Joint Access Point Placement and Power-Channel-Resource-Unit Assignment for 802.11ax-Based Dense WiFi with QoS Requirements

Qiu, Shuwei; Chu, Xiaowen; Leung, Yiu-Wing; Ng, Joseph Kee Yin

doi:10.1109/infocom41043.2020.9155490

Cited by 16 publications

(6 citation statements)

References 47 publications

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“…Rational planning (i.e., minimizing the number of APs) and effective planning of the wireless network (i.e., maximizing the throughput) for indoor/outdoor operation usually requires the consecutive evaluation of many candidate solutions using optimization algorithms. Among the optimization algorithms commonly used in network planning, we can mention: e.g., heuristics [12,[22][23][24], GA [25][26][27][28], or Particle Swarm Optimization (PSO) [29][30][31].…”

Section: Network Planning Algorithmsmentioning

confidence: 99%

“…Once the coverage requirement is met, it is investigated if any two closely spaced access points can be replaced by one. In [24], the authors designed an 802.11ax-based dense Wi-Fi network to provide service to a large number of users minimizing the number of APs and fulfilling the users' throughput requirement. The authors designed a heuristic algorithm with polynomial time complexity and compared the performance to the random and Greedy solution.…”

Section: Network Planning Algorithmsmentioning

confidence: 99%

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Indoor Genetic Algorithm-Based 5G Network Planning Using a Machine Learning Model for Path Loss Estimation

Santana

Alonso

Nieto³

et al. 2022

Applied Sciences

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Accurate wireless network planning is crucial for the deployment of new wireless services. This usually requires the consecutive evaluation of many candidate solutions, which is only feasible for simple path loss models, such as one-slope models or multi-wall models. However, such path loss models are quite straightforward and often do not deliver satisfactory estimations, eventually impacting the quality of the proposed network deployment. More advanced models, such as Indoor Dominant Path Loss models, are usually more accurate, but as their path loss calculation is much more time-consuming, it is no longer possible to evaluate a large set of candidate deployment solutions. Out of necessity, a heuristic network planning algorithm is then typically used, but the outcomes heavily depend on the quality of the heuristic. Therefore, this paper investigates the use of Machine Learning to approximate a complex 5G path loss model. The much lower calculation time allows using this model in a Genetic Algorithm-based network planning algorithm. The Machine Learning model is trained for two buildings and is validated on three other buildings, with a Mean Absolute Error below 3 dB. It is shown that the new approach is able to find a wireless network deployment solution with an equal, or smaller, amount of access points, while still providing the required coverage for at least 99.4% of the receiver locations and it does this 15 times faster. Unlike a heuristic approach, the proposed one also allows accounting for additional design criteria, such as maximal average received power throughout the building, or minimal exposure to radiofrequency signals in certain rooms.

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Section: Network Planning Algorithmsmentioning

confidence: 99%

Section: Network Planning Algorithmsmentioning

confidence: 99%

Indoor Genetic Algorithm-Based 5G Network Planning Using a Machine Learning Model for Path Loss Estimation

Santana

Alonso

Nieto³

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

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“…Concurrently, [15] established that adapting TX PWR can lead to increased throughput and reduced energy consumption. More recently, in 2020, [18] casts the issues of positioning the APs of a WLAN and choosing their TX PWR as an optimization problem. The authors provides a solution to this problem that delivers a static configuration of TX PWR for a WLAN.…”

Section: Related Workmentioning

confidence: 99%

INSPIRE: Distributed Bayesian Optimization for ImproviNg SPatIal REuse in Dense WLANs

Bardou¹,

Begin²

2022

Preprint

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WLANs, which have overtaken wired networks to become the primary means of connecting devices to the Internet, are prone to performance issues due to the scarcity of space in the radio spectrum. As a response, IEEE 802.11ax and subsequent amendments aim at increasing the spatial reuse of a radio channel by allowing the dynamic update of two key parameters in wireless transmission: the transmission power (TX POWER) and the sensitivity threshold (OBSS PD).In this paper, we present INSPIRE, a distributed solution performing local Bayesian optimizations based on Gaussian processes to improve the spatial reuse in WLANs. INSPIRE makes no explicit assumptions about the topology of WLANs and favors altruistic behaviors of the access points, leading them to find adequate configurations of their TX POWER and OBSS PD parameters for the "greater good" of the WLANs. We demonstrate the superiority of INSPIRE over other state-of-theart strategies using the ns-3 simulator and two examples inspired by real-life deployments of dense WLANs. Our results show that, in only a few seconds, INSPIRE is able to drastically increase the quality of service of operational WLANs by improving their fairness and throughput.

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“…With a well-designed WLAN, interference can be mitigated. Many research works focused on the planning and design phases to avoid co-channel interference in the WLAN systems [1][2][3][4][5]. However, even with a well-designed WLAN, some issues are exacerbated such as interference and growth in network size from new nodes being introduced into the system exacerbated.…”

Section: Preliminary Studymentioning

confidence: 99%

“…In [2], GA was adopted to provide a cost-effective WLAN in terms of coverage and capacity under budget constraints. In [5], both channel selection and the QoS of WLAN requirements were jointly optimized, while the authors in [4] suggested using GA to consider additional metrics such as capacity, cost, coverage, and interference. Apart from node placement optimization, GA in WLAN design can also be included in the design of patch antennas [12,13] as well as the handover process between Wi-Fi and LTE in the unlicensed spectrum, as described in [14].…”

Section: Preliminary Studymentioning

confidence: 99%

On Optimizing WiFi RSSI and Channel Assignment using Genetic Algorithm for WiFi Tuning

Apavatjrut

Kamdee

2021

ECTI-EEC

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In this work, we proposed a genetic algorithm-based Wi-Fi-tuning platform that could facilitate the network administrators to cope with co-channel interference triggered by other wireless sources. Generally, with a well-designed WLAN, signal interference from adjacent areas is usually minimized. Unfortunately, when other wireless sources are introduced into the WLAN system, co-channel interference is inevitable. Interference usually causes degradation and/or disruption of network services. Resolving this issue becomes even more complicated when the interfering signals come from access points owned by other ISPs and are not accessible by the network administrators. This paper proposed a Wi-Fi tuning platform that allowed automatic reconfiguration of WLAN settings by finding the best settings for channel assignment and transmission power level. When signal interference is detected, the platform attempts to find heuristic solutions for wireless settings based on a genetic algorithm. From our experiments, we could see that our proposed algorithm could regenerate WLAN settings that provided stronger signal levels, higher coverage ranges while reducing interference levels in the deployment area. With the proposed platform, troubleshooting became less complicated, requiring less cost and time. With the help of the Wi-Fi tuning platform, the network administrators could promptly react to the incidence leading to the enhancement of availability, reliability, and consistency of the WLAN system.

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Joint Access Point Placement and Power-Channel-Resource-Unit Assignment for 802.11ax-Based Dense WiFi with QoS Requirements

Abstract: If it is the author's pre-published version, changes introduced as a result of publishing processes such as copy-editing and formatting may not be reflected in this document. For a definitive version of this work, please refer to the published version.

Cited by 16 publications

References 47 publications

Indoor Genetic Algorithm-Based 5G Network Planning Using a Machine Learning Model for Path Loss Estimation

Indoor Genetic Algorithm-Based 5G Network Planning Using a Machine Learning Model for Path Loss Estimation

INSPIRE: Distributed Bayesian Optimization for ImproviNg SPatIal REuse in Dense WLANs

On Optimizing WiFi RSSI and Channel Assignment using Genetic Algorithm for WiFi Tuning

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