Next Generation Wi-Fi and 5G NR-U in the 6 GHz Bands: Opportunities and Challenges

Naik, Gaurang; Park, Jung‐Min; Ashdown, Jonathan; Lehr, William

doi:10.1109/access.2020.3016036

Cited by 125 publications

(57 citation statements)

References 66 publications

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“…Likewise, regarding the 5G New Radio on Unlicensed band (NR-U) and WiFi (5G NR-U/WiFi) coexistence, the authors in [9] addressed the coexistence of 5G NR-U/WiFi in the 6 GHz band and in [10] addressed the coexistence of WiFi with beam-based 5G NR-U in the mmWave bands. Moreover, in [11], the authors investigated the WiFi and 5G NR coexisted network by implementing a mode selection procedure in the NR base stations to use either the licensed spectrum band or the unlicensed spectrum band.…”

Section: Related Workmentioning

confidence: 99%

“…Note that if a UE of any MNO O\o in any TTI t on any floor ω fl of a building l does not exist, then N l,ω fl t � N o in (9), which results in M l,ω fl * o,t � M C . is implies that the whole countrywide 28 GHz or 60 GHz mmWave spectrum can be allocated in all TTIs t to UEs of small cells of an MNO o on any floor ω fl in a building l. e same process described above is applicable for all MNOs o ∈ O at each renewal term t rnw to update M l,ω fl o,t in any TTI t to avoid CCI.…”

Section: Optimal Amount Ofmentioning

confidence: 99%

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Spectrum Allocation and Reuse in 5G New Radio on Licensed and Unlicensed Millimeter-Wave Bands in Indoor Environments

Saha

2021

Mobile Information Systems

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In this paper, by exploiting the frequency-domain, we propose a countrywide millimeter-wave (mmWave) spectrum allocation and reuse technique to allocate and reuse spatially the countrywide 28 GHz licensed spectrum and 60 GHz unlicensed spectrum to small cells (SCs) on each floor of a building of each Fifth-Generation (5G) New Radio (NR) Mobile Network Operator (MNO) of an arbitrary country. We develop an interference management scheme, model user statistics per SC, and interferer statistics per apartment and formulate the amount of the 28 GHz and 60 GHz spectra per MNO. We derive average capacity, spectral efficiency (SE), energy efficiency (EE), and cost efficiency (CE) when employing the proposed technique, as well as the traditional static licensed spectrum allocation technique. We discuss the implementation of the proposed technique and evaluate the performance under two scenarios, namely, SCs operate only in the 28 GHz in scenario 1, and both 28 GHz and 60 GHz in scenario 2. Extensive results and analyses are carried out for four MNOs, i.e., MNOs 1, 2, 3, and 4, in scenario 1. However, in scenario 2, in addition to MNOs 1, 2, 3, and 4, an incumbent Wireless Gigabit (WiGig) operator is considered. It is shown that the proposed technique with no co-channel interference can improve average capacity, SE, EE, and CE of MNO 1 by 3 times, 1.65 times, 75%, and 60%, respectively, in scenario 1, whereas 6.12 times, 5.104 times, 85.8%, and 83.15%, respectively, in scenario 2. Moreover, with an increase in reuse factors, SE increases linearly and EE increases negative exponentially. Further, we show that the proposed technique can satisfy SE and EE requirements for sixth-generation (6G) mobile systems. Finally, we discuss offered benefits and point out key issues of the proposed technique for further studies.

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Section: Related Workmentioning

confidence: 99%

Section: Optimal Amount Ofmentioning

confidence: 99%

Spectrum Allocation and Reuse in 5G New Radio on Licensed and Unlicensed Millimeter-Wave Bands in Indoor Environments

Saha

2021

Mobile Information Systems

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“…One of the mitigation strategies consists of improving spectrum utilization efficiency by sharing the unlicensed operation of radio-access technologies (RATs). Recent examples include the use of the 5 GHz band, which Wi-Fi occupies (IEEE 802.11a) and an unlicensed version of Long-Term Evolution cellular standard (unlicensed-LTE) [ 1 , 2 ], as well as the unlocking of the 6 GHz band for Wi-Fi 6 (IEEE 802.11ax) and 5G New Radio Unlicensed (NR-U) standards that will operate along with incumbent primary users [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Robust Computationally-Efficient Wireless Emitter Classification Using Autoencoders and Convolutional Neural Networks

Almazrouei

Gianini

Almoosa

et al. 2021

Sensors

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This paper proposes a novel Deep Learning (DL)-based approach for classifying the radio-access technology (RAT) of wireless emitters. The approach improves computational efficiency and accuracy under harsh channel conditions with respect to existing approaches. Intelligent spectrum monitoring is a crucial enabler for emerging wireless access environments that supports sharing of (and dynamic access to) spectral resources between multiple RATs and user classes. Emitter classification enables monitoring the varying patterns of spectral occupancy across RATs, which is instrumental in optimizing spectral utilization and interference management and supporting efficient enforcement of access regulations. Existing emitter classification approaches successfully leverage convolutional neural networks (CNNs) to recognize RAT visual features in spectrograms and other time-frequency representations; however, the corresponding classification accuracy degrades severely under harsh propagation conditions, and the computational cost of CNNs may limit their adoption in resource-constrained network edge scenarios. In this work, we propose a novel emitter classification solution consisting of a Denoising Autoencoder (DAE), which feeds a CNN classifier with lower dimensionality, denoised representations of channel-corrupted spectrograms. We demonstrate—using a standard-compliant simulation of various RATs including LTE and four latest Wi-Fi standards—that in harsh channel conditions including non-line-of-sight, large scale fading, and mobility-induced Doppler shifts, our proposed solution outperforms a wide range of standalone CNNs and other machine learning models while requiring significantly less computational resources. The maximum achieved accuracy of the emitter classifier is 100%, and the average accuracy is 91% across all the propagation conditions.

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“…Today, the number of people demanding fast and efficient data speeds has increased exponentially. In this sense, there is a constant evolution towards networks with data speeds of 10 Gbps [ 1 , 2 ], which will allow the development of new applications, such as HD video and online games [ 3 ]. The New Generation Mobile (5G) wireless communication systems are called to provide 1000-times more capacity and support 100-times more smart devices than the current Fourth Generation (4G) [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

A Two-Hop mmWave MIMO NR-Relay Nodes to Enhance the Average System Throughput and BER in Outdoor-to-Indoor Environments

Peña

Alonso

2021

Sensors

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Millimeter-Wave (mmWave) bands are receiving enormous attention in 5G mobile communications, due to the capability to provide a multi-gigabit transmission rate. In this paper, a two-hop architecture for 5G communications with the capacity to support high end-to-end performance due to the use of Relay Nodes (RNs) in mmWave-bands is presented. One of the novelties of the paper is the implementation of Amplify-and-Forward (A&F) and Decode-and-Forward (D&F) RNs along with a mmWave-band transceiver chain (Tx/Rx). In addition, two approaches for channel estimation were implemented at the D&F RN for decoding the backhaul link. One of them assumes complete knowledge of the channel (PCE), and the other one performs the channel estimation through Least Square (LS) estimator. A large number of simulations, using MATLABTM and SimulinkTM software, were performed to verify the potential benefits of the proposal two-hop 5G architecture in an outdoor-to-indoor scenario. The main novelty in performing these simulations is the use of signals with 5G features, as DL-SCH transport channel coding, PDSCH generation, and SS Burst generation, which is another of the main contributions of the paper. On the other hand, mmWave transmitter and receiver chains were designed and implemented with off-the shelf components. The simulations show that the two-hop network substantially improves the Key Performance Indicators (KPIs), Bit Error Rate (BER), and Throughput, in the communications between the logical 5G Radio Node (gNodeB), and the New Radio User Equipment (NR-UE). For example, a throughput improvement of 22 Mbps is obtained when a 4 × 4 × 2 MIMO D&F with LS architecture is used versus a SISO D&F with PCE architecture for Signal-to-Noise Ratio (SNR) = 20 dB and 64-QAM signal. This improvement reaches 96 Mbps if a 256-QAM signal is considered. The improvement in BER is 11 dB and 10.5 dB, respectively, for both cases. This work also shows that the obtained results with D&F RNs are better than with A&F RNs. For example, an improvement of 17 Mbps in the use of SISO D&F with LS vs. SISO A&F, for the 64-QAM signal is obtained. Besides, this paper constitutes a first step to the implementation of a mmWave MIMO 5G cooperative network platform.

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Next Generation Wi-Fi and 5G NR-U in the 6 GHz Bands: Opportunities and Challenges

Cited by 125 publications

References 66 publications

Spectrum Allocation and Reuse in 5G New Radio on Licensed and Unlicensed Millimeter-Wave Bands in Indoor Environments

Spectrum Allocation and Reuse in 5G New Radio on Licensed and Unlicensed Millimeter-Wave Bands in Indoor Environments

Robust Computationally-Efficient Wireless Emitter Classification Using Autoencoders and Convolutional Neural Networks

A Two-Hop mmWave MIMO NR-Relay Nodes to Enhance the Average System Throughput and BER in Outdoor-to-Indoor Environments

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