3D non-stationary vehicle-to-vehicle MIMO channel model for 5G millimeter-wave communications

Eldowek, Basim Mohammed; El-atty, Saied M. Abd; El-Rabaie, El-Sayed M.; El‐Samie, Fathi E. Abd

doi:10.1016/j.dsp.2019.102580

Cited by 11 publications

(4 citation statements)

References 31 publications

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“…However, citing these technologies also introduces complications that complicate network management. AI‐based approaches can play a crucial role in numerous innovative heterogeneous network technologies and architectures 10 . By integrating AI with these methods, system capabilities can be further enhanced while addressing the problem of frequent mmWave interruptions.…”

Section: Related Workmentioning

confidence: 99%

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Double‐deep Q‐learning‐based handover management in mmWave heterogeneous networks with dual connectivity

Wang,

2023

Trans Emerging Tel Tech

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Millimeter wave (mmWave) technology, with its abundant spectrum resources and ultra‐high bandwidth, plays a crucial role in meeting the ultra‐high throughput requirements of future 6G communication and supporting a large number of device connections. However, mmWave signals are highly susceptible to congestion, and the channel quality in mmWave links can be extremely unstable, posing a significant challenge to mobility management in mmWave networks. In this article, we present a deep reinforcement learning‐based dual connectivity (DC) handover strategy for mmWave cellular networks. The DC architecture ensures a continuous connection to the core network, reducing the likelihood of frequent service interruptions. Additionally, the proposed handover strategy employs deep reinforcement learning to select the optimal handover target node, further enhancing system performance. Compared to traditional single connectivity (SC) handover strategies, DC involves more intricate handover trigger conditions and processes. We extend several typical SC handover algorithms for DC and compare them with the proposed algorithms in this study. Through numerical simulations, we evaluate the performance of these algorithms. The results demonstrate that the proposed algorithm minimizes unnecessary handovers, reduces service interruption time, and achieves the highest throughput.

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Section: Related Workmentioning

confidence: 99%

“…The variable u m n (t), which can take the values 0 or 1, represents whether UE nis connected to MN m in time slot t. A value of 1 indicates a connection, whereas a value of 0 indicates no connection. Therefore, the balance between maximizing system throughput and minimizing system downtime can be expressed as Equation (10) max…”

Section: Problem Formulationmentioning

confidence: 99%

Double‐deep Q‐learning‐based handover management in mmWave heterogeneous networks with dual connectivity

Wang,

2023

Trans Emerging Tel Tech

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“…Due to the mild precision and difficulty, the GBSM is one key form of the V2V channel model. Since the assumption of wide-sense stationary (WSS) is just for a brief amount of time, several non-WSS V2V GBSMs were proposed successively during the past [ 14 , 15 , 16 ]. Two-dimensional (2D) and 3D NS V2V GBSMs were analyzed with static clusters or moving scatterers in [ 17 , 18 , 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel GBSM for Non-Stationary V2V Channels Allowing 3D Velocity Variations

Ahmed

Hua

Zhu

et al. 2021

Sensors

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A new non-stationary (NS) geometry-based stochastic model (GBSM) is presented for developing and testing the communication systems of vehicle-to-vehicle (V2V) applications, which considers the three-dimensional (3D) scattering environments and allows 3D velocity as well. In this paper, the proposed GBSM for NS V2V channels allowed 3D velocity variations and was more suitable for actual V2V communications because it provided smoother transitions between the consecutive channel segments. The time-variant channel coefficient and the channel parameters, i.e., Doppler frequencies, path delay and power, angle of arrival (AoA), and angle of departure (AoD), were analyzed and derived. Likewise, the theoretical statistical properties as the probability density function (PDF), the auto-correlation function (ACF), and Doppler power spectral density (DPSD) were also analyzed and derived under the von Mises–Fisher (VMF) distribution. Finally, the theoretical and measured results were well coordinated alongside the implemented results, which confirmed the feasibility of the introduced model along with the theoretical expressions.

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“…Yuan et al [17] proposed a new 3D double spherical geometric scattering model. Eldowek et al [18–21] proposed a 3D geometric channel model to study the effects of channel parameters on envelope level crossing rate and the average fading duration in the millimetre‐wave band.…”

Section: Introductionmentioning

confidence: 99%

Channel modelling for vehicle‐to‐vehicle MIMO communications in geometrical rectangular tunnel scenarios

et al. 2020

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3D non-stationary vehicle-to-vehicle MIMO channel model for 5G millimeter-wave communications

Cited by 11 publications

References 31 publications

Double‐deep Q‐learning‐based handover management in mmWave heterogeneous networks with dual connectivity

Double‐deep Q‐learning‐based handover management in mmWave heterogeneous networks with dual connectivity

A Novel GBSM for Non-Stationary V2V Channels Allowing 3D Velocity Variations

Channel modelling for vehicle‐to‐vehicle MIMO communications in geometrical rectangular tunnel scenarios

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