A cooperative online learning scheme for resource allocation in 5G systems

The demand on mobile Internet related services has increased the need for higher bandwidth in cellular networks. The 5G technology is envisioned as a solution to satisfy this demand as it provides high data rates and scalable bandwidth. The multi-tier heterogeneous structure of 5G with dense base station deployment, relays, and device-to-device (D2D) communications intends to serve users with different QoS requirements. However, the multi-tier structure causes severe interference among the multi-tier users which further complicates the resource allocation problem. In this paper, we propose a cooperative scheme to tackle the interference problem, including both cross-tier interference that affects macro users from other tiers and co-tier interference, which is among users belong to the same tier. The scheme employs an online learning algorithm for efficient spectrum allocation with power and modulation adaptation capability. Our evaluation results show that our online scheme outperforms others and achieves significant improvements in throughput, spectral efficiency, fairness, and outage ratio.

[1]  Raj Jain,et al.  Analysis of the Increase and Decrease Algorithms for Congestion Avoidance in Computer Networks , 1989, Comput. Networks.

[2]  Kang G. Shin,et al.  CogWnet: A Resource Management Architecture for Cognitive Wireless Networks , 2013, 2013 22nd International Conference on Computer Communication and Networks (ICCCN).

[3]  Stefan Parkvall,et al.  Design aspects of network assisted device-to-device communications , 2012, IEEE Communications Magazine.

[4]  Gerard J. Foschini,et al.  A simple distributed autonomous power control algorithm and its convergence , 1993 .

[5]  Ahmad R. Sharafat,et al.  A Distributed Dynamic Target-SIR-Tracking Power Control Algorithm for Wireless Cellular Networks , 2010, IEEE Transactions on Vehicular Technology.

[6]  Manuela M. Veloso,et al.  Multiagent learning using a variable learning rate , 2002, Artif. Intell..

[7]  Zhong Fan,et al.  Emerging technologies and research challenges for 5G wireless networks , 2014, IEEE Wireless Communications.

[8]  Kang G. Shin,et al.  Enhanced cognitive Radio Resource Management for LTE systems , 2013, 2013 IEEE 9th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob).

[9]  Salah-Eddine Elayoubi,et al.  On frequency allocation in 3G LTE systems , 2006, 2006 IEEE 17th International Symposium on Personal, Indoor and Mobile Radio Communications.

[10]  Peter Dayan,et al.  Q-learning , 1992, Machine Learning.

[11]  Stefan Valentin,et al.  Context-aware resource allocation for cellular wireless networks , 2012, EURASIP J. Wirel. Commun. Netw..

[12]  Basem Shihada,et al.  Cognitive Aware Interference Mitigation Scheme for LTE Femtocells , 2015, CrownCom.

[13]  Peter Dayan,et al.  Technical Note: Q-Learning , 2004, Machine Learning.

[14]  Ahmad R. Sharafat,et al.  Pareto and Energy-Efficient Distributed Power Control With Feasibility Check in Wireless Networks , 2011, IEEE Transactions on Information Theory.

[15]  Chi Wan Sung,et al.  An opportunistic power control algorithm for cellular network , 2006, IEEE/ACM Trans. Netw..

[16]  Choong Seon Hong,et al.  Resource management in dense heterogeneous networks , 2015, 2015 17th Asia-Pacific Network Operations and Management Symposium (APNOMS).

[17]  Ahmed E. Kamal,et al.  Downlink spectrum allocation in 5G HetNets , 2014, 2014 International Wireless Communications and Mobile Computing Conference (IWCMC).

[18]  Predrag B. Rapajic,et al.  A joint resource allocation and link adaptation algorithm with carrier aggregation for 5G LTE-Advanced network , 2015, 2015 22nd International Conference on Telecommunications (ICT).

[19]  Xiqi Gao,et al.  Cellular architecture and key technologies for 5G wireless communication networks , 2014, IEEE Communications Magazine.

[20]  Ekram Hossain,et al.  5G cellular: key enabling technologies and research challenges , 2015, IEEE Instrumentation & Measurement Magazine.