[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Khan et al., 2017 - Google Patents

Comparative analysis of ANN techniques for predicting channel frequencies in cognitive radio

Khan et al., 2017

View PDF
Document ID
15327538474395339727
Author
Khan I
Wasi S
Waqar A
Khadim S
Publication year
Publication venue
International Journal of Advanced Computer Science and Applications

External Links

Snippet

Demand of larger bandwidth increases the spectrum scarcity problem. By using the concepts of Cognitive radio we can achieve an efficient spectrum utilization. The cognitive radio allows the unlicensed user to share the licensed user band. To sense the accessibility …
Continue reading at www.researchgate.net (PDF) (other versions)

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATIONS NETWORKS
    • H04W72/00—Local resource management, e.g. wireless traffic scheduling or selection or allocation of wireless resources
    • H04W72/04—Wireless resource allocation
    • H04W72/08—Wireless resource allocation where an allocation plan is defined based on quality criteria
    • H04W72/082—Wireless resource allocation where an allocation plan is defined based on quality criteria using the level of interference
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATIONS NETWORKS
    • H04W72/00—Local resource management, e.g. wireless traffic scheduling or selection or allocation of wireless resources
    • H04W72/04—Wireless resource allocation
    • H04W72/08—Wireless resource allocation where an allocation plan is defined based on quality criteria
    • H04W72/085—Wireless resource allocation where an allocation plan is defined based on quality criteria using measured or perceived quality
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATIONS NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/14—Spectrum sharing arrangements between different networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATIONS NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/02—Resource partitioning among network components, e.g. reuse partitioning
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATIONS NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organizing networks, e.g. ad-hoc networks or sensor networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna systems, i.e. transmission or reception using multiple antennas
    • H04B7/022—Site diversity; Macro-diversity
    • H04B7/024—Co-operative use of antennas of several sites, e.g. in co-ordinated multipoint or co-operative multiple-input multiple-output [MIMO] systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATIONS NETWORKS
    • H04W28/00—Network traffic or resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATIONS NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L5/00—Arrangements affording multiple use of the transmission path
    • H04L5/003—Arrangements for allocating sub-channels of the transmission path
    • H04L5/0058—Allocation criteria

Similar Documents

Publication Publication Date Title
Ge et al. Deep reinforcement learning for distributed dynamic MISO downlink-beamforming coordination
Liu et al. Deep learning based optimization in wireless network
Challita et al. Deep learning for proactive resource allocation in LTE-U networks
Mustapha et al. An energy efficient reinforcement learning based cooperative channel sensing for cognitive radio sensor networks
Zuo et al. Prediction-based spectrum access optimization in cognitive radio networks
Paul et al. Machine learning for spectrum information and routing in multihop green cognitive radio networks
Sekaran et al. 5G integrated spectrum selection and spectrum access using AI-based frame work for IoT based sensor networks
Zhang et al. Deep learning based user association in heterogeneous wireless networks
Iliya et al. Application of artificial neural network and support vector regression in cognitive radio networks for RF power prediction using compact differential evolution algorithm
Zhang et al. Spectrum prediction and channel selection for sensing-based spectrum sharing scheme using online learning techniques
CN110809893A (en) Electronic device and method for wireless communication
Lai et al. CQI-based interference detection and resource allocation with QoS provision in LTE-U systems
Fowdur et al. A review of machine learning techniques for enhanced energy efficient 5G and 6G communications
Wu et al. Proactive caching and bandwidth allocation in heterogenous networks by learning from historical numbers of requests
Alsharoa et al. Multi-band RF energy and spectrum harvesting in cognitive radio networks
Mohanakurup et al. 5G cognitive radio networks using reliable hybrid deep learning based on spectrum sensing
Naikwadi et al. A survey of artificial neural network based spectrum inference for occupancy prediction in cognitive radio networks
Ali et al. Optimizing Multi-Tier Cellular Networks With Deep Learning for 6G Consumer Electronics Communications
Khan et al. Comparative analysis of ANN techniques for predicting channel frequencies in cognitive radio
Iliya et al. Optimized artificial neural network using differential evolution for prediction of RF power in VHF/UHF TV and GSM 900 bands for cognitive radio networks
Elias et al. Multi-step-ahead spectrum prediction for cognitive radio in fading scenarios
Zhu et al. Channel sensing algorithm based on neural networks for cognitive wireless mesh networks
Zhang et al. Prediction of spectrum based on improved RBF neural network in cognitive radio
Adeogun et al. Power control for 6G in-factory subnetworks with partial channel information using graph neural networks
Sumith et al. Enhanced model for spectrum handoff in cognitive radio networks