Online modeling of wireless channels with hidden markov models and channel impulse responses for cognitive radios

Rondeau, Thomas W.; Rieser, Christian James; Gallagher, Tom; Bostian, C.W.

doi:10.1109/mwsym.2004.1339066

Cited by 18 publications

(6 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Experimental data recorded in the Chicago city area shows that the TV spectrum is severely under-utilized and the average spectrum occupancy of the TV spectrum is close to 30% [11]. Research has shown that PU's spectrum usage follows the Markov ON/OFF model [8][9][10] and therefore, in this paper we have the same assumption that PU's spectrum usage follows the Markov ON/OFF model. In our model, every discrete time interval corresponds to one super-frame i.e.…”

Section: A Markov On/off Model For Pu Activitymentioning

confidence: 96%

See 1 more Smart Citation

DS3: A Dynamic and Smart Spectrum Sensing Technique for Cognitive Radio Networks under denial of service attack

Amjad

Aslam

Zou

2013

2013 IEEE Global Communications Conference (GLOBECOM)

View full text Add to dashboard Cite

-IEEE 802.22 Cognitive Radio Networks (CRN) employ a two-stage quiet period mechanism based on a mandatory Fast Sensing and an optional Fine Sensing stage for Dynamic Spectrum Access (DSA) during every super frame. However, the two-stage spectrum sensing approach presents an opportunity to malicious users where they can launch a smart Denial of Service (DoS) attack by transmitting a very short jamming signal during the fast sensing stage and thus forcing the CRN to carry out fine sensing, thereby wasting spectrum opportunities for honest Secondary Users (SU). In this paper, we present 'DS3': A Dynamic and Smart Spectrum Sensing algorithm, which minimizes the effects of jamming as well as noise on the fast sensing phase of DSA. It improves system's efficiency by striking a balance between spectrum utilization by SUs and delay in the detection of Primary Users' (PU) presence on the spectrum, using a dynamic fine sensing decision algorithm with minimal overhead.

show abstract

Section: A Markov On/off Model For Pu Activitymentioning

confidence: 96%

“…The PU's use of spectrum is modeled as a Markov ON/OFF process [8][9][10] as shown in figure 2, with being the probability that the PU will transition from state 0 to 1 and being the transition probability from state 1 to 0. State 0 represents OFF and 1 represents ON state of the PU.…”

Section: System Modelmentioning

confidence: 99%

DS3: A Dynamic and Smart Spectrum Sensing Technique for Cognitive Radio Networks under denial of service attack

Amjad

Aslam

Zou

2013

2013 IEEE Global Communications Conference (GLOBECOM)

View full text Add to dashboard Cite

show abstract

“…A potential drawback when using HMMs is that a training sequence is needed, with the training process being potentially computationally complex. Other AI techniques such as GA are used to improve the model training efficiency [63]. We will further discuss the usage of HMMs in section VI-B where we will outline how HMM has been, or can be, used for solving certain modeling, planning and prediction tasks that relate to cognitive routing in CRNs.…”

Section: ) Key Problems In Hmmsmentioning

confidence: 99%

“…In [63], Rondeau et al proposed using HMM to model the wireless channel online with the HMM being trained using a genetic algorithm. In [151], techniques for modeling wireless network channel using Markov models are presented along with techniques for efficient estimation of Markov model parameters (including the number of states) to aid in reproducing and/or forecasting channel statistics accurately.…”

Section: ) Channel Quality Modeling and Predictionmentioning

confidence: 99%

Artificial intelligence based cognitive routing for cognitive radio networks

Qadir

2015

Artif Intell Rev

View full text Add to dashboard Cite

Abstract-Cognitive radio networks (CRNs) are networks of nodes equipped with cognitive radios that can optimize performance by adapting to network conditions. While cognitive radio networks (CRN) are envisioned as intelligent networks, relatively little research has focused on the network level functionality of CRNs. Although various routing protocols, incorporating varying degrees of adaptiveness, have been proposed for CRNs, it is imperative for the long term success of CRNs that the design of cognitive routing protocols be pursued by the research community. Cognitive routing protocols are envisioned as routing protocols that fully and seamless incorporate AI-based techniques into their design. In this paper, we provide a self-contained tutorial on various AI and machine-learning techniques that have been, or can be, used for developing cognitive routing protocols. We also survey the application of various classes of AI techniques to CRNs in general, and to the problem of routing in particular. We discuss various decision making techniques and learning techniques from AI and document their current and potential applications to the problem of routing in CRNs. We also highlight the various inference, reasoning, modeling, and learning sub tasks that a cognitive routing protocol must solve. Finally, open research issues and future directions of work are identified.

show abstract

“…The Genetic Algorithm is associated with the RAKE receiver to combat the inter-symbol interference (ISI) due to the frequency selective nature of UWB channels. Another GA based novel technique has been developed to train a hidden Markov model (HMM) with for the cognitive radio channel [22]. This paper has used the idea of HMM and SHMM in proposing new GA based FSMC model which is slightly different from the papers encountered in the literature.…”

Section: mentioning

confidence: 99%

Genetic Algorithm Based Finite State Markov Channel Modeling

Ranjan¹,

Bepari

Mitra

2013

WCMC

View full text Add to dashboard Cite

Statistical properties of the error sequences produced by fading channels with memory have a strong influence over the performance of high layer protocols and error control codes. Finite State Markov Channel (FSMC) models can represent the temporal correlations of these sequences efficiently and accurately. This paper proposes a simple genetic algorithm (GA) based search for the optimum state transition matrix for a block diagonal Markov model. The burst error statistics of the GA based FSMC model with respect to Autocorrelation Function and error free interval distribution of the original error sequence are presented to validate the proposed method. The superiority of the GA approach over the semi-hidden Markov model (SHMM) based Fritchman model is exhibited in significant improvement of closeness of match and in the usage of shorter length of error sequences. Another Baum-Welch algorithm (BWA) based GA search method has been proposed and compared with the BWA and SHMM methods for the same error sequence. Again the superiority of GA approaches is recognized, especially for the smaller error lengths.

show abstract

Online modeling of wireless channels with hidden markov models and channel impulse responses for cognitive radios

Cited by 18 publications

References 9 publications

DS3: A Dynamic and Smart Spectrum Sensing Technique for Cognitive Radio Networks under denial of service attack

DS3: A Dynamic and Smart Spectrum Sensing Technique for Cognitive Radio Networks under denial of service attack

Artificial intelligence based cognitive routing for cognitive radio networks

Genetic Algorithm Based Finite State Markov Channel Modeling

Contact Info

Product

Resources

About