Twenty-Five Years of Advances in Beamforming: From Convex and Nonconvex Optimization to Learning Techniques

Beamforming is a signal processing technique to steer, shape, and focus an electromagnetic wave using an array of sensors toward a desired direction. It has been used in several engineering applications such as radar, sonar, acoustics, astronomy, seismology, medical imaging, and communications. With the advances in multi-antenna technologies largely for radar and communications, there has been a great interest on beamformer design mostly relying on convex/nonconvex optimization. Recently, machine learning is being leveraged for obtaining attractive solutions to more complex beamforming problems. This article captures the evolution of beamforming in the last twenty-five years from convex-to-nonconvex optimization and optimization-to-learning approaches. It provides a glimpse of this important signal processing technique into a variety of transmit-receive architectures, propagation zones, paths, and conventional/emerging applications

Resource allocation optimization for future wireless communication systems

To meet the ever-increasing requirements of high data rate, extremely low latency, and ubiquitous connectivity for the fifth generation (5G) and beyond 5G (B5G) wireless communications, there is imperious demands for advanced communication system design. Particularly, efficient resource allocation is regarded as the fundamental challenge whereas an effective way to improve system performance. The term ”resource” refers to scare quantities such as limited bandwidth, power and time in wireless communications. Moreover, the development of wireless communication systems is accompanied by the innovation of applied technologies. Motivated by the above observations, efficient resource allocation strategies for several promising 5G and B5G technologies in terms of non-orthogonal multiple access (NOMA), mobile edge computing (MEC) and Long Range (LoRa) are addressed and investigated in this thesis. Firstly, the strong user’s data rate maximization problem for simultaneous wireless information and power transfer (SWIPT)-enabled cooperative NOMA system, considering the presence of channnel uncertainties, is proposed and investigated. Two major channel uncertainty design criteria in terms of the outage-based constraint design and the worst-case based optimization are adopted. In addition to the high-complexity optimal two-dimensional exhaustive search method, the low-complexity suboptimal solution is further proposed. The advantages of SWIPT-enabled cooperation in robust NOMA are confirmed with simulations. Secondly, considering the application of NOMA and user cooperation (UC) in a wireless powered MEC under the non-linear energy harvesting model, a computation efficiency maximization problem subject to the quality of service (QoS) and power budget constraint, is studied and analyzed. The formulated problem is nonconvex, which is challenging to solve. The semidefinite relaxation (SDR) approach is first applied, then the sequential convex approximation (SCA)-based solution is further proposed to maximize the system computation efficiency. Finally, taking into consideration the aspect of energy efficiency (EE), this thesis investigates the energy efficient resource allocation in LoRa networks to maximize the system EE (SEE) and the minimal EE (MEE) of LoRa users, respectively. The energy efficient resource allocation is formulated as NP-hard problems. A low-complexity user scheduling scheme based on matching theory is proposed to allocate users to channels, then the heuristic SF assignment solution is designed for LoRa users scheduled on the same channel. The optimal power allocation strategy is further proposed to maximize the corresponding EE