Capacity Bounds and Detection Schemes for Data Over Voice

Abstract

Cellular networks provide widespread and reliable voice communications among subscribers through mobile voice channels. These channels benefit from superior priority and higher availability compared with conventional cellular data communication services, such as General Packet Radio Service, Enhanced Data Rates for GSM Evolution, and High-Speed Downlink Packet Access. These properties are of major interest to applications that require transmitting small volumes of data urgently and reliably, such as an emergency call in vehicular applications. This encourages excessive research to make digital communication through voice channels feasible, leading to the emergence of Data over Voice (DoV) technology. In this research, we investigate the challenges of transmitting data through mobile voice channels. We introduce a simplified information-theoretic model of the vocoder channel and derive bounds on its capacity. By invoking detection theory concepts and conjecturing Weibull and chi-square distributions for approximately modeling the probability distribution of channel output, we propose improved detection schemes based on the mentioned distributions and compare the achieved performances with the calculated bounds and other state-of-the-art DoV structures. Moreover, in common mobile networks, the vocoder compression rate is adopted in accordance with the network traffic adaptively. Although this phenomenon affects the overall capacity significantly, it has been overlooked by previous research studies. In this research, we apply the Gilbert–Elliott (GE) model to the voice channel, extract the required model parameters from the Markov model, and bound the overall voice channel capacity by considering the adaptive rate adjustment phenomenon.