New Challenges in Wireless Systems

下一代无线蜂窝网络总是承诺比目前的无线蜂窝网络有巨大的改进，包括为用户提供更高的数据速率和更好的体验质量。为了实现这些承诺，新技术和新技术不断被提出。通常，它们是在简单的设置或非常严格的假设下进行分析的，因此，所宣传的性能收益并不总是现实的。过去，我们在展示不切实际的假设如何影响新技术性能研究结果方面取得了良好的成功。该计划侧重于两个长期挑战，这两个挑战在所有代人中反复出现，但正在变得更加关键，即：(C1)如何为不同的应用程序集提供适当的QOE？(C2)如何有效利用频谱？这些挑战并不新鲜，但它们尚未得到解决，而且在很大程度上变得更加尖锐。对于(C1)，我们将主要关注延迟和公平性。关于(C2)，我们将从两个角度来解决，第一个是关于先进的物理层(PHY)技术如何提供帮助，第二个将专注于更高层和智能运营。这两个挑战显然是相互关联的：例如，一些新应用严重依赖延迟(例如，自动驾驶、虚拟现实、游戏或远程手术)，而另一些则依赖于高速率；*要支持所有这些新应用，需要以最充分和高效的方式利用频谱。显然，公平性很重要，但可能会影响延迟和效率。公平是指确保用户不受歧视，例如基于位置、设备和应用程序。因此，这两个挑战可能会相互矛盾，这需要研究。我们将采取不偏不倚的研究方法，为实用和现实的系统提出完善的分析框架。其目标是提供洞察力以及新的工具和机制来操作、研究和调整蜂窝系统。我们还将提供关于哪些技术有效，哪些技术不好的指导方针。这些成果将显著推进无线系统的最新水平，并将帮助加拿大政策制定者做出明智的决定，例如未来对频谱的使用。该计划还将提供极好的HQP培训机会。我们将培训将无线蜂窝系统与分析技能相结合的HQP，如果加拿大工业研发要跟上这个快速发展领域的趋势，这样的HQP是必不可少的。

A next generation of wireless cellular networks always promises dramatic improvements over the current one including higher data rates for users and better Quality of Experience. To achieve these promises, new technologies and techniques are regularly being proposed. Often, they are analyzed in simple settings or under very strong assumptions and hence, the performance gains that are advertised are not always realistic. We have had in the past good success in showing how unrealistic assumptions bias the results of studies on the performance of new technologies. This program focuses on two long-term challenges that are recurrent in all generations but are becoming more critical, namely:(C1) How to provide appropriate QoE to a diverse set of applications? (C2) How to use the spectrum efficiently? These challenges are not new, but they have not been resolved as yet and they have become, to a large extent, even more acute. Regarding (C1), we will mainly focus on latency and fairness. Regarding (C2), we will address it from two standpoints, the first one will be about how advanced Physical Layer (PHY) technologies can help while the second one will focus on higher layers and smart operation. These two challenges are clearly linked: for example, some new applications are critically dependent on latency (e.g., autonomous driving, virtual reality, gaming, or remote surgery) while others are dependent on high rates;  to support all these new applications, the spectrum needs to be used to its fullest and in an efficient way. Clearly, fairness, which is the assurance that users are not discriminated against, based for example on locations, devices, and applications, matters but might affect latency and efficiency.  Hence, the two challenges can be at odds with each other and this needs to be studied. We will adopt an unbiased research approach to propose sound analytical frameworks for practical and realistic systems. The objective is to provide insights and new tools and mechanisms to operate, study, and size cellular systems. We will also provide guidelines on which techniques work well and which ones do not. The outcomes will significantly advance the state of the art on wireless systems and will help Canadian policy-makers make informed decisions, for example for future use of the spectrum. The program will also provide excellent HQP training opportunities. We will train HQP whose knowledge combines wireless cellular systems with analytical skills, Such HQP is essential if Canadian industrial R&D is going to keep pace with the trends in this fast- developing area.