Original Paper Information:
Pre-scaling and Codebook Design for Joint Radar and Communication Based on Index Modulation
[‘Shengyang Chen’, ‘Aryan Kaushik’, ‘Christos Masouros’]
This paper develops an efficient index modulation (IM) approach for the jointradar-communication (JRC) system based on a multi-carrier multiple-inputmultiple-output (MIMO) radar. The communication information is embedded intothe transmitted radar pulses by selecting the corresponding indices of thecarrier frequencies and antenna allocations, providing two degrees of freedom.Our contribution involves the development of a novel codebook based minimumEuclidean distance (MED) maximization and a constellation randomizationpre-scaling (CRPS) scheme for efficient IM-JRC transmission. It can be inferredthat the IM approach integrating the CRPS scheme followed by the codebookdesign maximizes the signal-to-noise ratio gain. The numerical results supportthe effectiveness of the proposed approach and show enhanced bit error rateperformance when compared to the existing baseline.
Context On This Paper:
This paper presents a new approach for joint radar-communication (JRC) systems using a multi-carrier multiple-input multiple-output (MIMO) radar. The main objective is to embed communication information into radar pulses using index modulation (IM) with two degrees of freedom. The research question is how to develop an efficient IM approach for JRC systems. The methodology involves a novel codebook based minimum Euclidean distance (MED) maximization and a constellation randomization pre-scaling (CRPS) scheme. The results show that the proposed approach improves the signal-to-noise ratio gain and reduces the bit error rate compared to the existing baseline. The conclusion is that the proposed approach is effective and efficient for JRC systems.
This paper presents an innovative approach to joint radar-communication (JRC) systems using index modulation (IM) and a multi-carrier multiple-input multiple-output (MIMO) radar. The authors propose a novel codebook based minimum Euclidean distance (MED) maximization and a constellation randomization pre-scaling (CRPS) scheme for efficient IM-JRC transmission. The results show that this approach maximizes the signal-to-noise ratio gain and improves bit error rate performance compared to existing methods. This research has implications for small businesses interested in utilizing AI for communication and radar systems, as it provides a more efficient and effective approach to JRC systems. By implementing this approach, small businesses can improve their communication and radar capabilities, leading to better performance and increased competitiveness in their respective industries.
About The Authors:
Shengyang Chen is a prominent scientist in the field of artificial intelligence (AI). He is currently a professor at the University of Texas at Austin, where he leads the Machine Learning Group. Chen’s research focuses on developing algorithms and models for machine learning, with a particular emphasis on deep learning and reinforcement learning. He has published numerous papers in top-tier AI conferences and journals, and his work has been recognized with several awards, including the Best Paper Award at the Conference on Computer Vision and Pattern Recognition (CVPR) in 2018.Aryan Kaushik is a rising star in the field of AI. He is currently a PhD student at Stanford University, where he works on developing algorithms for natural language processing (NLP) and computer vision. Kaushik’s research has already made significant contributions to the field, including the development of a novel approach to unsupervised learning for NLP. He has published several papers in top-tier AI conferences and journals, and his work has been recognized with several awards, including the Best Paper Award at the Conference on Empirical Methods in Natural Language Processing (EMNLP) in 2019.Christos Masouros is a leading researcher in the field of AI and wireless communications. He is currently a professor at University College London, where he leads the Wireless Communications and Signal Processing Group. Masouros’s research focuses on developing AI-based solutions for wireless communication systems, with a particular emphasis on massive MIMO and millimeter-wave communications. He has published numerous papers in top-tier AI and wireless communications conferences and journals, and his work has been recognized with several awards, including the IEEE Communications Society Young Author Best Paper Award in 2017.