Optimal caching policy for wireless content delivery in D2D networks

• Published in: Journal of network and computer applications Vol. 150; p. 102467
• Main Authors: Ali, Ahmed S., Mahmoud, Korany R., Naguib, Khaled M.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.01.2020
• Subjects: Average transmission delay; Caching policy; Device-to-Device caching; Frequency searching adaptive bat algorithm; Group mobility; Offloading probability; Particle swarm optimization; User preference; Caching policy; Device-to-Device caching; Offloading probability; Average transmission delay; User preference; Group mobility; Particle swarm optimization; Frequency searching adaptive bat algorithm
• ISSN: 1084-8045; 1095-8592
• DOI: 10.1016/j.jnca.2019.102467

The huge demand for multimedia services has exponentially grown in mobile networks and is expected to congest cellular traffic in the near future. Since network resources are limited, content caching may be considered a superior solution to offload data traffic during peak times. Content caching in mobile devices together with Device-to-Device (D2D) communications can improve the performance of cellular wireless networks. Predicting user demand and his mobility pattern allows the network to proceed proactive caching in order to relieve the network congestion and hence decreases the network load as well as its service cost. Moreover, performing an optimal caching policy is one of the important issues to maximize the offloading probability and as a result enhances the overall network performance. In this paper, we are introducing an incentive caching policy in which networks jointly considers the user preference and group mobility for the caching problem. Firstly, the cost optimal caching problem for the network is formulated. Then, the overall network cost is minimized due to the effect of user demand and group mobility using the Frequency Searching Adaptive Bat Algorithm (FSABA) by optimizing the cached portions of requested files. System performance analysis in terms of the overall network gain, average transmission delay and offloading probability are derived and evaluated according to the achieved optimal cached portions. Extended simulations are carried out to validate the beneficial of the presented optimal caching policy. Additionally, to verify the effectiveness of FSABA, the results are compared with those obtained using the Particle Swarm Optimization (PSO) algorithm. The results show that the proposed caching scheme outperforms both the baseline scenario and the random mobility-based schemes. It is worth mentioning that the FSABA can achieve a superior convergence capability compared to the PSO.