Effective Capacity Maximization in beyond 5G Vehicular Networks: A Hybrid Deep Transfer Learning Method

Abstract

How to improve delay-sensitive traffic throughput is an open issue in vehicular communication networks, where a great number of vehicle to infrastructure (V2I) and vehicle to vehicle (V2V) links coexist. To address this issue, this paper proposes to employ a hybrid deep transfer learning scheme to allocate radio resources. Specifically, the traffic throughput maximization problem is first formulated by considering interchannel interference and statistical delay guarantee. The effective capacity theory is then applied to develop a power allocation scheme on each channel reused by a V2I and a V2V link. Thereafter, a deep transfer learning scheme is proposed to obtain the optimal channel assignment for each V2I and V2V link. Simulation results validate that the proposed scheme provides a close performance guarantee compared to a globally optimal scheme. Besides, the proposed scheme can guarantee lower delay violation probability than the schemes aiming to maximize the channel capacity.

The Role of Knowledge Creation-Oriented Convolutional Neural Network in Learning Interaction

“…But this method does not pay attention to the fact that the content between the source domain and the target domain is not exactly the same. Therefore, in order to make up for the shortcomings of traditional methods, this paper improves ResNet and proposes a new deep transfer learning method [ 16 , 17 ]. After improving ResNet, a deep transfer learning model is reframed, as shown in Figure 6 .…”

Improving the detection of intrusion in vehicular ad-hoc networks with modified identity-based cryptosystem

“…Where: 𝑀 𝑘 is the membership authentication code for participants on the network, 𝐶 𝑘 is the control authentication key for the NCU, 𝐼𝐷 𝑇𝐴 is the identity of the TA, 𝑅𝐼𝐷 𝑣 is the real identity of the vehicle, 𝐼𝐷 𝑁𝐶𝑈 is the identity of NCU, '𝑠' is the master secret key, 𝛾 𝑖 is a set of integer randomly generated in 𝛾 𝑖 ∈ 𝑍 𝑞 * , 𝐻 is the hash function, || is the concatenation operation and ⊕ is the exclusive-OR operation. The NCU checks for the validity of the request using (7), and further checks the revocation list which contains the real identities of revoked vehicles received securely from the TA to ascertain the authenticity of the vehicles' membership.…”

“…Given the tremendous benefits expected from vehicular communications, just like any other wireless communication network, security has been a major challenge. Intruders can easily intercept, modify, and replay messages transmitted on the network or even impersonate other vehicles to broadcast incorrect information [6], [7]. Besides, the privacy of users on the network is also a challenge.…”

Coverage enhancements of vehicles users using mobile stations at 5G cellular networks

“…where ℎ m is the channel between eNB and 𝑀𝑆𝑥(𝑡) is the transmitted signal at any slot, ℎ a is access link between MS, and user equipment (UE) is user equipment [6], [21]. MS is the mobile small station placed on the top of vehicle to increase the coverage and the capacity for the passengers especially when the vehicle or bus is pass at cell boundaries, ψ is amplification factor of amplify and forward mobile relay station was selected in this work, therefore the re-transmitted signal form MS at the 𝑡 1 can be write as:…”

“…In order to evaluate the signal to noise ratio (SNR) for this system, there are very influencing factors such as, channel coefficient, quality of service (QoS), interference and noise [22], [21]. For simplicity, channel coefficient can be defined as |ℎ| 2 = 𝛫(ℓ) −𝑎 where 𝑎 is the path-loss exponent which is dependent on the environment , ℓ is the length of distance between the sender and receiver , 𝛫 is the transceivers' coefficients [22], [23] whereas, 𝛫 = 𝐺 𝑡 𝐺 𝑟 ℎ 𝑡 2 ℎ 𝑟 2 , (𝐺 𝑡 ℎ 𝑡 2 ) and (𝐺 𝑟 ℎ 𝑟 2 ) are the gains and heights of the sender and receiver antennas, respectively, whereas ℓ is the distance between the source and destination.…”