The Internet of Senses (IoS) holds the promise of flawless telepresence-style
communication for all human `receptors' and therefore blurs the difference of
virtual and real environments. We commence by highlighting the compelling use
cases empowered by the IoS and also the key network requirements. We then
elaborate on how the emerging semantic communications and Artificial
Intelligence (AI)/Machine Learning (ML) paradigms along with 6G technologies
may satisfy the requirements of IoS use cases. On one hand, semantic
communications can be applied for extracting meaningful and significant
information and hence efficiently exploit the resources and for harnessing a
priori information at the receiver to satisfy IoS requirements. On the other
hand, AI/ML facilitates frugal network resource management by making use of the
enormous amount of data generated in IoS edge nodes and devices, as well as by
optimizing the IoS performance via intelligent agents. However, the intelligent
agents deployed at the edge are not completely aware of each others' decisions
and the environments of each other, hence they operate in a partially rather
than fully observable environment. Therefore, we present a case study of
Partially Observable Markov Decision Processes (POMDP) for improving the User
Equipment (UE) throughput and energy consumption, as they are imperative for
IoS use cases, using Reinforcement Learning for astutely activating and
deactivating the component carriers in carrier aggregation. Finally, we outline
the challenges and open issues of IoS implementations and employing semantic
communications, edge intelligence as well as learning under partial
observability in the IoS context.

网络研究与人工智能技术可用于优化无缝遥感通信，语义通信可提取信息并利用资源，而强化学习适用于部分可观测环境下优化用户体验。