User association and resource allocation in 5G (AURA-5G): A joint optimization framework

Jain, Akshay; López-Aguilera, Elena; Demirkol, Ilker

doi:10.1016/j.comnet.2021.108063

Cited by 18 publications

(25 citation statements)

References 36 publications

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“…The multiplication of the two decision variables µ o j ,i and ω o j ,i,c j introduces a non-linearity in the objective function and constraints of the optimization problem formulated in (18). Similar to [70], a linearisation term is introduced that replaces the product with a single binary variable as depicted by (24) to avoid this non-linearity.…”

Section: Appendix Amentioning

confidence: 99%

Service-Aware User Association and Resource Allocation in Integrated Terrestrial and Non-Terrestrial Networks: A Genetic Algorithm Approach

2022

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In 6G networks and beyond, multiple radio access networks (RANs), including; the satellite, high altitude platforms, low altitude platforms, and the terrestrial network, will co-exist. These networks are characterized by different capabilities and limitations in meeting the envisioned 6G contrasting user requirements. Therefore, associating users with the appropriate radio access network (RAN) in such an integrated network is rigorous and complex. In this work, the user association and resource allocation problem is formulated as a multi-objective optimization problem (MOOP), aiming to maximize data rate while minimizing mobility-induced handoff in the integrated network. Moreover, the problem is formulated in such a way as to prioritize the service provisioning of mission-critical users. The weighted sum method is adopted to simplify and transform the MOOP into a single-objective optimization problem (SOOP). In order to solve the formulated NP-hard SOOP, a genetic algorithm (GA) whose fitness value is based on the user's service group is proposed. The performance of the proposed algorithm is evaluated by comparing it to the optimal solution, the greedy signal-to-interference-plus-noise ratio (SINR) based association, and the random user association algorithms. Simulation results show that as the number of access nodes in the network increases, the GA's spectrum efficiency (SE) remains within 0.4% of the optimal solution. Moreover, the GA outperforms all three schemes in user acceptance ratio (AR) and handoff probability. INDEX TERMS RAN user association, resource allocation, terrestrial networks, non-terrestrial networks, genetic algorithm I. INTRODUCTION A. BACKGROUND VOLUME 4, 2016

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Section: Appendix Amentioning

confidence: 99%

Service-Aware User Association and Resource Allocation in Integrated Terrestrial and Non-Terrestrial Networks: A Genetic Algorithm Approach

2022

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“…As a result, [29] expect peak information fees to remain high, even though the 5G device is a valuable resource for both highand low-portability customers. The latency requirement is usually more challenging to meet than the information charge because it needs the facts to be transmitted to the vacation spot [30].…”

Section: G Wireless Network Requirements For Mm-wave Hetnetmentioning

confidence: 99%

MM-Wave HetNet in 5G and beyond Cellular Networks Reinforcement Learning Method to improve QoS and Exploiting Path Loss Model

Bashir

Ali²,

Almasi‐Hashiani³

et al. 2022

Research Journal Of Computer Science And Information Technology

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This paper presents High density heterogeneous networks (HetNet) which are the most promising technology for the fifth generation (5G) cellular network. Since 5G will be available for a long time, previous generation networking systems will need customization and updates. We examine the merits and drawbacks of legacy and Q-Learning (QL)-based adaptive resource allocation systems. Furthermore, various comparisons between methods and schemes are made for the purpose of evaluating the solutions for future generation. Microwave macro cells are used to enable extra high capacity such as Long-Term Evolution (LTE), eNodeB (eNB), and Multimedia Communications Wireless technology (MC), in which they are most likely to be deployed. This paper also presents four scenarios for 5G mm-Wave implementation, including proposed system architectures. The WL algorithm allocates optimal power to the small cell base station (SBS) to satisfy the minimum necessary capacity of macro cell user equipment (MUEs) and small cell user equipment (SCUEs) in order to provide quality of service (QoS) (SUEs). The challenges with dense HetNet and the massive backhaul traffic they generate are discussed in this study. Finally, a core HetNet design based on clusters is aimed at reducing backhaul traffic. According to our findings, MM-wave HetNet and MEC can be useful in a wide range of applications, including ultra-high data rate and low latency communications in 5G and beyond. We also used the channel model simulator to examine the directional power delay profile with received signal power, path loss, and path loss exponent (PLE) for both LOS and NLOS using uniform linear array (ULA) 2X2 and 64x16 antenna configurations at 38 GHz and 73 GHz mmWave bands for both LOS and NLOS (NYUSIM). The simulation results show the performance of several path loss models in the mmWave and sub-6 GHz bands. The path loss in the close-in (CI) model at mmWave bands is higher than that of open space and two ray path loss models because it considers all shadowing and reflection effects between transmitter and receiver. We also compared the suggested method to existing models like Amiri, Su, Alsobhi, Iqbal, and greedy (non adaptive), and found that it not only enhanced MUE and SUE minimum capacities and reduced BT complexity, but it also established a new minimum QoS threshold. We also talked about 6G researches in the future. When compared to utilizing the dual slope route loss model alone in a hybrid heterogeneous network, our simulation findings show that decoupling is more visible when employing the dual slope path loss model, which enhances system performance in terms of coverage and data rate.

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“…Wei et al 39 used geometric model along with decision trees to predict users' target cell based on GPS trajectories. Akshay et al 40 formulated a joint user‐cell association and access point selection scheme to optimize bandwidth assignment in 5G networks. Table 1 summarizes recent cell association schemes for bandwidth optimization to optimize user QoE.…”

Section: Related Workmentioning

confidence: 99%

An LSTM‐based cell association scheme for proactive bandwidth management in 5G fog radio access networks

Manzoor

Mian

Mazhar

2021

Int J Communication

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Summary Cellular networks are evolving into dynamic, dense, and heterogeneous networks to meet the unprecedented traffic demands which introduced new challenges for cell resource management. To address this, various cell association schemes have been proposed. However, the current schemes ignore users' mobility information, and as a result, their cell admission and bandwidth allocation policy are reactive. In order to enable proactive bandwidth management in emerging 5G fog access radio networks (FRANs), we proposed a novel mobility‐aware cell association scheme (MACA) that exploits user's mobility and downlink rate demand information to associate it with the maximum rate offering cell. In MACA, the mobility prediction model consists of long‐short‐term memory (LSTM)‐based neural network that considers joint information of unique cell identification numbers with the corresponding sojourn times and predicts the user's most probable next cell. Later, the underlying future cell assignment is formulated as a convex problem and solved using the Lagrangian dual decomposition method and compared the proposed framework performance with Semi‐Markov, deep neural network (DNN), and MaxRSRP‐based cell association approaches in terms of the next cell prediction accuracy, the impact of downlink rate allocation, and user satisfaction percentage. MACA scheme is trained using two publicly available pedestrian datasets. Simulation results show that the proposed scheme performs significantly better than the other schemes and yields the average next cell prediction accuracy of 93.42%, 1.63 times higher downlink rates, and 56.8% users satisfied with the allocated bandwidth.

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User association and resource allocation in 5G (AURA-5G): A joint optimization framework

Cited by 18 publications

References 36 publications

Service-Aware User Association and Resource Allocation in Integrated Terrestrial and Non-Terrestrial Networks: A Genetic Algorithm Approach

Service-Aware User Association and Resource Allocation in Integrated Terrestrial and Non-Terrestrial Networks: A Genetic Algorithm Approach

MM-Wave HetNet in 5G and beyond Cellular Networks Reinforcement Learning Method to improve QoS and Exploiting Path Loss Model

An LSTM‐based cell association scheme for proactive bandwidth management in 5G fog radio access networks

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