Distributed DTX Alignment with Memory

This paper addresses the assignment of transmission and sleep time slots between interfering transmitters with the objective of minimal power consumption. In particular, we address the constructive alignment of Discontinuous Transmission (DTX) time slots under link rate constraints. Due to the complexity of the combinatorial optimization problem at hand, we resort to heuristic assignment strategies. We derive four time slot alignment solutions (sequential alignment, random alignment, p-persistent ranking and DTX alignment with memory) and identify trade-offs. One solution, DTX alignment with memory, addresses trade-offs of the other three by maintaining memory of past alignment and channel quality to buffer short term changes in channel quality. All strategies are found to exhibit similar convergence behavior, but different power consumption and retransmission probabilities. DTX alignment with memory is shown to achieve up to 40% savings in power consumption and more than 20% lower retransmission probability than the state of the art.Comment: 13 pages, In 2014 IEEE International Conference on Communications (ICC), Page(s): 3481 - 348

밀집된 펨토셀 네트워크를 위한 DTX 시간 할당 방법

학위논문 (석사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 8. 전화숙.To reduce rapidly growing power consumption in wireless network infrastructure, cell downlink discontinuous transmission (DTX) has been considered as a promising candidate under discussion by standardization organization. DTX is also regarded as an ideal solution to avoid inter-cell interference, especially when the cell is under low load condition. We proposed a graph-coloring based scheme to mitigate the femto-to-femto interference of densely deployed femtocell networks with the goal of minimizing supply power consumption while they utilize the DTX operation. If femtocell access points (FAPs) choose different slots to transmit their data and leave the other slots in the DTX mode, then femtocells could reduce mutual interference to achieve high SINR thereby resulting in the reduction of power consumption. FAPs report their interferers to femtocell management system (FMS), FMS then constructs an interference graph according to their reporting information. Based on the constructed interference graph, FMS determines which FAPs to include in which color groups (CGs) using our proposed graph-coloring algorithm. Then FMS allocates certain amounts of consecutive slots dedicated to FAPs in each CG and reusable slots to the unsatisfied FAPs by means of our proposed slots allocation algorithm. The simulation results indicate that our proposed solution has better performances than existing schemes in terms of mean power consumption, mean SINR and mean outage probability with a little introduction of signal overhead. In particular, compared with the state-of-the-art scheme, our scheme conserves 15% of power consumption and diminishes almost 85% outage probability.Contents Chapter 1 Introduction 7 Chapter 2 System Model 11 Chapter 3 Proposed Resource Allocation Scheme 14 3.1 Proposed Graph-coloring Scheme . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.2 Proposed Slots Allocation Scheme . . . . . . . . . . . . . . . . . . . . . . . . . 15 Chapter 4 Performance Evaluation 20 4.1 Simulation Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.2 Simulation Results . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . 20 Chapter 5 Conclusion 26 Bibliography 27 Abstract in Korean 29Maste

Enhancing the energy efficiency of radio base stations

This thesis is concerned with the energy efficiency of cellular networks. It studies the dominant power consumer in future cellular networks, the Long Term Evolution (LTE) radio Base Station (BS), and proposes mechanisms that enhance the BS energy efficiency by reducing its power consumption under target rate constraints. These mechanisms trade spare capacity for power saving. First, the thesis describes how much power individual components of a BS consume and what parameters affect this consumption based on third party experimental data. These individual models are joined into a component power model for an entire BS. The component model is an essential step in analysis but is too complex for many applications. It is therefore abstracted into a much simpler parameterized model to reduce its complexity. The parameterized model is further simplified into an affine model which can be applied in power minimization. Second, Power Control (PC) and Discontinuous Transmission (DTX) are identified as promising power-saving Radio Resource Management (RRM) mechanisms and applied to multi-user downlink transmission. PC reduces the power consumption of the Power Amplifier (PA) and is found to be most effective at high traffic loads. DTX mostly reduces the power consumption of the Baseband (BB) unit while interrupting transmission and is better applied in low traffic loads. Joint optimization of these two techniques is found to enable additional power-saving at medium traffic loads and to be a convex problem which can be solved efficiently. The convex problem is extended to provide a comprehensive power-saving Orthogonal Frequency Division Multiple Access (OFDMA) frame resource scheduler. The proposed scheduler is shown to reduce power consumption by 25-40% in computer simulations, depending on the traffic load. Finally, the thesis investigates the influence of interference on power consumption in a network of multiple power-saving BSs. It discusses three popular alternative distributed uncoordinated methods which align DTX mode between neighbouring BSs. To address drawbacks of these three, a fourth memory-based DTX alignment method is proposed. It decreases power consumption by up to 40% and retransmission probability by around 20%, depending on the traffic load

Depletion of stromal cells expressing fibroblast activation protein-α from skeletal muscle and bone marrow results in cachexia and anemia.

Fibroblast activation protein-α (FAP) identifies stromal cells of mesenchymal origin in human cancers and chronic inflammatory lesions. In mouse models of cancer, they have been shown to be immune suppressive, but studies of their occurrence and function in normal tissues have been limited. With a transgenic mouse line permitting the bioluminescent imaging of FAP(+) cells, we find that they reside in most tissues of the adult mouse. FAP(+) cells from three sites, skeletal muscle, adipose tissue, and pancreas, have highly similar transcriptomes, suggesting a shared lineage. FAP(+) cells of skeletal muscle are the major local source of follistatin, and in bone marrow they express Cxcl12 and KitL. Experimental ablation of these cells causes loss of muscle mass and a reduction of B-lymphopoiesis and erythropoiesis, revealing their essential functions in maintaining normal muscle mass and hematopoiesis, respectively. Remarkably, these cells are altered at these sites in transplantable and spontaneous mouse models of cancer-induced cachexia and anemia. Thus, the FAP(+) stromal cell may have roles in two adverse consequences of cancer: their acquisition by tumors may cause failure of immunosurveillance, and their alteration in normal tissues contributes to the paraneoplastic syndromes of cachexia and anemia

Quantum Information at the Interface of Light with Atomic Ensembles and Micromechanical Oscillators

This article reviews recent research towards a universal light-matter interface. Such an interface is an important prerequisite for long distance quantum communication, entanglement assisted sensing and measurement, as well as for scalable photonic quantum computation. We review the developments in light-matter interfaces based on room temperature atomic vapors interacting with propagating pulses via the Faraday effect. This interaction has long been used as a tool for quantum nondemolition detections of atomic spins via light. It was discovered recently that this type of light-matter interaction can actually be tuned to realize more general dynamics, enabling better performance of the light-matter interface as well as rendering tasks possible, which were before thought to be impractical. This includes the realization of improved entanglement assisted and backaction evading magnetometry approaching the Quantum Cramer-Rao limit, quantum memory for squeezed states of light and the dissipative generation of entanglement. A separate, but related, experiment on entanglement assisted cold atom clock showing the Heisenberg scaling of precision is described. We also review a possible interface between collective atomic spins with nano- or micromechanical oscillators, providing a link between atomic and solid state physics approaches towards quantum information processing

Neuroverkon inferenssi digitaalisessa signaalikäsittelyssä kovien reaaliaikavaatimusten alaisuudessa

The main objective of this thesis is to investigate how neural network inference can be efficiently implemented on a digital signal processor under hard real-time constraints from the execution speed point of view. Theories on digital signal processors and software optimization as well as neural networks are discussed. A neural network model for the specific use case is designed and a digital signal processor implementation is created based on the neural network model. A neural network model for the use case is created based on the data from the Matlab simulation model. The neural network model is trained and validated using the Python programming language with the Keras package. The neural network model is implemented on the CEVA-XC4500 digital signal processor. The digital signal processor implementation is written in C++ language with the processor specific vector-processing intrinsics. The neural network model is evaluated based on the model accuracy, precision, recall and f1-score. The model performance is compared to the conventional use case implementation by calculating 3GPP specified metrics of misdetection probability, false alarm rate and bit error rate. The execution speed of the digital signal processor implementation is evaluated with the CEVA integrated development environment profiling tool and also with the Lauterbach PowerTrace profiling module attached to the real base station product. Through this thesis, an optimized CEVA-XC4500 digital signal processor implementation was created for the specific neural network architecture. The optimized implementation showed to consume 88 percent less cycles than the conventional implementation. Also, the neural network model performance fulfills the 3GPP specification requirements.Tämän diplomityön tarkoituksena on tutkia miten neuroverkon inferenssi voidaan toteuttaa tehokkaasti digitaalisella signaaliprosessorilla suoritusnopeuden näkökulmasta, kun sovelluksella on kovat reaaliaikavaatimukset. Työssä käsitellään teoriaa digitaalisista signaaliprosessoreista, ohjelmistojen optimoinnista ja neuroverkoista. Työssä kehitetään neuroverkkomalli tiettyyn käyttötapaukseen, ja mallin pohjalta luodaan toteutus digitaaliselle signaaliprosessorille. Neuroverkkomalli luodaan Matlab-simulointimallin avulla kerätystä datasta. Neuroverkkomalli opetetaan ja varmennetaan Python-ohjelmointikiellellä ja Keras-paketilla. Neuroverkkomalli toteutetaan CEVA-XC4500 digitaaliselle signaaliprosessorille. Digitaalisen signaaliprosessorin toteutus kirjoitetaan C++-ohjelmointikielellä ja prosessorikohtaisilla vektorilaskentaoperaatioilla. Neuroverkkomalli varmennetaan mallin tarkkuuden, precision-arvon, recall-arvon ja f1-arvon perusteella. Mallin suorituskykyä verrataan käyttötapauksen tavanomaiseen toteutukseen laskemalla 3GPP-spesifikaation mukaiset mittarit virhehavaintotodennäköisyys, väärien hälytysten lukumäärä ja bittivirhemäärä. Suoritusnopeus määritetään sekä CEVA-ohjelmointiympäristön profilointityökalulla että tukiasematuotteeseen kytketyllä Lauterbach PowerTrace-yksiköllä. Työn tuloksena luotiin optimoitu CEVA-XC4500 digitaalinen signaaliprosessoritoteutus valitulle neuroverkkoarkkitehtuurille. Optimoitu toteutus kulutti 88% vähemmän laskentasyklejä kuin tavanomainen toteutus. Neuroverkkomalli täytti 3GPP-spesifikaation mukaiset vaatimukset

0-dimensional persistent homology analysis implementation in resource-scarce embedded systems

Persistent Homology (PH) analysis is a powerful tool for understanding many relevant topological features from a given dataset. PH allows finding clusters, noise, and relevant connections in the dataset. Therefore, it can provide a better view of the problem and a way of perceiving if a given dataset is equal to another, if a given sample is relevant, and how the samples occupy the feature space. However, PH involves reducing the problem to its simplicial complex space, which is computationally expensive and implementing PH in such Resource-Scarce Embedded Systems (RSES) is an essential add-on for them. However, due to its complexity, implementing PH in such tiny devices is considerably complicated due to the lack of memory and processing power. The following paper shows the implementation of 0-Dimensional Persistent Homology Analysis in a set of well-known RSES, using a technique that reduces the memory footprint and processing power needs of the 0-Dimensional PH algorithm. The results are positive and show that RSES can be equipped with this real-time data analysis tool.This work has been supported by FCT-Fundacao para a Ciencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020