Deep Reinforcement Learning for Interference Alignment Wireless Networks
Both caching and interference alignment (IA) are promising techniques for next generation wireless networks. Nevertheless, most existing works on cache-enabled IA wireless networks assume that the channel is invariant, which is unrealistic considering the time-varying nature of practical wireless environments. In this chapter, we consider realistic time-varying channels. Specifically, the channel is formulated as a finite-state Markov channel (FSMC). The complexity of the system is very high when we consider realistic FSMC models. Therefore, in this chapter, we propose a novel deep reinforcement learning approach, which is an advanced reinforcement learning algorithm that uses deep Q network to approximate the Q value-action function. We use Google TensorFlow to implement deep reinforcement learning in this chapter to obtain the optimal IA user selection policy in cache-enabled opportunistic IA wireless networks. Simulation results are presented to show that the performance of cache-enabled opportunistic IA networks in terms of the network’s sum rate and energy efficiency can be significantly improved by using the proposed approach.
- 7.C. Suh and D. Tse, “Interference alignment for cellular networks,” in Proc. 46th Annual Allerton Conf. on Commun., Control, and Computing,. Monticello, IL, Sep. 2008, pp. 1037–1044.Google Scholar
- 12.M. Deghel, E. Baştuğ, M. Assaad, and M. Debbah, “On the benefits of edge caching for MIMO interference alignment,” in Proc. IEEE SPAWC, 2015, pp. 655–659.Google Scholar
- 13.M. A. Maddah-Ali and U. Niesen, “Cache-aided interference channels,” in Proc. IEEE ISIT, 2015, pp. 809–813.Google Scholar
- 16.A. Z. Ghanavati, U. Pareek, S. Muhaidat, and D. Lee, “On the performance of imperfect channel estimation for vehicular ad-hoc networks,” in Proc. IEEE VTC’10Fall, Sept. 2010, pp. 1–5.Google Scholar
- 18.Y. Cai, F. R. Yu, C. Liang, B. Sun, and Q. Yan, “Software defined device-to-device (D2D) communications in virtual wireless networks with imperfect network state information (NSI),” IEEE Trans. Veh. Tech., no. 9, pp. 7349–7360, Sept. 2016.Google Scholar
- 24.Y. He, C. Liang, F. R. Yu, N. Zhao, and H. Yin, “Optimization of cache-enabled opportunistic interference alignment wireless networks: A big data deep reinforcement learning approach,” in Proc. IEEE ICC’17, Paris, France, June 2017.Google Scholar
- 25.Y. He, F. R. Yu, N. Zhao, V. C. M. Leung, and H. Yin, “Software-defined networks with mobile edge computing and caching for smart cities: A big data deep reinforcement learning approach,” IEEE Commun. Mag., vol. 55, no. 12, Dec. 2017.Google Scholar
- 27.A. Tatar, M. D. de Amorim, S. Fdida, and P. Antoniadis, “A survey on predicting the popularity of web content,” Springer J. Internet Services and Applications, vol. 5, no. 1, p. 8, 2014.Google Scholar
- 30.M. Abadi, A. Agarwal et al., “Tensorflow: Large-scale machine learning on heterogeneous systems,” arXiv:1603.04467, Nov. 2015.Google Scholar
- 31.“Tensorflow.org,” https://www.tensorflow.org/.