arXiv subjects

Zoraze Ali

Publications and source records attributed to Zoraze Ali.

5 recordsLinked to original sources

arXiv2026

An open-source implementation and validation of 5G NR Configured Grant for URLLC in ns-3 5G LENA: a scheduling case study in Industry 4.0 scenarios

Factories are undergoing a digital transformation towards cost-efficient, zero-defect manufacturing, creating the need for communication networks capable of meeting stringent latency and reliability requirements. 5G and beyond networks are being designed to support Ultra-Reliable Low-Latency Communications (URLLC). However, the use of dynamic scheduling for uplink transmissions introduces additional latency due to the signaling required to request and allocate radio resources. To reduce this latency, 5G NR defines Configured Grant (CG), which pre-allocates uplink resources and eliminates the need for per-packet scheduling requests. Accurate simulation tools implementing URLLC features are essential to evaluate the capability of 5G networks to support time-critical services and to investigate new solutions. Nevertheless, the availability of such tools is limited and, to the best of the authors' knowledge, no open-source 5G NR simulator currently supports CG. To address this gap, this paper presents the first implementation of CG in an open-source 5G NR simulator. Specifically, CG has been integrated into the ns-3 5G-LENA system-level simulator and made publicly available. In addition, the Orthogonal Frequency Division Multiple Access (OFDMA) implementation has been enhanced to better reflect the flexibility of 5G NR. The implementation is validated through Industry 4.0 scenarios, where the latency performance achieved with CG under different scheduling policies is analyzed. Results show that the latency obtained with 5G-LENA closely matches that reported in previous analytical studies. Furthermore, the study highlights the importance of efficient radio resource utilization to reduce latency and satisfy the requirements of critical industrial services.

cs.NI↗

arXiv2022

Calibration of the 5G-LENA System Level Simulator in 3GPP reference scenarios

Due to the rapid technology evolution and standardization activity in the mobile communication networks, there is the need for the research community to be able to develop, test and evaluate new and/or already xisting solutions before industrial or real-network implementation. As such, it is essential to have an open-source tool that provides an alternative solution to that of industrial proprietary simulators that are not available for public usage. ns-3 5G-LENA simulator is an end-to-end open-source NR system-level simulator that allows extensive research to be performed. However, it is of great importance to guarantee that the results obtained using the simulator can be comparable to that of industrial simulators and real networks. For this reason, calibrating the simulator based on 3GPP defined specifications is crucial. Based on the above, in this paper we calibrate the ns-3 5G-LENA simulator according to the 3GPP reference results for NR-based outdoor deployments. Moreover, we explore the REM feature provided by the simulator, to ease the calibration process and understand better the radio environment. Results show the resemblance of the simulator performance to that of simulators used as references by 3GPP.

cs.NI↗

arXiv2022

ns-3 and 5G-LENA Extensions to Support Dual-Polarized MIMO

MIMO spatial multiplexing is an essential feature to increase the communication data rates in current and future cellular systems. Currently, the ns-3 lte module leverages an abstraction model for 2x2 MIMO with spatial multiplexing of two streams; while mmwave and nr modules were lacking the spatial multiplexing option until this work, since the ns-3 models were not supporting the usage of multiple antennas for spatial multiplexing and an abstraction model such as the one used in the lte module is not suitable for the mmWave frequencies. In this paper, we propose, implement and evaluate models for ns-3 and the nr module to enable Dual-Polarized MIMO (DP-MIMO). The proposed extension for the ns-3 supports multiple antennas for DP-MIMO with spatial multiplexing of two streams and can be used by any ns-3 module that is compatible with the ns-3 antenna array-based models, such as nr and mmwave modules. We leverage this ns-3 extension to model DP-MIMO by exploiting dual-polarized antennas and their orthogonality under line-of-sight conditions, as it happens at high-frequency bands, to send the two data streams. The proposed model does not rely on abstraction, as the MIMO model in the ns-3 lte module, and can thus model more realistically the propagation differences of the two streams, correlation, inter-stream interference, and allows design and evaluation of the rank adaptation algorithms. Additionally, we propose and evaluate an adaptive rank adaptation scheme and compare it with a fixed scheme. The developed DP-MIMO spatial multiplexing models for the ns-3 simulator and the nr module are openly available.

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arXiv2021

On the impact of numerology in NR V2X Mode 2 with sensing and no-sensing resource selection

In this paper, we use a New Radio (NR) Vehicular-to-everything (V2X) standard compliant simulator based on ns-3, to study the impact of NR numerologies on the end-to-end performance. In particular, we focus on NR V2X Mode 2, used for autonomous resource selection in out-of-coverage communications, and consider the two key procedures defined in 3GPP: sensing and non-sensing based resource selection. We pay particular attention to the interplay between the operational numerology and the resource selection window length, a key parameter of NR V2X Mode 2. The results in a standard-compliant, end-to-end simulation platform show that in all cases, for basic service messages, a higher numerology is beneficial because of different reasons, depending on the way the resource selection window length is established.

eess.SP↗

arXiv2020

Recurrent Neural Networks for Handover Management in Next-Generation Self-Organized Networks

In this paper, we discuss a handover management scheme for Next Generation Self-Organized Networks. We propose to extract experience from full protocol stack data, to make smart handover decisions in a multi-cell scenario, where users move and are challenged by deep zones of an outage. Traditional handover schemes have the drawback of taking into account only the signal strength from the serving, and the target cell, before the handover. However, we believe that the expected Quality of Experience (QoE) resulting from the decision of target cell to handover to, should be the driving principle of the handover decision. In particular, we propose two models based on multi-layer many-to-one LSTM architecture, and a multi-layer LSTM AutoEncoder (AE) in conjunction with a MultiLayer Perceptron (MLP) neural network. We show that using experience extracted from data, we can improve the number of users finalizing the download by 18%, and we can reduce the time to download, with respect to a standard event-based handover benchmark scheme. Moreover, for the sake of generalization, we test the LSTM Autoencoder in a different scenario, where it maintains its performance improvements with a slight degradation, compared to the original scenario.

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