Deep Reinforcement Learning for Joint Spectrum and Power Allocation in Cellular Networks
Abstract
A wireless network operator typically divides the radio spectrum it possesses into a number of subbands. In a cellular network those subbands are then reused in many cells. To mitigate co-channel interference, a joint spectrum and power allocation problem is often formulated to maximize a sum-rate objective. The best known algorithms for solving such problems generally require instantaneous global channel state information and a centralized optimizer. In fact those algorithms have not been implemented in practice in large networks with time-varying subbands. Deep reinforcement learning algorithms are promising tools for solving complex resource management problems. A major challenge here is that spectrum allocation involves discrete subband selection, whereas power allocation involves continuous variables. In this paper, a learning framework is proposed to optimize both discrete and continuous decision variables. Specifically, two separate deep reinforcement learning algorithms are designed to be executed and trained simultaneously to maximize a joint objective. Simulation results show that the proposed scheme outperforms both the state-of-the-art fractional programming algorithm and a previous solution based on deep reinforcement learning.
- Publication:
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arXiv e-prints
- Pub Date:
- December 2020
- DOI:
- 10.48550/arXiv.2012.10682
- arXiv:
- arXiv:2012.10682
- Bibcode:
- 2020arXiv201210682S
- Keywords:
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- Electrical Engineering and Systems Science - Signal Processing;
- Computer Science - Information Theory;
- Computer Science - Machine Learning
- E-Print:
- 7 pages, 3 figures, to be submitted. To reproduce the results please see https://github.com/sinannasir/Spectrum-Power-Allocation