Abstract: Simultaneous wireless information and power transfer (SWIPT) is a promising solution for enabling long-life, and self-sustainable wireless networks. In this thesis, we propose a practical non-linear energy harvesting (EH) model and design a resource allocation algorithm for SWIPT systems. In particular, the algorithm design is formulated as a non-convex optimization problem for the maximization of the total harvested power at the EH receivers subject to quality of service (QoS) constraints for the information decoding (ID) receivers. To circumvent the non-convexity of the problem, we transform the corresponding non-convex sum-of-ratios objective function into an equivalent objec- tive function in parametric subtractive form. Furthermore, we design a computationally efficient iterative resource allocation algorithm to obtain the globally optimal solution. Numerical results illustrate significant performance gain in terms of average total har- vested power for the proposed non-linear EH receiver model, when compared to the traditional linear model.

Abstract: Simultaneous wireless information and power transfer (SWIPT) provides a promising solution for enabling perpetual wireless networks. As energy efficiency (EE) is an im- portant evaluation of system performance, this thesis studies energy-efficient resource allocation algorithm designs in SWIPT systems. We first investigate the trade-off between the EE for information transmission, the EE for power transfer, and the total transmit power in a basic SWIPT system with separated receivers. A multi-objective optimization problem is formulated under the constraint of maximum transmit power. We propose an algorithm which achieves flexible resource allocation for energy efficiencies maxi- mization and transmit power minimization. The trade-off region of the system design objectives is shown in simulation results. Further, we consider secure communication in a SWIPT system with power splitting receivers. Artificial noise is injected to the com- munication channel to combat the eavesdropping capability of potential eavesdroppers. A power-efficient resource allocation algorithm is developed when multiple legitimate information receivers and multi-antenna potential eavesdroppers co-exist in the system. Simulation results demonstrate a significant performance gain by the proposed optimal algorithm compared to suboptimal baseline schemes.

Abstract: Multicast radio resource allocation for multimedia applications is a challenging problem in wireless networks since different users experience different channel conditions. The simplest solution is to allocate the system resources with respect to the user with the weakest channel condition, e.g. the cell edge user. Although such method is able to guarantee the quality of service (QoS) of the weakest user, it does not fully utilize the radio resources and impairs the QoS of other users. In the literature, there are many approaches in striking a balance between spectral efficiency and guaranteeing QoS in multicast systems. One appealing approach to the problem is to use multiple layered coding. Basically, using layered multimedia transmission enable for more degrees of freedom for resource allocation, and hence allows all users to receive multimedia streams most of the time with different rates/qualities, depending on their own channel states. Indeed, the concept of layered coding has been applied to video standards such as H.264. Nevertheless, the introduction of layered coding requires an unequal error protection over different layers which are not considered in most resource allocation algorithms. In this Master thesis, we aim to design a practical resource allocation algorithm which takes into account unequal error protection for multicast wireless communication networks.

Abstract: In this thesis, we study the downlink multiuser scheduling and power allocation problem for systems with simultaneous wireless information and power transfer (SWIPT). In the first part of the thesis, we focus on multiuser scheduling. We design optimal scheduling algorithms that maximize the long-term average system throughput under different fairness requirements, such as proportional fairness and equal throughput fairness. In particular, the algorithm designs are formulated as non-convex optimization problems which take into account the minimum required average sum harvested energy in the system. The problems are solved by using convex optimization techniques and the proposed optimization framework reveals the tradeoff between the long-term average system throughput and the sum harvested energy in multiuser systems with fairness constraints. Simulation results demonstrate that substantial performance gains can be achieved by the proposed optimization framework compared to existing suboptimal scheduling algorithms from the literature. In the second part of the thesis, we investigate the joint user scheduling and power allocation algorithm design for SWIPT systems. The algorithm design is formulated as a non-convex optimization problem which maximizes the achievable rate subject to a minimum required average power transfer. Subsequently, the non-convex optimization problem is reformulated by big-M method which can be solved optimally. Furthermore, we show that joint power allocation and user scheduling is an efficient way to enlarge the feasible trade-off region for improving the system performance in terms of achievable data rate and harvested energy.

Abstract: High data rate, reliable communication, and low power consumption are the foremost demands for next generation of wireless communication systems. The key challenge to the design of communication systems is to combat the detrimental effects of channel fading, noise, and high power consumption. Wireless systems are often impaired by non-Gaussian noise, and the performance of systems designed for Gaussian noise can degrade if non-Gaussian noises are present but are not taken into account. Thus, it is imperative to analyze systems that are impaired by non-Gaussian noise and to manage their resources better to improve overall performance. Furthermore, there is significant interest in using renewable energy for wireless systems. However, energy harvesting (EH) is a random process and the harvested energy should be expended judiciously to maximize aggregate system throughput. In this thesis, we consider wireless systems that are impaired by Gaussian and non-Gaussian noise and powered by conventional energy sources and energy harvesters and propose appropriate resource allocation schemes for these systems. First, we propose optimal and fair power allocation schemes for a cooperative relay network with amplify-and-forward relays that employs best and partial relay selections and is impaired by Gaussian and non-Gaussian noise. We derive closed- form expressions of asymptotic bit error rate and use this expression to allocate transmit powers for different nodes with necessary energy consumption constraints. Second, we consider a network comprising a source, a relay, and a destination, where the source and the relay are EH nodes. We consider conventional and buffer- aided link adaptive relaying protocols, and propose offline and online resource allocation schemes that maximize the system throughput. Thirdly, we consider a multi-relay network with EH nodes and propose offline and online joint relay selection and power allocation schemes that maximize the system throughput. Fourth, we consider a single source-destination link, where the source has a hybrid energy supply comprised of constant energy source and energy harvester. We propose offline and online power allocation schemes that minimize the energy consumption from the constant energy source and thereby utilize the harvested energy effectively.