本論文針對都會環境內結合毫米波與公民寬頻無線服務(CBRS)架構形成雙頻協作中繼網路，設計一動態發射功率分配方法以最大化系統的平均傳輸速率，滿足使用者影片串流每秒3*10^7個位元的需求。網路中有一個發射毫米波的主要基地台，以波束成形方式與使用者和使用CBRS頻段的中繼節點連接。因為毫米波擁有比CBRS更多的使用頻寬，在LOS連接的傳輸速率會比使用CBRS還要更高。但在NLOS時，毫米波的訊號強度會急劇下降，導致傳輸距離縮減，因此使用協作中繼的方式，透過設置數個具毫米波接收、發射CBRS頻段與使用者連接、並稱為公民寬頻無線電服務設備(CBSD)的中繼節點，讓NLOS連接的使用者時能轉換為與CBSD連接。本論文中繼部分選擇使用CBRS架構是因為CBRS結合Wi-Fi的自由和簡單性與移動網絡的安全性和調整能力，而且使用3.5GHz的頻段，訊號穿透物體後的衰減會比毫米波還要少，再透過動態頻寬分配，獲得每秒傳輸10^9個位元的傳輸速率，滿足應用需求。但是在大量部署CBSD的情況下，雖然CBRS動態頻寬分配的服務能夠減少同頻干擾，但CBRS最多僅能夠分配150MHz的頻寬，仍然會有同頻干擾的問題，因此需要搭配功率分配機制來最小化系統中的同頻干擾。

因為本論文針對的都會環境，在不同時段人群會有不同的分佈方式、密度，所以不能僅依循同一種功率分配方式，需要在運算時間與系統平均傳輸速率上作權衡，因此提出基於環境中繼節點數量的非一致性基因功率分配演算法(BSIGA)。BSIGA有兩個策略，分別是Dual Probability (DP)與Higher Acceptance (HA)，並會根據環境中CBSD的數量自動選擇使用的策略。在系統平均傳輸速率的部分，在16平方公里中有25個CBSD以下時，DP擁有窮舉法的90%的效能，但在25個CBSD以上時，效能會急劇下降 ; HA則不管16平方公里中有幾個CBSD，都能擁有窮舉法99%的效能。在運算時間的部分，BSIGA的複雜度為O(NG)，其中DP花費的時間比HA還要少，在16平方公里中有17個CBSD以上時，HA所花的時間將比DP多上數倍 ; 在25個CBSD以上時，差距達10倍以上，因此DP能夠更頻繁的分配環境中基地台的功率，會比HA更適合在使用者移動性高、人潮變換大的環境。模擬結果顯示，本論文提出的BSIGA不管16平方公里中有多少個CBSD都能擁有窮舉法90%以上的效能，相較於其他方法擁有更好的效能，並且比窮舉法的複雜度還要低得多。

In this paper, set a dual-band cooperative relay network by combining millimeter wave (mmWave) and Citizen Broadband Radio Service (CBRS) architecture for users are using video streaming applications.
A mmWave main base station connects to the users and relay node by beamforming. Use cooperative relaying by setting several relay nodes with mmWave receiving and transmitting CBRS band connected to users, called Citizens Broadband Radio Service Devices (CBSD).
The CBRS architecture is chosen for relay because it combines the freedom and simplicity of Wi-Fi with the security and control of mobile networks.
However, in the case of setting large number of CBSD, although the CBRS dynamic channel allocation service can reduce the co-channel interference, CBRS can only allocate a maximum of 150 MHz of bandwidth, there will still be co-channel interference problems, so power allocation is required to minimize the co-channel interference in the system.

Because the urban environment has different distribution and density of population in different time, it is impossible to follow one power allocation but trade-off between the computing time and the average throughput of the system.
Therefore, the Base Station based Inconsistent Genetic Power Allocation (BSIGA) is proposed. BSIGA has two strategies, Dual Probability (DP) and Higher Acceptance (HA), and the strategy is automatically selected according to the number of CBSDs in the environment.
In terms of the average throughput of the system, DP has 90% exhaustive search performance when there are less than 25 CBSDs in the 16km^2 ; HA has 99% exhaustive search performance regardless of the number of CBSDs in the 16km^2.
In terms of execution time, DP takes less time than HA. When there are more than 17 CBSDs in the 16km^2, HA will take more time than DP, and the gap becomes larger as CBSD increases.
The simulation results show that the proposed BSIGA can have the 90% exhaustive search performance regardless of how many CBSDs are in the 16km^2. Compared with other methods, it has better performance and lower complexity than the exhaustive search.

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