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

October 18, 2019 Β· Declared Dead Β· πŸ› IEEE Transactions on Vehicular Technology

πŸ‘» CAUSE OF DEATH: Ghosted
No code link whatsoever

"No code URL or promise found in abstract"

Evidence collected by the PWNC Scanner

Authors Tiankui Zhang, Xinyuan Fang, Yuanwei Liu, Geoffrey Ye Li, Wenjun Xu arXiv ID 1910.08291 Category cs.NI: Networking & Internet Cross-listed cs.IT Citations 45 Venue IEEE Transactions on Vehicular Technology Last Checked 6 months ago
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.
Community shame:
Not yet rated
Community Contributions

Found the code? Know the venue? Think something is wrong? Let us know!

πŸ“œ Similar Papers

In the same crypt β€” Networking & Internet

Died the same way β€” πŸ‘» Ghosted