Abstract
Wireless communication is a significant auxiliary technology of data transmission for industrial Cyber-Physical system (CPS). While for the complex industrial scenario of coal mine with long and narrow laneway, lifetime of wireless perception nodes is a potential and nonnegligible problem for safety production. In order to deal with this problem, a power control algorithm based on deep Q network (DQN) is adopted to train micro base station (MBS) by two steps so that the MBS can learn an optimal policy to help the cognitive users (CUs) communicate with a proper transmit power. Firstly, the selection range of transmit power for CUs is calculated by the lower bound of Signal-to-Interference plus-Noise Ratio (SINR) to guarantee the transmission condition of both users. Then, the power control problem is modeled as a Markov Decision Process (MDP) with unknown transition function, where the energy consumption is decreased by giving the upper bound of CUs’ SINR or threshold of transmit power in formulation of reward. In modeled MDP, the system state, which collected by primary users (PUs) and fed back to MBS, is reduced dimension by method of principal component analysis and then treated as the input of DQN. After that, DQN is used to train a power control optimal policy by minimizing the loss function. Simulation results demonstrate that the proposed power control algorithm based on DQN has a good performance that the average transition step and energy utility are 3.56 and 1580h, which is better than the existing solutions.
Highlights
The complex industrial Cyber-Physical system (CPS) is a multi-dimensional complex system that integrates calculation, network, and physical environment in many industrial application fields [1]–[3]
The authors in [22] proposed a distributed power control method for the laneway of coal mine, in which the multi-sink nodes are performed as cluster heads, the optimal transmission range and power are allocated to each sink nodes combined with scoping routing algorithm, while the nodes transmit with multiple hops which does not benefit to the energy efficiency
Afterwards, reinforcement learning is used to train a policy for power control and the brief contents of the method is that, in a time slot, cognitive users (CUs) broadcast test signals to primary users (PUs), the received signal strength information (RSSI) of CUs are transmitted to micro base station (MBS)
Summary
The complex industrial CPS is a multi-dimensional complex system that integrates calculation, network, and physical environment in many industrial application fields [1]–[3]. The work in [18] investigated the maximization of energy efficiency for the cognitive femto users by optimizing power control scheme in 5G communications While they are not suitable for industrial applications because of the nonindustrial simulation environment. The authors in [22] proposed a distributed power control method for the laneway of coal mine, in which the multi-sink nodes are performed as cluster heads, the optimal transmission range and power are allocated to each sink nodes combined with scoping routing algorithm, while the nodes transmit with multiple hops which does not benefit to the energy efficiency. In order to prolong the lifetime of battery-supplied CUs and restrain the interference in the perception layer of coal mine CPS, a DQN based power control algorithm is proposed to optimize the energy consumption.
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