Prediction in wireless networks by Markov chains

Katsaros, Dimitrios; Manolopoulos, Yannis

doi:10.1109/mwc.2009.4907561

Cited by 52 publications

(35 citation statements)

References 17 publications

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“…As one can see in this The distribution generated by a variable order Markov model can indeed be generated by a fixed order Markov model of the same order. The only difference is that the variable order Markov model is able to do it in a much more succinct manner, which is computationally more efficient and consumes less memory [40,42]. When creating a fixed order Markov model we only have to worry about the conditional probabilities i.e.…”

Section: Proactive Channel Switchingmentioning

confidence: 99%

“…All Markov predictors depend on the property that, the probability distribution of the next symbol can be approximated by conditioning it on the previous D symbols, which is called by the name "short memory principle" [42]. If we choose the memory D to be too small than required, the distribution will be incapable of capturing all the dependencies between the symbols which degrades prediction efficiency [42].…”

Section: Algorithm To Build the Probabilistic Suffix Treementioning

confidence: 99%

“…If we choose the memory D to be too small than required, the distribution will be incapable of capturing all the dependencies between the symbols which degrades prediction efficiency [42]. On the other hand if we choose it to be too high it will over-fit the training sequence and give a high prediction error.…”

Section: Algorithm To Build the Probabilistic Suffix Treementioning

confidence: 99%

“…Therefore we can afford to keep D to be higher than required since the algorithm takes care of it [42]. But this advantage does not come free.…”

Section: Algorithm To Build the Probabilistic Suffix Treementioning

confidence: 99%

“…But this advantage does not come free. For the algorithm to take care of this issue, more tunable parameters are introduced [42]. This algorithm is governed by five parameters.…”

Section: Algorithm To Build the Probabilistic Suffix Treementioning

confidence: 99%

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Predictive Channel Access in Cognitive Radio Networks Based on Variable Order Markov Models

Devanarayana

Alfa

2011

2011 IEEE Global Telecommunications Conference - GLOBECOM 2011

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The concept of Cognitive radio enables the unlicensed users to share the spectrum with licensed users, on the condition that the licensed users have preemptive priority. The use of the channel by unlicensed users should not result in more than acceptable interference level to the licensed users, if interference occurs. The sense and react strategy by unlicensed users sometimes does not lead to acceptable level of interference while maintaining an acceptable data transfer rate for the unlicensed users.Proactive channel access has been proposed for the purpose of reducing the interference to primary users and to reduce the idle channel search delay for the secondary users. There are many methods used in the literature to model the channel state fluctuations. Based on these models the future channel states are predicted.In this thesis we introduce a predictive channel usage scheme which is capable of reducing the interference caused by the unlicensed users. Furthermore our scheme is capable of increasing the data rates the unlicensed users experience through the reduction of the idle channel identification delay. In our scheme no assumptions are made about the distribution of licensed user channel usage. We learn the traffic characteristics of the channels using a learning scheme called Probabilistic Suffix Tree algorithm.iii

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Section: Proactive Channel Switchingmentioning

confidence: 99%

Section: Algorithm To Build the Probabilistic Suffix Treementioning

confidence: 99%

Section: Algorithm To Build the Probabilistic Suffix Treementioning

confidence: 99%

“…Therefore we can afford to keep D to be higher than required since the algorithm takes care of it [42]. But this advantage does not come free.…”

Section: Algorithm To Build the Probabilistic Suffix Treementioning

confidence: 99%

“…But this advantage does not come free. For the algorithm to take care of this issue, more tunable parameters are introduced [42]. This algorithm is governed by five parameters.…”

Section: Algorithm To Build the Probabilistic Suffix Treementioning

confidence: 99%

See 3 more Smart Citations

Predictive Channel Access in Cognitive Radio Networks Based on Variable Order Markov Models

Devanarayana

Alfa

2011

2011 IEEE Global Telecommunications Conference - GLOBECOM 2011

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Predictive Location Tracking in Cellular and in Ad Hoc Wireless Networks

Dimokas

Katsaros

Bozanis

et al. 2010

Mobile Intelligence

Self Cite

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Predicting the future is mostly a matter of managing not to blink as you witness the present.-William Gibson INTRODUCTIONThe proliferation of cellular and ad hoc networks and the penetration of Internet services are changing many aspects of ubiquitous mobile computing. Constantly increasing mobile client populations utilize diverse mobile devices to access the Mobile Intelligence. Edited by Laurence T. Yang

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Dynamic PCI allocation on avoiding handover confusion via cell status prediction in LTE heterogeneous small cell networks

Xiao

Ding

et al. 2016

Wireless Communications

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In this paper, we investigate the inbound handover confusion in the two-tier macrocell-small cell networks with help of mobility prediction. Instead of studying the mobile user's (MU) movement, we propose an analytical model for the activity status of small cells, which is to exploit the statistical property of inbound handover events that would happen in small cells. We design the cell status prediction algorithm to obtain the optimal prediction outcome of the next status for the small cells. On avoiding the handover confusion, we develop a dynamic allocation approach of physical cell identifier according to the prediction results. We design (i) the cell status prediction-based strategy, by which the dedicated PCIs will be assigned to the small cells with busy activity status while the other small cells share the public PCIs, and (ii) an integrated strategy in order to fully exploit the usage of PCI. We formulate the preference relation for small cells via reference signal received quality relation integrated with status prediction information using Bayesian average method. Simulation results reveal that the proposed algorithms yield higher accuracy than the conventional methods; in the meantime, handover confusions can be reduced significantly during the inbound handover.

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Prediction in wireless networks by Markov chains

Cited by 52 publications

References 17 publications

Predictive Channel Access in Cognitive Radio Networks Based on Variable Order Markov Models

Predictive Channel Access in Cognitive Radio Networks Based on Variable Order Markov Models

Predictive Location Tracking in Cellular and in Ad Hoc Wireless Networks

Dynamic PCI allocation on avoiding handover confusion via cell status prediction in LTE heterogeneous small cell networks

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