Interference mitigation technique for self optimizing Picocell indoor LTE-A networks

Gaytare, Gelaye Geresu; Salau, Ayodeji Olalekan; Adame, Berhan Oumer

doi:10.1007/s11235-022-00966-3

Cited by 3 publications

(1 citation statement)

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“…In this type of interconnection, a higher received signal level results in a better signal-to-noise ratio; therefore, maximising this amount satisfies both objectives. Of course, it is also possible to increase the SNR by choosing the right cells with the right deployment of BSs [ 6 ], but for better testability, we always used one BS in the present study. The hardest part of the problem is that the UE can move arbitrarily and Non-Line-of-Sight (NLoS) connections may occur, which needs to be handled by the system.…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning-Based Antenna Alignment Prediction for Mobile Indoor Communication

Makara

Csathó

Rácz

et al. 2023

Sensors

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A significant innovation for future indoor wireless networks is the use of the mmWave frequency band. However, an important challenge comes from the restricted propagation conditions in this band, which necessitates the use of beamforming and associated beam management procedures, including, for instance, beam tracking or beam prediction. A possible solution to the beam management problem is to use artificial-intelligence-based procedures to learn the hidden spatial propagation patterns of the channel and to use this knowledge to predict the best beam directions. In this paper, we present a deep-neural-network-based method that has memory that can be used to predict the best reception directions for moving users. The best direction is the highest expected signal level at the next moment. The resulting method allows for a user-side antenna management system. The result was evaluated using three different metrics, thus detailing not only its predictive ability, but also its usability.

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Section: Introductionmentioning

confidence: 99%