Tez Koleksiyonu

Permanent URI for this collectionhttps://gcris.khas.edu.tr/handle/20.500.12469/1805

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Air quality prediction using a hybrid deep learning architecture
(Kadir Has Üniversitesi, 2020) Özmen, Atilla; Özmen, Atilla
Air pollution prediction is related to the variables in environmental monitoring data and modeling of the complex relationship between these variables. The objectives of the thesis are to develop a supervised model for the prediction of air pollution by using real sensor data and to transfer the model between cities. A CNN+LSTM deep neural network model was developed to predict the concentration of air pollutants in multiple locations by using a spatial-temporal relationship. The 2D input (univariate) contains the information of one pollutant; the 3D input (multivariate) contains the information of all pollutants and meteorology. There are three methods employed according to the input-output type: Method-1 is based on univariate-input and univariate-output; Method-2 is based on multivariate input and univariate-output; Method-3 is based on multivariate input and multivariate output. The study was carried out for different pollutants which are in publicly available data of the cities of Barcelona, Kocaeli, and İstanbul. The hyperparameters were tuned to determine the architecture that achieved the lowest test RMSE. Comparing the performance of the CNN+LSTM network with a 1-hidden layer LSTM network, the proposed model improved the prediction performance by the rates between 11%-53% for PM10, 20%-31% for O3, 9%-47% for NOX and 18%-46% for SO2. After, the network weights were transferred from the source domains to the target domain. The model has a more reliable prediction performance with the transfer of the network from Kocaeli to İstanbul because of the similarities between those two cities.
Analysis of visible light communation sytems and adaptive equalization effects
(Kadir Has Üniversitesi, 2015) Panayırcı, Erdal; Panayırcı, Erdal
Communication by means of visible light is a newly developing technology that has been brought up as an alternative to the current electromagnetic based communication systems. The recently conducted studies led to the use of LED Technology for illumination purposes and for this technology to have an edge over devices that consume excessive energy as presently being used by us. LED technology is predicted to become an important part of illumination systems especially in consideration of the low energy consumption low voltage use product longevity and small size the technology can provide. Furthermore the use of semi-conductor materials in the LED technology lends this illumination tool the capability to be turned on and off very fast in comparison to the other similar purpose devices. This capability makes it possible for LED technology to be used as a means of data transmission in addition to being a means of illumination. Studies are being conducted to use LEDs effectively as an optic transmission transmitter antenna and application of modulation techniques used in electromagnetic systems to VLC technology for purposes of achieving resistance against white noise reflections refractions and echoes. in literature the model of receiving data by means of adaptive filters and its processing has been used in very limited number of studies and in these studies generally LMS algorithms were used. in this dissertation an empty room in rectangular prism shape in pre-determined dimensions was used with LED panels in numbers and positions previously determined which sent data whereby the impulse response displayed by the channel until the data sent that reached the photo detector was analyzed according to different FOV angles in computer environment and later on the data was sent to the receiver through these channels that have differing impulse response based on specific FOV degrees. The data that is processed at the receiver was passed through adaptive filters to evaluate byte error rate performances in the computer environment. LMS and RLS algorithms were used in the adaptive filter. The simulation results have been displayed in graphics of bit error rate change based on SNR and it has been observed that the adaptive filter on which RLS algorithm is used provided better results than the adaptive filter on which LMS algorithm was used. bstract.
Artificial neural network based sparse channel estimation for OFDM systems
(Kadir Has Üniversitesi, 2017) Özmen, Atilla; Şenol, Habib; Özmen, Atilla
In order to increase the communication quality in frequency selective fading channel environment, orthogonal frequency division multiplexing (OFDM) systems are used to combat inter-symbol-interference (ISI). In this thesis, a channel estimation scheme for the OFDM system in the presence of sparse multipath channel is studied. The channel estimation is done by using the artificial neural networks (ANNs) with Resilient Backpropagation training algorithm. This technique uses the learning capability of artificial neural networks. By means of this feature we show how to obtain a channel estimate and how it allows the proposed technique to be less computationally complex; as there is no need for any matrix inversions. This proposed method is compared with the Matching Pursuit (MP) algorithm that is well known estimation technique for sparse channels. The results show that the ANN based channel estimate is computationally simpler and a small number of pilots are required to get a better estimate of the channel especially in low SNR levels. With this setting, the proposed algorithm leads to a better system throughput.
Autonomous vehicle control using reinforcement learning
(Kadir Has Üniversitesi, 2020) Özmen, Atilla; Özmen, Atilla
Autonomous vehicles have become an important research topic where artificial intelligence is applied. As the research increases, by means of the applications of artificial intelligence algorithms in different areas, enable the working mechanisms of the systems to become more optimal due to the change of factors such as human power, time, energy and control. It has been observed that deep learning and machine learning algorithms have advantages and disadvantages in different situations and conditions. Since deep learning algorithms require large amounts of data, studies on the reinforcement learning model based on the experience from the environment and based on the reward-punishment system have recently concentrated and some striking results have been obtained. Reinforcement learning is considered a powerful AI paradigm that can be used to teach machines through interaction with the environment and learning from their mistakes. In this thesis, an environment was created based on a two-dimensional vehicle scenario created using a pyglet simulation tool. A comparative simulation study of different reinforcement learning algorithms such as Q-Learning, SARSA and Deep Q-Network (DQN) is presented on this environment. While making this comparison, a certain learning criterion was added, and also, parameters such as epsilon value, step number were changed, and changes in training and test stages were analyzed. For this study, the actors (agent, sensor, obstacles etc.) provided by the simulator program were supported. Through the feedback provided by the sensors, the reinforcement learning agent trains himself on the basis of these algorithms and determines a movement strategy to explore the environment limited to a specific area.
Bayesian learning for cellular neural networks
(Kadir Has Üniversitesi, 2013) Özmen, Atilla; Ozmen, Atilla
Cellular Neural Networks have been an active research eld since their introduction in the late 80s. Several training algorithms are proposed since then. All have their advantages and disadvantages. Most of them uses deterministic methods to acquire the network parameters. in this thesis a new training method is proposed for Cellular Neural Networks and Discrete-Time Cellular Neural Networks are used for implemented applications. This new method is a probabilistic method. Maximum A Posteriori estimation is used to estimate the network parameters thus making this method a Bayesian learning method. A Cellular Neural Network is nonlinear in the sense of its activation function. For the same reason modeling of a Cellular Neural Network is also nonlinear. Using Maximum A Posteriori estimation on a nonlinear system causes some problems. To cope with this problems in the estimation process of network parameters Metropolis-Hastings algorithm which is one of Monte Carlo Markov Chain methods is used for generating the samples needed from the resulting distribution. After the network is trained it is tested against known algorithms to verify the training process. Discrete-Time Cellular Neural Networks are mostly used for image processing applications. Many dierent kind of applications can be applied using dierent network parameters without changing the cellular network architecture. A couple of applications are picked from this pool and using the estimated parameters Cellular Neural Networks are used to perform some image processing algorithms. This operations are performed by computer models and simulations. -- Abstract'tan.
Caching algorithm implementation for edge computing in IoT network
(Kadir Has Üniversitesi, 2020) Abduljabbar, Mohammed; Özmen, Atilla; Öğrenci, Arif Selçuk
The developing IoT concept brings new challenges to the service providers. The architecture of the networks changes to satisfy the needs arising by the large number of connected devices. Edge computing is the new architectural solution that will be used in the IoT networks. This architecture is more dynamic than the cloud computing network where the data can be quickly processed in the different layers of the network without going to the cloud. This will remove the problems faced by cloud computing: increase in data traffic and increase in latency of provided services. Research on edge computing in IoT networks encompass information-centric networks, use of 5G, and improving the hardware devices however a suitable solution for all the IoT use cases is not available yet. In this thesis, use of caching among IoT nodes is proposed as a solution to increase the efficiency of edge computing. Caching is an old but effective solution for dealing with data because it improves the real-time response of the system and can be used in IoT use cases. It will also not cause an extra hardware cost. In this research, two commonly used caching algorithms, LRU (Least Recently Used) and FIFO (First in First Out), are investigated and compared for their performance in sample IoT scenarios. Reductions in data processing time are observed where CPU and RAM utilizations are enhanced.
Coexistence of cognitive radio based networks in tv white space
(Kadir Has Üniversitesi, 2016) Erküçük, Serhat; Erküçük, Serhat
Due to increasing data rates in enhancing wireless communications RF spectrum which is one of the most crucial natural sources has become more valuable. in order to utilize the limited spectrum e_ciently and solve the scarcity problem regulatory agencies granted unlicensed networks or secondary users (SUs) access to licensed bands for wireless communication with the condition that they should not cause harmful interference to primary users (SUs). Cognitive radio (CR) technology enables devices to access the spectrum opportunistically. Using CR based networks licensed bands can be utilized more e_ectively for wireless communications. TV White Space (TVWS) refers to portions of the RF spectrum that was reserved only for licensed terrestrial TV broadcasting and is opened to unlicensed use under regulatory conditions. While regulations protect licensed systems in TVWS from harmful interference interference prevention among unlicensed systems is left mainly to manufacturers. Consequently there is a need to develop new coexistence approaches between TVWS networks. Busy tone broadcasting is a coexistence method which can be used by TVWS networks to announce that the selected frequency band is occupied. in this dissertation a busy tone based coexistence algorithm is proposed for wireless local area networks (WLANs) operating in TVWS (i.e. iEEE 802.11af based networks) where wireless regional area network (WRAN) (i.e. iEEE 802.22 based network) is assumed to be the busy tone broadcaster. The proposed algorithm is analyzed in detail considering the e_ects of log-normal shadowing client distribution around the access point and the number of clients where exact interfering packet rate and successful packet transmission rate expressions are obtained and validated by simulations for di_erent scenarios. The results show that with the proposed coexistence approach a WLAN can reliably detect the busy tone signal to change its frequency band and can reduce interference to WRAN. Even if there is no available frequency band for the WLAN the WRAN still maintains its enhanced successful packet transmission performance. The deployment of the proposed algorithm is important for successful coexistence between cognitive wireless regional and local area networks where interference among networks is not regulated such as in TVWS bands.
Computation of two-variable mixed element network functions
(Kadir Has Üniversitesi, 2017) Özmen, Atilla; Özmen, Atilla
in this dissertation the algorithm known as “Standard Decomposition Technique (SDT)” is used together with Belevitch’s canonic representation of scattering polynomial for two-port networks operate on high frequency to find the analytical solutions for “Fundamental equation set (FES)”. This FES is extracted by using Belevitch canonic polynomials “ ??(?? ??) ?(?? ??) and ??(?? ??)” used for the description of mixed lumped and distributed lossless two-port cascaded networks in two variables of degree five and the obtained solutions are further used to synthesis the realizable networks. The solution to the problem is also classified into two cases first case is discussed for three lumped and two distributed (???? = 3 ???? = 2 ) and the second is for three distributed and two lumped important (???? = 2 ???? = 3 ) the solution for both these cases are expressed separately with conclusive examples
Detection of interdependent multiband systems for cognitive radios and ultra-wideband systems
(Kadir Has Üniversitesi, 2012) Erküçük, Serhat; Erküçük, Serhat
As a result of advances in the wireless technology new systems have been proposed and hence more frequency bands are occupied in the spectrum. Therefore the number ofavailable frequency bands for future wireless communication systems is decreasing and alternative communication technologies have appeared in recent years. These are cognitive radio and ultra wideband systems which use the spectrum more efficiently. -- Abstract'dan.
Explicit solutions of two-variable scattering equations and broadband matching network design
(Kadir Has Üniversitesi, 2019) Şengül, Metin; Şengül, Metin
Karışık devre elemanı (toplu ve dağıtılmış eleman) içeren devreler mikrodalga mühendisliği için önemli bir konudur (Aksen, 1994). Toplu elemanlar arasındaki bağlantılar, iletim hattı olarak düşünülüp devre elemanı olarak tasarım sırasında denklemlere dahil edilirse, devrenin performansını bozmaları engellendiği gibi aynı zamanda devrenin istenen cevabı vermesi için kullanılmış olurlar. Bu tür devrelerde, iki farklı tipte eleman bulunduğundan, devre fonksiyonları iki değişken kullanılarak tanımlanır. Devrede yer alan toplu elemanlar için $p = \sigma + jw$ klasik frekans değişkeni ve dağıtılmış elemanlar için $\lambda$ = $tanh(p\tau)$ Richards değişkeni şeklinde tanımlanır(burada $\tau$ dağıtılmış elemanlar için gecikmedir). Dikkat edilirse bu iki değişken arasında hiperbolik bir bağımlılık vardır. Dolayısıyla bu tür devrelerin tanımlanmasında transandantal fonksiyonlar kullanılabilir. Fakat p ve $\lambda$ bağımsız değişkenler olarak kabul edilirse karışık elemanlı devreler iki-değişkenli fonksiyonlar kullanarak tanımlanabilir. Literatürde bu tür devreler üzerine birçok çalışma bulunmasına rağmen, bu denklemlerin çözümü için genel bir analitik method henüz bulunabilmiş değildir. Fakat yarı-analitik bir yaklaşım mevcuttur (Aksen, 1994). Bu yaklaşımda, iki-değişkenli saçılma denklemleri kullanılır ve sınırlı devre topolojileri için uygulanabilir durumdadır. Literatürde, bahsedilen yarı-analitik yaklaşım ile düşük dereceli alçak-geçiren birim elemanlarla ayrılmış LC merdiven devreler için bazı kısıtlamalar altında saçılma denklemlerinin çözümleri verilmiştir. Fakat bu tezde, hiç bir kısıtlama olmadan çözülen denklemler kullanılarak, genişbant uyumlaştırma devresi tasarımı yapılmış, elde edilen sonuçlar literatürde verilen denklemler kullanılarak tasarlanan uyumlaştırma devresi sonuçlarıyla karşılaştırılmıştırılaştırılmıştır.
Femtocell design for data communications in mobile networks
(Kadir Has Üniversitesi, 2013) Panayırcı, Erdal; Panayırcı, Erdal
in this thesis game theoretic utility-based adaptive power control algorithm for uplink of femtocell networks is performed for mitigation of interference in two different ways. The first one is allocation of femtocell interference threshold in randomly activated femtocell base stations in a known macrocell coverage area and the second one is adaptation of the power of the femtocell users for mitigating total interference from femtocell users at the macrocell base station. Accordingly we consider two different pricing schemes which are centralized pricing and de-centralized pricing for updating the power level at mobile station. in addition we examined two different technologies which are frequency division multiple access (FDMA) and code DMA (CDMA). The results show that each active user at network could reach the given signal to interference plus noiseratio (SiNR) threshold value with iterative power control algorithm and macrocell base station encountered with given interference threshold value with power control and user removal algorithms. -- Abstract'tan.
Load flow based electrical system design and short circuit analysis
(Kadir Has Üniversitesi, 2019) Rana, Subhan; Ö?renci, Arif Selçuk
Load flow and short circuit behaviour of electrical energy systems are investigated. The system to be investigated, is designed based on a 230kVA power grid analytical model. Load flow analysis is crucial for the design of electrial power systems where tests related to load flow are indispensible. This work includes modelling of several electrical power components (transformer, power grid, bus bar, circuit breakers, etc. ) to highlight the methods in the study of the system behaviour. Faults may occur in different scenarios, and all of the components have to be designed to withstand the worst case conditions based on the standards. Load flow and short circuit analysis are performed by variations of the load. Multiple design parameters are discussed and problems such as power factor correction, under voltages and over voltages are investigated.
Log analysis with anomaly detection
(Kadir Has Üniversitesi, 2019) Şahin, Uygar; Ö?renci, Arif Selçuk
Detection of anomalies in the data is an important data analysis job for server logs as they will reveal many benefits. Different types of methods can be used for anomaly detection: supervised, semi-supervised, and supervised anomaly detection. Similarly different algorithms exist for each category. In this work, four anomaly detection algorithms are utilized and their performance metrics are compared for public Hadoop Distributed File System (HDFS) data. Among the others, the support vector machines are identified as the best method for anomaly detection.
Low energy fixed clustering algorithm for wireless sensor networks
(Kadir Has Üniversitesi, 2016) Dağ, Tamer; Dağ, Tamer
Wireless sensor networks (WSNs) have become an important part of our lives as they can be used in vast application areas from disaster relief to health care. As a consequence the life span and the energy consumption of a WSN has become a challenging research area. According to the existing studies instead of using direct transmission or multi-hop routing clustering can significantly reduce the energy consumption of sensor nodes and can prolong the lifetime of a WSN. in this thesis low energy fixed clustering algorithm (LEFCA) and multihop low energy fixed clustering algorithm (M-LEFCA) are proposed for WSNs. With LEFCA the clusters are constructed during the set-up phase. A sensor node which becomes a member of a cluster stays in the same cluster throughout the life span of the network. LEFCA not only improves the lifetime of the network but also decreases the energy dissipation significantly. in addition proposed M-LEFCA uses multi-hop intra cluster communication approach. it selects optimum forward neighbor cluster heads (CHs) as relay nodes (RNs). M-LEFCA aims to reduce energy dissipation and prolong network lifetime of LEFCA by combining clustering and multi-hop routing approaches.
Modelling of visible light channels and performance analysis for optical ofdm systems
(Kadir Has Üniversitesi, 2016) Panayırcı, Erdal; Panayırcı, Erdal
This work is concerned with a challenging problem of modeling and simulation of visible light communications (VLC) channels for optical wireless communication (OWC) systems. A proper channel model for VLC is absent in the current literature. For this reason two different indoor channel model with different material types and different receiver-transmitter locations have been obtained and implemented for asymmetrically clipped optical orthogonal frequency division multiplexing (ACO-OFDM) technique. The channel models have been obtained from optical and illumination design software Zemax® by using sequential and non- sequential ray-tracing capabilities for VLC spectrum. The results of implementation of the realistic channel model has significant diversity from Gaussian and infra-red (iR) channel models implemented in studies.
Multi-sensor indoor positioning
(Kadir Has Üniversitesi, 2022) AYABAKAN, TARIK; Kerestecioğlu, Feza; Kerestecioglu, Feza
In this study, multi-sensor indoor positioning methods, which fuse the tri-laterated position data of the target are considered. The lateration is based on the dis tances that are obtained using the signal strengths received from different Wi-Fi access points. A new method, which is based on federated Kalman filtering (FKF) and makes use of the fingerprint data, namely, federated Kalman filter with skipped covariance updating (FKF-SCU) is proposed for indoor positioning. After that chal lenging issue of FKF, information sharing coefficient assignment is studied and two online adaptation methods based on received signal strength indication (RSSI) and distance information gathered from APs are proposed. Lastly, FKF-SCU structure is combined with adaptive FKF configuration. The data collected on two different test beds are used to compare the performance of the proposed positioning methods to those of the regular federated and centralized filters. It is shown on the test data that these algorithms improve the position accuracy and provide fault tolerance whenever signal reception is interrupted from an access point.
Neural network based channel estimation for time-varying OFDM systems
(Kadir Has Üniversitesi, 2023) Özmen, Atilla; Özmen, Atilla; Şenol, Habib
LTE gibi sistemler sayesinde, maksimum 100 Mbit/s'ye kadar veri hızlarına ulaşmak mümkün olmaktadır. Ancak, bu hızlara kullanıcı tarafındaki hareketliliğin olmadığı veya düşük olduğu senaryolarda erişilebilir. Kullanıcının hareket hızı arttıkça, kanal kestirimi yönteminin düşük kompleksiteye sahip olması gerekliliği de artmaktadır, çünkü kanalın zamana bağımlı özelliği kötüleşmektedir. Derin öğrenme, birçok sektörde geleneksel yöntemlerin yavaş yavaş yerini almaya başlayarak, çeşitli alanlarda sıkça kullanılır hale gelmektedir. Derin öğrenmenin hesaplama karmaşıklığını azaltmak ve sistem performansını artırmak hakkındaki kabiliyeti kanıtlanmıştır. Bu tez, derin sinir ağları (DNN) kullanarak zamana bağlı ortogonal frekans bölmeli çoklu erişim (OFDM) kanalları için bir kanal kestirimi yöntemi önermektedir. Kanal kestiriminin hesaplama karmaşıklığını azaltmak için zamana bağlı hızla değişen OFDM kanalını temsil etmek için Legendre polinom katsayıları kullanılmaktadır. Lineer minimum ortalama karesel hata (LMMSE) kullanılarak kanalı temsil eden polinom katsayılarının başlangıç değerleri kestirilmiş ve kestirim doğruluğu DNN ile arttırılmıştır. Sonuçlar, mekansal alternatif genelleştirilmiş beklenti maksimizasyonu - maksimum a posteriori olasılık (SAGE-MAP) ve LMMSE kanal kestirim yöntemi ile karşılaştırılmaktadır. Düşük sinyal-gürültü oranlarında DNN temelli kestirim daha küçük ortalama karesel hata (MSE) ve sembol hata oranları (SER) elde edildiği gösterilmiştir.
New channel estimation techniques for cooperative underwater acoustic OFDM systems
(Kadir Has Üniversitesi, 2013) Panayırcı, Erdal; Panayırcı, Erdal
Cooperative underwater acoustic communication systems come into prominence in recent years. Since underwater channels are sparse and additive noise entering the system is colored Gaussian noise it is very difficult to estimate the underwater channels and theoretically makes it interesting. in the first part of the thesis the transmission from source to the target recipient is realized by means of a relay system. Orthogonal frequency division multiplexing (OFDM)-based channel estimation problem is solved by using the matching pursuit (MP) algorithm and we obtained excellent error performance. in the second part of the thesis an efficient channel estimation algorithm is purposed for amplify-and-forward (AF) cooperative relay based OFDM system in the presence of sparse underwater acoustic channels and of the correlative non-Gaussian noise. The algorithm is based on the combinations of the MP and the space-alternating generalized expectation-maximization (SAGE) technique to improve the estimates of the channel taps and their location as well as the Gaussian mixture noise distribution parameters in an iterative way. Computer simulations show that underwater acoustic channel is estimated very effectively and the proposed algorithm has excellent symbol error rate (SER) and channel estimation performance as compared to the existing ones. -- Abstract'tan.
Optimal rate and power allocation algorithm in tdd-ofdm based two-tier femtocell networks
(Kadir Has Üniversitesi, 2014) Altabaa, Mhd Tahssin; Panayırcı, Erdal
People nowadays are witnessing the acceleration of development of electronics. The new technologies of electronic devices has made mobile phones smarter and more technologically advanced, where they support software platforms and applications that increased the demands on high data rates and gives the users the ability to play online games, video chat, upload-download data, and watch videos on social networks and other video-streaming websites. According to statistics of researches and telecommunication companies, the average usage area of a mobile data transfer users are generated from indoor places e.g. houses. Mobile operators seeks out for providing high data rates to increase their capacity to suit the demands of indoor users for those who experience weak signal power; However, the current studies suggests a distance decreasing between the Mobile Station (MS) and Base Station (BS) for a lower distance of wireless communication. Femtocell technology, also called Home Base Station (HBS), provides a high data transfer rates and better coverage area for limited number of indoor users, where the conventional base station used by the telecommunication company that has a wide coverage correspondingly named Macro Base Station (MBS). In home base station technology, the femtocell users uses the same spectrum for uplink and downlink frequencies that the other mobile stations are using for communication with the MBS. However, for such a communication system, it brings out a new area for research regarding interference management. In this thesis, an adaptive power control algorithm based on the network's present parameters with two constraints performed at the femtocell users' uplink channels for interference mitigation in a Time Division Duplex-Orthogonal Frequency Division Multiplexing (TDD-OFDM) wireless communication fashion. The constraints aims to ensure the quality of service of communication of MBS's user and to preserve the data rate of femtocell users from collapsing. The first constraint is regarding the mitigation of the interference coming from the femtocell user to the macro base station; this constraint depends on the availability of the time slot that both femtocell and macrocell users are sharing subject to a certain frequency. The second constraint is regarding the limitation of the femtocell user's transmitting power based on the maximization of the weighted rate sum of the femtocell users subject to the power summation of each femtocell user on each subchannel. Accordingly, we study the scenarios that considers both the cross-tier and co-tier interferences to mimic the realistic femtocell environment.
Performance analysis of low complexity maximal ratio transmission approaches in multi-relay networks
(Kadir Has Üniversitesi, 2014) Erdoğan, Eylem; Güçlüoğlu, Tansal; Dağ, Tamer
Kablosuz ileti sim a ğlarının artan talebi karşılamak için yüksek veri hızına sahip, yüksek güvenilirlikli ve düşük enerji tüketimli olması gerekmektedir. Bu nedenle, bu doktora tezi düşük karmaşıklığa sahip kuvvetlendir-aktar yapısındaki tek yönlü ve iki yönlü çok antenli ve çok röleli sistemlerin uygulama ve analizlerini sunmaktadır. İlk olarak, iki atamalı maksimum oranlı iletim tekniği kullanılan geleneksek ve fırsatçı yapıdaki çok röleli sistemlerin performansı Rayleigh sönümlemeli kanallarda incelenmektedir. Analiz, kümülatif dağılım fonksiyonu ve moment üreten fonksiyon gibi sinyal-gürültü oranına ait istatistiksel fonksiyonların türetimi ile başlamaktadır. Sonrasında, sembol hata oranı, kesinti olasılığı ve ergodik kapasite bulunmaktadır. İkinci çalışmada maksimum oranlı iletim tekniği ve röle seçimini içeren iki yönlü röleli ağ modeli üzerinde durulmaktadır. Bu model için, toplam sembol hata oranı ve sistem kesinti olasılığı Nakagami-m sönümlemeli kanallarda türetilmektedir. Son olarak, iki yönlü röleli ağ modeli anten ve röle seçimiyle beraber Nakagami-m sönümlemeli kanallarda analiz edilmekte ve sistem kesinti olasılığı elde edilmektedir.

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