9 research outputs found

    Exploitation of wireless control link in the software-defined LEO satellite network

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    software-defined satellite network, control link, cross layer optimization, power-efficient control link algorithmThe low earth orbit (LEO) satellite network can benefit from software-defined networking (SDN) by lightening forwarding devices and improving service diversity. In order to apply SDN into the network, however, reliable SDN control links should be associated from satellite gateways to satellites, with the wireless and mobile properties of the network taken into account. Since these characteristics affect both control link association and gateway power allocation, we define this new cross layer problem as an SDN control link problem. The problem is discussed from the viewpoint of multilayers such as automatic repeat request (ARQ) and gateway power allocation at the Link layer, and split transmit control protocol (TCP) and link scheduling at the Transport layer. A centralized SDN control framework constrained by maximum total power is introduced to enhance gateway power efficiency for control link setup. Based on the power control analysis of the problem, a power-efficient control link algorithm is developed, which establishes low latency control links with reduced power consumption. Along with the sensitivity analysis of the proposed control link algorithm, numerical results demonstrate low latency and high reliability of control links established by the algorithm, ultimately suggesting the feasibility, both technical and economical, of the software-defined LEO satellite network.open1. INTRODUCTION 1 1.1 Software-Defined Satellite Network 1 1.2 Wireless SDN Control Link Problem Statement 4 1.3 Contributions and Overview of Theses 5 1.4 Related Works 6 2. MODELING AND FORMULATION 8 2.1 Control Link Association 8 2.1.1 Graph Model 8 2.1.2 ARQ and Split TCP 9 2.1.3 Link Association Variable 10 2.2 Control Link Reliability and Expected Latency Formulation 12 2.2.1 Control Link Reliability and Gateway Power 12 2.2.2 Expected Latency Formulation 13 2.3 SDN Control Link Problem 16 2.3.1 Expected Latency Minimization Problem 16 2.3.2 Power-Efficient SDN Control Link Problem 17 3. SDN CONTROL LINK ALGORITHM 22 4. NUMERICAL RESULTS AND ANALYSIS 25 4.1 Latency Analysis and Feasibility of the Software-Defined Satellite Network 27 4.2 Sensitivity Analysis and Selection of the Maximum Total Power 33 5. CONCLUSION 37 APPENDIX 38 REFERENCES 40์ €๊ถค๋„(LEO) ์œ„์„ฑ ๋„คํŠธ์›Œํฌ๋Š” ๋ฐ์ดํ„ฐ ์ „๋‹ฌ ์žฅ์น˜๋ฅผ ๊ฐ„์†Œํ™”ํ•˜๊ณ  ์„œ๋น„์Šค ๋‹ค์–‘์„ฑ์„ ํ–ฅ์ƒ์‹œํ‚ค๋Š” ๋“ฑ, ์†Œํ”„ํŠธ์›จ์–ด ์ •์˜ ๋„คํŠธ์›Œํ‚น(SDN)๋กœ๋ถ€ํ„ฐ ๋‹ค์–‘ํ•œ ์ด์ ์„ ์–ป์„ ์ˆ˜ ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ SDN์„ ์œ„์„ฑ ๋„คํŠธ์›Œํฌ์— ์ ์šฉํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š”, ์‹ ๋ขฐ์„ฑ ์žˆ๋Š” SDN ์ œ์–ด ๋งํฌ๊ฐ€ ์œ„์„ฑ ๊ฒŒ์ดํŠธ์›จ์ด๋กœ๋ถ€ํ„ฐ ์œ„์„ฑ๊นŒ์ง€ ์—ฐ๊ฒฐ๋˜์–ด์•ผ ํ•˜๋ฉฐ, ์œ„์„ฑ ๋„คํŠธ์›Œํฌ์˜ ๋ฌด์„  ํŠน์„ฑ๊ณผ ์ด๋™์„ฑ์ด ๋™์‹œ์— ๊ณ ๋ ค๋˜์–ด์•ผ ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ํŠน์„ฑ๋“ค์€ ์ œ์–ด ๋งํฌ ์—ฐ๊ฒฐ๊ณผ ๊ฒŒ์ดํŠธ์›จ์ด ์ „๋ ฅ ํ• ๋‹น ๋ชจ๋‘์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๊ธฐ ๋•Œ๋ฌธ์—, ์šฐ๋ฆฌ๋Š” ์ด๋Ÿฌํ•œ ๊ต์ฐจ ๊ณ„์ธต ๋ฌธ์ œ๋ฅผ SDN ์ œ์–ด ๋งํฌ ๋ฌธ์ œ๋กœ ์ƒˆ๋กญ๊ฒŒ ์ •์˜ํ•œ๋‹ค. ์ด ๋ฌธ์ œ๋Š” ์ „์†ก ๊ณ„์ธต์˜ ์ž๋™ ์žฌ์ „์†ก ์š”๊ตฌ(ARQ) ๋ฐ ์ „์†ก ์ œ์–ด ํ”„๋กœํ† ์ฝœ(TCP), ๋„คํŠธ์›Œํฌ ๊ณ„์ธต์˜ ๋ผ์šฐํŒ…, ๋ฌผ๋ฆฌ ๊ณ„์ธต์˜ ์ „๋ ฅ ํ• ๋‹น๊ณผ ๊ฐ™์€ ๋‹ค์ค‘ ๊ณ„์ธต์˜ ๊ด€์ ์—์„œ ๋…ผ์˜๋œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ œ์–ด ๋งํฌ ์„ค์ •์— ํ•„์š”ํ•œ ๊ฒŒ์ดํŠธ์›จ์ด ์ „๋ ฅ ํšจ์œจ์„ ๋†’์ด๊ธฐ ์œ„ํ•ด ์ตœ๋Œ€ ์ด ์ „๋ ฅ์„ ์ œํ•œํ•˜๋Š” ์ค‘์•™์ง‘๊ถŒํ™” SDN ์ œ์–ด ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ๋„์ž…ํ•œ๋‹ค. ์ œ์•ˆ๋œ ๋ฌธ์ œ์— ๋Œ€ํ•œ ์ „๋ ฅ ํ• ๋‹น ๋ถ„์„์„ ๊ธฐ๋ฐ˜์œผ๋กœ, ์ „๋ ฅ ์†Œ๋น„๊ฐ€ ์ ์œผ๋ฉด์„œ๋„ ์ง€์—ฐ์ด ์ ์€ ์ œ์–ด ๋งํฌ๋ฅผ ์—ฐ๊ฒฐํ•˜๋Š” ์ „๋ ฅ ํšจ์œจ์ ์ธ ์ œ์–ด ๋งํฌ ์•Œ๊ณ ๋ฆฌ์ฆ˜์ด ์ œ์•ˆ๋œ๋‹ค. ์ œ์•ˆ๋œ ์ œ์–ด ๋งํฌ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ๋ฏผ๊ฐ๋„ ๋ถ„์„๊ณผ ํ•จ๊ป˜, ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ๋Š” ์•Œ๊ณ ๋ฆฌ์ฆ˜์— ์˜ํ•ด ์„ค์ •๋˜๋Š” ์ œ์–ด ๋งํฌ์˜ ๋‚ฎ์€ ์ง€์—ฐ๊ณผ ๋†’์€ ์‹ ๋ขฐ์„ฑ์„ ๋ณด์—ฌ์ฃผ๋ฉฐ, ๊ถ๊ทน์ ์œผ๋กœ ์†Œํ”„ํŠธ์›จ์–ด ์ •์˜ LEO ์œ„์„ฑ ๋„คํŠธ์›Œํฌ์˜ ๊ธฐ์ˆ ์  ๋ฐ ๊ฒฝ์ œ์  ํƒ€๋‹น์„ฑ์„ ์ œ์‹œํ•œ๋‹ค.MasterdCollectio

    Frequency divided group beamforming with sparse spacefrequency code for above 6 GHz URLLC systems

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    In this study, we propose a limited feedback-based frequency divided group beamforming with sparse space-frequency transmit diversity coded orthogonal frequency division multiplexing (OFDM) system for ultrareliable low latency communication (URLLC) scenario. The proposed scheme has several advantages over the traditional hybrid beamforming approach, including not requiring downlink channel state information for baseband precoding, supporting distributed multipoint transmission structures for diversity, and reducing beam sweeping latency with little uplink overhead. These are all positive aspects of physical layer characteristics intended for URLLC. It is suggested in the system to manage the multipoint transmission structure realized by distributed panels using a power allocation method based on cooperative game theory. Link-level simulations demonstrate that the proposed scheme offers reliability by achieving both higher diversity order and array gain in a nonline-of-sight channel of selectivity and limited spatial scattering

    Comparison of Bacterial Populations in the Ceca of Swine at Two Different Stages and Their Functional Annotations

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    The microbial composition in the cecum of pig influences host health, immunity, nutrient digestion, and feeding requirements significantly. Advancements in metagenome sequencing technologies such as 16S rRNAs have made it possible to explore cecum microbial population. In this study, we performed a comparative analysis of cecum microbiota of crossbred Korean native pigs at two different growth stages (stage L = 10 weeks, and stage LD = 26 weeks) using 16S rRNA sequencing technology. Our results revealed remarkable differences in microbial composition, α and β diversity, and differential abundance between the two stages. Phylum composition analysis with respect to SILVA132 database showed Firmicutes to be present at 51.87% and 48.76% in stages L and LD, respectively. Similarly, Bacteroidetes were present at 37.28% and 45.98% in L and LD, respectively. The genera Prevotella, Anaerovibrio, Succinivibrio, Megasphaera were differentially enriched in stage L, whereas Clostridium, Terrisporobacter, Rikenellaceae were enriched in stage LD. Functional annotation of microbiome by level-three KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis revealed that glycine, serine, threonine, valine, leucine, isoleucine arginine, proline, and tryptophan metabolism were differentially enriched in stage L, whereas alanine, aspartate, glutamate, cysteine, methionine, phenylalanine, tyrosine, and tryptophan biosynthesis metabolism were differentially enriched in stage LD. Through machine-learning approaches such as LEfSe (linear discriminant analysis effect size), random forest, and Pearson’s correlation, we found pathways such as amino acid metabolism, transport systems, and genetic regulation of metabolism are commonly enriched in both stages. Our findings suggest that the bacterial compositions in cecum content of pigs are heavily involved in their nutrient digestion process. This study may help to meet the demand of human food and can play significant roles in medicinal application
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