This thesis presents packet-level channel modeling, spectrum efficiency optimization and channel estimation for wireless cooperative communication systems with diversity combining. Cooperative transmission in a wireless network allows neighboring nodes to share their communication resources to create a virtual antenna array by distributed transmission and signal processing, which is useful to exploit spatial diversity, increase channel capacity, and attain wider service coverage with single-antenna terminals. How to exploit spatial diversity and leverage the multi-hop channel structure is an important research issue for the cooperative network. In this thesis, two cooperative schemes are considered, amplify and forward (AF) and demodulation and forward (DMF). For AF cooperative systems, finite state Markov chain (FSMC) models are designed in analyzing the system performance considering time-varying channel behaviors and facilitating fast channel simulation. For DMF cooperative systems, first we formulate the optimization problem that jointly chooses the modulation schemes at the source and relay nodes, to maximize the throughput of cooperative systems under the BER constraint. Second, we propose to use the soft values of each bit to devise a simple and effective combining scheme, which can be applied for both AF and DMF cooperative systems. Third, as the soft values from demodulation process can also be used for measuring the channel estimation accuracy, a soft value-assisted channel estimation has been proposed by iteratively utilizing soft values to refine the accurate channel estimation. In addition, we also implement the soft value module in OFDM-based transceiver system based on a GNU Radio/USRP2 platform, and verify the effectiveness and performance improvement for the proposed SVC systems. As considering wireless cooperative systems has attracted increasing attentions from both academic and industry to meet the demanding of the high data rate transmission, the packet-level channel modeling, adaptive modulation, spectrum efficiency improvement frameworks based on soft value combining and accurate channel estimation algorithms proposed in this thesis are essential for future proliferation of high data rate, reliable and efficient wireless communication networks. / Graduate / 0537 / 0544 / 0984

Reconfigurable intelligent surfaces (RISs) are now considered among the key enabling technologies catering to the ever-increasing demand for traffic rate in the future fifth-generation beyond or even sixth-generation. RISs can be leveraged to transform the propagation environment into a smart space that can be programmable for the benefit of the communication application. Throughout this proposal, we study RIS-assisted systems from different perspectives to analyze and enhance the operation of such systems in different setups. In this context, we first analyze the performance of the RIS-assisted single-input single-output (SISO) system and make a fair comparison with the conventional relaying system. Then, we investigates the use of a RIS to aid point-to-point multi-data-stream multiple-input multiple-output (MIMO) wireless communications. With practical finite alphabet input, the reflecting elements at the RIS and the precoder at the transmitter are alternatively optimized to minimize the symbol error rate. Considering the same RIS-assisted MIMO system, We further explore the potential of RIS for acting as an active modulator and piggybacking its own information when helping the information transmission between the transmitter and the receiver at the same time. Furthermore, considering a RIS-assisted SISO system over the millimeter wave channel, we propose an appropriate design of the phase shifts of each element at the RIS so as to maximize the received signal power at the desired user, while nulling the received interference signal power at the undesired user. However, most of the works investigated the use of continuous phase shift designs, which cannot be implemented in practice. It motivates us to investigate the control of the phases shifts under the assumption that they belong to a finite discrete set. As the above-mentioned performance analysis and optimization of RIS-assisted system requires the channel state information, we thus address the channel estimation problem for a point-to-point SISO system and a point-to-point multiple-input single-output system, respectively. Finally, we highlight some possible future research directions to be considered for the thesis.

No description available.

The use of orthogonal frequency-division multiplexing (OFDM) by wireless standards is often preferred due to its high spectral efficiency and ease of implementation. However, data transmission via OFDM still suffers when passing through a noisy channel. In order to maximize the abilities of OFDM, channel effects must be corrected. Unfortunately, channel estimation is often difficult due to the nonlinearity and randomness present in a practical communication channel. Recently, machine learning based approaches have been used to improve existing channel estimation algorithms for a more efficient transmission. This thesis investigates the application of artificial neural networks (ANNs) as a means of improving existing channel estimation techniques. Multi-layer feed forward neural networks (FNNs) and convolutional neural networks (CNNs) are tested on a variety of random fading channels with different signal-to-noise ratios (SNRs) via computer simulations. Compared to the conventional least squares (LS) algorithm, the approach based on CNN can reduce the bit error rate (BER) of data transmission by an average of 47.59%.

No description available.

Present trends in communication links between devices have opted for wireless instead of wired solutions. As a consequence, unlicensed bands have seen a rise in the interference level as more and more devices are introduced into the market place that take advantage of these free bands for their communication needs. Under these conditions, the receiver's ability to recognize and identify the presence of interference becomes increasingly important. In order for the receiver to make an optimal decision on the signal-of-interest, it has to be aware of the type (modulation) of interference as well as how the received signals are affected (channel) by these impediments in order to appropriately mitigate them. This dissertation addresses the blind (unaided) identification of the signal modulations and the channel in a Multiple Input Multiple Output (MIMO) system. The method presented herein takes advantage of the modulation induced periodicities of the signals in the system and uses higher-order cyclostationary statistics to extract the signal and channel unknowns. This method can be used to identify more signals in the system than antenna elements at the receiver (overloaded case). This dissertation presents a system theoretic analysis of the problem as well as describes the development of an algorithm that can be used in the identification of the channel and the modulation of the signals in the system. Linear and non-linear receivers are examined at the beginning of the manuscript in order to review the a priori information that is needed for each receiver configuration to function properly. / Ph. D.

High transmission data rate, spectral efficiency, and reliability are necessary for future wireless communications systems. In a multipath-rich wireless channel, deploying multiple antennas at both the transmitter and receiver achieves high data rate, without increasing the total transmission power or bandwidth. When perfect knowledge of the wireless channel conditions is available at the receiver, the capacity has been shown to grow linearly with the number of antennas. However, the channel conditions must be estimated since perfect channel knowledge is never known a priori. In practice, the channel estimation procedure can be aided by transmitting pilot symbols that are known at the receiver. System performance depends on the quality of channel estimate, and the number of pilot symbols. It is desirable to limit the number of transmitted pilot symbols because pilot symbols reduce spectral efficiency. This thesis analyzes the system performance of coded multiple-input multiple-output (MIMO) systems for the quasi-static fading channel. The assumption that perfect channel knowledge is available at the receiver must be removed, in order to more accurately examine the system performance. Emphasis is placed on developing channel estimation strategies for an iterative Vertical Bell-Labs Layered Space Time (V-BLAST) architecture. The channel estimate can be sequentially improved between successive iterations of the iterative V-BLAST algorithm. For both the coded and uncoded systems, at high signal to noise ratio only a minimum number of pilot symbols per transmit antenna are required to achieve perfect channel knowledge performance. / Master of Science

Yes / A rigorous model for automatic modulation classification (AMC) in cognitive radio (CR) systems is proposed in this paper. This is achieved by exploiting the Kalman filter (KF) integrated with an adaptive interacting multiple model (IMM) for resilient estimation of the channel state information (CSI). A novel approach is proposed, in adding up the squareroot singular values (SRSV) of the decomposed channel using the singular value decompositions (SVD) algorithm. This new scheme, termed Frobenius eigenmode transmission (FET), is chiefly intended to maintain the total power of all individual effective eigenmodes, as opposed to keeping only the dominant one. The analysis is applied over multiple-input multiple-output (MIMO) antennas in combination with a Rayleigh fading channel using a quasi likelihood ratio test (QLRT) algorithm for AMC. The expectation-maximization (EM) is employed for recursive computation of the underlying estimation and classification algorithms. Novel simulations demonstrate the advantages of the combined IMM-KF structure when compared to the perfectly known channel and maximum likelihood estimate (MLE), in terms of achieving the targeted optimal performance with the desirable benefit of less computational complexity loads.

FundaÃÃo de Amparo à Pesquisa do Estado do Cearà / Atualmente a internet à uma ferramenta largamente utilizada e o grande desenvolvimentoe popularidade de tecnologias de acesso sem-fio (wireless) nos levam a um futuro no qual uma conexÃo caracterizada por estar disponÃvel âanytime, anywhereâ, ou seja, a qualquer hora e em qualquer lugar, serà essencial. Tal caracterÃstica à considerada obrigatÃria em sistemas4G (quarta geraÃÃo), mas para uma experiÃncia satisfatÃria para o usuÃrio à necessÃrio que uma conexÃo segura e eficiente esteja disponÃvel. A fim de obter tal eficiÃncia, a comunidade de pesquisa tem gerado algumas soluÃÃes promissoras que obtÃm ganhos significativos no desempenho do sistema, tais como modulaÃÃo e codificaÃÃo adaptativas, codificaÃÃo espaÃo-temporal, mÃltiplas antenas e canais MIMO (Multiple Input Multiple Output ), modulaÃÃo multiportadora, detecÃÃo multiusuÃrio, etc. [1]. Dentre essas soluÃÃes, destaca-se a adaptaÃÃo do sistema, ou seja, o sistema deve estar em constante adaptaÃÃo para obter sempre o melhor desempenho possÃvel para cada situaÃÃo em que se encontra. No entanto, uma importante premissa para a adaptaÃÃo do sistema consiste em conhecer o estado atual em que o sistema se encontra (informaÃÃo sobre o canal de comunicaÃÃo). Para isso diversas tÃcnicas de estimaÃÃo de canal sÃo propostas na literatura, cada uma possuindo vantagens e desvantagens. Nesse trabalho o impacto da estimaÃÃo de canal na adaptaÃÃo de enlace à analisado atravÃs de simulaÃÃes computacionais1. Em particular, duas tÃcnicas de estimaÃÃo de canal com caracterÃsticas diferentes sÃo analisadas, para alguns cenÃrios especÃficos em um sistema MIMO-OFDM (Multiple Input Multiple Output - Orthogonal Frequency Division Multiplexing ), atravÃs de uma mÃtrica que considera tanto a redundÃncia introduzida para estimar o canal quanto o erro de estimaÃÃo de canal de cada tÃcnica. Os resultados encontrados constituem curvas que podem ser utilizadas para efetuar a adaptaÃÃo de enlace do sistema de maneira mais realista, ou seja, considerando o efeito da estimaÃÃo de canal, alÃm de incluir a prÃpria tÃcnica de estimaÃÃo de canal como um parÃmetro a ser adaptado. / Atualmente a internet à uma ferramenta largamente utilizada e o grande desenvolvimentoe popularidade de tecnologias de acesso sem-fio (wireless) nos levam a um futuro no qual uma conexÃo caracterizada por estar disponÃvel âanytime, anywhereâ, ou seja, a qualquer hora e em qualquer lugar, serà essencial. Tal caracterÃstica à considerada obrigatÃria em sistemas4G (quarta geraÃÃo), mas para uma experiÃncia satisfatÃria para o usuÃrio à necessÃrio que uma conexÃo segura e eficiente esteja disponÃvel. A fim de obter tal eficiÃncia, a comunidade de pesquisa tem gerado algumas soluÃÃes promissoras que obtÃm ganhos significativos no desempenho do sistema, tais como modulaÃÃo e codificaÃÃo adaptativas, codificaÃÃo espaÃo-temporal, mÃltiplas antenas e canais MIMO (Multiple Input Multiple Output ), modulaÃÃo multiportadora, detecÃÃo multiusuÃrio, etc. [1]. Dentre essas soluÃÃes, destaca-se a adaptaÃÃo do sistema, ou seja, o sistema deve estar em constante adaptaÃÃo para obter sempre o melhor desempenho possÃvel para cada situaÃÃo em que se encontra. No entanto, uma importante premissa para a adaptaÃÃo do sistema consiste em conhecer o estado atual em que o sistema se encontra (informaÃÃo sobre o canal de comunicaÃÃo). Para isso diversas tÃcnicas de estimaÃÃo de canal sÃo propostas na literatura, cada uma possuindo vantagens e desvantagens. Nesse trabalho o impacto da estimaÃÃo de canal na adaptaÃÃo de enlace à analisado atravÃs de simulaÃÃes computacionais1. Em particular, duas tÃcnicas de estimaÃÃo de canal com caracterÃsticas diferentes sÃo analisadas, para alguns cenÃrios especÃficos em um sistema MIMO-OFDM (Multiple Input Multiple Output - Orthogonal Frequency Division Multiplexing ), atravÃs de uma mÃtrica que considera tanto a redundÃncia introduzida para estimar o canal quanto o erro de estimaÃÃo de canal de cada tÃcnica. Os resultados encontrados constituem curvas que podem ser utilizadas para efetuar a adaptaÃÃo de enlace do sistema de maneira mais realista, ou seja, considerando o efeito da estimaÃÃo de canal, alÃm de incluir a prÃpria tÃcnica de estimaÃÃo de canal como um parÃmetro a ser adaptado. / Nowadays the internet is a widely used tool and the great development and popularity of wireless technologies leads us to a future where the connectivity will be characterized as âanywhere, anytimeâ. Such characteristic is considered essential in 4G systems. However, for a satisfactory user experience a secure and efficient connectivity has to be always available. To obtain such efficiency, the research community has generated a number of promising solutions that achieve significative improvements in system performance, such as adaptive modulation and coding, space-time coding, multiple antennas and MIMO (Multiple Input Multiple Output ) channels, multicarrier modulation, multiuser detection, etc. [1]. Among these solutions, the system adaptation is a particularly interesting one, there is, the system must constantly adapt itself to achieve the best performance for each situation. However, one important premise for the system adaptation is the knowledge of the channel state information (CSI). To obtain this knowledge, several channel estimation strategies were proposed in the literature, each one with advantages and disadvantages. In this work we analyze the impact of channel estimation in the link adaptation through computer simulations1. Two channel estimation techniques with different characteristics were analyzed for some specific scenarios in a MIMO-OFDM (Multiple Input Multiple Output - Orthogonal Frequency Division Multiplexing ) system. To perform the analysis it was used a metric that consider the redundancy introduced to estimate the channel and the channel estimation error of each technique. The obtained results constitute curves that can be used to perform link adaptation in a more realistic way, that is, considering the effect of channel estimation. Besides, it is shown that even the choice of the channel estimation strategy can be an adaptable parameter so that the most adequate channel estimation strategy for each system state is used. / Nowadays the internet is a widely used tool and the great development and popularity of wireless technologies leads us to a future where the connectivity will be characterized as âanywhere, anytimeâ. Such characteristic is considered essential in 4G systems. However, for a satisfactory user experience a secure and efficient connectivity has to be always available. To obtain such efficiency, the research community has generated a number of promising solutions that achieve significative improvements in system performance, such as adaptive modulation and coding, space-time coding, multiple antennas and MIMO (Multiple Input Multiple Output ) channels, multicarrier modulation, multiuser detection, etc. [1]. Among these solutions, the system adaptation is a particularly interesting one, there is, the system must constantly adapt itself to achieve the best performance for each situation. However, one important premise for the system adaptation is the knowledge of the channel state information (CSI). To obtain this knowledge, several channel estimation strategies were proposed in the literature, each one with advantages and disadvantages. In this work we analyze the impact of channel estimation in the link adaptation through computer simulations1. Two channel estimation techniques with different characteristics were analyzed for some specific scenarios in a MIMO-OFDM (Multiple Input Multiple Output - Orthogonal Frequency Division Multiplexing ) system. To perform the analysis it was used a metric that consider the redundancy introduced to estimate the channel and the channel estimation error of each technique. The obtained results constitute curves that can be used to perform link adaptation in a more realistic way, that is, considering the effect of channel estimation. Besides, it is shown that even the choice of the channel estimation strategy can be an adaptable parameter so that the most adequate channel estimation strategy for each system state is used.

The increasing demand for high data rate transmission over broadband radio channels has imposed significant challenges in wireless communications. Accurate channel estimation has a major impact on the whole system performance. Specifically, reliable estimate of the channel state information (CSI) is more challenging for orthogonal frequency division multiplexing (OFDM) systems in doubly selective fading channels than for the slower fading channels over which OFDM has been deployed traditionally. With the help of a basis expansion model (BEM), a novel multivariate autoregressive (AR) process is developed to model the time evolution of the fast fading channel. Relying on pilot symbol aided modulation (PSAM), a novel Kalman smoothing algorithm based on a second-order dynamic model is exploited, where the mean square error (MSE) of the channel estimator is near to that of the optimal Wiener filter. To further improve the performance of channel estimation, a novel low-complexity iterative joint channel estimation and symbol detection procedure is developed for fast fading channels with a small number of pilots and low pilot power to achieve the bit error rate (BER) performance close to when the CSI is known perfectly. The new channel estimation symbol detection technique is robust to variations of the radio channel from the design values and applicable to multiple modulation and coding types. By use of the extrinsic information transfer (EXIT) chart, we investigate the convergence behavior of the new algorithm and analyze the modulation, pilot density, and error correction code selection for good system performance for a given power level. The algorithms developed in this thesis improve the performance of the whole system requiring only low ratios of pilot to data for excellent performance in fast fading channels. / Graduate