Learning Optimal Fronthauling and Decentralized Edge Computation in Fog Radio Access Networks

Fog radio access networks (F-RANs), which consist of a cloud and multiple edge nodes (ENs) connected via fronthaul links, have been regarded as promising network architectures. The F-RAN entails a joint optimization of cloud and edge computing as well as fronthaul interactions, which is challenging for traditional optimization techniques. This paper proposes a Cloud-Enabled Cooperation-Inspired Learning (CECIL) framework, a structural deep learning mechanism for handling a generic F-RAN optimization problem. The proposed solution mimics cloud-aided cooperative optimization policies by including centralized computing at the cloud, distributed decision at the ENs, and their uplink-downlink fronthaul interactions. A group of deep neural networks (DNNs) are employed for characterizing computations of the cloud and ENs. The forwardpass of the DNNs is carefully designed such that the impacts of the practical fronthaul links, such as channel noise and signling overheads, can be included in a training step. As a result, operations of the cloud and ENs can be jointly trained in an end-to-end manner, whereas their real-time inferences are carried out in a decentralized manner by means of the fronthaul coordination. To facilitate fronthaul cooperation among multiple ENs, the optimal fronthaul multiple access schemes are designed. Training algorithms robust to practical fronthaul impairments are also presented. Numerical results validate the effectiveness of the proposed approaches.Comment: to appear in IEEE Transactions on Wireless Communication

Learning Robust Beamforming for MISO Downlink Systems

This paper investigates a learning solution for robust beamforming optimization in downlink multi-user systems. A base station (BS) identifies efficient multi-antenna transmission strategies only with imperfect channel state information (CSI) and its stochastic features. To this end, we propose a robust training algorithm where a deep neural network (DNN), which only accepts estimates and statistical knowledge of the perfect CSI, is optimized to fit to real-world propagation environment. Consequently, the trained DNN can provide efficient robust beamforming solutions based only on imperfect observations of the actual CSI. Numerical results validate the advantages of the proposed learning approach compared to conventional schemes.Comment: to appear in IEEE Communications Letters (5 pages, 5 figures, 1 tables

A Bipartite Graph Neural Network Approach for Scalable Beamforming Optimization

Deep learning (DL) techniques have been intensively studied for the optimization of multi-user multiple-input single-output (MU-MISO) downlink systems owing to the capability of handling nonconvex formulations. However, the fixed computation structure of existing deep neural networks (DNNs) lacks flexibility with respect to the system size, i.e., the number of antennas or users. This paper develops a bipartite graph neural network (BGNN) framework, a scalable DL solution designed for multi-antenna beamforming optimization. The MU-MISO system is first characterized by a bipartite graph where two disjoint vertex sets, each of which consists of transmit antennas and users, are connected via pairwise edges. These vertex interconnection states are modeled by channel fading coefficients. Thus, a generic beamforming optimization process is interpreted as a computation task over a weight bipartite graph. This approach partitions the beamforming optimization procedure into multiple suboperations dedicated to individual antenna vertices and user vertices. Separated vertex operations lead to scalable beamforming calculations that are invariant to the system size. The vertex operations are realized by a group of DNN modules that collectively form the BGNN architecture. Identical DNNs are reused at all antennas and users so that the resultant learning structure becomes flexible to the network size. Component DNNs of the BGNN are trained jointly over numerous MU-MISO configurations with randomly varying network sizes. As a result, the trained BGNN can be universally applied to arbitrary MU-MISO systems. Numerical results validate the advantages of the BGNN framework over conventional methods.Comment: accepted for publication on IEEE Transactions on Wireless Communication

Deep Learning Methods for Universal MISO Beamforming

This letter studies deep learning (DL) approaches to optimize beamforming vectors in downlink multi-user multi-antenna systems that can be universally applied to arbitrarily given transmit power limitation at a base station. We exploit the sum power budget as side information so that deep neural networks (DNNs) can effectively learn the impact of the power constraint in the beamforming optimization. Consequently, a single training process is sufficient for the proposed universal DL approach, whereas conventional methods need to train multiple DNNs for all possible power budget levels. Numerical results demonstrate the effectiveness of the proposed DL methods over existing schemes.Comment: to appear in IEEE Wireless Communications Letters (5 pages, 3 figures, 2 tables

Effects of Wet-Pressing and Cross-Linking on the Tensile Properties of Carbon Nanotube Fibers

To increase the strength of carbon nanotube (CNT) fibers (CNTFs), the mean size of voids between bundles of CNTs was reduced by wet-pressing, and the CNTs were cross-linked. Separate and simultaneous physical (roller pressing) and chemical methods (cross-linking) were tested to confirm each method's effects on the CNTF strength. By reducing the fraction of pores, roller pressing decreased the cross-sectional area from 160 mu m(2) to 66 mu m(2) and increased the average load-at-break from 2.83 +/- 0.25 cN to 4.41 +/- 0.16 cN. Simultaneous injection of crosslinker and roller pressing augmented the cross-linking effect by increasing the infiltration of the crosslinker solution into the CNTF, so the specific strength increased from 0.40 +/- 0.05 N/tex to 0.67 +/- 0.04 N/tex. To increase the strength by cross-linking, it was necessary that the size of the pores inside the CNTF were reduced, and the infiltration of the solution was increased. These results suggest that combined physical and chemical treatment is effective to increase the strength of CNTFs.11Ysciescopu

Genetic and Molecular Characterization of a New EMS-Induced Mutant without the Third Glucose Moiety at the C-3 Sugar Chain of Saponin in Glycine max (L.) Merr.

Saponin, a secondary metabolite, is produced by various plant species, including soybean (Glycine max (L.) Merr.). Soybeans synthesize triterpenoid saponins, which are classified by their aglycone structure and sugar chain composition. Here, we characterized an ethyl methanesulfonate-induced mutant, PE1539, without saponin and with a glucose moiety at the third position of the C-3 sugar chain. The saponin phenotype of PE1539 is described by the accumulation of Ab-gamma g saponin and deficiency of Ab-alpha g saponin and DDMP-alpha g saponin, similar to a previously reported sg-3 mutant in soybean. Genetic analysis showed that the saponin phenotype of PE1539 is controlled by a recessive mutation. We mapped the gene responsible for the phenotype of PE1539 and the mapped region included Sg-3 (Glyma.10G104700). Further analysis of Sg-3 in PE1539 using DNA sequencing revealed a single-nucleotide substitution in the exon (G804A), resulting in a premature stop codon; thus, PE1539 produced a PSPG box-truncated protein. Saponin phenotype analysis of the F-2 population-from a cross between wild-type Uram and PE1539-showed that the phenotype of saponin was cosegregated with the genotype of Sg 3. Quantitative real-time PCR showed reduced expression of Sg-3 in PE1539 cells. Together, our data indicate that the saponin phenotype of PE1539 results from a mutation in Sg-3

Successful Management of a Rare Case of Stent Fracture and Subsequent Migration of the Fractured Stent Segment Into the Ascending Aorta in In-Stent Restenotic Lesions of a Saphenous Vein Graft

Stent fracture is a complication following implantation of drug eluting stents and is recognized as one of the risk factors for in-stent restenosis. We present the first case of successfully managing a stent fracture and subsequent migration of the fractured stent into the ascending aorta that occurred during repeat revascularization for in-stent restenosis of an ostium of saphenous vein graft after implantation of a zotarolimus-eluting stent. Although the fractured stent segment had migrated into the ascending aorta with a pulled balloon catheter, it was successfully repositioned in the saphenous vein graft using an inflated balloon catheter. Then, the fractured stent segment was successfully connected to the residual segment of the zotarolimus-eluting stent by covering it with an additional sirolimuseluting stent