Data-Throughput Enhancement Using Data Mining-Informed Cognitive Radio

Kotobi, Khashayar; Mainwaring, Philip B.; Tucker, Conrad S.; Bilén, Sven G.

doi:10.3390/electronics4020221

Cited by 23 publications

(10 citation statements)

References 25 publications

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“…To the best of our knowledge, mapping traffic patterns using OSN data has not been studied in academia (see industrial progress [8]) and the closest example is using OSN data to infer channel occupancy in cognitive radio [9] and customer complaints [10], [11]. In this paper, the authors demonstrate that structured OSN data can act as a superior predictor for long-term and short-term data demand.…”

Section: Introductionmentioning

confidence: 97%

Estimating Mobile Traffic Demand Using Twitter

Yang

Guo

Chen

et al. 2016

IEEE Wireless Commun. Lett.

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In this paper, the authors show that structured social media data can act as an accurate predictor for wireless data demand patterns at a high spatial-temporal resolution. A casestudy is performed on Greater London covering a 5000km 2 area. The data used includes over 0.6 million geo-tagged Twitter data, over 1 million mobile phone data demand records, and UK census data. The analysis shows that social media activity (Tweets/s n) can accurately predict the long-term traffic demand for both the uplink and downlink channels. The relationship between social media activity and traffic demand obeys a power law and the model explains for over 71-79% of the variance in real traffic demand. This is a significant improvement over existing methods of long-term traffic prediction such as census population data (R 2 =0.57). The authors also show that social media data can also forward predict short-term traffic demand for up to 2 hours on the same day and for the same time in the following 2-3 days.

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Section: Introductionmentioning

confidence: 97%

Estimating Mobile Traffic Demand Using Twitter

Yang

Guo

Chen

et al. 2016

IEEE Wireless Commun. Lett.

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“…Because D upb 2 is a decreasing function of P k s and P tot (see Appendix X-B), solving (19) for P k s, we find that…”

Section: A Coupled Scheme For Minimizing D Amentioning

confidence: 98%

“…where α k = (4τ 2 k /3)||g k || 2 and ||g k || 2 is the squared Euclidean norm of the k-th column of G. Let L({L k , P k , µ k } K k=1 , λ) be the Lagrangian for (19), where µ k and λ are the Lagrange multipliers. The corresponding KKT conditions are as follows:…”

Section: A Coupled Scheme For Minimizing D Amentioning

confidence: 99%

See 1 more Smart Citation

Distributed Vector Estimation for Power- and Bandwidth-Constrained Wireless Sensor Networks

Sani

Vosoughi

2016

IEEE Trans. Signal Process.

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We consider the distributed estimation of a Gaussian vector with a linear observation model in an inhomogeneous wireless sensor network, in which a fusion center (FC) reconstructs the unknown vector using a linear estimator. Sensors employ uniform multi-bit quantizers and binary PSK modulation, and they communicate with the FC over orthogonal power-and bandwidth-constrained wireless channels.We study transmit power and quantization rate (measured in bits per sensor) allocation schemes that minimize the mean-square error (MSE). In particular, we derive two closed-form upper bounds on the MSE in terms of the optimization parameters and propose "coupled" and "decoupled" resource allocation schemes that minimize these bounds. We show that the bounds are good approximations of the simulated MSE and that the performance of the proposed schemes approaches the clairvoyant centralized estimation when the total transmit power or bandwidth is very large. We investigate how the power and rate allocations are dependent on the sensors' observation qualities and channel gains and on the total transmit power and bandwidth constraints. Our simulations corroborate our analytical results and demonstrate the superior performance of the proposed algorithms. Index TermsDistributed estimation, Gaussian vector, upper bounds on MSE, power and rate allocation, ellipsoid method, quantization, linear estimator, linear observation model.

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“…As an important form of the underlying network technologies of Internet of Things (IoT) [8,9], the node deployment cost [10,11], limited energy [12] and the shortage of wireless spectrum resources [13] has restricted the development of WSNs. In order to solve the spectrum crisis, cognitive radio (CR) technology is becoming a hot research topic [14,15]. The core idea of CR is to make the radio devices intelligent and able to perform cognitive behaviors such as sensing, reasoning, learning, decision making and executing [16][17][18], which is consistent with the idea of artificial intelligence (AI) [19][20][21][22][23] to some extent.…”

Section: Introductionmentioning

confidence: 99%

Non-Cooperative Spectrum Access Strategy Based on Impatient Behavior of Secondary Users in Cognitive Radio Networks

Zeng

Wang

et al. 2019

Electronics

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In the cognitive radio network (CRN), secondary users (SUs) compete for limited spectrum resources, so the spectrum access process of SUs can be regarded as a non-cooperative game. With enough artificial intelligence (AI), SUs can adopt certain spectrum access strategies through their learning ability, so as to improve their own benefit. Taking into account the impatience of the SUs with the waiting time to access the spectrum and the fact that the primary users (PUs) have preemptive priority to use the licensed spectrum in the CRN, this paper proposed the repairable queueing model with balking and reneging to investigate the spectrum access. Based on the utility function from an economic perspective, the relationship between the Nash equilibrium and the socially optimal spectrum access strategy of SUs was studied through the analysis of the system model. Then a reasonable spectrum pricing scheme was proposed to maximize the social benefits. Simulation results show that the proposed access mechanism can realize the consistency of Nash equilibrium strategy and social optimal strategy to maximize the benefits of the whole cognitive system.

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Data-Throughput Enhancement Using Data Mining-Informed Cognitive Radio

Cited by 23 publications

References 25 publications

Estimating Mobile Traffic Demand Using Twitter

Estimating Mobile Traffic Demand Using Twitter

Distributed Vector Estimation for Power- and Bandwidth-Constrained Wireless Sensor Networks

Non-Cooperative Spectrum Access Strategy Based on Impatient Behavior of Secondary Users in Cognitive Radio Networks

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