We investigate the effects of quantized channel state information (CSI) on the performance of resource allocation algorithms in wireless networks. The thesis starts with a brief overview of a specific type of quantizer, referred to as a conservative quantizer where we propose the optimality and sufficiency conditions as well as practical methods to find such quantizers. We apply this theory to the quantization of transmitter CSI in point-to-point Gaussian channels and transmission under short-term power constraints. Next, we show that in a multiple-node decode-and-forward (DF) cooperative network, the same structure for quantizer is close to op- timal for the sum-rate objective function. Based on a proposed upper bound on the rate loss in such scenarios, we also argue that the quantizer should assign uneven numbers of quantization bits to different links in the network. The simulation results show that given a target rate loss level, through quantization and bit allocation, there is, on average, 0.5−1 bits per link savings in CSI feedback requirements compared to the uniform and equal bit allocation approaches. Given the many benefits in non-uniform allocation of CSI rate in the network, we formulate a generalized bit allocation scheme which is extensible to arbitrary classes of network resource allocation problems. In the last part of this thesis, we focus on power control in an interference network and then, investigate the effects of CSI imperfections on the performance of power control algorithms. First, we propose an iterative power control algorithm based on a fixed-point iteration and prove its local convergence. Then, we show that for a centralized implementation of the power control algorithm, a uniform in dB (geometric) quantizer of channel power is efficient. Based on this choice of channel quantizer, we propose a bound on rate loss in terms of the resolution of the ii deployed quantizer, where a 3 dB in quantization error is shown to contribute to a maximum of 1 bit rate loss at each user. Similarly to the previous scenario, the upper bound suggests that an uneven assignment of numbers of quantization levels leads to smaller distortion. Based on this bound, we develop the corresponding bit allocation laws. We also investigate the effects of CSI errors on the performance of distributed power control algorithms and show that, compared to the centralized case, the distributed algorithm could lead to a further SINR loss of up to 3 dB for one or more transmitters. This error is due to the fact that because of CSI errors, the estimated interference level at each receiver is different from the induced interference wireless transmitters expect.

We investigate the effects of quantized channel state information (CSI) on the performance of resource allocation algorithms in wireless networks. The thesis starts with a brief overview of a specific type of quantizer, referred to as a conservative quantizer where we propose the optimality and sufficiency conditions as well as practical methods to find such quantizers. We apply this theory to the quantization of transmitter CSI in point-to-point Gaussian channels and transmission under short-term power constraints. Next, we show that in a multiple-node decode-and-forward (DF) cooperative network, the same structure for quantizer is close to op- timal for the sum-rate objective function. Based on a proposed upper bound on the rate loss in such scenarios, we also argue that the quantizer should assign uneven numbers of quantization bits to different links in the network. The simulation results show that given a target rate loss level, through quantization and bit allocation, there is, on average, 0.5−1 bits per link savings in CSI feedback requirements compared to the uniform and equal bit allocation approaches. Given the many benefits in non-uniform allocation of CSI rate in the network, we formulate a generalized bit allocation scheme which is extensible to arbitrary classes of network resource allocation problems. In the last part of this thesis, we focus on power control in an interference network and then, investigate the effects of CSI imperfections on the performance of power control algorithms. First, we propose an iterative power control algorithm based on a fixed-point iteration and prove its local convergence. Then, we show that for a centralized implementation of the power control algorithm, a uniform in dB (geometric) quantizer of channel power is efficient. Based on this choice of channel quantizer, we propose a bound on rate loss in terms of the resolution of the ii deployed quantizer, where a 3 dB in quantization error is shown to contribute to a maximum of 1 bit rate loss at each user. Similarly to the previous scenario, the upper bound suggests that an uneven assignment of numbers of quantization levels leads to smaller distortion. Based on this bound, we develop the corresponding bit allocation laws. We also investigate the effects of CSI errors on the performance of distributed power control algorithms and show that, compared to the centralized case, the distributed algorithm could lead to a further SINR loss of up to 3 dB for one or more transmitters. This error is due to the fact that because of CSI errors, the estimated interference level at each receiver is different from the induced interference wireless transmitters expect.

The need for reliable, high-throughput, mobile wireless communication technologies has never been greater as increases in the demand for on-the-go access to information, entertainment, and other electronic services continues. Two such technologies, which are at the forefront of current research efforts, are orthogonal frequency division multiplexing (OFDM) and multiple-input multiple-output (MIMO) systems, their union being known simply as MIMO-OFDM. The successful performance of these technologies depends upon the availability of accurate information concerning the wireless communication channel. In this dissertation, several issues related to quality of this channel state information (CSI) are studied. Specifically, the first part of this dissertation considers the design of optimal pilot signals for OFDM systems. The optimization is addressed via lower bounds on the estimation error variance, which bounds are given by formulations of the Cram'{e}r-Rao bound (CRB). The second part of this dissertation uses the CRB once again, this time as a tool for evaluating the potential performance of MIMO-OFDM channel estimation and prediction. Bounds are found for several parametric time-varying wideband MIMO-OFDM channel models, and numerical evaluations of these bounds are used to illuminate several interesting features regarding the estimation and prediction of MIMO-OFDM channels. The final part of this dissertation considers the problem of MIMO multiplexing using SVD-based methods when only imperfect CSI is available. For this purpose, general per-MIMO-subchannel signal and interference-plus-noise power expressions are derived to quantify the effects of CSI imperfections, and these expressions are then used to find robust MIMO-SVD power and bit allocations which maintain good overall performance in spite of imperfect CSI.

Dans la première partie de la thèse, on se concentre sur la conception d'un système de communication par satellite complet se basant sur la construction de faisceaux adaptatifs aux terminaux mobiles. Comparé à la construction classique de faisceaux fixes, le système à faisceaux adaptatifs peut considérablement améliorer la capacité du système en termes du nombre de STs desservies et de l'efficacité énergétique. Pour la conception du système à faisceaux adaptatifs, les informations sur l'état de canal (CSI) sont essentielles. Vu que le temps de propagation est trop long par rapport au temps de cohérence du canal, le CSI instantané est déjà périmé lorsqu'il est reçu pour la construction des faisceaux. Cependant, une partie de l'information du canal, plus particulièrement, les vecteurs de directivité ont une variation assez lente. On utilise cette connaissance partielle du CSI pour concevoir le système à base de faisceaux adaptatifs. Afin d'estimer les vecteurs de directivité, on propose un algorithme basé sur un critère de minimisation de l'erreur quadratique. Puis, basées sur l'estimation des vecteurs de directivité, on présente deux approches heuristiques pour la conception des faisceaux. En outre, on propose également deux approches qui reposent sur l'estimation de la directivité pour la détection des STs et la résolution possible des collisions sur le canal d'accès aléatoire au satellite. Comme la performance du système SDMA dépend fortement des positions spatiales des STs co-existants, on propose deux algorithmes de faible complexité pour l'attribution des fréquences dans le système de communication par satellite / In the first part of the thesis, we focus on the design of a complete satellite communication system adopting adaptive beamforming with mobile satellite terminals. Compared with conventional fixed beamforming, adaptive beamforming can signi_cantly improve the capacity of a satellite system in terms of served satellite terminals (ST) and power e_ciency. For the design of an adaptive beamforming system, channel state information (CSI) is critical. Since the propagation delay is too long compared to the coherence time of the channel, the instantaneous CSI is already stale when processed for beamforming. However, some parts of the channel, more speci_cally, directivity vectors change quite slowly. We utilize this partial knowledge of CSI to design an adaptive beamforming system. In order to estimate the directivity vectors, we propose an algorithm based on a least square error criterion. Then, based on the estimation of directivity vectors, we propose two heuristics approaches to the design of adaptive beamforming. Additionally, we also propose two approaches, based on directivity estimation for the detection of transmitting terminals and the possible resolution of collisions in the random access channel of the satellite system. Since SDMA system performance depends strongly on the spatial locations of co-existing terminals, we also propose two low complexity algorithms for frequency allocation in a satellite communication system. Finally, we simulate a complete satellite system, including a random access channel and a connection-oriented channel. We analyze the system performance and compare it to conventional fixed beamforming systems

Multi-antenna communication systems are attracting research interest as a means to increase the information capacity, reliability, and spectral efficiency of wireless information transfer. Ray-tracing methods predict the behavior of wireless channels using a model of the propagation environment and are a low-cost alternative to direct measurements. We use ray tracing simulations to validate the statistical time and angle of arrival characteristics of an indoor multipath channel and compare model parameter estimates with estimates derived from channel sounding measurements. Ray tracing predicts the time and angle clustering of multipaths observed in the measurements and provides model parameter estimates which are closely correlated with measured estimates. The ray tracing parameters relating to power characteristics show more deviation from measurements than the time and angle related parameters. Our results also indicate that the description of reflective scatterers in the propagation environment is more important to the quality of the predicted statistical behavior than the description of bulk materials. We use a ray synthesis model to investigate means of efficiently representing the channel for feedback information to the transmitter as a means to increase the information capacity. Several methods of selecting the ray-model feedback information are demonstrated with results from simulated and measured channels. These results indicate that an ESPRIT algorithm coupled with ad hoc transmit/receive pairing can yield better than 90% of the ideal waterfilling capacity when adequate training-based channel estimates are available. Additionally, we investigate a covariance feedback method for providing channel feedback for increased capacity. Both the ray-based and covariance-based feedback methods yield their highest capacity improvements when the signal to noise ratio is low. This results because of the larger benefit of focusing transmit power into the most advantageous eigenmodes of the channel when fewer eigenmodes have power allocated to them by the waterfilling capacity solution. In higher signal to noise ratio cases, more eigenmodes of the channel receive power when waterfilling, and the capacity improvement from feedback information decreases relative to a uniform power allocation. In general, ray model feedback methods are preferable because the covariance feedback quickly requires higher computational effort as the array sizes increase and typically results in lower capacity for a given amount of feedback information.

In telecommunications, distributed cooperative communications refer to techniques which allow different users in a wireless network to share or combine their information in order to increase diversity gain or power gain. Unlike conventional point-to-point communications maximizing the performance of the individual link, distributed cooperative communications enable multiple users to collaborate with each other to achieve an overall improvement in performance, e.g., improved range and data rates. The first part of this dissertation focuses the problem of jointly decoding binary messages from a single distant transmitter to a cooperative receive cluster. The outage probability of distributed reception with binary hard decision exchanges is compared with the outage probability of ideal receive beamforming with unquantized observation exchanges. Low- dimensional analysis and numerical results show, via two simple but surprisingly good approximations, that the outage probability performance of distributed reception with hard decision exchanges is well-predicted by the SNR of ideal receive beamforming after subtracting a hard decision penalty of slightly less than 2 dB. These results, developed in non-asymptotic regimes, are consistent with prior asymptotic results (for a large number of nodes and low per-node SNR) on hard decisions in binary communication systems. We next consider the problem of estimating and tracking channels in a distributed transmission system with multiple transmitters and multiple receivers. In order to track and predict the effective channel between each transmit node and each receive node to facilitate coherent transmission, a linear time-invariant state- space model is developed and is shown to be observable but nonstabilizable. To quantify the steady-state performance of a Kalman filter channel tracker, two methods are developed to efficiently compute the steady-state prediction covariance. An asymptotic analysis is also presented for the homogenous oscillator case for systems with a large number of transmit and receive nodes with closed-form results for all of the elements in the asymptotic prediction covariance as a function of the carrier frequency, oscillator parameters, and channel measurement period. Numeric results confirm the analysis and demonstrate the effect of the oscillator parameters on the ability of the distributed transmission system to achieve coherent transmission. In recent years, the development of efficient radio frequency (RF) radiation wireless power transfer (WPT) systems has become an active research area, motivated by the widespread use of low-power devices that can be charged wirelessly. In this dissertation, we next consider a time division multiple access scenario where a wireless access point transmits to a group of users which harvest the energy and then use this energy to transmit back to the access point. Past approaches have found the optimal time allocation to maximize sum throughput under the assumption that the users must use all of their harvested power in each block of the "harvest-then-transmit" protocol. This dissertation considers optimal time and energy allocation to maximize the sum throughput for the case when the nodes can save energy for later blocks. To maximize the sum throughput over a finite horizon, the initial optimization problem is separated into two sub-problems and finally can be formulated into a standard box- constrained optimization problem, which can be solved efficiently. A tight upper bound is derived by relaxing the energy harvesting causality. A disadvantage of RF-radiation based WPT is that path loss effects can significantly reduce the amount of power received by energy harvesting devices. To overcome this problem, recent investigations have considered the use of distributed transmit beamforming (DTB) in wireless communication systems where two or more individual transmit nodes pool their antenna resources to emulate a virtual antenna array. In order to take the advantages of the DTB in the WPT, in this dissertation, we study the optimization of the feedback rate to maximize the energy efficiency in the WPT system. Since periodic feedback improves the beamforming gain but requires the receivers to expend energy, there is a fundamental tradeoff between the feedback period and the efficiency of the WPT system. We develop a new model to combine WPT and DTB and explicitly account for independent oscillator dynamics and the cost of feedback energy from the receive nodes. We then formulate a "Normalized Weighted Mean Energy Harvesting Rate" (NWMEHR) maximization problem to select the feedback period to maximize the weighted averaged amount of net energy harvested by the receive nodes per unit of time as a function of the oscillator parameters. We develop an explicit method to numerically calculate the globally optimal feedback period.

Advanced mobile communication systems are characterized by a dense deployment of different types of wireless access points. Since these systems are primarily limited by interference, multiple-input multiple-output (MIMO) techniques as well as coordinated transmission and detection schemes are necessary to mitigate this limitation. Thus, mobile communication systems become more complex which requires that also the mathematical tools for their theoretical analysis must evolve. These must be able to take the most important system characteristics into account, such as fading, path loss, and interference. The aim of this thesis is to develop such tools based on large random matrix theory and to demonstrate their usefulness with the help of several practical applications, such as the performance analysis of network MIMO and large-scale MIMO systems, the design of low-complexity polynomial expansion detectors, and the study of random beamforming techniques as well as multi-hop relay and double-scattering channels. The methods developed in this work provide deterministic approximations of the system performance which become arbitrarily tight in the large system regime with an unlimited number of transmitting and receiving devices. This leads in many cases to simple and close approximations of the finite-size system performance and allows one to draw relevant conclusions about the most significant parameters. One can think of these methods as a way to provide a deterministic abstraction of the physical layer which substantially reduces the system complexity. Due to this complexity reduction, it is possible to carry out a system optimization which would be otherwise intractable.

The topic of this thesis is signaling design for data transmission through wireless channels between a transmitter and a receiver that can both be equipped with one or more antennas. In particular, the focus is on channels where the propagation coefficients between each transmitter--receiver antenna pair are only partially known or completetly unknown to the receiver and unknown to the transmitter. A standard signal design approach for this scenario is based on separate training for the acquisition of channel knowledge at the receiver and subsequent error-control coding for data detection over channels that are known or at least approximately known at the receiver. If the number of parameters to estimate in the acquisition phase is high as, e.g., in a frequency-selective multiple-input multiple-output channel, the required amount of training symbols can be substantial. It is therefore of interest to study signaling schemes that minimize the overhead of training or avoid a training sequence altogether. Several approaches for the design of such schemes are considered in this thesis. Two different design methods are investigated based on a signal representation in the time domain. In the first approach, the symbol alphabet is preselected, the design problem is formulated as an integer optimization problem and solutions are found using simulated annealing. The second design method is targeted towards general complex-valued signaling and applies a constrained gradient-search algorithm. Both approaches result in signaling schemes with excellent detection performance, albeit at the cost of significant complexity requirements. A third approach is based on a signal representation in the frequency domain. A low-complexity signaling scheme performing differential space--frequency modulation and detection is described, analyzed in detail and evaluated by simulation examples. The mentioned design approaches assumed that the receiver has no knowledge about the value of the channel coefficients. However, we also investigate a scenario where the receiver has access to an estimate of the channel coefficients with known error statistics. In the case of a frequency-flat fading channel, a design criterion allowing for a smooth transition between the corresponding criteria for known and unknown channel is derived and used to design signaling schemes matched to the quality of the channel estimate. In particular, a constellation design is proposed that offers a high level of flexibility to accomodate various levels of channel knowledge at the receiver. / QC 20101014

<p>The topic of this thesis is signaling design for data transmission through wireless channels between a transmitter and a receiver that can both be equipped with one or more antennas. In particular, the focus is on channels where the propagation coefficients between each transmitter--receiver antenna pair are only partially known or completetly unknown to the receiver and unknown to the transmitter.</p><p>A standard signal design approach for this scenario is based on separate training for the acquisition of channel knowledge at the receiver and subsequent error-control coding for data detection over channels that are known or at least approximately known at the receiver. If the number of parameters to estimate in the acquisition phase is high as, e.g., in a frequency-selective multiple-input multiple-output channel, the required amount of training symbols can be substantial. It is therefore of interest to study signaling schemes that minimize the overhead of training or avoid a training sequence altogether.</p><p>Several approaches for the design of such schemes are considered in this thesis. Two different design methods are investigated based on a signal representation in the time domain. In the first approach, the symbol alphabet is preselected, the design problem is formulated as an integer optimization problem and solutions are found using simulated annealing. The second design method is targeted towards general complex-valued signaling and applies a constrained gradient-search algorithm. Both approaches result in signaling schemes with excellent detection performance, albeit at the cost of significant complexity requirements.</p><p>A third approach is based on a signal representation in the frequency domain. A low-complexity signaling scheme performing differential space--frequency modulation and detection is described, analyzed in detail and evaluated by simulation examples.</p><p>The mentioned design approaches assumed that the receiver has no knowledge about the value of the channel coefficients. However, we also investigate a scenario where the receiver has access to an estimate of the channel coefficients with known error statistics. In the case of a frequency-flat fading channel, a design criterion allowing for a smooth transition between the corresponding criteria for known and unknown channel is derived and used to design signaling schemes matched to the quality of the channel estimate. In particular, a constellation design is proposed that offers a high level of flexibility to accomodate various levels of channel knowledge at the receiver.</p>

The coding and feedback inaccuracies of the channel state information (CSI) in limited feedback multiple-input multiple-output (MIMO) wireless systems can severely impact the achievable data rate and reliability. The CSI is mathematically represented as a Grassmann manifold or manifold of unitary matrices. These are non-Euclidean spaces with special constraints that makes efficient and high fidelity coding especially challenging. In addition, the CSI inaccuracies may occur due to digital representation, time variation, and delayed feedback of the CSI. To overcome these inaccuracies, the manifold structure of the CSI can be exploited. The objective of this dissertation is to develop a new signal processing techniques on the manifolds to harvest the benefits of MIMO wireless systems. First, this dissertation presents the Kerdock codebook design to represent the CSI on the Grassmann manifold. The CSI inaccuracy due to digital representation is addressed by the finite alphabet structure of the Kerdock codebook. In addition, systematic codebook construction is identified which reduces the resource requirement in MIMO wireless systems. Distance properties on the Grassmann manifold are derived showing the applicability of the Kerdock codebook to beam-forming and spatial multiplexing systems. Next, manifold-constrained algorithms to predict and encode the CSI with high fidelity are presented. Two prominent manifolds are considered; the Grassmann manifold and the manifold of unitary matrices. The Grassmann manifold is a class of manifold used to represent the CSI in MIMO wireless systems using specific transmission strategies. The manifold of unitary matrices appears as a collection of all spatial information available in the MIMO wireless systems independent of specific transmission strategies. On these manifolds, signal processing building blocks such as differencing and prediction are derived. Using the proposed signal processing tools on the manifold, this dissertation addresses the CSI coding accuracy, tracking of the CSI under time variation, and compensation techniques for delayed CSI feedback. Applications of the proposed algorithms in single-user and multiuser systems show that most of the spatial benefits of MIMO wireless systems can be harvested. / text