Energy-Efficient Computation Offloading in Delay-Constrained Massive MIMO Enabled Edge Network Using Data Partitioning
Energy-Efficient Computation Offloading in Delay-Constrained Massive MIMO Enabled Edge Network Using Data Partitioning
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DOI：
10.1109/twc.2020.3007616
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发表时间：
2020-10
影响因子：
10.4
通讯作者：
Rafia Malik;M. Vu
中科院分区：
文献类型：
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作者：
Rafia Malik;M. Vu
我们研究了一种无线边缘计算系统，该系统允许多个用户同时将计算密集型任务卸载到多个MIMO接入点，每个接入点都有一个并置的多访问边缘计算（MEC）服务器。Massive-MIMO使所有用户能够同时进行上行传输，与顺序协议相比，大大缩短了数据卸载时间，并使数据卸载、计算和下载的三个阶段具有可比的持续时间。基于这三个阶段的结构，我们制定了一个新的问题，以最大限度地减少在往返延迟约束下的用户和MEC服务器的能源消耗的加权和，在用户和服务器端的数据分区，发射功率控制和CPU频率缩放的组合。我们设计了一种新的嵌套算法，由内部原始对偶算法和外部延迟感知下降算法来有效地解决这个问题。优化的解决方案表明，对于较大的请求，更多的数据被卸载到MEC，以减少本地计算时间，以满足延迟约束，尽管无线传输的能量成本较高。导频污染下的大规模MIMO信道估计误差也会导致更多的数据被卸载到MEC。与二进制卸载相比，使用数据分区的部分卸载上级，并且导致整体能耗的显著降低。
We study a wireless edge-computing system which allows multiple users to simultaneously offload computation-intensive tasks to multiple massive-MIMO access points, each with a collocated multi-access edge computing (MEC) server. Massive-MIMO enables simultaneous uplink transmissions from all users, significantly shortening the data offloading time compared to sequential protocols, and makes the three phases of data offloading, computing, and downloading have comparable durations. Based on this three-phase structure, we formulate a novel problem to minimize a weighted sum of the energy consumption at both the users and the MEC server under a round-trip latency constraint, using a combination of data partitioning, transmit power control and CPU frequency scaling at both the user and server ends. We design a novel nested algorithm consisting of an inner primal-dual algorithm and an outer latency-aware descent algorithm to solve this problem efficiently. Optimized solutions show that for larger requests, more data is offloaded to the MECs to reduce local computation time in order to meet the latency constraint, despite higher energy cost of wireless transmissions. Massive-MIMO channel estimation errors under pilot contamination also causes more data to be offloaded to the MECs. Compared to binary offloading, partial offloading with data partitioning is superior and leads to significant reduction in the overall energy consumption.