Energy Efficient Relay-Assisted Cellular Network Model using Base Station Switching

Cellular network planning strategies have tended to focus on peak traffic scenarios rather than energy efficiency. By exploiting the dynamic nature of traffic load profiles, the prospect for greener communications in cellular access networks is evolving. For example, powering down base stations (BS) and applying cell zooming can significantly reduce energy consumption, with the overriding design priority still being to uphold a minimum quality of service (QoS). Switching off cells completely can lead to both coverage holes and performance degradation in terms of increased outage probability, greater transmit power dissipation in the up and downlinks, and complex interference management, even at low traffic loads. In this paper, a cellular network model is presented where certain BS rather than being turned off, are switched to low-powered relay stations (RS) during zero-to-medium traffic periods. Neighbouring BS still retain all the baseband signal processing and transmit signals to corresponding RS via backhaul connections, under the assumption that the RS covers the whole cell. Experimental results demonstrate the efficacy of this new BS-RS Switching technique from both an energy saving and QoS perspective, in the up and downlinks

Energy savings using an adaptive base station-to-relay station switching paradigm

Applying a Base Station (BS) sleep approach during low traffic periods has recently been advocated as a strategy for reducing energy consumption in cellular networks. The complete switching off of certain BS however, can lead to coverage holes and severe performance degradation in terms of off-cell user throughput, greater transmit power dissipation in both the up and downlinks, and more complex interference management. This paper presents a novel cellular network energy saving model in which certain BS rather being turned off are switched to Relay Station (RS) mode during low traffic periods. The switched RS and other shared RS deployed at the cross border of each cell are responsible for upholding the same quality of service (QoS) provision as when all BS are active. A centralised adaptive switching threshold algorithm is also introduced to undertake the switching decision, instead of using a fixed threshold. Simulation results confirm the new BS-RS Switching model using an adaptive threshold can reduce network energy consumption by more than half, as well as improving off-cell users’ throughput

Dynamic spectrum access based on cognitive radio within cellular networks

Overlay transmissions in cognitive radio (CR) permit a secondary system to use spectrum concomitantly with a primary system, though adopting this spectrum sharing strategy presents a number of challenges, such as the requirement for a secondary user to have a priori knowledge as side information about the primary user. In this paper, a cognitive cellular network is proposed which uses an overlay approach to dynamically share its radio resource by incorporating cognition, leading to enhanced cell capacity. To compensate for the interference caused by the overlay, cognitive base stations use robust dirty-paper coding in combination with variable transmission powers, which are set depending upon the location of the mobile stations. A detailed performance analysis is presented to corroborate the improved spectrum utilization achieved using this technique

Traffic-and-interference aware base station switching for green cellular networks

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-and-interference 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

Energy-Efficient Cloud Radio Access Networks by Cloud Based Workload Consolidation for 5G

YesNext-generation cellular systems like fth generation (5G) is are expected to experience tremendous tra c growth. To accommodate such tra c 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 signi cant amount of energy. As a result, cloud radio access networks (C-RAN) have been proposed as anenergy e cient (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 tra c periods and 12% during peak tra c periods compared to baseline LTE system. Moreover, the proposed scheme saves 38% of energy compared to the baseline system on a daily average

Evaluating energy-efficient cloud radio access networks for 5G

YesNext-generation cellular networks such as fifth-generation (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 energy-efficient 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

Fuzzy-Logic Based Call Admission Control in 5G Cloud Radio Access Networks with Pre-emption

YesFifth generation (5G) cellular networks will be comprised of millions of connected devices like wearable devices, Androids, iPhones, tablets and the Internet of Things (IoT) with a plethora of applications generating requests to the network. The 5G cellular networks need to cope with such sky-rocketing tra c requests from these devices to avoid network congestion. As such, cloud radio access networks (C-RAN) has been considered as a paradigm shift for 5G in which requests from mobile devices are processed in the cloud with shared baseband processing. Despite call admission control (CAC) being one of radio resource management techniques to avoid the network congestion, it has recently been overlooked by the community. The CAC technique in 5G C-RAN has a direct impact on the quality of service (QoS) for individual connections and overall system e ciency. In this paper, a novel Fuzzy-Logic based CAC scheme with pre-emption in C-RAN is proposed. In this scheme, cloud bursting technique is proposed to be used during congestion, where some delay tolerant low-priority connections are pre-empted and outsourced to a public cloud with a penalty charge. Simulation results show that the proposed scheme has low blocking probability below 5%, high throughput, low energy consumption and up to 95% of return on revenue

Active Inference for Sum Rate Maximization in UAV-Assisted Cognitive NOMA Networks

Given the surge in wireless data traffic driven by the emerging Internet of Things (IoT), unmanned aerial vehicles (UAVs), cognitive radio (CR), and non-orthogonal multiple access (NOMA) have been recognized as promising techniques to overcome massive connectivity issues. As a result, there is an increasing need to intelligently improve the channel capacity of future wireless networks. Motivated by active inference from cognitive neuroscience, this paper investigates joint subchannel and power allocation for an uplink UAV-assisted cognitive NOMA network. Maximizing the sum rate is often a highly challenging optimization problem due to dynamic network conditions and power constraints. To address this challenge, we propose an active inference-based algorithm. We transform the sum rate maximization problem into abnormality minimization by utilizing a generalized state-space model to characterize the time-changing network environment. The problem is then solved using an Active Generalized Dynamic Bayesian Network (Active-GDBN). The proposed framework consists of an offline perception stage, in which a UAV employs a hierarchical GDBN structure to learn an optimal generative model of discrete subchannels and continuous power allocation. In the online active inference stage, the UAV dynamically selects discrete subchannels and continuous power to maximize the sum rate of secondary users. By leveraging the errors in each episode, the UAV can adapt its resource allocation policies and belief updating to improve its performance over time. Simulation results demonstrate the effectiveness of our proposed algorithm in terms of cumulative sum rate compared to benchmark schemes.Comment: This paper has been accepted for the 2023 IEEE 9th World Forum on Internet of Things (IEEE WFIoT2023

Optimal datalink selection for future aeronautical telecommunication networks

YesModern aeronautical telecommunication networks (ATN) make use of different simultaneous datalinks to deliver robust, secure and efficient ATN services. This paper proposes a Multiple Attribute Decision Making based optimal datalink selection algorithm which considers different attributes including safety, QoS, costs and user/operator preferences. An intelligent TRigger-based aUtomatic Subjective weighTing (i-TRUST) method is also proposed for computing subjective weights necessary to provide user flexibility. Simulation results demonstrate that the proposed algorithm significantly improves the performance of the ATN system.Innovate U.K. Project SINCBAC-Secure Integrated Network Communications for Broadband and ATM Connectivity: Application number 18650-134196