基于時延優(yōu)化的蜂窩D2D通信聯(lián)合用戶關(guān)聯(lián)及內(nèi)容部署算法
doi: 10.11999/JEIT180408 cstr: 32379.14.JEIT180408
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重慶郵電大學(xué)通信與信息工程學(xué)院 ??重慶 ??400065
基金項目: 國家自然科學(xué)基金(61571073)，國家科技重大專項(2016ZX03001010-004)
Joint Clustering and Content Deployment Algorithm for Cellular D2D Communication Based on Delay Optimization
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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)
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摘要: 針對蜂窩網(wǎng)絡(luò)傳輸性能及基站(BS)緩存能力受限，多用戶內(nèi)容請求難以滿足用戶服務(wù)質(zhì)量(QoS)需求等問題，該文提出一種蜂窩終端直通(D2D)通信聯(lián)合用戶關(guān)聯(lián)及內(nèi)容部署算法?？紤]到位于特定區(qū)域的多用戶可能對于相同內(nèi)容存在內(nèi)容請求，該文引入成簇思想，提出一種成簇及內(nèi)容部署機制，通過為各簇頭推送熱點內(nèi)容，而簇成員基于D2D通信模式關(guān)聯(lián)簇頭獲取所需內(nèi)容，可實現(xiàn)高效內(nèi)容獲取。綜合考慮成簇數(shù)量、用戶關(guān)聯(lián)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(wù)時延最小化的聯(lián)合成簇及內(nèi)容部署優(yōu)化模型。該優(yōu)化問題是一個非凸的混合整數(shù)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(zhuǎn)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(lián)合求解各子問題，從而得到聯(lián)合成簇及內(nèi)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。
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關(guān)鍵詞:
- 蜂窩網(wǎng)絡(luò) /
- D2D通信 /
- 用戶關(guān)聯(lián) /
- 內(nèi)容部署 /
- 業(yè)務(wù)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. -
表 1 聯(lián)合用戶關(guān)聯(lián)及內(nèi)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(shè)置最大迭代次數(shù)${T^{\ \max }}$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}}$； (5) 重復(fù)主程序； (6) 求解用戶關(guān)聯(lián)子問題得到局部變量值${\delta _{i,j}}$； 求解內(nèi)容部署子問題得到局部變量值${\beta _{j,k}}$； 求解聯(lián)合優(yōu)化子問題得到局部變量值${\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(fù)步驟(6)—步驟(10)，直到算法收斂或$t = {T^{\ \max }}$；
(12) $l = l + 1$，重復(fù)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(wù)時延，選擇最優(yōu)用戶關(guān)聯(lián)及內(nèi)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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