Integrated Waveform and Intelligence (IWAI): Physical Layer Solutions to Sustainable 6G

通信和信息网络在我们的日常生活中无处不在，据估计，信息和通信技术（ICT）贡献了1.8%-2.8%的碳排放。5G的成功部署揭示了5G通信系统的总功耗比4G高得多，这是由于5G中更密集地放置基站。最近的一项研究还显示，由于天线数量更多，频谱带宽占用更广，基站部署策略更密集，潜在的6 G系统将消耗比5G系统高近50倍的功率。5G技术已经标准化并部署在真实的生活中，而没有优先考虑净零可持续性。因此，要引领下一代零净通信的可持续创新，我们应该重塑6 G的物理层技术。提高能源效率已成为全球的优先事项，并已被确定为下一代6 G系统的关键技术使能器，许多行业和研究机构正在寻求净零可持续技术，以实现减少能源使用和碳排放的目标排气中该项目旨在支持可持续通信系统的研究，这与英国政府在无线基础设施战略：2030年愿景（来源：GOV.UK）中关于净零的首要任务和雄心保持一致。通信系统的功耗直接与物理组件（诸如物理硬件和物理信号）相关联。通过先进制造进行硬件升级可以降低功耗，但在6 G中，当需要更多的基站来为给定区域提供服务时，其贡献有限。因此，在节能物理信号设计方面的根本性突破，更准确地说是波形设计，是及时和专门定位的，以实现6 G的净零目标。自1924年Harry Nyquist开发了当今数字通信信号的基础以来，已经取得了许多进步。然而，通信系统中的现有波形设计面临许多基本挑战（a）现有的能量高效空中接口波形设计受到马佐限制的限制，其在没有任何性能损失的情况下只能节省高达20%的功率。然而，20%的功率节省不再是足够的，并且20%是以复杂且耗能的信号处理为代价来实现的。(b)数学导出的波形受到已知数学模型的限制，并且不太可能是最佳的。(c)机器学习可以帮助信号具有更好的性能，但机器学习算法需要大量的处理能力。(d)现有的信号波形和AI模型通常使用高能耗硬件部署，因为需要强大的计算资源。为了应对上述挑战，提出了一个雄心勃勃的计划，包括i）对传统20%节电限制之外的新波形的基础探索，ii）波形和人工智能的共同设计，以获得高级波形格式并降低AI模型的复杂性，iii）低-低成本的硬件概念验证和节能验证。智能信号波形设计的基础研究突破将对许多领域和领域的研究人员的工作产生了影响，从电路设计到新的通信系统、人工智能算法、传感器和传感器网络、绿色通信技术、高级信号处理、生物医学信号和其他领域。

Communications and information networks are ubiquitous in our daily life, and information and communications technology (ICT) is estimated to contribute 1.8%-2.8% of carbon emissions. The successful deployment of 5G has revealed that the total power consumption for a 5G communication system is much higher than that in 4G due to more densely placed base stations in 5G. A recent study also revealed that a potential 6G system would consume nearly 50 times higher power than a 5G system due to a larger number of antennas, a wider spectral bandwidth occupation, and a denser base station deployment strategy. 5G techniques have already been standardized and deployed in real life without the priority on net-zero sustainability. Therefore, to lead the sustainability innovations for next generation net-zero communications, we should reshape the physical layer techniques for 6G.Improving energy efficiency has become the priority worldwide and has been identified as a key technology enabler for next generation 6G systems, and a number of industries and research institutes are chasing for net zero sustainable technology to achieve the objectives of cutting energy usage and carbon emission. This project aims for underpinning research in sustainable communication systems, which aligns with the UK government's top priority and ambitions on net zero in Wireless Infrastructure Strategy: a vision for 2030 (Source: GOV.UK). Power consumption for a communication system is directly linked to physical components such as physical hardware and physical signals. Hardware upgrade via advanced manufacturing can cut power consumption but with limited contributions in 6G when more base stations are required to serve a given area. Therefore, a fundamental breakthrough in energy efficient physical signal design, more precisely waveform design, is timely and specially positioned to achieve net-zero goals in 6G.A number of advancements have been achieved since 1924 when Harry Nyquist developed the foundation of today's digital communication signals. However, the existing waveform design in communication systems faces a number of fundamental challenges (a) Existing energy efficient air interface waveform designs are limited by the Mazo limit, which can only save power by up to 20% without any performance loss. However, the 20% power saving is no longer sufficient and the 20% is achievable at the cost of sophisticated and energy consuming signal processing. (b) Mathematically derived waveforms are limited by known mathematical models and are unlikely to be the optimal. (c) Machine learning can assist signals to have better performance but machine learning algorithms require lots of processing power. (d) Existing signal waveforms and AI models are commonly deployed using high energy consuming hardware because powerful computing resources are needed. To address the above challenges, an ambitious program is proposed including i) fundamental explorations of new waveforms beyond the conventional 20% power saving limit, ii) co-design of waveform and artificial intelligence to derive advanced waveform formats and cut the complexity of AI models, iii) low-cost hardware proof of concept with power saving validations.The fundamental research breakthrough in the signal waveform design with intelligence will have impacts on researchers' work across many areas and fields, from circuit design to new communication systems, artificial intelligence algorithms, sensors and sensor networks, green communication techniques, advanced signal processing, biomedical signals, and other areas.