The ubiquitous demand for high data rate services necessitates breakthroughs in network system architecture. With a progressive move towards full spectrum reuse and a positive trend in small-cell deployment, cloud-radio access networks (CRANs) and fog-radio access networks (FRANs) become essential in large-scale interference management for beyond 5G wireless systems (B5G). By connecting the base-stations to the centralized cloud, CRANs provide a strong ability to allocate resources in a coordinated way across base-stations through inter-basestation coordination and through the proper use of the interconnecting fronthaul links. CRANs amalgamates both the ability of joint signal processing of data belonging to different users, and the strong processors capability of computing high complexity algorithms that conventional base-stations are incapable of. FRANs, on the other hand, are designed to reduce the delay caused by the fronthaul links by pushing control and storage abilities to the network edge. Such functionality of FRANs allows overcoming CRANs latency and signalling overhead issues by partially moving part of the network intelligence capabilities, i.e., cloud computing and storage capabilities, closer to the network edge (access points, mobile users and devices).
Such interplay between CRANs strong centralized processing and FRANs distributed intelligence capabilities provides a fertile arena for efficient resource allocation strategies. This is particularly the case since both CRANs and FRANs (albeit at different scales) make optimized resource sharing strategies feasible between the different entities through the cloud, by means of jointly encoding (decoding) the messages using downlink (uplink) coordinated resource allocation techniques (beamforming, scheduling, cache placement, etc.). Recent studies have also suggested that CRANs and FRANs platforms are particularly suitable for the practical implementation of rate-splitting strategies, i.e., by dividing each user message into two parts, a private part decodable at the intended user only, and a common part which can be decoded by another user, for the purpose of reducing large-scale interference.
This special issue is, therefore, devoted to investigating large scale interference management techniques in the context of CRANs and FRANs for B5G systems, through the proper optimization of resource allocation and ratesplitting techniques, both jointly and separately. High quality technical papers submissions reporting on original algorithmic, theoretical, numerical, and experimental results are welcome. Exceptional survey/tutorial-like papers may also be considered. We solicit original submissions in the following areas:
• Resource allocation in CRANs/FRANs (beamforming, power control, base-station association, scheduling, cache placement, etc.)
• Interference analysis, avoidance, and alignment for CRANs/FRANs
• Hybrid RF/Optical backhauling/fronthauling in CRANs/FRANs
• Machine learning and artificial intelligence for cloud efficiency
• Joint operation and optimization of radio-access and backhaul networks for CRANs/FRANs
• Centralized/decentralized computing and processing in CRANs/FRANs
• Flexible assignment of functionalities and entities in CRANs/FRANs
• Analysis and trade-off of delay, throughput, energy-efficiency, QoS, and cost-efficiency in CRANs vs FRANs
• Inter-cloud and intra-cloud interference management in multi-cloud networks
• Cache placement and optimization for cache-enabled CRANs/FRANs
• Rate-splitting and common message decoding for CRANs/FRANs
• Cross-layer optimization and performance analysis of rate-splitting enabled CRANs/FRANs
• CRANs/FRANs interplay with large-intelligent surfaces (LIS), massive MIMO, THz communications, massive IoT, V2X, cellular, UAV and satellite networks
• High-mobility consideration and performance analysis in CRANs/FRANs
The ubiquitous demand for high data rate services necessitates breakthroughs in network system architecture. With a progressive move towards full spectrum reuse and a positive trend in small-cell deployment, cloud-radio access networks (CRANs) and fog-radio access networks (FRANs) become essential in large-scale interference management for beyond 5G wireless systems (B5G). By connecting the base-stations to the centralized cloud, CRANs provide a strong ability to allocate resources in a coordinated way across base-stations through inter-basestation coordination and through the proper use of the interconnecting fronthaul links. CRANs amalgamates both the ability of joint signal processing of data belonging to different users, and the strong processors capability of computing high complexity algorithms that conventional base-stations are incapable of. FRANs, on the other hand, are designed to reduce the delay caused by the fronthaul links by pushing control and storage abilities to the network edge. Such functionality of FRANs allows overcoming CRANs latency and signalling overhead issues by partially moving part of the network intelligence capabilities, i.e., cloud computing and storage capabilities, closer to the network edge (access points, mobile users and devices).
Such interplay between CRANs strong centralized processing and FRANs distributed intelligence capabilities provides a fertile arena for efficient resource allocation strategies. This is particularly the case since both CRANs and FRANs (albeit at different scales) make optimized resource sharing strategies feasible between the different entities through the cloud, by means of jointly encoding (decoding) the messages using downlink (uplink) coordinated resource allocation techniques (beamforming, scheduling, cache placement, etc.). Recent studies have also suggested that CRANs and FRANs platforms are particularly suitable for the practical implementation of rate-splitting strategies, i.e., by dividing each user message into two parts, a private part decodable at the intended user only, and a common part which can be decoded by another user, for the purpose of reducing large-scale interference.
This special issue is, therefore, devoted to investigating large scale interference management techniques in the context of CRANs and FRANs for B5G systems, through the proper optimization of resource allocation and ratesplitting techniques, both jointly and separately. High quality technical papers submissions reporting on original algorithmic, theoretical, numerical, and experimental results are welcome. Exceptional survey/tutorial-like papers may also be considered. We solicit original submissions in the following areas:
• Resource allocation in CRANs/FRANs (beamforming, power control, base-station association, scheduling, cache placement, etc.)
• Interference analysis, avoidance, and alignment for CRANs/FRANs
• Hybrid RF/Optical backhauling/fronthauling in CRANs/FRANs
• Machine learning and artificial intelligence for cloud efficiency
• Joint operation and optimization of radio-access and backhaul networks for CRANs/FRANs
• Centralized/decentralized computing and processing in CRANs/FRANs
• Flexible assignment of functionalities and entities in CRANs/FRANs
• Analysis and trade-off of delay, throughput, energy-efficiency, QoS, and cost-efficiency in CRANs vs FRANs
• Inter-cloud and intra-cloud interference management in multi-cloud networks
• Cache placement and optimization for cache-enabled CRANs/FRANs
• Rate-splitting and common message decoding for CRANs/FRANs
• Cross-layer optimization and performance analysis of rate-splitting enabled CRANs/FRANs
• CRANs/FRANs interplay with large-intelligent surfaces (LIS), massive MIMO, THz communications, massive IoT, V2X, cellular, UAV and satellite networks
• High-mobility consideration and performance analysis in CRANs/FRANs
