Advanced wireless communications and signal processing techniques for 6G wireless networks.

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Since 2019, commercial fifth-generation (5G) mobile networks have been rolled out worldwide and have already reached an enormous scale in some countries. For example, China has deployed over 500 000 5G base stations at the end of 2020, serving more than 100 million 5G subscribers. Since the original rollout of the first generation (1G), a new generation has appeared every one decade. Thus, in a decade, the successor of 5G is 6G wireless. Why is 6G needed? Currently, global wireless traffic is escalating exponentially, up to 5016 EB (exabyte) per month in 2030 compared with 62 EB per month in 2020. Thus, 5G may not fit the tremendous volume of mobile traffic in 2030 and beyond. Thus 2030 and beyond, the 6G system will provide extreme capacity, reliability, efficiency, etc. However, many classical wireless technologies have plateaued their performance and efficiency metrics (e.g., spectral efficiency (SE) and energy efficiency (EE)). This calls for novel system designs to attack these bottlenecks. To this end, it is necessary to optimize complex communication tasks such as energy use, spectrum sharing, multiple access, parameter estimation, resource allocation, and others. Thus, 6G requirements of high reliability, lower latency, and massive connectivity are realizable. Despite the exponential growth of wireless, the available spectrum is limited and cannot sustain the 1000 times expansion in users and devices with high data requirements. Therefore, researchers must improve the SE (the data transfer rate achieved per unit bandwidth). The primary way of enhancing the SE is with cognitive radio, which offers flexible, opportunistic spectrum access. Another way is non-orthogonal multiple access, where multiple users reuse the same spectrum simultaneously. These solutions thus provide higher SE than orthogonal approaches but have a significantly higher signal processing complexity. In the 6G landscape, the unlicensed parts of the new spectrum may suffer from interference by other systems and environmental noises. 6G devices, therefore, must be able to dynamically and cognitively select the best operating band. However, the cost of using these techniques is the high complexity of adaptation and estimation. Thus, this project will exploit concepts/techniques from machine learning techniques (deep learning, reinforcement learning, and others). Therefore, this project aims to develop holistic, integrated solutions involving classical techniques such as multiple-antenna wireless and emerging techniques such as intelligent panels, drones, energy harvesting, etc. By integrating these, research can find synergies and efficiencies. The project will develop new analysis and design methods, algorithms, and applications for beyond-5G and 6G wireless to achieve those gains. The timing is perfect, as the global research efforts into these systems are expanding right now.