帶有特征感知的D2D內(nèi)容緩存策略

楊靜; 李金科

doi:10.11999/JEIT190691

帶有特征感知的D2D內(nèi)容緩存策略

doi: 10.11999/JEIT190691

楊靜,
李金科^,

1.
重慶郵電大學(xué) 通信與信息工程學(xué)院重慶 400065
2.
重慶高校市級(jí)光通信與網(wǎng)絡(luò)重點(diǎn)實(shí)驗(yàn)室重慶 400065
3.
泛在感知與互聯(lián)重慶市重點(diǎn)實(shí)驗(yàn)室重慶 400065

基金項(xiàng)目: 國(guó)家自然科學(xué)基金(61871062, 61771082)，重慶市高校創(chuàng)新團(tuán)隊(duì)建設(shè)計(jì)劃項(xiàng)目(CXTDX201601020)

詳細(xì)信息

作者簡(jiǎn)介:
楊靜：女，1972年生，高級(jí)工程師，研究方向?yàn)榉涸跓o(wú)線通信網(wǎng)絡(luò)、物聯(lián)網(wǎng)技術(shù)等

李金科：男，1995年生，碩士生，研究方向?yàn)镈2D通信

通訊作者:
李金科　s170131104@stu.cqupt.edu.cn

中圖分類號(hào): TN919
計(jì)量
- 文章訪問數(shù): 1899
- HTML全文瀏覽量: 635
- PDF下載量: 75
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2019-09-05
- 修回日期: 2020-05-03
- 網(wǎng)絡(luò)出版日期: 2020-05-17
- 刊出日期: 2020-09-27

Feature-Aware D2D Content Caching Strategy

Jing YANG,
Jinke LI^,

1.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Optical Communication and Networks Key Laboratory of Chongqing, Chongqing 400065, China
3.
Ubiquitous Sensing and Networking Key Laboratory of Chongqing, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (61871062, 61771082), The Program for Innovation Team Building at Institutions of Higher Education in Chongqing (CXTDX201601020)

摘要

摘要: 設(shè)備到設(shè)備通信(D2D)可以有效地卸載基站流量，在D2D網(wǎng)絡(luò)中不僅需要共享大眾化內(nèi)容還需要個(gè)性化內(nèi)容緩存。該文對(duì)緩存內(nèi)容選擇問題進(jìn)行了深入研究，提出一種結(jié)合特征感知的內(nèi)容社交價(jià)值預(yù)測(cè)(CSVP)方法。價(jià)值預(yù)測(cè)不僅可以降低時(shí)延也可以減少緩存替換次數(shù)降低緩存成本。首先結(jié)合用戶特征和內(nèi)容特征計(jì)算內(nèi)容當(dāng)前價(jià)值，然后通過用戶社交關(guān)系計(jì)算未來(lái)價(jià)值。微基站根據(jù)內(nèi)容的價(jià)值為用戶提供個(gè)性化內(nèi)容緩存服務(wù)，宏基站則在每個(gè)微基站的緩存內(nèi)容中選擇價(jià)值較大部分的內(nèi)容。仿真結(jié)果表明，該文提出的緩存策略可以有效緩解基站流量，與其他方法相比降低時(shí)延約20%～40%。
- 邊緣網(wǎng)絡(luò) /
- D2D通信 /
- 內(nèi)容緩存 /
- 探索-利用 /
- 價(jià)值預(yù)測(cè)
Abstract: Device to Device (D2D) communications can effectively offload base station traffic. In D2D networks, the popular content is not only needs to be shared, but also the individual content is needs to be cached. In this paper, the problem of cache content selection is researched. A Content Social Value Prediction(CSVP) method based on feature perception is proposed. Value prediction can not only reduce latency, but also reduce the number of cache replacements and reduce cache costs. Firstly, the current value of content is calculated by combining user features and content features, and then future value is calculated through user social relationships. The small base station provides the user with a personalized content caching service according to the value of the content, and the base station selects a content with a larger value in the individual cache content of each small base station as popular content. Simulation results show that the caching strategy based on the proposed method can alleviate the base station traffic effectively, and reduce the delay by about 20%～40%.
- Edge network /
- D2D communication /
- Content caching /
- Exploration - exploitation /
- Value prediction

HTML全文

圖 1 網(wǎng)絡(luò)架構(gòu)

下載: 全尺寸圖片幻燈片

圖 2 用戶間社交對(duì)價(jià)值的影響

下載: 全尺寸圖片幻燈片

圖 3 不同策略下的命中率

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圖 4 不同策略下的請(qǐng)求時(shí)延

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圖 5 不同齊夫參數(shù)下的命中率

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圖 6 不同學(xué)習(xí)率下的命中率

下載: 全尺寸圖片幻燈片

表 1 算法1 內(nèi)容社交價(jià)值預(yù)測(cè)

輸入：$T,r,\alpha ,\lambda ,{\lambda _1},{\lambda _2},{\beta _1},{\beta _2},\xi $
輸出：價(jià)值列表，觀察用戶請(qǐng)求情況
(1) 　隨機(jī)初始化參數(shù)$\widehat {{U}},\widehat {{C}},{{L}},{{W}}$
(2) 　For t=1, 2, ···, T do
(3) 　　感知內(nèi)容特征及用戶特征
(4) 　　For all k ∈C do
(5) 　　　如果內(nèi)容是新內(nèi)容：
(6) 　　　　初始化參數(shù)：${{{A}}_k} \leftarrow {{{I}}_d}$, ${{{B}}_k} \leftarrow {0_{d*1}}$
(7) 　　　結(jié)束
(8) 　　　更新參數(shù)：${\theta _{t,k}} \leftarrow {{A}}_{t,k}^{ - 1}{{{B}}_{t,k}}$, 　　　　　 ${P_{t,i,k}} \leftarrow {{X}}_{t,k}^{\rm{T}}{\theta _k} + \alpha \sqrt {{{X}}_{t,k}^{\rm{T}}{{A}}_{t,k}^{ - 1}{{X}}_{t,k}^{\rm{T}}} $, 　　　　　 ${{{A}}_{t,k}} \leftarrow {{{A}}_{t,k}} + {{{X}}_{t,k}}{{X}}_{t,k}^{\rm{T}}$, ${{{B}}_{t,k}} \leftarrow {{{B}}_{t,k}} + {r_t}{{{X}}_{t,k}}$
(9) 　　　當(dāng)式(29)的值沒有收斂時(shí)：
(10) 　　　　根據(jù)梯度更新參數(shù)
(11) 　　　結(jié)束
(12) 　　　計(jì)算未來(lái)價(jià)值
(13) 　　　計(jì)算總價(jià)值
(14) 　　結(jié)束
(15) 　按降序輸出價(jià)值列表，觀察用戶請(qǐng)求情況
(16) 結(jié)束

下載: 導(dǎo)出CSV

表 2 仿真參數(shù)設(shè)置

參數(shù)	參數(shù)值
內(nèi)容庫(kù)數(shù)量	5000
內(nèi)容包大小	20 MB
SBS-UE延時(shí)	20 ms
BS-UE延時(shí)	50 ms
CDN-UE延時(shí)	100 ms
D2D延時(shí)	10 ms

下載: 導(dǎo)出CSV

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