DURATS: Distributed User-centric Radio Access Technology Selection framework

Mamadou, Ali Mamadou; Chalhoub, Gérard

doi:10.1109/wimob52687.2021.9606417

Cited by 2 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Meanwhile, in the UC approach, a vehicle gathers information on RAT condition and compute the optimum RAT selection. However, one drawback of UC is the unforeseen lack of RAT information that can cause an undesirable effect in RAT selection [11].…”

Section: Figure 1: Multi-rat V2i Communication Throughout Vehicle Jou...mentioning

confidence: 99%

“…For a simultaneous downlink communication in C-V2X, eNB allocates orthogonal channel to each vehicle without interference. The theoretical rate for each vehicle is calculating using equation (11), where W L is the eNB bandwidth in MHz, K number of sub-channels that can be allocated to a vehicle at time slot t, p is the mean transmission power, g is the transmission gain, no is the background noise density [57].…”

Section: Figure 11: Available Bw Vs Number Of Attached Vehiclesmentioning

confidence: 99%

See 1 more Smart Citation

Service-Oriented LSTM Multi-Criteria RAT Selection Scheme for Vehicle-to-Infrastructure Communication

2022

View full text Add to dashboard Cite

The evolution towards future Intelligent Transportation Systems (ITS) is significantly influenced by enabling telematics and safety services for vehicular use-cases, especially for autonomous driving. These vehicular use-cases have their own network requirements, with a variety of instantaneous requirements and dynamic characterizations that make delivering data via a single Radio Access Technology (RAT) severely challenging. By utilizing current and future network context and vehicular use-case requirements, the RAT selection scheme must select the optimum network that guarantees the continuity and high throughput data transfer. Moreover, the consideration of on-time stochastic queue backlog of all use-cases in each RATs, while maximizing throughput is important for a reliable vehicular use-case deployment. In this paper, we developed a Service-Oriented Joint LSTM Multi-Criteria (SOLMC) RAT selection scheme for Vehicle-to-Infrastructure (V2I) networks. The objective of this scheme is to maximize the overall network throughput while reducing the current stochastic queue backlog in deployed RATs. LSTM prediction technique is used as a network filter process that rejects the worst channel quality before the initialization RAT selection scheme. Transmission capacity, connectivity time, data delivery cost, and queue backlog are the decision criteria in our proposed SOLMC RAT scheme. In particular, to characterize the importance of these criteria factors, we construct Analytical Hierarchy Process (AHP) for each use-case. Extensive simulations are carried out under different network context conditions. The results demonstrate that the SOLMC scheme can significantly achieve up to 47.5% network throughput maximization and 20.42% packet delivery ratio improvement, using lower queue length compared to nearest-RAT-based selection. In addition, implementing LSTM in SOLMC improves the total average throughput by up to 3.1% and 6% packet delivery ratio.

show abstract

Section: Figure 1: Multi-rat V2i Communication Throughout Vehicle Jou...mentioning

confidence: 99%

Section: Figure 11: Available Bw Vs Number Of Attached Vehiclesmentioning

confidence: 99%

Service-Oriented LSTM Multi-Criteria RAT Selection Scheme for Vehicle-to-Infrastructure Communication

2022

View full text Add to dashboard Cite

show abstract

“…While the energy efficiency of the proposal is shown compared to legacy transmissions, it lacks consideration of QoS constraints of enduser applications in the utility decision function. This issue is in turn considered in [11]. Yet, the efficiency the proposal has to be shown in scenarios where multiple applications are used at the same time, which is realistic and closer to reallife usage.…”

Section: Introductionmentioning

confidence: 99%

MCDM-based RAT Selection method between LTE-D2D and IEEE 802.11

MAMADOU

Chalhoub

2022

Preprint

View full text Add to dashboard Cite

Radio Access Technology (RAT) selection is an essential component in designing heterogeneous network coexistence to enable the diversity of future applications. In the 5G paradigm, many wireless technologies are available to ensure ubiquitous radio access. However, there are still open issues related to the decision-making process and Quality of Service (QoS) constraints consideration when it comes to selecting the most suitable wireless interface for a specific data transmission e.g. in Device-To-Device (D2D) communications. In this paper, we present MCDM-RAT: RAT Selection method between LTE-D2D and IEEE 802.11 formulated as a Multiple Criteria Decision Making (MCDM) problem with a decision data analysis mechanism that allows accurate network performance estimation in the decision making process. Full-stack network simulation results show the benefits of MCDM-RAT compared to baseline decision mechanisms. Our findings show the benefits of MCDM-RAT, under multiple network load conditions, in enhancing network performance perceived by end-users (in terms of delay, through-put, and data delivery rate), while considering simultaneously multiple QoS constraints.

show abstract

DURATS: Distributed User-centric Radio Access Technology Selection framework

Cited by 2 publications

References 11 publications

Service-Oriented LSTM Multi-Criteria RAT Selection Scheme for Vehicle-to-Infrastructure Communication

Service-Oriented LSTM Multi-Criteria RAT Selection Scheme for Vehicle-to-Infrastructure Communication

MCDM-based RAT Selection method between LTE-D2D and IEEE 802.11

Contact Info

Product

Resources

About