Search Results (7)

This paper presents an autonomous perfectly secure low-bit-rate voice communication system (APS-VCS) based on the mixed-excitation linear prediction voice coder (MELPe), Vernam cipher, and sequential key distillation (SKD) protocol by public discussion. An authenticated public channel can be selected in a wide range, from internet connections to specially leased radio channels. We found the source of common randomness between the locally synthesized speech signal at the transmitter and the reconstructed speech signal at the receiver side. To avoid information leakage about open input speech, the SKD protocol is not executed on the actual transmitted speech signal but on artificially synthesized speech obtained by random selection of the linear spectral pairs (LSP) parameters of the speech production model. Experimental verification of the proposed system was performed on the Vlatacom Personal Crypto Platform for Voice encryption (vPCP-V). Empirical measurements show that with an adequate selection of system parameters for voice transmission of 1.2 kb/s, a secret key rate (KR) of up to 8.8 kb/s can be achieved, with a negligible leakage rate (LR) and bit error rate (BER) of order ${10}^{-3}$ for various communications channels, including GSM 3G and GSM VoLTE networks. At the same time, by ensuring perfect secrecy within symmetric encryption systems, it further highlights the importance of the symmetry principle in the field of information-theoretic security. To our knowledge, this is the first autonomous, perfectly secret system for low-bit-rate voice communication that does not require explicit prior generation and distribution of secret keys. Full article

This article aims to test, measure and evaluate the quality of voice calls made in a mobile cellular network. A set of drive tests were conducted, during which logs were collected using specialized measurement terminals equipped with a dedicated voice evaluation application. Three different scenarios were considered: the first scenario consisted of a series of mobile-to-mobile calls in a circuit-switched (CS) domain over the GSM network, the second scenario involved similar calls using the VoLTE service in a packet-switched (PS) domain of a 4G network, and the third scenario employed an over-the-top (OTT) media service type via the WhatsApp application in the same PS domain of the 4G network. The measurement results highlight the user experience in each scenario and compare the voice quality evaluated through the Mean Opinion Score (MOS) across the CS and PS domains. The originality of this work consists of in situ measurements performed in Bucharest, Romania, providing detailed, context-specific insights regarding the network performance that can drive local improvements and support policy and investment decisions. Full article

In this presented study, we measured in situ the uplink duty cycles of a smartphone for 5G NR and 4G LTE for a total of six use cases covering voice, video, and data applications. The duty cycles were assessed at ten positions near a 4G and 5G base-station site in Belgium. For Twitch, VoLTE, and WhatsApp, the duty cycles ranged between 4% and 22% in time, both for 4G and 5G. For 5G NR, these duty cycles resulted in a higher UL-allotted time due to time division duplexing at the 3.7 GHz frequency band. Ping showed median duty cycles of 2% for 5G NR and 50% for 4G LTE. FTP upload and iPerf resulted in duty cycles close to 100%. Full article

The most effective automatic speech recognition (ASR) approaches are based on artificial neural networks (ANN). ANNs need to be trained with an adequate amount of matched conditioned data. Therefore, performing training adaptation of an ASR model using augmented data of matched condition as the real environment gives better results for real data. Real-world speech recordings can vary in different acoustic aspects depending on the recording channels and environment such as the Long Term Evolution (LTE) channel of mobile telephones, where data are transmitted with voice over LTE (VoLTE) technology, wireless pin mics in a classroom condition, etc. Acquiring data with such variation is costly. Therefore, we propose training ASR models with simulated augmented data and fine-tune them for domain adaptation using deep neural network (DNN)-based simulated data along with re-recorded data. DNN-based feature transformation creates realistic speech features from recordings of clean conditions. In this research, a comparative investigation is performed for different recording channel adaptation methods for real-world speech recognition. The proposed method yields 27.0% character error rate reduction (CERR) for the DNN–hidden Markov model (DNN-HMM) hybrid ASR approach and 36.4% CERR for the end-to-end ASR approach for the target domain of the LTE channel of telephone speech. Full article

A heterogeneous vehicular network (HetVNET) is a promising network architecture that combines multiple network technologies such as IEEE 802.11p, dedicated short-range communication (DSRC), and third/fourth generation cellular networks (3G/4G). In this network area, vehicle users can use wireless fidelity access points (Wi-Fi APs) to offload 4G long-term evolution (4G-LTE) networks. However, when using Wi-Fi APs, the vehicles must organize themselves and select an appropriate mobile gateway (MGW) to communicate to the cellular infrastructure. Researchers are facing the problem of selecting the best MGW vehicle to aggregate vehicle traffic and reduce LTE load in HetVNETs when the Wi-Fi APs are unavailable for offloading. The selection process utilizes extra network overhead and complexity due to the frequent formation of clusters in this highly dynamic environment. In this study, we proposed a non-cluster adaptive QoS-aware gateway selection (AQAGS) scheme that autonomously picks a limited number of vehicles to act as LTE gateways based on the LTE network’s load status and vehicular ad hoc network (VANET) application’s QoS requirements. The present AQAGS scheme focuses on highway scenarios. The proposed scheme was evaluated using simulation of Urban mobility (SUMO) and network simulator version 2 (NS2) simulators and benchmarked with the clustered and non-clustered schemes. A comparison was made based on the end-to-end delay, throughput, control packet overhead (CPO), and packet delivery ratio (PDR) performance metrics over Voice over Internet Protocol (VoIP) and File Transfer Protocol (FTP) applications. Using VoIP, the AQAGS scheme achieved a 26.7% higher PDR compared with the other schemes. Full article

Sharing network infrastructure is carried out by a few network operators in the world and is regarded as an effective means to accelerate the commercial 5G with seamless coverage and user experience guarantees but significantly reduced investment. Voice via IMS has been defined as the voice-bearing solution from 3rd-Generation Partnership Project (3GPP) Release 5. Release 15 pointed out that 5G still adopts the IMS-based voice service architecture. In such a background, and in the process of global 5G network evolution from non-stand-alone (NSA) to stand-alone (SA), how to bear 5G voice services in the sharing network infrastructure has quite a few technical options. This paper investigates the 5G access network sharing technical solutions and presents the voice bearer technology under different new radio (NR) evolution stages. Analysis was performed for the different stages of voice handover. Performance results from field tests are provided to verify the feasibility of the solution, and performance analysis such as end-to-end call setup delay was also carried out. From the theoretical and practical analysis, the voice over long-term evolution (VoLTE) non-back-to-home solution has a relatively short access delay in the NSA sharing stage; EPS fallback based on either handover or redirection introduces a large time delay, so EPS fallback can only be used as a transition solution in the early stage of SA sharing deployment; voice over new radio (VoNR) has the lowest access time delay and the simplest implementation solution, so it is the final voice solution for 5G SA sharing network. The comparison of different voice-bearing solutions in different network development stages provides a reference for countries around the world. Full article

The competition of speech recognition technology related to smartphones is now getting into full swing with the widespread internet of thing (IoT) devices. For robust speech recognition, it is necessary to detect speech signals in various acoustic environments. Speech/music classification that facilitates optimized signal processing from classification results has been extensively adapted as an essential part of various electronics applications, such as multi-rate audio codecs, automatic speech recognition, and multimedia document indexing. In this paper, we propose a new technique to improve robustness of a speech/music classifier for an enhanced voice service (EVS) codec adopted as a voice-over-LTE (VoLTE) speech codec using long short-term memory (LSTM). For effective speech/music classification, feature vectors implemented with the LSTM are chosen from the features of the EVS. To overcome the diversity of music data, a large scale of data is used for learning. Experiments show that LSTM-based speech/music classification provides better results than the conventional EVS speech/music classification algorithm in various conditions and types of speech/music data, especially at lower signal-to-noise ratio (SNR) than conventional EVS algorithm. Full article