Simple, Accurate and Computationally Efficient Wireless Channel Modeling Algorithm

Moltchanov, Dmitri; Koucheryavy, Yevgeni; Harju, Jarmo

doi:10.1007/11424505_23

Cited by 19 publications

(18 citation statements)

References 7 publications

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“…To fully parameterize a covariance-stationary binary process, only the mean and the value of the normalized autocorrelation function of bit error observations at lag 1 have to be captured. In our previous work [23], we have demonstrated that there is a SBP model exactly matching the mean and the lag-1 NACF of covariance-stationary bit error observations. This model is given by…”

Section: Bit Error Processmentioning

confidence: 92%

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Modeling TCP SACK performance over wireless channels with semi-reliable ARQ/FEC

Moltchanov

Dunaytsev

2009

Wireless Netw

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Providing reliable data communications over wireless channels is a challenging task because timevarying wireless channel characteristics often lead to bit errors. These errors result in loss of IP packets and, consequently, TCP segments encapsulated into these packets. Since TCP cannot distinguish packet losses due to bit corruption from those due to network congestion, any packet loss caused by wireless channel impairments leads to unnecessary execution of the TCP congestion control algorithms and, hence, sub-optimal performance. Automatic Repeat reQuest (ARQ) and Forward Error Correction (FEC) try to improve communication reliability and reduce packet losses by detecting and recovering corrupted bits. Most analytical models that studied the effect of ARQ and FEC on TCP performance assumed that the ARQ scheme is perfectly persistent (i.e., completely reliable), thus a frame is always successfully transmitted irrespective of the number of transmission attempts it takes. In this paper, we develop an analytical cross-layer model for a TCP connection running over a wireless channel with a semireliable ARQ scheme, where the amount of transmission attempts is limited by some number. The model allows to evaluate the joint effect of stochastic properties of the wireless channel characteristics and various implementation-specific parameters on TCP performance, which makes it suitable for performance optimization studies. The input parameters include the bit error rate, the value of the normalized autocorrelation function of bit error observations at lag 1, the strength of the FEC code, the persistency of ARQ, the size of protocol data units at different layers, the raw data rate of the wireless channel, and the bottleneck link buffer size.

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Section: Bit Error Processmentioning

confidence: 92%

“…Details of the algorithm are outlined in [23]. The proposed model captures first-and second-order statistical characteristics in terms of the BER and the lag-1 NACF.…”

Section: Bit Error Processmentioning

confidence: 99%

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Modeling TCP SACK performance over wireless channels with semi-reliable ARQ/FEC

Moltchanov

Dunaytsev

2009

Wireless Netw

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“…We found our traces to be partially covariance stationary and explicitly capture statistical characteristics of covariance stationary segments including bit error rate and empirical ACF. The proposed approach is not only applicable for bit error traces but can be used to model frame error traces of wireless channels [21] as long as empirical ACFs can be sufficiently well approximated by a single geometrically distributed component.…”

Section: Bit Error Model Of the Wireless Channelmentioning

confidence: 99%

“…The mean number of correctly received packets, E[N P ], can be obtained from (21) and given by the following expression E½N P Z p S ðD A ð1Þ5D P ð0ÞÞe:…”

Section: Performance At the Ip Layermentioning

confidence: 99%