Abstract Reverse link feedback power control in subject to a feedback delay and in conjuction with diversity is considered over a frequency-nonselective slow Rayleigh fading channel. The transmission power of a mobile station is adjusted as a function of fed back estimated channel state information, so that the average error probability is minimized when the average transmission power is fixed. The channel state is estimated by using known, constant-power pilot symbols. In each frame, a time multiplexed pilot symbol is transmitted in addition to the antipodal data symbols. In the literature, feedback MMSE (minimum mean-square error) power control has been analyzed in the case of a random time-invariant channel. Therein the frame size was two, i.e. one data and one pilot symbol were transmitted in each frame. Also, the fading gain was estimated by a one-shot MMSE estimator. This author's main contribution is that the aforementioned analysis has been extended to a more general system model in which the frame size is arbitrary, and in which the time-variant fading gain is estimated by an optimal MMSE estimator. For power control purposes, the estimator has to be a predictor since feedback requires causality. First, in order to avoid a delay in detection, the predictor is used in both power control and detection. In the case of a frame size of two, the performance of feedback MMSE power control employing the predictor is compared to that of a system using the one-shot estimator. Then, the performance of feedback MMSE power control with an optimal frame size is evaluated. Finally, the system performance is derived when a smoother is employed in detection, and the additional effects of a feedback delay and diversity on the performance are investigated. The performance difference between optimal (channel states are assumed to be known) and MMSE power control using a one-shot estimator is found to be significant at large signal-to-noise ratios (SNR's). This is in contradiction with the result presented earlier in the literature. The reason for the large performance difference is that the SNR of the channel estimate is small, since each estimate is computed using only one pilot symbol. The performance difference between optimal and MMSE power control with the predictor is smaller than said difference in the case of the one-shot estimator because the estimate is averaged over many pilot symbols. It is also observed that the lag error of the estimator considerably reduces the benefit of MMSE power control, even when the channel changes very slowly. To diminish the lag error, and to achieve good performance, a large number of estimator coefficients is required. It is well known that fixed-step adjustment closed loop power control attempts to compensate for all changes caused by the channel. In contrast, according to Monte Carlo simulations, MMSE power control did not attempt to compensate for the deepest fades. At other time instants, it strives to set the received SNR to an approximately constant level, which depends on the bit-error rate (BER) target. Increasing the frame size from the value of two not only improves the spectrum utilization, but was also shown to yield better performance for the pilot symbol system with MMSE power control over a slowly fading channel. Also, a clear performance improvement was achieved by using the smoother in detection. The performance loss resulting from a feedback delay of 10-20 % from the channel coherence time was shown to be small with reasonable BER values. Estimation errors were shown to diminish the benefit of power control when the diversity order was two, compared to the case of no diversity.

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

In mobile communication channels information symbols are transmitted through a communication channel that is prone to fading and multipath propagation. At the receiver, the effect of multipath propagation is reduced by a process called channel equalization. Channel equalization relies on an accurate estimate of the channel state information (CSI). This estimate is obtained using a channel estimation algorithm. Mobile WiMAX is a recently released technology that makes use of an orthogonal frequency division multiplexing (OFDM) based physical layer to transmit information over a wireless communication channel. In this dissertation, frequency and time domain channel estimation methods typically used in classical OFDM systems, using block and comb type pilot insertion schemes, were analyzed and adopted for mobile WiMAX. Least squares (LS) and linear minimum mean square error (LMMSE) channel estimation methods were considered in the case of block type pilot insertion. In the case of comb type pilot insertion, piecewise constant, linear, spline cubic as well as discrete Wiener interpolation methods were considered. A mobile WiMAX simulation platform was developed as part of the dissertation to evaluate and compare the performance of these different channel estimation methods. It was found that the performance of the channel estimation methods, applied to a real world mobile WiMAX simulation platform, conforms to the expected performance of the corresponding classical OFDM channel estimation methods found in literature. / Dissertation (MEng)--University of Pretoria, 2009. / Electrical, Electronic and Computer Engineering / unrestricted

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.

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.

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

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.