الگوریتم های زمان بندی در شبکه های بی سیم
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الگوریتم های زمان بندی در شبکه های بی سیم

عنوان فارسی مقاله: یادگیری الگوریتم های زمان بندی در شبکه های بی سیم با آمار کانال ناشناخته
عنوان انگلیسی مقاله: Learning algorithms for scheduling in wireless networks with unknown channel statistics
مجله/کنفرانس: شبکه های ادهاک - Ad Hoc Networks
رشته های تحصیلی مرتبط: مهندسی کامپیوتر
گرایش های تحصیلی مرتبط: مهندسی الگوریتم ها و محاسبات، معماری سیستم های کامپیوتری، برنامه نویسی کامپیوتر
کلمات کلیدی فارسی: شبکه های بی سیم، کنترل شبکه، زمان بندی انتقال
کلمات کلیدی انگلیسی: Wireless networks، Network control، Transmission scheduling
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journals List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.adhoc.2018.10.006
دانشگاه: MIT Lincoln Laboratory, 244 Wood St., Lexington, MA 02421, United States
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 4/301 در سال 2018
شاخص H_index: 79 در سال 2019
شاخص SJR: 0/648 در سال 2018
شناسه ISSN: 1570-8705
شاخص Quartile (چارک): Q1 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 14
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: دارد
کد محصول: E12750
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


1- Introduction


2- Network model and problem formulation


3- Scheduling with uncertain channel statistics


4- Learning greedy maximal matchings


5- Performance analysis under primary interference


6- Stochastic network control


7- Extensions to other scheduling constraints


8- Simulation results


9- Conclusion


References

نمونه متن انگلیسی مقاله

Abstract


We study the problem of learning channel statistics to efficiently schedule transmissions in wireless networks subject to interference constraints. We propose an algorithm that uses greedily-constructed schedules in order to learn the channels’ transmission rates, while simultaneously exploiting previous observations to obtain high throughput. Comparison to the offline solution shows our algorithm to have good performance that scales well with the number of links in the network. We then turn our attention to the stochastic setting where packets randomly arrive to the network and await transmission in queues at the nodes. We develop a queue-length-based scheduling policy that uses the channel learning algorithm as a component. We analyze our method in time-varying environments and show that it achieves the same stability region as that of a greedy policy with full channel knowledge.


Introduction


A major challenge in the design of wireless networks is the need to schedule transmissions so that the nodes in the network can efficiently share the common spectrum. Nodes that are nearby can interfere with one another, and, therefore, network controllers must choose simultaneous link activations that do not violate interference constraints. Additionally, scheduling decisions must take channel state information into account. In many settings, it is not possible to know the channel in advance. The nodes can only learn the channel statistics by transmitting on the channels and using receiver feedback to observe the results. Due to multi-path fading and environmental interference, the channel state is often time-varying, and multiple observations are needed in order to obtain an accurate estimate of the channel statistics. This introduces a tradeoff between exploration and exploitation. The nodes may have to forgo scheduling those channels that, so far, have given the highest throughput in order to improve their understanding of under-observed channels. With perfect channel knowledge, it is well known that the maxweight policy, first explored in [2], provides a throughput optimal scheduling scheme that activates edges based on queue length and channel conditions, subject to interference constraints. The maxweight policy weights each edge by the product of its queue backlog and channel rate and chooses a link activation that maximizes the summation of weights in the set [3]. Depending on the interference constraints imposed on the network, this requires that the controller solves a complex combinatorial optimization problem at each time step of the algorithm. A drawback of the max-weight policy is that it is heavily centralized and assumes that the network controller knows the queue backlog at every edge in the network. For this reason, the use of greedy scheduling methods have been explored in the literature [4–8]. Under these methods, at each time step, the activation set is chosen according to a greedy combinatorial algorithm. In many settings, this construction can be performed in a distributed manner by the nodes of the network. The cost of using greedy algorithms is that, in general, they are not able to guarantee throughput optimality. However, under certain interference constraints, they can obtain network stability over a guaranteed fraction of the network’s throughput region.

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