Samuel Onidare

Bio: Samuel Onidare is an academic researcher from Lancaster University. The author has contributed to research in topics: Spectral efficiency & Licensee. The author has an hindex of 3, co-authored 6 publications receiving 24 citations. Previous affiliations of Samuel Onidare include University of Ilorin.

Large scale spectrum survey in rural and urban environments within the 50 MHz–6 GHz bands

Abstract: In-depth spectrum measurement was conducted in rural and urban locations, covering 50 MHz–6 GHz bands, during the weekdays and weekends. A modified duty cycle metric is presented by introducing a space variable into the existing metrics available today. An adaptive energy detection threshold technique was employed, the results indicate the average spectral occupancy of 0.18%, and 5.08% in rural and urban locations respectively during weekdays and 1.45% on weekends for urban locations. Furthermore, short and long term temporal variations of the duty cycle for each of the bands were studied, and it was found that GSM 900 shows significant temporal variation when compared with GSM 1800. It was also found that the choice of the detection threshold would significantly affect the duty cycle as GSM 900 and 1800 give exponential decay with increase in detection threshold while TV band shows very sharp exponential decay which becomes invariant after −85 dBm.

Towards statistical machine learning for edge analytics in large scale networks: Real-time Gaussian function generation with generic DSP

Abstract: The smart grid (SG) is a large-scale network and it is an integral part of the Internet of Things (IoT). For a more effective big data analytic in large-scale IoT networks, reliable solutions are being designed such that many real-time decisions will be taken at the edge of the network close to where data is being generated. Gaussian functions are extensively applied in the field of statistical machine learning, pattern recognition, adaptive algorithms for function approximation, etc. It is envisaged that soon, some of these machine learning solutions and other gaussian function based applications that have low computation and low-memory footprint will be deployed for edge analytics in large-scale IoT networks. Hence, it will be of immense benefit if an adaptive, low-cost, method of designing gaussian functions becomes available. In this paper, gaussian distribution functions are designed using C28x real-time digital signal processor (DSP) that is embedded in the TMS320C2000 modem designed for powerline communication (PLC) at the low voltage distribution end of the smart grid, where numerous devices that generate massive amount of data exist. Open-source embedded C programming language is used to program the C28x for real-time gaussian function generation. The designed gaussian waveforms are stored in lookup tables (LUTs) in the C28x embedded DSP, and could be deployed for a variety of applications at the edge of the SG and IoT network. The novelty of the design is that the gaussian functions are designed with a generic, low-cost, fixed-point DSP, different from state of the art in which gaussian functions are designed using expensive arbitrary waveform generators and other specialized circuits. C28x DSP is selected for this design since it is already existing as an embedded DSP in many smart grid applications and in other numerous industrial systems that are part of the large scale IoT network, hence it is envisaged that integration of any gaussian function based solution using this DSP in the smart grid and other IoT systems may not be too challenging.

On the Spectrum and Energy Efficiency in Dynamic Licensed Shared Access Systems:A Multiobjective Optimization Approach

Abstract: In licensed shared access (LSA) systems, the protection of the incumbent in the shared spectrum may degrade the spectrum and energy efficiency of the licensee. In this paper, we examine the optimization of these two performance metrics in a LSA vertical sharing scenario between an airport incumbent, and a mobile network operator licensee. Considering a restriction zone of a pre-defined radius, we derive the probability of the incumbent's interference threshold and then formulate a power allocation scheme as a multi-objective optimization of both energy and spectrum efficiency. We then adopt the weighted sum method to convert this multi-objective optimization into a single objective optimization and convert that into a quasi concave optimization problem. The optimum power allocation is then obtained using fractional programming. We further investigate the impact of various critical operational parameters in conjunction with the two performance metrics. Simulation results indicate a significantly improved energy efficiency in the licensee network as well as the spectrum efficiency comparable to even when the LSA spectrum utilization is unrestricted by the incumbent's maximum interference threshold. Furthermore, we show that with careful selection of the licensee eNodeB coverage radius, transmit power, and users number per eNodeB coverage area, one can engineer the best possible trade-off between the spectrum and energy efficiency.

Spectral Efficiency of Dynamic Licensed Shared Access

Abstract: In licensed shared access (LSA) the radio spectrum is dynamically shared between an incumbent and one or more licensee systems. Protective measures are applied to the licensees’ communication activity to protect the normal operation of the incumbent system. Such measures are therefore crucial components of the LSA, and thus fundamentally affect the achievable spectrum efficiency. In this paper, we investigate a vertical LSA including an airport traffic control system, as the incumbent, and a mobile network as the licensee. While some previous works only consider the licensee uplink, we analytically obtain the interference received by the incumbent from the licensee's transmission both in the uplink and downlink. We then obtain optimal uplink and downlink power allocation in the licensee using an optimisation problem with the objective of maximizing licensee's spectral efficiency (SE) subject to the incumbent interference threshold. Furthermore, we investigate the effect of the number of users and cell size on the SE. Our results provide quantitative insights for practical system design and deployment of LSA system. We then examine the whole LSA spectrum utilization by characterising the availability of the LSA spectrum using a tandem queue setting. Using this model we obtain an expression for the spectral utilization as a function of the licensee's achievable spectral efficiency and the statistics of the LSA spectrum availability. Simulation results show more than a seven-fold improvement in the licensee SE using the optimal power allocation. It is also seen that a higher SE gain is achieved with the proposed optimal power allocation in cases where the number of user equipment in the eNodeB coverage area is very small. Furthermore, higher spectrum utilization efficiency is achieved as a result of shorter busy period and higher achievable SE for distant cells.

Maximum Achievable Sum Rate in Highly Dynamic Licensed Shared Access

Abstract: In this paper, we propose a novel power allocation scheme that maximizes the sum spectral efficiency of the licensee in a dynamic Licensed Shared Access (LSA) system. In particular, our focus is on the time intervals in which the incumbent system is active in the spectrum. We derive an expression for the interference distribution of the licensee, e.g., a mobile network operator, utilizing a spectrum belonging to an airport incumbent under the LSA spectrum sharing. Formulating an optimization problem to maximize the sum spectrum efficiency subject to the interference threshold constraint at the licensee, we then show its convexity, and obtain its optimal solutions. We further investigate the impact of sum rate maximization on the fairness of network resource allocations. Simulation results show a significant gain in the achievable spectrum efficiency, especially during the intervals in which the incumbent system is active in the LSA band. This paper provides quantitative insights on the maximum achievable sum rate in an LSA system in which both the licensee and the incumbent systems are active at the same time.

Millimeter wave propagation measurements and characteristics for 5g system

Abstract: In future 5G systems, the millimeter wave (mmWave) band will be used to support a large capacity for current mobile broadband. Therefore, the radio access technology (RAT) should be made available for 5G devices to help in distinct situations, for example device-to-device communications (D2D) and multi-hops. This paper presents ultra-wideband channel measurements for millimeter wave bands at 19, 28, and 38 GHz. We used an ultra-wideband channel sounder (1 GHz bandwidth) in an indoor to outdoor (I2O) environment for non-line-of-sight (NLOS) scenarios. In an NLOS environment, there is no direct path (line of sight), and all of the contributed paths are received from different physical objects by refection propagation phenomena. Hence, in this work, a directional horn antenna (high gain) was used at the transmitter, while an omnidirectional antenna was used at the receiver to collect the radio signals from all directions. The path loss and temporal dispersion were examined based on the acquired measurement data—the 5G propagation characteristics. Two different path loss models were used, namely close-in (CI) free space reference distance and alpha-beta-gamma (ABG) models. The time dispersion parameters were provided based on a mean excess delay, a root mean square (RMS) delay spread, and a maximum excess delay. The path loss exponent for this NLOS specific environment was found to be low for all of the proposed frequencies, and the RMS delay spread values were less than 30 ns for all of the measured frequencies, and the average RMS delay spread values were 19.2, 19.3, and 20.3 ns for 19, 28, and 38 GHz frequencies, respectively. Moreover, the mean excess delay values were found also at 26.1, 25.8, and 27.3 ns for 19, 28, and 38 GHz frequencies, respectively. The propagation signal through the NLOS channel at 19, 28, and 38 GHz was strong with a low delay; it is concluded that these bands are reliable for 5G systems in short-range applications.

DEW: A New Edge Computing Component for Distributed Dynamic Networks

Abstract: The paper proposes the introduction of a new component in the family of distributed computer networks, with specific functions for edge computing devices. The DEW component is a network layer located between Fog and Edge, a position currently attributed to the Mist concept. Specifically, DEW is designed as a Mist counterpart that can be used in Distributed Dynamical Networks (DDN). The DEW characteristics and the associated specific algorithms are discussed on a particular type of DDN - microgrids and microgrids clusters.

Real-Time Distributed Computing at Network Edges for Large Scale Industrial IoT Networks

Abstract: Low-cost, real-time digital signal processors (DSPs) embedded in generic Internet of Things (IoT) edge devices can make significant contributions to distributed edge computing for industrial IoT (IIoT) networks. The DSP considered in this paper is the Texas Instruments (TI) TMS320C28x DSP (C28x). At the edge of the network, the C28x is programmed using low-level Embedded C programming language to construct the Morlet wavelet. Our implementation at this layer is the first known construction of the Morlet wavelet for C28x DSP using Embedded C. At the fog layer, near the edge of the IoT network, where more computing resources exist, the wavelet is then convolved with healthcare (electrocardiogram) and electrical network signals, using Matlab to reduce signal noise, and to identify important parts of examined signals. Convolution results indicates that the distributed computing approach for low-cost generic devices considered in this paper is suitable for use in large IIoT networks

Osmotic Collaborative Computing for Machine Learning and Cybersecurity Applications in Industrial IoT Networks and Cyber Physical Systems with Gaussian Mixture Models

Abstract: To implement machine learning algorithms and other useful algorithms in industrial Internet of Things (IIoT), new computing approaches are needed to prevent costs associated with having to install state of the art edge analytic devices. A suitable approach may include collaborative edge computing using available, resource-constrained IoT edge analytic hardware. In this paper, collaborative computing method is used to construct a popular and very useful waveform for IoT analytics, the Gaussian Mixture Model (GMM). GMM parameters are learned in the cloud, but the GMMs are constructed at the IIoT edge layer. GMMs are constructed using C28x, a ubiquitous, low-cost, embedded digital signal processor (DSP) that is widely available in many pre-existing IIoT infrastructures and in many edge analytic devices. Several GMMs including 2-GMM and 3-GMMs are constructed using the C28x DSP and Embedded C to show that GMM designs could be achieved in form of an osmotic microservice from the IIoT edge to the IIoT fog layer. Designed GMMs are evaluated using their differential and zero-crossings and are found to satisfy important waveform design criteria. At the fog layer, constructed GMMs are then applied for novelty detection, an IIoT cybersecurity and fault-monitoring application and are found to be able to detect anomalies in IIoT machine data using Hampel identifier, 3-Sigma rule, and the Boxplot rule. The osmotic collaborative computing method advocated in this paper will be crucial in ensuring the possibility of shifting many complex applications such as novelty detection and other machine learning based cybersecurity applications to edges of large scale IoT networks using low-cost widely available DSPs.

IIoT Gateway for Edge Computing Applications

Abstract: With the emergence of IoT applications Cloud architecture proves to be inefficient in handling massive amounts of data, mainly because of the variable latency and limited bandwidth. More specific, major requirements of Industrial Internet of Things (IIoT) like control and real-time decision making could not be addressed. These limitations along with the increasing intelligence in the lower levels of the data transmission architecture led to the development of an intermediate edge processing layer, closer to the process, enabling distributed computing and near real-time communication. In this paper a new perspective on edge architectures is presented and a model for a new edge gateway is designed. This device aims to facilitate new distributed computing methods while being able to handle both operational and functional requirements. Three case studies analyse how this device can be used to improve existing solutions: a hydroponic greenhouse, Smart Grid implementation for power systems and a video surveillance system in a manufacturing application.