scholarly journals Autonomous Power Allocation Based on Distributed Deep Learning for Device-to-Device Communication Underlaying Cellular Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 107853-107864
Author(s):  
Jeehyeong Kim ◽  
Joohan Park ◽  
Jaewon Noh ◽  
Sunghyun Cho
Keyword(s):  
Deep Learning ◽  
Power Allocation ◽  
Cellular Network ◽  
Device To Device ◽  
Device To Device Communication

scholarly journals Joint power allocation and subcarrier assignment for device to device in 5G time division duplex networks

2018 ◽  
Vol 14 (11) ◽  
pp. 155014771881109 ◽  
Author(s):  
Pan Zhao ◽  
Lei Feng ◽  
Peng Yu ◽  
Wenjing Li ◽  
Xuesong Qiu
Keyword(s):  
Quality Of Service ◽  
Power Allocation ◽  
Base Station ◽  
Joint Power ◽  
Time Division Duplex ◽  
Device To Device ◽  
Minlp Problem ◽  
Device To Device Communication ◽  
Time Division

The explosive demands for mobile broadband service bring a major challenge to 5G wireless networks. Device-to-device communication, adopting side links for user-direct communication, is regarded as a main technical source for offloading large volume of mobile traffic from cellular base station. This article investigates the joint power and subcarrier allocation scheme for device-to-device communication in 5G time division duplex systems. In time division duplex system, instead of utilizing an exclusive portion of the precious cellular spectrum, device-to-device pairs reuse the subcarriers occupied by cellular users, thus producing harmful interference to cellular users in both uplink and downlink communication, and strongly limiting the spectrum efficiency of the system. To this end, we focus on the maximization of device-to-device throughput while guaranteeing both uplink and downlink channel quality of service of cellular users as well as device-to-device pairs. The problem is formulated as a mixed integer non-linear programming (MINLP) problem. To make it tractable, we separate the original MINLP problem into two sub problems: power allocation and sub-carrier reusing. The former is to develop optimal power allocation for each device-to-device pair and each cellular user, with the constraints of maximum power and quality of service. It is solved by geometric programming technique in convex optimization method. The latter is derived as a one-to-many matching problem for scheduling multiple subcarriers occupied by cellulars to device-to-device pairs. It is solved by Hungarian method. Simulation results show that the proposed scheme significantly improves system capacity of the device-to-device underlay network, with quality of service of both device-to-device users and cellular users guaranteed.

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