5G New Radio Base-Station Sensitivity and Performance

Peralta, Elena; Levanen, Toni; Ihalainen, Tero; Nielsen, Sari; Ng, Man Hung; Renfors, Markku; Valkama, Mikko

doi:10.1109/iswcs.2018.8491061

Cited by 9 publications

(4 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We consider three SNR boundaries: 25 dB, 14 dB, and 4 dB. These represent SNRs at which 256QAM 4/5, 16QAM 1/2, and QPSK 3/10 modulation and coding schemes (MCS) may be received with goodput factors of 0.7, 0.7, and 1.0 respectively [49][50][51]. We can estimate link rates using an impaired Shannon capacity D = 𝜌𝛽𝐵 log 2 (1 + 10 (𝑆𝑁 𝑅−𝐶)/10 ), where 𝜌 = 0.6 is the overhead factor, 𝛽 is the goodput factor, and 𝐶 = 3 dB is implementation loss.…”

Section: Glass-dependent Oti Data Ratesmentioning

confidence: 99%

Outdoor-to-Indoor 28 GHz Wireless Measurements in Manhattan: Path Loss, Environmental Effects, and 90% Coverage

Kohli¹,

Adhikari²,

Avci³

et al. 2022

Preprint

View full text Add to dashboard Cite

Outdoor-to-indoor (OtI) signal propagation further challenges the already tight link budgets at millimeter-wave (mmWave). To gain insight into OtI mmWave scenarios at 28 GHz, we conducted an extensive measurement campaign consisting of over 2,200 link measurements. In total, 43 OtI scenarios were measured in West Harlem, New York City, covering seven highly diverse buildings. The measured OtI path gain can vary by up to 40 dB for a given link distance, and the empirical path gain model for all data shows an average of 30 dB excess loss over free space at distances beyond 50 m, with an RMS fitting error of 11.7 dB. The type of glass is found to be the single dominant feature for OtI loss, with 20 dB observed difference between empirical path gain models for scenarios with low-loss and high-loss glass. The presence of scaffolding, tree foliage, or elevated subway tracks, as well as difference in floor height are each found to have an impact between 5-10 dB. We show that for urban buildings with high-loss glass, OtI coverage can support 500 Mbps for 90% of indoor user equipment (UEs) with a base station (BS) antenna placed up to 49 m away. For buildings with low-loss glass, such as our case study covering multiple classrooms of a public school, data rates over 2.5/1.2 Gbps are possible from a BS 68/175 m away from the school building, when a line-of-sight path is available. We expect these results to be useful for the deployment of mmWave networks in dense urban environments as well as the development of relevant scheduling and beam management algorithms.

show abstract

Section: Glass-dependent Oti Data Ratesmentioning

confidence: 99%

Outdoor-to-Indoor 28 GHz Wireless Measurements in Manhattan: Path Loss, Environmental Effects, and 90% Coverage

Kohli¹,

Adhikari²,

Avci³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Fifth-generation network applications such as the tactile internet, telehospitals, M2M communication, and vehicle to everything (V2X) communication require an exceptionally low latency rate, which cannot be achieved easily due to the presence of this propagation challenges [294]. In [295], the authors analyzed the receiver sensitivity prerequisites in a wireless 5G NR environment related to the single-to-noise ratio (SNR) constraints at BS to achieve maximum results for throughput. NR primarily has two frequency ranges: (1) 450-6000 MHz is defined for FR1, and (2) 24,250-52,600 MHz is defined for FR2.…”

Section: • Far End Crosstalk (Fext)mentioning

confidence: 99%

Machine Learning for Physical Layer in 5G and beyond Wireless Networks: A Survey

et al. 2021

View full text Add to dashboard Cite

Fifth-generation (5G) technology will play a vital role in future wireless networks. The breakthrough 5G technology will unleash a massive Internet of Everything (IoE), where billions of connected devices, people, and processes will be simultaneously served. The services provided by 5G include several use cases enabled by the enhanced mobile broadband, massive machine-type communications, and ultra-reliable low-latency communication. Fifth-generation networks potentially merge multiple networks on a single platform, providing a landscape for seamless connectivity, particularly for high-mobility devices. With their enhanced speed, 5G networks are prone to various research challenges. In this context, we provide a comprehensive survey on 5G technologies that emphasize machine learning-based solutions to cope with existing and future challenges. First, we discuss 5G network architecture and outline the key performance indicators compared to the previous and upcoming network generations. Second, we discuss next-generation wireless networks and their characteristics, applications, and use cases for fast connectivity to billions of devices. Then, we confer physical layer services, functions, and issues that decrease the signal quality. We also present studies on 5G network technologies, 5G propelling trends, and architectures that help to achieve the goals of 5G. Moreover, we discuss signaling techniques for 5G massive multiple-input and multiple-output and beam-forming techniques to enhance data rates with efficient spectrum sharing. Further, we review security and privacy concerns in 5G and standard bodies’ actionable recommendations for policy makers. Finally, we also discuss emerging challenges and future directions.

show abstract

“…Additionally, inter-numerology interference (INI) needs to garner more attention in recent researches. NR reference sensitivity, based on the signal-to-noise ratio (SNR), has been evaluated in [21] for both sub-6 GHz and millimeter-wave frequency ranges, only for UL streams. The performance results in terms of throughput and block error rate (BLER) have been presented with low-density parity-check (LDPC) code compared to turbo code.…”

Section: Introductionmentioning

confidence: 99%

Dynamic resource management algorithms for interference prediction in 5G new radio scenarios

Angri¹,

Mahfoudi²,

Najid³

2021

Preprint

View full text Add to dashboard Cite

Efficient Radio Resource Management is a key mechanism in interference management in 5G New Radio (NR) networks, specifically in the case of the presence of mobile users moving at high speed. To this end, the prediction and the evaluation of the propagation channel sensitivity requires that the radio resources allocation in NR must be efficient and powerful. Therefore, several scheduling algorithms have been developed and tested using the mmWave model of NS-3 simulator, with the aim of enhancing their contribution to improving the quality of the signal received by users. The performances have been evaluated in terms of Signal-to-Interference-and-Noise-Ratio (SINR) and signal Block Error Rate (BLER). The simulations were run for different types of data flows, and achieved satisfactory results for most schemes. The achievements clearly show the importance of scheduling algorithms in lowering received interference, but they have also demonstrated the stability and reliability of some of those strategies.

show abstract

5G New Radio Base-Station Sensitivity and Performance

Cited by 9 publications

References 4 publications

Outdoor-to-Indoor 28 GHz Wireless Measurements in Manhattan: Path Loss, Environmental Effects, and 90% Coverage

Outdoor-to-Indoor 28 GHz Wireless Measurements in Manhattan: Path Loss, Environmental Effects, and 90% Coverage

Machine Learning for Physical Layer in 5G and beyond Wireless Networks: A Survey

Dynamic resource management algorithms for interference prediction in 5G new radio scenarios

Contact Info

Product

Resources

About