Algorithms for 5G physical layer

There is a great activity in the research community towards the investigations of the various aspects of 5G at different protocol layers and parts of the network. Among all, physical layer design plays a very important role to satisfy high demands in terms of data rates, latency, reliability and number of connected devices for 5G deployment. This thesis addresses he latest developments in the physical layer algorithms regarding the channel coding, signal detection, frame synchronization and multiple access technique in the light of 5G use cases. These developments are governed by the requirements of the different use case scenarios that are envisioned to be the driving force in 5G. All chapters from chapter 2 to 5 are developed around the need of physical layer algorithms dedicated to 5G use cases. In brief, this thesis focuses on design, analysis, simulation and he advancement of physical layer aspects such as 1. Reliability based decoding of short length Linear Block Codes (LBCs) with very good properties in terms of minimum hamming istance for very small latency requiring applications. In this context, we enlarge the grid of possible candidates by considering, in particular, short length LBCs (especially extended CH codes) with soft-decision decoding; 2. Efficient synchronization of preamble/postamble in a short bursty frame using modified Massey correlator; 3. Detection of Primary User activity using semiblind spectrum sensing algorithms and analysis of such algorithms under practical imperfections; 4. Design of optimal spreading matrix for a Low Density Spreading (LDS) technique in the context of non-orthogonal multiple access. In such spreading matrix, small number of elements in a spreading sequences are non zero allowing each user to spread its data over small number of chips (tones), thus simplifying the decoding procedure using Message Passing Algorithm (MPA)

Multi-antenna energy detector under unknown primary user traffic

In cognitive radio (CR) networks, the knowledge of primary user (PU) traffic plays a crucial role in designing the sensing slot duration and synchronization with PU traffic. However, the secondary user (SU) sensing unit usually does not have the knowledge of the exact time slot structure in the primary network. Moreover, it is also possible that the communication among PUs are not based on synchronous schemes at all. In this paper, the effect of unknown primary user (PU) traffic on the performance of multi-antenna spectrum sensing is evaluated under a flat fading channel. In contrast to the commonly used continuous time Markov model of the existing literature, a realistic and simple PU traffic model is proposed which is based only on the discrete time distribution of PU free and busy periods. Furthermore, in order to assess the effect of PU traffic on the detection performance, analytical expressions for the probability density functions of the decision statistic are derived considering Energy Detection (ED) test as spectrum sensing method. It is shown that the time varying PU traffic severely affects the spectrum sensing performance. Most importantly, our results show that the performance gain due to multiple antennas in the sensing unit is significantly reduced by the effect of PU traffic when the mean lengths of free and busy periods are of the same order of magnitude of the sensing slot

Performance Analysis of Multi-Antenna Hybrid Detectors and Optimization with Noise Variance Estimation

In this paper, a performance analysis of multi-antenna spectrum sensing techniques is carried out. Both well known algorithms, such as Energy Detector (ED) and eigenvalue based detectors, and an eigenvector based algorithm, are considered. With the idea of auxiliary noise variance estimation, the performance analysis is extended to the hybrid approaches of the considered detectors. Moreover, optimization for Hybrid ED under constant estimation plus detection time is performed. Performance results are evaluated in terms of Receiver Operating Characteristic (ROC) curves and performance curves, i.e., detection probability as a function of the Signal-to-Noise Ratio (SNR). It is concluded that the eigenvector based detector and its hybrid approach are able to approach the optimal Neyman-Pearson performance

Hybrid approach analysis of energy detection and eigenvalue based spectrum sensing algorithms with noise power estimation

Two particular semi-blind spectrum sensing algorithms are taken into account in this paper: Energy Detection (ED) and Roy's Largest Root Test (RLRT). Both algorithms require the knowledge of the noise power in order to achieve optimal performance. Since by its nature the noise power is unpredictable, noise variance estimation is needed in order to cope with the absence of prior knowledge of the noise power: this leads to a new hybrid approach for both considered detectors. Probability of detection and false alarm with this new approach are derived in closed-form expressions. The impact of noise estimation accuracy for ED and RLRT is evaluated in terms of Receiver Operating Characteristic (ROC) curves and performance curves, i.e., detection/misdetection probability as a function of the Signal to Noise Ratio (SNR). Analytical results have been confirmed by numerical simulations under a flat-fading channel scenario. It is concluded that both hybrid approaches tend to their ideal cases when a large number of slots is used for noise variance estimation and that the impairment due to noise uncertainty is reduced on RLRT w.r.t. E

New Constraints on Macroscopic Dark Matter Using Radar Meteor Detectors

We show that dark-matter candidates with large masses and large nuclear interaction cross sections are detectable with terrestrial radar systems. We develop our results in close comparison to successful radar searches for tiny meteoroids, aggregates of ordinary matter. The path of a meteoroid (or suitable dark-matter particle) through the atmosphere produces ionization deposits that reflect incident radio waves. We calculate the equivalent radar echoing area or `radar cross section' for dark matter. By comparing the expected number of dark-matter-induced echoes with observations, we set new limits in the plane of dark-matter mass and cross section, complementary to pre-existing cosmological limits. Our results are valuable because (A) they open a new detection technique for which the reach can be greatly improved and (B) in case of a detection, the radar technique provides differential sensitivity to the mass and cross section, unlike cosmological probes.Comment: Main text 14 pages and 11 figures, Appendix 2 pages and 3 figure

Gurkha Warriors as Entrepreneurs in Britain: A Social Anchoring Lens on Martial Heritage and Migrant Enterprises

Using the social anchoring approach, this article investigates the entrepreneur experience of one of the newest migrant groups in Britain, the Nepali Gurkhas. The findings derived from the semi-structured interviews show how these migrant entrepreneurs employ multiple ‘anchors’ to engage in family-based enterprises and to navigate structural constraints. Their military heritage, which has provided them with psycho-social resources in the form of subjective and mixed anchors, has been central to their exercise of agency and enabling them to gain a foothold in Britain. This has rendered Gurkha entrepreneurs a distinct group within migrant entrepreneurship. The article contributes to the literature on migrant entrepreneurship by delineating how agential capacity, by deploying different anchors, can cause variations in migrant enterprises, which in turn imbue migrant entrepreneurship with distinct characteristics

Gurkha Warriors as Entrepreneurs in Britain: A Social Anchoring Lens on Martial Heritage and Migrant Enterprises

Using the social anchoring approach, this article investigates the entrepreneur experience of one of the newest migrant groups in Britain, the Nepali Gurkhas. The findings derived from the semi-structured interviews show how these migrant entrepreneurs employ multiple ‘anchors’ to engage in family-based enterprises and to navigate structural constraints. Their military heritage, which has provided them with psycho-social resources in the form of subjective and mixed anchors, has been central to their exercise of agency and enabling them to gain a foothold in Britain. This has rendered Gurkha entrepreneurs a distinct group within migrant entrepreneurship. The article contributes to the literature on migrant entrepreneurship by delineating how agential capacity, by deploying different anchors, can cause variations in migrant enterprises, which in turn imbue migrant entrepreneurship with distinct characteristics

Phenotyping Fusarium head blight through seed morphology characteristics using RGB imaging

Fusarium head blight (FHB) is an economically important disease affecting wheat and thus poses a major threat to wheat production. Several studies have evaluated the effectiveness of image analysis methods to predict FHB using disease-infected grains; however, few have looked at the final application, considering the relationship between cost and benefit, resolution, and accuracy. The conventional screening of FHB resistance of large-scale samples is still dependent on low-throughput visual inspections. This study aims to compare the performance of two cost-benefit seed image analysis methods, the free software "SmartGrain " and the fully automated commercially available instrument "Cgrain Value (TM) " by assessing 16 seed morphological traits of winter wheat to predict FHB. The analysis was carried out on a seed set of FHB which was visually assessed as to the severity. The dataset is composed of 432 winter wheat genotypes that were greenhouse-inoculated. The predictions from each method, in addition to the predictions combined from the results of both methods, were compared with the disease visual scores. The results showed that Cgrain Value (TM) had a higher prediction accuracy of R (2) = 0.52 compared with SmartGrain for which R (2) = 0.30 for all morphological traits. However, the results combined from both methods showed the greatest prediction performance of R (2) = 0.58. Additionally, a subpart of the morphological traits, namely, width, length, thickness, and color features, showed a higher correlation with the visual scores compared with the other traits. Overall, both methods were related to the visual scores. This study shows that these affordable imaging methods could be effective to predict FHB in seeds and enable us to distinguish minor differences in seed morphology, which could lead to a precise performance selection of disease-free seeds/grains

The Sociomateriality of Digitalisation in Nepalese NGOs

Drawing on the concept of sociomateriality, this paper investigates the digitalisation of Non-Governmental Organisations (NGOs) in developing countries during the COVID-19 pandemic. NGOs represent one sector in which the consequences of digitalisation have been particularly striking. Nationwide lockdowns, travel restrictions and strict government guidelines led to NGOs embarking on a transition towards digitalisation for their continuity and survival. Adhering to a qualitative approach, data for the study have been derived through semi-structured interviews with stakeholders, focus group discussions with beneficiaries and a review of documentary sources. Outlining both the benefits and consequences of digitalisation, the findings of the study illustrate the way how the NGOs’ digitalisation has triggered changes in both their operations and modes of communication, altered their relationships with beneficiaries and other stakeholders, and transformed their identity. The key contribution made by the paper involves moving beyond the human-centred and techno-centric approaches to digitalisation, which dominate the existing accounting literature, and illustrating how the performance of technologies evolves in everyday life. In doing so, the paper delineates the role that the technology itself can play in shaping NGOs’ day-to-day practices in developing countries

Phenotyping Fusarium head blight through seed morphology characteristics using RGB imaging

Fusarium head blight (FHB) is an economically important disease affecting wheat and thus poses a major threat to wheat production. Several studies have evaluated the effectiveness of image analysis methods to predict FHB using disease-infected grains; however, few have looked at the final application, considering the relationship between cost and benefit, resolution, and accuracy. The conventional screening of FHB resistance of large-scale samples is still dependent on low-throughput visual inspections. This study aims to compare the performance of two cost–benefit seed image analysis methods, the free software “SmartGrain” and the fully automated commercially available instrument “Cgrain Value™” by assessing 16 seed morphological traits of winter wheat to predict FHB. The analysis was carried out on a seed set of FHB which was visually assessed as to the severity. The dataset is composed of 432 winter wheat genotypes that were greenhouse-inoculated. The predictions from each method, in addition to the predictions combined from the results of both methods, were compared with the disease visual scores. The results showed that Cgrain Value™ had a higher prediction accuracy of R2 = 0.52 compared with SmartGrain for which R2 = 0.30 for all morphological traits. However, the results combined from both methods showed the greatest prediction performance of R2 = 0.58. Additionally, a subpart of the morphological traits, namely, width, length, thickness, and color features, showed a higher correlation with the visual scores compared with the other traits. Overall, both methods were related to the visual scores. This study shows that these affordable imaging methods could be effective to predict FHB in seeds and enable us to distinguish minor differences in seed morphology, which could lead to a precise performance selection of disease-free seeds/grains