A5054723260- Advanced MIMO Systems Optimization
- Advanced Wireless Communication Techniques
- Cooperative Communication and Network Coding
- Antenna Design and Analysis
- Wireless Communication Networks Research
Rice University
2022-2024
As wireless communication systems strive to improve spectral efficiency, there has been a growing interest in employing machine learning (ML)-based approaches for adaptive modulation and coding scheme (MCS) selection. In this paper, we introduce new MCS selection framework massive MIMO that operates without any feedback from users <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">by solely relying on instantaneous uplink channel estimates.Ou</sup>...
In cell-free massive MIMO systems with multiple distributed access points (APs) serving users over the same time-frequency resources, downlink beamforming is done through spatial precoding. Precoding vectors can be optimally designed to use minimum transmit power while satisfying a quality-of-service requirement for each user. However, existing centralized solutions optimization pose challenges such as high communication overhead and processing delay. On other hand, approaches either require...
As wireless communication systems strive to improve spectral efficiency, there has been a growing interest in employing machine learning (ML)-based approaches for adaptive modulation and coding scheme (MCS) selection. In this paper, we introduce new MCS selection framework massive MIMO that operates without any feedback from users by solely relying on instantaneous uplink channel estimates. Our proposed method can effectively operate multi-user scenarios where user imposes excessive delay...
In this paper, we study the robustness of distributed beamforming in presence hardware imperfections. particular, characterize impact carrier frequency offset (CFO) between antennas on multi-user receive beamforming. We performance CFO using a massive MIMO platform. Using datasets collected from platform, measure and compare absence CFO. Further, effect variation among through simulations. Interestingly, observe that drop is higher as more are included Our results can be used to determine...