDM-IM, Permutation mode, ML detection.

Abstract

Orthogonal Frequency Division Multiplexing (OFDM) is a type of Multi-Carrier Modulation that operates by dividing a single channel into numerous orthogonal subchannels. By doing this, it facilitates the transformation of high-speed digital signals into parallel subdata streams. These streams are then modulated onto the subchannels, thereby enabling efficient transmission. OFDM with Index Modulation (OFDM-IM) further enhances this process by combining the benefits of OFDM with the high spectral efficiency of spatial modulation techniques, leading to significant improvements in the bit-error rate performance of the system. Despite the advantages of OFDM-IM systems, several challenges exist. For instance, the presence of inconsistent sampling rates can result in amplitude distortion. Furthermore, a high peak-to-average power ratio can adversely impact the efficiency of the RF power amplifier. These problems necessitate the exploration of diverse solutions to improve the performance of OFDM-IM systems. The focus of this thesis is to investigate and assess different strategies aimed at addressing these issues within OFDM-IM systems. We will explore these strategies, compare their effectiveness, and offer comprehensive recommendations on their optimal applications based on specific system requirements and scenarios. In doing so, the goal is to pave the way for more effective and efficient implementation of OFDM-IM systems in diverse contexts.

Downloads

Download data is not yet available.

References

Wang, Y., Jiang, B., & Liu, J. (2019). A survey of multimodal emotion recognition research. Information Fusion, 52, 222-237.

Baltrusaitis, T., Robinson, P., & Morency, L. P. (2018). Multimodal emotion analysis in the wild. Image and Vision Computing, 68, 101-115.

Karray, F., & Saleh, J. A. (2018). Multimodal Emotion Recognition Using Deep Neural Networks: A Review. IEEE Transactions on Affective Computing, 9(3), 377-401.

Song, X., Lu, D., & Li, H. (2020). Multimodal Emotion Recognition Based on Deep Learning: A Survey. IEEE Transactions on Cognitive and Developmental Systems, 12(3), 567-579.

Yin, M., Duan, J., Zhang, Y., Zhang, S., & Yan, Q. (2019). Multimodal emotion recognition based on deep neural network. IEEE Access, 7, 7093-7103.

Li, Y., Mao, Y., Chen, L., & Lin, C. Y. (2018). A survey of multimodal emotion recognition using physiological signals. Information Fusion, 45, 99-113.

Zhao, Y., Wu, X., Wang, L., Zhang, J., & Chen, Y. (2020). A Survey on Multimodal Emotion Recognition Methods and Applications. Frontiers in Psychology, 11, 335.

Guan, J., Wang, D., & Hong, X. (2017). Multimodal emotion recognition using dynamic fuzzy support vector machine. IEEE Transactions on Cybernetics, 48(5), 1393-1404.

Liu, Y., Wang, Y., & Zhang, J. (2019). Convolutional neural network based multimodal emotion recognition applied to video game.

Wang, S., Li, Y., & Li, Q. (2017). Multimodal emotion recognition using sparse autoencoder neural network. IEEE Transactions on Industrial Electronics, 64(8), 6486-6495.