Resource allocation for licensed and unlicensed spectrum in 5G heterogeneous networks

Ali, Mudassar; Qaisar, Saad; Naeem, Muhammad; Rodrigues, Joel J. P. C.; Qamar, Farhan

doi:10.1002/ett.3299

Cited by 14 publications

(9 citation statements)

References 40 publications

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“…Document [15] proposes a distributed resource allocation scheme that balances the fairness of network throughput and distribution. The authors in [16] use grid adaptive search amplification to provide an optimal resource allocation scheme for heterogeneous networks, reducing interference and increasing network throughput and speed. Layered protocols can effectively improve the performance of heterogeneous WSNs [17].…”

Section: Related Workmentioning

confidence: 99%

ADAI and Adaptive PSO-Based Resource Allocation for Wireless Sensor Networks

et al. 2019

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Resource allocation in the Internet of Things (IoT) applications for Wireless Sensor Networks (WSNs) is a challenging problem that requires tasks processing from the appropriate sensor nodes without compromising the Quality-of-Service (QoS). Due to heterogeneity in sensors, the inter-cluster and intra-cluster cooperative communication between sensor nodes hinders the overall resource allocation of the network in terms of energy consumption and response time. Therefore, this paper establishes a multi-agent clustering WSN model, i.e., Adaptive Distributed Artificial Intelligence (ADAI) technique with a hierarchical resource allocation strategy to address the issue of resource allocation in these types of network. For the inter-cluster power allocation, we are considering QoS and energy consumption factors with DAI. Moreover, for intra-cluster resource allocation, this paper introduces Adaptive Particle Swarm Optimization (APSO), which uses its objective functions as the node distance and respective energy loads. The mathematical analysis and simulation results validate the propose method in terms of energy consumption and response time of the network.

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Section: Related Workmentioning

confidence: 99%

ADAI and Adaptive PSO-Based Resource Allocation for Wireless Sensor Networks

et al. 2019

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“…The reason is that when γ is large, serving customers with LTE-U leads to less congestion, which allows the incumbent to serve more customers. We can also look at equivalent bandwidth in (7). When LTE-U is used, the increase of licensed equivalent bandwidth is multiplied by the spectral efficiency γ.…”

Section: A Monopoly Marketmentioning

confidence: 99%

“…Without LTE-U, the equivalent bandwidth is γB + W . With LTE-U, the equivalent bandwidth is B e + W e , where B e is in (7) and W e is in (5). Then by solving the inequality…”

Section: Appendix I Proof Of Theorem 47mentioning

confidence: 99%

“…Indeed, in 2016, there was more mobile data traffic offloaded to unlicensed bands than served in licensed spectrum worldwide [5]. These trends are expected to continue with 5G [6], [7] and have led to the development of technologies for unlicensed access that are based on the LTE technology that SPs utilize in licensed spectrum. The two main examples of this are LTE in unlicensed spectrum (LTE-U) and License Assisted Access (LAA).…”

Section: Introductionmentioning

confidence: 99%

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Price Competition with LTE-U and WiFi

Wang

Berry

2019

IEEE INFOCOM 2019 - IEEE Conference on Computer Communications

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LTE-U is an extension of the Long Term Evolution (LTE) standard for operation in unlicensed spectrum. LTE-U differs from WiFi, the predominant technology used in unlicensed spectrum in that it utilizes a duty cycle mode for accessing the spectrum and allows for a more seamless integration with LTE deployments in licensed spectrum. There have been a number of technical studies on the co-existence of LTE-U and WiFi in unlicensed spectrum In this paper, we instead investigate the impact of such a technology from an economic perspective. We consider a model in which an incumbent service provider (SP) deploys a duty cycle-based technology like LTE-U in an unlicensed band along with operating in a licensed band and competes with one or more entrants that only operate in the unlicensed band using a different technology like WiFi. We characterize the impact of a technology like LTE-U on the market outcome and show that the welfare impacts of this technology are subtle, depending in part on the amount of unlicensed spectrum and number of entrants. The difference in spectral efficiency between LTE and WiFi also plays a role in the competition among SPs. Finally, we investigate the impact of the duty cycle and the portion of unlicensed spectrum used by the technology.

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“…The optimal contention period is obtained by using the Nash bargaining solution. In the work of Ali et al, a joint user association and power allocation for licensed and unlicensed spectrum algorithm was proposed to maximize maximize sum rate of LTE‐U/WiFi heterogeneous networks.…”

Section: Introductionmentioning

confidence: 99%

Energy efficient and fair resource allocation for LTE‐unlicensed uplink networks: A two‐sided matching approach with partial information

Gao

et al. 2018

Trans Emerging Tel Tech

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Long‐Term Evolution–unlicensed (LTE‐U) has recently attracted worldwide interest to meet the explosion in cellular traffic data. By using carrier aggregation, licensed and unlicensed bands are integrated to enhance transmission capacity while maintaining reliable and predictable performance. As there may exist other conventional unlicensed band users, such as WiFi users, LTE‐U users have to share the same unlicensed bands with them. Thus, an optimized resource allocation scheme to ensure the fairness between LTE‐U users and conventional unlicensed band users is critical for the deployment of LTE‐U networks. In this paper, we investigate an energy efficient resource allocation problem in LTE‐U coexisting with other wireless networks, which aims at guaranteeing fairness among the users of different radio access networks. We formulate the problem as a multiobjective optimization problem and propose a semidistributed matching framework with a partial information‐based algorithm to solve it. We demonstrate our contributions with simulations in which various network densities and traffic load levels are considered.

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Resource allocation for licensed and unlicensed spectrum in 5G heterogeneous networks

Cited by 14 publications

References 40 publications

ADAI and Adaptive PSO-Based Resource Allocation for Wireless Sensor Networks

ADAI and Adaptive PSO-Based Resource Allocation for Wireless Sensor Networks

Price Competition with LTE-U and WiFi

Energy efficient and fair resource allocation for LTE‐unlicensed uplink networks: A two‐sided matching approach with partial information

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