Frequency Diversity-Aware Wi-Fi Using OFDM-Based Bloom Filters

Lee, Suchul; Choi, Jaehyuk; Yoo, Joon; Kim, Chong-Kwon

doi:10.1109/tmc.2014.2326602

Cited by 16 publications

(10 citation statements)

References 21 publications

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“…The proposed covariate multi-armed bandit model is capable of dealing with the exploitation-exploration dilemma of the relay selection process. Lee et al [271] proposed a Kϵ-greedy multi-armed bandit based framework for exploiting the gains provided by frequency diversity in Wi-Fi channels. They struck a trade-off between the achievable gain stemming from frequency diversity and the resource consumption imposed by channel estimation and coordination.…”

Section: A Multi-armed Bandit and Its Applications 1) Methodsmentioning

confidence: 99%

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Wang

Jiang

Zhang

et al. 2020

IEEE Commun. Surv. Tutorials

470

200

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Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (Het-Nets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.

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Section: A Multi-armed Bandit and Its Applications 1) Methodsmentioning

confidence: 99%

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Wang

Jiang

Zhang

et al. 2020

IEEE Commun. Surv. Tutorials

470

200

View full text Add to dashboard Cite

show abstract

“…At the PHY layer, a variety of actions can be supported by ML techniques to improve the performance of WiFi networks. Issues that can be addressed include collision detection characterization [65] and its mitigation [66], [67], interference power-level characterization [70] and its mitigation [73], signal de-noising [69], source detection to improve spectral efficiency [95], prediction of signal strength variability [72], or the enhanced modeling of the PHY and MAC layer interactions to improve throughput [68]. As depicted in Fig.…”

Section: E Phy Featuresmentioning

confidence: 99%

Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance with Machine Learning

Szott,

Kosek-Szott,

Gawłowicz

et al. 2021

Preprint

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Wireless local area networks (WLANs) empowered by IEEE 802.11 (WiFi) hold a dominant position in providing Internet access thanks to their freedom of deployment and configuration as well as affordable and highly interoperable devices. The WiFi community is currently deploying WiFi 6 and developing WiFi 7, which will bring higher data rates, better multi-user and multi-AP support, and, most importantly, improved configuration flexibility. These technical innovations, including the plethora of configuration parameters, are making next-generation WLANs exceedingly complex as the dependencies between parameters and their joint optimization usually have a non-linear impact on network performance. The complexity is further increased in the case of dense deployments and coexistence in shared bands. While classic optimization approaches fail in such conditions, machine learning (ML) is well known for being able to handle complexity. Much research has been published on using ML to improve WiFi performance and solutions are slowly being adopted in existing deployments. In this survey, we adopt a structured approach to describing the various areas where WiFi can be enhanced using ML. To this end, we analyze over 200 papers in the field providing readers with an overview of the main trends. Based on this review, we identify both open challenges in each WiFi performance area as well as general future research directions.

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“…In [23], Two separate graphics processing unit implementation methods in a Software Defined Radio set-up were evaluated and it was found that only one proposed method was environmentally friendly. In [24], A new PHY / MAC protocol, known as Diversity-aware Wi-Fi, is developed and tested using USRP / GRC platform, and compared to existing methods. Despite this variety of features, GRC can be considered to be one of the strongest SDR apps [25].…”

Section: Softwarementioning

confidence: 99%

Testing different Channel Estimation Techniques in Real-Time Software Defined Radio Environment

Ponnaluru¹,

Satyanarayana²

2020

IJACSA

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In modern wireless communication to maximize spectral efficiency and to minimize the bit error rate OFDM (Orthogonal frequency-domain multiplexing) is used. OFDM is used broadly in networks using various protocols, including wireless vehicular environment IEEE 802.11p, IEEE 802.16d/e Wireless Metropolitan Area Networks, Long-Term Evolution 3GPP networks and IEEE 802.11a/g/n Wireless Local Area Networks. The main challenges involved when using OFDM for wireless communications are short channel-coherence bandwidth and the narrow coherence time, and both have a major effect on the reliability and latency of data packet communication. These properties increase the difficulty of channel equalization because the channel may change drastically over the period of a single packet. Spectral Temporal Averaging is an enhanced decisiondirected channel equalization technique that improves communication performance (as far as the frame delivery ratio (FDR) and throughput) in typical channel conditions. This paper reports tests of Spectral Temporal Averaging channel equalization in an IEEE 802.11a network, compared with other channel equalization techniques in terms of the FDR in a realtime environment. Herein, a software defined Radio (SDR) platform was used for estimating the channel. This proves that the system can provide over 90% of delivery ratio at 25 db of Signal to Noise Ratio (SNR) for various digital modulation techniques. For this purpose, an experimental setup consisting of software-defined radio, Universal Software Radio Peripheral (USRP) N210 along with wide bandwidth daughter board as hardware and GNU radio is used.

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Frequency Diversity-Aware Wi-Fi Using OFDM-Based Bloom Filters

Cited by 16 publications

References 21 publications

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance with Machine Learning

Testing different Channel Estimation Techniques in Real-Time Software Defined Radio Environment

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