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Deep Learning-Aided User Association and Power Control With Renewable Energy Sources.
- Source :
-
IEEE Transactions on Communications . Apr2022, Vol. 70 Issue 4, p2387-2403. 17p. - Publication Year :
- 2022
-
Abstract
- The renewable energy source (RES)-powered small cell base station (SBS) is a promising technology for the next-generation networks because RESs provide sustainable energy without being depleted. In RES-assisted networks, joint optimization of user association (UA) and power control (PC) is required to enhance the sum-rate performance by reducing inter-cell interference between SBSs. This paper tackles the UA and PC optimization for the sum-rate maximization under quality-of-service (QoS) and backhaul constraints. We formulate the problem as mixed-integer non-linear programming, in which UA and PC variables of different time slots are tightly coupled due to the RES energy dynamics model. Hence, designing a dynamic policy-based UA and PC with consideration of the future environments such as the quantity of channel gain and energy harvesting remains a challenge. First, we propose a deep unsupervised learning (DUL)-based UA scheme for a fixed PC variable. To lower the computational complexity and accelerate the convergence of the proposed learning-based optimization, we relax the UA variable, which originally has an extremely high dimension, into a low-dimensional continuous variable inspired by the Lagrangian method. Next, we propose a deep reinforcement learning (DRL)-based PC scheme, in which the stringent QoS and backhaul constraints are considered penalty terms on the reward design. The proposed DRL-based PC scheme facilitates dynamic PC inferring the relationship between the future environment and current PC. Simulation results demonstrate that the proposed scheme enhances the sum-rate by 10%, accommodates 3.3 percent point (%p) more QoS-qualified users, and reduces the computation time by 20 times compared to the conventional optimization-based method. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00906778
- Volume :
- 70
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Communications
- Publication Type :
- Academic Journal
- Accession number :
- 156342918
- Full Text :
- https://doi.org/10.1109/TCOMM.2022.3148775