In heterogeneous cellular network, task scheduling for computation offloading is one of the biggest challenges. Most works focus on alleviating heavy burden of macro base stations by moving the computation tasks on macro cell user equipment (MUE) to remote cloud or small cell base stations. But the selfishness of network users is seldom considered. Motivated by the cloud edge computing, this paper provides incentive for task transfer from macro cell users to small cell base stations. The proposed incentive scheme utilizes small cell user equipment to provide relay service. The problem of computation offloading is modeled as a two-stage auction, in which the remote MUEs with common social character can form a group and then buy the computation resource of small-cell base stations with the relay of small cell user equipment. A two-stage auction scheme named TARCO is contributed to maximize utilities for both sellers and buyers in the network. The truthful, individual rationality and budget balance of the TARCO are also proved in this paper. In addition, two algorithms are proposed to further refine TARCO on the social welfare of the network. One can achieve higher utility of MUEs and the other can obtain higher total social welfare. Extensive simulation results demonstrate that, TARCO is better than random algorithm by 104.90% in terms of average utility of MUEs, while the performance of TARCO is further improved up to 28.75% and 17.06% by the proposed two algorithms, respectively.

中文翻译：

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TARCO：HetNet 中 D2D 中继辅助计算资源分配的两阶段拍卖

在异构蜂窝网络中，用于计算卸载的任务调度是最大的挑战之一。大多数工作侧重于通过将宏小区用户设备 (MUE) 上的计算任务转移到远程云或小小区基站来减轻宏基站的沉重负担。但很少考虑网络用户的自私。在云边缘计算的推动下，本文为从宏小区用户到小小区基站的任务转移提供了激励。提议的激励方案利用小小区用户设备来提供中继服务。计算卸载问题被建模为两阶段拍卖，其中具有共同社会特征的远程MUE可以组成一个组，然后通过小基站用户设备的中继购买小基站的计算资源。名为 tarco 的两阶段拍卖计划有助于最大限度地提高网络中卖家和买家的效用。本文还证明了TARCO的真实性、个体合理性和预算平衡性。此外，还提出了两种算法来进一步细化 TARCO 对网络社会福利的影响。一个可以获得更高的 MUE 效用，另一个可以获得更高的社会总福利。大量的仿真结果表明，TARCO 在 MUE 的平均效用方面比随机算法好 104.90%，而提出的两种算法分别使 TARCO 的性能进一步提高了 28.75% 和 17.06%。本文还证明了 TARCO 的个体合理性和预算平衡。此外，还提出了两种算法来进一步细化 TARCO 对网络社会福利的影响。一个可以获得更高的 MUE 效用，另一个可以获得更高的社会总福利。大量的仿真结果表明，TARCO 在 MUE 的平均效用方面比随机算法好 104.90%，而提出的两种算法分别使 TARCO 的性能进一步提高了 28.75% 和 17.06%。本文还证明了 TARCO 的个体合理性和预算平衡。此外，还提出了两种算法来进一步细化 TARCO 对网络社会福利的影响。一个可以获得更高的 MUE 效用，另一个可以获得更高的社会总福利。大量的仿真结果表明，TARCO 在 MUE 的平均效用方面比随机算法好 104.90%，而提出的两种算法分别使 TARCO 的性能进一步提高了 28.75% 和 17.06%。