Cooperative Interference Avoidance Scheduler for Radio Resource Management in NB-IoT Systems

Mwakwata, Collins Burton; Alam, Muhammad Mahtab; Moullec, Yannick Le; Malik, Hassan; Pärand, Sven

doi:10.1109/eucnc48522.2020.9200967

Cited by 7 publications

(4 citation statements)

References 10 publications

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“…For scenarios with over 24 stations, the proposed method with a trade-off parameter of V = 0.3 shows better throughput performance than the random method. Note that decreasing V places more emphasis on stability represented by Lyapunov drift, while increasing V prioritizes the required cooperation delay at the fronthaul network in scheduling decisions as represented in (12). The results highlight that the proposed cooperative scheduling method with well-adjusted trade-off parameter V enhances network throughput performance in dense networks.…”

Section: Performance Evaluationmentioning

confidence: 85%

“…where V is a non-negative weight that adjusts the relation between the Lyapunov drift term and the penalty term. The proposed uplink scheduling method minimizes the upper bound of the Lyapunov drift-plus-penalty denoted in (12) to stably achieve enhanced network throughput without a significant increase in required delay for cooperative scheduling. Algorithm 1 describes the simple heuristic algorithm for the Lyapunov drift-pluspenalty-based cooperative uplink scheduling decision.…”

Section: Lyapunov Drift-plus-penalty-based Uplink Modeling and Decisionmentioning

confidence: 99%

“…The cooperative scheduling approach has also been investigated for various applications and scenarios beyond Wi-Fi networks. Mwakwata et al investigated a cooperative scheduler-based interference avoidance method in NB-IoT systems [ 12 ]. In the considered scenario, cellular base stations supporting NB-IoT services share their scheduling table for inter-cell interference avoidance scheduling.…”

Section: Related Workmentioning

confidence: 99%

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Lyapunov Drift-Plus-Penalty-Based Cooperative Uplink Scheduling in Dense Wi-Fi Networks

Kim,

Kim

2024

Sensors

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In high-density network environments with multiple access points (APs) and stations, individual uplink scheduling by each AP can severely interfere with the uplink transmissions of neighboring APs and their associated stations. In congested areas where concurrent uplink transmissions may lead to significant interference, it would be beneficial to deploy a cooperative scheduler or a central coordinating entity responsible for orchestrating cooperative uplink scheduling by assigning several neighboring APs to support the uplink transmission of a single station within a proximate service area to alleviate the excessive interference. Cooperative uplink scheduling facilitated by cooperative information sharing and management is poised to improve the likelihood of successful uplink transmissions in areas with a high concentration of APs and stations. Nonetheless, it is crucial to account for the queue stability of the stations and the potential delays arising from information exchange and the decision-making process in uplink scheduling to maintain the overall effectiveness of the cooperative approach. In this paper, we propose a Lyapunov drift-plus-penalty framework-based cooperative uplink scheduling method for densely populated Wi-Fi networks. The cooperative scheduler aggregates information, such as signal-to-interference-plus-noise ratio (SINR) and queue status. During the aggregation procedure, propagation delays are also estimated and utilized as a value of expected cooperation delays in scheduling decisions. Upon aggregating the information, the cooperative scheduler calculates the Lyapunov drift-plus-penalty value, incorporating a predefined model parameter to adjust the system accordingly. Among the possible scheduling candidates, the proposed method proceeds to make uplink decisions that aim to reduce the upper bound of the Lyapunov drift-plus-penalty value, thereby improving the network performance and stability without a severe increase in cooperation delay in highly congested areas. Through comprehensive performance evaluations, the proposed method effectively enhances network performance with an appropriate model parameter. The performance improvement is particularly notable in highly congested areas and is achieved without a severe increase in cooperation delays.

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Section: Performance Evaluationmentioning

confidence: 85%

Section: Lyapunov Drift-plus-penalty-based Uplink Modeling and Decisionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Lyapunov Drift-Plus-Penalty-Based Cooperative Uplink Scheduling in Dense Wi-Fi Networks

Kim,

Kim

2024

Sensors

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“…When the base stations receive the list of these users and the available resources, power allocation is performed to reduce the unnecessary energy consumption due to excessive transmit power allocation for the cell edge users. We adopt simulation parameters as presented in [16], unless specified otherwise. The overview of the proposed scheme is shown in Algorithm 1.…”

Section: Proposed Cooperative Scheduler and Simulation Setupmentioning

confidence: 99%

Inter-cell Interference Reduction Scheme for Uplink Transmission in NB-IoT Systems

Mwakwata

Elgarhy

Moullec

et al. 2021

2021 International Wireless Communications and Mobile Computing (IWCMC)

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In this paper, we propose an inter-cell interference (ICI) minimization scheme for uplink transmission in the narrowband internet of things (NB-IoT) systems. We first establish the theoretical ICI problem formulation and propose its corresponding solution for the orthogonal multiple access (OMA) NB-IoT system. Based on the theoretical formulation, we design a cooperative radio resource scheduler that reduces the impact of ICI and allocates transmit powers to reduce the energy consumption to the scheduled users in a multi-cell scenario. We compare the performance of the proposed scheme with that of some benchmark OMA schedulers. The results show that the proposed technique significantly reduces the impact of ICI and hence is more suitable for the massive connectivity of the NB-IoT system. For example, the users operating under the proposed approach experience up to 50% reduced energy consumption when compared to the best channel quality indicator (CQI) scheme. Furthermore, 30% and 35% improvements in terms of achieved user's data rates are obtained as compared to the MaxMin and round-robin schemes, respectively.

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The Development of Encroaching Control System for Managing the Optimized Communication of Networks Through SN

Al-Farouni,

Kapoor,

Shweta

et al. 2024

2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE)

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Cooperative Interference Avoidance Scheduler for Radio Resource Management in NB-IoT Systems

Cited by 7 publications

References 10 publications

Lyapunov Drift-Plus-Penalty-Based Cooperative Uplink Scheduling in Dense Wi-Fi Networks

Lyapunov Drift-Plus-Penalty-Based Cooperative Uplink Scheduling in Dense Wi-Fi Networks

Inter-cell Interference Reduction Scheme for Uplink Transmission in NB-IoT Systems

The Development of Encroaching Control System for Managing the Optimized Communication of Networks Through SN

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