Relativistic Positioning Systems: The Emission Coordinates

This paper introduces some general properties of the gravitational metric and the natural basis of vectors and covectors in 4-dimensional emission coordinates. Emission coordinates are a class of space-time coordinates defined and generated by 4 emitters (satellites) broadcasting their proper time by means of electromagnetic signals. They are a constitutive ingredient of the simplest conceivable relativistic positioning systems. Their study is aimed to develop a theory of these positioning systems, based on the framework and concepts of general relativity, as opposed to introducing `relativistic effects' in a classical framework. In particular, we characterize the causal character of the coordinate vectors, covectors and 2-planes, which are of an unusual type. We obtain the inequality conditions for the contravariant metric to be Lorentzian, and the non-trivial and unexpected identities satisfied by the angles formed by each pair of natural vectors. We also prove that the metric can be naturally split in such a way that there appear 2 parameters (scalar functions) dependent exclusively on the trajectory of the emitters, hence independent of the time broadcast, and 4 parameters, one for each emitter, scaling linearly with the time broadcast by the corresponding satellite, hence independent of the others.Comment: 13 pages, 3 figures. Only format changed for a new submission. Submitted to Class. Quantum Gra

Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays

Massive MIMO (multiple-input multiple-output) is no longer a "wild" or "promising" concept for future cellular networks - in 2018 it became a reality. Base stations (BSs) with 64 fully digital transceiver chains were commercially deployed in several countries, the key ingredients of Massive MIMO have made it into the 5G standard, the signal processing methods required to achieve unprecedented spectral efficiency have been developed, and the limitation due to pilot contamination has been resolved. Even the development of fully digital Massive MIMO arrays for mmWave frequencies - once viewed prohibitively complicated and costly - is well underway. In a few years, Massive MIMO with fully digital transceivers will be a mainstream feature at both sub-6 GHz and mmWave frequencies. In this paper, we explain how the first chapter of the Massive MIMO research saga has come to an end, while the story has just begun. The coming wide-scale deployment of BSs with massive antenna arrays opens the door to a brand new world where spatial processing capabilities are omnipresent. In addition to mobile broadband services, the antennas can be used for other communication applications, such as low-power machine-type or ultra-reliable communications, as well as non-communication applications such as radar, sensing and positioning. We outline five new Massive MIMO related research directions: Extremely large aperture arrays, Holographic Massive MIMO, Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive MIMO.Comment: 20 pages, 9 figures, submitted to Digital Signal Processin

Improvement of indoor VLC network downlink scheduling and resource allocation

Indoor visible light communications (VLC) combines illumination and communication by utilizing the high-modulation-speed of LEDs. VLC is anticipated to be complementary to radio frequency communications and an important part of next generation heterogeneous networks. In order to make the maximum use of VLC technology in a networking environment, we need to expand existing research from studies of traditional point-to-point links to encompass scheduling and resource allocation related to multi-user scenarios. This work aims to maximize the downlink throughput of an indoor VLC network, while taking both user fairness and time latency into consideration. Inter-user interference is eliminated by appropriately allocating LEDs to users with the aid of graph theory. A three-term priority factor model is derived and is shown to improve the throughput performance of the network scheduling scheme over those previously reported. Simulations of VLC downlink scheduling have been performed under proportional fairness scheduling principles where our newly formulated priority factor model has been applied. The downlink throughput is improved by 19.6% compared to previous two-term priority models, while achieving similar fairness and latency performance. When the number of users grows larger, the three-term priority model indicates an improvement in Fairness performance compared to two-term priority model scheduling

Inventory drivers in a pharmaceutical supply chain

In recent years, inventory reduction has been a key objective of pharmaceutical companies, especially within cost optimization initiatives. Pharmaceutical supply chains are characterized by volatile and unpredictable demands –especially in emergent markets-, high service levels, and complex, perishable finished-good portfolios, which makes keeping reasonable amounts of stock a true challenge. However, a one-way strategy towards zero-inventory is in reality inapplicable, due to the strategic nature and importance of the products being commercialised. Therefore, pharmaceutical supply chains are in need of new inventory strategies in order to remain competitive. Finished-goods inventory management in the pharmaceutical industry is closely related to the manufacturing systems and supply chain configurations that companies adopt. The factors considered in inventory management policies, however, do not always cover the full supply chain spectrum in which companies operate. This paper works under the pre-assumption that, in fact, there is a complex relationship between the inventory configurations that companies adopt and the factors behind them. The intention of this paper is to understand the factors driving high finished-goods inventory levels in pharmaceutical supply chains and assist supply chain managers in determining which of them can be influenced in order to reduce inventories to an optimal degree. Reasons for reducing inventory levels are found in high inventory holding and scrap related costs; in addition to lost sales for not being able to serve the customers with the adequate shelf life requirements. The thesis conducts a single case study research in a multi-national pharmaceutical company, which is used to examine typical inventory configurations and the factors affecting these configurations. This paper presents a framework that can assist supply chain managers in determining the most important inventory drivers in pharmaceutical supply chains. The findings in this study suggest that while external and downstream supply chain factors are recognized as being critical to pursue inventory optimization initiatives, pharmaceutical companies are oriented towards optimizing production processes and meeting regulatory requirements while still complying with high service levels, being internal factors the ones prevailing when making inventory management decisions. Furthermore, this paper investigates, through predictive modelling techniques, how various intrinsic and extrinsic factors influence the inventory configurations of the case study company. The study shows that inventory configurations are relatively unstable over time, especially in configurations that present high safety stock levels; and that production features and product characteristics are important explanatory factors behind high inventory levels. Regulatory requirements also play an important role in explaining the high strategic inventory levels that pharmaceutical companies hold

PresenceSense: Zero-training Algorithm for Individual Presence Detection based on Power Monitoring

Non-intrusive presence detection of individuals in commercial buildings is much easier to implement than intrusive methods such as passive infrared, acoustic sensors, and camera. Individual power consumption, while providing useful feedback and motivation for energy saving, can be used as a valuable source for presence detection. We conduct pilot experiments in an office setting to collect individual presence data by ultrasonic sensors, acceleration sensors, and WiFi access points, in addition to the individual power monitoring data. PresenceSense (PS), a semi-supervised learning algorithm based on power measurement that trains itself with only unlabeled data, is proposed, analyzed and evaluated in the study. Without any labeling efforts, which are usually tedious and time consuming, PresenceSense outperforms popular models whose parameters are optimized over a large training set. The results are interpreted and potential applications of PresenceSense on other data sources are discussed. The significance of this study attaches to space security, occupancy behavior modeling, and energy saving of plug loads.Comment: BuildSys 201

Power Optimization for Network Localization

Reliable and accurate localization of mobile objects is essential for many applications in wireless networks. In range-based localization, the position of the object can be inferred using the distance measurements from wireless signals exchanged with active objects or reflected by passive ones. Power allocation for ranging signals is important since it affects not only network lifetime and throughput but also localization accuracy. In this paper, we establish a unifying optimization framework for power allocation in both active and passive localization networks. In particular, we first determine the functional properties of the localization accuracy metric, which enable us to transform the power allocation problems into second-order cone programs (SOCPs). We then propose the robust counterparts of the problems in the presence of parameter uncertainty and develop asymptotically optimal and efficient near-optimal SOCP-based algorithms. Our simulation results validate the efficiency and robustness of the proposed algorithms.Comment: 15 pages, 7 figure