IPTV deployment - Trigger for advanced network services!

C

Fixed-Mobile Convergence in the 5G era: From Hybrid Access to Converged Core

The availability of different paths to communicate to a user or device introduces several benefits, from boosting enduser performance to improving network utilization. Hybrid access is a first step in enabling convergence of mobile and fixed networks, however, despite traffic optimization, this approach is limited as fixed and mobile are still two separate core networks inter-connected through an aggregation point. On the road to 5G networks, the design trend is moving towards an aggregated network, where different access technologies share a common anchor point in the core. This enables further network optimization in addition to hybrid access, examples are userspecific policies for aggregation and improved traffic balancing across different accesses according to user, network, and service context. This paper aims to discuss the ongoing work around hybrid access and network convergence by Broadband Forum and 3GPP. We present some testbed results on hybrid access and analyze some primary performance indicators such as achievable data rates, link utilization for aggregated traffic and session setup latency. We finally discuss the future directions for network convergence to enable future scenarios with enhanced configuration capabilities for fixed and mobile convergence.Comment: to appear in IEEE Networ

Distributed Cognitive RAT Selection in 5G Heterogeneous Networks: A Machine Learning Approach

The leading role of the HetNet (Heterogeneous Networks) strategy as the key Radio Access Network (RAN) architecture for future 5G networks poses serious challenges to the current cell selection mechanisms used in cellular networks. The max-SINR algorithm, although effective historically for performing the most essential networking function of wireless networks, is inefficient at best and obsolete at worst in 5G HetNets. The foreseen embarrassment of riches and diversified propagation characteristics of network attachment points spanning multiple Radio Access Technologies (RAT) requires novel and creative context-aware system designs. The association and routing decisions, in the context of single-RAT or multi-RAT connections, need to be optimized to efficiently exploit the benefits of the architecture. However, the high computational complexity required for multi-parametric optimization of utility functions, the difficulty of modeling and solving Markov Decision Processes, the lack of guarantees of stability of Game Theory algorithms, and the rigidness of simpler methods like Cell Range Expansion and operator policies managed by the Access Network Discovery and Selection Function (ANDSF), makes neither of these state-of-the-art approaches a favorite. This Thesis proposes a framework that relies on Machine Learning techniques at the terminal device-level for Cognitive RAT Selection. The use of cognition allows the terminal device to learn both a multi-parametric state model and effective decision policies, based on the experience of the device itself. This implies that a terminal, after observing its environment during a learning period, may formulate a system characterization and optimize its own association decisions without any external intervention. In our proposal, this is achieved through clustering of appropriately defined feature vectors for building a system state model, supervised classification to obtain the current system state, and reinforcement learning for learning good policies. This Thesis describes the above framework in detail and recommends adaptations based on the experimentation with the X-means, k-Nearest Neighbors, and Q-learning algorithms, the building blocks of the solution. The network performance of the proposed framework is evaluated in a multi-agent environment implemented in MATLAB where it is compared with alternative RAT selection mechanisms

Towards reliable and low-latency vehicular edge computing networks

Abstract. To enable autonomous driving in intelligent transportation systems, vehicular communication is one of the promising approaches to ensure safe, efficient, and comfortable travel. However, to this end, there is a huge amount of application data that needs to be exchanged and processed which makes satisfying the critical requirement in vehicular communication, i.e., low latency and ultra-reliability, challenging. In particular, the processing is executed at the vehicle user equipment (VUE) locally. To alleviate the VUE’s computation capability limitations, mobile edge computing (MEC), which pushes the computational and storage resources from the network core towards the edge, has been incorporated with vehicular communication recently. To ensure low latency and high reliability, jointly allocating resources for communication and computation is a challenging problem in highly dynamics and dense environments such as urban areas. Motivated by these critical issues, we aim to minimize the higher-order statistics of the end-to-end (E2E) delay while jointly allocating the communication and computation resources in a vehicular edge computing scenario. A novel risk-sensitive distributed learning algorithm is proposed with minimum knowledge and no information exchange among VUEs, where each VUE learns the best decision policy to achieve low latency and high reliability. Compared with the average-based approach, simulation results show that our proposed approach has the better network-wide standard deviation of E2E delay and comparable average E2E delay performance