本文選題：多跳無線網絡 + MIMO技術�。� 參考：《浙江理工大學》2017年碩士論文

【摘要】：多輸入多輸出(Multiple Input Multiple Output,簡稱MIMO)技術因其能夠顯著改善傳輸容量限制和提高通信的可靠性,給無線通信領域帶來了重大突破,與此同時,仍然有很多的應用場景等待深入探索。另一方面,多跳無線網絡得益于其魯棒性好、結構靈活、帶寬高、可分布式部署等優(yōu)勢在無線網絡通信領域得到了廣泛應用。如果能夠將兩者有效結合,充分發(fā)揮其潛力,將大大改善現有通信質量。本文在充分了解目前國內外關于多跳無線網絡資源優(yōu)化分配相關研究的基礎上,深入研究了MIMO技術在多跳無線網絡中的調度問題,利用多天線帶來的空間自由度優(yōu)勢來提供每條傳輸鏈路上的MIMO信道模式,并針對MIMO多跳無線網絡這一場景,分別引入了預測隊列和雙層隊列理念來設計改善網絡模型并給出了分布式實施方案,本文的主要研究工作如下:1)提出基于信道模式的分布式跨層優(yōu)化。針對MIMO多跳無線網絡在長時間平均下網絡效用最大化這一優(yōu)化問題,提出了基于MIMO信道模式的動態(tài)感知調度模型,使得網絡中的每個節(jié)點能夠感知當前網絡狀態(tài)從而選擇合適的MIMO信道模式來進行數據傳輸以滿足通信需求。相比傳統(tǒng)MIMO多跳無線網絡,這種動態(tài)感知MIMO信道模式并進行調度決策的方式更加智能,也可以獲得更好的網絡效用,結合李雅普諾夫優(yōu)化算法,保證了網絡運行的穩(wěn)定性,接著通過對偶分解算法將耦合項分離,最終實現整個跨層資源優(yōu)化分配問題的分布式實施。2)提出基于預測隊列的分布式跨層優(yōu)化。在(1)的基礎上,引入預測服務模型,即在原始隊列模型中加入預測隊列,網絡中的每個節(jié)點基于一個預測窗口進行預測并發(fā)送未來一定范圍內時隙的數據包。通過對未來數據的預測和提前決策規(guī)劃,可以使整個網絡在保證效用的情況下,有效減少數據包的時延。本文采用等效隊列的方式,將真實數據隊列和預測隊列在宏觀上先等效為一個求和隊列結合李雅普諾夫漂移理論和對偶次梯度算法實現分布式求解,再根據具體的映射及更新規(guī)則分拆為每個具體優(yōu)化項的決策。3)提出基于雙層隊列的分布式跨層優(yōu)化。在(1)的基礎上,引入雙層隊列模型來彌補經典背壓式算法的不足。將整個網絡的架構進行分離,在網絡層和數據鏈路層分別構建隊列,網絡層部分負責每個節(jié)點的路由選擇決策,數據鏈路層部分負責節(jié)點在鏈路上的調度決策,從而使得原來需要聯合優(yōu)化的方案可以分離。本文通過李雅普諾夫優(yōu)化算法和對偶分解算法實現了網絡效用最優(yōu)并可分布式實施。
[Abstract]:Multiple Input Multiple Output (MIMO) technology has brought great breakthroughs in the field of wireless communication because it can significantly improve the transmission capacity limitation and improve the reliability of communication. At the same time, there are still many applications waiting for further exploration. On the other hand, multi hop wireless networks benefit from its good robustness, The advantages of flexible structure, high bandwidth and distributed deployment have been widely used in the field of wireless network communication. If it is possible to combine the two effectively and give full play to its potential, it will greatly improve the existing communication quality. This paper is based on a thorough understanding of the research on the optimal allocation of multi hop wireless network resources at home and abroad. The scheduling problem of MIMO technology in multi hop wireless networks is studied. The MIMO channel pattern on each transmission link is provided by using the spatial freedom advantage of multiple antennas. The predictive queue and double queue concept are introduced to improve the network model and give the distributed reality for the scene of MIMO multi hop wireless network. The main research work of this paper is as follows: 1) put forward the distributed cross layer optimization based on channel mode. Aiming at the optimization problem of MIMO multi hop wireless network with long time average network utility maximization, a dynamic perception scheduling model based on MIMO channel mode is proposed, so that each node in the network can perceive the current network. The state then selects the appropriate MIMO channel mode to carry on the data transmission to meet the communication needs. Compared with the traditional MIMO multi hop wireless network, this dynamic perception of MIMO channel mode and scheduling decision can be more intelligent, and can also obtain better network utility. Combined with the Li Ya prize optimization algorithm, the stability of the network operation is guaranteed. On the basis of (1), the prediction service model is introduced, that is, the prediction queue is added to the original queue model, and each node in the network is based on one, based on the (1). The prediction window predicts and sends data packets in a certain range of time slot in the future. Through the prediction of the future data and the early decision planning, the whole network can effectively reduce the time delay of the packet under the condition of guaranteeing the utility. In this paper, the equivalent queue is used to equip the real data queue and prediction queue at the macro level first. A summation queue combines Lyapunov drift theory and dual gradient algorithm to realize distributed solution. Then, based on the specific mapping and updating rules, the distributed cross layer optimization based on double queue is proposed based on the decision.3 of each specific optimization. On the basis of (1), a double queue model is introduced to make up for the classic back pressure calculation. The structure of the whole network is separated, the queues are constructed in the network layer and the data link layer respectively. The network layer is responsible for the routing decision of each node, and the data link layer is responsible for the scheduling decision of the nodes on the link. Thus, the original scheme which needs joint optimization can be separated. This paper through lyapuno is used in this paper. The optimal algorithm and dual decomposition algorithm achieve the best network utility and distributed implementation.
【學位授予單位】：浙江理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN919.3

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