D2D-Enabled Mobile User Edge Caching: A Multi-Winner Auction Approach

TL;DR

This paper introduces a multi-winner auction approach for device-to-device (D2D) edge caching in cellular networks, optimizing content placement to reduce redundancy, traffic load, and delay, while ensuring fairness among user terminals.

Contribution

It proposes a novel multi-winner auction model for UT caching placement, including SDP-based optimization and a fair payment strategy, along with heuristic algorithms for complexity reduction.

Findings

The algorithms significantly reduce traffic load and content access delay.

The proposed methods outperform existing caching algorithms.

Heuristic approach achieves near-optimal performance with lower complexity.

Abstract

In device-to-device (D2D)-enabled caching cellular networks, the user terminals (UTs) collaboratively store and share a large volume of popular contents from the base station (BS) for traffic offloading and delivery delay reduction. In this article, the multi-winner auction based caching placement in D2D-enabled caching cellular networks is investigated for UT edge caching incentive and content caching redundancy reduction. Firstly, a multi-winner once auction for UT edge caching is modeled which auctions multiple contents for multiple UTs. Then the optimization problem for content caching revenue maximization is formulated. Specifically, the "cache conflict" restriction relationship among UTs is used as one of the constraints in the problem to reduce the content caching redundancy in a UT movement scenario. The problem is solved by semidefinite programming (SDP) relaxation to obtain an approximate optimal caching placement. Moreover, the payment strategy of the auction is developed as a Nash bargaining game for personal profit fairness among the UTs who win the auction for content caching. Subsequently, a multi-winner once auction based caching (MOAC) placement algorithm is proposed. In addition, due to the high complexity of MOAC, we further propose a heuristic multi-winner repeated auction based caching placement (MRAC) algorithm, which can greatly reduce the complexity with only tiny performance loss. Simulation results show that the proposed algorithms can reduce the traffic load and average content access delay effectively compared with the existing caching placement algorithms.