支援物聯通訊之最佳群組形成及無線資源管理技術

物聯網的一大特色為物聯裝置的數量龐大且分佈密集，當這些裝置需要通訊時，如何有效使用現有無線通訊系統的頻譜資源，並透過分群以提升頻譜之空間利用率，被視為支援物聯通訊的關鍵技術。過去相關文獻所提出之分群演算法及通訊協定，為了減少設計的複雜度，對於群組之間的通訊干擾估計大多相當簡略，因此無法運用於對信號干擾問題較為重視之無線通訊系統。為了解決這個問題，本論文提出群組結構形成、傳輸功率控制以及無線資源分配應該統籌作一最佳設計，以俾物聯裝置重複使用現有無線通訊系統之頻譜資源，而又不致對現有通訊裝置造成不利的干擾。在這個目標下，我們首先提出一混和整數及實數之非線性規劃問題，透過群組形成、功率控制以及資源分配之聯合最佳化，最大化系統可支援的物聯裝置數量。由於此一類型的問題，其複雜度隨著物聯裝置數量的增加而呈指數性成長，為了能支援物聯網龐大的裝置數量，我們將此問題拆解成「群組結構搜尋」的子問題以及「無線資源管理」的子問題，並提出一可隨意控制計算時間之演算法。透過電腦模擬與數據分析，可發現我們提出的演算法能以極高的效率產生理想的群組結構，同時算出各裝置的最佳傳輸功率大小與無線資源分配比例。此外，我們發現透過群組形成以及無線資源管理之聯合最佳化，能被系統支援的物聯裝置數量將可大幅增加，尤其當群組數量多且互相鄰近時，更可達 70% 的增幅。因此，我們認為群組形成必須同時考慮功率控制以及資源分配，才能有效於現有無線通訊系統下支援物聯通訊。

關鍵字

物聯網；群組形成；無線資源分配；傳輸功率控制；混和整數非線性規劃

並列摘要

Clustering of machines for better spatial reuse has been considered as one key technology for supporting machine-to-machine (M2M) communications with a large number of communicating devices. Most related work, however, focuses on developing distributed clustering algorithms and protocols with simple or no wireless interference models, and thus they can not be applied for interference-limited M2M communications with high machine density. In this thesis, we consider a scenario where clustered machines through proper transmission power control are allowed to reuse the spectrum occupied by human devices and we investigate the optimization problem that jointly handles cluster formation and resource management for all machines in the network. To maximize the number of machines that can communicate without violating the data rate constraints of human devices and machines themselves, we formulate a mixed-integer non-linear programming (MINLP) problem. Since the MINLP problem becomes too complex when the number of machines increases, we propose an algorithm that transforms the problem into a coalition structure generation sub-problem embedded with a resource allocation sub-problem. The proposed algorithm is an anytime algorithm and hence the length of the running time can be arbitrarily controlled while still yielding a feasible solution with non-decreasing quality. Compared with other approaches of solving the original MINLP problem, we show through numerical results that the proposed algorithm can e ectively solve the joint cluster formation and resource management problem in the target scenario. Evaluation results also show the benefits of joint optimization with an increase in the number of supported machines up to 70% for a dense network with a large number of clusters. We conclude that clustering formation should take power control and resource allocation into consideration for e ectively supporting M2M communications with high density.

並列關鍵字

M2M communication ； cluster formation ； resource allocation ； power control ； mixed-integer non-linear programming

參考文獻

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支援物聯通訊之最佳群組形成及無線資源管理技術

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