Secure data communication through GSM Adaptive Multi Rate voice channel

Boloursaz, Mohammad Reza; Hadavi, Amir Hossein; Kazemi, Reza; Behnia, Fereidoon

doi:10.1109/istel.2012.6483136

Cited by 8 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…) are supposed to be negative. In each iteration the algorithm chooses the child codebook with the least Δf value and as seen in (17) and (18), D H X|Y ( ) (…”

Section: Lemmamentioning

confidence: 99%

See 1 more Smart Citation

Efficient codebook design for digital communication through compressed voice channels

Mashhadi

Behnia

2016

IET Communications

View full text Add to dashboard Cite

The common voice channels existing in cellular communication networks provide reliable, ubiquitously available and top priority communication mediums. These properties make voice dedicated channels an ideal choice for high priority, real time communication. However, such channels include voice codecs that hamper the data flow by compressing the waveforms prior to transmission. This study designs codebooks of speech-like symbols for reliable data transfer through the voice channel of cellular networks. An efficient algorithm is proposed to select proper codebook symbols from a database of natural speech to optimise a desired objective. Two variants of this codebook optimisation algorithm are presented: One variant minimises the symbol error rate and the other maximises the capacity achievable by the codebook. It is shown both analytically and by the simulation results that under certain circumstances, these two objective functions reach the same performance. Simulation results also show that the proposed codebook optimisation algorithm achieves higher data rates and lower symbol error rates compared with previously reported results while requiring lower computational complexity for codebook optimisation. The Gilbert-Elliot channel model is utilised to study the effects of adaptive compression rate adjustment of the vocoder on overall voice channel capacity. Finally, practical implementation issues are addressed.

show abstract

“…) are supposed to be negative. In each iteration the algorithm chooses the child codebook with the least Δf value and as seen in (17) and (18), D H X|Y ( ) (…”

Section: Lemmamentioning

confidence: 99%

“…As classified by [3, 4], prior researches on data transmission over voice channels have proposed three different approaches of ‘parameter mapping’ [15], ‘codebook optimisation’ [16–18] and ‘modulation optimisation’ [19, 20] to overcome voice channel non‐idealities [3, 4, 21]. These approaches are well compared in [4].…”

Section: Introductionmentioning

confidence: 99%

Efficient codebook design for digital communication through compressed voice channels

Mashhadi

Behnia

2016

IET Communications

View full text Add to dashboard Cite

show abstract

“…The error probability in this case can be calculated similar to the orthogonal case P e = i P S i P e|S i (27) As before assumed P(e|S 1 ) = max i P(e|S i ). Since f (r i |S i ) is centralised chi-square function as in (19), then having its cumulative distribution function [22], we obtain…”

Section: General Signallingmentioning

confidence: 99%

“…Shahbazi et al [21] used exhaustive search algorithm over observed human speech waveforms to find optimum symbols. Boloursaz et al [22][23][24] and Kazemi et al [25] proposed a heuristic algorithm optimising the codebook for a maximised capacity. The highest achieved data rate of this group is 4 kbps with the symbol error rate (SER) of 2.5%.…”

Section: Introductionmentioning

confidence: 99%