论文标题
数据包完成时间通过关节D2D和蜂窝通信最小化:一种统一的网络编码方法
Packet Completion Time Minimization via Joint D2D and Cellular Communication: A Unified Network Coding Approach
论文作者
论文摘要
本文通过间歇性连接的D2D链接通过可立即解释的网络编码(IDNC)来解决许多蜂窝用户的问题。特别令人感兴趣的是广播实时应用程序,例如视频按需广播,由于蜂窝链路上的数据包擦除,蜂窝用户可能会部分收到常见内容。具体来说,我们研究了数据包完成时间的问题,该问题定义为向所有用户提供通用内容所需的传输插槽数量。利用图理论,我们通过构建两层IDNC冲突图来开发最佳数据包完成时间策略。高层图允许我们确定可以通过蜂窝链路传输的所有可行数据包组合,而较低层的图使我们能够找到所有可行的网络编码数据包,并确定可以通过间歇性连接的D2D链接来生成和传输这些数据包的用户集。通过将较高层和低层IDNC冲突图组合在一起,我们证明,找到最佳的IDNC数据包来最大程度地减少数据包完成时间问题,等于找到两层IDNC冲突图的最大独立集,这是NP-HARD问题。我们设计了一种调用Bron-Kerbosch算法以找到最佳策略的方案。为了避免达到全局最佳距离所需的高计算复杂性,我们建立了一个多项式可溶可溶剂的低复杂性启发式,以找到有效的亚最佳溶液。通过广泛的数值结果验证了我们提出的方案的有效性,这些结果表明与现有方法相比,性能的重大提高。
This paper tackles the problem of transmitting a common content to a number of cellular users by means of instantly decodable network coding (IDNC) with the help of intermittently connected D2D links. Of particular interest are broadcasting real-time applications such as video-on-demand, where common contents may be partially received by cellular users due to packet erasures over cellular links. Specifically, we investigate the problem of packet completion time, defined as the number of transmission slots necessary to deliver a common content to all users. Drawing on graph theory, we develop an optimal packet completion time strategy by constructing a two-layer IDNC conflict graph. The higher-layer graph permits us to determine all feasible packet combinations that can be transmitted over the cellular link, while the lower-layer graph enables us to find all feasible network coded packets and identify the set of users that can generate and transmit these packets via intermittently connected D2D links. By combining the higher-layer and the lower-layer IDNC conflict graphs, we demonstrate that finding the optimal IDNC packets to minimize the packet completion time problem is equivalent to finding the maximum independent set of the two-layer IDNC conflict graph, which is known to be an NP-hard problem. We design a scheme that invokes the Bron-Kerbosch algorithm to find the optimal policy. To circumvent the high computational complexity required to reach the global optimum, we establish a polynomial-time solvable low-complexity heuristic to find an efficient sub-optimal solution. The effectiveness of our proposed scheme is verified through extensive numerical results which indicate substantial performance improvement in comparison with existing methods.