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20242026
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8 papers · 1 filter

eess.SP2026

Multi-Hop RIS ISAC for Target Positioning: A Tensor Decomposition-based Approach

Yirui Luo, Xiaoyan Ma, Yong Liang Guan +2

Reconfigurable intelligent surface (RIS) has demon- strated remarkable potential to enhance the performance of integrated sensing and communication (ISAC), particularly when the li…

eess.SP2026

Optimal Multi-RIS Placement: Coverage-Guaranteed Sum Rate Maximization Under Inhomogeneous User Distributions

Abhishek Rajasekaran, Mehdi Karbalayghareh, Xiaoyan Ma +2

The realization of the full potential of Reconfigurable Intelligent Surfaces (RIS) in a wireless system is tied to their strategic spatial deployment. While existing literature pri…

eess.SP2026

Optimal RIS Placement in Multi-User MISO Systems with User Randomness

Abhishek Rajasekaran, Mehdi Karbalayghareh, Xiaoyan Ma +2

It is well established that the performance of reconfigurable intelligent surface (RIS)-assisted systems critically depends on the optimal placement of the RIS. Previous works cons…

eess.SP2025

Constant Modulus Waveform Design with Space-Time Sidelobe Reduction for DFRC Systems

Byunghyun Lee, Anindya Bijoy Das, David Love +2

Dual-function radar-communication (DFRC) is a key enabler of location-based services for next-generation communication systems. In this paper, we investigate the problem of designi…

eess.SP2025

Learning-Based Two-Way Communications: Algorithmic Framework and Comparative Analysis

David R. Nickel, Anindya Bijoy Das, David J. Love +1

Machine learning (ML)-based feedback channel coding has garnered significant research interest in the past few years. However, there has been limited research exploring ML approach…

eess.SP2025

Physics-based Generative Models for Geometrically Consistent and Interpretable Wireless Channel Synthesis

Satyavrat Wagle, Akshay Malhotra, Shahab Hamidi-Rad +3

In recent years, machine learning (ML) methods have become increasingly popular in wireless communication systems for several applications. A critical bottleneck for designing ML s…