Cognitive Radio Energy Saving and Optimization

2016 ◽  
pp. 273-296
Author(s):  
Yunfei Chen
Keyword(s):  
Cognitive Radio ◽  
Energy Saving

scholarly journals Smart Energy Consumption of IoT With Millimeter-Wave Cognitive Radiofor 5G Cellular Network

2021 ◽  
Author(s):  
Dan Ye
Keyword(s):  
Energy Consumption ◽  
Cognitive Radio ◽  
Energy Saving ◽  
Millimeter Wave ◽  
Resource Sharing ◽  
Network Capacity ◽  
Visible Light Communications ◽  
Radio Access ◽  
Data Rates ◽  
Iot Devices

Abstract Millimeter-wave technology is rising as a crucial component for 5G radio access and other emerging ancillary wireless networks including Gb/s device-to-device communication and mobile backhaul. This paper envisions that millimeter-wave cognitive radio in 5G network is a proposed smart energy consumption solution of Internet of Things (IoT) devices. Improving resource efficiency and enhancing data rates, resource sharing is a proposed advantage over millimeter wave cognitive radio in 5G IoT network. IoT Fog collaboration is proposed to apply artificial intelligence techniques to offer important energy-saving services allowing integrated systems to perceive, reason, learn, and act intelligently in intelligent gateway control. Smart energy meters are the current energy-saving utility in the flexible deployment of IoT architecture. NarrowBand IoT (NB-IoT) delivers Low Power Wide Area access (LPWA) to a new generation of connected things in the race to 5G IoT network, reducing energy computation and achieving promising network capacity. The renewable energy strategy is a proposed energy-efficiency solution in IoT network, maximizing the power supply while minimizing power consumption. A novel kind of visible light communications (VLC) is proposed to enable mmWave cognitive radio receiver in 5G IoT network. Simulation results show the proposed solution can reap the benefits of higher data rates, more IoT device connectivity, and lower energy consumption.

The Research of Self-Adaptative Power Adjustment Routing

2014 ◽  
Vol 986-987 ◽  
pp. 2082-2085
Author(s):  
Zhi Ping Ding
Keyword(s):  
Cognitive Radio ◽  
Power Consumption ◽  
Energy Saving ◽  
Real Time ◽  
Ad Hoc Network ◽  
Ad Hoc ◽  
Power Type ◽  
Power Adjustment ◽  
Routing Mechanism ◽  
Internal Capacity

Cognitive Radio Ad Hoc Network (CRAHN), established on the cognitive radio environment, configures the Ad Hoc Network which possesses the internal capacity. How to effectively control the power consumption is an important issue in the CRAHN for CRAHN‘s main power type is battery. At present, the energy-saving routing research in CRAHN is mainly based on the real-time routing mechanism. It means that it should be routed again after transferring an archive. In order to increase the network route’s surviving time and save the power consumption, it introduces a routing mechanism whose purpose is to optimize the communication algorithm’s performance by improving the existing routing mechanism in the Ad Hoc network.

scholarly journals Social Optimization and Pricing Policy in Cognitive Radio Networks with an Energy Saving Strategy

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Shunfu Jin ◽  
Si Chen ◽  
Jinliang Zhang
Keyword(s):  
Cognitive Radio ◽  
Energy Saving ◽  
Cognitive Radio Networks ◽  
Process Model ◽  
Arrival Rate ◽  
Radio Networks ◽  
Base Station ◽  
Single Server ◽  
Pricing Policy ◽  
Optimal Behavior

The rapid growth of wireless application results in an increase in demand for spectrum resource and communication energy. In this paper, we firstly introduce a novel energy saving strategy in cognitive radio networks (CRNs) and then propose an appropriate pricing policy for secondary user (SU) packets. We analyze the behavior of data packets in a discrete-time single-server priority queue under multiple-vacation discipline. With the help of a Quasi-Birth-Death (QBD) process model, we obtain the joint distribution for the number of SU packets and the state of base station (BS) via the Matrix-Geometric Solution method. We assess the average latency of SU packets and the energy saving ratio of system. According to a natural reward-cost structure, we study the individually optimal behavior and the socially optimal behavior of the energy saving strategy and use an optimization algorithm based on standard particle swarm optimization (SPSO) method to search the socially optimal arrival rate of SU packets. By comparing the individually optimal behavior and the socially optimal behavior, we impose an appropriate admission fee to SU packets. Finally, we present numerical results to show the impacts of system parameters on the system performance and the pricing policy.

QoS provisioning energy saving dynamic access policy for overlay cognitive radio networks with hidden Markov channels

2013 ◽  
Vol 10 (12) ◽  
pp. 92-101 ◽  
Author(s):  
Wei Yifei ◽  
Teng Yinglei ◽  
Wang Li ◽  
Song Mei ◽  
Wang Xiaojun
Keyword(s):  
Cognitive Radio ◽  
Energy Saving ◽  
Cognitive Radio Networks ◽  
Hidden Markov ◽  
Radio Networks ◽  
Qos Provisioning ◽  
Access Policy

Interference Mitigation and Energy Management in Heterogeneous Networks

2014 ◽  
pp. 124-142 ◽  
Author(s):  
Chungang Yang ◽  
Jiandong Li
Keyword(s):  
Cognitive Radio ◽  
Energy Saving ◽  
Energy Management ◽  
Heterogeneous Networks ◽  
Interference Mitigation ◽  
Interference Management ◽  
Interference Control ◽  
Energy Aware ◽  
Scheme Design ◽  
Improved Performance

In Long Term Evolution (LTE) 4G systems, coexistence of multiple in-band smallcells defines what is called heterogeneous cellular networks. There is no doubt that the development of heterogeneous networks and the popularization of intelligent terminals facilitate subscribers with great convenience, better Quality of Experience (QoE) guarantee, and much higher traffic rate. However, interference management will be indispensable in heterogeneous networks. Meanwhile, with emerging various energy-hungry services of subscribers, energy-aware design attracts a wide attention. Motivated by interference mitigation and energy-saving challenges of the heterogeneous networks and the promising cognitive radio techniques, more advanced energy-saving and interference control techniques based on cognitive radio should be developed for better QoE. In this chapter, the authors first review cognitive radios, multiple types of smallcells, and introduce the benefits of cognitive radio-enabled heterogeneous networks. Then, focusing on the scheme design of cognitive interference management and energy management, finally, simulation results are provided to show the improved performance of these proposed cognitive schemes.

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