Joint Clustering and Content Deployment Algorithm for Cellular D2D Communication Based on Delay Optimization
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摘要: 针对蜂窝网络传输性能及基站(BS)缓存能力受限，多用户内容请求难以满足用户服务质量(QoS)需求等问题，该文提出一种蜂窝终端直通(D2D)通信联合用户关联及内容部署算法。考虑到位于特定区域的多用户可能对于相同内容存在内容请求，该文引入成簇思想，提出一种成簇及内容部署机制，通过为各簇头推送热点内容，而簇成员基于D2D通信模式关联簇头获取所需内容，可实现高效内容获取。综合考虑成簇数量、用户关联簇头、簇头缓存容量及传输速率等限制条件，建立基于用户总业务时延最小化的联合成簇及内容部署优化模型。该优化问题是一个非凸的混合整数优化问题，该文运用拉格朗日部分松弛法，将原优化问题等价转换为3个凸优化的子问题，并基于迭代算法及Kuhn-Munkres算法联合求解各子问题，从而得到联合成簇及内容部署优化策略。最后通过MATLAB仿真验证所提算法的有效性。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.
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表 1 联合用户关联及内容部署算法
(1) 确定L种簇头组合策略； (2) for $l = 1$，针对第$l$种簇头组合策略； (3) 设置最大迭代次数${T^{\ \max }}$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}}$； (5) 重复主程序； (6) 求解用户关联子问题得到局部变量值${\delta _{i,j}}$； 求解内容部署子问题得到局部变量值${\beta _{j,k}}$； 求解联合优化子问题得到局部变量值${\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) 算法收敛； 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) 否则 $t = t + 1$； (11) 重复步骤(6)—步骤(10)，直到算法收敛或$t = {T^{\ \max }}$；
(12) $l = l + 1$，重复步骤(5)—步骤(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) 比较$L$种簇头组合下的最优业务时延，选择最优用户关联及内容部署优化策略，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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