Dissertationen zum Thema „Received signal power (RSS)“

The objective of this work is to enhance the awareness of the indoor propagation behaviour, by a set of investigations including simulations and measurements. These investigations include indoor propagation behaviour, local mean power estimation, proposing new indoor path loss model and introducing a case study on 60 GHz propagation in indoor environments using ray tracing and measurements. A summary of propagation mechanisms and manifestations in the indoor environment is presented. This comprises the indoor localization techniques using channel parameters in terms of angle of arrival (AOA), time of arrival (TOA) and received signal strength (RSS). Different models of path loss, shadowing and fast fading mechanisms are explored. The concept of MIMO channels is studied using many types of deterministic channel modelling such as Finite Difference Time Domain, Ray tracing and Dominant path model. A comprehensive study on estimating local average of the received signal strength (RSS) for indoor multipath propagation is conducted. The effect of the required number of the RSS data and their Euclidian distances between the neighbours samples are investigated over 1D, 2D and 3D configurations. It was found that the effect of fast fading was reduced sufficiently using 2D horizontal’s arrangement with larger spacing configuration. A modified indoor path loss prediction model is presented namely effective wall loss model (EWLM). The modified model with wall correction factors is compared to other indoor path loss prediction models using simulation data (for 2.4, 5, 28, 60 and 73.5 GHz) and real-time measurements (for 2.4 and 5 GHz). Different operating frequencies and antenna polarizations are considered to verify the observations. In the simulation part, EWLM shows the best performance among other models. Similar observations were recorded from the experimental results. Finally, a detailed study on indoor propagation environment at 60 GHz is conducted. The study is supported by Line of Sight (LoS) and Non-LoS measurements data. The results were compared to the simulated ones using Wireless-InSite ray tracing software. Several experiments have confirmed the reliability of the modelling process based on adjusted material properties values from measurements.

Avec le développement rapide de l'Internet des objets (IoT), le besoin de services de localisation en intérieur, tels que la gestion des actifs, la navigation et le suivi, a également augmenté au fil du temps. Pour la localisation indoor, les systèmes de navigation par satellite tels que le GPS ont un usage limité par l'absence de visibilité directe avec les satellites.Diverses solutions ont été proposées pour la localisation en intérieur, telles que la trilatération, la triangulation et la navigation à l'estime, mais leurs performances sont limitées par les conditions du canal intérieur, telles que masquage et l'évanouissement par trajets multiples. En exploitant la correspondance entre les mesures des caractéristiques du signal sans fil et les positions, les méthodes basées sur l'empreinte digitale ont montré qu'elles pouvaient fournir de bonnes performances de localisation avec suffisamment de données. Cependant, la localisation indoor est toujours confrontée à des défis tels que l'évolutivité, le coût et la complexité, la confidentialité, etc.L'objectif de cette thèse est d'améliorer l'efficience de la localisation indoor en utilisant des techniques d'apprentissage automatique. Nous divisons le processus de localisation en deux phases : la phase de cartographie radio hors ligne et la phase de localisation en ligne. Pendant la phase hors ligne, nous introduisons l'analyse des jeux de données comme une étape intermédiaire entre la création des jeux de données et la localisation. Nous proposons deux indicateurs numériques de qualité des jeux de données qui peuvent fournir un retour d'information pour améliorer la carte radio. De plus, l'extraction de caractéristiques et le traitement des données à l'aide d'outils d'apprentissage automatique sont intégrés dans un but d'efficience, en réduisant la taille des données et la complexité de calcul, tout en améliorant les performances de localisation. Pour être plus précis, nous proposons une méthode de cartographie radio basée sur les k-means qui peut réduire le nombre d'empreintes de plus de 90 % sans perdre d'informations utiles dans la carte radio ni dégrader les performances de localisation. En explorant la nature hiérarchique des grands ensembles de données, nous proposons une méthode d'extraction de caractéristiques hiérarchiques qui peut réduire davantage la complexité de la localisation sans en compromettre les performances.Pour la phase de localisation en ligne, nous explorons à la fois l'apprentissage automatique traditionnel et l'apprentissage profond. Nous présentons d'abord plusieurs méthodes traditionnelles d'apprentissage automatique et comparons les performances de localisation sur des ensembles de données publiques. Nous cherchons à améliorer les performances de localisation des méthodes traditionnelles.Pour faire face aux problèmes de confidentialité et de complexité, nous introduisons un cadre d'apprentissage fédéré pour le problème de la localisation indoor. Dans ce cadre, les clients partagent uniquement leurs modèles locaux avec le serveur central au lieu des données d'empreintes radios. Nous comparons d'abord les performances de l'apprentissage fédéré et centralisé. Ensuite, nous étudions l'impact du nombre de clients et de la taille des données locales. Toujours dans un objectif d'efficience, afin d'économiser les ressources, nous cherchons à réduire le coût de communication pendant le processus d'apprentissage. Nous évaluons différentes techniques, notamment la sélection des clients, l'accumulation du gradient et la compression du modèle. Une méthode de compression efficace est proposée pour compresser les modèles locaux, ce qui permet de réduire le coût de communication de la liaison montante de 91,5 % sans compromettre les performances de localisation. Enfin, nous considérons des limites sur la capacité des liens montants et évaluons différentes stratégies de compression
With rapid development of Internet of Things (IoT), the need of indoor location-based services such as asset management, navigation and tracking has also grown overtime. For indoor localization, navigation satellite systems such as GPS has limited usage since a direct line-of-sight to satellites is unavailable.Various solutions have been proposed for indoor localization such as trilateration, triangulation, dead reckoning, but their performance is limited by indoor channel conditions, such as shadowing and multipath fading. By exploiting the mapping between wireless signal feature measurements and positions, fingerprinting based methods have shown the potential to provide good localization performance with sufficient data. However, indoor localization still faces challenges like scalability, cost and complexity, privacy, etc.The focus of this thesis is to improve efficiency of indoor localization using machine learning techniques. We divide the localization process into two phases: offline radio mapping phase and online localization phase. During the offline phase, we introduce dataset analysis as an intermediate step between dataset creation and localization. We propose two numerical dataset quality indicators which can provide feedback to improve the radio map. Moreover, feature extraction and dataset processing using machine learning tools are integrated to improve efficiency by reducing the data size and computation complexity while improving localization performance. We propose a k-means based radio mapping method which can reduce the number of fingerprints by over % without losing useful information in the radio map or degrading localization performance. By exploring the hierarchical nature of large datasets, we propose a hierarchical feature extraction method which can further reduce localization complexity without compromising localization performance.For the online localization phase, we explore both traditional machine learning and deep learning. We first introduce several traditional machine learning methods and compare the localization performance on public datasets. We aim to improve localization performance of traditional methods.To cope with privacy and complexity issue, we introduce federated learning framework for indoor localization problem. In this framework, the clients share only their local models to the central server instead of the fingerprinting data. We first compare the performance with federated and centralized learning. Then, we further study the impact on different client numbers and local data size. To reduce communication cost during the training process, we evaluate different measures including client selection, gradient accumulation and model compression. An efficient compression method is proposed to compress local models which can reduce the uplink communication cost by 91.5% without compromising localization performance. At last, we consider a limit on uplink capacity and evaluate different compression strategies

While WiFi-based indoor localization is attractive, the need for a significant degree of pre-deployment effort is a key challenge. In this paper, indoor localization with no pre-deployment effort in an indoor space, such as an office building corridor, with WiFi coverage but no apriori knowledge of the placement of the access points(APs) is implemented for mobile devices. WiFi Received Signal Strength(RSS) in the considered environment is used to build radio maps using WiFi fingerprinting approach. Two architectures are developed based on this localization algorithm. The first one involves a client-server approach where the localization algorithm runs on the server whereas the second one is a standalone architecture and the algorithm runs on the SD card of the mobile device.

Location estimation when deployed on wireless networks supports a range of services including user tracking and monitoring, health care support and push and pull marketing. The main subject of this thesis is improving indoor and outdoor location estimation accuracy using received signal strength (RSS) from neighbouring base stations (BSs) or access points (APs), without using the global positioning system (GPS) or triangulation methods. For the outdoor environment, state-of-the-art deterministic and probabilistic algorithms are adapted to exploit principal components (PCs) and clustering. The accuracy is compared with K-nearest neighbour (KNN) algorithms using different partitioning models. The proposed scheme clusters the RSS tuples based on deviations from an estimated RSS attenuation model and then transforms the raw RSS in each cluster into new uncorrelated dimensions, using PCs. As well as simple global dimensionality reduction using PCs, the data reduction and rotation within each cluster improves estimation accuracy because a) each cluster can model the different local RSS distributions and b) it efficiently preserves the RSS correlations that are observed (some of which are substantial) in local regions and which independence approximations ignore. Different simulated and real environments are used for the comparisons. Experimental results show that positioning accuracy is significantly improved and fewer training samples are needed compared with traditional methods. Furthermore, a technique to adjust RSS data so that radio maps collected in different environmental conditions can be used together to enhance accuracy is also demonstrated. Additionally, in the radio coverage domain, a non-parametric probability approach is used for the radio reliability estimation and a semi-supervised learning model is proposed for the monitoring model training and evolution according to real-time mobile users’ RSS feedback. For the indoor environment, an approach for a large multi-story indoor location estimaiii tion using clustering and rank order matching is described. The accuracies using WiFi RSS alone, cellular GSM RSS alone and integrated WiFi and GSM RSS are presented. The methods were tested on real indoor environments. A hierarchical clustering method is used to partition the RSS space, where a cluster is defined as a set of mobile users who share exactly the same strongest RSS ranking set of transmitters. The experimental results show that while integrating of WiFi RSS with GSM RSS creates a marginal improvement, the GSM data can be used to ameliorate the loss of accuracy when APs fail.

Traditional satellite positioning systems have limited resolution and have proved inaccuratein areas such as urban canyons where signals are subject to bounce phenomena or indeed may be entirely unavailable. An alternative method of positioning is that of tri/multilateration, which uses known positions and distances from beacon points to locate a receiver. In this project, a software was developed which used DSRC Basic Safety Messages (containing locational information) in combination with Received Signal Strength metrics (translated to distance information) to carry out such positioning in static environments. Initial studies confirmed that a signal received on the Craton 2 hardware was subject to considerable signal strength spread approximating a Gaussian distribution. A software was developed to simulate BSMs, including a measure of perturbation, over TCP. Three different traffic scenarios were constructed. Furthermore, multilaterationsoftware was developed to receive the BSMs and calculate position using three separate algorithms. The performance of these algorithms in the three different traffic scenarios was then evaluated. Lastly, the multilateration software was further developed to allow for the capture and processing of real BSMs sent on the 5.9 GHzband. The multilateration software was capable of determining the location of the receiver to varying degrees of accuracy, depending on the geometrical distribution of surrounding vehicles and the algorithm used to multilaterate. The 3D Linear Least Squares method performed well in situations where beacons were well spaced in three dimensions. Other implemented multilateration algorithms, i.e., a 2D Linear Least Squares method and a 3D Gauss Newton method, performed better in typical traffic scenarios where vehicles tend to be coplanar.The software developed provides a useful starting point for further developmentof static, but also dynamic, multilateration algorithms.

This master thesis investigates the possibility of locating LTE base stations, known as eNodeBs, using signal measurements collected by routers on trains. Four existing algorithms for transmitter localization are adopted: the centroid, strongest signal, Monte Carlo path loss simulation and power difference of arrival (PDoA) methods. An improved version of Monte Carlo path loss simulation called logloss fitting is proposed. Furthermore, a novel localization method called sector fitting is presented, which operates solely on the cell identity and geographical distribution of the measurements. The methods are evaluated for a set of manually located eNodeBs, and the results are compared to other external systems that can be used to locate eNodeBs. It is found that the novel sector fitting algorithm is able to considerably improve the accuracy of the logloss fitting and PDoA methods, but weighted centroid is overall the most accurate of the considered methods, providing a median error of approximately 1 km. The Google Geolocation API and Mozilla Location Service still provides estimates that are generally closer to the true location than any of the considered methods. However, for a subset of eNodeBs where measurements from all sectors are available, the novel sector fitting algorithm combined with logloss fitting outperforms the external systems. Therefore, a hybrid approach is suggested, where sector fitting combined with logloss fitting or weighted centroid is used to locate eNodeBs that have measurements from all sectors, while Google Geolocation API or Mozilla Location Service is used to locate the remaining eNodeBs. It is concluded that while the localization performance for those eNodeBs that have measurements from all sectors is relatively good, further improvements to the overall results can likely be obtained in future work by considering environmental factors, the angular losses introduced by directional antennas, and the effects of downlink power control.

Inter-Cell Interference (ICI) will be one of main problems for degrading the performance of future wireless networks at cell edge. This adverse situation will become worst in the presence of dense deployment of micro and macro cells. In this context, the Coordinated Multi-Point (CoMP) technique was introduced to mitigate ICI in Next Generation Wireless Networks (NGWN) and increase their network performance at cell edge. Even though the CoMP technique provides satisfactory solutions of various problems at cell edge, nevertheless existing CoMP handover schemes do not prevent unnecessary handover initialisation decisions and never discuss the drawbacks of CoMP handover technique such as excessive feedback and resource sharing among UEs. In this research, new CoMP-based handover schemes are proposed in order to minimise unnecessary handover decisions at cell edge and determine solution of drawbacks of CoMP technique in conjunction with signal measurements such as Reference Signal Received Power (RSRP) and Received Signal Received Quality (RSRQ). A combination of calculations of RSRP and RSRQ facilitate a credible decision making process of CoMP mode and handover mode at cell edge. Typical numerical experiments indicate that by triggering the CoMP mode along with solutions of drawbacks, the overall network performance is constantly increase as the number of unnecessary handovers is progressively reduced.

The ultra-wideband (UWB) technology has a vast unlicensed frequency spectrum, which can support precise indoor positioning in orders of centimeters. The features of UWB signals can be utilized for variety of applications. In this project first we present an empirical channel models to analyze the localization accuracy of the UWB technology for interactive electronic gaming (Ping-Pong) in Line-of-Sight (LOS) and Obstructed LOS (OLOS) scenarios. Then we introduce a new concept that we refer to as micro-gesture detection to handle the more refined motions of the hand, such as rotation, while one antenna is held by the user using features of UWB signal. We use four specific features of the UWB signals: time of arrival, power of the first peak, total power, and the Root-Mean Square (RMS) of the delay spread, for this purpose. As the hand rotates the position of the antenna in the hand and the external antenna changes from LOS to OLOS. We demonstrate that features of the UWB signals are more useful than the RSS signal of the Wi-Fi to detect this class of micro-gestures. We foresee this micro-gesture detection capabilities become helpful for the people with limited ability or visually impaired for implementation of simplified sign language to communication with electronic devices located away from a person. We compare gesture detection using multiple features of the UWB signal with traditional gesture detection using the received signal strength (RSS) of the Wi-Fi signal.

La navigation pédestre est un domaine de recherche en pleine croissance qui vise à développer des services assurant le positionnement et la navigation en continu des personnes à l'extérieur comme à l'intérieur de bâtiments. Dans cette thèse, nous proposons un prototype de service pour la navigation pédestre ubiquitaire qui tient compte des préférences de l'utilisateur et de la technologie de positionnement optimale disponible. Notre objectif principal est d'estimer, d'une façon continue, la position d'un piéton muni d'un smartphone. En premier lieu, nous proposons un nouvel algorithme, nommé UCOSA, qui permet de sélectionner la technologie de positionnement à adopter à tout moment le long du processus de navigation. L'algorithme UCOSA commence par inférer la nécessité de déclencher un processus de "handover" (changement de technologie) entre les technologies de positionnement détectées (i.e. quand les zones de couvertures se chevauchent) en utilisant la technique de la logique floue. Ensuite, il sélectionne la technologie optimale à l'aide d'une fonction qui calcule un score pour chaque technologie disponible et qui se compose de deux parties. La première partie représente les poids, calculés en utilisant la méthode d'analyse hiérarchique (AHP). Tandis que, la deuxième partie fournit les valeurs normalisées des paramètres considérés. L'algorithme UCOSA intègre aussi la technique de positionnement à l'estime appelé PDR afin d'améliorer le calcul de la position du smartphone. En second lieu, nous portons l'intérêt à la technique de positionnement par empreintes RSS dont le principe consiste à calculer la position du smartphone en comparant les valeurs RSSs enregistrées, en temps réel, avec les valeurs RSSs stockées dans une base de données (radiomap). La majorité des radiomaps sont représentées sous forme de grilles composées de points de référence (PR). Nous proposons une nouvelle conception de radiomap qui ajoute d'autres PRs au centre de gravité de chaque carré de la grille. En troisième lieu, nous abordons le problème de la construction du graphe modélisant un bâtiment multi-étages. Nous proposons un algorithme qui crée tout d'abord un graphe plan pour chaque étage, séparément, et qui relie ensuite les différents étages par des liens verticaux. En dernier lieu, nous étudions un nouvel algorithme nommé SIONA qui calcule et qui affiche d'une manière continue le chemin entre deux points situés à l'intérieur ou à l'extérieur d'un bâtiment. Plusieurs expériences réelles ont été réalisées pour évaluer les performances des algorithmes proposés avec des résultats prometteurs en termes de continuité et de précision (de l'ordre de 1.8 m) du service de navigation
Pedestrian navigation is a growing research field, which aims at developing services and applications that ensure the continuous positioning and navigation of people inside and outside covered areas (e.g. buildings). In this thesis, we propose a ubiquitous pedestrian navigation service based on user preferences and the most suitable efficient available positioning technology (e.g. WiFi, GNSS). Our main objective is to estimate continuously the position of a pedestrian carrying a smartphone equipped with a variety of technologies and sensors. First, we propose a novel positioning technology selection algorithm, called UCOSA for the complete ubiquitous navigation service in indoor and outdoor environments. UCOSA algorithm starts by inferring the need of a handover between the available positioning technologies on the overlapped coverage areas using fuzzy logic technique. If a handover process is required, a score is calculated for each captured Radio Frequency (RF) positioning technology. The score function consists of two parts: the first part represents the user preferences weights computed based on the Analytic Hierarchy Process (AHP). Whereas, the second part provides the user requirements (normalized values). UCOSA algorithm also integrates the Pedestrian Dead Reckoning (PDR) positioning technique through the navigation process to enhance the estimation of the smartphone's position. Second, we focus on the RSS fingerprinting positioning technique as it is the most widely used technique, which principle is to return the smartphone's position by comparing the real time recorded RSS values with the radiomap (i.e. a database of previous stored RSS values). Most of radiomap are organized in a grid, formed or Reference Point (RP): we propose a new design of radiomap which complements the grid with other RPs located at the center of gravity of each grid square. Third, we address the challenge of constructing a graph for a multi-floor building. We propose an algorithm that starts by creating the horizontal graph of each floor, separately, and then, adds vertical links between the different floors. Finally, we implement a novel algorithm, called SIONA that calculates and displays in a continuous manner the pathway between two distinct points being located indoor or outdoor. We conduct several real experiments inside the campus of the University of Passau in Germany to evaluate the performance of the proposed algorithms. They yield promising results in terms of continuity and accuracy (around 1.8 m indoor) of navigation service

A wide range of services and applications become possible when accurate position information for a radio terminal is available. These include: location-based services; navigation; safety and security applications. The commercial, industrial and military value of radio-location is such that considerable research effort has been directed towards developing related technologies, using satellite, cellular or local area network infrastructures or stand-alone equipment. This work studies and investigates two location techniques. The first one presents an implementation scheme for a wideband transmission and direction finding system using OFDM multi-carrier communications systems. This approach takes advantage of delay discrimination to improve angle-of-arrival estimation in a multipath channel with high levels of additive white Gaussian noise. A new methodology is interpreted over the multi carrier modulation scheme in which the simulation results of the estimated channel improves the performance of OFDM signal by mitigating the effect of frequency offset synchronization to give error-free data at the receiver, good angle of arrival accuracy and improved SNR performance. The full system simulation to explore optimum values such as channel estimation and AoA including the antenna array model and prove the operational performance of the OFDM system as implemented in MATLAB. The second technique proposes a low cost-effective method of tracking and monitoring objects (examples: patient, device, medicine, document) by employing passive radio frequency identification (RFID) systems. A multi-tag, (totalling fifty-six tags) with known ID values are attached to the whole patient’s body to achieve better tracking and monitoring precision and higher accuracy. Several tests with different positions and movements are implemented on six patients. The aim is to be able to track the patient if he/she is walking or sitting; therefore, the tests considered six possible movements for the patient including walking, standing, sitting, resting, laying on the floor and laying on the bed, these placements are important to monitor the status of the patient like if he collapsed and fall on the ground so that the help will be quick. The collected data from the RFID Reader in terms of Time Stamp, RSS, Tag ID, and a number of channels are processed using the MATLAB code.

Network lifetime is an important performance metric in Wireless Sensor Networks (WSNs). Transmission Power Control (TPC) is a well-established method to minimise energy consumption in transmission in order to extend node lifetime and, consequently, lead to solutions that help extend network lifetime. The accurate lifetime estimation of sensor nodes is useful for routing to make more energy-efficient decisions and prolong lifetime. This research proposes an Energy-Efficient TPC (EETPC) mechanism using the measured Received Signal Strength (RSS) to calculate the ideal transmission power. This includes the investigation of the impact factors on RSS, such as distance, height above ground, multipath environment, the capability of node, noise and interference, and temperature. Furthermore, a Dynamic Node Lifetime Estimation (DNLE) technique for WSNs is also presented, including the impact factors on node lifetime, such as battery type, model, brand, self-discharge, discharge rate, age, charge cycles, and temperature. In addition, an Energy-Efficient and Lifetime Aware Routing (EELAR) algorithm is designed and developed for prolonging network lifetime in multihop WSNs. The proposed routing algorithm includes transmission power and lifetime metrics for path selection in addition to the Expected Transmission Count (ETX) metric. Both simulation and real hardware testbed experiments are used to verify the effectiveness of the proposed schemes. The simulation experiments run on the AVRORA simulator for two hardware platforms: Mica2 and MicaZ. The testbed experiments run on two real hardware platforms: the N740 NanoSensor and Mica2. The corresponding implementations are on two operating systems: Contiki and TinyOS. The proposed TPC mechanism covers those investigated factors and gives an overall performance better than the existing techniques, i.e. it gives lower packet loss and power consumption rates, while delays do not significantly increase. It can be applied for single-hop with multihoming and multihop networks. Using the DNLE technique, node lifetime can be predicted more accurately, which can be applied for both static and dynamic loads. EELAR gives the best performance on packet loss rate, average node lifetime and network lifetime compared to the other algorithms and no significant difference is found between each algorithm with the packet delay.

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FAPEAM - Fundação de Amparo à Pesquisa do Estado do Amazonas
An RFID system is typically composed of a reader and a set of tags. An anti-collision algorithm is necessary to avoid collision between tags that respond simultaneously to a reader. The most widely used anti-collision algorithm is DFSA (Dynamic Framed Slotted ALOHA) due to its simplicity and low computational cost. In DFSA algorithms, the optimal TDMA (Time Division Multiple Access) frame size must be equal to the number of unread tags. If the exact number of tags is unknown, the DFSA algorithm needs a tag estimator to get closer to the optimal performance. Currently, applications have required the identification of large numbers of tags, which causes an increase in collisions and hence the degradation in performance of the traditional algorithms DFSA. This work proposes a power control mechanism to estimate the number of tags for radio frequency identification networks (RFID). The mechanism divides the interrogation zone into subgroups of tags and then RSSI (Received Signal Strength Indicator) measurements estimate the number of tags in a subarea. The mechanism is simulated and evaluated using a simulator developed in C/C++ language. In this study, we compare the number of slots and identification time, with ideal DFSA algorithm and Q algorithm EPCglobal standard. Simulation results shows the proposed mechanism provides 99% performance of ideal DFSA in dense networks, where there are many tags. Regarding the Q algorithm, we can see the improvement in performance of 6.5%. It is also important to highlight the lower energy consumption of the reader comparing to ideal DFSA is 63%.
Um sistema de identificação por rádio frequência (RFID) é composto basicamente de um leitor e etiquetas. Para que o processo de identificação das etiquetas seja bem sucedido, é necessário um algoritmo anticolisão a fim de evitar colisões entre etiquetas que respondem simultaneamente à interrogação do leitor. O algoritmo anticolisão mais usado é o DFSA (Dynamic Framed Slotted ALOHA) devido à sua simplicidade e baixo custo computacional. Em algoritmos probabilísticos, tal como o DFSA, o tamanho ótimo do quadro TDMA (Time Division Multiple Access) utilizado para leitura das etiquetas deve ser igual à quantidade de etiquetas não lidas. Uma vez que no processo de leitura, normalmente não se sabe a quantidade exata de etiquetas, o algoritmo DFSA faz uso de um estimador para obter um desempenho mais próximo do ideal. Atualmente, as aplicações têm demandado a identificação de grandes quantidades de etiquetas, o que ocasiona um aumento das colisões e, consequentemente, a degradação no desempenho dos algoritmos DFSA tradicionais. Este trabalho propõe um mecanismo de controle de potência para estimar a quantidade de etiquetas em redes de identificação por rádio frequência (RFID). O mecanismo baseia-se na divisão da área de interrogação em subáreas e, consequentemente, subgrupos de etiquetas. Tal divisão é utilizada para realizar medições de RSSI (Received Signal Strength Indicator) e, assim, estimar a quantidade de etiquetas por subárea. O mecanismo é simulado e avaliado utilizando um simulador próprio desenvolvido em linguagem C/C++. Neste estudo, comparam-se os resultados de quantidade de slots e tempo de identificação das etiquetas, com os obtidos a partir da utilização dos algoritmos DFSA ideal e algoritmo padrão Q da norma EPCglobal. A partir dos resultados da simulação, é possível perceber que o mecanismo proposto apresenta desempenho 99% do DFSA ideal em redes densas, onde há grande quantidade de etiquetas. Em relação ao algoritmo Q, percebe-se a melhoria de 6,5% no desempenho. É importante ressaltar também a redução no consumo de energia do leitor em torno de 63% em relação ao DFSA ideal.

Um estudo comparativo entre os principais modelos empíricos e semi-empíricos de predição de nível de sinal para comunicações móveis celulares é descrito neste trabalho. Medidas de cobertura outdoor em ambiente urbano foram comparadas com os resultados simulados a partir dos modelos de Okumura-Hata e Lee, lkegami, Walfisch-Bertoni e Walfisch-Ikegami. As medidas de potência de sinal recebido foram realizadas na cidade de Conceição das Alagoas, MG, a partir da única estação rádio-base (ERB) da cidade, operando na banda A com tecnologia AMPS/TDMA. Foi utilizada como portadora de teste o canal de controle analógico 328. As informações foram coletadas em algumas ruas da cidade, por meio de equipamento instalado em veículo, em diferentes posicionamentos em relação a ERB, de forma a se obter amostras de cobertura em diferentes cenários, seja em visada direta, em obstrução parcial ou total. O modelo de Ikegami se mostrou apropriado para predição de níveis de sinal recebido no ambiente estudado, apresentando desvio médio 5,81 dB em relação às medidas realizadas.
A comparative study among the main empiric and semi-empiric models of prediction of signal leveI for cellular mobile communications is described in this work. Measurements of covering outdoor in an urban environment were compared to the simulated results from the models of Okumura-Hata, Lee, Ikegami, Walfisch-Bertoni and Walfisch-Ikegami. The measurements of received signal level were accomplished in some streets of the city of Conceição das Alagoas, MG, starting from the only radio base station of the city, operating in the A band with AMPS/TDMA technology. The 328-analog control channel was used as test carrier. The measurements were carried out using some equipment installed in a vehicle, in different positions in relation to the radio base station, in order to obtain covering profile in different circunstances such as line-of-sight, non line-of-sight, and partial obstruction. The model of Ikegami was shown appropriate for prediction of the received signal levels in the studied environment, exhibiting an average deviation of 5,81 dB in relation to the accomplished measurements.

This thesis studies parametric optimization in cellular and cell-free networks, exploring data-based and expert-based paradigms. Power allocation and power control, which adjust the transmit power to meet different fairness criteria such as max-min or max-product, are crucial tasks in wireless communications that fall into the parametric optimization category. The state-of-the-art approaches for power control and power allocation often demand huge computational costs and are not suitable for real-time applications. To address this issue, we develop a general-purpose unsupervised-learning approach for solving parametric optimizations; and extend the well-known fractional power control algorithm. In the data-based paradigm, we create an unsupervised learning framework that defines a custom neural network (NN), incorporating expert knowledge to the NN loss function to solve the power control and power allocation problems. In this approach, a feedforward NN is trained by repeatedly sampling the parameter space, but, rather than solving the associated optimization problem completely, a single step is taken along the gradient of the objective function. The resulting method is applicable for both convex and non-convex optimization problems. It offers two-to-three orders of magnitude speedup in the power control and power allocation problems compared to a convex solver—whenever appliable. In the expert-driven paradigm, we investigate the extension of fractional power control to cell-free networks. The resulting closed-form solution can be evaluated for uplink and downlink effortlessly and reaches an (almost) optimum solution in the uplink case. In both paradigms, we place a particular focus on large scale gains—the amount of attenuation experienced by the local-average received power. The slow-varying nature of the large-scale gains relaxes the need for a frequent update of the solutions in both the data-driven and expert-driven paradigms, enabling real-time application for both methods.
Aqueta tesis estudia l’optimització paramètrica a les xarxes cel.lulars i xarxes cell-free, explotant els paradigmes basats en dades i basats en experts. L’assignació i control de la potencia, que ajusten la potencia de transmissió per complir amb diferents criteris d’equitat com max-min o max-product, son tasques crucials en les telecomunicacions inalàmbriques pertanyents a la categoria d’optimització paramètrica. Les tècniques d’última generació per al control i assignació de la potència solen exigir enormes costos computacionals i no son adequats per aplicacions en temps real. Per abordar aquesta qüestió, desenvolupem una tècnica de propòsit general utilitzant aprenentatge no supervisat per resoldre optimitzacions paramètriques; i al mateix temps ampliem el reconegut algoritme de control de potencia fraccionada. En el paradigma basat en dades, creem un marc d’aprenentatge no supervisat que defineix una xarxa neuronal (NN, sigles de Neural Network en Anglès) especifica, incorporant coneixements experts a la funció de cost de la NN per resoldre els problemes de control i assignació de potència. Dins d’aquest enfocament, s’entrena una NN de tipus feedforward mitjançant el mostreig repetit en l’espai de paràmetres, però, en lloc de resoldre completament el problema d’optimització associat, es pren un sol pas en la direcció del gradient de la funció objectiu. El mètode resultant ´es aplicable tant als problemes d’optimització convexos com no convexos. Això ofereix una acceleració de dos a tres ordres de magnitud en els problemes de control i assignació de potencia en comparació amb un algoritme de resolució convexa—sempre que sigui aplicable. En el paradigma dirigit per experts, investiguem l’extensió del control de potencia fraccionada a les xarxes sense cèl·lules. La solució tancada resultant pot ser avaluada per a l’enllaç de pujada i el de baixada sense esforç i assoleix una solució (gaire) òptima en el cas de l’enllaç de pujada. En ambdós paradigmes, ens centrem especialment en els guanys a gran escala—la quantitat d’atenuació que experimenta la potencia mitja local rebuda. La naturalesa de variació lenta dels guanys a gran escala relaxa la necessitat d’una actualització freqüent de les solucions tant en el paradigma basat en dades com en el basat en experts, permetent d’aquesta manera l’ús dels dos mètodes en aplicacions en temps real.
Esta tesis estudia la optimización paramétrica en las redes celulares y redes cell-free, explorando los paradigmas basados en datos y en expertos. La asignación y el control de la potencia, que ajustan la potencia de transmisión para cumplir con diferentes criterios de equidad como max-min o max-product, son tareas cruciales en las comunicaciones inalámbricas pertenecientes a la categoría de optimización paramétrica. Los enfoques más modernos de control y asignación de la potencia suelen exigir enormes costes computacionales y no son adecuados para aplicaciones en tiempo real. Para abordar esta cuestión, desarrollamos un enfoque de aprendizaje no supervisado de propósito general que resuelve las optimizaciones paramétricas y a su vez ampliamos el reconocido algoritmo de control de potencia fraccionada. En el paradigma basado en datos, creamos un marco de aprendizaje no supervisado que define una red neuronal (NN, por sus siglas en inglés) específica, incorporando conocimiento de expertos a la función de coste de la NN para resolver los problemas de control y asignación de potencia. Dentro de este enfoque, se entrena una NN de tipo feedforward mediante el muestreo repetido del espacio de parámetros, pero, en lugar de resolver completamente el problema de optimización asociado, se toma un solo paso en la dirección del gradiente de la función objetivo. El método resultante es aplicable tanto a los problemas de optimización convexos como no convexos. Ofrece una aceleración de dos a tres órdenes de magnitud en los problemas de control y asignación de potencia, en comparación con un algoritmo de resolución convexo—siempre que sea aplicable. Dentro del paradigma dirigido por expertos, investigamos la extensión del control de potencia fraccionada a las redes cell-free. La solución de forma cerrada resultante puede ser evaluada para el enlace uplink y el downlink sin esfuerzo y alcanza una solución (casi) óptima en el caso del enlace uplink. En ambos paradigmas, nos centramos especialmente en las large-scale gains— la cantidad de atenuación que experimenta la potencia media local recibida. La naturaleza lenta y variable de las ganancias a gran escala relaja la necesidad de una actualización frecuente de las soluciones tanto en el paradigma basado en datos como en el basado en expertos, permitiendo el uso de ambos métodos en aplicaciones en tiempo real.

No
A comparison between two indoor localization algorithms using received signal strength is carried out. The first algorithm is the vector algorithm; the second is the matrix algorithm. The comparison considered the effects of the reference points, the access point, and the frequency on the accuracy of the localization process. The experiments were carried out using ray tracing software and MATLAB. This paper justifies the use of adopting the matrix algorithm.

This thesis presents and discusses research associated with locating wireless devices. Several algorithms have been developed to determine the physical location of the wireless device and a subset of these algorithms only rely on received signal strength (RSS). Two of the most promising RSS-based algorithms are the LC and dwMDS algorithms; however each algorithm has only been tested via computer simulations with different environmental parameters. To determine which algorithm performs better (i.e., produces estimates that are closer to the true location of the wireless device), a fair comparison needs to be made using the same set of data. The goal of this research is to compare the performance of these two algorithms using not only the same set of data, but data that is collected from the field. An extensive measurement campaign at different environments provided a vast amount of data as input to these algorithms. Both of these algorithms are evaluated in a onedimensional (straight line) and two-dimensional (grid) setting. In total, six environments were used to test these algorithms; three environments for each setting. The results show that on average, the LC algorithm outperforms dwMDS in most of the environments. Since the same data was inputted for each algorithm, a fair comparison can be made and doesn’t give any unfair advantage to any particular algorithm. In addition, since the data was taken directly from the field as opposed to computer simulations, this provides a better degree of confidence for a successful realworld implementation.

no
A comparison is presented between two indoor localization algorithms using received signal strength, namely the vector algorithm and the Comparative Received Signal Strength (CRSS) algorithm. Signal values were obtained using ray tracing software and processed with MATLAB to ascertain the effects on localization accuracy of radio map resolution, number of access points and operating frequency. The vector algorithm outperforms the CRSS algorithm, which suffers from ambiguity, although that can be reduced by using more access points and a higher operating frequency. Ambiguity is worsened by the addition of more reference points. The vector algorithm performance is enhanced by adding more access points and reference points while it degrades with increasing frequency provided that the statistical mean of error increased to about 60 cm for most studied cases.
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碩士
國立臺灣大學
電子工程學研究所
107
This thesis studies power detectors at V-band and Q-band for millimeter wave RSSI system and is proposed to measure power in real time for wireless communication system. First, this thesis reviews literatures related to power detector circuits as well as introduces their structures, theory and applications. This thesis presents a low noise amplifier and two power detectors. This thesis first proposes a V-band low noise amplifier. It attains low noise and high gain by using source degeneration and double-transformer-coupling structure, along with common source structure to maintain outstanding linearity. The measured maximum gain achieves 21.5 dB with minimum noise figure 5.7 dB, IP1dB -21.4 dBm and IIP3 -10.6 dBm at 60 GHz. The amplifier consumes 16.3 mW dc power. Secondly, in order to convert rf power into dc output voltage with single circuit, this thesis proposes a V-band power detector by utilizing successive millimeter amplifiers and rectifiers in parallel to fulfill its design. Transformer-based common gate amplifier, common source amplifier and cascode rectifier along with switch control are used to increase detector’s dynamic range. The measured detectable range is from -33 dBm to over 5 dBm with dynamic range of at least 38 dB, maximum sensitivity of 14.5 mV/dB at 55 GHz and only 8.8 mW for quasi-dc power consumption. Finally, a Q-band power detector is proposed to meet the demand of sensitivity for next stage like ADC. Taking sensitivity as the first priority, Transformer-based common gate amplifier and common gate rectifier along with switch control are used to offer wider voltage range. The measured detectable range is from -20 dBm to 8.5 dBm, with dynamic range of 28.5 dB, with maximum sensitivity of 46 mV/dB at 34 GHz and only 13.2 mW for quasi-dc power consumption.

碩士
國立清華大學
電機工程學系
105
In recent years, applications of positioning have become more and more popular. Since the signals transmitted from global positioning system (GPS) satellites cannot penetrate inside the buildings, there are demands for indoor positioning systems with other technologies. In this thesis, we propose a indoor positioning system based on Bluetooth low energy (BLE) with characteristics of low power, low cost and high portability . The proposed system consists of BLE tags, BLE/WiFi repeaters and a fusion server. The BLE tag in our system is a device which broadcasts BLE beacons. The BLE/WiFi repeaters collect the beacons transmitted from the tag and extract the received signal strength (RSS). The RSS values are then transmitted to our fusion server through Wi-Fi, and the server will estimate the position of the BLE tag with RSS-based positioning algorithm. We propose a indoor positioning algorithm which is a hybrid from received signal strength indication (RSSI)-fingerprint and cell of origin (CoO). Some modifications are made to typical RSSI-fingerprint and CoO algorithm to get better accuracy. To verify the performance of our system, we take two indoor environments into considerate. The first is the rest area of Delta building in National Tsing-Hua University (NTHU). The second is the office of GYRO system company. Each environment is covered by four BLE/WiFi repeaters. The mean error distance of these two environments are 1.2 m and 1.37 m respectively. Moreover, the size of the BLE tag is 1.7 cm in radius and 0.5 cm thick, that can be easily attached to the localization target. Each BLE tag costs 3 US dollars. So it is friendly for those who need large amounts of them for multi-objects positioning. The current consumption of the tag is 50 A which can be used without charge for 136 days with a CR2025 battery.

The localization of the sensor nodes is a fundamental issue in the area of wireless sensor networks (WSNs). An attractive way for estimating the location of mobile or static wireless objects is by using the received signal strength (RSS) attenuation with the distance, which does not require any additional hardware. This is possible due to the fact that in most sensor nodes radios the received signal strength indicator (RSSI) is a standard feature and can be obtained automatically by the received messages. On the other hand the RSS is known for being noisy, unstable, variable and difficult to use in practice. For achieving a better understanding of the nature of these difficulties and limitations, and for identifying the range of applicability of the RSS in localization and tracking scenarios, a thorough study about the RSS and its dependence on the various factors and environmental conditions is essential. The present doctoral dissertation investigates the feasibility of sensor node localization and target tracking with the resources of the WSN technology, when using only the RSS of the exchanged messages. Moreover, it offers experimental support to the hypothesis that proper modeling of the RSS behavior and appropriate selection of the topology parameters are essential for the applicability of WSN in real world conditions. In brief, the present doctoral dissertation concerns with: (i) identifying the main factors that influence the accuracy, the variability and the reliability of the obtained RSS, (ii) modeling the RF signal propagation in the context of WSNs, and (iii)defining the basic deployment constraints and evaluation of the topology parameters that can guarantee successful localization and tracking. For assessing the practical value of various RF-models, experiments using Tmote Sky and TelosB sensor nodes in real-field outdoor environment were carried out. The impact of a number of factors, such as the operating frequency of the radio, the transmitter–receiver distance, the variation of transceivers hardware due to manufacturing tolerances, the antenna orientation, and the environmental conditions, on the RSS was investigated. The influence of the various factors that affect the RF signal propagation and some constraints imposed by the WSN nature was accounted in order to design practical models, suitable for outdoor unobstructed and outdoor tree-obstructed environments. A pre-deployment simulation framework has been introduced and in its context a RF signal propagation-based connectivity strategy (RFCS) has been developed to fulfill three deployment provisions: (i) discovering the most appropriate height from the ground and distances for the sensor nodes, (ii) reducing the transmission power, and (iii) minimizing the interference from non-neighbor nodes. The RFCS uses a RF signal propagation model to predict the RSS in order to identify the most appropriate communication-based deployment parameters, i.e. T-R distance, height from the ground and transmission power. The localization and tracking considerations, by means of localization and tracking techniques, topology parameters and factors influencing the localization and tracking accuracy, are combined in illustrative simulation examples to evaluate their significance concerning the performance of the localization and tracking task. Furthermore, the propagation model and the topology parameters being identified were validated in real outdoor sensor node localization and target tracking tests.
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碩士
國立中正大學
電機工程所
94
Two research subjects for electromagnetic glottal micro-movement detection system were presented in this thesis work. The purpose of system is using an electromagnetic sensor based on radar technologies to detect the motions of the vocal organs. The first subject is to design power detectors for this homodyne radar circuits. Two power detectors were designed and realized either in hybrid or monolithic way. The hybrid power detector was designed at 915 MHz based on lumped elements loaded with Schottky diodes. Additionally, it has been employed into an auto-level-control (ALC) circuit application. The monolithic power detector, which was fabricated by TSMC 0.18μm CMOS process, is a single-chip logarithmic amplifier that also can be used to detect the power. The other subject of this work is to develop a LabVIEW program for data acquisition and signal processing. The signals coming from both the microphone and the electromagnetic glottal micro-movement detection system were acquired and transferred into digital form for further analysis.

This dissertation addresses the target localization problem in wireless sensor networks (WSNs). WSNs is now a widely applicable technology which can have numerous practical applications and offer the possibility to improve people’s lives. A required feature to many functions of a WSN, is the ability to indicate where the data reported by each sensor was measured. For this reason, locating each sensor node in a WSN is an essential issue that should be considered. In this dissertation, a performance analysis of two recently proposed distributed localization algorithms for cooperative 3-D wireless sensor networks (WSNs) is presented. The tested algorithms rely on distance and angle measurements obtained from received signal strength (RSS) and angle-of-arrival (AoA) information, respectively. The measurements are then used to derive a convex estimator, based on second-order cone programming (SOCP) relaxation techniques, and a non-convex one that can be formulated as a generalized trust region sub-problem (GTRS). Both estimators have shown excellent performance assuming a static network scenario, giving accurate location estimates in addition to converging in few iterations. The results obtained in this dissertation confirm the novel algorithms’ performance and accuracy. Additionally, a change to the algorithms is proposed, allowing the study of a more realistic and challenging scenario where different probabilities of communication failure between neighbor nodes at the broadcast phase are considered. Computational simulations performed in the scope of this dissertation, show that the algorithms’ performance holds for high probability of communication failure and that convergence is still achieved in a reasonable number of iterations.

Ubiquitous location-aware systems and services are becoming a reality as made evident by the widely known Global Position System (GPS). However, indoor location-aware sensing systems are not yet commercially viable since: (i) for a GPS-based system, the signals attenuate and can multipath indoors causing weak signal and poor location (ii) for a Radiolocation-based system, the propagation of radio signals are complex and difficult to model. In this paper, we present RadLoco, a location-sensing system that uses IEEE 802.11 Wireless LAN survey techniques to create a radio Received Signal Strength (RSS) map of the propagation environment. To provide accurate location estimation, we make use of a kernel-window estimation algorithm that is used to approximate the probability density function of RSS measurement and location. Unlike parametric estimators, this non-parametric kernel approach requires less knowledge of the distributions of location and measurements, and also makes use of the prior knowledge of mobile terminal location to reduce the estimation error. The novelty of the system is an innovative radio/ultrasonic sensory network which allows for rapid data collection whereas the standard technique of defining a grid of survey points with measuring rulers, chalk, and tape would require a great amount of manpower. Using this sensory network, a 2000m sq. office building is surveyed in four hours by a single technician. Our experimental results indicate the mobile terminal is located on the correct oor with over 98% accuracy and with a mean error of less than 2.5m from the true location.

This thesis addresses the target localization problem in wireless sensor networks (WSNs) by employing statistical modeling and convex relaxation techniques. The first and the second part of the thesis focus on received signal strength (RSS)- and RSS-angle of arrival (AoA)-based target localization problem, respectively. Both non-cooperative and cooperative WSNs are investigated and various settings of the localization problem are of interest (e.g. known and unknown target transmit power, perfectly and imperfectly known path loss exponent). For all cases, maximum likelihood (ML) estimation problem is first formulated. The general idea is to tightly approximate the ML estimator by another one whose global solution is a close representation of the ML solution, but is easily obtained due to greater smoothness of the derived objective function. By applying certain relaxations, the solution to the derived estimator is readily obtained through general-purpose solvers. Both centralized (assumes existence of a central node that collects all measurements and carries out all necessary processing for network mapping) and distributed (each target determines its own location by iteratively solving a local representation of the derived estimator) algorithms are described. More specifically, in the case of centralized RSS-based localization, second-order cone programming (SOCP) and semidefinite programming (SDP) estimators are derived by applying SOCP and SDP relaxation techniques in non-cooperative and cooperative WSNs, respectively. It is also shown that the derived SOCP estimator can be extended for distributed implementation in cooperative WSNs. In the second part of the thesis, derivation procedure of a weighted least squares (WLS) estimator by converting the centralized non-cooperative RSS-AoA localization problem into a generalized trust region sub-problem (GTRS) framework, and an SDP estimator by applying SDP relaxations to the centralized cooperative RSS-AoA localization problem are described. Furthermore, a distributed SOCP estimator is developed, and an extension of the centralized WLS estimator for non-cooperative WSNs to distributed conduction in cooperative WSNs is also presented. The third part of the thesis is committed to RSS-AoA-based target tracking problem. Both cases of target tracking with fixed/static anchors and mobile sensors are investigated. First, the non-linear measurement model is linearized by applying Cartesian to polar coordinates conversion. Prior information extracted from target transition model is then added to the derived model, and by following maximum a posteriori (MAP) criterion, a MAP algorithm is developed. Similarly, by taking advantage of the derived model and the prior knowledge, Kalman filter (KF) algorithm is designed. Moreover, by allowing sensor mobility, a simple navigation routine for sensors’ movement management is described, which significantly enhances the estimation accuracy of the presented algorithms even for a reduced number of sensors. The described algorithms are assessed and validated through simulation results and real indoor measurements.

LOW power operation for wireless radio receivers has been gaining importance lately on account of the recent spurt of growth in the usage of ubiquitous embedded mobile devices. These devices are becoming relevant in all domains of human influence. In most cases battery life for these devices continue to be an us-age bottleneck as energy storage techniques have not kept pace with the growing demand of such mobile computing devices. Many applications of these radios have limitations on recharge cycle, i.e. the radio needs to last out of a battery for long duration. This will specially be true for sensor network applications and for im-plantable medical devices. The search for low power wireless receivers has become quite advanced with a plethora of techniques, ranging from circuit to architecture to system level approaches being formulated as part of standard design procedures. However the next level of optimization towards “Smart” receiver systems has been gaining credence and may prove to be the next challenge in receiver design and de-velopment. We aim to proceed further on this journey by proposing Power Scalable Wireless Receivers (PSRX) which have the capability to respond to instantaneous performance requirements to lower power even further. Traditionally low power receivers were designed for worst-case input conditions, namely low signal and high interference, leading to large dynamic range of operation which directly im-pacts the power consumption. We propose to take into account the variation in performance required out of the receiver, under varying Signal and Interference conditions, to trade-off power. We have analyzed, designed and implemented a Power Scalable Receiver tar-geted towards low data-rate receivers which can work for Zigbee or Bluetooth Low Energy (BLE) type standards. Each block of such a receiver system was evaluated for performance-power trade-offs leading to identification of tuning/control knobs at the circuit architecture level of the receiver blocks. Then we developed an usage algorithm for finding power optimal operational settings for the tuning knobs, while guaranteeing receiver reception performance in terms of Bit-Error-Rate (BER). We have proposed and demonstrated a novel signal measurement system to gen-erate digitized estimates of signal and interference strength in the received signal, called Received Signal Quality Indicator (RSQI). We achieve a RSQI average energy consumption of 8.1nJ with a peak energy consumption of 9.4nJ which is quite low compared to the packet reception energy consumption for low power receivers, and will be substantially lower than the energy savings which will be achieved from a power scalable receiver employing a RSQI. The full PSRX system was fabricated in UMC 130nm RF-CMOS process to test out our concepts and to formally quantify the power savings achieved by following the design methodology. The test chip occupied an area of 2.7mm2 with a peak power consumption of 5.5mW for the receiver chain and 18mW for the complete PSRX. We were able to meet the receiver performance requirements for Zigbee standard and achieved about 5X power savings for the range of input condition variations.

LOW power operation for wireless radio receivers has been gaining importance lately on account of the recent spurt of growth in the usage of ubiquitous embedded mobile devices. These devices are becoming relevant in all domains of human influence. In most cases battery life for these devices continue to be an us-age bottleneck as energy storage techniques have not kept pace with the growing demand of such mobile computing devices. Many applications of these radios have limitations on recharge cycle, i.e. the radio needs to last out of a battery for long duration. This will specially be true for sensor network applications and for im-plantable medical devices. The search for low power wireless receivers has become quite advanced with a plethora of techniques, ranging from circuit to architecture to system level approaches being formulated as part of standard design procedures. However the next level of optimization towards “Smart” receiver systems has been gaining credence and may prove to be the next challenge in receiver design and de-velopment. We aim to proceed further on this journey by proposing Power Scalable Wireless Receivers (PSRX) which have the capability to respond to instantaneous performance requirements to lower power even further. Traditionally low power receivers were designed for worst-case input conditions, namely low signal and high interference, leading to large dynamic range of operation which directly im-pacts the power consumption. We propose to take into account the variation in performance required out of the receiver, under varying Signal and Interference conditions, to trade-off power. We have analyzed, designed and implemented a Power Scalable Receiver tar-geted towards low data-rate receivers which can work for Zigbee or Bluetooth Low Energy (BLE) type standards. Each block of such a receiver system was evaluated for performance-power trade-offs leading to identification of tuning/control knobs at the circuit architecture level of the receiver blocks. Then we developed an usage algorithm for finding power optimal operational settings for the tuning knobs, while guaranteeing receiver reception performance in terms of Bit-Error-Rate (BER). We have proposed and demonstrated a novel signal measurement system to gen-erate digitized estimates of signal and interference strength in the received signal, called Received Signal Quality Indicator (RSQI). We achieve a RSQI average energy consumption of 8.1nJ with a peak energy consumption of 9.4nJ which is quite low compared to the packet reception energy consumption for low power receivers, and will be substantially lower than the energy savings which will be achieved from a power scalable receiver employing a RSQI. The full PSRX system was fabricated in UMC 130nm RF-CMOS process to test out our concepts and to formally quantify the power savings achieved by following the design methodology. The test chip occupied an area of 2.7mm2 with a peak power consumption of 5.5mW for the receiver chain and 18mW for the complete PSRX. We were able to meet the receiver performance requirements for Zigbee standard and achieved about 5X power savings for the range of input condition variations.