Search results for: Krobba Benharzallah
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 2

Search results for: Krobba Benharzallah

2 Adhesion Study of Repair Mortar Based in Dune and Crushed Limestone Sand

Authors: Krobba Benharzallah, Kenai Said, Bouhicha Mohamed, Lakhdari Mohammed Fatah, Merah Ahmed

Abstract:

In recent years, great interest has been directed towards the use of local materials and natural resources in building and public works. This is to satisfy the enormous need for these materials and contribute to sustainable development. Among these resources, dune sand and limestone crushed sand, which can be an interesting alternative to the replacement of siliceous alluvial sands for the formulation of a repair mortar. The results found show that the particle size correction of dune sand by limestone sand and the addition of a superplasticizer are very beneficial in terms of adhesion and mechanical strength.

Keywords: repair mortar, dune sand, crushed limestone sand, adhesion, mechanical strength

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1 Deep Learning to Improve the 5G NR Uplink Control Channel

Authors: Ahmed Krobba, Meriem Touzene, Mohamed Debeyche

Abstract:

The wireless communications system (5G) will provide more diverse applications and higher quality services for users compared to the long-term evolution 4G (LTE). 5G uses a higher carrier frequency, which suffers from information loss in 5G coverage. Most 5G users often cannot obtain high-quality communications due to transmission channel noise and channel complexity. Physical Uplink Control Channel (PUCCH-NR: Physical Uplink Control Channel New Radio) plays a crucial role in 5G NR telecommunication technology, which is mainly used to transmit link control information uplink (UCI: Uplink Control Information. This study based of evaluating the performance of channel physical uplink control PUCCH-NR under low Signal-to-Noise Ratios with various antenna numbers reception. We propose the artificial intelligence approach based on deep neural networks (Deep Learning) to estimate the PUCCH-NR channel in comparison with this approach with different conventional methods such as least-square (LS) and minimum-mean-square-error (MMSE). To evaluate the channel performance we use the block error rate (BLER) as an evaluation criterion of the communication system. The results show that the deep neural networks method gives best performance compared with MMSE and LS

Keywords: 5G network, uplink (Uplink), PUCCH channel, NR-PUCCH channel, deep learning

Procedia PDF Downloads 10