Reinforcement learning for 5G O-RAN
AI is a key component of the Open Radio Access Network (O-RAN) and is used for various optimization tasks. The network architecture provides data and control interfaces between the RAN Intelligent Controller (RIC) and the Radio Access Network (RAN), enabling AI agents to optimize the RAN across different time scales.
A typical application of AI in O-RAN is the xApp, which operates in the Near-Real-Time RIC to perform learning-based optimization at the NET, MAC, and PHY layers. We have developed a 5G O-RAN system comprising three RUs and 15 cellphones from diverse vendors to evaluate different online learning algorithms. These algorithms run within the xApp to optimize spectrum and energy efficiency in near-real-time, with a time scale ranging from 10 ms to 1 s.
The key features of our design include: (i) Near-real-time online optimization for resource slicing, inter-cell interference management, and energy efficiency, (ii) enhanced optimization for actor and critic agents, and (iii) diffusion policy for reinforcement learning.
A typical application of AI in O-RAN is the xApp, which operates in the Near-Real-Time RIC to perform learning-based optimization at the NET, MAC, and PHY layers. We have developed a 5G O-RAN system comprising three RUs and 15 cellphones from diverse vendors to evaluate different online learning algorithms. These algorithms run within the xApp to optimize spectrum and energy efficiency in near-real-time, with a time scale ranging from 10 ms to 1 s.
The key features of our design include: (i) Near-real-time online optimization for resource slicing, inter-cell interference management, and energy efficiency, (ii) enhanced optimization for actor and critic agents, and (iii) diffusion policy for reinforcement learning.
Achieving anti-jamming 5G mmWave communications
Reliable 5G mmWave communications are critical for many applications. However, maintaining the reliability of mmWave communications is challenging in scenarios where networks are affected by radio interference or jamming attacks. In this project, we present a joint analog and digital beamforming scheme for a 5G mmWave receiver designed to decode data packets in the presence of jamming signals. The beamforming scheme incorporates two key techniques: i) an online-learning Bayesian optimization framework for analog beamforming, and ii) a Modified Minimum Mean Square Error (M-MMSE) detector for digital beamforming. The combination of analog and digital beamformings enables the receiver to suppress jamming signals in both the analog and digital domains, allowing it to successfully decode data packets despite interference. We have built a prototype of the proposed receiver on a 28 GHz testbed and extensively evaluated its performance in different scenarios.
Key features of this system include: (i) joint analog and digital beamforming, (ii) online learning for beam selection, (iii) real-time jamming mitigation, and (iv) realistic OTA evaluation.
Key features of this system include: (i) joint analog and digital beamforming, (ii) online learning for beam selection, (iii) real-time jamming mitigation, and (iv) realistic OTA evaluation.
A polarization-based mmWave MIMO communication system
This real-time mmWave (28 GHz) communication system demonstrates that horizontal and vertical polarizations provide two spatial degrees of freedom (DoF) for transmitting two independent data streams. The components of this system include: i) USRP X310 devices for baseband signal processing, ii) Sivers EVK02004 for 28 GHz up/down conversion, and iii) computers for digital signal processing. Most of the code was written in C++.
This mmWave communication system has the following features: (i) real-time video streaming, (ii) real-time beam steering at Tx and Rx, (iii) support 2x2 MIMO transmission, and (iv) support Tx and Rx synchronization
This mmWave communication system has the following features: (i) real-time video streaming, (ii) real-time beam steering at Tx and Rx, (iii) support 2x2 MIMO transmission, and (iv) support Tx and Rx synchronization
A practical spectrum sharing solution for wireless systems
In this project, we designed a practical underlay spectrum sharing scheme for cognitive radio networks (CRNs) in which primary users are unaware of the presence of secondary users. The key components of our scheme include two MIMO-based interference cancellation (IC) techniques to manage cross-network interference on the secondary network side. We implemented the proposed underlay spectrum sharing scheme on a GNURadio-USRP2 wireless testbed. Experimental results demonstrate that secondary users can achieve an average spectrum efficiency of 1 bps/Hz without degrading the performance of primary users in a real-world office environment.
Key features of this system include: (i) no across-system coordination is needed for spectrum sharing, (ii) no inter-system synchronization is needed for spectrum sharing, and (iii) no central controller is needed for spectrum sharing.
Key features of this system include: (i) no across-system coordination is needed for spectrum sharing, (ii) no inter-system synchronization is needed for spectrum sharing, and (iii) no central controller is needed for spectrum sharing.