Effective space utilization is an important consideration in logistics systems and is especially important in dense storage environments. Dense storage systems provide high-space utilization; however, because not all items are immediately accessible, storage and retrieval operations often require shifting of other stored items in order to access the desired item, which results in item location uncertainty when asset tracking is insufficient. Given an initial certainty in item location, we use Markovian principles to quantify the growth of uncertainty as a function of retrieval requests and discover that the steady state probability distribution for any communicating class of storage locations approaches uniform. Using this result, an expected search time model is developed and applied to the systems analyzed. We also develop metrics that quantify and characterize uncertainty in item location to aid in understanding the nature of that uncertainty. By incorporating uncertainty into our logistics model and conducting numerical experiments, we gain valuable insights into the uncertainty problem such as the benefit of multiple item copies in reducing expected search time and the varied response to different retrieval policies in otherwise identical systems.

A geração e o uso dos mapas de ambiente de rádio (REM - Radio Environment Map) em sistemas de comunicações sem fio vêm sendo alvo de pesquisas recentes na literatura científica. Dentre as possíveis aplicações, o REM fornece informações importantes para os processos de predição e otimização de cobertura em sistemas de comunicações sem fio, pois é baseado em medidas coletadas diretamente da rede. Neste contexto, a geração do REM depende do processamento das medidas e suas localizações para a construção dos mapas, por meio de predições espaciais. Entretanto, a incerteza de localização das medidas coletadas pode degradar a acurácia das predições de forma significativa e, consequentemente, impactar as posteriores decições baseadas no REM. Este trabalho aborda o problema de geração do REM de forma mais realística, formulando um modelo de predição espacial que introduz erros de localização no ambiente de rádio de um sistema de comunicação sem fio. As investigações mostram que os impactos provocados pela incerteza de localização na geração do REM são significativos, especialmente nas técnicas de estimação utilizadas para a aprendizagem de parâmetros do modelo de predição espacial. Com isso, uma técnica de predição espacial é proposta e utiliza ferramentas da área geoestatística para superar os efeitos negativos causados pela incerteza de localização nas medidas. Simulações computacionais são desenvolvidas para a avaliação de desempenho das principais técnicas de predição no contexto de geração do REM, considerando o problema da incerteza de localização. Os resultados de simulação da técnica proposta são promissores e mostram que levar em conta a distribuição estatística dos erros de localização pode resultar em predições com maior acurácia para a geração do REM. A influência de diferentes aspectos da modelagem do ambiente de rádio também é analisada e reforçam a ideia de que a aprendizagem de parâmetros do ambiente de rádio tem um papel importante na acurácia das predições espaciais, que são fundamentais para a geração confiável do REM. Finalmente, um estudo experimental do REM é realizado por meio de uma campanha de medidas, permitindo explorar o desempenho dos algoritmos de aprendizagem de parâmetros e predições desenvolvidos neste trabalho. / The generation and use of radio environment maps (REM) in wireless systems has been the subject of recent research in the scientific literature. Among the possible applications, the REM provides important information for the coverage predicfition and optimization processes in wireless systems, since it is based on measurements collected directly on the network. In this context, the REM generation process depends on the processing of the measurements and their locations for the construction of the maps through spatial predictions. However, the location uncertainty related to the measurements collected can signicantly degrade the accuracy of the spatial predictions and, consequently, impact the decisions based on REM. This work addresses the problem of the REM generation in a more realistic way, through the formulation of a spatial prediction model that introduces location errors in the radio environment of a wireless communication system. The investigations show that the impacts of the location uncertainty on the REM generation are significant, especially in the estimation techniques used to learn the parameters of the spatial prediction model. Thus, a spatial prediction technique is proposed, based on geostatistical tools, to overcome the negative effects caused by the location uncertainty of the REM measurements. Computational simulations are developed for the performance evaluation of the main prediction techniques in the context of REM generation, considering the problem of location uncertainty. The simulation results of the proposed technique are promising and show that taking into account the statistical distribution of location errors can result in more accurate predictions for the REM generation process. The influence of different aspects of the radio environment modeling is also analyzed and reinforce the idea that the learning of radio environment parameters plays an important role in the accuracy of spatial predictions, which are fundamental for the reliable REM generation. Finally, an experimental study is carried out through a measurement campaign with the purpose of generating the REM in practice and to explore the performance of the learning and prediction algorithms developed in this work.

Millimeter wave (mmWave) plays a critical role in the Fifth-generation (5G) new radio due to the rich bandwidth it provides. However, one shortcoming of mmWave is the substantial path loss caused by poor diffraction at high frequencies, and consequently highly directional beams are applied to mitigate this problem. A typical way of beam management is to cluster users based on their locations. However, localization uncertainty is unavoidable due to measurement accuracy, system performance fluctuation, and so on. Meanwhile, the traffic demand may change dynamically in wireless environments, which increases the complexity of network management. Therefore, a scheme that can handle both the uncertainty of localization and dynamic radio resource allocation is required. Moreover, since the localization uncertainty will influence the network performance, more state-of-the-art localization methods, such as vision-aided localization, are expected to reduce the localization error. In this thesis, we proposed two algorithms for joint radio resource allocation and beam management in 5G mmWave networks, namely UK-means-based Clustering and Deep Reinforcement Learning-based resource allocation (UK-DRL) and UK-medoids-based Clustering and Deep Reinforcement Learning-based resource allocation (UKM-DRL). Specifically, we deploy UK-means and UK-medoids clustering method in UK-DRL and UKM-DRL, respectively, which is designed to handle the clustering under location uncertainties. Meanwhile, we apply Deep Reinforcement Learning (DRL) for intra-beam radio resource allocations in UK-DRL and UKM-DRL. Moreover, to improve the localization accuracy, we develop a vision-aided localization scheme, where pixel characteristics-based features are extracted from satellite images as additional input features for location prediction. The simulations show that UK-DRL and UKM-DRL successfully improve the network performance in data rate and delay than baseline algorithms. When the traffic load is 4 Mbps, UK-DRL has a 172.4\% improvement in sum rate and 64.1\% improvement in latency than K-means-based Clustering and Deep Reinforcement Learning-based resource allocation (K-DRL). UKM-DRL has 17.2\% higher throughput and 7.7\% lower latency than UK-DRL, and 231\% higher throughput and 55.8\% lower latency than K-DRL. On the other hand, the vision-aided localization scheme can significantly reduce the localization error from 17.11 meters to 3.6 meters.