مشخصات مقاله | |
ترجمه عنوان مقاله | تشکیل پرتو سازگار و ارتباط کاربر در شبکه های دسترسی رادیویی ابر ناهمگن: یک مبادله هزینه و عملکرد آگاه از تحرک |
عنوان انگلیسی مقاله | Adaptive beamforming and user association in heterogeneous cloud radio access networks: A mobility-aware performance-cost trade-off |
انتشار | مقاله سال 2019 |
تعداد صفحات مقاله انگلیسی | 14 صفحه |
هزینه | دانلود مقاله انگلیسی رایگان میباشد. |
پایگاه داده | نشریه الزویر |
نوع نگارش مقاله |
مقاله پژوهشی (Research Article) |
مقاله بیس | این مقاله بیس نمیباشد |
نمایه (index) | Scopus – Master Journals List – JCR |
نوع مقاله | ISI |
فرمت مقاله انگلیسی | |
ایمپکت فاکتور(IF) |
4.205 در سال 2018 |
شاخص H_index | 119 در سال 2019 |
شاخص SJR | 0.592 در سال 2018 |
شناسه ISSN | 1389-1286 |
شاخص Quartile (چارک) | Q1 در سال 2018 |
مدل مفهومی | ندارد |
پرسشنامه | ندارد |
متغیر | ندارد |
رفرنس | دارد |
رشته های مرتبط | مهندسی کامپیوتر، مهندسی فناوری اطلاعات و ارتباطات |
گرایش های مرتبط | رایانش ابری، مهندسی الگوریتم و محاسبات، مخابرات سیار |
نوع ارائه مقاله |
ژورنال |
مجله / کنفرانس | شبکه های کامپیوتری – Computer Networks |
دانشگاه | LRI Laboratory, CNRS – Univ. Paris-Saclay – Univ. Paris-Sud, Orsay, France |
کلمات کلیدی | شبکه دسترسی رادیویی ابر ناهمگن، تشکیل پرتو، خوشه بندی، ارتباط کاربر با رادیو از راه دور، سربار اطلاعات وضعیت کانال |
کلمات کلیدی انگلیسی | H-CRAN، Beamforming، Clustering، User-to-RRH association، CSI Overhead |
شناسه دیجیتال – doi |
https://doi.org/10.1016/j.comnet.2019.05.005 |
کد محصول | E13677 |
وضعیت ترجمه مقاله | ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید. |
دانلود رایگان مقاله | دانلود رایگان مقاله انگلیسی |
سفارش ترجمه این مقاله | سفارش ترجمه این مقاله |
فهرست مطالب مقاله: |
Abstract 1. Introduction 2. State of the art 3. System model 4. Problem formulation and reference schemes 5. Proposed adaptive beamforming and user clustering (ABUC) algorithm 6. Cost analysis of the proposed ABUC algorithm 7. Optimizing ABUC’s feedback parameters using Q-learning 8. Simulation results 9. Conclusion Conflicts of Interest Acknowledgements Appendix A. Supplementary materials Research Data References |
بخشی از متن مقاله: |
Abstract
Heterogeneous Cloud Radio Access Network (H-CRAN) is a promising network architecture for the future 5G mobile communication system to address the increasing demand for mobile data traffic. In this work, we consider the design of efficient joint beamforming and user clustering (user-to-Remote Radio Head (RRH) association) in the downlink of a H-CRAN where users have different mobility profiles. Given the rapidly time-varying nature of such wireless environment, it becomes very challenging to enable optimized beamforming and user clustering without incurring large Channel State Information (CSI) and signaling overheads. The main objective of this work is to investigate and evaluate the trade-off between system throughput and the incurred costs in terms of complexity and signaling overhead, including the impact of different CSI feedback strategies given different user mobility profiles. We propose the Adaptive Beamforming and User Clustering (ABUC) algorithm which adapts its feedback parameters, namely the period of dynamic user clustering and the type of CSI feedback, in function of user mobility. Furthermore, we design a reinforcement-learning framework which enables the proposed ABUC algorithm to optimize its scheduling parameters on-the-fly, given each user mobility profile. Based on computer simulations, an analysis of the effect of mobility on system performance metrics is presented and conclusions are drawn regarding the algorithm’s adequate parameter tuning for different mobility scenarios. Introduction Next generation of mobile and wireless communications system (5G) will revolutionize the way people communicate and extend the boundaries of the wireless industry. 5G will move beyond networks that are purpose-built for mobile broadband alone, toward systems that connect far more different types of devices at different speeds. The Internet of Things (IoT) is one of the primary contributors to global mobile traffic growth and this progression will lead to a huge mobile and wireless traffic volume predicted to increase a thousand-fold over the next decade [2]. Besides sustaining the tremendous growth of the traffic load, 5G system will be designed to fulfill diverse application requirements: far more stringent latency and reliability levels are expected to be necessary to support applications related to healthcare, security, logistics, automotive applications, or mission-critical control; Network scalability and flexibility are required to support a large number of devices with very low complexity and to enable long battery lifetimes [3]. 5G system is envisioned to meet such challenges thanks to the combination of several breakthroughs and technological advances such as ultra-dense small-cell deployments, intelligent multi-antenna, full duplex radios, millimeter wave transmissions, and cloud computing abilities. Particularly, the Cloud Radio Access Network (CRAN) is a network architecture based on cloud computing and centralized processing. It has been shown to provide high spectral and energy efficiencies while reducing both capital and operating expenditures [4]. |