Deep Reinforcement Learning for Resource Management in Network Slicing

Network slicing is born as an emerging business to operators, by allowing them to sell the customized slices to various tenants at different prices. In order to provide better-performing and cost-efficient services, network slicing involves challenging technical issues and urgently looks forward to intelligent innovations to make the resource management consistent with users' activities per slice. In that regard, deep reinforcement learning (DRL), which focuses on how to interact with the environment by trying alternative actions and reinforcing the tendency actions producing more rewarding consequences, is assumed to be a promising solution. In this paper, after briefly reviewing the fundamental concepts of DRL, we investigate the application of DRL in solving some typical resource management for network slicing scenarios, which include radio resource slicing and priority-based core network slicing, and demonstrate the advantage of DRL over several competing schemes through extensive simulations. Finally, we also discuss the possible challenges to apply DRL in network slicing from a general perspective.Comment: The manuscript has been accepted by IEEE Access in Nov. 201

Toward a High-Efficient Utilization of Solar Radiation by Quad-Band Solar Spectral Splitting

The promising quad-band solar spectral splitter incorporates the properties of the optical filter and the spectrally selective solar thermal absorber can direct PV band to PV modules and absorb thermal band energy for thermal process with low thermal losses. It provides a new strategy for spectral splitting and offers potential ways for hybrid PVT system design.United States. Department of Energy (contract DE-AR0000471)United States. Advanced Research Projects Agency-Energy (contract DE-AR0000471)United States. Department of Energy. Office of Science. Solid-State Solar Thermal Energy Conversion Center (Award # DE-FG02-09ER46577

Three-dimensional echo-shifted EPI with simultaneous blip-up and blip-down acquisitions for correcting geometric distortion

Purpose: Echo-planar imaging (EPI) with blip-up/down acquisition (BUDA) can provide high-quality images with minimal distortions by using two readout trains with opposing phase-encoding gradients. Because of the need for two separate acquisitions, BUDA doubles the scan time and degrades the temporal resolution when compared to single-shot EPI, presenting a major challenge for many applications, particularly functional MRI (fMRI). This study aims at overcoming this challenge by developing an echo-shifted EPI BUDA (esEPI-BUDA) technique to acquire both blip-up and blip-down datasets in a single shot. Methods: A three-dimensional (3D) esEPI-BUDA pulse sequence was designed by using an echo-shifting strategy to produce two EPI readout trains. These readout trains produced a pair of k-space datasets whose k-space trajectories were interleaved with opposite phase-encoding gradient directions. The two k-space datasets were separately reconstructed using a 3D SENSE algorithm, from which time-resolved B0-field maps were derived using TOPUP in FSL and then input into a forward model of joint parallel imaging reconstruction to correct for geometric distortion. In addition, Hankel structured low-rank constraint was incorporated into the reconstruction framework to improve image quality by mitigating the phase errors between the two interleaved k-space datasets. Results: The 3D esEPI-BUDA technique was demonstrated in a phantom and an fMRI study on healthy human subjects. Geometric distortions were effectively corrected in both phantom and human brain images. In the fMRI study, the visual activation volumes and their BOLD responses were comparable to those from conventional 3D echo-planar images. Conclusion: The improved imaging efficiency and dynamic distortion correction capability afforded by 3D esEPI-BUDA are expected to benefit many EPI applications.Comment: 8 figures, peer-reviewed journal pape

Improvement of Sciatic Nerve Regeneration Using Laminin-Binding Human NGF-β

Sciatic nerve injuries often cause partial or total loss of motor, sensory and autonomic functions due to the axon discontinuity, degeneration, and eventual death which finally result in substantial functional loss and decreased quality of life. Nerve growth factor (NGF) plays a critical role in peripheral nerve regeneration. However, the lack of efficient NGF delivery approach limits its clinical applications. We reported here by fusing with the N-terminal domain of agrin (NtA), NGF-β could target to nerve cells and improve nerve regeneration. was also measured. Using the rat sciatic nerve crush injury model, the nerve repair and functional restoration by utilizing LBD-NGF were tested.. In the rat sciatic nerve crush injury model, we found that LBD-NGF could be retained and concentrated at the nerve injury sites to promote nerve repair and enhance functional restoration following nerve damages.Fused with NtA, NGF-β could bind to laminin specifically. Since laminin is the major component of nerve extracellular matrix, laminin binding NGF could target to nerve cells and improve the repair of peripheral nerve injuries