مکان یابی بهینۀ BTS برای پوشش و ترافیک شبکه های سلولی بی سیم (مقاله علمی وزارت علوم)
درجه علمی: نشریه علمی (وزارت علوم)
آرشیو
چکیده
با توجه به اینکه قرارگیری مکان مناسب ایستگاه های پایه(BTS) [i]</sup> از جهت بهبود همپوشانی، هزینه، برقراری ترافیک مدنظر و کنترل تداخل در شبکه های بی سیم، از اهمیت بسزایی برخوردار است، در این مقاله، هدف بهینه سازی تعداد ایستگاه های پایه و مکان آنها در محیط مدنظر، برای کاهش هزینه و پوشش دهی کامل آن، با توجه به تعداد کاربران موجود است؛ به طوری که ترافیک منطقه تأمین و تداخل ناشی از برقراری ارتباطات کنترل شود. توجه به این نکته ضروری است که ابزارهای طراحی واقعی موجود در صنعت، توانایی انتخاب مکان بهینه ایستگاه های پایه را با در نظر گرفتن احتیاجات تعریف شده در شبکه ندارند. به این منظور مدلی بر مبنای برنامه ریزی خطی عدد صحیح برای پوشش برخی از عوامل تأثیرگذار بر مکان یابی تدوین شده است. در این مدل حداقل هزینه، توان، شعاع تحت پوشش، حداکثر ظرفیت هر آنتن، فاصله ایستگاه های مجاور با در نظر گرفتن امکان انتخاب نوع ایستگاه و فاصله بین هر کاربر و ایستگاه لحاظ شده است. عملکرد مدل پیشنهادی با ایجاد یک نمونه تصادفی از کاربران و ایستگاه ها بررسی و با مدل ارائه شده دیگری، مقایسه شده است. [i]Base Transceiver StationOptimal BTS location for coverage and traffic of cellular wireless networks
Purpose: This paper aims to efficiently find the number and optimal placement of BTSs in the coverage area such that the implementation cost is minimized, while the user's traffic in the corresponding area and the channel interference constraints are satisfied.Design/methodology/approach: An integer linear mathematical model has been proposed to assign all customer points to transceiver-based stations. The objective function has been assumed to minimize fixed costs while considering a penalty for reducing interference. An optimization solver has been applied to solve the model.Findings: The proposed mathematical model was compared to another existing model in the literature from three points of view: coverage cost, construction costs, and running time. The results indicated that the proposed model has the appropriate efficiency to find solutions to real practical problems. In more detail, the proposed model succeeded to find solutions with less coverage or construction cost in a shorter time, compared with the model in literature, which has been referred to as the SSH model in the paper. However, there have been some samples in which the SSH model overtakes the proposed model in one or two mentioned aspects for comparison.Research limitations/implications: The limitation of this study is that the channel model was considered based on the simple free space path loss model, while the real channel model based on the environmental effects can be suggested for future work. Another important issue is that for BTS localization design, knowing the status of the real environment leads to the proper design in such networks.Social/practical implications: BTS localization is the most important issue in mobile networks' design. In a practical situation, there is not any software available to design a mobile network in the corresponding coverage area. Therefore, this paper can help communication engineers to implement the proposed approach in a real environment.Originality/value: In this paper, the problem of the optimal location of transceiver-based stations (BTS) in different models in the coverage area has been addressed such that the implementation cost is minimized, while the user's traffic in the corresponding area and the channel interference constraints are satisfied. This has not been considered in the literature. Any of the existing models focus on one aspect to optimize, such as maximizing the coverage, minimizing the interference, and minimizing coverage or construction costs. The novelties of the proposed model are twofold: i) two goals were considered in the objective function, i.e., minimizing construction costs and minimizing interference. Since interference with full coverage is unavoidable, the model added a penalty to the objective function to reduce the interference as much as possible; and ii) it was assumed that there is more than one single type BTS, any of which has different power, radius, and capacity. This makes the model more real and indeed more complicated to solve.
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