Downlink Transmit Power Control in Ultra-Dense UAV Network Based on Mean Field Game and Deep Reinforcement Learning

Author(s):  
Lixin Li ◽  
Qianqian Cheng ◽  
Kaiyuan Xue ◽  
Chungang Yang ◽  
Zhu Han
2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Arbab Waheed Ahmad ◽  
Heekwon Yang ◽  
Gul Shahzad ◽  
Chankil Lee

In Long Term Evolution-Advanced (LTE-A) heterogeneous networks (HetNets), small cells are deployed within the coverage area of macrocells having 1 : 1 frequency reuse. The coexistence of small cells and a macrocell in the same frequency band poses cross-tier interference which causes outage for macrocells users and/or small cell users. To address this problem, in this paper, we propose two algorithms that consider the received interference level at the evolved NodeB (eNB) while allocating transmit power to the users. In the proposed algorithm, the transmit power of all users is updated according to the target and instantaneous signal-to-noise-plus-interference ratio (SINR) condition as long as the effective received interference at the serving eNB is below the given threshold. Otherwise, if the effective received interference at the eNB is greater than the threshold, the transmit power of small cell users is gradually reduced in order to guarantee the target SINR for all macrocells users, aiming for zero-outage for macrocells users at the cost of an increased outage ratio for small cell users. Further, in the second algorithm, the transmit power of all users is additionally controlled by the power headroom report that considers the current channel condition while updating the transmit power which results in the outage ratio decreasing for small cell users. The extensive system-level simulations show significant improvements in the average throughput and outage ratio when compared with the conventional transmit power control technique.


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