Genetic algorithm-based content distribution strategy for F-RAN architectures

  • Li, Xujie (College of Computer and Information, Hohai University) ;
  • Wang, Ziya (College of Computer and Information, Hohai University) ;
  • Sun, Ying (College of Computer and Information, Hohai University) ;
  • Zhou, Siyuan (College of Computer and Information, Hohai University) ;
  • Xu, Yanli (Department of Information Engineering, Shanghai Maritime University) ;
  • Tan, Guoping (College of Computer and Information, Hohai University)
  • Received : 2018.05.10
  • Accepted : 2018.10.08
  • Published : 2019.06.03


Fog radio access network (F-RAN) architectures provide markedly improved performance compared to conventional approaches. In this paper, an efficient genetic algorithm-based content distribution scheme is proposed that improves the throughput and reduces the transmission delay of a F-RAN. First, an F-RAN system model is presented that includes a certain number of randomly distributed fog access points (F-APs) that cache popular content from cloud and other sources. Second, the problem of efficient content distribution in F-RANs is described. Third, the details of the proposed optimal genetic algorithm-based content distribution scheme are presented. Finally, simulation results are presented that show the performance of the proposed algorithm rapidly approaches the optimal throughput. When compared with the performance of existing random and exhaustive algorithms, that of the proposed method is demonstrably superior.


Supported by : Central Universities, National Natural Science Foundation of China


  1. C. Zhang et al., Social‐aware content downloading for fog radio access networks supported device‐to‐device communications, in Int. Conf. Ubiquitous Wireless Broadband (ICUWB), Nanjing, China, 2016, pp. 1-4.
  2. M. Peng et al., Fog‐computing‐based radio access networks: issues and challenges, IEEE Netw. 30 (2016), no. 4, 46-53.
  3. M. Peng, K. Zhang, Recent advances in fog radio access networks: performance analysis and radio resource allocation, IEEE Access 4 (2016), 5003-5009.
  4. H. Zhang et al., Computing resource allocation in three‐tier IoT fog networks: a joint optimization approach combining Stackelberg game and matching, IEEE Internet Things J. 4 (2017), no. 5, 1204-1215.
  5. Y. Shih et al., Enabling low‐latency applications in fog‐radio access network, IEEE Netw. 31 (2017), no. 1, 52-58.
  6. M. Chiang, T. Zhang, Fog and IoT: an overview of research opportunities, IEEE Internet Things J. 3 (2016), no. 6, 854-864.
  7. D. Zeng et al., Joint optimization of task scheduling and image placement in fog computing supported software‐defined embedded system, IEEE Trans. Comput. 65 (2016), no. 12, 3702-3712.
  8. S. Jia et al., Hierarchical content caching in fog radio access networks: ergodic rate and transmit latency, China Commun. 13 (2016), no. 12, 1-14.
  9. S. Avik, T. Ravi, S. and Osvaldo, Fog‐aided wireless networks for content delivery: fundamental latency tradeoffs, IEEE Trans. Inf. Theory 63 (2017), no. 10, 6650-6678.
  10. X. Wang, S. Leng, and K. Yang, Social‐aware edge caching in fog radio access networks, IEEE Access 5 (2017), 8492-8501.
  11. H. Zhang et al., Fog radio access networks: mobility management, interference mitigation, and resource optimization, IEEE Wireless Commun. 24 (2017), no. 6, 120-127.
  12. D. Ahmed and S. Sameh, Data dissemination using instantly decodable binary codes in fog‐radio access networks, IEEE Trans. Commun. 66 (2018), no. 99, 2052-2064.
  13. X. Huang and A. Nirwan, Content caching and distribution in smart grid enabled wireless networks, IEEE Internet Things J. 4 (2017), no. 2, 513-520.
  14. K. Shahriar et al., Dynamic content distribution for decentralized sharing in tourist spots using demand and supply, in Int. Wireless Commun. and Mobile Comput. Conf. (IWCMC), Valencia, Spain, 2017, pp. 2121-2126.
  15. Z. Su and Q. Xu, Content distribution over content centric mobile social networks in 5G, IEEE Commun. Mag. 53 (2015), no. 6, 66-72.
  16. Y. Sun, T. Dang, and J. Zhou, User scheduling and cluster formation in fog computing based radio access networks, in IEEE Int. Conf. Ubiquitous Wireless Broadband (ICUWB), Nanjing, China, 2016, pp. 1-4.
  17. K. Karla et al., A methodology for the design of self‐optimizing, decentralized content‐caching strategies, IEEE/ACM Trans. Netw. 24 (2016), no. 5, 2634-2647.
  18. X. Qiu et al., Cost‐minimizing dynamic migration of content distribution services into hybrid clouds, IEEE Trans. Parallel Distrib. Syst. 26 (2015), no. 12, 3330-3345.
  19. N. Lotti et al., Improving quality of experience in future wireless access networks through fog computing, IEEE Internet Comput. 21 (2017), no. 2, 26-33.
  20. L. Jaime et al., Energy efficient dynamic content distribution, IEEE J. Sel. Areas Commun. 33 (2015), no. 12, 2826-2836.
  21. S. Yan, M. Peng, and W. Wang, User access mode selection in fog computing based radio access networks, in Int. Conf. Commun. (ICC), Kuala Lumpur, Malaysia, 2016, pp. 1-6.
  22. E. Flavio and C. Walter, Integrating piece and peer selection in content distribution networks, in Global Commun. Conf. (GLOBECOM), San Diego, CA, USA, 2015, pp. 1-6.
  23. K. Neeraj, J. P. C. R. Joel, and C. Naveen, Bayesian coalition game as‐a‐service for content‐caching distribution in internet of vehicles, IEEE Internet Things J. 1 (2014), no. 6, 544-555.
  24. H. Xiang et al., Joint mode selection and resource allocation for downlink fog radio access networks supported D2D, in Heterogeneous Netw. for Quality, Reliability, Security and Robustness (QSHINE), Taipei, Taiwan, 2015, pp. 177-182.
  25. J. Liu et al., Cache placement in Fog‐RANs: from centralized to distributed algorithms Fog‐RAN, IEEE Trans. Wireless Commun. 16 (2017), no. 11, 7039-7051.
  26. B. Andrea, M. and Reza, M. Michela, Energy consumption for data distribution in content delivery networks, in IEEE Int. Conf. Commun. (ICC), Kuala Lumpur, Malaysia, 2016, pp. 1-6.
  27. H. Li et al., Cooperative RAN caching based on local altruistic game for single and joint transmissions, IEEE Commun. Lett. 21 (2017), 853-856.
  28. Z. Wang, H. Li, Z. and Xu, Real‐world traffic analysis and joint caching and scheduling for in‐RAN caching networks, Sci. China Inf. Sci. 60 (2017), no. 6, 1-15.
  29. L. Lei et al., Collaborative edge caching through service function chaining: architecture and challenges, IEEE Wireless Commun. 25 (2018), no. 3, 94-102.
  30. H. John, Holland, genetic algorithms and the optimal allocation of trials, SIAM J. Comput. 2 (1972), no. 2, 88-105.
  31. S. Luca et al., Energy‐efficient power control: a look at 5G wireless technologies, IEEE Trans. Signal Process. 64 (2016), no. 7, 1668-1683.
  32. D. Bai, J. Lee, and I. Kang, Advanced interference management for 5G cellular networks, IEEE Commun. Mag. 52 (2014), no. 5, 52-60.
  33. K. Manolakis et al., The role of small cells, coordinated multipoint, and massive MIMO in 5G, IEEE Commun. Mag. 52 (2014), no. 5, 44-51.
  34. J. Montalban et al., Multimedia multicast services in 5G networks: subgrouping and non‐orthogonal multiple access techniques, IEEE Commun. Mag. 56 (2018), no. 3, 91-95.
  35. A. Lipowski and D. Lipowska, Roulette‐wheel selection via stochastic acceptance, Phys. A 391 (2012), 2193-2196.
  36. M. Srinivas and L. Patnaik, Adaptive probabilities of crossover and mutation in genetic algorithms, IEEE Trans. Syst. Man Cybern. Syst. 24 (1994), no. 4, 656-667.
  37. L. Zhang, N. Wang, and X. He, RNA genetic algorithm with adaptive crossover probability for estimating parameters of heavy oil thermal cracking model, - in Chinese Control Conf., Xi'an, China, 2013, 1866-1870.