TARCO: Two-Stage Auction for D2D Relay Aided Computation Resource Allocation in Hetnet

TL;DR

This paper introduces TARCO, a two-stage auction mechanism that incentivizes task offloading in heterogeneous networks by leveraging small cell user equipment as relays, improving utility and social welfare.

Contribution

The paper proposes a novel two-stage auction scheme, TARCO, that incentivizes relay-assisted computation offloading and proves its truthfulness, individual rationality, and budget balance.

Findings

TARCO outperforms random algorithms by 104.90% in average utility.

Two algorithms further improve TARCO's social welfare by up to 28.75% and 17.06%.

Extensive simulations validate TARCO's effectiveness in heterogeneous networks.

Abstract

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 modelled 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. Extensive simulation results demonstrate that, TARCO is better than random algorithm by about 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.