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

自2019年以来，商用第五代（5G）移动的网络已在全球范围内推出，并已在一些国家达到巨大规模。例如，截至2020年底，中国已部署超过50万个5G基站，服务超过1亿5G用户。自第一代（1G）最初推出以来，每十年就会出现新一代。因此，在十年内，5G的继任者是6 G无线。为什么需要6 G？目前，全球无线流量呈指数级增长，到2030年将达到每月5016 EB（EB），而2020年为每月62 EB。因此，5G可能无法适应2030年及以后的巨大移动的流量。因此，2030年及以后，6 G系统将提供极端的容量、可靠性、效率等。然而，许多经典无线技术已经使其性能和效率指标趋于稳定（例如，频谱效率（SE）和能量效率（EE））。这就需要新的系统设计来解决这些瓶颈。为此，有必要优化复杂的通信任务，例如能量使用、频谱共享、多址接入、参数估计、资源分配等。因此，高可靠性、低延迟和大规模连接的6 G要求是可以实现的。尽管无线技术呈指数级增长，但可用频谱有限，无法承受高数据需求的用户和设备1000倍的扩展。因此，研究人员必须提高SE（每单位带宽实现的数据传输速率）。增强SE的主要方法是使用认知无线电，它提供灵活的机会主义频谱接入。另一种方式是非正交多址，其中多个用户同时重用相同的频谱。因此，这些解决方案提供比正交方法更高的SE，但是具有显著更高的信号处理复杂度。在6 G环境中，新频谱的未授权部分可能会受到其他系统和环境噪声的干扰。因此，6 G设备必须能够动态和认知地选择最佳工作频段。然而，使用这些技术的成本是适应和估计的高复杂性。因此，该项目将利用机器学习技术（深度学习，强化学习等）的概念/技术。 因此，该项目旨在开发整体的集成解决方案，涉及多天线无线等经典技术和智能面板、无人机、能量收集等新兴技术。该项目将为超5G和6 G无线开发新的分析和设计方法、算法和应用，以实现这些收益。时机是完美的，因为全球对这些系统的研究正在扩大。

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.