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Reverse TDD-Based Massive MIMO Systems With Underlay Spectrum Sharing
DOI:10.1109/TWC.2019.2911292.png)
摘要
En 中文
Multi-cell multi-user underlay spectrum-sharing massive multiple-input multiple-output systems operating with reverse time division duplexing (R-TDD) are investigated. By primarily aiming at fully mitigating intra-cell pilot contamination and coherent interference, in the proposed R-TDD scheme, the primary/secondary systems are allowed to operate only in the opposite transmission directions. In order to establish fundamental performance limits, the secondary transmit power constraints and achievable rates are derived in the presence of trainingbased channel estimation. Thereby, the joint detrimental effects of spatial correlation, beamforming uncertainty, and inter-/intra-cell coherence interference due to pilot contamination are quantified and compared against the conventional TDD (C-TDD) counterpart. A max-min optimal power control policy is designed, and thereby, the common achievable rates and power control coefficients are derived. It is shown that by invoking R-TDD, the secondary power constraints and sum rates can be made to become asymptotically independent of primary interference threshold. Thus, the secondary system can be operated with its maximum average transmit power, independent of the primary system without hindering its asymptotically achievable rates by the virtue of the inherent intra-cell coherent interference mitigation benefit of the R-TDD. By exploiting the pilot decontamination feature of R-TDD, the achievable rates of primary/secondary systems can be significantly boosted, compared to the underlay spectrum sharing with C-TDD.
Keyword:
Massive MIMO
spectrum-sharing
reverse TDD
max-min power control
achievable rate analysis
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期刊
IF:
10.7
论文数:
1.3W
被引数:
5.3W
机构
引用论文
Breaking Spectrum Gridlock With Cognitive Radios: An Information Theoretic Perspective
PROCEEDINGS OF THE IEEE
IF25.9
QoS Aware Power Allocation and User Selection in Massive MIMO Underlay Cognitive Radio Networks大规模MIMO底层认知无线电网络中QoS感知的功率分配和用户选择

