A Survey on Beyond 5G Network With the Advent of 6G: Architecture and Emerging Technologies

Dogra, Anutusha; Jha, Rakesh Kumar; Jain, Shubha

doi:10.1109/access.2020.3031234

Cited by 283 publications

(129 citation statements)

References 78 publications

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“…These targets can be summarized into: high data rate, massive device connectivity, energy saving, cost reduction and reduced latency. An effective approach to satisfy the 5G and beyond high transmission rate requirement is reducing the communication distance between users and base stations (BS)s [1], and [2]. However, this distance issue becomes a more complex problem in the next generation because of the high user mobility due to the 5G large network size and decreased coverage radius [3] - [4].…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Dynamic Downlink PPP Cellular Networks Over Generalized Fading Channels With MRC Diversity

et al. 2021

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This paper proposes novel and generalized expressions to characterize the performance of modern cellular networks under realistic user mobility behavior. The η-µ distribution is employed to derive the received power probability density function, the average bit error rate for different modulation schemes, and the coverage probability assuming a Poisson point process spatial distribution of base stations in downlink. The user is assumed to experience fading with Maximum Ratio Combining (MRC) and move according to a random way-point mobility model. To get more insights on the achivable diversity order, accurate asymptotic expressions for the coverage probability and average bit error rate are derived. The derived expressions are applicable to different widely-used fading environments, such as Rayleigh and Nakagami-m as particular cases, by an appropriate selection of the η-µ parameters. Monte Carlo simulation was used to show the validity of the proposed expressions. In addition, the generalized expressions allow the system designer to quantify the effects of user mobility on the cellular network performance, in different propagation environments, and network topologies as a function of the number of base stations and MRC branches.

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Section: Introductionmentioning

confidence: 99%

Performance Analysis of Dynamic Downlink PPP Cellular Networks Over Generalized Fading Channels With MRC Diversity

et al. 2021

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“…Therefore, in this survey, we focus on the application of ML algorithms to HO management in cellular networking with special attention aimed at 5G and B5G networks in order to keep the discussion timely as 5G has already become a reality, and visionary works about 6G has started to appear in the literature [25]- [27]. One of the common grounds of these studies, which try to plot the framework of 6G, is that they all agree that artificial intelligence (AI) and subsequently ML will play a key role in 6G networks as intelligence is expected to lie at the core of 6G networks.…”

Section: Introductionmentioning

confidence: 99%

“…One of the common grounds of these studies, which try to plot the framework of 6G, is that they all agree that artificial intelligence (AI) and subsequently ML will play a key role in 6G networks as intelligence is expected to lie at the core of 6G networks. Moreover, terahertz (THz) frequencies have been projected to be used in 6G due to the abundance of the bandwidth available in these frequencies [26]. However, this makes the HO management concept even more crucial as THz band includes much higher frequencies that mm-wave band, and therefore the smaller footprint (and more frequent HOs) will be much more significant.…”

Section: Introductionmentioning

confidence: 99%

A Survey of Machine Learning Applications to Handover Management in 5G and Beyond

et al. 2021

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Handover (HO) is one of the key aspects of next-generation (NG) cellular communication networks that need to be properly managed since it poses multiple threats to quality-of-service (QoS) such as the reduction in the average throughput as well as service interruptions. With the introduction of new enablers for fifth-generation (5G) networks, such as millimetre wave (mm-wave) communications, network densification, Internet of things (IoT), etc., HO management is provisioned to be more challenging as the number of base stations (BSs) per unit area, and the number of connections has been dramatically rising. Considering the stringent requirements that have been newly released in the standards of 5G networks, the level of the challenge is multiplied. To this end, intelligent HO management schemes have been proposed and tested in the literature, paving the way for tackling these challenges more efficiently and effectively. In this survey, we aim at revealing the current status of cellular networks and discussing mobility and HO management in 5G alongside the general characteristics of 5G networks. We provide an extensive tutorial on HO management in 5G networks accompanied by a discussion on machine learning (ML) applications to HO management. A novel taxonomy in terms of the source of data to be utilized in training ML algorithms is produced, where two broad categories are considered; namely, visual data and network data. The stateof-the-art on ML-aided HO management in cellular networks under each category is extensively reviewed with the most recent studies, and the challenges, as well as future research directions, are detailed. INDEX TERMS Handover, machine learning, mobility management, fifth generation.

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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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A Survey on Beyond 5G Network With the Advent of 6G: Architecture and Emerging Technologies

Cited by 283 publications

References 78 publications

Performance Analysis of Dynamic Downlink PPP Cellular Networks Over Generalized Fading Channels With MRC Diversity

Performance Analysis of Dynamic Downlink PPP Cellular Networks Over Generalized Fading Channels With MRC Diversity

A Survey of Machine Learning Applications to Handover Management in 5G and Beyond

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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