Wireless Data Traffic Estimation Using a State-Space Model

Kohandani, F.; McAvoy, D.W.; Khandani, Amir K.

doi:10.1109/tvt.2008.923663

Cited by 5 publications

(4 citation statements)

References 30 publications

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“…Kalman filtering [23] is well known for its abilities in estimating the state of a dynamical process given the independent measurements step by step.…”

Section: Kalman Filtering-based Interference Predictionmentioning

confidence: 99%

“…where I k l,f (k,l) (t) is the combined interferences power level from neighbouring cells to the kth cell on subchannel l at (19), employing a prediction on this combined interferences through Kalman filtering will be able to provide a distributed solution to the original sum rate optimisation problem (4). Kalman filtering [23] is well known for its abilities in estimating the state of a dynamical process given the independent measurements step by step.…”

Section: Kalman Filtering-based Interference Predictionmentioning

confidence: 99%

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Joint and distributed scheduling with dynamic power control in multicell orthogonal frequency division multiple access networks

et al. 2013

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This study addresses the issue of intercell interference coordination in a multicell orthogonal frequency division multiple access network with universal frequency reuse, which aims at improving the network spectral efficiency, especially for the cell edge areas. According to different degrees of information sharing within the engaged network, both a joint and a distributed solution are proposed respectively trying to determine the most appropriate power control strategy along with the user scheduling policy. The joint power control and user scheduling scheme with full access to the network information is capable of gaining considerable enhancement on the network capacity and also a performance improvement for users suffering from strong intercell interferences. The distributed scheme with the same purpose manages to adapt the power control and user scheduling strategy on the basis of dynamic programming with respect to both current and future utility of the network with only local information available. Numerical results have shown the advantages of the proposed schemes in achieving better spectral efficiency under both joint and distributed circumstances.

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“…Kalman filtering [23] is well known for its abilities in estimating the state of a dynamical process given the independent measurements step by step.…”

Section: Kalman Filtering-based Interference Predictionmentioning

confidence: 99%

Section: Kalman Filtering-based Interference Predictionmentioning

confidence: 99%

Joint and distributed scheduling with dynamic power control in multicell orthogonal frequency division multiple access networks

et al. 2013

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“…The main difference between these two criteria is the knowledge of a priori probability. In this paper, the radio environment map (REM) forecasts the trend and seasonality of primary system based on the transmission log [4], and provides a priori information to the secondary system [5], [6]. It is also assumed that the secondary system assesses its own utility depending on the sensing outcome and acquires the utility values of primary system via REM.…”

Section: Introductionmentioning

confidence: 99%

Optimal threshold adaptation with radio environment map for cognitive radio networks

Seol

Lim

2009

2009 IEEE International Symposium on Information Theory

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Spectrum sensing is a key enabling technology of cognitive radio. Reliable detection increases access opportunity to temporarily unused bands and prevents harmful interference to the licensed users. Due to the receiver noise, signal attenuation, and multi-path fading effect, however, it is usually not possible to determine the existence of primary signal with absolute certainty. Without the information of primary user activity, Neyman-Pearson criterion has been commonly used to minimize the missed detection probability for a given false alarm rate. In this paper, we assume that the traffic statistic of primary system is logged into the radio environment map (REM) and can be accessed by the secondary system. Considering sensing errors, Bayes criterion is adopted for total utility maximization of primary and secondary systems. The threshold of energy detector is adapted according to the utility values and a priori information from REM, i.e., both false alarm and detection probabilities are dynamically adjusted.

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“…The main difference between these two criteria is the knowledge of a priori probability. In this paper, the radio environment map (REM) forecasts the trend and seasonality of primary system based on the transmission log [7], and provides a priori information to the secondary system [8], [9]. It is also assumed that the secondary system assesses its own utility depending on the sensing outcome and acquires the utility values of primary system via REM.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Spectrum Sensing with Dynamic Threshold Adaptation

Seol

Lim

2009

GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference

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Spectrum sensing is a key enabling technology of cognitive radio. Reliable detection increases access opportunity to temporarily unused bands and prevents harmful interference to the licensed users. Due to the receiver noise, signal attenuation, and multi-path fading effect, however, it is usually not possible to determine the existence of primary signal with absolute certainty. By extracting a global decision from shared local sensing results, cooperative sensing achieves high reliability over fading channels. In this paper, we assume that the traffic statistic of primary system is logged into the radio environment map (REM) and can be accessed by the secondary systems. The threshold of each energy detector is dynamically adapted according to the utility values and a priori information from REM. Then, decision results and corresponding operating points are collected by a fusion center, which makes a global decision with high confidence.

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Wireless Data Traffic Estimation Using a State-Space Model

Cited by 5 publications

References 30 publications

Joint and distributed scheduling with dynamic power control in multicell orthogonal frequency division multiple access networks

Joint and distributed scheduling with dynamic power control in multicell orthogonal frequency division multiple access networks

Optimal threshold adaptation with radio environment map for cognitive radio networks

Cooperative Spectrum Sensing with Dynamic Threshold Adaptation

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