2002
DOI: 10.1109/tie.2002.804974
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Measurements of a wireless link in an industrial environment using an IEEE 802.11-compliant physical layer

Abstract: Abstract-The design and simulation of coding schemes, Medium Access Control (MAC), and link-layer protocols for future industrial wireless local area networks can be supported by some understanding of the statistical properties of the bit error patterns delivered by a wireless link (which is an ensemble of transmitter, channel, receiver, modems). We present results of bit error measurements taken with an IEEE 802.11-compliant radio modem in an industrial environment. In addition to reporting the most important… Show more

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Cited by 288 publications
(165 citation statements)
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“…The former paper points out that the instantaneous packet error probability varies by approximately 30% around its mean. The latter paper, as well as Willig et al [3], both show that the packet-error stochastic process exhibits significant long-term dependence.…”
Section: Introductionmentioning
confidence: 80%
“…The former paper points out that the instantaneous packet error probability varies by approximately 30% around its mean. The latter paper, as well as Willig et al [3], both show that the packet-error stochastic process exhibits significant long-term dependence.…”
Section: Introductionmentioning
confidence: 80%
“…Measurements conducted on the wireless channel in an IEEE 802.11 WLAN [22] show that the Gilbert-Elliot model gives a good prediction of WLAN performance. To evaluate the performance of the proposed LD-ARF algorithm under variable conditions and compare with that of the ARF algorithm, in this paper we use an extension model of the original Gilbert-Elliot model.…”
Section: Performance Evaluation Of Loss Differentiating Arf Algormentioning
confidence: 99%
“…The last line of Table 1 refers to a simulation where N 2 = N 1 , which results in the most precise estimation, because at each step at least one data (and at most two) for the network state estimate is available. As experimental validation, we have run Algorithm 3.1 on a dataset obtained from a real wireless sensor network composed of Telos T-mote Sky nodes where an object was sometimes shadowing the receiver node, similarly to what discussed in [6]. Part of the dataset was used to estimate the transition probabilities of the Markov chain, and the packet drops probabilities in each state, obtaining p R,U = 10 −3 , p U,U = 1.5 · 10 −3 , p r = 0.10, p u = 0.75.…”
Section: Simulations and Experimentsmentioning
confidence: 99%