Matching Game-Based Cell Association in Multi-RAT HetNet Considering Device Requirements

Anany, M. G.; Elmesalawy, Mahmoud M.; El-Haleem, Ahmed M. Abd

doi:10.1109/jiot.2019.2931448

Cited by 24 publications

(35 citation statements)

References 30 publications

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“…The application of cooperative GT to wireless communications requires communication among network entities and possibly extensive message exchange; this may invalidate the advantage of distributed solutions over centralized schemes. Even though cooperative games have been in general used to a lesser extent than non-cooperative models, for RAT selection scenarios, relevant work in the context of cooperative user association and resource allocation can be found for example in [118]- [124].…”

Section: A Cooperative Vs Non-cooperative Gamesmentioning

confidence: 99%

User-Centric Radio Access Technology Selection: A Survey of Game Theory Models and Multi-Agent Learning Algorithms

et al. 2021

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User-centric radio access technology (RAT) selection is a key communication paradigm, given the increased number of available RATs and increased cognitive capabilities at the user end. When considered against traditional network-centric approaches, user-centric RAT selection results in reduced network-side management load, and leads to lower operational costs for RATs, as well as improved quality of service (QoS) and quality of experience (QoE) for users. The complex between-users interactions involved in RAT selection require, however, specific analyses, toward developing reliable and efficient schemes. Two theoretical frameworks are most often applied to user-centric RAT selection analysis, i.e., game theory (GT) and multi-agent learning (MAL). As a consequence, several GT models and MAL algorithms have been recently proposed to solve the problem at hand. A comprehensive discussion of such models and algorithms is, however, currently missing. Moreover, novel issues introduced by next-generation communication systems also need to be addressed. This paper proposes to fill the above gaps by providing a unified reference for both ongoing research and future research directions in the field. In particular, the review addresses the most common GT and MAL models and algorithms, and scenario settings adopted in user-centric RAT selection in terms of utility function and network topology. Regarding GT, the review focuses on non-cooperative models, because of their widespread use in RAT selection; as for MAL, a large number of algorithms are described, ranging from game-theoretic to reinforcement learning (RL) schemes, and also including most recent approaches, such as deep RL (DRL) and multi-armed bandit (MAB). Models and algorithms are analyzed by comparatively reviewing relevant literature. Finally, open challenges are discussed, in light of ongoing research and standardization activities.

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Section: A Cooperative Vs Non-cooperative Gamesmentioning

confidence: 99%

User-Centric Radio Access Technology Selection: A Survey of Game Theory Models and Multi-Agent Learning Algorithms

et al. 2021

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“…On the other hand, the MBS path loss model can be obtained by the standard LTE model which is based on the distance between them(d) as [17,18].…”

Section: System Modelmentioning

confidence: 99%

“…where, is the uplink transmitted power from NB-IoT device , is the channel gain, and the interference from the UBS NB-IoT device is . According to the adaptive modulation and coding scheme (MCS) used in LTE, the actual uplink data rate of any NB-IoT device can be calculated physically depending on mapping the corresponding SINR [18] as,…”

Section: System Modelmentioning

confidence: 99%

No-Regret Matching Game Algorithm for NOMA Based UAV-Assisted NB-IoT Systems

Metwaly¹,

El-Haleem²,

Elghandour³

2021

ISI

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NB-IoT is the standardized technology for machine type communication (MTC) in Long Term Evolution (LTE). NB-IoT can achieve IoT requirements nevertheless, it suffers a low rate and capacity. On the other hand, Unmanned aerial vehicles (UAV) and Non-Orthogonal Multiple Access (NOMA) are promising technology used to enhance the throughput, capacity, and coverage of wireless communication networks. In this paper, we propose a heterogeneous network scenario where a UAV small Base Station (UBS) is used to assist the LTE Macro Base Station (MBS) with the help of the Non-Orthogonal Multiple Access technique to solve the NB-IoT throughput and capacity issues. Matching game based no-regret learning algorithm is proposed to optimize the NB-IoT device association and using NOMA pairing at each base station to provide the maximum system total rate and capacity. Simulation results show that our proposed scheme increases the total rate of the system by 60% and the system capacity by at least 80%, compared to NOMA without UAV and the total rate and capacity of the system by 200% and 85% respectively, with OMA scheme.

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“…The channel gain between the two paired devices and eNB are hm, hn.. According to our goal to enhance the overall NB-IoT network data rate, the uplink data rate of each VNB-IoT device is calculated, however, the actual uplink data rate cannot be calculated simply using Shannon channel model, instead of that the physical uplink data rate can be used [13,14]. By using the physical rate Eq.…”

Section: System Modelmentioning

confidence: 99%

NOMA Based Matching Game Algorithm for Narrowband Internet of Things (NB-IoT) System

Metwaly¹,

El-Haleem²,

Elghandour³

2020

ISI

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Narrowband Internet of Things (NB-IoT) is introduced by the third generation partnership project (3GPP) as a standardized technology for machine type communication (MTC) in Long Term Evolution (LTE). NB-IoT can satisfy many IoT requirements, Nevertheless, NB-IoT suffers a low data rate and low network capacity. This paper provides nonorthogonal multiple access (NOMA) scheme based matching game for uplink in NB-IoT systems to enhance the capacity and data rate by providing more connectivity for massive MTC devices. We formulate our optimization problem to maximize the total system rate by using a matching game. Simulation results show that the proposed scheme increases the total system rate by at least 150% and the system capacity by at least 125%, compared to OMA, and NOMA-water filling scheme.

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Matching Game-Based Cell Association in Multi-RAT HetNet Considering Device Requirements

Cited by 24 publications

References 30 publications

User-Centric Radio Access Technology Selection: A Survey of Game Theory Models and Multi-Agent Learning Algorithms

User-Centric Radio Access Technology Selection: A Survey of Game Theory Models and Multi-Agent Learning Algorithms

No-Regret Matching Game Algorithm for NOMA Based UAV-Assisted NB-IoT Systems

NOMA Based Matching Game Algorithm for Narrowband Internet of Things (NB-IoT) System

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