Morteza Kheirkhah

6G will provide a new opportunity to integrate Artificial Intelligence (AI) and Machine Learning (ML) into wireless communication. The current 5G Core (5GC) architecture possesses limitations that restrict its ability to effectively deploy AI functions, including the lack of a standardized, unified access layer for network data, limited service exposure models that cannot fully support AI feedback loops, and tight coupling between logic and signaling that inhibits the insertion of AI-based decision-making components. This paper explores approaches to address the 5GC shortcomings in the future 6G Core (6GC) network. Recommendations for architectural evolution and standardization are discussed to facilitate scalable, efficient, and adaptable AI-enabled 6G networks.

We address the challenge of backhaul connectivity for rural and developing regions, which is essential for universal fixed/mobile Internet access. To this end, we propose to exploit the TV white space (TVWS) spectrum for its attractive properties: low cost, abundance in under-served regions and favorable propagation characteristics. Specifically, we propose a system called WhiteHaul for the efficient aggregation of the TVWS spectrum tailored for the backhaul use case. At the core of WhiteHaul are two key innovations: (i) a TVWS conversion substrate that can efficiently handle multiple non-contiguous chunks of TVWS spectrum using multiple low cost 802.11n/ac cards but with a single antenna; (ii) novel use of MPTCP as a link-level tunnel abstraction and its use for efficiently aggregating multiple chunks of the TVWS spectrum via a novel uncoupled, cross-layer congestion control algorithm. Through extensive evaluations using a prototype implementation of WhiteHaul, we show that: (a) WhiteHaul can aggregate almost the whole of TV band with 3 interfaces and achieve nearly 600Mbps TCP throughput; (b) the WhiteHaul MPTCP congestion control algorithm provides an order of magnitude improvement over state of the art algorithms for typical TVWS backhaul links. We also present additional measurement and simulation based results to evaluate other aspects of the WhiteHaul design.

To ensure high Quality of Experience (QoE) for end users, many media applications require significant quantities of computing and network resources, making their realization challenging in resource constrained environments. In this paper, we present the approach of the 5G-MEDIA project, providing an integrated programmable service platform for the development, design and operations of media applications in 5G networks, facilitating media service management across the service life cycle. The platform offers tools to service developers for efficient development, testing and continuous correction of services. One step further, it provides a service virtualization platform offering horizontal services, such as a Media Service Catalogue and accounting services, as well as optimization mechanisms to flexibly adapt service operations to dynamic conditions with efficient use of infrastructure resources. The paper outlines three use cases where the platform was tested and validated.

Modern data centres provide large aggregate network capacity and multiple paths among servers. Traffic is very diverse; most of the data is produced by long, bandwidth hungry flows but the large majority of flows, which commonly come with strict deadlines regarding their completion time, are short. It has been shown that TCP is not efficient for any of these types of traffic in modern data centres. More recent protocols such MultiPath TCP (MPTCP) are very efficient for long flows, but are ill-suited for short flows. In this paper, we present Maximum MultiPath TCP (MMPTCP), a novel transport protocol which, compared to TCP and MPTCP, reduces short flows' completion times, while providing excellent goodput to long flows. To do so, MMPTCP runs in two phases; initially, it randomly scatters packets in the network under a single congestion window exploiting all available paths. This is beneficial to latency-sensitive flows. After a specific amount of data is sent, MMPTCP switches to a regular MultiPath TCP mode. MMPTCP is incrementally deployable in existing data centres as it does not require any modifications outside the transport layer and behaves well when competing with legacy TCP and MPTCP flows. Our extensive experimental evaluation in simulated FatTree topologies shows that all design objectives for MMPTCP are met.

In this paper, we introduce MMPTCP, a novel transport protocol which aims at unifying the way data is transported in data centres. MMPTCP runs in two phases; initially, it randomly scatters packets in the network under a single congestion window exploiting all available paths. This is beneficial to latency-sensitive flows. During the second phase, MMPTCP runs in Multi-Path TCP (MPTCP) mode, which has been shown to be very efficient for long flows. Initial evaluation shows that our approach significantly improves short flow completion times while providing high throughput for long flows and high overall network utilisation.