2 1 The innovative transport systems and the CityMobil project 10 1.1 The research questions 10 2 The state of art in the field of automatic transport systems 12 2.1 Case studies and demand studies for innovative transport systems 12 3 The design and implementation of surveys 14 3.1 Definition of experimental design 14 3.2 Questionnaire design and delivery 16 3.3 First analyses on the collected sample 18 4 Calibration of Logit Multionomial demand models 21 4.1 Methodology 21 4.2 Calibration of the “full” model. 22 4.3 Calibration of the “final” model 24 4.4 The demand analysis through the final Multinomial Logit model 25 5 The analysis of interaction between the demand and socioeconomic attributes 31 5.1 Methodology 31 5.2 Application of Mixed Logit models to the demand 31 5.3 Analysis of the interactions between demand and socioeconomic attributes through Mixed Logit models 32 5.4 Mixed Logit model and interaction between age and the demand for the CTS 38 5.5 Demand analysis with Mixed Logit model 39 6 Final analyses and conclusions 45 6.1 Comparison between the results of the analyses 45 6.2 Conclusions 48 6.3 Answers to the research questions and future developments 52

The European commission

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Table Of Integrals Series And Products

Cognitive radios hold tremendous promise for increasing spectral efficiency in wireless systems. This paper surveys the fundamental capacity limits and associated transmission techniques for different wireless network design paradigms based on this promising technology. These paradigms are unified by the definition of a cognitive radio as an intelligent wireless communication device that exploits side information about its environment to improve spectrum utilization. This side information typically comprises knowledge about the activity, channels, codebooks, and/or messages of other nodes with which the cognitive node shares the spectrum. Based on the nature of the available side information as well as a priori rules about spectrum usage, cognitive radio systems seek to underlay, overlay, or interweave the cognitive radios' signals with the transmissions of noncognitive nodes. We provide a comprehensive summary of the known capacity characterizations in terms of upper and lower bounds for each of these three approaches. The increase in system degrees of freedom obtained through cognitive radios is also illuminated. This information-theoretic survey provides guidelines for the spectral efficiency gains possible through cognitive radios, as well as practical design ideas to mitigate the coexistence challenges in today's crowded spectrum.

Breaking Spectrum Gridlock With Cognitive Radios: An Information Theoretic Perspective

Information Theory and Reliable Communication

It is now well known that employing channel adaptive signaling in wireless communication systems can yield large improvements in almost any performance metric. Unfortunately, many kinds of channel adaptive techniques have been deemed impractical in the past because of the problem of obtaining channel knowledge at the transmitter. The transmitter in many systems (such as those using frequency division duplexing) can not leverage techniques such as training to obtain channel state information. Over the last few years, research has repeatedly shown that allowing the receiver to send a small number of information bits about the channel conditions to the transmitter can allow near optimal channel adaptation. These practical systems, which are commonly referred to as limited or finite-rate feedback systems, supply benefits nearly identical to unrealizable perfect transmitter channel knowledge systems when they are judiciously designed. In this tutorial, we provide a broad look at the field of limited feedback wireless communications. We review work in systems using various combinations of single antenna, multiple antenna, narrowband, broadband, single-user, and multiuser technology. We also provide a synopsis of the role of limited feedback in the standardization of next generation wireless systems.

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An overview of limited feedback in wireless communication systems

We consider multi-input convolutional codes with unequal error protection (UEP) among the inputs. Using conventional metrics including the free input distance and free input sum weight as a measure for UEP degree, we design efficient and optimal algorithms to evaluate these metrics for any number of inputs. Next, we discuss several ambiguities arisen from using these metrics to estimate convolutional code UEP property and propose two new metrics, the average weight per path and the ant colony system UEP (ACS-UEP) index. The ACS-UEP index can accurately and consistently capture UEP properties over all ranges of SNR. Using these metrics, we propose an algorithmic process for designing convolutional codes with bounded external degree complexity to minimize a weighted sum of input error probabilities. We analyze the effectiveness of the design approach and show significant improvements over existing UEP convolutional code designing approach in terms of both the absolute and UEP input error protections. Our proposed algorithms for designing multi-input convolutional codes with UEP are flexible, adaptable, and provide high performance codes within the specified complexity.

Metrics and Algorithms for Designing Convolutional Codes With Unequal Error Protection

Using a multi-armed bandit technique, we propose centralized and semi-distributed online algorithms for load balancing user association and handover in mmWave-enabled networks. Load balancing at all base stations (BSs) imposes explicit constraints that makes the actions of all user equipment (UEs) co-dependent, a challenging twist to reinforcement learning. We propose a central load balancer to guarantee load balancing at all BSs for every learning step. We consider two association vectors: one for leaning update, and one best-to-date for data transmission, allowing UEs to engage in best-result data transmission while effectively participating in a background learning process indefinitely. For dynamic networks, we introduce a measurement model capturing rapid channel variations and user mobility. To minimize handover rate, we also differentiate between handover costs for transmission and for learning, and introduce a learning handover cost decreasing with sojourn time. The proposed algorithms can be implemented online as they require no offline training and can effectively adapt to network dynamics. Numerical results show that the proposed algorithms exhibit fast learning convergence and outperform 3GPP handover by achieving an order of magnitude lower handover rate at a significantly higher network sum-rate, reaching within 94-97% of the near-optimal worst connection swapping benchmark algorithm.

Reinforcement Learning for User Association and Handover in mmWave-enabled Networks

Wireless charging coupled with computation offloading in edge networks offers a promising solution for realizing power-hungry and computation intensive applications on user devices. We consider a mutil-access edge computing (MEC) system with collocated MEC servers and base-stations/access points (BS/AP) supporting multiple users requesting data computation and wireless charging. We propose an integrated solution with computation offloading to satisfy the largest proportion of requested wireless charging while keeping the energy consumption at the minimum subject to the MEC-AP transmit power and latency constraints. We propose a novel algorithm to perform data partitioning, time allocation, transmit power control and design the optimal energy beamforming for wireless charging. Our resource allocation scheme offers an energy minimizing solution compared to other schemes while also delivering higher amount of transferred charge to the users.

Energy-efficient Wireless Charging and Computation Offloading In MEC Systems

We design user-assisted relaying strategies and analyze their performance in a multiple-input-multiple-output uplink cellular network. In user-assisted relaying, the base station serves an active user equipment (UE) using a transmission scheme adaptive between direct transmission and relaying via another UE according to a cooperation strategy. Modeling the network based on stochastic geometry and Poisson point processes, we propose a practical, geometric-based cooperation strategy determining how to select the relaying UE and when to perform relaying transmission. The proposed relaying transmission employs a simple transmit beamforming and one of two optimized receive combining schemes depending on interference awareness. The extra interference generated by relaying UEs is captured in an interference model that accounts for the adaptive transmission and random locations of these UEs. Integrating the proposed cooperation strategy, transmit and receive beamforming design, and interference model, we analyze the network outage rate performance. Provided sufficient density of potential relay UEs, results demonstrate user-assisted relaying to be most beneficial for cell edge UEs when the relaying UEs are equipped with multiple antenna elements and their signals propagate in relatively high shadowing environments.

MIMO Cellular Networks Performance Under User-Assisted Relaying

This paper introduces and studies a model in which two relay channels interfere with each other. Motivated by practical scenarios in heterogeneous wireless access networks, each relay is assumed to be connected to its intended receiver through a digital link with finite capacity. Inner and outer bounds for achievable rates are derived and shown to be tight for new discrete memoryless classes, which generalize and unify several known cases involving interference and relay channels. Capacity region and sum capacity for multiple Gaussian scenarios are also characterized to within a constant gap. The results show the optimality or near-optimality of the quantize-bin-and-forward coding scheme for practically relevant relay-interference networks, which brings important engineering insight into the design of wireless communications systems.

Mai Vu

Papers

Metrics and Algorithms for Designing Convolutional Codes With Unequal Error Protection

Reinforcement Learning for User Association and Handover in mmWave-enabled Networks

Energy-efficient Wireless Charging and Computation Offloading In MEC Systems

MIMO Cellular Networks Performance Under User-Assisted Relaying

Interfering Relay Channels