Deep Reinforcement Learning-Based Long Short-Term Memory for Satellite IoT Channel Allocation

Abstract

In recent years, the demand for smart wireless communication technology has increased tremendously, and it urges to extend internet services globally with high reliability, less cost and minimal delay. In this connection, low earth orbit (LEO) satellites have played prominent role by reducing the terrestrial infrastructure facilities and providing global coverage all over the earth with the help of satellite internet of things (SIoT). LEO satellites provide wide coverage area to dynamically accessing network with limited resources. Presently, most resource allocation schemes are designed only for geostationary earth orbit (GEO) satellites. For LEO satellites, resource allocation is challenging due to limited availability of resources. Moreover, due to uneven distribution of users on the ground, the satellite remains unaware of the users in each beam and therefore cannot adapt to changing state of users among the beams. In this paper, long short-term memory (LSTM) neural network has been implemented for efficient allocation of channels with the help of deep reinforcement learning (DRL) model. We name this model as DRL-LSTM scheme. Depending on the pool of resources available to the satellite, a channel allocation method based on the user density in each beam is designed. To make the satellite aware of the number of users in each beam, previous information related to the user density is provided to LSTM. It stores the information and allocates channels depending upon the requirement. Extensive simulations have been carried out which have shown that the DRL-LSTM scheme performs better as compared to the traditional and recently proposed schemes.

Star-earth cooperative network channel allocation algorithm based on power grid and deep learning

“…The simulation parameters are shown in Table 1, and the specific simulation parameters are set in [20]. [15,120]sub/h Business durationt [1,5]min Learning rate 0.001…”

“…It can be used as a complementary coverage to the terrestrial mobile network to construct a star-earth collaborative network that meets the needs of power communication in remote areas [3,4]. The construction of a dedicated star-earth collaborative network can make up for the problems of poor signal coverage, limited bandwidth, low security, and expensive costs existing in the existing wireless public network [5,6]. It can meet the needs of power monitoring in key areas.…”

“…It can meet the needs of power monitoring in key areas. It also provides methods to solve power system communication problems [5][6][7]. Although there is an increasing demand for star-earth collaborative network communication in the power system, star-earth collaborative network also suffers from resource constraints.…”