A Learning Aided Long-term User Association Scheme for Ultra-dense Networks

Author(s):  
Biling Zhang ◽  
Shaobo Liu ◽  
Jung-Lang Yu ◽  
Zhu Han
2020 ◽  
Author(s):  
Long Zhang ◽  
Guobin Zhang ◽  
Xiaofang Zhao ◽  
Yali Li ◽  
Chuntian Huang ◽  
...  

A coupling of wireless access via non-orthogonal multiple access and wireless backhaul via beamforming is a promising way for downlink user-centric ultra-dense networks (UDNs) to improve system performance. However, ultra-dense deployment of radio access points in macrocell and user-centric view of network design in UDNs raise important concerns about resource allocation and user association, among which notably is energy efficiency (EE) balance. To overcome this challenge, we develop a framework to investigate the resource allocation problem for energy efficient user association in such a scenario. The joint optimization framework aiming at the system EE maximization is formulated as a large-scale non-convex mixed-integer nonlinear programming problem, which is NP-hard to solve directly with lower complexity. Alternatively, taking advantages of sum-of-ratios decoupling and successive convex approximation methods, we transform the original problem into a series of convex optimization subproblems. Then we solve each subproblem through Lagrangian dual decomposition, and design an iterative algorithm in a distributed way that realizes the joint optimization of power allocation, sub-channel assignment, and user association simultaneously. Simulation results demonstrate the effectiveness and practicality of our proposed framework, which achieves the rapid convergence speed and ensures a beneficial improvement of system-wide EE.<br>


2019 ◽  
Vol 25 (1) ◽  
pp. 274-284 ◽  
Author(s):  
H. T. Nguyen ◽  
H. Murakami ◽  
K. Nguyen ◽  
K. Ishizu ◽  
F. Kojima ◽  
...  

2019 ◽  
Vol 68 (8) ◽  
pp. 7733-7746 ◽  
Author(s):  
Rui Dong ◽  
Ang Li ◽  
Wibowo Hardjawana ◽  
Yonghui Li ◽  
Xiaohu Ge ◽  
...  

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