HMM Based Channel Status Predictor for Cognitive Radio

Park, Chang-Hyun; Kim, Sangwon; Lim, Sunmin; Song, Mee

doi:10.1109/apmc.2007.4554696

Cited by 56 publications

(37 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, in [10] the accuracy of channel status prediction was not provided. Another HMM based predictor was also proposed in [11], but it only deals with deterministic traffic scenarios, making it not applicable for the actual environment. There are several drawbacks in using the HMM based spectrum prediction schemes [10], [11] such as, determining an optimal 1 In this paper, channel and spectrum are used interchangeably.…”

Section: Related Workmentioning

confidence: 99%

“…Another HMM based predictor was also proposed in [11], but it only deals with deterministic traffic scenarios, making it not applicable for the actual environment. There are several drawbacks in using the HMM based spectrum prediction schemes [10], [11] such as, determining an optimal 1 In this paper, channel and spectrum are used interchangeably. 1 model (number of states in the HMM) is difficult and even impossible in practice, a huge memory space is needed to store a large number of past observations, and model estimation has high computational complexity.…”

Section: Related Workmentioning

confidence: 99%

“…Unlike the HMM based prediction schemes [10], [11] which require continuous training, the neural network predictor is trained only once in an offline fashion when the observed process is stationary. Once the neural networks are trained, the computational complexity is significantly reduced.…”

Section: Spectrum Prediction Using Neural Networkmentioning

confidence: 99%

See 2 more Smart Citations

A Neural Network Based Spectrum Prediction Scheme for Cognitive Radio

Tumuluru

Wang

Niyato

2010

2010 IEEE International Conference on Communications

179

101

View full text Add to dashboard Cite

Abstract-The Cognitive Radio (CR) technology enables the unlicensed users to share the spectrum with the licensed users on a non-interfering basis. Spectrum sensing is an important function for the unlicensed users to determine availability of a channel in the licensed user's spectrum. However, spectrum sensing consumes considerable energy which can be reduced by employing predictive methods for discovering spectrum holes. Using a reliable prediction scheme, the unlicensed users will sense only those channels which are predicted to be idle. By achieving a low probability of error in predicting the idle channels, the spectrum utilization can also be improved. Since the traffic characteristics of most licensed user systems encountered in real life are not known a priori, we design the spectrum predictor using the neural network model, multilayer perceptron (MLP), which does not require a prior knowledge of the traffic characteristics of the licensed user systems. The performance of the spectrum predictor is analyzed through extensive simulations.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Neural Network Based Spectrum Prediction Scheme for Cognitive Radio

Tumuluru

Wang

Niyato

2010

2010 IEEE International Conference on Communications

179

101

View full text Add to dashboard Cite

show abstract

“…A spectrum detection system based on several HMMs, each trained for a particular signal spectrum, was presented in [6]. Besides, channel state prediction models based on HMMs were proposed in [7,8]. The one addressed in [7] could only predict the channel state for observation sequences of a 1 symbol period or 2 symbols period; and could not carry out the prediction of non periodic sequences as real measurements from a frequency band actually are.…”

Section: Introductionmentioning

confidence: 99%

“…Besides, channel state prediction models based on HMMs were proposed in [7,8]. The one addressed in [7] could only predict the channel state for observation sequences of a 1 symbol period or 2 symbols period; and could not carry out the prediction of non periodic sequences as real measurements from a frequency band actually are. Furthermore, the model proposed in [8] was based on Higher-Order HMM and took into account the latency between spectrum sensing and Wi-Fi data transmissions.…”

Section: Introductionmentioning

confidence: 99%

HF spectrum activity prediction model based on HMM for cognitive radio applications

Melián-Gutiérrez

Zazo

Blanco-Murillo

et al. 2013

Physical Communication

View full text Add to dashboard Cite

Although most of the research on Cognitive Radio is focused on communication bands above the HF upper limit (30 MHz), Cognitive Radio principles can also be applied to HF communications to make use of the extremely scarce spectrum more efficiently. In this work we consider legacy users as primary users since these users transmit without resorting to any smart procedure, and our stations using the HFDVL (HF Data+Voice Link) architecture as secondary users. Our goal is to enhance an efficient use of the HF band by detecting the presence of uncoordinated primary users and avoiding collisions with them while transmitting in different HF channels using our broad-band HF transceiver.A model of the primary user activity dynamics in the HF band is developed in this work to make short-term predictions of the sojourn time of a primary user in the band and avoid collisions. It is based on Hidden Markov Models (HMM) which are a powerful tool for modelling stochastic random processes and are trained with real measurements of the 14 MHz band.By using the proposed HMM based model, the prediction model achieves an average 10.3% prediction error rate with one minute-long channel knowledge but it can be reduced when this knowledge is extended: with the previous 8 min knowledge, an average 5.8% prediction error rate is achieved.These results suggest that the resulting activity model for the HF band could actually be used to predict primary users activity and included in a future HF cognitive radio based station.

show abstract

A prediction and scheduling framework in centralized cognitive radio network for energy efficient non‐real time communication

Manna

Misra

2018

Int J Communication

View full text Add to dashboard Cite

Advent of Internet of Things led to an exponential rise in battery-operated sensors transmitting small non-real time (NRT) data regularly. To this end, this work proposes a framework for centralized cognitive radio network (CRN) that facilitates better spectrum utilization and low-cost opportunistic NRT data transfer with high energy efficiency. The novelty of this framework is to incorporate Hidden Markov Model-based prediction within the traditional cognitive radio sensing-transmission cycle. To minimize the prediction time, we design a Hardware-based Hidden Markov Model engine (H2M2) to be used by the cognitive base station (CBS). CBS exploits the H2M2 engine over high primary user (PU) activity channels to minimize the collisions between PUs and NRT secondary users, thereby reducing the SU energy consumption. However, this is at the cost of reduced throughput. Taking this into account, we propose an Intersensing-Prediction Time Optimization algorithm that identifies the predictable PU activity channels and maximizes the throughput within a PU interference threshold. Furthermore, to minimize the total battery consumption of all the SUs within CRN, a Battery Consumption Minimizing Scheduler is designed at the CBS that efficiently allocates the predictable PU channels to the NRT SUs. By exploiting the unutilized high PU activity channels, the proposed Centralized Scheduling, Sensing and Prediction (CSSP) framework improves the spectral efficiency of the CRN. Exhaustive performance studies show that CSSP outperforms traditional nonpredictive sensing techniques in terms of energy efficiency and interference management. Finally, through a proof of concept, we validate the ability of CSSP framework in enabling NRT communication.

show abstract

HMM Based Channel Status Predictor for Cognitive Radio

Cited by 56 publications

References 3 publications

A Neural Network Based Spectrum Prediction Scheme for Cognitive Radio

A Neural Network Based Spectrum Prediction Scheme for Cognitive Radio

HF spectrum activity prediction model based on HMM for cognitive radio applications

A prediction and scheduling framework in centralized cognitive radio network for energy efficient non‐real time communication

Contact Info

Product

Resources

About