Recent developments in spectrum policy and regulatory domains, notably the release of the National Broadband Plan, the publication of final rules for TV white spaces, and the ongoing proceeding for secondary use of the 2360-2400 MHz band for medical body area networks, will allow more flexible and efficient use of spectrum in the future. These important changes open up exciting opportunities for cognitive radio to enable and support a variety of emerging applications, ranging from smart grid, public safety and broadband cellular, to medical applications. This article presents a high-level view on how cognitive radio (primarily from a dynamic spectrum access perspective) would support such applications, the benefits that cognitive radio would bring, and also some challenges that are yet to be resolved. We also illustrate related standardization that uses cognitive radio technologies to support such emerging applications.

Emerging cognitive radio applications: A survey

Spectrum decision is the ability of a cognitive radio (CR) to select the best available spectrum band to satisfy secondary users' (SUs') quality of service (QoS) requirements, without causing harmful interference to licensed or primary users (PUs). Each CR performs spectrum sensing to identify the available spectrum bands and the spectrum decision process selects from these available bands for opportunistic use. Spectrum decision constitutes an important topic which has not been adequately explored in CR research. Spectrum decision involves spectrum characterization, spectrum selection and CR reconfiguration functions. After the available spectrum has been identified, the first step is to characterize it based not only on the current radio environment conditions, but also on the PU activities. The second step involves spectrum selection, whereby the most appropriate spectrum band is selected to satisfy SUs' QoS requirements. Finally, the CR should be able to reconfigure its transmission parameters to allow communication on the selected band. Key to spectrum characterization is PU activity modelling, which is commonly based on historical data to provide the means for predicting future traffic patterns in a given spectrum band. This paper provides an up-to-date survey of spectrum decision in CR networks (CRNs) and addresses issues of spectrum characterization (including PU activity modelling), spectrum selection and CR reconfiguration. For each of these issues, we highlight key open research challenges. We also review practical implementations of spectrum decision in several CR platforms.

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Spectrum Decision in Cognitive Radio Networks: A Survey

The Internet of Things (IoT) is one of the most promising technologies for the near future. Healthcare and well-being will receive great benefits with the evolution of this technology. This paper presents a review of techniques based on IoT for healthcare and ambient-assisted living, defined as the Internet of Health Things (IoHT), based on the most recent publications and products available in the market from industry for this segment. Also, this paper identifies the technological advances made so far, analyzing the challenges to be overcome and provides an approach of future trends. Through selected works, it is possible to notice that further studies are important to improve current techniques and that novel concept and technologies of IoHT are needed to overcome the identified challenges. The presented results aim to serve as a source of information for healthcare providers, researchers, technology specialists, and the general population to improve the IoHT.

Enabling Technologies for the Internet of Health Things

The development of Industrial Internet of Things (IIoT) has been limited due to the shortage of spectrum resources. Based on cognitive radio, the cognitive IIoT (CIIoT) has been proposed to improve spectrum utilization via sensing and accessing the idle spectrum. To improve sensing and transmission performance of the CIIoT, a cluster-based CIIoT is proposed, in this article, wherein the cluster heads perform cooperative spectrum sensing to get available spectrum, and the nodes transmit via nonorthogonal multiple access (NOMA). The frame structure of the CIIoT is designed, and the spectrum access probability and average total throughput of the CIIoT are deduced. A joint resource optimization for sensing time, node powers, and the number of clusters is formulated to maximize the average total throughput. The optimal solution is obtained via sensing and power optimization. The clustering algorithm and cluster head alternation are proposed to improve transmission performance and ensure energy balance, respectively. The simulations have indicated that the NOMA for the cluster-based CIIoT can better guarantee the transmission performance of each node, especially the node decoded first, than the traditional NOMA and orthogonal multiple access.

NOMA-Based Resource Allocation for Cluster-Based Cognitive Industrial Internet of Things

Fifth-generation (5G) wireless networks are expected to operate at both microwave and millimeter-wave (mmWave) frequency bands, including frequencies in the range of 24 to 86 GHz. Radio propagation models are used to help engineers design, deploy, and compare candidate wireless technologies, and have a profound impact on the decisions of almost every aspect of wireless communications. This paper provides a comprehensive overview of the channel models that will likely be used in the design of 5G radio systems. We start with a discussion on the framework of channel models, which consists of classical models of path loss versus distance, large-scale, and small-scale fading models, and multiple-input multiple-output channel models. Then, key differences between mmWave and microwave channel models are presented, and two popular mmWave channel models are discussed: the 3rd Generation Partnership Project model, which is adopted by the International Telecommunication Union, and the NYUSIM model, which was developed from several years of field measurements in New York City. Examples on how to apply the channel models are then given for several diverse applications demonstrating the wide impact of the models and their parameter values, where the performance comparisons of the channel models are done with promising hybrid beamforming approaches, including leveraging coordinated multipoint transmission. These results show that the answers to channel performance metrics, such as spectrum efficiency, coverage, hardware/signal processing requirements, etc., are extremely sensitive to the choice of channel models.

Propagation Models and Performance Evaluation for 5G Millimeter-Wave Bands

SmartDR:A device-to-device communication for post-disaster recovery

On the shaping of 5G technologies and networks, scope for wider service and applications innovation and UK strengths and opportunities.

5G innovation opportunities-- A discussion paper

The present approaches on predicting stability of delta-sigma (Δ-Σ) modulators are mostly confined to DC inputs. This poses limitations as practical applications of Δ-Σ modulators involve a wide range of signals other than DC such as multiple-sinusoidal inputs for speech modelling. In this study, a quasi-linear model for Δ-Σ modulators with non-linear feedback control analysis is presented that accurately predicts stability of single-loop one-bit higher-order Δ-Σ modulators for multiple sinusoids. Theoretical values are shown to match closely with the simulation results. The results of this study would significantly speed up the design and evaluation of higher-order single-loop Δ-Σ modulators with increased dynamic ranges for various applications that require multiple-sinusoidal inputs or any general input composed of a finite number of sinusoidal components.

Accurate stability prediction of one-bit higher-order delta-sigma modulators for multiple-sinusoidal inputs

Broadband power line (BPL) data transmission deals with transfer of data via the existing power line systems and is a fast emerging technology. The main advantage of BPL is being able to use the existing power line infrastructure, thereby reducing the cost. However, power line systems were not designed for high-speed data transmission as they consist of various branches and power line elements such as bridges, taps, transformers and capacitor banks. Therefore, the power line transmission medium not only introduces noise but is also adverse to high-speed data transfer in terms of the channel bandwidth. In this paper a power line channel has been modelled using Matlab and the effects of variations in the direct length, branch length and branch load on the channel frequency response are investigated. Simulations indicate suitability of multi-carrier transmission over the power line channels.

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Data transfer over low-voltage European power distribution networks

Recent trends for vehicular localization in millimetre-wave (mmWave) channels include employing a combination of parameters such as angle of arrival (AOA), angle of departure (AOD), and time of arrival (TOA) of the transmitted/received signals. These parameters are challenging to estimate, which along with the scattering and random nature of mmWave channels, and vehicle mobility lead to errors in localization. To circumvent these challenges, this paper proposes mmWave vehicular localization employing difference of arrival for time and frequency, with multiuser (MU) multiple-input-multiple-output (MIMO) hybrid beamforming; rather than relying on AOD/AOA/TOA estimates. The vehicular localization can exploit the number of vehicles present, as an increase in the number of vehicles reduces the Cramér-Rao bound (CRB) of error estimation. At 10 dB signal-to-noise ratio (SNR) both spatial multiplexing and beamforming result in comparable localization errors. At lower SNR values, spatial multiplexing leads to larger errors compared to beamforming due to formation of spurious peaks in the cross ambiguity function. Accuracy of the estimated parameters is improved by employing an extended Kalman filter leading to a root mean square (RMS) localization error of approximately 6.3 meters.

https://repository.uel.ac.uk/download/3759f141d3b2d7658d6be3b4aeec1076f06b272ee2b5a3a7f34f445f595204ed/1430492/MmWave_V2V_Localization_in_MU-MIMO_Hybrid_Beamforming.pdf

Jaswinder Lota

Papers

SmartDR:A device-to-device communication for post-disaster recovery

5G innovation opportunities-- A discussion paper

Accurate stability prediction of one-bit higher-order delta-sigma modulators for multiple-sinusoidal inputs

Data transfer over low-voltage European power distribution networks

mmWave V2V Localization in MU-MIMO Hybrid Beamforming