Abstract-Base station (BS) sleeping in cellular networks has emerged as a promising solution for more energy efficient communications, concomitant with lowering the network carbon footprint. Switching off specific BS entirely however, can lead to coverage holes and severe performance degradation. To avoid coverage holes, the transmit power of neighbouring BS must be commensurately increased, which can cause higher interference to other cell users. Recently a BS-RS (relay station) switching model has been proposed where the BS changes operating mode to a RS during off-peak periods rather than being completely turned off. This paper presents a traffic-aware and traffic-andinterference aware switching strategy for both the BS sleeping and BS-RS switching paradigms, which dynamically establishes the conditions for a BS to alter its working mode. The switching is based upon a dynamic traffic threshold allied with the received BS interference level. Analysis corroborates both new algorithms significantly improve network energy efficiency, while upholding the requisite quality of service provision.
Next-generation cellular networks such as fifthgeneration (5G) will experience tremendous growth in traffic. To accommodate such traffic demand, there is a necessity to increase the network capacity that eventually requires the deployment of more base stations (BSs). Nevertheless, BSs are very expensive and consume a significant amount of energy. Meanwhile, cloud radio access networks (C-RAN) has been proposed as an energyefficient architecture that leverages cloud computing technology where baseband processing is performed in the cloud, i.e., the computing servers or baseband processing units (BBUs) are located in the cloud. With such an arrangement, more energy saving gains can be achieved by reducing the number of BBUs used. This paper proposes a bin packing scheme with three variants such as First-fit (FT), First-fit decreasing (FFD) and Next-fit (NF) for minimizing energy consumption in 5G C-RAN. The number of BBUs are reduced by matching the right amount of baseband computing load with traffic load. In the proposed scheme, BS traffic items that are mapped into processing requirements, are to be packed into computing servers, called bins, such that the number of bins used are minimized and idle servers can then be switched off to save energy. Simulation results demonstrate that the proposed bin packing scheme achieves an enhanced energy performance compared to the existing distributed BS architecture.
Long-Range (LoRa) communication technology is considered as a promising connectivity solutions for Internet of Things (IoT) dense applications. In particular, LoRa has drawn the interest due to its low power consumption and wide area coverage. Despite the benefits of LoRaWAN protocol, it still suffers from excessive random and simultaneous transmissions due to the adoption of ALOHA protocol. Therefore, resulting in severe packet collision rate as the network scales up. This leads to continuous retransmission attempts, which in return increase the transmission delay and energy consumption. Thus, this paper proposes a dynamic transmission Priority Scheduling Technique (PST) based on the unsupervised learning clustering algorithm to reduce the packet collision rate and enhance the network's transmission delay and energy consumption. Particularly, the LoRa gateway classifies the nodes into different transmission priority clusters. While the dynamic PST allows the gateway to configure the transmission intervals for the nodes according to the transmission priorities of the corresponding clusters. This work allows scaling up the network density while maintaining low packet collision rate and significantly enhances the transmission delay & the energy consumption. Simulation results show that the proposed work outperforms the typical LoRaWAN and recent clustering & scheduling schemes. Therefore, the proposed work is well suited for dense applications in LoRaWAN.
Next-generation cellular systems like fth generation (5G) is are expected to experience tremendous trac growth. To accommodate such trac demand, there is a need to increase the network capacity that eventually requires the deployment of more base stations (BSs). Nevertheless, BSs are very expensive and consume a lot of energy. With growing complexity of signal processing, baseband units are now consuming a signicant amount of energy. As a result, cloud radio access networks (C-RAN) have been proposed as an energy ecient (EE) architecture that leverages cloud computing technology where baseband processing is performed in the cloud. This paper proposes an energy reduction technique based on baseband workload consolidation using virtualized general purpose processors (GPPs) in the cloud. The rationale for the cloud based workload consolidation technique model is to switch o idle baseband units (BBUs) to reduce the overall network energy consumption. The power consumption model for C-RAN is also formulated with considering radio side, fronthaul and BS cloud power consumption. Simulation results demonstrate that the proposed scheme achieves an enhanced energy performance compared to the existing distributed long term evolution (LTE) RAN system. The proposed scheme saves up to 80% of energy during low trac periods and 12% during peak trac periods compared to baseline LTE system. Moreover, the proposed scheme saves 38% of energy compared to $ This document is a collaborative eort.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.