Spectral and Energy Efficiency Analysis of mmWave Communications With Channel Inversion in Outband D2D Network

Chevillon, Romain; Andrieux, Guillaume; Diouris, Jean-François

doi:10.1109/access.2018.2882679

Cited by 14 publications

(9 citation statements)

References 42 publications

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“…• The coverage probability for this LoRa network with the improved path-loss model and the α-stable distributed uncoordinated networks interference is given for various values of ED and GW densities. Note that the analytical results for one gateway have been validated by numerical simulations in [25], and the system model with multiple gateways based on stochastic geometry calculations was compared and validated with simulations in our previous works [16], [36]. • The values of the SF-based ED densities are not obtained with analytical approximations (as made in related works) but with Monte-Carlo simulations that converge to results that are much closer to the true values.…”

Section: B Contributions and Organizationmentioning

confidence: 84%

“…Different means to model a network are possible. In this work, we propose to use stochastic geometry [16]. In [17], the authors use a Poisson rain (that is a space-time Poisson process of packets) to model a LoRa network, and analyze the successful reception probability versus the node density for all the spreading factors.…”

Section: A Related Workmentioning

confidence: 99%

“…7 shows the Signal-to-Interference Ratio success probability vs. distance d = d i0 between the i-th ED and its relative 0-th GW. The curves are obtained thanks to (16), for λ E = 5 EDs per km 2 and λ G = 0.005, 0.01, 0.05 (orange curve, green curve and purple curves, respectively) GWs per km 2 .…”

Section: B Sir Analysismentioning

confidence: 99%

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Stochastic Geometry-Based Analysis of the Impact of Underlying Uncorrelated IoT Networks on LoRa Coverage

et al. 2022

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IoT networks are more and more present nowadays. Some IoT protocols share the same bandwidth leading to interference on neighboring networks and decrease of overall coverage. To contribute to this problem, an analytical study of the coverage of a LoRa network with underlying uncoordinated IoT networks for uplink transmissions is presented in this paper. Using stochastic geometry, closed form analytical expressions are proposed allowing to analyze the success and coverage probabilities for a LoRa network. An appropriate model of the path loss including real-life values is used to characterize the log-distance propagation parameters. The interference comes from both the LoRa network itself and the underlying IoT networks, modeled with an α-stable distribution based on recent measurements. It is shown that for an environment with a huge amount of surrounding uncoordinated IoT networks, the gateways deployment should be doubled to reach a decent coverage probability, compared to an environment where the underlying interfering networks are not considered.

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Section: B Contributions and Organizationmentioning

confidence: 84%

Section: A Related Workmentioning

confidence: 99%

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Stochastic Geometry-Based Analysis of the Impact of Underlying Uncorrelated IoT Networks on LoRa Coverage

et al. 2022

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“…In this paper, frequencyselective fading is not considered because measurements made in [50] demonstrate that the delay spread is generally small. Moreover, using techniques like orthogonal frequencydivision multiplexing (OFDM) or frequency domain equalization limits the effect of the frequency-selectivity in fading [51]. In addition, small-scale fading at mmWave cellular systems is less severe than that in Long-Term Evolution (LTE) systems when using base station antennas with narrow beams, as the measurement results show [52].…”

Section: Propagation Channel Modelmentioning

confidence: 99%

Enhancing 5G Small Cell Selection: A Neural Network and IoV-Based Approach

Alablani

Arafah

2021

Sensors

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The ultra-dense network (UDN) is one of the key technologies in fifth generation (5G) networks. It is used to enhance the system capacity issue by deploying small cells at high density. In 5G UDNs, the cell selection process requires high computational complexity, so it is considered to be an open NP-hard problem. Internet of Vehicles (IoV) technology has become a new trend that aims to connect vehicles, people, infrastructure and networks to improve a transportation system. In this paper, we propose a machine-learning and IoV-based cell selection scheme called Artificial Neural Network Cell Selection (ANN-CS). It aims to select the small cell that has the longest dwell time. A feed-forward back-propagation ANN (FFBP-ANN) was trained to perform the selection task, based on moving vehicle information. Real datasets of vehicles and base stations (BSs), collected in Los Angeles, were used for training and evaluation purposes. Simulation results show that the trained ANN model has high accuracy, with a very low percentage of errors. In addition, the proposed ANN-CS decreases the handover rate by up to 33.33% and increases the dwell time by up to 15.47%, thereby minimizing the number of unsuccessful and unnecessary handovers (HOs). Furthermore, it led to an enhancement in terms of the downlink throughput achieved by vehicles.

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“…The subcarrier allocation problem was solved with the Hungarian algorithm obtaining a near optimal solution. The concept of channel inversion was applied in [13] to develop an energy efficient outband D2D communication network in the mmWave band. It considered the effect of sectored antenna models and the number of elements in the uniform linear array antennas for SE and EE maximization.…”

Section: Related Workmentioning

confidence: 99%

Energy Efficient Resource Allocation in Millimeter-Wave D2D Enabled 5G Cellular Networks

Ombongi¹,

Absaloms

Kibet

2020

Eng. Technol. Appl. Sci. Res.

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The current trend has seen the data capacity and traffic density increase due to the increased demand for multimedia services. Since this cannot be handled successfully by the current 4G networks, there is a need to integrate the mmWave and the Device-to-Device (D2D) communication 5G technologies to meet this increased demand and traffic density. However, there is the challenge of increased interference between dense D2D users and cellular users if D2D users are allowed to reuse the resources allocated to cellular users. This degrades the performance of the D2D users in terms of achievable data rate and Energy Efficiency (EE). The paper formulates a match theoretic resource allocation scheme to maximize the achievable D2D sum rate. In addition, an EE optimization problem is formulated for D2D users by considering the rate and power constraints. The EE optimization problem is solved by the Lagrangian dual decomposition method. The algorithms were simulated in MATLAB and the results were compared to Hungarian and heuristic optimization algorithms. The results showed that the match theoretic resource allocation is on average 1.82 times better than the Hungarian algorithm. At the same time, the match theoretic resource allocation algorithm increases fairness in resource allocation as it maintains a higher sum rate for low and high-density number of users. The proposed EE optimization algorithm improved the D2D performance by 8.2% compared to the heuristic algorithm.

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Spectral and Energy Efficiency Analysis of mmWave Communications With Channel Inversion in Outband D2D Network

Cited by 14 publications

References 42 publications

Stochastic Geometry-Based Analysis of the Impact of Underlying Uncorrelated IoT Networks on LoRa Coverage

Stochastic Geometry-Based Analysis of the Impact of Underlying Uncorrelated IoT Networks on LoRa Coverage

Enhancing 5G Small Cell Selection: A Neural Network and IoV-Based Approach

Energy Efficient Resource Allocation in Millimeter-Wave D2D Enabled 5G Cellular Networks

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