Dynamic spectrum management using frequency selection at licensed and unlicensed bands for efficient vehicle-to-vehicle communication

Keshvinder Singh Randhava; Mardeni Roslee; Zubaida Yusoff

doi:10.12688/f1000research.73481.1

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Method Article

Dynamic spectrum management using frequency selection at licensed and unlicensed bands for efficient vehicle-to-vehicle communication

[version 1; peer review: 1 approved with reservations, 1 not approved]

Keshvinder Singh Randhava¹, Mardeni Roslee¹, Zubaida Yusoff ¹

PUBLISHED 22 Dec 2021

Author details Author details

¹ Centre for Wireless Technology, Faculty of Engineering., Multimedia University, Cyberjaya, Selangor, 63100, Malaysia

Keshvinder Singh Randhava
Roles: Data Curation, Formal Analysis, Investigation, Methodology, Writing – Original Draft Preparation

Mardeni Roslee
Roles: Conceptualization, Methodology, Project Administration, Resources, Supervision, Writing – Review & Editing

Zubaida Yusoff
Roles: Conceptualization, Project Administration, Supervision, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS

This article is included in the Research Synergy Foundation gateway.

Abstract

Background: The exponential increase in pattern of vehicles on the roads demands a need to develop a vehicular infrastructure that may not only ease congestions and provide a better experience but also pivot the levels of safety among users. The development of wireless technology has made it convenient for machines, devices and vehicles to interact with one another. The efficacy of this wireless communications relies on utilising current and available technology to enable information to be shared efficiently. In the wake of the available advancement in wireless technology, a new dynamic spectrum management (DSM) in vehicle-to-vehicle (V2V) communication that coexists with the existing Long-Term Evolution (LTE) network to increase the throughput in V2V communication is proposed. This will provide some solutions to enable a more efficient vehicular infrastructure.
Methods: This paper focuses on the utilization of DSM in V2V communications by selecting an appropriate frequency band through the selection of available licensed and unlicensed frequency bands for vehicles. Further investigations are done to identify the effect of interference in the dynamic spectrum by observing the path loss, SINR, and the throughput with various interfering users.
Results: The results show that the performance of the proposed DSM augments a significant improvement in the overall throughput and the signal-to-interference-plus-noise ratio (SINR) value is reduced by up to 60% when compared to the fixed spectrum allocation.
Conclusions: Although the dynamic spectrum is still affected by the interference from the existing cellular users, the throughput of the dynamic spectrum remains sufficient to transmit the information to other vehicles.

Keywords

Vehicle-to-vehicle (V2V), dynamic spectrum, the fifth-generation technology standard(5G), Long Term Evolution (LTE), throughput, interference.

Corresponding author: Zubaida Yusoff

Competing interests: No competing interests were disclosed.

Grant information: The author(s) declared that no grants were involved in supporting this work.

Copyright: © 2021 Randhava KS et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Randhava KS, Roslee M and Yusoff Z. Dynamic spectrum management using frequency selection at licensed and unlicensed bands for efficient vehicle-to-vehicle communication [version 1; peer review: 1 approved with reservations, 1 not approved]. F1000Research 2021, 10:1309 (https://doi.org/10.12688/f1000research.73481.1) First published: 22 Dec 2021, 10:1309 (https://doi.org/10.12688/f1000research.73481.1) Latest published: 22 Dec 2021, 10:1309 (https://doi.org/10.12688/f1000research.73481.1)

Introduction

Vehicle-to-vehicle (V2V) communication allows vehicles to interact and trade data or information directly with one another without the need of accessing a base station. This may involve data exchange with regards to speed, position and traffic environment.¹ This method of communication correspondingly benefits users to avoid collisions, reduce traffic congestions and subsequently improves road safety.² As the current wireless network technology moves from the fourth generation (4G) towards the fifth generation (5G),³^,⁴ there lies a promising future for V2V communication in terms of improved latency, throughput and connections⁵^,⁶ between vehicles. This enhancement in wireless network technology compels researchers to identify in detail the improvements to be made.

Literature review

V2V communication is initially utilised in dedicated short-range communications (DSRC) through the exchange of information with each other.⁷ The DSRC generally works on the 5.9 GHz band⁸ of the radio frequency range and is powerful and efficient for vehicle communication that involves short to medium distance coverage. However, this technology does not offer high-speed communication.

Due to the innovations driven by the 3^rd Generation Partnership Project (3GPP), the most recent vehicle-to-everything are utilising the cellular networks. This network is known as Cellular V2X (C-V2X) to separate it from the wireless local area network (WLAN)-based vehicle technology.⁹ The 3GPP began to normalise work regarding cellular vehicular communication in 2014 during Release 14.¹⁰ This technology is known as Long-Term Evolution (LTE)-V2X since the functionalities of this technology relies on LTE networks. The C-V2X functionalities are extended to help 5G technology in the 3GPP Release 15.¹¹

Each technology has its assigned spectrum band where the users operate on the listened spectrum. Due to the expanding number of various kinds of wireless services and the number of vehicles on the road, the current fixed spectrum allocation policy such as dedicated short-range communications (DSRC) and LTE-V2X cannot cope with the growing demands of the wireless networks.¹² Hence, the Federal Communications Commission (FCC) has proposed the usage of the underutilised spectrum and spectrum portions that are not in use by the user. The technology that can solve this problem is known as dynamic spectrum management (DSM) where it can combine a few bands in a wireless service.

DSM is a technique subject to the theoretical concept in the communication network created to enhance the performance and proficiency of the network. Various studies have been done to explore the use of DSM by utilising diverse frequency ranges simultaneously in the vehicular network. Research¹³ was conducted in 2016 to demonstrate and analyse the efficiency of dynamic spectrum sharing among DSRC with the support of Wi-Fi systems. The author used the Poisson point process to dynamically construct a vehicular network where DSRC vehicles and Wi-Fi devices coincide and proposed the quality measures on the spectrum efficiency and data rate transmission. Results of this study indicate that dynamic spectrum allocation in the 5.9 GHz band improves the efficiency of the Wi-Fi network without excessively reducing the quality of the DSRC system.

The range optimisation for dynamic spectrum sharing using DSRC and 5G technology in V2V communication links was studied by Elias Alwan.¹⁴ Since DSRC provides a limited capacity for high-speed data, Alwan combined the usage of the 5G network to provide a high data rate in the V2V network. The channel link for this research work uses the free-space path loss model to investigate the link budget of the communication channel. Results from this paper show that the maximum data rate produced by this network model is 27 Mbps with a network coverage up to 500 m.

Haibo Zhou et al.¹⁵ developed a dynamic sharing spectrum between 5G and DSRC frequency bands to prepare vehicular communication with massive system capacity while reducing the delay in communication and reducing cost. The idea proposed in this paper meets the developing expectations of vehicular communication services. However, this study does not consider that vehicles have high versatility and due to the heterogeneity of 5G, accomplishing an effective dynamic spectrum sharing is challenging in the real world. Hence, this paper makes realistic studies of the dynamic sharing spectrum of 5G and DSRC for the vivid experience in the vehicular communication network through the usage of available licensed and unlicensed frequency bands available. This paper will study the results of the path loss, signal-to-interference-plus-noise ratio (SINR), and the throughput with various interfering users.

Table 1 shows that the LTE cellular network and mm-Wave technology may offer a high data rate in the vehicular network. Hence, this work will utilize LTE and mm-Wave technology that supports 5G to be used dynamically in the V2V communications to achieve better throughput in the network.

Table 1. Specifications of the technology in vehicular communications.

	Cellular	DSRC	mm-Wave
Standardization	3GPP LTE-A	IEEE 802.11p/WAVE	IEEE 802.11ad
Frequency band	Licensed	5.7-5.92 GHz	30-300 GHz
Mobility support	350 km/h	140 km/h	140 km/h
Data rate	300 Mbps	3-27 Mbps	~ 10 Gbps
Coverage	>4 km	<1 M	<100 m
Method	D2D	Ad hoc	D2D/Ad hoc

Methods

Ethical approval

This study got ethical approval from the Multimedia University Research Ethics Committee with approval number EA2832021.

Dynamic spectrum management using licensed and unlicensed bands

This paper proposes an effective dynamic spectrum by utilising both licensed and unlicensed bands. The user’s spectrum selection is determined by the distance between the V2V pairs. Selection is made to determine the most suitable frequency band to be utilised in establishing a V2V communication channel. The proposed dynamic spectrum deals with the distance between the transmitter and receiver of the V2V link and selects either the licensed or unlicensed band that may significantly reduce the overall system interference while boosting the throughput in vehicular communication.

The licensed spectrum with a frequency band of 2.4 GHz is chosen since it is generally used by the LTE network. Similarly, the unlicensed spectrum band that uses the mm-Wave frequency band (5G) of 60 GHz¹⁶ is chosen. Due to the short coverage of 5G, the supporting LTE network is utilized to support a larger coverage area by providing communication between vehicles.¹⁷

Network communication model

The network system is designed in a hexagonal shape with a cell radius of 500 m (Figure 1). The network system is assumed to support both cellular and V2V communication in the network, where there exist n number of the existing cellular users which shall be considered as interfering devices in this paper, denoted as CUEs, and m number of vehicles doing local V2V high-capacity data exchange denoted as VUEs. It is assumed that all the vehicle and cellular user equipment is capable of supporting both licensed and unlicensed spectrum communications simultaneously. The value of the vehicle is more than the number of interfering devices, m >> n in this network setup.

Figure 1. Vehicular network setup for dynamic spectrum management (DSM).

V2V = vehicle to vehicle; LTE = Long-Term Evolution.

The base station is placed in the middle of the network cell. The number of V2V pairs will increase gradually to analyse the algorithm of the network. The V2V pair will consist of one vehicle that will be a transmitter and another vehicle as a receiver. The cellular user and the V2V users are distributed uniformly over a hexagon cell area. In addition, it is assumed that all the devices in the network can support V2V mode and cellular mode in the setup.

The two frequency bands that are supported by the devices to transmit the data in the network are the licensed spectrum (LTE-V2V communication) and the unlicensed spectrum (5G cellular network supported by the mm-Wave technology).⁴ Additionally, the interfering devices that are connected to the vehicle are set to communicate using licensed spectrum, meanwhile, the V2V communication system will use either one of the spectrum bands depending on the best choice at that particular distance.

Channel model setup

The path-loss for the proposed dynamic spectrum is designed by considering the free space, urban, and Line of Sight (LOS) models. It is also designed by considering the mm-Wave propagation models where the heavy oxygen absorption from the environment and heavy attenuation at 60 GHz is considered.¹⁸ The path loss in the transmission link between the V2V pair for long and short distances is modelled as:

(1)

Pathloss V 2 V_{RX}^{long} = 20 {log}_{10} (distance) + 20 {log}_{10} (f_{c}) + 32.45

(2)

Pathloss V 2 V_{RX}^{short} = 12 {log}_{10} (distance) + 12 {log}_{10} (f_{c}) + 19.45

Meanwhile, the path loss due to the interfering users, CUE is modelled as:

(3)

{Pathloss Int}_{RX}^{long} = 34 {log}_{10} (distance) + 34 {log}_{10} (f_{c}) + 64.9

(4)

Pathloss {Int}_{RX}^{short} = 20 {log}_{10} (distance) + 20 {log}_{10} (f_{c}) + 32.45

For equations (1) to (4), the f_c is in MHz and d is in metres. The constant value represents the shadowing effect of signal power fluctuations due to the surrounding obstacles. This dynamic spectrum V2V model is designed according to a Log-normal shadowing distribution and using a Rayleigh fast fading in the channel link.¹⁹

SINR in V2V communication is obtained in this work to evaluate the performance of the spectrum and to compare the differences between the SINR fixed spectrum allocation of the unlicensed band and licensed band and the dynamic spectrum allocation in vehicular communication.

(5)

{SINR}_{V 2 V}^{long} = \frac{Received power, long}{N_{o} + Σ_{P Interfering}^{long}}

(6)

{SINR}_{V 2 V}^{short} = \frac{Received power, short}{N_{o} + Σ_{P Interfering}^{short}}

The received power signal is calculated by power transmitted minus path loss in the V2V link. The noise power density is stated as N₀. It has a dimension of power over frequency, which is measured in watts per hertz or equivalent to watt-seconds. Meanwhile, the total power of the interfering devices received by the V2V receiver is label as $Σ_{P Interfering}^{long}$ and $Σ_{P Interfering}^{short}$ for long and short distances, respectively.

The Shannon capacity theorem is considered when calculating the throughput of vehicular communication. It characterises the maximum value of the successful message transmitted or the data capacity which is sent over any channel or communication medium. The system’s throughput for long and short distances is through equations below:

(7)

T_{V 2 V}^{long} = B_{licensed} \times 10 {log}_{10} (1 + {SINR}_{V 2 V}^{long})

(8)

T_{V 2 V}^{short} = B_{unlicensed} \times 10 {log}_{10} (1 + {SINR}_{V 2 V}^{short})

$B_{licensed}$ : bandwidth in Hz in the licensed spectrum.

$B_{unlicensed}$ : bandwidth in Hz in the unlicensed spectrum.

SINR: received signal power over the total noise power across the channel model.

Flowchart of the dynamic spectrum simulation

The simulation is done using MATLAB version R2020a (RRID: SCR 001622) Communications Toolbox 2020 using the algorithm flow chart depicted in Figure 2. Alternative software that may be used are GNU Octave and SAGE (Phython SageMath). The parameters are based on Table 2 and the distance between the vehicle transmitter and receiver is calculated. The equations used after this is dependent upon the distances between the two vehicles. When the distance of the link is less than 15 m, the 5G unlicensed band setup is set up for the vehicle to transmit the data where the communication link now utilises the equations of $Pathloss V 2 V_{RX}^{short}$ , ${SINR}_{V 2 V}^{short}$ , ${Throughput}_{V 2 V}^{short}$ , which are equations (2), (6) and (8), respectively.

Figure 2. Flow chart of the dynamic spectrum simulations.

Table 2. Simulation parameters.

Parameters	Values
Carrier frequency	60 GHz
Carrier frequency	2.4 GHz
Unlicensed bandwidth	1.5 GHz
Licensed bandwidth	5 MHz
Number of interfering users	20
Number of vehicle users	300
Number of V2V links	1 (increased gradually)
Cell radius	500 m
Noise power density	−174 dBm/Hz
VUE transmitting power	26 dBm
CUE transmitting power	23 dBm

As the communication channel is set up using the LTE communication network, equations used to analyse the communication are equations (1), (5) and (7). Table 2 shows the parameter used in the simulation.

The code used in this paper is available from GitHub and is archived with Zenodo.²¹

Algorithm of the dynamic spectrum

Proposed performance test on the DSMin V2V communication.

1: Initialise $CUE = 20$ , $VUE = 300$ ;

2: Generate the network setup model

3: Calculate the distance between Vtx and Vrx

4: if distance >15 % use licensed parameter

5: f2 = 2.4 GHz,

6: for V2V communication link

7: Calculate $Pathloss V 2 V_{RX}^{long}$

8: Calculate $Received power, long$

9: For Interference link

10: Calculate $Pathloss {Int}_{RX}^{long}$

11: Calculate $Interfering power, long$

12: Find ${SINR}_{V 2 V}^{long}$

13: Calculate $T_{V 2 V}^{long}$

14: else% if distance less than 15 m

15: f1 = 60 GHz % use unlicensed parameter

16: For V2V communication link

17: Calculate $Pathloss V 2 V_{RX}^{short}$ ;

18: Calculate $Received power, short$

19: For Interference link

20: Calculate $Pathloss {Int}_{RX}^{short}$

21: Calculate $Interfering power, short$

22: Find ${SINR}_{V 2 V}^{short}$

23: Calculate $T_{V 2 V}^{short}$

24: end

Results and discussion

Vehicle-to-vehicle network setup

The network communication set up in the proposed DSM in V2V technology is simulated in Figure 3. The network model is designed in a hexagonal shape since it resembles the real-world communication coverage area.²⁰ The radius of this system is set to 500 m. As mentioned previously, the number of vehicle users is set up more than the value of the interfering devices in the cellular network. The V2V receiver is denoted as ‘●’ meanwhile the V2V Transmitter is set as ‘►’ in Figure 3 where its location is fixed throughout the simulation. The interfering and V2V receivers are distributed uniformly over the network setup and the base station is located at the centre where it is symbolised as ‘o’.

Figure 3. V2V (vehicle-to-vehicle) network setup in the dynamic spectrum allocation.

Figure 4 shows a significant improvement in the overall throughput in the network. The dynamic spectrum combines the technology of LTE and 5G in the vehicular communication network based on distance. Hence, there is still a throughput should the distance be more than 15 m as the proposed dynamic spectrum will utilise the LTE technology to expand the coverage area while maintaining a decent throughput in the channel. If the graph is focused at one point where the distance between the transmitter and receiver is 10 m, it can be observed that the throughput of the proposed dynamic spectrum is 224.5 Kbps, followed by the throughput of the unlicensed spectrum 168.1 Kbps and the licensed spectrum 72.44 Kbps, which denotes that the throughput of the dynamic spectrum suggests an improvement of 67% compared to the throughput in the licensed spectrum.

Figure 4. Throughput of the proposed dynamic algorithm supporting 4G and 5G technologies.

Figure 5 shows that the static licensed spectrum offers a better path loss interference since the licensed spectrums are initially assigned for licensed users. Hence, the amount of interfering effects to this licensed spectrum is minimised compared to the other spectrum. Observing the point where the unlicensed spectrum is utilised, the unlicensed static spectrum suffered a higher path loss attenuation. The reason for this is because this spectrum was not initially assigned to V2V technology. It is actually an unutilised spectrum that people generally use when conducting experiments in the innovation of wireless communication technology.

Figure 5. Path loss vs distance.

Conclusion

In this paper, the DSM is implemented by choosing the more suitable frequency band based on the distance between V2V links to minimise the total system interference while increasing the throughput of the network. A simulation is performed using MATLAB R2020a to prove that the proposed dynamic spectrum guarantees improvement from the existing fixed spectrum allocation. Results show that the SINR of the proposed dynamic system is reduced by up to 60% compared to the unlicensed spectrum. It is also observed that the throughput of the proposed system shows a significant improvement of up to 67% compared to the licensed spectrum allocation. Towards the end of this paper, the effect of interference is studied in the dynamic spectrum. Through the graph, it can be observed that the interference power becomes higher as the distance between the transmitter and receiver is higher and this interference can be influenced by the number of interfering devices. Despite this, it can still provide sufficient throughput for the vehicles to share the information between them.

Data availability

All data underlying the results are available as part of the article and no additional source data are required.

Software availability

• Source code available from: https://github.com/kesh3141/V2V-Comms_5
• Archived source code at time of publication: https://doi.org/10.5281/zenodo.5760098.²¹
• License: The GNU General Public License v3.0

References

1. U.S. Department of Transportation: Vehicle to Vehicle Communications for Trucks.2014; p. 2.
2. Li B, He D, Feng Y, et al.: Spectrum Resource Allocation Scheme for Alarm Information Delivery in V2V Communication. IEEE Veh. Technol. Conf. 2018; vol. 2018-August pp. 0–4. Publisher Full Text
3. Storck CR, Duarte-Figueiredo F: A Survey of 5G Technology Evolution, Standards, and Infrastructure Associated with Vehicle-to-Everything Communications by Internet of Vehicles. IEEE Access. 2020; 8: 117593–117614. Publisher Full Text
4. Storck CR, Duarte-Figueiredo F: 5G V2X Ecosystem Providing Entertainment on Board Using Mm Wave Communications. Proc. - 2018 10th IEEE Latin-American Conf. Commun. LATINCOM 2018. 2019. Publisher Full Text
5. Parvez I, Rahmati A, Guvenc I, et al.: A survey on low latency towards 5G: RAN, core network and caching solutions. arXiv. 2017; vol. 20(no. 4): pp. 3098–3130.
6. El Zorkany M, Yasser A, Galal AI: Vehicle to Vehicle ‘V2V’ Communication: Scope, Importance, Challenges, Research Directions and Future. Open Transp. J. 2020; 14(1): 86–98. Publisher Full Text
7. Kenney BJB: Dedicated Short-Range Communications (DSRC) Standards in the United States.2011; vol. 99(no. 7).
8. Guo J, Balon N: Vehicular Ad Hoc Networks and Dedicated Short-Range Communication.2006; vol. 6439: pp. 1–56.
9. Molina-Masegosa R, Gozalvez J, Sepulcre M: Configuration of the C-V2X Mode 4 Sidelink PC5 Interface for Vehicular Communication. Proc. - 14th Int. Conf. Mob. Ad-Hoc Sens. Networks, MSN 2018. 2018; pp. 43–48. Publisher Full Text
10. E. U. Terrestrial: Evolved Universal Terrestrial Radio Access (E-UTRA); Overall description stage 2; (Release 10), document ‘TS 36.300 V10.2.0,’ 3GPP, 2011-11.2013; vol. 0.
11. Naik G, Choudhury B, Park JM: IEEE 802.11bd 5G NR V2X: Evolution of Radio Access Technologies for V2X Communications. IEEE Access. 2019; 7: 70169–70184. Publisher Full Text
12. Gyawali S, Xu S, Qian Y, et al.: Challenges and Solutions for Cellular based V2X Communications. IEEE Commun. Surv. Tutorials. 2020; 23: 1–1. Publisher Full Text
13. Xu W, Wu, Daneshmand, et al.: A data privacy-protective mechanism for WBAN. Wirel. Commun. Mob. Comput. 2015. Publisher Full Text
14. Govindarajulu SR, Alwan EA: Range Optimization for DSRC and 5G Millimeter-Wave Vehicle-to-Vehicle Communication Link. 2019 Int. Work. Antenna Technol. iWAT 2019. 2019; pp. 228–230. Publisher Full Text
15. Zhou H, Xu W, Bi Y, et al.: Toward 5G spectrum sharing for immersive-experience-driven vehicular communications. IEEE Wirel. Commun. 2017; 24(6): 30–37. Publisher Full Text
16. 5G Automotive Association: An assessment of direct communications technologies for improved road safety in the EU.2017; (no. December): pp. 1–80. Publisher Full Text Reference Source
17. Chen S, et al.: Vehicle-to-Everything (v2x) Services Supported by LTE-Based Systems and 5G. IEEE Commun. Stand. Mag. 2017; 1(2): 70–76. Publisher Full Text
18. Simić L, Riihijärvi J, Mähönen P: Can statistical propagation models be saved by real 3D city data?: A regionalized study of radio coverage in New York City. 2015 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN). 2015; pp. 285–288. Publisher Full Text
19. Kompella S, Ephremides A: Stable Throughput Regions in Wireless Networks. 2014. Publisher Full Text
20. Nasri R, Jaziri A: Analytical Tractability of Hexagonal Network Model with Random User Location. IEEE Trans. Wirel. Commun. May 2016; 15(5): 3768–3780. Publisher Full Text
21. Randhava KS, Yusoff Z, Roslee M: Vehicle to Vehicle Comms Using Licensed & Unlicensed Frequecies (MATLAB Communications Toolbox 2018a (License No:40598489).) [Data set]. Zenodo. 2021. Publisher Full Text

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Version 1

VERSION 1 PUBLISHED 22 Dec 2021

Author details Author details

¹ Centre for Wireless Technology, Faculty of Engineering., Multimedia University, Cyberjaya, Selangor, 63100, Malaysia

Keshvinder Singh Randhava
Roles: Data Curation, Formal Analysis, Investigation, Methodology, Writing – Original Draft Preparation

Mardeni Roslee
Roles: Conceptualization, Methodology, Project Administration, Resources, Supervision, Writing – Review & Editing

Zubaida Yusoff
Roles: Conceptualization, Project Administration, Supervision, Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

The author(s) declared that no grants were involved in supporting this work.

Article Versions (1)

version 1

Published: 22 Dec 2021, 10:1309

https://doi.org/10.12688/f1000research.73481.1

Copyright

© 2021 Randhava KS et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Randhava KS, Roslee M and Yusoff Z. Dynamic spectrum management using frequency selection at licensed and unlicensed bands for efficient vehicle-to-vehicle communication [version 1; peer review: 1 approved with reservations, 1 not approved]. F1000Research 2021, 10:1309 (https://doi.org/10.12688/f1000research.73481.1)

NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.

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Open Peer Review

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VERSION 1

PUBLISHED 22 Dec 2021

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Reviewer Report 03 Apr 2023

Mustafa Maad Hamdi, Communications and Computers Engineering, Al-Maarif University College, Ramadi, Iraq

Not Approved

https://doi.org/10.5256/f1000research.77135.r168198

There are some points to improve the article:

Abstract

Need to mention the issue of this research paper and mention the value of results.
The abstract needs a

There are some points to improve the article:

Abstract

Need to mention the issue of this research paper and mention the value of results.
The abstract needs a rewrite, because the abstract must contain three points like: problem, method and results. Also, there are some problems with the grammar.

Introduction

Need more explanation in the introduction and mention the current issue and the objective.
Need to add the figure showing dynamic spectrum management (DSM).

Literature review

Need to mention the strengths and weaknesses for all studies.
Need to update all references and use more specific references in five recent years.

Method

Need to adding more explanation in methods and presented in clearly. Explain the steps of proposed method DSM with V2V ) and how observing the path loss, SINR and throughput with various interference user.
Need to adding flow chart. A flowchart would help make this article very simple to read and understand the proposed method (DSM) step by step.

Results

Need to compare with benchmark.
Need to add more explanation in the results. Explain the figure of result in detail and discuss each value.
All figures in the results need to be redrawn again. They are not clear when the reader wants to see and read in this article.

Conclusion

The conclusion need to be rewritten and mention the future work. This conclusion must contain the contribution and the value of results. Also there are some problem with the language.

Is the rationale for developing the new method (or application) clearly explained?

Partly
Is the description of the method technically sound?

No
Are sufficient details provided to allow replication of the method development and its use by others?

No
If any results are presented, are all the source data underlying the results available to ensure full reproducibility?

Partly
Are the conclusions about the method and its performance adequately supported by the findings presented in the article?

No

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: VANETS

I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.

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Reviewer Report 13 Mar 2023

Muhammad Waqas, Computer Engineering Department, University of Bahrain, Bahrain, 32038, Bahrain

Approved with Reservations

https://doi.org/10.5256/f1000research.77135.r159120

The authors proposed the DSM in V2V communications by selecting an appropriate frequency band by selecting available licensed and unlicensed frequency bands for vehicles. Although the paper has merit, it requires extensive revision before indexing. My comments are given as ... Continue reading

The authors proposed the DSM in V2V communications by selecting an appropriate frequency band by selecting available licensed and unlicensed frequency bands for vehicles. Although the paper has merit, it requires extensive revision before indexing. My comments are given as follows.

In the abstract, the authors should not mention the headings. It should be in a continuous one-paragraph with word limits. No need to mention the headings.
The introduction section is not convincing. The authors need to mention the details in the introduction about the work done, the problem statement, the background and the contributions of the work.
In the literature, most of the related works are too old, for instance, most of the papers in the literature are from 2010 to 2015. There have been many advancements in this field since 2015. The authors need to update the related work section by citing up-to-date papers from 2020 to 2023. Some of the suggested references to be cited are given below. All the references are related to vehicular networks and are up-to-date:

- Badshah et al. (2022)¹
- Haider et al. (2023)²
- Ullah et al. (2022)³
- Munawar et al. (2022)⁴
- Abbas et al. (2022)⁵
- Waqas et al. (2022)⁶
- Ullah et al. (2021)⁷
- Ullah et al. (2021)⁸
- Haider et al. (2020)⁹
- Waqas et al. (2020)¹⁰
- Waqas et al. (2020)¹¹
The algorithm is not in the standard format. The current format is not professional. I suggest seeing the published paper and taking the idea of how to write the algorithm in the technical papers.
All the figures must be revised. The current figures are not clear and don't attract the readers. The figures must be clear, clean and neat, and the text inside the figure should be in the same font and style as the text on the paper.
The authors also need to describe each figure clearly. The current version of the explanation is not convincing and doesn't provide the proper meaning of the work done by the authors.
More simulation results are needed to claim the work done.
It is better to write notations in the equation rather than writing the words.
The manuscript also needs an extensive revision to remove all the English mistakes, grammatical issues and typo errors.

The authors need an extensive revision of the paper and I will be happy to see the revise version of the manuscript.

Is the rationale for developing the new method (or application) clearly explained?

No
Is the description of the method technically sound?

Yes
Are sufficient details provided to allow replication of the method development and its use by others?

Yes
If any results are presented, are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions about the method and its performance adequately supported by the findings presented in the article?

Yes

References

1. Badshah A, Waqas M, Muhammad F, Abbas G, et al.: AAKE-BIVT: Anonymous Authenticated Key Exchange Scheme for Blockchain-Enabled Internet of Vehicles in Smart Transportation. IEEE Transactions on Intelligent Transportation Systems. 2022. 1-17 Publisher Full Text
2. Haider U, Waqas M, Hanif M, Alasmary H, et al.: Network load prediction and anomaly detection using ensemble learning in 5G cellular networks. Computer Communications. 2023; 197: 141-150 Publisher Full Text
3. Ullah S, Abbas G, Waqas M, Abbas Z, et al.: RSU assisted reliable relay selection for emergency message routing in intermittently connected VANETs. Wireless Networks. 2022. Publisher Full Text
4. Munawar S, Ali Z, Waqas M, Tu S, et al.: Cooperative Computational Offloading in Mobile Edge Computing for Vehicles: A Model-based DNN Approach. IEEE Transactions on Vehicular Technology. 2022. 1-16 Publisher Full Text
5. Abbas G, Ullah S, Waqas M, Abbas Z, et al.: A position-based reliable emergency message routing scheme for road safety in VANETs. Computer Networks. 2022; 213. Publisher Full Text
6. Waqas M, Tu S, Halim Z, Rehman S, et al.: The role of artificial intelligence and machine learning in wireless networks security: principle, practice and challenges. Artificial Intelligence Review. 2022; 55 (7): 5215-5261 Publisher Full Text
7. Ullah S, Abbas G, Waqas M, Abbas ZH, et al.: Position-based Vehicular Clustering for Emergency Messages Dissemination in Zone of Interest. 2021 2nd Asia Symposium on Signal Processing (ASSP). 2021. 61-66 Publisher Full Text
8. Ullah S, Abbas G, Waqas M, Abbas ZH, et al.: EEMDS: An Effective Emergency Message Dissemination Scheme for Urban VANETs.Sensors (Basel). 2021; 21 (5). PubMed Abstract | Publisher Full Text
9. Haider S, Abbas Z, Abbas G, Waqas M, et al.: A Novel Cross-Layer V2V Architecture for Direction-Aware Cooperative Collision Avoidance. Electronics. 2020; 9 (7). Publisher Full Text
10. Waqas M, Tu S, Ur Rehman S, Halim Z, et al.: Authentication of Vehicles and Road Side Units in Intelligent Transportation System. Computers, Materials & Continua. 2020; 64 (1): 359-371 Publisher Full Text
11. Waqas M, Niu Y, Li Y, Ahmed M, et al.: A Comprehensive Survey on Mobility-Aware D2D Communications: Principles, Practice and Challenges. IEEE Communications Surveys & Tutorials. 2020; 22 (3): 1863-1886 Publisher Full Text

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Wireless Communication, Vehicular Networks

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

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VERSION 1 PUBLISHED 22 Dec 2021

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Version 1 22 Dec 21	read	read

Muhammad Waqas, University of Bahrain, Bahrain, Bahrain
Mustafa Maad Hamdi, Al-Maarif University College, Ramadi, Iraq

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Reviewer Report

10 Views

03 Apr 2023 | for Version 1

Mustafa Maad Hamdi, Communications and Computers Engineering, Al-Maarif University College, Ramadi, Iraq

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Not Approved

There are some points to improve the article:

Abstract

Need to mention the issue of this research paper and mention the value of results.
The abstract needs a rewrite, because the abstract must contain three points like: problem, method and results. Also, there are some problems with the grammar.

Introduction

Need more explanation in the introduction and mention the current issue and the objective.
Need to add the figure showing dynamic spectrum management (DSM).

Literature review

Need to mention the strengths and weaknesses for all studies.
Need to update all references and use more specific references in five recent years.

Method

Need to adding more explanation in methods and presented in clearly. Explain the steps of proposed method DSM with V2V ) and how observing the path loss, SINR and throughput with various interference user.
Need to adding flow chart. A flowchart would help make this article very simple to read and understand the proposed method (DSM) step by step.

Results

Need to compare with benchmark.
Need to add more explanation in the results. Explain the figure of result in detail and discuss each value.
All figures in the results need to be redrawn again. They are not clear when the reader wants to see and read in this article.

Conclusion

The conclusion need to be rewritten and mention the future work. This conclusion must contain the contribution and the value of results. Also there are some problem with the language.

Is the rationale for developing the new method (or application) clearly explained?

Partly
Is the description of the method technically sound?

No
Are sufficient details provided to allow replication of the method development and its use by others?

No
If any results are presented, are all the source data underlying the results available to ensure full reproducibility?

Partly
Are the conclusions about the method and its performance adequately supported by the findings presented in the article?

No

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

VANETS

I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.

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Reviewer Report

7 Views

13 Mar 2023 | for Version 1

Muhammad Waqas, Computer Engineering Department, University of Bahrain, Bahrain, 32038, Bahrain

7 Views Cite this report Responses(0)

Approved With Reservations

The authors proposed the DSM in V2V communications by selecting an appropriate frequency band by selecting available licensed and unlicensed frequency bands for vehicles. Although the paper has merit, it requires extensive revision before indexing. My comments are given as follows.

In the abstract, the authors should not mention the headings. It should be in a continuous one-paragraph with word limits. No need to mention the headings.
The introduction section is not convincing. The authors need to mention the details in the introduction about the work done, the problem statement, the background and the contributions of the work.
In the literature, most of the related works are too old, for instance, most of the papers in the literature are from 2010 to 2015. There have been many advancements in this field since 2015. The authors need to update the related work section by citing up-to-date papers from 2020 to 2023. Some of the suggested references to be cited are given below. All the references are related to vehicular networks and are up-to-date:

- Badshah et al. (2022)¹
- Haider et al. (2023)²
- Ullah et al. (2022)³
- Munawar et al. (2022)⁴
- Abbas et al. (2022)⁵
- Waqas et al. (2022)⁶
- Ullah et al. (2021)⁷
- Ullah et al. (2021)⁸
- Haider et al. (2020)⁹
- Waqas et al. (2020)¹⁰
- Waqas et al. (2020)¹¹
The algorithm is not in the standard format. The current format is not professional. I suggest seeing the published paper and taking the idea of how to write the algorithm in the technical papers.
All the figures must be revised. The current figures are not clear and don't attract the readers. The figures must be clear, clean and neat, and the text inside the figure should be in the same font and style as the text on the paper.
The authors also need to describe each figure clearly. The current version of the explanation is not convincing and doesn't provide the proper meaning of the work done by the authors.
More simulation results are needed to claim the work done.
It is better to write notations in the equation rather than writing the words.
The manuscript also needs an extensive revision to remove all the English mistakes, grammatical issues and typo errors.

The authors need an extensive revision of the paper and I will be happy to see the revise version of the manuscript.

Is the rationale for developing the new method (or application) clearly explained?

No
Is the description of the method technically sound?

Yes
Are sufficient details provided to allow replication of the method development and its use by others?

Yes
If any results are presented, are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions about the method and its performance adequately supported by the findings presented in the article?

Yes

References

1. Badshah A, Waqas M, Muhammad F, Abbas G, et al.: AAKE-BIVT: Anonymous Authenticated Key Exchange Scheme for Blockchain-Enabled Internet of Vehicles in Smart Transportation. IEEE Transactions on Intelligent Transportation Systems. 2022. 1-17 Publisher Full Text
2. Haider U, Waqas M, Hanif M, Alasmary H, et al.: Network load prediction and anomaly detection using ensemble learning in 5G cellular networks. Computer Communications. 2023; 197: 141-150 Publisher Full Text
3. Ullah S, Abbas G, Waqas M, Abbas Z, et al.: RSU assisted reliable relay selection for emergency message routing in intermittently connected VANETs. Wireless Networks. 2022. Publisher Full Text
4. Munawar S, Ali Z, Waqas M, Tu S, et al.: Cooperative Computational Offloading in Mobile Edge Computing for Vehicles: A Model-based DNN Approach. IEEE Transactions on Vehicular Technology. 2022. 1-16 Publisher Full Text
5. Abbas G, Ullah S, Waqas M, Abbas Z, et al.: A position-based reliable emergency message routing scheme for road safety in VANETs. Computer Networks. 2022; 213. Publisher Full Text
6. Waqas M, Tu S, Halim Z, Rehman S, et al.: The role of artificial intelligence and machine learning in wireless networks security: principle, practice and challenges. Artificial Intelligence Review. 2022; 55 (7): 5215-5261 Publisher Full Text
7. Ullah S, Abbas G, Waqas M, Abbas ZH, et al.: Position-based Vehicular Clustering for Emergency Messages Dissemination in Zone of Interest. 2021 2nd Asia Symposium on Signal Processing (ASSP). 2021. 61-66 Publisher Full Text
8. Ullah S, Abbas G, Waqas M, Abbas ZH, et al.: EEMDS: An Effective Emergency Message Dissemination Scheme for Urban VANETs.Sensors (Basel). 2021; 21 (5). PubMed Abstract | Publisher Full Text
9. Haider S, Abbas Z, Abbas G, Waqas M, et al.: A Novel Cross-Layer V2V Architecture for Direction-Aware Cooperative Collision Avoidance. Electronics. 2020; 9 (7). Publisher Full Text
10. Waqas M, Tu S, Ur Rehman S, Halim Z, et al.: Authentication of Vehicles and Road Side Units in Intelligent Transportation System. Computers, Materials & Continua. 2020; 64 (1): 359-371 Publisher Full Text
11. Waqas M, Niu Y, Li Y, Ahmed M, et al.: A Comprehensive Survey on Mobility-Aware D2D Communications: Principles, Practice and Challenges. IEEE Communications Surveys & Tutorials. 2020; 22 (3): 1863-1886 Publisher Full Text

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Wireless Communication, Vehicular Networks

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

Respond to this report

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[1] 1. U.S. Department of Transportation: Vehicle to Vehicle Communications for Trucks.2014; p. 2.

[2] 2. Li B, He D, Feng Y, et al.: Spectrum Resource Allocation Scheme for Alarm Information Delivery in V2V Communication. IEEE Veh. Technol. Conf. 2018; vol. 2018-August pp. 0–4. Publisher Full Text

[3] 3. Storck CR, Duarte-Figueiredo F: A Survey of 5G Technology Evolution, Standards, and Infrastructure Associated with Vehicle-to-Everything Communications by Internet of Vehicles. IEEE Access. 2020; 8: 117593–117614. Publisher Full Text

[4] 4. Storck CR, Duarte-Figueiredo F: 5G V2X Ecosystem Providing Entertainment on Board Using Mm Wave Communications. Proc. - 2018 10th IEEE Latin-American Conf. Commun. LATINCOM 2018. 2019. Publisher Full Text

[5] 5. Parvez I, Rahmati A, Guvenc I, et al.: A survey on low latency towards 5G: RAN, core network and caching solutions. arXiv. 2017; vol. 20(no. 4): pp. 3098–3130.

[6] 6. El Zorkany M, Yasser A, Galal AI: Vehicle to Vehicle ‘V2V’ Communication: Scope, Importance, Challenges, Research Directions and Future. Open Transp. J. 2020; 14(1): 86–98. Publisher Full Text

[7] 7. Kenney BJB: Dedicated Short-Range Communications (DSRC) Standards in the United States.2011; vol. 99(no. 7).

[8] 8. Guo J, Balon N: Vehicular Ad Hoc Networks and Dedicated Short-Range Communication.2006; vol. 6439: pp. 1–56.

[9] 9. Molina-Masegosa R, Gozalvez J, Sepulcre M: Configuration of the C-V2X Mode 4 Sidelink PC5 Interface for Vehicular Communication. Proc. - 14th Int. Conf. Mob. Ad-Hoc Sens. Networks, MSN 2018. 2018; pp. 43–48. Publisher Full Text

[10] 10. E. U. Terrestrial: Evolved Universal Terrestrial Radio Access (E-UTRA); Overall description stage 2; (Release 10), document ‘TS 36.300 V10.2.0,’ 3GPP, 2011-11.2013; vol. 0.

[11] 11. Naik G, Choudhury B, Park JM: IEEE 802.11bd 5G NR V2X: Evolution of Radio Access Technologies for V2X Communications. IEEE Access. 2019; 7: 70169–70184. Publisher Full Text

[12] 12. Gyawali S, Xu S, Qian Y, et al.: Challenges and Solutions for Cellular based V2X Communications. IEEE Commun. Surv. Tutorials. 2020; 23: 1–1. Publisher Full Text

[13] 13. Xu W, Wu, Daneshmand, et al.: A data privacy-protective mechanism for WBAN. Wirel. Commun. Mob. Comput. 2015. Publisher Full Text

[14] 14. Govindarajulu SR, Alwan EA: Range Optimization for DSRC and 5G Millimeter-Wave Vehicle-to-Vehicle Communication Link. 2019 Int. Work. Antenna Technol. iWAT 2019. 2019; pp. 228–230. Publisher Full Text

[15] 15. Zhou H, Xu W, Bi Y, et al.: Toward 5G spectrum sharing for immersive-experience-driven vehicular communications. IEEE Wirel. Commun. 2017; 24(6): 30–37. Publisher Full Text

[16] 16. 5G Automotive Association: An assessment of direct communications technologies for improved road safety in the EU.2017; (no. December): pp. 1–80. Publisher Full Text Reference Source

[17] 17. Chen S, et al.: Vehicle-to-Everything (v2x) Services Supported by LTE-Based Systems and 5G. IEEE Commun. Stand. Mag. 2017; 1(2): 70–76. Publisher Full Text

[18] 18. Simić L, Riihijärvi J, Mähönen P: Can statistical propagation models be saved by real 3D city data?: A regionalized study of radio coverage in New York City. 2015 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN). 2015; pp. 285–288. Publisher Full Text

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[20] 20. Nasri R, Jaziri A: Analytical Tractability of Hexagonal Network Model with Random User Location. IEEE Trans. Wirel. Commun. May 2016; 15(5): 3768–3780. Publisher Full Text

[21] 21. Randhava KS, Yusoff Z, Roslee M: Vehicle to Vehicle Comms Using Licensed & Unlicensed Frequecies (MATLAB Communications Toolbox 2018a (License No:40598489).) [Data set]. Zenodo. 2021. Publisher Full Text

Dynamic spectrum management using frequency selection at licensed and unlicensed bands for efficient vehicle-to-vehicle communication

Abstract

Keywords

Introduction

Literature review

Table 1. Specifications of the technology in vehicular communications.

Methods

Ethical approval

Dynamic spectrum management using licensed and unlicensed bands

Network communication model

Figure 1. Vehicular network setup for dynamic spectrum management (DSM).

Channel model setup

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Flowchart of the dynamic spectrum simulation

Figure 2. Flow chart of the dynamic spectrum simulations.

Table 2. Simulation parameters.

Algorithm of the dynamic spectrum

Results and discussion

Vehicle-to-vehicle network setup

Figure 3. V2V (vehicle-to-vehicle) network setup in the dynamic spectrum allocation.

Figure 4. Throughput of the proposed dynamic algorithm supporting 4G and 5G technologies.

Figure 5. Path loss vs distance.

Conclusion

Data availability

Software availability

References

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