階層式感知無線電網路波束權重及使用者排程之研究

Joing beamforming and scheduling design in hierarchical cognitive radio networks

劉祐榕(Yu-Jung Liu)

指導教授 ： 王蒞君

國立交通大學/電機學院/電信工程系所/碩士(2010年)

https://doi.org/10.6842/NCTU.2010.00188

摘要

感知無線電(Cognitive Radio)在近年來被視為一種能有效地增加頻譜使用效率的方法。在本論文中，我們在階層式感知無線電網路中，提出一個同時設計能量分配、波束權重和使用者排程的演算法以達到最大化系統傳輸速率的目的。在考慮付費使用者和非付費使用者同時使用相同的頻段的情況下，這個問題主要的挑戰在於要盡量減輕非付費使用者對付費使用者的干擾，這篇論文主要的貢獻在於提出一個可行的演算法能同時設計能量分配、波束權重和使用者排程並且滿足嚴格限制對付費使用者的條件，最大化系統傳輸速率。我們將原本混合整數非線性規劃(Mixed Integer Non-linear Programming)利用半正定規劃(Semi-definite Programming)轉換成凸函數最佳化(Convex Optimization)問題。模擬結果顯示本論文提出的方法跟強制為零波束(Zero-forcing Beamforming)加上窮舉使用者排程(exhaustive search user scheduling)在傳輸速率方面有20%的提升。本論文提出的方法可以做為無線電網路設計和佈建法則的重要參考。

關鍵字

感知無線電 ； 波束權重 ； 使用者排程 ； 凸函數最佳化

並列摘要

Cognitive radio (CR) has been recently regarded as an important technique to improve spectrum utilization.
In the thesis, we present a joint design approach to integrate power allocation, antenna beamforming and user scheduling to maximize the system sum rate of multiple users in the hierarchical CR networks with multicarrier transmissions.
Concurrent transmissions between CR users and licensed users are considered to improve the spectrum efficiency.
The challenge of hierarchical CR networks is to manage mutual interference between the CR and licensed systems.
We first apply the semi-definite relaxation (SDR) technique to transfer the original mixed integer non-linear problem (MINLP) to a convex problem.
Then, a sum rate optimization algorithm is proposed to determine the optimal power allocation, antenna beamforming and user scheduling under the limitation of the interference to the licensed networks.
The simulation results show that the proposed algorithm can improve the sum rate at least 20$\%$ compared to an exhaustive search user scheduling plus zero-forcing beamforming, while overcoming the primary system with sacrifice of 10$\%$ sum rate below the maximum sum rate.
The proposed methodology provides many important insights into the system design and deployment principles for future hierarchical CR networks.

並列關鍵字

Cognitive radio ； Beamforming ； Scheduling ； Convex optimization

參考文獻

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