scholarly journals Robust Baseband Compression Against Congestion in Packet-Based Fronthaul Networks Using Multiple Description Coding

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 433 ◽  
Author(s):  
Seok-Hwan Park ◽  
Osvaldo Simeone ◽  
Shlomo Shamai (Shitz)
Keyword(s):  
Access Network ◽  
General Purpose ◽  
Multiple Description Coding ◽  
Network Function Virtualization ◽  
Path Diversity ◽  
Network Function ◽  
Multiple Description ◽  
Radio Access ◽  
Multiple Routes ◽  
Description Coding

In modern implementations of Cloud Radio Access Network (C-RAN), the fronthaul transport network will often be packet-based and it will have a multi-hop architecture built with general-purpose switches using network function virtualization (NFV) and software-defined networking (SDN). This paper studies the joint design of uplink radio and fronthaul transmission strategies for a C-RAN with a packet-based fronthaul network. To make an efficient use of multiple routes that carry fronthaul packets from remote radio heads (RRHs) to cloud, as an alternative to more conventional packet-based multi-route reception or coding, a multiple description coding (MDC) strategy is introduced that operates directly at the level of baseband signals. MDC ensures an improved quality of the signal received at the cloud in conditions of low network congestion, i.e., when more fronthaul packets are received within a tolerated deadline. The advantages of the proposed MDC approach as compared to the traditional path diversity scheme are validated via extensive numerical results.

Multiple Description Coding for Multipath Video Streaming

2016 ◽  
pp. 63-117
Author(s):  
Pedro Correia ◽  
Pedro A. Amado Assuncao ◽  
Vítor Silva
Keyword(s):  
Video Coding ◽  
Video Streaming ◽  
Multiple Description Coding ◽  
Video Adaptation ◽  
Path Diversity ◽  
Coding Efficiency ◽  
Multiple Description ◽  
Research Findings ◽  
Advanced Video Coding ◽  
Description Coding

This Chapter addresses robust video coding and adaptation of compressed streams in multipath communications environments, using Multiple Description Coding (MDC). A review of Multiple Description (MD) video coding is presented, covering different video coding approaches. Different path diversity topologies and MDC networking applications are described, including MD video adaptation schemes to operate at network edges, for robust video streaming. A multi-loop architecture for Advanced Video Coding (AVC) to prevent drift distortion accumulation is also described. A simulation study of MDC for AVC is presented to evaluate the coding efficiency, the effects of distortion propagation and streaming performance in lossy networks. These research findings extend the current state-of-the-art MDC methods by developing new networking capabilities in different application scenarios maintaining coding efficiency, and increasing error robustness, when subject to transmission losses.

Multiple Description Coding for Multipath Video Streaming

Biometrics ◽  
2017 ◽  
pp. 836-891
Author(s):  
Pedro Correia ◽  
Pedro A. Amado Assuncao ◽  
Vítor Silva
Keyword(s):  
Video Coding ◽  
Video Streaming ◽  
Multiple Description Coding ◽  
Video Adaptation ◽  
Path Diversity ◽  
Coding Efficiency ◽  
Multiple Description ◽  
Research Findings ◽  
Advanced Video Coding ◽  
Description Coding

This Chapter addresses robust video coding and adaptation of compressed streams in multipath communications environments, using Multiple Description Coding (MDC). A review of Multiple Description (MD) video coding is presented, covering different video coding approaches. Different path diversity topologies and MDC networking applications are described, including MD video adaptation schemes to operate at network edges, for robust video streaming. A multi-loop architecture for Advanced Video Coding (AVC) to prevent drift distortion accumulation is also described. A simulation study of MDC for AVC is presented to evaluate the coding efficiency, the effects of distortion propagation and streaming performance in lossy networks. These research findings extend the current state-of-the-art MDC methods by developing new networking capabilities in different application scenarios maintaining coding efficiency, and increasing error robustness, when subject to transmission losses.

scholarly journals AMF Optimal Placement based on Deep Reinforcement Learning in Heterogeneous Radio Access Network

2020 ◽  
Author(s):  
hao jin ◽  
Wenzhe Pang ◽  
Chenglin Zhao
Keyword(s):  
Reinforcement Learning ◽  
Mobility Management ◽  
Network Architecture ◽  
Access Network ◽  
Optimal Placement ◽  
Network Function Virtualization ◽  
User Mobility ◽  
Radio Access Network ◽  
Network Function ◽  
Radio Access

Abstract To support various service requirements such as massive Machine Type Communications, Ultra-Reliable and Low-Latency Communications in 5G scenario, Network Function Virtualization (NFV) plays an important role in the 5G network architecture to manage and orchestrate network services. As the key network function responsible for mobility management, Access and Mobility Management Function (AMF) can be deployed flexibly at the edge of the radio access network to improve the performance of mobility management based on NFV. In this paper, the optimal placement of AMF is addressed based on Deep Reinforcement Learning (DRL) in a heterogeneous radio access network, which aims to minimize the network utility including the average delay of mobility management requests at AMF, the average wired hops to relay the requests and the cost of AMF instances. By considering time-varying features including user mobility and the arrival rate of user mobility management requests, an AMF optimal placement approach is proposed for the long term optimization. Simulation results show that the performance of the proposed DRL based AMF optimal placement approach outperforms that of the baselines.

scholarly journals Orchestration and Control in Software-Defined 5G Networks: Research Challenges

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Gianfranco Nencioni ◽  
Rosario G. Garroppo ◽  
Andres J. Gonzalez ◽  
Bjarne E. Helvik ◽  
Gregorio Procissi
Keyword(s):  
Access Network ◽  
Network Function Virtualization ◽  
Future Research ◽  
Major Step ◽  
Core Networks ◽  
Network Function ◽  
Research Challenges ◽  
Radio Access ◽  
Cost Efficient ◽  
And Control

The fifth generation (5G) of cellular networks promises to be a major step in the evolution of wireless technology. 5G is planned to be used in a very broad set of application scenarios. These scenarios have strict heterogeneous requirements that will be accomplished by enhancements on the radio access network and a collection of innovative wireless technologies. Softwarization technologies, such as Software-Defined Networking (SDN) and Network Function Virtualization (NFV), will play a key role in integrating these different technologies. Network slicing emerges as a cost-efficient solution for the implementation of the diverse 5G requirements and verticals. The 5G radio access and core networks will be based on a SDN/NFV infrastructure, which will be able to orchestrate the resources and control the network in order to efficiently and flexibly and with scalability provide network services. In this paper, we present the up-to-date status of the software-defined 5G radio access and core networks and a broad range of future research challenges on the orchestration and control aspects.

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