A framework for mobile relay node selection for serving outdoor cell edge users

Sarieddine, Khaled; Charaf, Malak; Ayad, Mohammad; Artail, Hassan

doi:10.1016/j.comnet.2020.107359

Cited by 5 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Outage probability is the result of received SNR falls below a certain threshold γ th [26]. The received signal at the MS via access link is expressed as (11):…”

Section: Proposed Modelmentioning

confidence: 99%

“…this scheme. In order to get more benefits in terms of throughput and signal to interference and noise ratio (SINR) the authors in [11], proposed association framework to relay distribution where they use mobile relays to achieve a significant gain for public transportation vehicles. Assegie et al [12] explored a models employed for mobile stations for improving its performance within software.…”

mentioning

confidence: 99%

See 1 more Smart Citation

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

Aldhaibani

Alkhazraji

Mohammed

et al. 2022

IJEECS

View full text Add to dashboard Cite

High mobility requirements are one of the challenges face fifth-generation wireless (5G) cellular networks by providing acceptable wireless services to users traveling at speed up to 350 km/h. This paper presents a new scenario to increase the bit rate and coverage for passengers that use the vehicles for traveling through the installation a mobile station (MS) on these vehicles to provide a high-quality service to users. Based on signal to noise ratio (SNR’s) mathematical derivation and the outage probability of the user link, the proposed system is evaluated. Numerical results indicate an enhancement for users who received signal strength (RSS) from (-72 to -55) dBm and (15 to 38) Mbps in bit rate. Moreover, their number of users increased by proposed system adoption.

show abstract

“…Outage probability is the result of received SNR falls below a certain threshold γ th [26]. The received signal at the MS via access link is expressed as (11):…”

Section: Proposed Modelmentioning

confidence: 99%

mentioning

confidence: 99%

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

Aldhaibani

Alkhazraji

Mohammed

et al. 2022

IJEECS

View full text Add to dashboard Cite

show abstract

“…Multiple criteria for relay positioning have been previously considered in the literature [3], [4]. For example, maximizing the algebraic connectivity (AC) of relay-assisted network was considered for 3-dimensional (3-D) UAV positioning in [5], [6] or 2-D relay positioning in [7].…”

Section: Introductionmentioning

confidence: 99%

Relay Placement for Maximum Flow Rate via Learning and Optimization Over Riemannian Manifolds

Nasim,

Ibrahim

2023

Trans. Mach. Learn. Comm. Netw.

View full text Add to dashboard Cite

Networks topology can be represented over Riemannian manifolds (i.e., curved surfaces), given the symmetric positive definite (SPD) property of their spectral graphs. Moreover, maximizing flow rate of a baseline network topology through relay placement can be equivalent to finding the relay location that maximizes the geodesic distance (i.e., Riemannian metric) between the representations of a relayassisted network topology and the baseline one over Riemannian manifolds. Therefore in this paper, we propose two complementary approaches to find relay locations that maximize Riemannian metrics, such as Log-Euclidean metric (LEM), and hence maximize the network flow rate. First, we propose a Riemannian multi-armed bandit (RMAB) reinforcement learning model to track the relay positions, which increase the LEM towards the baseline network. Particularly, selecting a possible relay location is considered as an action, whereas the LEM represents the reward of the RMAB model. Second, we propose a Riemannian Particle Swarm Optimization (RPSO) algorithm that iteratively attempts to find the representation of relayassisted network topology with maximum LEM towards that of the baseline network over the Riemannian manifold. Simulation results show that both the RMAB and RPSO approaches converge to near-optimum solutions, which in the case of single relay placement achieve 94.3% and 90.6%, respectively, of the maximum possible network flow rate.INDEX TERMS Multi-armed bandit, network flow rate, particle swarm optimization, relay placement, reinforcement learning, Riemannian manifolds.

show abstract