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Extreme Learning Machine-based Channel Estimation in IRS-Assisted Multi-User ISAC System
Multi-user integrated sensing and communication (ISAC) assisted by intelligent reflecting surface (IRS) has been recently investigated to provide a high spectral and energy efficiency transmission.
Deep-Learning-Based Channel Estimation for IRS-Assisted ISAC System
A deep-learning framework is proposed to estimate the sensing and communication (S&C) channels in such a system.
Deep-Learning Channel Estimation for IRS-Assisted Integrated Sensing and Communication System
This problem is challenging due to the lack of signal processing capacity in passive IRS, as well as the presence of mutual interference between sensing and communication (SAC) signals in ISAC systems.
Deep Reinforcement Learning for RIS-Assisted FD Systems: Single or Distributed RIS?
It is further shown that the proposed algorithm significantly improves the sum-rate compared to the non-optimized scenario in both single and distributed RIS deployment schemes.
Deep Reinforcement Learning for Optimizing RIS-Assisted HD-FD Wireless Systems
The complexity analysis and Monte Carlo simulations illustrate that the proposed DRL algorithm significantly improves the rate compared to the non-optimized scenario in both HD and FD operating modes using a single parameter setting.
On the Complexity Reduction of Uplink Sparse Code Multiple Access for Spatial Modulation
The first algorithm is referred to as successive user detection (SUD), while the second algorithm is the modified version of SUD, namely modified SUD (MSUD).
