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.

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.