Deep Reinforcement Learning for Joint Spectrum and Power Allocation in Cellular Networks
This work provides a more practical and efficient solution for wireless network operators to manage spectrum and power allocation, improving network performance.
This paper addresses the joint spectrum and power allocation problem in cellular networks, where spectrum allocation is discrete and power allocation is continuous. The proposed learning framework, which uses two simultaneously trained deep reinforcement learning algorithms, outperforms both state-of-the-art fractional programming and a previous deep reinforcement learning solution.
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.