arXiv subjects

Andrea Bonfante

Publications and source records attributed to Andrea Bonfante.

8 recordsLinked to original sources

arXiv2024

5G-Advanced AI/ML Beam Management: Performance Evaluation with Integrated ML Models

The legacy beam management (BM) procedure in 5G introduces higher measurement and reporting overheads for larger beam codebooks resulting in higher power consumption of user equipment (UEs). Hence, the 3rd generation partnership project (3GPP) studied the use of artificial intelligence (AI) and machine learning (ML) in the air interface to reduce the overhead associated with the legacy BM procedure. The usage of AI/ML in BM is mainly discussed with regard to spatial-domain beam prediction (SBP) and time-domain beam prediction (TBP). In this study, we discuss different sub-use cases of SBP and TBP and evaluate the beam prediction accuracy of AI/ML models designed for each sub-use case along with AI/ML model generalization aspects. Moreover, a comprehensive system-level performance evaluation is presented in terms of user throughput with integrated AI/ML models to a 3GPP-compliant system-level simulator. Based on user throughput evaluations, we present AI/ML BM design guidelines for the deployment of lightweight, low-complexity AI/ML models discussed in this study.

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arXiv2021

Performance of Predictive Indoor mmWave Networks with Dynamic Blockers

In this paper, we consider millimeter Wave (mmWave) technology to provide reliable wireless network service within factories where links may experience rapid and temporary fluctuations of the received signal power due to dynamic blockers, such as humans and robots, moving in the environment. We propose a novel beam recovery procedure that leverages Machine Learning (ML) tools to predict the starting and finishing of blockage events. This erases the delay introduced by current 5G New Radio (5G-NR) procedures when switching to an alternative serving base station and beam, and then re-establish the primary connection after the blocker has moved away. Firstly, we generate synthetic data using a detailed system-level simulator that integrates the most recent 3GPP 3D Indoor channel models and the geometric blockage Model-B. Then, we use the generated data to train offline a set of beam-specific Deep Neural Network (DNN) models that provide predictions about the beams' blockage states. Finally, we deploy the DNN models online into the system-level simulator to evaluate the benefits of the proposed solution. Our prediction-based beam recovery procedure guarantee higher signal level stability and up to $82\%$ data rate improvement with respect detection-based methods when blockers move at speed of $2$ m/s.

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arXiv2020

Performance Evaluation of Scheduling in 5G-mmWave Networks under Human Blockage

The millimetre-wave spectrum provisions enormous enhancement to the achievable data rate of 5G networks. However, human blockages affecting the millimetre-wave signal can severely degrade the performance if proper resource allocation is not considered. In this paper, we assess how conventional schedulers, such as the Proportional Fair scheduler, react to the presence of blockage. Our results show that the resource allocation may disfavour users suffering from blockage, leading to low data rate for those users. To circumvent this problem, we show that the data rate of those users can be improved by using a scheduler adapted to react to upcoming blockage events. The adapted scheduler aims at proactively allocating the resources before a blockage happens, mitigating losses. Such adaptation is motivated by recent progress in blockage prediction for millimetre-wave signals in a dynamic human blockage scenario. Our extensive simulations indicate gains in the 1st percentile rate and fairness with respect to Proportional Fair scheduler when blockage conditions are severe.

cs.IT↗

arXiv2019

5G Massive MIMO Architectures: Self-Backhauled Small Cells versus Direct Access

In this paper, we focus on one of the key technologies for the fifth-generation wireless communication networks, massive multiple-input-multiple-output (mMIMO), by investigating two of its most relevant architectures: 1) to provide in-band backhaul for the ultra-dense network (UDN) of self-backhauled small cells (SCs), and 2) to provide direct access (DA) to user equipments (UEs). Through comprehensive 3GPP-based system-level simulations and analytical formulations, we show the end-to-end UE rates achievable with these two architectures. Differently from the existing works, we provide results for two strategies of self-backhauled SC deployments, namely random and ad-hoc, where in the latter SCs are purposely positioned close to UEs to achieve line-of-sight (LoS) access links. We also evaluate the optimal backhaul and access time resource partition due to the in-band self-backhauling (s-BH) operations. Our results show that the ad-hoc deployment of self-backhauled SCs closer to the UEs with optimal resource partition and with directive antenna patterns, provides rate improvements for cell-edge UEs that amount to 30% and tenfold gain, as compared to mMIMO DA architecture with pilot reuse 3 and reuse 1, respectively. On the other hand, mMIMO s-BH underperforms mMIMO DA above the median value of the UE rates when the effect of pilot contamination is less severe, and the LoS probability of the DA links improves.

cs.NI↗

arXiv2018

Performance of Massive MIMO Self-Backhauling for Ultra-Dense Small Cell Deployments

A key aspect of the fifth-generation wireless communication network will be the integration of different services and technologies to provide seamless connectivity. In this paper, we consider using massive multiple-input multiple-output (mMIMO) to provide backhaul links to a dense deployment of self-backhauling (s-BH) small cells (SCs) that provide cellular access within the same spectrum resources of the backhaul. Through a comprehensive system-level simulation study, we evaluate the interplay between access and backhaul and the resulting end-to-end user rates. Moreover, we analyze the impact of different SCs deployment strategies, while varying the time resource allocation between radio access and backhaul links. We finally compare the above mMIMO-based s-BH approach to a mMIMO direct access (DA) architecture accounting for the effects of pilot reuse schemes, together with their associated overhead and contamination mitigation effects. The results show that dense SCs deployments supported by mMIMO s-BH provide significant rate improvements for cell-edge users (UEs) in ultra-dense deployments with respect to mMIMO DA, while the latter outperforms mMIMO s-BH from the median UEs' standpoint.

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arXiv2017

Massive MIMO Unlicensed: A New Approach to Dynamic Spectrum Access

Nowadays, the demand for wireless mobile services is copious, and will continue increasing in the near future. Mobile cellular operators are therefore looking at the unlicensed spectrum as an economical supplement to augment the capacity of their soon-to-be overloaded networks. The same unlicensed bands are luring internet service providers, venue owners, and authorities into autonomously setting up and managing their high-performance private networks. In light of this exciting future, ensuring coexistence between multiple unlicensed technologies becomes a pivotal issue. So far this issue has been merely addressed via inefficient sharing schemes based on intermittent transmission. In this article, we present the fundamentals and the main challenges behind massive MIMO unlicensed, a brand-new approach for technology coexistence in the unlicensed bands, which is envisioned to boost spectrum reuse for a plethora of use cases.

cs.IT↗

arXiv2017

Enhancing Coexistence in the Unlicensed Band with Massive MIMO

We consider cellular base stations (BSs) equipped with a large number of antennas and operating in the unlicensed band. We denote such system as massive MIMO unlicensed (mMIMO-U). We design the key procedures required to guarantee coexistence between a cellular BS and nearby Wi-Fi devices. These include: neighboring Wi-Fi channel covariance estimation, allocation of spatial degrees of freedom for interference suppression, and enhanced channel sensing and data transmission phases. We evaluate the performance of the so-designed mMIMO-U, showing that it allows simultaneous cellular and Wi-Fi transmissions by keeping their mutual interference below the regulatory threshold. The same is not true for conventional listen-before-talk (LBT) operations. As a result, mMIMO-U boosts the aggregate cellular-plus-Wi-Fi data rate in the unlicensed band with respect to conventional LBT, exhibiting increasing gains as the number of BS antennas grows.

cs.IT↗

arXiv2017

Operating Massive MIMO in Unlicensed Bands for Enhanced Coexistence and Spatial Reuse

We propose to operate massive multiple-input multiple output (MIMO) cellular base stations (BSs) in unlicensed bands. We denote such system as massive MIMO unlicensed (mMIMO-U). We design the key procedures required at a cellular BS to guarantee coexistence with nearby Wi-Fi devices operating in the same band. In particular, spatial reuse is enhanced by actively suppressing interference towards neighboring Wi-Fi devices. Wi-Fi interference rejection is also performed during an enhanced listen-before-talk (LBT) phase. These operations enable Wi-Fi devices to access the channel as though no cellular BSs were transmitting, and vice versa. Under concurrent Wi-Fi and BS transmissions, the downlink rates attainable by cellular user equipment (UEs) are degraded by the Wi-Fi-generated interference. To mitigate this effect, we select a suitable set of UEs to be served in the unlicensed band accounting for a measure of the Wi-Fi/UE proximity. Our results show that the so-designed mMIMO-U allows simultaneous cellular and Wi-Fi transmissions by keeping their mutual interference below the regulatory threshold. Compared to a system without interference suppression, Wi-Fi devices enjoy a median interference power reduction of between 3 dB with 16 antennas and 18 dB with 128 antennas. With mMIMO-U, cellular BSs can also achieve large data rates without significantly degrading the performance of Wi-Fi networks deployed within their coverage area.

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