H-CRAN網(wǎng)絡(luò)下聯(lián)合擁塞控制和資源分配的網(wǎng)絡(luò)切片動(dòng)態(tài)資源調(diào)度策略

唐倫; 魏延南; 譚頎; 唐睿; 陳前斌

doi:10.11999/JEIT190439

H-CRAN網(wǎng)絡(luò)下聯(lián)合擁塞控制和資源分配的網(wǎng)絡(luò)切片動(dòng)態(tài)資源調(diào)度策略

doi: 10.11999/JEIT190439 cstr: 32379.14.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(xué)通信與信息工程學(xué)院移動(dòng)通信技術(shù)重點(diǎn)實(shí)驗(yàn)室重慶 400065
2.
重慶大學(xué)光電工程學(xué)院重慶 400044

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

詳細(xì)信息

作者簡(jiǎn)介:
唐倫：男，1973年生，教授，研究方向?yàn)橄乱淮鸁o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?yàn)?G網(wǎng)絡(luò)切片、虛擬資源分配、隨機(jī)優(yōu)化理論

譚頎：女，1995年生，碩士生，研究方向?yàn)?G網(wǎng)絡(luò)切片、資源分配、隨機(jī)優(yōu)化理論

陳前斌：男，1967年生，教授，博士生導(dǎo)師，研究方向?yàn)閭€(gè)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(hào): TN929.5
計(jì)量
- 文章訪問數(shù): 2514
- HTML全文瀏覽量: 716
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- 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(luò)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(duì)異構(gòu)云無線接入網(wǎng)絡(luò)(H-CRAN)網(wǎng)絡(luò)下基于網(wǎng)絡(luò)切片的在線無線資源動(dòng)態(tài)優(yōu)化問題，該文通過綜合考慮業(yè)務(wù)接入控制、擁塞控制、資源分配和復(fù)用，建立一個(gè)以最大化網(wǎng)絡(luò)平均和吞吐量為目標(biāo)，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(wù)質(zhì)量(QoS)需求和資源分配等約束的隨機(jī)優(yōu)化模型，并進(jìn)而提出了一種聯(lián)合擁塞控制和資源分配的網(wǎng)絡(luò)切片動(dòng)態(tài)資源調(diào)度算法。該算法會(huì)在每個(gè)資源調(diào)度時(shí)隙內(nèi)動(dòng)態(tài)地為性能需求各異的網(wǎng)絡(luò)切片中的用戶分配資源。仿真結(jié)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(luò)穩(wěn)定的基礎(chǔ)上，提升網(wǎng)絡(luò)整體吞吐量，并且還可通過調(diào)整控制參量的取值實(shí)現(xiàn)時(shí)延和吞吐量間的動(dòng)態(tài)平衡。
- 異構(gòu)云無線接入網(wǎng)絡(luò) /
- 網(wǎng)絡(luò)切片 /
- 資源分配 /
- 李雅普諾夫隨機(jī)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(luò)切片的H-CRAN下行傳輸場(chǎng)景

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圖 2 平均和吞吐量與控制參量V

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圖 3 平均隊(duì)列時(shí)延與控制參量V

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圖 4 平均和速率與平均業(yè)務(wù)到達(dá)率$\lambda $

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圖 5 平均隊(duì)列時(shí)延與平均業(yè)務(wù)到達(dá)率$\lambda $

下載: 全尺寸圖片幻燈片

表 1 H-CRAN網(wǎng)絡(luò)下聯(lián)合擁塞控制和資源分配的網(wǎng)絡(luò)切片動(dòng)態(tài)資源調(diào)度算法

　(1) 初始化控制參量$V > 0$、各用戶的初始隊(duì)列長(zhǎng)度${Q_u}(0),\forall u \in {\cal{U}}$和最大時(shí)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(jì)算各用戶當(dāng)前時(shí)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(jì)算子載波$s$當(dāng)前時(shí)隙(近似)最優(yōu)的子載波復(fù)用、分配和功率分配策略${\alpha _s}^*$, ${{\beta}} _s^*$和${{{P}}_s}^*$，進(jìn)而更新各用戶剩余的排隊(duì)隊(duì)列長(zhǎng)度

　(8)　　若某用戶$u \in {\cal{U}}$已經(jīng)獲得了足夠的子載波(即其隊(duì)列長(zhǎng)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(fù)用、分配和功率分配策略${\alpha ^*}$, ${\beta ^*}$和${P^*}$計(jì)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(shí)隙的業(yè)務(wù)隊(duì)列長(zhǎng)度

　(14) end for

　(15) 輸出：(近似)最優(yōu)流量接入控制、子載波復(fù)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$。

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