Imperfect CSI Muting With Time-Domain Thresholding for Cooperative Intercell Interference Cancellation

Maruta, Kazuki

doi:10.1109/jsyst.2021.3074468

Cited by 1 publication

(2 citation statements)

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“…The similar interference cancellation strategy has been extended to the time domain by configuring almost blank subframes (ABS) [8], which is also known as enhanced ICIC (eICIC) in the third generation partnership project (3GPP) [9]. Based on ICIC and eICIC schemes, many promising technologies have been developed in recent years [10][11][12][13]. For example, in [10], the paper proposed a beam-based ABS allocation and beam-based cell range expansion (CRE) algorithm to reduce the interference on small cell edge users.…”

Section: Introductionmentioning

confidence: 99%

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An Interference-Aware Resource-Allocation Scheme for Non-Cooperative Multi-Cell Environment

et al. 2023

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Inter-cell interference cancellation has been investigated for several decades and has become an elementary technique for modern wireless networks. However, the existing interference cancellation mechanism rarely considers the historical channel variations and interference characteristics. In this paper, we propose an interference-aware prediction-based resource-allocation strategy to deal with multi-cell interference, where the historical noisy channel state and the acknowledgment feedback are fully utilized. Together with the predicted interference patterns, our proposed joint sub-channel allocation and rate selection mechanism can achieve better average throughput performance. Through the numerical as well as the prototyping results, we show that our proposed scheme is able to provide more than 9.7% and 8% average throughput improvement compared with many existing baselines.

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Section: Introductionmentioning

confidence: 99%

“…With the formulation result, their scheme has a lower computation cost. In [11], the paper used CSI muting with time-domain thresholding to improve the spectral efficiency performance of the ICIC. In [12], the paper used the independent deep Q-learning method to control both the transmit power and beamforming in a distributed manner.…”

Section: Introductionmentioning

confidence: 99%