BEAR: A Bursty Error Auto-Regressive Model for Indoor Wireless Environments

Agüero, Ramón; García, María Jesús Márquez; Mufioz, L.

doi:10.1109/pimrc.2007.4394738

Cited by 4 publications

(5 citation statements)

References 5 publications

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“…In [5] it is shown that, under slow fading conditions, the SNR (Signal to Noise Ratio) of indoor wireless channels can be well approximated by an autoregressive (AR) model. This means that, under such conditions, we can model the channel attenuations as…”

Section: Modelmentioning

confidence: 99%

“…Therefore, we are allowed to formulate the game as a stochastic game in which each user j controls its own Markov chain on the state space 5j. As in the single user case 5j is the set of all the possible states (5). Formally, the state space of the stochastic game at hand is the Cartesian product 5* = 51 X 52 .…”

Section: Multi User: Stochastic Game Formulationmentioning

confidence: 99%

“…In this work, we consider a wireless network where trans mitters can select a frequency band from a shared pool to communicate on. The evolution of the slow fading channel attenuation associated to each frequency band and each trans mitter is a random process that can be well approximated by an autoregressive process [5]. We assume that all such random processes are independent of each other.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Slow fading channel selection: A restless multi-armed bandit formulation

Avrachenkov

Cottatellucci

Maggi

2012

2012 International Symposium on Wireless Communication Systems (ISWCS)

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We deal with a multi-access wireless network in which transmitters dynamically select a frequency band to communicate on. The slow fading channel attenuations follow an autoregressive model. In the single user case, we formulate this selection problem as a restless multi-armed bandit problem and we propose two strategies to dynamically select a band at each time slot. Our objective is to maximize the SNR in the long run. Each of these strategies is close to the optimal strategy in different regimes. In the general case with several users, we formulate the problem as a stochastic game with uncountable state space, where the objective is the SINR. Then we propose two strategies to approximate the best response policy for one user when the other users' strategy is fixed.

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Section: Modelmentioning

confidence: 99%

Section: Multi User: Stochastic Game Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Slow fading channel selection: A restless multi-armed bandit formulation

Avrachenkov

Cottatellucci

Maggi

2012

2012 International Symposium on Wireless Communication Systems (ISWCS)

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“…The Bursty Error model based on Auto-Regressive filter (BEAR) is able to adequate channel behavior depending on link quality, in terms of the received SNR per frame, and thus it can be used to study cross layer optimization techniques, as the one we are analyzing in this paper. BEAR is able to precisely reflect the behavior of typical office environments, especially in terms of the appearance of erroneous frame bursts [12], [15], which are quite likely to happen within real propagation conditions and may have a strong impact over the ETX estimation, as reported in [8].…”

Section: Channel Model For Indoor Wireless Linksmentioning

confidence: 99%

Using SNR to Improve Multi-Hop Routing

Agüero

Galache

Muñoz

2009

VTC Spring 2009 - IEEE 69th Vehicular Technology Conference

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This paper analyzes the use of link quality, based on the Signal to Noise Ratio, as a metric to improve routing in multi-hop wireless networks. We compare the behavior and performance with those of other approaches, namely a simple minimum-hop selection process, as well as the Expected Transmission Count (ETX) metric, which has recently gathered relevant attention from the scientific community. We use a simulation based analysis, but we account for a realistic channel model, able to mimic the Gray Zones effect, which has been proven to have a great impact on wireless-based communications in general and over multi-hop topologies in particular. The results show that important improvements can be brought about by making use of the proposed metric to enhance route selection procedures.

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“…Since states are normally distributed, the objectives of exploitation and exploration naturally correspond to the conditional mean and variance of an arm, which at the same time contain all relevant information concerning its state (and thus fully describe the belief state of the arm). The AR(1) model has been found useful for example for modeling channels in wireless networks [1]. It seems that in the context of decision making under reward observability it has previously only been considered in [3], where the myopic (greedy) policy was compared numerically to an ad hoc randomized policy.…”

Section: Introductionmentioning

confidence: 99%

Exploration vs Exploitation with Partially Observable Gaussian Autoregressive Arms

Kühn¹,

Mandjes²,

Nazarathy³

2015

Proceedings of the 8th International Conference on Performance Evaluation Methodologies and Tools

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We consider a restless bandit problem with Gaussian autoregressive arms, where the state of an arm is only observed when it is played and the state-dependent reward is collected. Since arms are only partially observable, a good decision policy needs to account for the fact that information about the state of an arm becomes more and more obsolete while the arm is not being played. Thus, the decision maker faces a tradeoff between exploiting those arms that are believed to be currently the most rewarding (i.e. those with the largest conditional mean), and exploring arms with a high conditional variance. Moreover, one would like the decision policy to remain tractable despite the infinite state space and also in systems with many arms. A policy that gives some priority to exploration is the Whittle index policy, for which we establish structural properties. These motivate a parametric index policy that is computationally much simpler than the Whittle index but can still outperform the myopic policy. Furthermore, we examine the many-arm behavior of the system under the parametric policy, identifying equations describing its asymptotic dynamics. Based on these insights we provide a simple heuristic algorithm to evaluate the performance of index policies; the latter is used to optimize the parametric index.

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BEAR: A Bursty Error Auto-Regressive Model for Indoor Wireless Environments

Cited by 4 publications

References 5 publications

Slow fading channel selection: A restless multi-armed bandit formulation

Slow fading channel selection: A restless multi-armed bandit formulation

Using SNR to Improve Multi-Hop Routing

Exploration vs Exploitation with Partially Observable Gaussian Autoregressive Arms

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