H-CRAN網(wǎng)絡下聯(lián)合擁塞控制和資源分配的網(wǎng)絡切片動態(tài)資源調度策略

唐倫; 魏延南; 譚頎; 唐睿; 陳前斌

doi:10.11999/JEIT190439

H-CRAN網(wǎng)絡下聯(lián)合擁塞控制和資源分配的網(wǎng)絡切片動態(tài)資源調度策略

doi: 10.11999/JEIT190439

唐倫¹,
魏延南^1, ,,
譚頎¹,
唐睿²,
陳前斌¹

1.
重慶郵電大學通信與信息工程學院移動通信技術重點實驗室重慶 400065
2.
重慶大學光電工程學院重慶 400044

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

詳細信息

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

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

譚頎：女，1995年生，碩士生，研究方向為5G網(wǎng)絡切片、資源分配、隨機優(yōu)化理論

陳前斌：男，1967年生，教授，博士生導師，研究方向為個人通信、多媒體信息處理與傳輸、異構蜂窩網(wǎng)絡等

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): 2472
- HTML全文瀏覽量: 701
- PDF下載量: 80
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2019-06-17
- 修回日期: 2020-01-03
- 網(wǎng)絡出版日期: 2020-01-11
- 刊出日期: 2020-06-04

Joint Congestion Control and Resource Allocation Dynamic Scheduling Strategy for Network Slices in Heterogeneous Cloud Raido Access Network

1.
Key Laboratory of Mobile Communication Technology, School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
School of Measurement and Control Technology and Instruments, Chongqing University, Chongqing 400044, 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:
針對異構云無線接入網(wǎng)絡(H-CRAN)網(wǎng)絡下基于網(wǎng)絡切片的在線無線資源動態(tài)優(yōu)化問題，該文通過綜合考慮業(yè)務接入控制、擁塞控制、資源分配和復用，建立一個以最大化網(wǎng)絡平均和吞吐量為目標，受限于基站(BS)發(fā)射功率、系統(tǒng)穩(wěn)定性、不同切片的服務質量(QoS)需求和資源分配等約束的隨機優(yōu)化模型，并進而提出了一種聯(lián)合擁塞控制和資源分配的網(wǎng)絡切片動態(tài)資源調度算法。該算法會在每個資源調度時隙內動態(tài)地為性能需求各異的網(wǎng)絡切片中的用戶分配資源。仿真結果表明，該文算法能在滿足各切片用戶QoS需求和維持網(wǎng)絡穩(wěn)定的基礎上，提升網(wǎng)絡整體吞吐量，并且還可通過調整控制參量的取值實現(xiàn)時延和吞吐量間的動態(tài)平衡。
- 異構云無線接入網(wǎng)絡 /
- 網(wǎng)絡切片 /
- 資源分配 /
- 李雅普諾夫隨機優(yōu)化
Abstract:
For online dynamic radio resources optimization for network slices in Heterogeneous Cloud Raido Access Network (H-CRAN), by comprehensively considering traffic admission control, congestion control, resource allocation and reuse, the problem is formulated as a stochastic optimization programming which maximizes network average total throughput subject to Base Station (BS) transmit power, system stability, Quality of Service (QoS) requirements of different slices and resource allocation constraints. Then, a joint congestion control and resource allocation dynamic scheduling algorithm is proposed which will dynamically allocate resources to users in network slices with distinct performance requirements within each resource scheduling time slot. The simulation results show that the proposed algorithm can improve the network overall throughput while satisfying the QoS requirement of each slice user and maintaining network stability. Besides, it could also flexibly strike a dynamic balance between delay and throughput by simply tuning an introduced control parameter.
- Heterogeneous Cloud Raido Access Network (H-CRAN) /
- Network Slice (NS) /
- Resource allocation /
- Lyapunov stochastic optimization

HTML全文

圖 1 基于網(wǎng)絡切片的H-CRAN下行傳輸場景

下載: 全尺寸圖片幻燈片

圖 2 平均和吞吐量與控制參量V

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圖 3 平均隊列時延與控制參量V

下載: 全尺寸圖片幻燈片

圖 4 平均和速率與平均業(yè)務到達率$\lambda $

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圖 5 平均隊列時延與平均業(yè)務到達率$\lambda $

下載: 全尺寸圖片幻燈片

表 1 H-CRAN網(wǎng)絡下聯(lián)合擁塞控制和資源分配的網(wǎng)絡切片動態(tài)資源調度算法

　(1) 初始化控制參量$V > 0$、各用戶的初始隊列長度${Q_u}(0),\forall u \in {\cal{U}}$和最大時隙數(shù)${T^{\max }}$初始化最大迭代次數(shù)$T_0^{\max }$和允許誤差$\delta $

　(2) for $t = 0,1, ··· ,{T^{\max } } - 1$

　(3)　根據(jù)式(24)分別計算各用戶當前時隙最優(yōu)的流量接入控制策略

　(4)　Repeat：

　(5)　令迭代索引$n = 1$，初始化拉格朗日乘子${{\lambda}} $, ${{\eta }}$和${{\mu}} $

　(6)　for $s \in {\cal{S}}$

　(7)　　計算子載波$s$當前時隙(近似)最優(yōu)的子載波復用、分配和功率分配策略${\alpha _s}^*$, ${{\beta}} _s^*$和${{{P}}_s}^*$，進而更新各用戶剩余的排隊隊列長度

　(8)　　若某用戶$u \in {\cal{U}}$已經獲得了足夠的子載波(即其隊列長度為0)，則將其從接下來的子載波分配過程中排除。

　(9)　　若所有用戶均分配到足夠的子載波，則break

　(10)　end for

　(11)　根據(jù)得到的(近似)最優(yōu)子載波復用、分配和功率分配策略${\alpha ^*}$, ${\beta ^*}$和${P^*}$計算拉格朗日函數(shù)${\cal{L}}{\left( {\alpha ,\beta ,P,\lambda ,\eta ,\mu } \right)^{(n)}}$

　(12) Until$\left| { {\cal{L} }{ {\left( {\alpha ,\beta ,P,\lambda ,\eta ,\mu } \right)}^{(n)} } - {\cal{L} }{ {\left( {\alpha ,\beta ,P,\lambda ,\eta ,\mu } \right)}^{(n - 1)} } } \right| \le \delta $ or $n > T_0^{\max }$, then stop Otherwise, 利用次梯度法更新拉格朗日乘子$\lambda $,
$\eta $和$\mu $，令$n = n + 1$并返回第6步

　(13) 根據(jù)式(17)更新各用戶在下一時隙的業(yè)務隊列長度

　(14) end for

　(15) 輸出：(近似)最優(yōu)流量接入控制、子載波復用和分配以及功率分配策略$r$, $\alpha $, $\beta $和$P$,${Q_u}(t),\forall u \in {\cal{U}},t$。

下載: 導出CSV

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