Beam selection and user scheduling method of large-scale antenna planar array in sea area

Abstract

The present application provides a method for beam selection and user scheduling of a large-scale planar antenna array in a sea area, which relates to the technical field of sea area scheduling. Step R1, the shipborne AIS system interacts with the shore-based base station equipped with a large-scale antenna planar array to obtain the location information of each user; then obtain the height information of the shore-based base station and the height information of the ship terminal through the altimeter. Step R2, according to the user position information obtained in step R1, the height information of the shore-based base station and the ship terminal, divide the sea area covered by the beam corresponding to the large-scale antenna planar array, and divide each user into each corresponding beam; step R3. Select the user with the highest priority in each beam, and put it into the user set to be served in turn; Step R4. In each scheduling cycle, schedule the users in the user set to be served for service according to the system weight and the maximum rate criterion, Turn off unused beams. In this application, beam selection and user scheduling are fast, and algorithm complexity is low.

Description

technical field [0001] The present application relates to the technical field of sea area scheduling, and in particular to a beam selection and user scheduling method of a large-scale antenna planar array in the sea area. Background technique [0002] With the rapid development of the marine industry, marine tourism, offshore aquaculture and marine mineral exploration, marine multimedia services are becoming more and more important. In order to improve the transmission rate of the maritime communication system, we considered using a massive multiple-input multiple-output system (MMIMO) in the base station. First, MMIMO can deeply mine spatial dimension resources, so that multiple users can communicate with the base station on the same time-frequency resource. Communicate at the same time, thereby greatly increasing spectrum resources; second, MMIMO can greatly reduce uplink and downlink transmission power, thereby improving power efficiency; third, MMIMO concentrates beams i...

AI Technical Summary

Problems solved by technology

[0003] The overall user distribution in the sea scene is sparse, but it is crowded in the local area so that it is impossible to distinguish these users in the angle domain

Therefore, we need to schedule some users to serve to ensure the maximum weighted sum rate. The traditional scheduling method is based on the exhaustive method, scheduling the user combination with the largest sum rate for service, although this algorithm can schedule the best combination of users to be served , but it cannot adapt to the time-varying channel in the actual system, and the calculation complexity is high

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