Interference management for LTE‐Advanced Het‐Nets: stochastic scheduling approach in frequency domain

Zheng, Zhong; Dowhuszko, Alexis A.; Hämäläinen, Jyri

doi:10.1002/ett.2570

Cited by 26 publications

(34 citation statements)

References 28 publications

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“…This enhancement is fundamental since femtocells are deployed by the users with no prior frequency planning. Due to lack of careful resource planning as done by the operator deployed base stations, femtocells may experience the challenge of uncontrolled interference [7]. Hence, it is paramount to provide efficient interference management.…”

Section: Related Workmentioning

confidence: 99%

Energy efficiency impact of cognitive femtocells in heterogeneous wireless networks

Gür

Bayhan

Alagöz

2013

Proceedings of the 1st ACM Workshop on Cognitive Radio Architectures for Broadband

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The recent surge in mobile broadband traffic has been accompanied by diminishing revenues per bit and spectral bottleneck for mobile cellular networks. For this purpose, cognitive radio concept embedded in smaller cells, i.e. cognitive femtocell networks (CFNs), is promising as a potential component of future heterogeneous networks. However, the ongoing mobile traffic explosion is also expected to result in higher energy consumption and larger carbon footprint which is a major challenge against green communications objective. Thus, energy efficiency (EE) has become a key research focus for these broadband access systems. In this work, we analyze the introduction of cognitive femtocells into wireless networks from energy efficiency perspective. We propose an analytical model for such CFN-deployed heterogeneous networks and evaluate the impact of CFN proliferation on energy consumption discussing the relevant tradeoffs and practical issues. We observe two fundamental findings: 1-CFN is beneficial for improving EE. 2-The resultant EE gains are dependent on intricate factors entailing femtocell deployment density, CFBS proliferation rate, sensing period selection and interference management.

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Section: Related Workmentioning

confidence: 99%

Energy efficiency impact of cognitive femtocells in heterogeneous wireless networks

Gür

Bayhan

Alagöz

2013

Proceedings of the 1st ACM Workshop on Cognitive Radio Architectures for Broadband

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“…Moreover, the state‐action pair ( s , a ) is represented by the Q‐value of the evaluation function

Q_{t}^{n MathClass-punc, k} ((), s MathClass-punc, a)

. In order to have the simplified representation, we are using Q ( s , a ) instead of

Q_{t}^{n MathClass-punc, k} ((), s MathClass-punc, a)

and is expressed in as

Q (s MathClass-punc, a) MathClass-rel= R (s MathClass-punc, a) MathClass-bin+ γ {MathClass-op\sum}_{s^{MathClass-bin*} MathClass-rel\in S} P_{s MathClass-punc, s^{MathClass-bin*}} (a) Q ((), s^{MathClass-bin*} MathClass-punc, a)

The expression for optimal strategy

V_{π}^{MathClass-bin*} (s)

can be written in using . Where in equation , V π ( s ) is the value of the expected infinite reward with the initial state‐action pair ( s 0 , a 0 ).…”

Section: Distributive Learning Techniquesmentioning

confidence: 99%

A docitive Q‐learning approach towards joint resource allocation and power control in self‐organised femtocell networks

Shahid

Aslam

Kim

et al. 2014

Trans. Emerging Tel. Tech.

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Femtocell is a technology that contributes towards the escalation of coverage as well as throughput. By virtue of uncertain deployment of femtocells, self‐organisation is a viable solution for resource allocation. In this study, we are projecting a docitive Q‐learning (DQL) paradigm for joint resource allocation and power control (JRAPC). Moreover, the proposed learning paradigm is compared with independent Q‐learning for the same JRAPC problem. In the proposed DQL paradigm, femto base stations, which are agents, learn the strategies by exploiting Q‐learning and share their learned strategies with their neighbours. Concerning the shared channel environment, the problem function is formulated as the maximisation of femtocell capacity while maintaining the quality of service requirement of the macrousers. The impact of the proposed DQL paradigm is investigated on system capacity and femtocell capacity. Furthermore, comparison is carried out with the considered independent learning paradigm in terms of convergence, min‐max capacity and the effect of femtocell density. Also, the fairness index is computed to have further insight. The results illustrate that DQL‐based JRAPC outperforms its counterpart. Copyright © 2014 John Wiley & Sons, Ltd.

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“…It is possible to handle the interference in the heterogeneous networks using different approaches [3,4]. Interference alignment (IA) is one of the techniques to effectively mitigate the interference in wireless networks [5].…”

Section: Introductionmentioning

confidence: 99%

On stream selection for interference alignment with limited feedback in heterogeneous networks

Beyazıt

Özbek

LeźRuyet

2016

Trans. Emerging Tel. Tech.

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This paper presents a stream selection based interference alignment approach with imperfect channel state information for heterogeneous networks. The proposed solution constructs stream sequences by selecting only the strongest stream of each user where the first stream of the constructed stream sequences is associated to a pico user. While selecting the streams, the channel matrices of the unselected streams are projected orthogonally to the virtual transmit and receive channels of the selected stream in order to align the interference in the null space of these virtual channels. In addition, the influence of imperfect channel state information on the proposed algorithm is analysed. A bit allocation scheme is given by deriving an upper bound on the rate loss because of quantisation. The simulation results are carried out by considering various scenarios with different locations of pico cells at the cell edge regions of the macro cell. The performance results show that the proposed algorithm with the imperfect channel state information achieves higher performance than the existing algorithms.

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Interference management for LTE‐Advanced Het‐Nets: stochastic scheduling approach in frequency domain

Cited by 26 publications

References 28 publications

Energy efficiency impact of cognitive femtocells in heterogeneous wireless networks

Energy efficiency impact of cognitive femtocells in heterogeneous wireless networks

A docitive Q‐learning approach towards joint resource allocation and power control in self‐organised femtocell networks

On stream selection for interference alignment with limited feedback in heterogeneous networks

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