Efficient Globally Optimal Resource Allocation in Wireless Interference Networks

Radio resource allocation in communication networks is essential to achieve optimal performance and resource utilization. In modern interference networks the corresponding optimization problems are often nonconvex and their solution requires significant computational resources. Hence, practical systems usually use algorithms with no or only weak optimality guarantees for complexity reasons. Nevertheless, asserting the quality of these methods requires the knowledge of the globally optimal solution. State-of-the-art global optimization approaches mostly employ Tuy's monotonic optimization framework which has some major drawbacks, especially when dealing with fractional objectives or complicated feasible sets. In this thesis, two novel global optimization frameworks are developed. The first is based on the successive incumbent transcending (SIT) scheme to avoid numerical problems with complicated feasible sets. It inherently differentiates between convex and nonconvex variables, preserving the low computational complexity in the number of convex variables without the need for cumbersome decomposition methods. It also treats fractional objectives directly without the need of Dinkelbach's algorithm. Benchmarks show that it is several orders of magnitude faster than state-of-the-art algorithms. The second optimization framework is named mixed monotonic programming (MMP) and generalizes monotonic optimization. At its core is a novel bounding mechanism accompanied by an efficient BB implementation that helps exploit partial monotonicity without requiring a reformulation in terms of difference of increasing (DI) functions. While this often leads to better bounds and faster convergence, the main benefit is its versatility. Numerical experiments show that MMP can outperform monotonic programming by a few orders of magnitude, both in run time and memory consumption. Both frameworks are applied to maximize throughput and energy efficiency (EE) in wireless interference networks. In the first application scenario, MMP is applied to evaluate the EE gain rate splitting might provide over point-to-point codes in Gaussian interference channels. In the second scenario, the SIT based algorithm is applied to study throughput and EE for multi-way relay channels with amplify-and-forward relaying. In both cases, rate splitting gains of up to 4.5% are observed, even though some limiting assumptions have been made

Research Issues, Challenges, and Opportunities of Wireless Power Transfer-Aided Full-Duplex Relay Systems

We present a comprehensive review for wireless power transfer (WPT)-aided full-duplex (FD) relay systems. Two critical challenges in implementing WPT-aided FD relay systems are presented, that is, pseudo FD realization and high power consumption. Existing time-splitting or power-splitting structure based-WPT-aided FD relay systems can only realize FD operation in one of the time slots or only forward part of the received signal to the destination, belonging to pseudo FD realization. Besides, self-interference is treated as noise and self-interference cancellation (SIC) operation incurs high power consumption at the FD relay node. To this end, a promising solution is outlined to address the two challenges, which realizes consecutive FD realization at all times and forwards all the desired signal to the destination for decoding. Also, active SIC, that is, analog/digital cancellation, is not required by the proposed solution, which effectively reduces the circuit complexity and releases high power consumption at the FD relay node. Specific classifications and performance metrics of WPT-aided FD relay systems are summarized. Some future research is also envisaged for WPT-aided FD systems

Resource Allocation for Wireless-Powered Full-Duplex Relaying Systems with Nonlinear Energy Harvesting Efficiency

In wireless power transfer (WPT)-assisted relaying systems, spectral efficiency (SE) of source-relay link plays a dominant role in system SE performance due to the limited transmission power at the WPT-aided relay. In this paper, we propose a novel protocol for a downlink orthogonal frequency division multiple access (OFDMA) system with a WPT-aided relay operating in full-duplex (FD) decode-and-forward (DF) mode, where the time slot durations of the source-relay and relay-users hops are designed to be dynamic, to enhance the utilization of degrees of freedom and hence the system SE. In particular, a multiple-input and signal-output (MISO) source-relay channel is considered to satisfy the stringent sensitivity of the energy harvesting (EH) circuit at the relay, while a single-input and single-output (SISO) relay-user channel is considered to alleviate the power consumption at the relay node. Taking into account the non-linearity of EH efficiency, a near-optimal iteration-based dynamic WPT-aided FD relaying (A-FR) algorithm is developed by jointly optimizing the time slot durations, subcarriers, and transmission power at the source and the relay. Furthermore, self-interference generated at the relay is utilized as a vital energy source rather than being canceled, which increases substantially the total energy harvested at the FD relay. We also reveal some implicit characteristics of the considered WPT-aided FD relaying system through intensive discussions. Simulation results confirm that the proposed A-FR achieves a significant enhancement in terms of SE with different relay's locations and the number of users, compared to the conventional symmetric WPT-aided FD relaying (S-FR) and the time-switching-based WPT-aided FD relaying (TS-FR) benchmarks

Robust Transceiver Design for SWIPT DF MIMO Relay Systems With Time-Switching Protocol

In this article, we investigate a dual-hop simultaneous wireless information and power transfer decode-and-forward multiple-input and multiple-output relay communication system, in which the relay node harvests energy based on the radio frequency (RF) signal transmitted from the source node through the time-switching (TS) protocol to decode and forward the re-encoded information to the destination node. With the consideration of the channel estimation error, the joint optimization of the TS factor and source and relay precoding matrices is proposed with robustness against the channel state information mismatch to maximize the mutual information (MI) between the source and destination nodes. We derive the optimal structure of the source and relay precoding matrices to simplify the transceiver optimization problem under fixed and flexible power constraints. Numerical examples demonstrate that the proposed algorithms with robustness provide better MI performance compared to the nonrobust algorithm

Transceiver Optimization for Wireless Powered Time-Division Duplex MU-MIMO Systems: Non-Robust and Robust Designs

Wireless powered communication (WPC) has been considered as one of the key technologies in the Internet of Things (IoT) applications. In this paper, we study a wireless powered time-division duplex (TDD) multiuser multiple-input multiple-output (MU-MIMO) system, where the base station (BS) has its own power supply and all users can harvest radio frequency (RF) energy from the BS. We aim to maximize the users' information rates by jointly optimizing the duration of users' time slots and the signal covariance matrices of the BS and users. Different to the commonly used sum rate and max-min rate criteria, the proportional fairness of users' rates is considered in the objective function. We first study the ideal case with the perfect channel state information (CSI), and show that the non-convex proportionally fair rate optimization problem can be transformed into an equivalent convex optimization problem. Then we consider practical systems with imperfect CSI, where the CSI mismatch follows a Gaussian distribution. A chance-constrained robust system design is proposed for this scenario, where the Bernstein inequality is applied to convert the chance constraints into the convex constraints. Finally, we consider a more general case where only partial knowledge of the CSI mismatch is available. In this case, the conditional value-at-risk (CVaR) method is applied to solve the distributionally robust system rate optimization problem. Simulation results are presented to show the effectiveness of the proposed algorithms