Data Mining Practical Machine Learning Tools and Techniques

Radio access technologies for cellular mobile communications are typically characterized by multiple access schemes, e.g., frequency division multiple access (FDMA), time division multiple access (TDMA), code division multiple access (CDMA), and OFDMA. In the 4th generation (4G) mobile communication systems such as Long-Term Evolution (LTE) (Au et al., Uplink contention based SCMA for 5G radio access. Globecom Workshops (GC Wkshps), 2014. doi:10.1109/GLOCOMW.2014.7063547) and LTE-Advanced (Baracca et al., IEEE Trans. Commun., 2011. doi:10.1109/TCOMM.2011.121410.090252; Barry et al., Digital Communication, Kluwer, Dordrecht, 2004), standardized by the 3rd Generation Partnership Project (3GPP), orthogonal multiple access based on OFDMA or single carrier (SC)-FDMA is adopted. Orthogonal multiple access was a reasonable choice for achieving good system-level throughput performance with simple single-user detection. However, considering the trend in 5G, achieving significant gains in capacity and system throughput performance is a high priority requirement in view of the recent exponential increase in the volume of mobile traffic. In addition the proposed system should be able to support enhanced delay-sensitive high-volume services such as video streaming and cloud computing. Another high-level target of 5G is reduced cost, higher energy efficiency and robustness against emergencies.

Non-Orthogonal Multiple Access (NOMA) for cellular future radio access

Due to the under-utilization problem of the allocated radio spectrum, cognitive radio (CR) communications have recently emerged as a reliable and effective solution. Among various network models, this survey paper focuses on the enabling techniques for interweave CR networks which have received great attention from standards perspective due to its reliability to achieve the required quality-of-service. Spectrum sensing provides the essential information to enable this interweave communications in which primary and secondary users are not allowed to access the medium concurrently. Several researchers have already considered various aspects to realize efficient techniques for spectrum sensing. In this direction, this survey paper provides a detailed review of the state-of-the-art related to the application of spectrum sensing in CR communications. Starting with the basic principles and the main features of interweave communications, this paper provides a classification of the main approaches based on the radio parameters. Subsequently, we review the existing spectrum sensing works applied to different categories such as narrowband sensing, narrowband spectrum monitoring, wideband sensing, cooperative sensing, practical implementation considerations for various techniques, and the recent standards that rely on the interweave network model. Furthermore, we present the latest advances related to the implementation of the legacy spectrum sensing approaches. Finally, we conclude this survey paper with some suggested open research challenges and future directions for the CR networks in next generation Internet-of-Things applications.

Advances on Spectrum Sensing for Cognitive Radio Networks: Theory and Applications

As the next generation digital TV (DTV) standard, the ATSC 3.0 system is developed to provide significant improvements on the spectrum efficiency, the service reliability, the system flexibility, and system forward compatibility. One of the top-priority requirements for the ATSC 3.0 is the capability to deliver reliable mobile TV services to a large variety of mobile and indoor devices. Layered-division-multiplexing (LDM) is a physical-layer non-orthogonal-multiplexing technology to efficiently deliver multiple services with different robustness and throughputs in one TV channel. A two-layer LDM structure is accepted by ATSC 3.0 as a baseline physical-layer technology. This LDM system is capable of delivering robust high-definition (HD) mobile TV and ultra-HDTV services in one 6 MHz channel, with a higher spectrum efficiency than the traditional time/frequency-division-multiplexing (T/FDM)-based DTV systems. This paper presents a detailed overview on the LDM technology, and its application in the ATSC 3.0 systems. First, the fundamental advantages of the LDM over the traditional TDM/FDM systems are analyzed from information theory point of view. The performance advantages of the LDM are then confirmed by extensive simulations of the ATSC 3.0 system. It is shown that, LDM can realize the potential gain offered by superposition coding over the TDM/FDM systems, by properly configuring the transmission power, channel coding, and modulation, and using different multiple antenna technologies in the multiple layers. Next, the efficient implementation of LDM in the ATSC 3.0 system is presented to show that the performance advantages of the LDM are obtained with small additional complexity. This is achieved by carefully aligning the transmission signal structure and the signal processing chains in the multiple layers. Finally, we show that the LDM can be further integrated with different multiple antenna technologies to achieve further transmission capacity.

Layered-Division-Multiplexing: Theory and Practice

This paper provides an overview of the physical layer specification of Advanced Television Systems Committee (ATSC) 3.0, the next-generation digital terrestrial broadcasting standard. ATSC 3.0 does not have any backwards-compatibility constraint with existing ATSC standards, and it uses orthogonal frequency division multiplexing-based waveforms along with powerful low-density parity check (LDPC) forward error correction codes similar to existing state-of-the-art. However, it introduces many new technological features such as 2-D non-uniform constellations, improved and ultra-robust LDPC codes, power-based layered division multiplexing to efficiently provide mobile and fixed services in the same radio frequency (RF) channel, as well as a novel frequency pre-distortion multiple-input single-output antenna scheme. ATSC 3.0 also allows bonding of two RF channels to increase the service peak data rate and to exploit inter-RF channel frequency diversity, and to employ dual-polarized multiple-input multiple-output antenna system. Furthermore, ATSC 3.0 provides great flexibility in terms of configuration parameters (e.g., 12 coding rates, 6 modulation orders, 16 pilot patterns, 12 guard intervals, and 2 time interleavers), and also a very flexible data multiplexing scheme using time, frequency, and power dimensions. As a consequence, ATSC 3.0 not only improves the spectral efficiency and robustness well beyond the first generation ATSC broadcast television standard, but also it is positioned to become the reference terrestrial broadcasting technology worldwide due to its unprecedented performance and flexibility. Another key aspect of ATSC 3.0 is its extensible signaling, which will allow including new technologies in the future without disrupting ATSC 3.0 services. This paper provides an overview of the physical layer technologies of ATSC 3.0, covering the ATSC A/321 standard that describes the so-called bootstrap, which is the universal entry point to an ATSC 3.0 signal, and the ATSC A/322 standard that describes the physical layer downlink signals after the bootstrap. A summary comparison between ATSC 3.0 and DVB-T2 is also provided.

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An Overview of the ATSC 3.0 Physical Layer Specification

Due to its paramount relevance in transport planning and logistics, road traffic forecasting has been a subject of active research within the engineering community for more than 40 years. In the beginning most approaches relied on autoregressive models and other analysis methods suited for time series data. More recently, the development of new technology, platforms and techniques for massive data processing under the Big Data umbrella, the availability of data from multiple sources fostered by the Open Data philosophy and an ever-growing need of decision makers for accurate traffic predictions have shifted the spotlight to data-driven procedures. This paper aims to summarize the efforts made to date in previous related surveys towards extracting the main comparing criteria and challenges in this field. A review of the latest technical achievements in this field is also provided, along with an insightful update of the main technical challenges that remain unsolved. The ultimate goal of this work is to set an updated, thorough, rigorous compilation of prior literature around traffic prediction models so as to motivate and guide future research on this vibrant field.

Road Traffic Forecasting: Recent Advances and New Challenges

This paper provides a review of the second generation of terrestrial digital video broadcasting standard (DVB-T2). DVB-T2 is the evolution of DVB-T and, together with DVB-S2 and DVB-C2, inaugurated a new transition from the first-generation digital broadcasting systems, similar to the transition from analog-to-digital systems. In this paper, the most relevant features of DVB-T2 are explained in detail, along with their benefits and trade-offs. This paper also presents a comprehensive review of the laboratory and field trial results available so far. Especial emphasis is placed in the results of the measurements carried out to test the mobile reception and the novel technologies as multiple input single output and time frequency slicing.

/pdf/dvb-t2-the-second-generation-of-terrestrial-digital-video-24cv6toxen.pdf

DVB-T2: The Second Generation of Terrestrial Digital Video Broadcasting System

The increasing scarcity in the available spectrum for wireless communication is one of the current bottlenecks impairing further deployment of services and coverage The proper exploitation of white spaces in the radio spectrum requires fast, robust, and accurate methods for their detection This paper proposes a new strategy to detect adaptively white spaces in the radio spectrum Such strategy works in cognitive radio (CR) networks whose nodes perform spectrum sensing based on energy detection in a cooperative way or not The main novelty of the proposal is the use of a cost-function that depends upon a single parameter which, by itself, contains the aggregate information about the presence or absence of primary users The detection of white spaces based on this parameter is able to improve significantly the deflection coefficient associated with the detector, as compared to other state-of-the-art algorithms In fact, simulation results show that the proposed algorithm outperforms by far other competing algorithms For example, our proposal can yield a probability of miss-detection 20 times smaller than that of an optimal soft-combiner solution in a cooperative setup with a predefined probability of false alarm of 01

Energy Detection Technique for Adaptive Spectrum Sensing

Cloud transmission (Cloud Txn) is a flexible multilayer system that uses spectrum overlay technology to simultaneously deliver multiple program streams with different characteristics and robustness for different services (mobile TV, HDTV, and UHDTV) in one radio frequency channel. Cloud Txn is a multilayer transmission system like layered-division multiplexing. The transmitted signal is formed by superimposing a number of independent signals at desired power levels to form a multilayered signal. The signals of different layers can have different coding, bit rate, and robustness. The upper layer system parameters are chosen to provide very robust transmission that can be used for high-speed mobile broadcasting. The bit rate is traded for powerful coding and robustness so that the signal-to-noise ratio (SNR) threshold at the receiver is in the range of ${-}{2}$ to ${-}{\rm 3}~{\rm dB}$ . The top layer is designed to withstand combined noise, co-channel interference and multipath distortion power levels higher than the desired signal power. The lower-layer signal can be a DVB-T2 signal or another new system to deliver HDTV/UHDTV to fixed receivers. The system concept is open to technological advances that might come in the future: BICM/non uniform-QAM, rotated constellations, time frequency slicing or MIMO techniques can be implemented in the Cloud Txn lower (high data rate) layer. The system can have backward compatible future extensions, adding more lower layers for additional services without impact legacy services. This paper describes the performance of Cloud Txn broadcasting system.

Cloud Transmission: System Performance and Application Scenarios

Vehicle flow forecasting is of crucial importance for the management of road traffic in complex urban networks, as well as a useful input for route planning algorithms. In general traffic predictive models rely on data gathered by different types of sensors placed on roads, which occasionally produce faulty readings due to several causes, such as malfunctioning hardware or transmission errors. Filling in those gaps is relevant for constructing accurate forecasting models, a task which is engaged by diverse strategies, from a simple null value imputation to complex spatio-temporal context imputation models. This work elaborates on two machine learning approaches to update missing data with no gap length restrictions: a spatial context sensing model based on the information provided by surrounding sensors, and an automated clustering analysis tool that seeks optimal pattern clusters in order to impute values. Their performance is assessed and compared to other common techniques and different missing data generation models over real data captured from the city of Madrid (Spain). The newly presented methods are found to be fairly superior when portions of missing data are large or very abundant, as occurs in most practical cases.

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Manuel Velez

Papers

Road Traffic Forecasting: Recent Advances and New Challenges

DVB-T2: The Second Generation of Terrestrial Digital Video Broadcasting System

Energy Detection Technique for Adaptive Spectrum Sensing

Cloud Transmission: System Performance and Application Scenarios

On the imputation of missing data for road traffic forecasting: New insights and novel techniques