虛擬化云無線接入網(wǎng)絡(luò)下基于在線學(xué)習(xí)的網(wǎng)絡(luò)切片虛擬資源分配算法

唐倫; 魏延南; 馬潤琳; 賀小雨; 陳前斌

doi:10.11999/JEIT180771

虛擬化云無線接入網(wǎng)絡(luò)下基于在線學(xué)習(xí)的網(wǎng)絡(luò)切片虛擬資源分配算法

doi: 10.11999/JEIT180771 cstr: 32379.14.JEIT180771

1.
重慶郵電大學(xué)通信與信息工程學(xué)院? ?重慶? ?400065
2.
重慶郵電大學(xué)移動通信技術(shù)重點實驗室? ?重慶? ?400065

基金項目: 國家自然科學(xué)基金(61571073)，重慶市教委科學(xué)技術(shù)研究項目(KJZD-M201800601)

詳細(xì)信息

作者簡介:
唐倫：男，1973年生，教授，主要研究方向為下一代無線通信網(wǎng)絡(luò)、異構(gòu)蜂窩網(wǎng)絡(luò)、軟件定義無線網(wǎng)絡(luò)等

魏延南：男，1995年生，碩士生，研究方向為5G網(wǎng)絡(luò)切片、虛擬資源分配、隨機(jī)優(yōu)化理論

馬潤琳：女，1993年生，碩士生，研究方向為5G網(wǎng)絡(luò)切片、網(wǎng)絡(luò)功能虛擬化、無線資源分配

賀小雨：女，1995年生，碩士生，研究方向為5G網(wǎng)絡(luò)切片、無線網(wǎng)絡(luò)虛擬化、智能優(yōu)化理論

陳前斌：男，1967年生，教授，博士生導(dǎo)師，主要研究方向為個人通信、多媒體信息處理與傳輸、異構(gòu)蜂窩網(wǎng)絡(luò)等

通訊作者:
魏延南　weiyannan_cqupt@163.com

中圖分類號: TN929.5
計量
- 文章訪問數(shù): 3035
- HTML全文瀏覽量: 1223
- PDF下載量: 145
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2018-08-03
- 修回日期: 2019-02-20
- 網(wǎng)絡(luò)出版日期: 2019-03-19
- 刊出日期: 2019-07-01

Online Learning-based Virtual Resource Allocation for Network Slicing in Virtualized Cloud Radio Access Network

1.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Key Laboratory of Mobile Communication Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (61571073), The Science and Technology Research Program of Chongqing Municipal Education Commission (KJZD-M201800601)

摘要

摘要: 針對現(xiàn)有研究中缺乏云無線接入網(wǎng)絡(luò)(C-RAN)場景下對網(wǎng)絡(luò)切片高效的動態(tài)資源分配方案的問題，該文提出一種虛擬化C-RAN網(wǎng)絡(luò)下的網(wǎng)絡(luò)切片虛擬資源分配算法。首先基于受限馬爾可夫決策過程(CMDP)理論建立了一個虛擬化C-RAN場景下的隨機(jī)優(yōu)化模型，該模型以最大化平均切片和速率為目標(biāo)，同時受限于各切片平均時延約束以及網(wǎng)絡(luò)平均回傳鏈路帶寬消耗約束。其次，為了克服CMDP優(yōu)化問題中難以準(zhǔn)確掌握系統(tǒng)狀態(tài)轉(zhuǎn)移概率的問題，引入決策后狀態(tài)(PDS)的概念，將其作為一種“中間狀態(tài)”描述系統(tǒng)在已知動態(tài)發(fā)生后，但在未知動態(tài)發(fā)生前所處的狀態(tài)，其包含了所有與系統(tǒng)狀態(tài)轉(zhuǎn)移有關(guān)的已知信息。最后，提出一種基于在線學(xué)習(xí)的網(wǎng)絡(luò)切片虛擬資源分配算法，其在每個離散的資源調(diào)度時隙內(nèi)會根據(jù)當(dāng)前系統(tǒng)狀態(tài)為每個網(wǎng)絡(luò)切片分配合適的資源塊數(shù)量以及緩存資源。仿真結(jié)果表明，該算法能有效地滿足各切片的服務(wù)質(zhì)量(QoS)需求，降低網(wǎng)絡(luò)回傳鏈路帶寬消耗的壓力并同時提升系統(tǒng)吞吐量。
- 5G網(wǎng)絡(luò)切片 /
- 云無線接入網(wǎng)絡(luò) /
- 資源分配 /
- 馬爾可夫決策過程
Abstract: To solve the problem of lacking efficient and dynamic resource allocation schemes for 5G Network Slicing (NS) in Cloud Radio Access Network (C-RAN) scenario in the existing researches, a virtual resource allocation algorithm for NS in virtualized C-RAN is proposed. Firstly, a stochastic optimization model in virtualized C-RAN network is established based on the Constrained Markov Decision Process (CMDP) theory, which maximizes the average sum rates of all slices as its objective, and is subject to the average delay constraint for each slice as well as the average network backhaul link bandwidth consumption constraint in the meantime. Secondly, in order to overcome the issue of having difficulties in acquiring the accurate transition probabilities of the system states in the proposed CMDP optimization problem, the concept of Post-Decision State (PDS) as an " intermediate state” is introduced, which is used to describe the state of the system after the known dynamics, but before the unknown dynamics occur, and it incorporates all of the known information about the system state transition. Finally, an online learning based virtual resource allocation algorithm is presented for NS in virtualized C-RAN, where in each discrete resource scheduling slot, it will allocate appropriate Resource Blocks (RBs) and caching resource for each network slice according to the observed current system state. The simulation results reveal that the proposed algorithm can effectively satisfy the Quality of Service (QoS) demand of each individual network slice, reduce the pressure of backhaul link on bandwidth consumption and improve the system throughput.
- 5G Network Slicing (NS) /
- Cloud Radio Access Network (C-RAN) /
- Resource allocation /
- Markov Decision Process (MDP)

HTML全文

圖 1 虛擬化C-RAN網(wǎng)絡(luò)系統(tǒng)場景

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圖 2 不同平均時延約束下的平均切片和速率

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圖 3 不同平均時延約束下的平均切片總時延

下載: 全尺寸圖片幻燈片

圖 4 不同數(shù)據(jù)包到達(dá)率${\lambda _1}$下的平均切片和速率

下載: 全尺寸圖片幻燈片

圖 5 不同數(shù)據(jù)包到達(dá)率${\lambda _1}$下的平均切片總時延

下載: 全尺寸圖片幻燈片

表 1 虛擬化C-RAN網(wǎng)絡(luò)下基于在線學(xué)習(xí)的網(wǎng)絡(luò)切片虛擬資源分配算法

　輸入　系統(tǒng)狀態(tài)空間$C$，動作空間$A$，拉格朗日回報函數(shù)
$g({c_t}, {\text{π}} ({c_t}))$，有限信道狀態(tài)集合${\text{H}}$。

　初始化：初始化決策后狀態(tài)的狀態(tài)值函數(shù)${\tilde V_0}(\tilde c) \in R, \forall \tilde c \in C\,$，令
$t \leftarrow 0$, ${c_t} \leftarrow c \in C\,$。

　學(xué)習(xí)階段：
　　(1) 求解
${a_t} = \mathop {\arg \min }\limits_{a \in A} \left\{ {g({c_t}, a) + \gamma {{\tilde V}_t}({S^{M, a}}({c_t}, a))} \right\}$； (27)

　　(2) 觀察PDS狀態(tài)${\tilde c_t}$和下一時隙狀態(tài)${c_{t + 1}}$：${\tilde c_t} = {S^{M, a}}({c_t}, {a_t})$,
${c_{t + 1}} = {S^{M, W}}({\tilde c_t}, {{\text{A}}_t}, {{\text{H}}_{t + 1}})$；

　　(3) 計算${c_{t + 1}}$的狀態(tài)值函數(shù)：
　　　${V_t}({c_{t + 1}}) = \mathop {\min }\limits_{a \in A} \left\{ {g({c_{t + 1}}, a) + \gamma {{\tilde V}_t}({S^{M, a}}({c_{t + 1}}, a))} \right\}$； (28)

　　(4) 更新${\tilde V_{t + 1}}({\tilde c_t})$：

　　　${\tilde V_{t + 1}}({\tilde c_t}) = (1 - {\alpha _t}){\tilde V_t}({\tilde c_t}) + {\alpha _t}{V_t}({c_{t + 1}})$；　　　　 (29)

　　(5) 利用隨機(jī)次梯度法更新拉格朗日乘子${\text{β}} :{\beta _i} \ge 0$。

　輸出　最優(yōu)策略${\text{π}} _{{\rm{PDS}}}^ * $。

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表 2 仿真參數(shù)

仿真參數(shù)	值
遠(yuǎn)端射頻頭(RRH)最大發(fā)射功率	20 dBm
各切片最大隊列長度${Q_{s, \max }}$	20 packets
噪聲功率譜密度	–174 dBm/Hz
數(shù)據(jù)包大小$L$	4 kbit/packet
路徑損耗模型	104.5+20lg(d) (d[km])
時隙長度$\tau $	1 ms

下載: 導(dǎo)出CSV

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NIU Binglai, ZHOU Yong, SHAH-MANSOURI H, et al. A dynamic resource sharing mechanism for cloud radio access networks[J]. IEEE Transactions on Wireless Communications, 2016, 15(12): 8325–8338. doi: 10.1109/TWC.2016.2613896

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