Spectral Efficiency of Random Time-Hopping CDMA

Traditionally paired with impulsive communications, Time-Hopping CDMA (TH-CDMA) is a multiple access technique that separates users in time by coding their transmissions into pulses occupying a subset of $N_\mathsf{s}$ chips out of the total $N$ included in a symbol period, in contrast with traditional Direct-Sequence CDMA (DS-CDMA) where $N_\mathsf{s}=N$. This work analyzes TH-CDMA with random spreading, by determining whether peculiar theoretical limits are identifiable, with both optimal and sub-optimal receiver structures, in particular in the archetypal case of sparse spreading, that is, $N_\mathsf{s}=1$. Results indicate that TH-CDMA has a fundamentally different behavior than DS-CDMA, where the crucial role played by energy concentration, typical of time-hopping, directly relates with its intrinsic "uneven" use of degrees of freedom.Comment: 26 pages, 13 figure

Consonant gemination in Italian: the affricate and fricative case

Consonant gemination in Italian affricates and fricatives was investigated, completing the overall study of gemination of Italian consonants. Results of the analysis of other consonant categories, i.e. stops, nasals, and liquids, showed that closure duration for stops and consonant duration for nasals and liquids, form the most salient acoustic cues to gemination. Frequency and energy domain parameters were not significantly affected by gemination in a systematic way for all consonant classes. Results on fricatives and affricates confirmed the above findings, i.e., that the primary acoustic correlate of gemination is durational in nature and corresponds to a lengthened consonant duration for fricative geminates and a lengthened closure duration for affricate geminates. An inverse correlation between consonant and pre-consonant vowel durations was present for both consonant categories, and also for both singleton and geminate word sets when considered separately. This effect was reinforced for combined sets, confirming the hypothesis that a durational compensation between different phonemes may serve to preserve rhythmical structures. Classification tests of single vs. geminate consonants using the durational acoustic cues as classification parameters confirmed their validity, and highlighted peculiarities of the two consonant classes. In particular, a relatively poor classification performance was observed for affricates, which led to refining the analysis by considering dental vs. non-dental affricates in two different sets. Results support the hypothesis that dental affricates, in Italian, may not appear in intervocalic position as singletons but only in their geminate form.Comment: Submitted to Speech Communication. arXiv admin note: substantial text overlap with arXiv:2005.0696

MoMo: a group mobility model for future generation mobile wireless networks

Existing group mobility models were not designed to meet the requirements for accurate simulation of current and future short distance wireless networks scenarios, that need, in particular, accurate, up-to-date informa- tion on the position of each node in the network, combined with a simple and flexible approach to group mobility modeling. A new model for group mobility in wireless networks, named MoMo, is proposed in this paper, based on the combination of a memory-based individual mobility model with a flexible group behavior model. MoMo is capable of accurately describing all mobility scenarios, from individual mobility, in which nodes move inde- pendently one from the other, to tight group mobility, where mobility patterns of different nodes are strictly correlated. A new set of intrinsic properties for a mobility model is proposed and adopted in the analysis and comparison of MoMo with existing models. Next, MoMo is compared with existing group mobility models in a typical 5G network scenario, in which a set of mobile nodes cooperate in the realization of a distributed MIMO link. Results show that MoMo leads to accurate, robust and flexible modeling of mobility of groups of nodes in discrete event simulators, making it suitable for the performance evaluation of networking protocols and resource allocation algorithms in the wide range of network scenarios expected to characterize 5G networks.Comment: 25 pages, 17 figure

Automatic best wireless network selection based on key performance indicators

Introducing cognitive mechanisms at the application layer may lead to the possibility of an automatic selection of the wireless network that can guarantee best perceived experience by the final user. This chapter investigates this approach based on the concept of Quality of Experience (QoE), by introducing the use of application layer parameters, namely Key Performance Indicators (KPIs). KPIs are defined for different traffic types based on experimental data. A model for an ap- plication layer cognitive engine is presented, whose goal is to identify and select, based on KPIs, the best wireless network among available ones. An experimenta- tion for the VoIP case, that foresees the use of the One-way end-to-end delay (OED) and the Mean Opinion Score (MOS) as KPIs is presented. This first implementation of the cognitive engine selects the network that, in that specific instant, offers the best QoE based on real captured data. To our knowledge, this is the first example of a cognitive engine that achieves best QoE in a context of heterogeneous wireless networks

Cooperative sensing of spectrum opportunities

Reliability and availability of sensing information gathered from local spectrum sensing (LSS) by a single Cognitive Radio is strongly affected by the propagation conditions, period of sensing, and geographical position of the device. For this reason, cooperative spectrum sensing (CSS) was largely proposed in order to improve LSS performance by using cooperation between Secondary Users (SUs). The goal of this chapter is to provide a general analysis on CSS for cognitive radio networks (CRNs). Firstly, the theoretical system model for centralized CSS is introduced, together with a preliminary discussion on several fusion rules and operative modes. Moreover, three main aspects of CSS that substantially differentiate the theoretical model from realistic application scenarios are analyzed: (i) the presence of spatiotemporal correlation between decisions by different SUs; (ii) the possible mobility of SUs; and (iii) the nonideality of the control channel between the SUs and the Fusion Center (FC). For each aspect, a possible practical solution for network organization is presented, showing that, in particular for the first two aspects, cluster-based CSS, in which sensing SUs are properly chosen, could mitigate the impact of such realistic assumptions

Cognitive routing models

This paper investigates the effect of introducing cognitive mechanisms in the routing module of a wireless network. A routing cost function that incorporates measurements of both internal network status and instantaneous behavior of external world is described. The proposed cost function is analyzed by simulation in the framework of IEEE 802.1.5.4a-like low data rate and low cost networks for mixed indoor/outdoor communications. The analysis focuses on the impact of MUI modeling on network performance. Results indicate that MUI-awareness, as provided by the proposed cognitive cost function, may improve network performance in terms of network lifetime. Based on this analysis, a mechanism for learning from previous routing decisions and adapting the routing cost function to MUI conditions is introduced

Editorial

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A mixed approach to similarity metric selection in affinity propagation-based WiFi fingerprinting indoor positioning

The weighted k-nearest neighbors (WkNN) algorithm is by far the most popular choice in the design of fingerprinting indoor positioning systems based on WiFi received signal strength (RSS). WkNN estimates the position of a target device by selecting k reference points (RPs) based on the similarity of their fingerprints with the measured RSS values. The position of the target device is then obtained as a weighted sum of the positions of the k RPs. Two-step WkNN positioning algorithms were recently proposed, in which RPs are divided into clusters using the affinity propagation clustering algorithm, and one representative for each cluster is selected. Only cluster representatives are then considered during the position estimation, leading to a significant computational complexity reduction compared to traditional, flat WkNN. Flat and two-step WkNN share the issue of properly selecting the similarity metric so as to guarantee good positioning accuracy: in two-step WkNN, in particular, the metric impacts three different steps in the position estimation, that is cluster formation, cluster selection and RP selection and weighting. So far, however, the only similarity metric considered in the literature was the one proposed in the original formulation of the affinity propagation algorithm. This paper fills this gap by comparing different metrics and, based on this comparison, proposes a novel mixed approach in which different metrics are adopted in the different steps of the position estimation procedure. The analysis is supported by an extensive experimental campaign carried out in a multi-floor 3D indoor positioning testbed. The impact of similarity metrics and their combinations on the structure and size of the resulting clusters, 3D positioning accuracy and computational complexity are investigated. Results show that the adoption of metrics different from the one proposed in the original affinity propagation algorithm and, in particular, the combination of different metrics can significantly improve the positioning accuracy while preserving the efficiency in computational complexity typical of two-step algorithms