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Table Of Integrals Series And Products

In order to effectively provide ultra reliable low latency communications and pervasive connectivity for Internet of Things (IoT) devices, next-generation wireless networks can leverage intelligent, data-driven functions enabled by the integration of machine learning (ML) notions across the wireless core and edge infrastructure. In this context, this paper provides a comprehensive tutorial that overviews how artificial neural networks (ANNs)-based ML algorithms can be employed for solving various wireless networking problems. For this purpose, we first present a detailed overview of a number of key types of ANNs that include recurrent, spiking, and deep neural networks, that are pertinent to wireless networking applications. For each type of ANN, we present the basic architecture as well as specific examples that are particularly important and relevant wireless network design. Such ANN examples include echo state networks, liquid state machine, and long short term memory. Then, we provide an in-depth overview on the variety of wireless communication problems that can be addressed using ANNs, ranging from communication using unmanned aerial vehicles to virtual reality applications over wireless networks as well as edge computing and caching. For each individual application, we present the main motivation for using ANNs along with the associated challenges while we also provide a detailed example for a use case scenario and outline future works that can be addressed using ANNs. In a nutshell, this paper constitutes the first holistic tutorial on the development of ANN-based ML techniques tailored to the needs of future wireless networks.

Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial

Direct radio-based vehicle-to-vehicle communication can help prevent accidents by providing accurate and up-to-date local status and hazard information to the driver. In this paper, we assume that two types of messages are used for traffic safety-related communication: 1) Periodic messages (ldquobeaconsrdquo) that are sent by all vehicles to inform their neighbors about their current status (i.e., position) and 2) event-driven messages that are sent whenever a hazard has been detected. In IEEE 802.11 distributed-coordination-function-based vehicular networks, interferences and packet collisions can lead to the failure of the reception of safety-critical information, in particular when the beaconing load leads to an almost-saturated channel, as it could easily happen in many critical vehicular traffic conditions. In this paper, we demonstrate the importance of transmit power control to avoid saturated channel conditions and ensure the best use of the channel for safety-related purposes. We propose a distributed transmit power control method based on a strict fairness criterion, i.e., distributed fair power adjustment for vehicular environments (D-FPAV), to control the load of periodic messages on the channel. The benefits are twofold: 1) The bandwidth is made available for higher priority data like dissemination of warnings, and 2) beacons from different vehicles are treated with ldquoequal rights,rdquo and therefore, the best possible reception under the available bandwidth constraints is ensured. We formally prove the fairness of the proposed approach. Then, we make use of the ns-2 simulator that was significantly enhanced by realistic highway mobility patterns, improved radio propagation, receiver models, and the IEEE 802.11p specifications to show the beneficial impact of D-FPAV for safety-related communications. We finally put forward a method, i.e., emergency message dissemination for vehicular environments (EMDV), for fast and effective multihop information dissemination of event-driven messages and show that EMDV benefits of the beaconing load control provided by D-FPAV with respect to both probability of reception and latency.

/pdf/vehicle-to-vehicle-communication-fair-transmit-power-control-cwgzu7utkg.pdf

Vehicle-to-Vehicle Communication: Fair Transmit Power Control for Safety-Critical Information

Ultra Wideband Signals and Systems in Communication Engineering

Machine learning algorithms for wireless sensor networks: A survey

The capacity and error probability of orthogonal space-time block codes (STBCs) are considered for pulse-amplitude modulation/phase shift keying/quadrature-amplitude modulation (PAM/PSK/QAM) in fading channels. The approach is based on an equivalent scalar additive white Gaussian noise channel with a channel gain proportional to the Frobenius norm of the matrix channel for the STBC. Using this effective channel, capacity and probability of error expressions are derived for PSK/PAM/QAM modulation with space-time block coding. Rayleigh-, Ricean-, and Nakagami-fading channels are considered. As an application, these results are extended to obtain the capacity and probability of error for a multiuser direct sequence code-division multiple-access system employing space-time block coding.

Capacity and error probability analysis for orthogonal space-time block codes over fading channels

A new cooperative received signal strength-based localization algorithm is proposed which employs relative error estimation and semidefinite programming (SDP). First, the log-normal shadowing RSS measurement model is transformed into an equivalent multiplicative model. Then, a relative error estimation criterion is used with this model to develop a nonconvex estimator to approximate the maximum likelihood solution. Finally, semidefinite relaxation is applied to the nonconvex estimator to obtain an SDP estimator. The proposed algorithm is first derived for noncooperative RSS-based localization and then extended to the cooperative case. The Cramer–Rao lower bound is derived for cooperative RSS-based localization. Performance results are presented, which demonstrate that the proposed SDP estimator provides a significant improvement over existing localization methods.

Cooperative RSS-Based Localization in Wireless Sensor Networks Using Relative Error Estimation and Semidefinite Programming

In this paper, we propose a new modulation scheme called biorthogonal pulse position modulation (BPPM) for ultra-wideband (UWB) communication systems. A set of N=2/sup k+1/ BPPM signals are constructed from 2/sup k/ orthogonal PPM signals by including the antipodal version of the orthogonal PPM signals. The channel capacity of BPPM is determined for a time-hopping multiple access UWB communication system. The error probability and performance bounds are derived for a multiuser environment. It is shown that N-ary BPPM has better performance than N-ary PPM with the same throughput and half the computational complexity.

Biorthogonal pulse position modulation for time-hopping multiple access UWB communications

This paper considers the detection and localization of a human subject in complex environments using an ultra-wideband impulse radar. The subject is remotely sensed by extracting micro-motion information, such as the respiration and heartbeat frequencies. It is challenging to extract this information due to the low signal to noise and clutter ratio in typical disaster environments. To improve the localization accuracy, a new method is proposed using the characteristics of vital sign signals. The range is determined using a short-time Fourier transform of the kurtosis and standard deviation of the received signals. Furthermore, an improved arctangent demodulation technique is used to determine the frequency of human micro-motion based on a multiple frequency accumulation method. Performance results are presented, which show that the proposed method is superior to several well-known techniques.

An Improved Algorithm for Through-Wall Target Detection Using Ultra-Wideband Impulse Radar

In this letter, we propose a new modulation scheme called pulse position amplitude modulation (PPAM) for ultra-wideband (UWB) communication systems. PPAM combines pulse position modulation and pulse amplitude modulation to provide good system performance and low computational complexity. The channel capacity of PPAM is determined for a time-hopping multiple-access UWB communication system. The error probability and performance bounds are derived for a multiuser environment.

Hao Zhang

Papers

Capacity and error probability analysis for orthogonal space-time block codes over fading channels

Cooperative RSS-Based Localization in Wireless Sensor Networks Using Relative Error Estimation and Semidefinite Programming

Biorthogonal pulse position modulation for time-hopping multiple access UWB communications

An Improved Algorithm for Through-Wall Target Detection Using Ultra-Wideband Impulse Radar

Pulse position amplitude Modulation for time-hopping multiple-access UWB communications