Journal
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
Volume 21, Issue 1, Pages 398-412Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TWC.2021.3096383
Keywords
Interference; Microcell networks; Throughput; Analytical models; OFDM; Microprocessors; Computer architecture; Dynamic time-division duplex; fractional frequency reuse; cell clustering; mean packet throughput; small cell networks
Funding
- National Key Research and Development Program of China [2018YFB1801104]
- National Natural Science Foundation Program of China (NSFC) [61771427]
- SUTD-ZJU IDEA [ZJUVP1800104]
- National Research Foundation, Singapore
- Infocomm Media Development Authority under its Future Communications Research and Development Program
- MOE ARF Tier 2 [T2EP20120-0006]
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In this paper, an interference coordination technology for D-TDD small cell networks is proposed by integrating fractional frequency reuse (FFR) with cell clustering. Numerical results show that the proposed scheme outperforms clustered D-TDD and traditional D-TDD in terms of improving uplink performance while slightly decreasing downlink performance, and can maximize spatially averaged MPT by jointly optimizing network parameters.
In dense small cell networks, dynamic time-division duplex (D-TDD) technology has emerged as a promising solution to accommodate the fast variants of volatile traffic conditions because it allows each cell to dynamically configure the uplink and downlink transmission directions. However, the flexibility of traffic configuration introduces additional inter-cell interference, which largely deteriorates network throughput. This paper proposes an interference coordination technology for D-TDD small cell networks by integrating fractional frequency reuse (FFR) with cell clustering. To evaluate the system performance, we develop a theoretical framework to analytically characterize the mean packet throughput (MPT) performance by considering the impact of spatio-temporal traffic. The analytical model can be extended to further study the FFR-based D-TDD, clustered D-TDD, and traditional D-TDD networks. We verify the accuracy of our analysis through simulations and whereby explore the effect of different network parameters. Numerical results demonstrate that the proposed scheme outperforms clustered D-TDD and traditional D-TDD for both the downlink and uplink spatially averaged MPT, and can significantly improve the performance in uplink while slightly decreasing that in downlink compared with FFR-based D-TDD. Furthermore, by jointly optimizing network parameters, the spatially averaged MPT can be maximized while enduring MPT per user.
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