Radio Access Networks (RANs) are increasingly softwarized and disaggregated, resulting in diverse and interoperable components running on commodity hardware and compute platforms. In this demo, we present a real-time RAN testing framework, called ETHOS, designed to capture fine-grained metrics on performance, energy consumption, and computational efficiency for O-RAN-compliant systems across a range of compute platforms. ETHOS also interfaces with the RAN stack for real-time controlling to test various scheduling algorithms. ETHOS leverages a shared memory-based inter-process communication (IPC) framework and automates testing through the emulation of diverse user traffic and channel conditions. We deploy ETHOS on the NVIDIA ARC-OTA platform in this demonstration to show its effectiveness. ETHOS is designed to be easily portable to other commercial and open-source RAN software, offering the potential for a unified testing and evaluation framework to accelerate O-RAN research and innovation.

The highly directional nature of the millimeter wave (mmWave) beams causes several challenges in using that spectrum to meet the communication demands of immersive applications. The mmWave beams are especially susceptible to misalignments and blockages caused by user movements. As a result, mmWave channels are vulnerable to large quality fluctuations, which in turn, degrades the end-to-end performance of immersive applications. In this paper, we propose a reinforcement learning (RL) based application-layer plugin that works in conjunction with the QUIC protocol to combat the challenges of mmWave networks. The plug-in called Millimeter wave based Immersive QUIC Agent (MIQA) uses the RL model to help modulate the sending rate along with the congestion control scheme of QUIC. To evaluate the effectiveness of MIQA, we conduct experiments on a mmWave augmented immersive testbed. The evaluation results show that MIQA significantly improves the immersive experience by increasing the end-to-end throughput and by decreasing the end-to-end latency.

The highly directional nature of the millimeter wave (mmWave) beams pose several challenges in using that spectrum for meeting the communication needs of immersive applications. In particular, the mmWave beams are susceptible to misalignments and blockages caused by user movements. As a result, mmWave channels are vulnerable to large fluctuations in quality, which in turn, cause disproportionate degradation in end-to-end performance of Transmission Control Protocol (TCP) based applications. In this paper, we propose a reinforcement learning (RL) integrated transport-layer plugin, Millimeter wave based Immersive Agent (MIA), for immersive content delivery over the mmWave link. MIA uses the RL model to predict mmWave link bandwidth based on the real-time measurement. Then, MIA cooperates with TCP’s congestion control scheme to adapt the sending rate in accordance with the predictions of the mmWave bandwidth. To evaluate the effectiveness of the proposed MIA, we conduct experiments using a mmWave augmented immersive testbed and network simulations. The evaluation results show that MIA improves end-to-end immersive performance significantly on both throughput and latency.

Millimeter wave (mmWave) access networks have the potential to meet the high-throughput and low-latency needs of immersive applications. However, due to the highly directional nature of the mmWave beams and their susceptibility to beam misalignment and blockage resulting from user movements and rotations, the associated mmWave links are vulnerable to large channel fluctuations. These fluctuations result in disproportionately adverse effects on performance of transport layer protocols such as Transmission Control Protocol (TCP). To overcome this challenge, we propose a network layer solution, COded Taking And Giving (COTAG) scheme to sustain low-latency and high-throughput end-to-end TCP performance in dually connected networks. In particular, COTAG creates network encoded packets at the network gateway and each access point (AP) aiming to adaptively take the spare bandwidth on each link for transmission. Further, if one link bandwidth drops due to user movements, COTAG actively abandons the transmission opportunity by conditionally dropping packets. Consequently, COTAG actively adapts to link quality changes in mmWave access network and enhances the TCP performance without jeopardizing the latency of immersive content delivery. To evaluate the effectiveness of the proposed COTAG, we conduct experiments using off-the-shelf APs and network simulations. The evaluation results show that COTAG improves end-to-end TCP performance significantly on both throughput and latency.