Location Prediction Methods with Markovian Approach and Extended Random Walk Model

Fülöp, Péter; Szabó, Sándor; Szálka, Tamás

doi:10.1109/cisim.2007.45

Cited by 3 publications

(4 citation statements)

References 4 publications

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“…The work of [36] applies Markov chains to predict the next cell for both simple and complex environments. Specifically, for simple scenarios (e.g.…”

Section: ) Markov Chainmentioning

confidence: 99%

A Survey on Next-Cell Prediction in Cellular Networks: Schemes and Applications

2020

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Mobility prediction is a powerful tool for network operators to optimize network performance. From cell level, if network operators know the cells to which the users will be connected in advance, wireless resources can be pre-allocated to improve network performance and better user experience can be provided in location-based services. Many next-cell prediction models and methods have been suggested and implemented. This paper is devoted to next-cell prediction (cell level mobility prediction) in cellular networks, and provides a thorough survey of the prediction schemes and applications. Particularly, a two-level classification methodology was proposed and applied. We first divided the prediction schemes into three categories based on the mobility data used for prediction, i.e. Current Movement State based Approaches (CMSA), Historical Movement Pattern based Approaches (HMPA), and HybriD Approaches (HDA). Prediction schemes in each category were further classified based on the used prediction methods. The typical application scenarios were introduced as well, including handover management, resource allocation, etc. Finally, current challenges and potential trends in the near future were further discussed.

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“…The work of [36] applies Markov chains to predict the next cell for both simple and complex environments. Specifically, for simple scenarios (e.g.…”

Section: ) Markov Chainmentioning

confidence: 99%

A Survey on Next-Cell Prediction in Cellular Networks: Schemes and Applications

2020

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“…The inequalities (19) and (21) can ensure that the value of α always meets the condition of inequality (18). As such, we can choose the optimal random walk model with α = 1/(3β) in order to achieve the optimal delay performance of primary network.…”

Section: B Modified Cooperative Mechanismmentioning

confidence: 99%

“…In this paper, we focus on such CRN, where primary nodes stay static while secondary nodes are mobile following hybrid random walk models. These models have been widely used to simulate the movements of terminals in mobile wireless networks due to its Markovian nature [6], [7], [19], [20]. In the proposed primary protocol, the primary packets can approach the intended destinations with the aid of secondary nodes.…”

Section: Introductionmentioning

confidence: 99%

Cooperation Improves Delay in Cognitive Networks With Hybrid Random Walk

Zheng

Luo

Zhang

et al. 2015

IEEE Trans. Commun.

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In this paper, we study the capacity and delay scaling laws of cognitive radio networks (CRN) with static primary nodes (PNs) and mobile secondary nodes (SNs). The primary network consists of randomly distributed primary nodes of density n, which have a higher priority to access the spectrum. The secondary network consists of randomly distributed secondary nodes of density m = n β , where β represents the density relationship in CRN. Secondary nodes move according to hybrid random walk models with parameter α (0 ≤ α < 1/2), and the step size of the mobility model is Θ(m −2α ). Motivated by observation that the performance of CRN can benefit from the cooperation among primary nodes and secondary nodes, we propose a novel cooperative scheduling mechanism to fully utilize the mobility and geographic information of secondary nodes to enhance the performance of the primary network. For both networks, the delay performance varies with α. We show that the delay performance of primary network can be significantly improved from O(n/log n) [16] to Θ(n β/3 log n) when β < 3 for an optimal value of α, while a near-optimal throughput of Θ(1/log n) is obtained. Furthermore, the secondary network can still achieve the same throughput and delay scaling laws as a stand-alone network simultaneously.Index Terms-Throughput, delay, cognitive radio networks, hybrid random walk, geographic information. science (e.g., online social networks and large scale network with data implications), network economics (e.g., game theoretic analysis on communication networks and pricing and incentive design in network applications), and network optimization (e.g., resource allocation and pricing in cloud computing and information centric networks).Xiaohua Tian received the B.E. and M.E. degrees in communication engineering from Northwestern

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“…They are very simple methods that were developed for maximizing the performance of CPU by predicting the branch instructions [12]. Recently, studies have been conducted to achieve more precise prediction results using complex models such as the Markovian approach or the Bayesian network [13,14].…”

Section: Introductionmentioning

confidence: 99%

Using an Adaptive Search Tree to Predict User Location

Oh¹

2012

Journal of Information Processing Systems

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In this paper, we propose a method for predicting a user's location based on their past movement patterns. There is no restriction on the length of past movement patterns when using this method to predict the current location. For this purpose, a modified search tree has been devised. The search tree is constructed in an effective manner while it additionally learns the movement patterns of a user one by one. In fact, the time complexity of the learning process for a movement pattern is linear. In this process, the search tree expands to take into consideration more details about the movement patterns when a pattern that conflicts with an existing trained pattern is found. In this manner, the search tree is trained to make an exact matching, as needed, for location prediction.In the experiments, the results showed that this method is highly accurate in comparison with more complex and sophisticated methods. Also, the accuracy deviation of users of this method is significantly lower than for any other methods. This means that this method is highly stable for the variations of behavioral patterns as compared to any other method. Finally, 1.47 locations were considered on average for making a prediction with this method. This shows that the prediction process is very efficient

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Location Prediction Methods with Markovian Approach and Extended Random Walk Model

Cited by 3 publications

References 4 publications

A Survey on Next-Cell Prediction in Cellular Networks: Schemes and Applications

A Survey on Next-Cell Prediction in Cellular Networks: Schemes and Applications

Cooperation Improves Delay in Cognitive Networks With Hybrid Random Walk

Using an Adaptive Search Tree to Predict User Location

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