scholarly journals Optimization Methods for Power Allocation and Interference Coordination Simultaneously with MIMO and Full Duplex for Multi-Robot Networks

2021 ◽  
Vol 15 (1) ◽  
Keyword(s):  
Power Allocation ◽  
Optimization Methods ◽  
Full Duplex ◽  
Interference Coordination ◽  
Robot Networks ◽  
Multi Robot

Precoding and Power Allocation for Full-Duplex MIMO Relays

2011 ◽  
Vol E94-B (8) ◽  
pp. 2316-2327 ◽  
Author(s):  
Jong-Ho LEE ◽  
Oh-Soon SHIN
Keyword(s):  
Power Allocation ◽  
Full Duplex

scholarly journals Fast Power Allocation for Secure Communication With Full-Duplex Radio

2017 ◽  
Vol 65 (14) ◽  
pp. 3846-3861 ◽  
Author(s):  
Lei Chen ◽  
Qiping Zhu ◽  
Weixiao Meng ◽  
Yingbo Hua
Keyword(s):  
Power Allocation ◽  
Secure Communication ◽  
Full Duplex ◽  
Fast Power

Power Allocation for NOMA-Based Two-Way Full-Duplex Relaying Systems

2021 ◽  
Author(s):  
Rui Tang ◽  
Ruizhi Zhang ◽  
Yihong Zhao ◽  
Jinpu He ◽  
Huizhi Zhang ◽  
...  
Keyword(s):  
Power Allocation ◽  
Full Duplex ◽  
Relaying Systems

Deep Reinforcement Learning for Task Offloading and Power Allocation in UAV-assisted MEC System

2021 ◽  
Vol 12 (4) ◽  
pp. 0-0
Keyword(s):  
Reinforcement Learning ◽  
Power Allocation ◽  
Optimization Methods ◽  
Gradient Algorithm ◽  
Wireless Access Networks ◽  
Computing Services ◽  
Large State Space ◽  
Markov Decision ◽  
Training Cost ◽  
Task Offloading

Mobile edge computing (MEC) can provide computing services for mobile users (MUs) by offloading computing tasks to edge clouds through wireless access networks. Unmanned aerial vehicles (UAVs) are deployed as supplementary edge clouds to provide effective MEC services for MUs with poor wireless communication condition. In this paper, a joint task offloading and power allocation (TOPA) optimization problem is investigated in UAV-assisted MEC system. Since the joint TOPA problem has a strong non-convex characteristic, a method based on deep reinforcement learning is proposed. Specifically, the joint TOPA problem is modeled as Markov decision process. Then, considering the large state space and continuous action space, a twin delayed deep deterministic policy gradient algorithm is proposed. Simulation results show that the proposed scheme has lower smoothing training cost than other optimization methods.

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