Doppler Shift Characterization of Wideband Mobile Radio Channels

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Experimental Assessment of a Forward-Collision Warning System Fusing Deep Learning and Decentralized Radio Sensing

This paper presents the idea of an automatic forward-collision warning system based on a decentralized radio sensing (RS) approach. In this framework, a vehicle in receiving mode employs a continuous waveform (CW) transmitted by a second vehicle as a probe signal to detect oncoming vehicles and warn the driver of a potential forward collision. Such a CW can easily be incorporated as a pilot signal within the data frame of current multicarrier vehicular communication systems. Detection of oncoming vehicles is performed by a deep learning (DL) module that analyzes the features of the Doppler signature imprinted on the CW probe signal by a rapidly approaching vehicle. This decentralized CW RS approach was assessed experimentally using data collected by a series of field trials conducted in a two-lanes high-speed highway. Detection performance was evaluated for two different DL models: a long short-term memory network and a convolutional neural network. The obtained results demonstrate the feasibility of the envisioned forward-collision warning system based on the fusion of DL and decentralized CW RS

A novel and adaptive angle diversity-based receiver for 6G underground mining VLC systems

Visible light communication (VLC) is considered an enabling technology for future 6G wireless systems. Among the many applications in which VLC systems are used, one of them is harsh environments such as Underground Mining (UM) tunnels. However, these environments are subject to degrading environmental and intrinsic challenges for optical links. Therefore, current research should focus on solutions to mitigate these problems and improve the performance of Underground Mining Visible Light Communication (UM-VLC) systems. In this context, this article presents a novel solution that involves an improvement to the Angle Diversity Receivers (ADRs) based on the adaptive orientation of the Photo-Diodes (PDs) in terms of the Received Signal Strength Ratio (RSSR) scheme. Specifically, this methodology is implemented in a hemidodecahedral ADR and evaluated in a simulated UM-VLC scenario. The performance of the proposed design is evaluated using metrics such as received power, user data rate, and bit error rate (BER). Furthermore, our approach is compared with state-of-the-art ADRs implemented with fixed PDs and with the Time of Arrival (ToA) reception method. An improvement of at least 60% in terms of the analyzed metrics compared to state-of-the-art solutions is obtained. Therefore, the numerical results demonstrate that the hemidodecahedral ADR, with adaptive orientation PDs, enhances the received optical signal. Furthermore, the proposed scheme improves the performance of the UM-VLC system due to its optimum adaptive angular positioning, which is completed according to the strongest optical received signal power. By improving the performance of the UM-VLC system, this novel method contributes to further consideration of VLC systems as potential and enabling technologies for future 6G deployments

Vehicular Communications over TV White Spaces in the Presence of Secondary Users

The lack of available spectrum for wireless communications is a threat to the successful deployment of applications designed for intelligent transportation systems (ITSs). The ITS services should be available to a high number of road users and have a fast response time. The interworking between radio access networks is one way to increase spectrum availability. In particular, the joint operation of the dedicated short-range communication (DSRC) technology and TV white spaces (TVWS) has been proposed to increase the dissemination distance for safety messages in vehicular networking. However, previous works have often assumed that the only restriction on the opportunistic access of TVWS is the presence of a TV transmitter (i.e., the primary user). Other secondary users, such as the popular White-Fi networks to be deployed in TV bands, are omitted from the analysis of opportunistic channel access over TVWS. This is despite several proposals in the literature that use secondary networks for purposes other than vehicular networking over TVWS. In this paper, we analyze the opportunistic use of TVWS when other fixed users, such as White-Fi networks, are present. We estimate channel access opportunities and introduce a new metric, the channel availability for opportunistic vehicular access (CAFOVA), which relates the channel occupancy of the White-Fi network, the speed of the vehicle, and the channel verification distance. The results show that there are opportunities for vehicular access even when a White-Fi network occupies the TVWS. Vehicles may use these opportunities for transmission, instead of spending time looking for a new available TVWS and establishing a new link with another vehicle. Therefore, even when a White-Fi network occupies the same TVWS, it may be possible to exploit dynamic spectrum access to extend the available spectrum for vehicular communications

MIMO millimeter-wave channel estimation using coalitional games

In millimeter-wave massive multiple input multiple output (MIMO) antenna systems, channel estimation is a crucial component. In this paper, we propose a virtual channel representation channel estimation method us-ing out-of-band spatial information to reduce training overheads and a cooperative channel allocation method based on coalitional game framework. The proposed cooperative chan-nel allocation method enhances throughput performance in mm-wave small cell networks

Depth Maps-Based Human Segmentation and Action Recognition Using Full-Body Plus Body Color Cues Via Recognizer Engine

Assessment of human behavior during performance of daily routine actions at indoor areas plays a significant role in healthcare services and smart homes for elderly and disabled people. During this consideration, initially, depth images are captured using depth camera and segment human silhouettes due to color and intensity variation. Features considered spatiotemporal properties and obtained from the human body color joints and depth silhouettes information. Joint displacement and specific-motion features are obtained from human body color joints and side-frame differentiation features are processed based on depth data to improve classification performance. Lastly, recognizer engine is used to recognize different activities. Unlike conventional results that were evaluated using a single dataset, our experimental results have shown state-of-the-art accuracy of 88.9% and 66.70% over two challenging depth datasets. The proposed system should be serviceable with major contributions in different consumer application systems such as smart homes, video surveillance and health monitoring systems

Improved Nyquist-I Pulses to Enhance the Performance of OFDM-Based Systems

Pulse shaping is used in orthogonal frequency division multiplexing (OFDM) based systems to reduce inter-carrier interference (ICI) power and peak-to-average power ratio (PAPR), which are considered the major weaknesses in OFDM-based systems. A novel family of Nyquist-I pulses called sinc exponential pulse (SEP) is proposed, and it is characterized by two new design parameters that provide extra degrees of freedom for a certain roll-off factor, alpha. SEP effectively decreases the relative magnitude of the two largest side lobes of the SEP frequency function, which minimizes the ICI power and reduces the PAPR in OFDM systems. Furthermore, the SEP possesses a broader main lobe, which provides sufficient improvement in bit-error-rate (BER). The behavior of the SEP is examined in the time and frequency domain by tuning its design parameters to obtain the sub-optimum SEP. Theoretical and simulation results show that the sub-optimum SEP performs better than other existing pulses in terms of ICI power, signal-to-interference ratio (SIR) power, BER, and PAPR in OFDM-based systems

Implementation of the LMS Algorithm for Noise Cancellation on Speech Using the ARM LPC2378 Processor.

On this thesis project, the LMS algorithm has been applied for speech noise filteringand different behaviors were tested under different circumstances by using Matlabsimulations and the LPC2378 ARM Processor, which does the task of filtering in realtime. The thesis project is divided into two parts: the theoretical and practical part. In the theoretical part there is a brief description of the different aspects of signalprocessing systems, filter theory, and a general description of the Least-Mean-SquareAdaptive Filter Algorithm. In the practical part of the report a general description of the procedure will besummarized, the results of the tests that were conducted will be analyzed, a generaldiscussion of the problems that were encounter during the simulations will be mention,and suggestion for the problems will be given