Millimeter Wave (mmWave) is a key technology to meet the challenge of data rates and the lack of bandwidth in sub-6GHz networks. Due to a high operation frequency, the mmWave network has unique channel characteristics and a relatively high pathloss. Therefore, a dense deployment of Base Station (BS) is necessary, leading to a more frequent handover, which may cause a degradation of User Equipment (UE) experience. Furthermore, a massive number of devices cause an interference issue and a high dropping probability. In this project, we propose a handover method based on Reinforcement Learning (RL). This handover method provides a seamless connection and considers the load balancing. To verify the proposed method, Q-learning is selected to solve this RL problem and a simulation environment of mmWave is set up, including the pathloss model, system model, and beamforming. The average data rate, number of handovers, and number of available resources are evaluated during the movement of UEs. The results are compared with rate-max method and random backup method in different interference scenarios. Our proposed method shows a notable performance in terms of data rate, for example, while doubling the interference, the data rate decreases 8.6% with our method while it decreases 20% with the random-backup method. Moreover, our method has the minimum number of handovers in the trajectory. The performance in multiple trajectories is also illustrated and it performs as expected. / Millimeter Wave (mmWave) är en nyckelteknologi för att möta utmaningen med datahastigheter och bristen på bandbredd i sub-6GHz-nätverk. På grund av den höga driftsfrekvensen har mmWave-nätverket unika kanalegenskaper och en relativt hög banförlust. Därför är en tät användning av basstationen (BS) nödvändig vilket leder till en mer frekvent överlämning, vilket kan orsaka en försämring av User Equipment (UE) upplevelse. Dessutom orsakar ett stort antal enheter störningsproblem och en hög dropping probability. I det här projektet föreslår vi en överlämningsmetod baserad på Reinforcement Learning (RL). Denna överlämningsmetod ger en sömlös anslutning och tar hänsyn till lastbalanseringen. För att verifiera den föreslagna metoden har en simuleringsmiljö på mmWave ställts in, inklusive banförlust-modellen, systemmodellen och strålformning. Genomsnitt datahastighet, antal överlämningar och antal tillgängliga resurser utvärderas under förflyttning av UE: er. Resultaten jämförs med rate-max metod och slumpmässig säkerhetskopieringsmetod i olika störningsscenarier. Vår föreslagna metod visar en anmärkningsvärd prestanda när det gäller datahastighet, till exempel, när interferensen fördubblas minskar datahastigheten 8,6% med vår metod medan den minskar 20% med slumpmässig säkerhetskopieringsmetod. Dessutom har vår metod det minsta antalet överlämningar i banan. Prestandan i flera banor illustreras också och den fungerar som förväntat.

With the increasing maturity of unmanned aerial vehicles (UAVs), also known as drones, wireless ecosystem is experiencing an unprecedented paradigm shift. These aerial platforms are specifically appealing for a variety of applications due to their rapid and flexible deployment, cost-effectiveness, and high chance of forming line-of-sight (LoS) links to the ground nodes. As with any new technology, the benefits of incorporating UAVs in existing cellular networks cannot be characterized without completely exploring the underlying trade space. This requires a detailed system-level analysis of drone cellular networks by taking the unique features of UAVs into account, which is the main objective of this dissertation. We first focus on a static setup and characterize the performance of a three-dimensional (3D) two-hop cellular network in which terrestrial base stations (BSs) coexist with UAVs to serve a set of ground user equipment (UE). In particular, a UE connects either directly to its serving terrestrial BS by an access link or connects first to its serving UAV which is then wirelessly backhauled to a terrestrial BS (joint access and backhaul). We consider realistic antenna radiation patterns for both BSs and UAVs using practical models developed by the third generation partnership project (3GPP). We assume a probabilistic channel model for the air-to-ground transmission, which incorporates both LoS and non-LoS links. Assuming the max-power association policy, we study the performance of the network in both amplify-and-forward (AF) and decode-and-forward (DF) relaying protocols. Using tools from stochastic geometry, we analyze the joint distribution of distance and zenith angle of the closest (and serving) UAV to the origin in a 3D setting. Further, we identify and extensively study key mathematical constructs as the building blocks of characterizing the received signal-to-interference-plus-noise ratio (SINR) distribution. Using these results, we obtain exact mathematical expressions for the coverage probability in both AF and DF relaying protocols. Furthermore, considering the fact that backhaul links could be quite weak because of the downtilted antennas at the BSs, we propose and analyze the addition of a directional uptilted antenna at the BS that is solely used for backhaul purposes. The superiority of having directional antennas with wirelessly backhauled UAVs is further demonstrated via extensive simulations. Second, we turn our attention to a mobile setup and characterize the performance of several canonical mobility models in a drone cellular network in which UAV base stations serve UEs on the ground. In particular, we consider the following four mobility models: (i) straight line (SL), (ii) random stop (RS), (iii) random walk (RW), and (iv) random waypoint (RWP), among which the SL mobility model is inspired by the simulation models used by the 3GPP for the placement and trajectory of UAVs, while the other three are well-known canonical models (or their variants) that offer a useful balance between realism and tractability. Assuming the nearest-neighbor association policy, we consider two service models for the UEs: (i) UE independent model (UIM), and (ii) UE dependent model (UDM). While the serving UAV follows the same mobility model as the other UAVs in the UIM, it is assumed to fly towards the UE of interest in the UDM and hover above its location after reaching there. We then present a unified approach to characterize the point process of UAVs for all the mobility and service models. Using this, we provide exact mathematical expressions for the average received rate and the session rate as seen by the typical UE. Further, using tools from the calculus of variations, we concretely demonstrate that the simple SL mobility model provides a lower bound on the performance of other general mobility models (including the ones in which UAVs follow curved trajectories) as long as the movement of each UAV in these models is independent and identically distributed (i.i.d.). Continuing our analysis on mobile setups, we analyze the handover probability in a drone cellular network, where the initial positions of the UAVs serving the ground UEs are modeled by a homogeneous Poisson point process (PPP). Inspired by the mobility model considered in the 3GPP studies, we assume that all the UAVs follow the SL mobility model, i.e., move along straight lines in random directions. We further consider two different scenarios for the UAV speeds: (i) same speed model (SSM), and (ii) different speed model (DSM). Assuming nearest-neighbor association policy, we characterize the handover probability of this network for both mobility scenarios. For the SSM, we compute the exact handover probability by establishing equivalence with a single-tier terrestrial cellular network, in which the BSs are static while the UEs are mobile. We then derive a lower bound for the handover probability in the DSM by characterizing the evolution of the spatial distribution of the UAVs over time. After performing these system-level analyses on UAV networks, we focus our attention on the air-to-ground wireless channel and attempt to understand its unique features. For that, we first study the impact of UAV wobbling on the coherence time of the wireless channel between UAVs and a ground UE, using a Rician multi-path channel model. We consider two different scenarios for the number of UAVs: (i) single UAV scenario (SUS), and (ii) multiple UAV scenario (MUS). For each scenario, we model UAV wobbling by two random processes, i.e., the Wiener and sinusoidal processes, and characterize the channel autocorrelation function (ACF) which is then used to derive the coherence time of the channel. For the MUS, we further show that the UAV-UE channels for different UAVs are uncorrelated from each other. One key observation that is revealed from our analysis is that even for small UAV wobbling, the coherence time of the channel may degrade quickly, which may make it difficult to track the channel and establish a reliable communication link. Finally, we develop an impairments-aware air-to-ground unified channel model that incorporates the effect of both wobbling and hardware impairments, where the former is caused by random physical fluctuations of UAVs, and the latter by intrinsic radio frequency (RF) nonidealities at both the transmitter and receiver, such as phase noise, in-phase/quadrature (I/Q) imbalance, and power amplifier (PA) nonlinearity. The impact of UAV wobbling is modeled by two stochastic processes, i.e., the canonical Wiener process and the more realistic sinusoidal process. On the other hand, the aggregate impact of all hardware impairments is modeled as two multiplicative and additive distortion noise processes, which is a well-accepted model. For the sake of generality, we consider both wide-sense stationary (WSS) and nonstationary processes for the distortion noises. We then rigorously characterize the ACF of the wireless channel, using which we provide a comprehensive analysis of four key channel-related metrics: (i) power delay profile (PDP), (ii) coherence time, (iii) coherence bandwidth, and (iv) power spectral density (PSD) of the distortion-plus-noise process. Furthermore, we evaluate these metrics with reasonable UAV wobbling and hardware impairment models to obtain useful insights. Similar to our observation above, this work again demonstrates that the coherence time severely degrades at high frequencies even for small UAV wobbling, which renders air-to-ground channel estimation very difficult at these frequencies. / Doctor of Philosophy / With the increasing maturity of unmanned aerial vehicles (UAVs), also known as drones, wireless ecosystem is changing dramatically. Owing to their ease of deployment and high chance of forming direct line-of-sight (LoS) links with the other UAVs and ground users, they are very appealing for numerous wireless applications. As with any new technology, exploring the full extent of the benefits of UAVs requires careful exploration of the underlying trade space. Therefore, in this dissertation, our main focus is on the analysis of such aerial networks, their interplay with the current terrestrial networks, and the unique features of UAVs that make them different from conventional ground nodes. One important aspect of aerial communication systems is their integration into our current cellular networks. Clearly, the addition of these new aerial components has the potential of benefiting both the ground users (such as mobile users watching a concert who need cellular connectivity to share the moments) and the cellular base station (BS). Therefore, careful analysis of these ``aerial-terrestrial" networks is of utmost importance. In the first phase of this dissertation, we perform this analysis by interpreting the network as a combination of one-hop (from the BS to the user) and two-hop (from the BS to the UAV and then from the UAV to the UE) links. Since the locations of BSs, UAVs, and users are irregular in general, we use tools from stochastic geometry to carry out our analysis, which is a field of mathematics that studies random shapes and patterns. Also, because existing terrestrial BSs are primarily designed to serve the ``ground", we propose the addition of a separate set of antennas at the BS site that is solely used to serve the ``air", i.e., to communicate with the UAVs, and demonstrate the benefits of this additional infrastructure in detail. One of our assumptions in the first phase of this dissertation was that the considered network was static, i.e., the UAVs were hovering in the air and the BSs/users were also not moving. In the second phase, on the other hand, we explore the benefits and challenges of a mobile network of UAVs and characterize the performance of several canonical mobility models in a drone cellular network. In particular, one of the models that we studied extensively is the so-called straight line (SL) mobility model, which was inspired by the simulation models used by the third generation partnership project (3GPP) for the placement and trajectory of UAVs. Since the locations of UAVs could be assumed random in general, we use tools from stochastic geometry and present a unified approach to characterize the point process of UAVs, using which we obtained exact mathematical expressions for the average received rate (i.e., throughput) as seen by the users. Continuing our analysis on mobile setups and using the SL mobility model, we also analyze the handover probability in a drone cellular network, which is defined as the event when the serving UAV of a user changes. By establishing equivalence between our aerial setup with a terrestrial cellular network, we compute the exact handover probability in drone cellular networks. In the final phase of this dissertation, we focus our attention on the air-to-ground wireless channel and attempt to understand its unique features. For that, we propose an impairments-aware unified channel model for an air-to-ground wireless communication system and extensively analyze the link between a hovering UAV in the air and a static user on the ground. In particular, we consider two different types of impairments: (i) UAV wobbling, and (ii) hardware impairments, where the former is caused by random physical fluctuations, and the latter by intrinsic radio frequency (RF) nonidealities at both the transmitter and receiver. Using appropriate models for each type of impairment, we rigorously characterize the autocorrelation function (ACF) of the wireless channel, using which we provide a comprehensive analysis of key channel-related metrics, such as coherence time and coherence bandwidth. One key observation that is revealed from our analysis is that even for small UAV wobbling and low hardware impairment levels, the coherence time of the channel may degrade quickly at high frequencies, which could make it difficult to track the channel and establish a reliable communication link at these frequencies.

Third Generation (3G) wireless networks have been well studied and optimized with traditional radio resource management techniques, but still there is room for improvement. Cognitive radio technology can bring significantcant network improvements by providing awareness to the surrounding radio environment, exploiting previous network knowledge and optimizing the use of resources using machine learning and artificial intelligence techniques. Cognitive radio can also co-exist with legacy equipment thus acting as a bridge among heterogeneous communication systems. In this work, an approach for applying cognition in wireless networks is presented. Also, two machine learning techniques are used to create a hybrid cognitive engine. Furthermore, the concept of cognitive radio resource management along with some of the network applications are discussed. To evaluate the proposed approach cognition is applied to three typical wireless network problems: improving coverage, handover management and determining recurring policy events. A cognitive engine, that uses case-based reasoning and a decision tree algorithm is developed. The engine learns the coverage of a cell solely from observations, predicts when a handover is necessary and determines policy patterns, solely from environment observations. / Ph. D.

The health applications aimed at monitoring patients remotely have reached great proportions with the advancement of wireless networks. This paper presents a study of performance evaluation of biosignal traffic, which was simulated the transmission of patient data in emergency situations. The simulation scenario considered the transmission of signals from an ambulance through wireless network and collected in a medical monitoring center. On the way to the hospital, while the mobile broadcast biosignals moved between areas covered by different network technologies, featuring vertical handover situation. Based on the minimum QoS requirements prevailing in the scientific community, the most important parameters in healthcare applications such as loss rate, delay, throughput and jitter were evaluated. Was still considered a minimum bandwidth required for transmission of vital signs, taking into account rates of known samples to physicians signs such as electrocardiogram (ECG), blood pressure, heart rate, body temperature and rate of oxygen saturation blood. To evaluate the performance, were carried computer simulations using an implementation of the IEEE 802.21 standard for the simulator NS-2. The simulated scenario used the networks of Wi-Fi and WiMAX technologies, mobile with multiple interfaces and nodes cargo, which made transmissions with constant rates. The results showed that the network technologies in use can meet the minimum QoS requirements for medical applications. / As aplicações de saúde voltadas para monitoramento de pacientes a distância têm atingido grandes proporções com o avanço das redes sem fio. Este trabalho apresenta um estudo de avaliação de desempenho do tráfego de biosinais, no qual foi simulado a transmissão de dados de pacientes em situações de emergência. O cenário de simulação considerou a transmissão dos sinais a partir de uma ambulância, através de rede sem fio e coletados em um centro de monitoramento médico. No percurso até o hospital, o móvel transmitiu biosinais enquanto transitou entre áreas cobertas por tecnologias de rede distintas, caracterizando situação de handover vertical. Com base nos requisitos mínimos de QoS praticados na comunidade científica, foram avaliados os parâmetros mais importantes em aplicações de saúde como taxa de perdas, atraso, vazão e jitter. Ainda foi considerada uma largura de banda mínima necessária para transmissão de sinais vitais, levando-se em conta as taxas de amostragens conhecidas para sinais médicos como Eletrocardiograma (ECG), Pressão arterial, Frequência cardíaca, Temperatura do corpo e Taxa de saturação de oxigênio no sangue. Para avaliar o desempenho, foram realizadas simulações computacionais com o uso de uma implementação do padrão IEEE 802.21 para o simulador NS-2. O cenário simulado utilizou as redes das tecnologiasWi-Fi eWiMAX, dispositivo móvel com múltipla interface e nós de carga, os quais realizaram transmissões com taxas constantes. Os resultados mostraram que as tecnologias de rede em uso podem atender aos requisitos mínimos de QoS para aplicações médicas.

One of the enabling technologies for Long Term Evolution (LTE) deployments is the femtocell technology. By having femtocells deployed indoors and closer to the user, high data rate services can be provided efficiently. These femtocells are expected to be depolyed in large numbers which raises many technical challenges including the handover management. In fact, managing handovers in femtocell environments, with the conventional manual adjustment techniques, is almost impossible to keep pace with in such a rapidly growing femtocell environment. Therefore, doing this automatically by implementing Self Organizing Network (SON) use cases becomes a necessity rather than an option. However, having multiple SON use cases operating simultaneously with a shared objective could cause them to interact either negatively or positively. In both cases, designing a suitable coordination policy is critical in solving negative conflicts and building upon positive benefits. In this work, we focus on studying the interactions between three self optimization use cases aiming at improving the overall handover procedure in LTE femtocell networks. These self optimization use cases are handover, Call Admission Control (CAC) and load balancing. We develop a comprehensive, unified LTE compliant evaluation environment. This environment is extendable to other radio access technologies including LTE-Advanced (LTE-A), and can also be used to study other SON use cases. Various recommendations made by main bodies in the area of femtocells are considered including the Small Cell Forum, the Next Generation Mobile Networks (NGMN) alliance and the 3rd Generation Partnership Project (3GPP). Additionally, traffic sources are simulated in compliance with these recommendations and evaluation methodologies. We study the interaction between three representative handover related self optimization schemes. We start by testing these schemes separately, in order to make sure that they meet their individual goals, and then their mutual interactions when operating simultaneously. Based on these experiments, we recommend several guidelines that can help mobile network operators and researchers in designing better coordination policies. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2012-12-05 22:35:27.538

There has been a steady growth in the traffic generated by Mobile Network Operators (MNOs), and by 2020 it is expected to overload the existing licensed spectrum capacity and lead to the problem of scarce resources. One method to deal with this traffic overload is to access unlicensed and shared spectrum bands using an opportunistic approach. The use of Licensed Shared Access (LSA) is a novel approach for spectrum sharing between the incumbent user (i.e., the current owner of the shared spectrum) and the LSA licensee (i.e., the temporary user of frequencies, such as an MNO). The LSA system allows the incumbent users to temporarily provide the LSA licensee with access to its spectrum resources. However, licensees must adopt vertical handover and traffic steering procedures to vacate their customers from the LSA band without causing interference, whenever this is required by the incumbent. These procedures should be carried out, de facto, before the base station is turned off as a part of a rapid release of unscheduled LSA band facing evacuation scenarios. Thus, in this dissertation, a cognitive mechanism is proposed to make decisions in advance to find the best target network(s) for evacuated customers in connected mode and with active traffic per class of service. On the basis of these decisions, the vertical handover and traffic steering procedures are carried out for the best target network(s), which are selected in advance and undertaken immediately to avoid interference between the licensee and incumbent services. Furthermore, this guarantees the seamless connectivity and QoS of evacuated customers and their traffic respectively, during and after the unscheduled evacuation scenarios. A performance evaluation conducted in a simulating scenario consisting of one LTE-LSA and three Wi-Fi networks, demonstrated that the proposed solution could be completed within the time required for the unscheduled evacuation, as well as, being able to ensure the QoS and seamless connectivity of the evacuees. The total execution time obtained during the performance evaluation of the proposed solution was around 46% faster than of two related works and could thus avoid interference between the licensee and incumbent services.

This thesis deals with the problem of estimating the velocity of a mobile station (MS)in a mobile communication system using the instantaneous frequency (IF) of the received signal at the MS antenna. This estimate is essential for satisfactory handover performance, effective dynamic channel assignment, and optimisation of adaptive multiple access wireless receivers. Conventional methods for estimating the MS velocity are based either on the statistics of the envelope or quadrature components of the received signal. In chapter 4 of the thesis, we show that their performance deteriorates in the presence of shadowing. Other velocity estimators have also been proposed which require prior estimation of the channel or the average received power. These are generally difficult to obtain due to the non-stationary nature of the received signal. An appropriate window which depends on the unknown MS velocity must first be applied in order to accurately estimate the required quantities. Using the statistics of the IF of the received signal at the MS antenna given in chapter 3, new velocity estimators are proposed in chapter 4 of this thesis. The proposed estimators are based on the moments, zero-crossing rate, and covariance of the received IF. Since the IF of the received signal is not affected by any amplitude distortion, the proposed IF-based estimators are robust to shadowing and propagation path-loss. The estimators for the MS velocity in a macro- and micro-cellular system are presented separately. A macro-cell system can be considered as a special case of a micro-cell in which there is no line-of-sight component at the receiver antenna. It follows that those estimators which are derived for micro-cells can be used in a macro-cell as well. In chapter 4, we analyse the performance of the proposed velocity estimators in the presence of additive noise, non-isotropic scattering, and shadowing. We also prove analytically that the proposed velocity estimators outperform the existing methods in the presence of shadowing and additive noise. The proposed IF-based estimators need prior estimation of both the IF of the received signal and Ricean K-factor. The IF estimation in a typical wireless environment, can be considered as a special case of a general problem of IF estimation in the presence of multiplicative and additive noise. In chapter 5, we show that current time-frequency approaches to this problem which are based on the peak of a time-frequency distribution (TFD) of the signal, fail because of the special shape of the power spectral density of the multiplicative noise in a wireless environment. To overcome this drawback, the use of the first-order moment of a TFD is studied in chapter 5. Theoretical analysis and simulations show that the IF estimator based on the first-order moment of a TFD exhibits negligible bias when the signal-to-additive noise ratio is more than 10 dB. The Ricean K-factor is not only necessary for velocity estimation in micro-cells, but also is a measure of the severity of fading and a good indicator of the channel quality. Two new methods for estimating the Ricean K-factor based on the first two moments of the envelope of the received signal, are proposed in chapter 6. Performance analysis presented in chapter 6, prove that the proposed K estimators are robust to non-isotropic scattering. Theoretical analysis and simulations which are presented in chapters 4 and 7 of this thesis, prove that the proposed velocity and K estimators outperform existing estimators in the presence of shadowing and additive noise.

Secure information exchanges over cyberspace is on the increase due to the convergence of wireless and mobile access technologies in all businesses. Accordingly, with the proliferation of diverse multicast group service subscriptions that are possible to co-exist within a single broadband network, there is also huge demand by the mobile subscribers to ubiquitously access these services over high speed broadband using their portable devices. Likewise, the Network Providers (NPs) invest hugely in infrastructure deployment to disseminate these services efficiently and concomitantly. Therefore, cyber security in any business is obligatory to restrict access of disseminated services to only authorised personnel. This becomes a vital requirement for a successful commercialisation of exchanged group services. The standard way to achieve cyber security in a wireless mobile multicast communication environment is through confidentiality using Group Key Management (GKM).The existing GKM schemes for secure wireless multicast from literature only target single group service confidentiality; however, the adoption of multiple group service confidentiality in them involve inefficient management of keys that induce huge performance overheads unbearable for real time computing. Therefore, a novel authenticated GKM scheme for multiple multicast group subscriptions known as slot based multiple group key management (SMGKM) is proposed. In the SMGKM, the handovers move across diverse decentralised clusters of homogeneous or heterogeneous wireless access network technologies while participating in multiple group service subscriptions. Unlike the conventional art, the SMGKM advances its security by integrating location based authentication and GKM functions. Both functions are securely offloaded from the Domain Key Distributor (DKD) to the intermediate cluster controllers, Area Key Distributors (AKDs), in a distributed fashion, using the proposed location based authenticated membership list (SKDL). A significant upgrade of fast handoff performance with reduced performance overheads of the SMGKM scheme is achieved. The developed numerical analysis and the simulation results display significant resource economy in terms of reduced rekeying transmission, communication bandwidth and storage overheads while providing enhanced security. The performance of the SMGKM in a high speed environment is also evaluated and has demonstrated that SMGKM outperforms the previous work. Finally, the SMGKM correctness against various attacks is verified using BAN logic, the eminent tool for analysing the widely deployed security protocols. The security analysis demonstrates that SMGKM can counteract the security flaws and redundancies identified in the chosen related art.

There has been a steady growth in the traffic generated by Mobile Network Operators (MNOs), and by 2020 it is expected to overload the existing licensed spectrum capacity and lead to the problem of scarce resources. One method to deal with this traffic overload is to access unlicensed and shared spectrum bands using an opportunistic approach. The use of Licensed Shared Access (LSA) is a novel approach for spectrum sharing between the incumbent user (i.e., the current owner of the shared spectrum) and the LSA licensee (i.e., the temporary user of frequencies, such as an MNO). The LSA system allows the incumbent users to temporarily provide the LSA licensee with access to its spectrum resources. However, licensees must adopt vertical handover and traffic steering procedures to vacate their customers from the LSA band without causing interference, whenever this is required by the incumbent. These procedures should be carried out, de facto, before the base station is turned off as a part of a rapid release of unscheduled LSA band facing evacuation scenarios. Thus, in this dissertation, a cognitive mechanism is proposed to make decisions in advance to find the best target network(s) for evacuated customers in connected mode and with active traffic per class of service. On the basis of these decisions, the vertical handover and traffic steering procedures are carried out for the best target network(s), which are selected in advance and undertaken immediately to avoid interference between the licensee and incumbent services. Furthermore, this guarantees the seamless connectivity and QoS of evacuated customers and their traffic respectively, during and after the unscheduled evacuation scenarios. A performance evaluation conducted in a simulating scenario consisting of one LTE-LSA and three Wi-Fi networks, demonstrated that the proposed solution could be completed within the time required for the unscheduled evacuation, as well as, being able to ensure the QoS and seamless connectivity of the evacuees. The total execution time obtained during the performance evaluation of the proposed solution was around 46% faster than of two related works and could thus avoid interference between the licensee and incumbent services.

With increasing demands on network reliability and speed, network suppliers need to effectivize their communications algorithms. Frequency measurements are a core part of mobile network communications, increasing their effectiveness would increase the effectiveness of many network processes such as handovers, load balancing, and carrier aggregation. This study examines the possibility of using supervised learning to predict the signal of inter-frequency measurements by investigating various learning algorithms and pre-processing techniques. We found that random forests have the highest predictive performance on this data set, at 90.7\% accuracy. In addition, we have shown that undersampling and varying the discriminator are effective techniques for increasing the performance on the positive class on frequencies where the negative class is prevalent. Finally, we present hybrid algorithms in which the learning algorithm for each model depends on attributes of the training data set. These algorithms perform at a much higher efficiency in terms of memory and run-time without heavily sacrificing predictive performance.