基于時延優(yōu)化的蜂窩D2D通信聯合用戶關聯及內容部署算法

柴蓉; 王令; 陳明龍; 陳前斌

doi:10.11999/JEIT180408

基于時延優(yōu)化的蜂窩D2D通信聯合用戶關聯及內容部署算法

doi: 10.11999/JEIT180408

重慶郵電大學通信與信息工程學院 ??重慶 ??400065

基金項目: 國家自然科學基金(61571073)，國家科技重大專項(2016ZX03001010-004)

詳細信息

作者簡介:
柴蓉：女，1974年生，教授，研究方向為通信網絡架構及關鍵技術、無線資源管理及移動性管理技術等

王令：女，1993年生，碩士生，研究方向為無線通信、無線資源管理及網絡虛擬化等

陳明龍：男，1993年生，碩士生，研究方向為無線資源管理、網絡虛擬化及內容緩存等

陳前斌：男，1967年生，教授，研究方向為無線通信、通信網絡理論、軟件定義網絡、多媒體技術等

通訊作者:
柴蓉　chairong@cqupt.edu.cn

中圖分類號: TN915
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- 被引次數: 0
出版歷程
- 收稿日期: 2018-05-02
- 修回日期: 2019-05-21
- 網絡出版日期: 2019-07-19
- 刊出日期: 2019-11-01

Joint Clustering and Content Deployment Algorithm for Cellular D2D Communication Based on Delay Optimization

School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The National Science Foundation of China (61571073), The National Science and Technology Specific Project of China (2016ZX03001010-004)

摘要

摘要: 針對蜂窩網絡傳輸性能及基站(BS)緩存能力受限，多用戶內容請求難以滿足用戶服務質量(QoS)需求等問題，該文提出一種蜂窩終端直通(D2D)通信聯合用戶關聯及內容部署算法。考慮到位于特定區(qū)域的多用戶可能對于相同內容存在內容請求，該文引入成簇思想，提出一種成簇及內容部署機制，通過為各簇頭推送熱點內容，而簇成員基于D2D通信模式關聯簇頭獲取所需內容，可實現高效內容獲取。綜合考慮成簇數量、用戶關聯簇頭、簇頭緩存容量及傳輸速率等限制條件，建立基于用戶總業(yè)務時延最小化的聯合成簇及內容部署優(yōu)化模型。該優(yōu)化問題是一個非凸的混合整數優(yōu)化問題，該文運用拉格朗日部分松弛法，將原優(yōu)化問題等價轉換為3個凸優(yōu)化的子問題，并基于迭代算法及Kuhn-Munkres算法聯合求解各子問題，從而得到聯合成簇及內容部署優(yōu)化策略。最后通過MATLAB仿真驗證所提算法的有效性。
- 蜂窩網絡 /
- D2D通信 /
- 用戶關聯 /
- 內容部署 /
- 業(yè)務時延
Abstract: Due to the limited transmission performance of cellular network and the buffering capabilities of the Base Station (BS), it is very difficult to achieve the Quality of Service (QoS) requirements of multi-user content requests. In this paper, a joint user association and content deployment algorithm is proposed for cellular Device-to-Device (D2D) communication network. Assuming that multiple users located in a specific area may have content requests for the same content, a clustering and content deployment mechanism is presented in order to achieve efficient content acquisition. A joint clustering and content deployment optimization model is formulated to minimize total user service delay, which can be solved by Lagrange partial relaxation, iterative algorithm and Kuhn-Munkres algorithm, and the joint clustering and content deployment optimization strategies can be obtained. Finally, the effectiveness of the proposed algorithm is verified by MATLAB simulation.
- Cellular network /
- Device-to-Device (D2D) communication /
- User association /
- Content deployment /
- Service delay

HTML全文

圖 1 蜂窩D2D通信系統場景圖

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圖 2 基于用戶成簇機制的蜂窩D2D系統場景圖

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圖 3 總業(yè)務時延與迭代次數關系圖

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圖 4 總業(yè)務時延與業(yè)務到達速率關系圖(不同服務速率)

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圖 5 總業(yè)務時延與業(yè)務到達速率關系圖(不同最低傳輸速率)

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圖 6 總業(yè)務時延與子信道帶寬關系圖(不同噪聲功率譜密度)

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圖 7 總業(yè)務時延與子信道帶寬關系圖(不同業(yè)務到達速率)

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表 1 聯合用戶關聯及內容部署算法

(1) 確定L種簇頭組合策略；
(2) for $l = 1$，針對第$l$種簇頭組合策略；
(3) 設置最大迭代次數${T^{\ \max }}$和最大容忍值$\varepsilon $；
(4) 初始化拉格朗日因子${\eta _{i,j,k}},\;{\varphi _{i,j,k}},\;{\theta _{i,j,k}}$；
(5) 重復主程序；
(6) 求解用戶關聯子問題得到局部變量值${\delta _{i,j}}$；
求解內容部署子問題得到局部變量值${\beta _{j,k}}$；
求解聯合優(yōu)化子問題得到局部變量值${\alpha _{i,j,k}}$；
(7) 更新拉格朗日因子；
${\eta _{i,j,k}}(t + 1) = {\left[ {{\eta _{i,j,k}}(t) - {\omega _1}\left( {{\alpha _{i,j,k}}(t) + 1 - {\delta _{i,j}}(t) - {\beta _{j,k}}(t)} \right)} \right]^ + },$
${\varphi _{i,j,k}}(t + 1) = {\left[ {{\varphi _{i,j,k}}(t) - {\omega _2}\left( {{\delta _{i,j}}(t) - {\alpha _{i,j,k}}(t)} \right)} \right]^ + },$
${\theta _{i,j,k}}(t + 1) = {\left[ {{\theta _{i,j,k}}(t) - {\omega _3}\left( {{\beta _{j,k}}(t) - {\alpha _{i,j,k}}(t)} \right)} \right]^{\rm{ + }}};$
(8) 若$ \sum\nolimits_{i = 1}^M \sum\nolimits_{j = 1}^M \sum\nolimits_{k = 1}^K \left[ \left\| {{\eta _{i,j,k}}(t + 1) - {\eta _{i,j,k}}(t)} \right\| \right. $ 　　　　$\left.+ \left\| {{\varphi _{i,j,k}}(t + 1) - {\varphi _{i,j,k}}(t)} \right\| + \left\| {{\theta _{i,j,k}}(t + 1) - {\theta _{i,j,k}}(t)} \right\| \right] \le \varepsilon $ ；
(9) 算法收斂；
返回 $\delta _{i,j}^{\left( l \right) * }{\rm{ = }}{\delta _{i,j}},\beta _{j,k}^{\left( l \right) * }{\rm{ = }}{\beta _{j,k}},\alpha _{i,j,k}^{\left( l \right) * }{\rm{ = }}{\alpha _{i,j,k}};$
(10) 否則 $t = t + 1$；
(11) 重復步驟(6)—步驟(10)，直到算法收斂或$t = {T^{\ \max }}$；　(12) $l = l + 1$，重復步驟(5)—步驟(11)，得到$\delta _{i,j}^{\left( l \right)},\;\beta _{j,k}^{\left( l \right) },\;\alpha _{i,j,k}^{\left( l \right) * }$及${D^{\left( l \right) * }}$，直至$l = L$；
(13) 比較$L$種簇頭組合下的最優(yōu)業(yè)務時延，選擇最優(yōu)用戶關聯及內容部署優(yōu)化策略，即$\left\{ {\delta _{i,j}^{\left( l \right) * },\beta _{j,k}^{\left( l \right) * },\alpha _{i,j,k}^{\left( l \right) * }} \right\} = \arg \min {D^{\left( l \right) * }}。$

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