Distributed Q-Learning for Interference Control in OFDMA-Based Femtocell Networks

Galindo-Serrano, Ana; Giupponi, Lorenza

doi:10.1109/vetecs.2010.5493950

Cited by 79 publications

(82 citation statements)

References 6 publications

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“…In these studies, the solutions rely on finding subchannel allocations for the femtocells in a way that interference is mitigated by frequency diversity. These approaches are basically formal implementations of LTE's ICIC [13], [14]. It is also possible to enhance interference mitigation in white space networks by going beyond the information available about spectrum availability in a regulator's approved databases.…”

Section: Related Workmentioning

confidence: 99%

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Self-organized Clustering for Improved Interference Mitigation in White Spaces

Aráuz¹,

Sánchez²

2017

JTIT

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Abstract-In this paper a collaborative coexistence mechanism for white space base stations is proposed. We look at the case where these base stations operate in geographical areas where the density of used TV channels is such that only one channel is left for broadband access. We show how with cooperative closed loop control and a clustering strategy, it is possible to find feasible power assignments that provide a flexible and stable coverage solution. The framework under which we study our proposal is based on the IEEE 802.22 standard, which provides white space guidelines for applications in broadband access or machine-to-machine communications. We propose and evaluate a self-organized, collaborative power control and design strategy to enable effective coexistence of base stations under extreme bandwidth constraints. Finally, we also portray how proposed approach positively compares against others from different wireless access technologies.

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Section: Related Workmentioning

confidence: 99%

“…Under these assumptions, and considering that due to clustering each CPE reports SNIR values similar to each other, the control loop can be modeled as shown in Eq. (13). A schematic diagram of the complete system is shown in Fig.…”

Section: Stability Analysis and Convergencementioning

confidence: 99%

Self-organized Clustering for Improved Interference Mitigation in White Spaces

Aráuz¹,

Sánchez²

2017

JTIT

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“…The considered cost of 500 is only for the case that the total transmit power P n tot is larger than the maximum transmit power of a BS. It provides the best performance and convergence trade-off in our simulations as shown in Section VI-D and has been heuristically selected [34]. Being in state s after selecting action a and receiving the immediate cost c, the agent updates its knowledge Q(s, a) for this particular state-action pair as follows:…”

Section: B Q-learning Based Time-domain Icicmentioning

confidence: 99%

“…where α = 0.5 is the player's willingness to learn from its environment, λ = 0.9 is the discount factor, and s ′ is the next state [34], [35]. Hereby, the agent's previous knowledge about the state-action pair (s, a) is represented by the first term in (9).…”

Section: B Q-learning Based Time-domain Icicmentioning

confidence: 99%

Learning Based Frequency- and Time-Domain Inter-Cell Interference Coordination in HetNets

Simsek

Bennis

Güvenç

2015

IEEE Trans. Veh. Technol.

100

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Abstract-In this article, we focus on inter-cell interference coordination (ICIC) techniques in heterogeneous network (HetNet) deployments, whereby macro-and picocells autonomously optimize their downlink transmissions, with loose coordination. We model this strategic coexistence as a multi-agent system, aiming at joint interference management and cell association. Using tools from Reinforcement Learning (RL), agents (i.e., macro-and picocells) sense their environment, and self-adapt based on local information so as to maximize their network performance. Specifically, we explore both time-and frequency domain ICIC scenarios, and propose a two-level RL formulation. Here, picocells learn their optimal cell range expansion (CRE) bias and transmit power allocation, as well as appropriate frequency bands for multi-flow transmissions, in which a user equipment (UE) can be simultaneously served by two or more base stations (BSs) from macro-and pico-layers. To substantiate our theoretical findings, Long Term Evolution Advanced (LTE-A) based system level simulations are carried out in which our proposed approaches are compared with a number of baseline approaches, such as resource partitioning (RP), static CRE, and single-flow Carrier Aggregation (CA). Our proposed solutions yield substantial gains up to 125% compared to static ICIC approaches in terms of average UE throughput in the timedomain. In the frequency-domain our proposed solutions yield gains up to 240% in terms of cell-edge UE throughput.

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“…More power control algorithms for the HetNets have been proposed for both uplink [15,20] and downlink scenarios [21,22] based on optimization frameworks with objectives of minimizing the transmit power of small cells under a required SINR constraint. In addition, learning based noncooperative power control algorithms [23][24][25] were proposed. In 3GPP Rel-10 standard, enhanced intercell interference coordination (eICIC) technique has been introduced for interference mitigation in the HetNets.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Resource Scheduling for Dual Connectivity in Heterogeneous IoT Cellular Networks

Kim

2016

International Journal of Distributed Sensor Networks

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As massive distributed sensor devices are integrated into Internet for Internet of things (IoT) and generate tremendous data from simple measurement to rich multimedia information, wireless cellular networks like LTE are enforced to deploy more small cells to accommodate data from the countless IoT devices. In 3GPP Rel-12 specification, dual connectivity helps deploying the small cell eNBs by separating a control and data plane to a macro and small cell, respectively. The dual connectivity also improves per-user throughput and mobility robustness. Meanwhile, dynamic TDD configuration in the Rel-12 can enhance radio resource utilization of TDD-based small cells even though intercell interference can be worse than legacy static configuration within a small cell cluster. In this paper, we propose a heterogeneous cellular IoT network architecture using the aforementioned two small cell features, as well as scheduling algorithms for load balancing in the dual connectivity and for dynamic TDD configuration to mitigate interference in the small cell cluster. We evaluate proposed algorithms using LTE system level simulator and show that our approach improves network throughput.

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Distributed Q-Learning for Interference Control in OFDMA-Based Femtocell Networks

Cited by 79 publications

References 6 publications

Self-organized Clustering for Improved Interference Mitigation in White Spaces

Self-organized Clustering for Improved Interference Mitigation in White Spaces

Learning Based Frequency- and Time-Domain Inter-Cell Interference Coordination in HetNets

Adaptive Resource Scheduling for Dual Connectivity in Heterogeneous IoT Cellular Networks

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