• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.3
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• pp.477-483
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• 2016

In this paper, we propose a distributed user scheduling with interference-aware power control (IAPC) to maximize signal to generating interference plus noise ratio (SGINR) in uplink multi-cell networks. Assuming that the channel reciprocity time-division duplexing (TDD) system is used, the channel state information (CSI) can be obtained at each user from pilot signals from other BSs. In the proposed scheduling, to be specific, each user reduces the transmit power if its generating interference to other BSs is larger than a predetermined threshold. Each BS selects the user with the largest SGINR among users. Simulation results show that the proposed technique significantly outperforms the existing user scheduling algorithms. It is worth noting that the proposed technique operates with distributed manner without information exchange among cells. Hence, it can be easily applied to the practical wireless systems like 3GPP LTE without significant modifications of the specification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.5
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• pp.1063-1070
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• 2015

In this paper, we propose a distributed user scheduling with transmit power control based on the amount of generating interference to other base stations (BSs) in multi-cell multi-input multi-output (MIMO) networks. Assuming that the time-division duplexing (TDD) system is used, the interference channel from users to other cell BSs is obtained at each user. In the proposed scheduling, each user first generates a transmit beamforming vector by using singular value decompositon (SVD) over MIMO channels and reduces the transmit power if its generating interference to other BSs is larger than a predetermined threshold. Each BS selects the user with the largest effective channel gains among users, which reflects the adjusted power of users. Simulation results show that the proposed technique significantly outperforms the existing user scheduling algorithms.