Mobile cloud and edge computing protocols make it possible to offer computationally heavy applications to mobile devices via computational offloading from devices to nearby edge servers or more powerful, but remote, cloud servers. Previous work assumed that computational tasks can be fractionally offloaded at both cloud processor (CP) and at a local edge node (EN) within a conventional Distributed Radio Access Network (D-RAN) that relies on non-cooperative ENs equipped with one-way uplink fronthaul connection to the cloud. In this paper, we propose to integrate collaborative fractional computing across CP and ENs within a Cloud RAN (C-RAN) architecture with finite-capacity two-way fronthaul links. Accordingly, tasks offloaded by a mobile device can be partially carried out at an EN and the CP, with multiple ENs communicating with a common CP to exchange data and computational outcomes while allowing for centralized precoding and decoding. Unlike prior work, we investigate joint optimization of computing and communication resources, including wireless and fronthaul segments, to minimize the end-to-end latency by accounting for a two-way uplink and downlink transmission. The problem is tackled by using fractional programming (FP) and matrix FP. Extensive numerical results validate the performance gain of the proposed architecture as compared to the previously studied D-RAN solution.

中文翻译：

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C-RAN 系统中的协作云和边缘移动计算，具有最小的端到端延迟


移动云和边缘计算协议使得通过从设备到附近的边缘服务器或更强大但远程的云服务器的计算卸载，可以向移动设备提供计算量大的应用程序。先前的工作假设计算任务可以在传统分布式无线接入网络 (D-RAN) 内的云处理器 (CP) 和本地边缘节点 (EN) 处部分卸载，该传统分布式无线接入网络 (D-RAN) 依赖于配备单向的非协作 EN到云的上行链路前传连接。在本文中，我们建议将跨 CP 和 EN 的协作分数计算集成到具有有限容量双向前传链路的 Cloud RAN (C-RAN) 架构中。因此，由移动设备卸载的任务可以在 EN 和 CP 处部分执行，其中多个 EN 与公共 CP 通信以交换数据和计算结果，同时允许集中预编码和解码。与之前的工作不同，我们研究了计算和通信资源（包括无线和前传段）的联合优化，以通过考虑双向上行链路和下行链路传输来最小化端到端延迟。该问题通过使用分数规划 (FP) 和矩阵 FP 来解决。大量的数值结果验证了所提出的架构与之前研究的 D-RAN 解决方案相比的性能增益。