A Survey of Resource Management Toward 5G Radio Access Networks

Olwal, Thomas O.; Djouani, Karim; Kurien, Anish Mathew

doi:10.1109/comst.2016.2550765

Cited by 225 publications

(106 citation statements)

References 114 publications

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“…In [17] a reinforcement learning approach against satellite channel impairments such as orbital perturbations and atmospheric effects is presented. As a side remark, we point out that the problem of radio resource management (RRM) has been studied also in the context of terrestrial networks [18] [19]. However, the payload constraints and the different network topologies make the two optimization problems significantly different.…”

Section: Introductionmentioning

confidence: 99%

Radio Resource Management Optimization of Flexible Satellite Payloads for DVB-S2 Systems

Cocco

Cola

Angelone

et al. 2018

IEEE Trans. on Broadcast.

128

147

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The increasing demand for high-rate broadcast and multicast services over satellite networks has pushed for the development of High Throughput Satellites characterized by a large number of beams (e.g., more than 100). This, together with the variable distribution of data traffic request across beams and over time, has called for the design of a new generation of satellite payloads, able to flexibly allocate bandwidth and power. In this context, this paper studies the problem of radio resource allocation in the forward link of multibeam satellite networks adopting the Digital Video Broadcasting -Satellite -Second Generation (DVB-S2) standard. We propose a novel objective function with the aim to meet as close as possible the requested traffic across the beams while taking fairness into account. The resulting non-convex optimization problem is solved using a modified version of the simulated annealing algorithm, for which a detailed complexity analysis is presented. Simulation results obtained under realistic conditions confirm the effectiveness of the proposed approach and shed some light on possible payload design implications.

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Section: Introductionmentioning

confidence: 99%

Radio Resource Management Optimization of Flexible Satellite Payloads for DVB-S2 Systems

Cocco

Cola

Angelone

et al. 2018

IEEE Trans. on Broadcast.

128

147

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“…Furthermore, the contribution in [51] provided a tutorial overview of 5G research activities, deployment challenges and standardization trials. Another survey article [52] provided a comprehensive review of existing radio interference and resource management schemes for 5G radio access networks and classified the existing schemes in terms of radio interference, energy efficiency and spectrum efficiency. In the direction of energy-efficient 5G communications, authors in [53,54] provided a detailed survey of the existing works in the areas of energy-efficient techniques for 5G networks and analyzed various green trade-offs, namely, spectrum efficiency versus energy efficiency, delay versus power, deployment efficiency versus energy efficiency, and bandwidth versus power for the effective design of energy-efficient 5G networks [53].…”

Section: B Related Workmentioning

confidence: 99%

“…The authors in [52] have provided a summary on the merits and demerits of several 5G [57,84]. Several researchers have recently studied the applications of FD in massive MIMO systems in various settings [84][85][86].…”

Section: Full-duplex In 5g Networkmentioning

confidence: 99%

Dynamic Spectrum Sharing in 5G Wireless Networks With Full-Duplex Technology: Recent Advances and Research Challenges

Sharma

Bogale

et al. 2018

IEEE Commun. Surv. Tutorials

203

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Full-Duplex (FD) wireless technology enables a radio to transmit and receive on the same frequency band at the same time, and it is considered to be one of the candidate technologies for the fifth generation (5G) of wireless communications due to its advantages including potential doubling of the capacity, reduced end-to-end and feedback delays, improved network secrecy and efficiency, and starting with a detailed overview of FD-enabled DSS, we provide a comprehensive survey of recent advances in this domain. We then highlight several potential techniques for enabling FD operation in DSS wireless systems. Subsequently, we propose a novel communication framework to enable CST in DSS systems by employing a power control-based SI mitigation scheme and carry out the throughput performance analysis of this proposed framework. Finally, we discuss some open research issues and future directions with the objective of stimulating future research efforts in the emerging FD-enabled DSS wireless systems.

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“…Alongside standard RRM functions (i.e. power control, interference management, mobility control, resource allocation, packet scheduling [4]), a flexible RRM involves more dynamic functionalities able to adapt to unpredictable network conditions. Some studies have shown an increased interest of integrating Machine Learning (ML) methodologies to learn the optimal RRM strategies based on some centralized user-centric (i.e.…”

Section: Introductionmentioning

confidence: 99%

Towards 5G: A Reinforcement Learning-Based Scheduling Solution for Data Traffic Management

Comşa

Zhang

Aydın

et al. 2018

IEEE Trans. Netw. Serv. Manage.

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Abstract-Dominated by delay-sensitive and massive data applications, radio resource management in 5G access networks is expected to satisfy very stringent delay and packet loss requirements. In this context, the packet scheduler plays a central role by allocating user data packets in the frequency domain at each predefined time interval. Standard scheduling rules are known limited in satisfying higher Quality of Service (QoS) demands when facing unpredictable network conditions and dynamic traffic circumstances. This paper proposes an innovative scheduling framework able to select different scheduling rules according to instantaneous scheduler states in order to minimize the packet delays and packet drop rates for strict QoS requirements applications. To deal with real-time scheduling, the Reinforcement Learning (RL) principles are used to map the scheduling rules to each state and to learn when to apply each. Additionally, neural networks are used as function approximation to cope with the RL complexity and very large representations of the scheduler state space. Simulation results demonstrate that the proposed framework outperforms the conventional scheduling strategies in terms of delay and packet drop rate requirements.

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A Survey of Resource Management Toward 5G Radio Access Networks

Cited by 225 publications

References 114 publications

Radio Resource Management Optimization of Flexible Satellite Payloads for DVB-S2 Systems

Radio Resource Management Optimization of Flexible Satellite Payloads for DVB-S2 Systems

Dynamic Spectrum Sharing in 5G Wireless Networks With Full-Duplex Technology: Recent Advances and Research Challenges

Towards 5G: A Reinforcement Learning-Based Scheduling Solution for Data Traffic Management

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