In next 5 years we want to set the foundations for future wireless networks, addressing the development of fully flexible and adaptive solutions able to simultaneously achieve an efficient spectrum utilization in time, frequency and space, while satisfying the miscellaneous user requirements in the large, complex and extremely dynamic expected future scenarios. To achieve that goal, the following main research directions will be considered:
- Disruptive strategies and protocols for maximizing the reuse of the spectrum resources in all dimensions (space, frequency and time), which include the use of novel techniques such as interference cancellation/full-duplex communications, dynamic spectrum access, transmit power control and directional transmissions. In addition to the design of adaptive protocols able to efficiently use these features, we aim to study the optimal capacity regions when all those mechanisms are considered simultaneously. This is very challenging because the interactions/dependencies between the different techniques are not yet fully known, and the number of possible configurations is large.
- In order to properly employ all previous features in highly-dynamic scenarios aiming to operate as close as possible to optimal conditions, decisions about next actions/configurations can be made at the network edge or in the cloud depending on the amount of available information and the maximum tolerable delay. Combining efficiently "short-term" local decisions based on partial information with much more accurate but "mid/long term" decisions based on a global view of the network is still an unsolved challenge. We will address this challenge by developing new Machine Learning and Information Extraction techniques that will take into account the specificities of future wireless networks.
- Understanding the dynamics and fundamental performance limits of adaptive systems: an ultimate goal is to understand and characterize formally the implications and effects of including self-adaptive capabilities in the performance of wireless networks, identifying their fundamental properties such as the presence of dominant states (most common states), the distribution of paths and switching times between them, and the cause/effect relationships over multiple temporal scales. Understanding all these aspects will open the door for new paradigms in the design of future wireless networks.
Resource sharing strategies
- Coexistence issues / Interactions between independent wireless networks
- Channel Access protocols
- Properties and characteristics of dynamic CSMA networks
- Multi-user batch-service systems
- Network state-dependent service times
Energy-constrained Wireless Networks
- Low-energy MAC protocols for multi-hop WSNs
- Information-aware WSNs
- Power Saving Mechanisms in WLANs for sensor-like communication
- Self-adaptive WSNs
High-performance WLANs (< 5 GHz, mmW)
- (Dynamic) Channel bonding, channel selection, OFDMA
- Spatial reuse
- Collision-free decentralized MAC protocols
- Spatial Multiplexing
Artificial Intelligence (AI) in Networking
- Machine Learning (ML)-based architecture for future WLANs
- Reinforcement Learning (RL) for decentralized and distributed networks
- ML and Software-Defined Networks (SDNs) for WLANs