Cloud computing based cognitive radio networks (CCCRN) is an eye-catching research area in recent years to improve the spectrum sensing and spectrum management. Cognitive radio networks (CRN) are capable of adaptive learning and reconfiguration to provide consistent communications in dynamic environments. The adoption and learning in CRN demand fast process of big data. The performance and security in CRN do not meet such requirements due to its low computational power capabilities, particularly in low computational power devices. The advent of cloud capabilities mitigate these constraints. Due to this reason, we suggest the steganography with Advanced Encryption Standard (AES) cryptography technique to protect the cloud data. We identify the critical issues and challenges to implementing CCCRN and provide possible solutions. Even though, both techniques have the same objective, the cloud data in cognitive radio network requires a combination to keep the hackers away from the classified and unclassified data.
Integration of cloud computing and cognitive radio increases the performance with added security threats of cloud computing. If the integration overcome these security threats, CCCRN will replace traditional methods of radio operation. The proposed security model incorporated in CCCRN can help the primary user emulation and many other jamming problems. Integrating cognitive radio in cloud arrives secure problems along with real-time processing and energy supply problems. Cloud integration provides resource pooling with additional antennas to meet the real-time performance. Therefore, the cloud is one of the solutions that is facing by CRN. We discuss these problems in the current research paper.
Deep learning (DL) is a set of methods that automatically classify the raw data fed into the machine. Deep Convolutional nets composed of multiple processing layers to learn and representation of data with multiple levels of abstraction to process images, video, speech and audio. H2o deep learning architecture has many features that include supervised training protocol, memory efficient Java implementation, adaptive learning, and with related CRAN packages. H2o uses supervised training protocol with a uniform adaptive option which is an optimization based on the size of the network. It can take clusters of computing nodes to train on the entire data set but automatically shuffling the training examples for each iteration locally. The framework supports regularization techniques to prevent overfitting. H2o R has intuitive web interface using localhost and IP address. Using the H2o package in R is easy. The computations are performed in the H2o cluster and initiated by REST calls (in highly optimized Java code) from R. Since SPARK is available in R, H2o uses a single R session and communicates to the H2o Java cluster via REST calls. H2o runs inside the Spark executor JVM. Using these packages in R, we demonstrate the classification and automatic recognition of objects. Further, we use the h2o deep learning package in R Language to classify the NOAA VIIRS Night fires data to detect the persistent fire activity at a given location around the globe.
Dynamic spectrum access is a way of gaining access to individual frequencies on a temporary basis. This makes use of
the frequency assigned to a specific user (primary user) by using specific devices and/or spectrum management
techniques. The spectrum management techniques can be done by allocating the spectrum (a) through auctions (market
based), (b) using management techniques (c) spectrum sharing (detects and utilizes the unutilized part of the spectrum)
(d) command and control, and (e) through opportunistic spectrum access. In opportunistic spectrum access, the
secondary or unlicensed user transfers the data with high speed and at short distances with tolerable interference
(without disturbing) to the primary signal. Efficient spectrum allocation techniques were discussed using stochastic
models, economic models, genetic algorithms, and optimization techniques. The existing models need to be tuned for
better performance with optimum utilization of the power.
In this paper, we proposed a model that provides access with tolerable interference from secondary users to the
primary users while maximizing the spectrum utilization. Furthermore, we designed a congestion game model for
efficient utilization of the spectrum by secondary users with minimum interference to primary users. The simulation
results show that the congestion game model helps to utilize the spectrum efficiently.
KEYWORDS: Genetic algorithms, Orthogonal frequency division multiplexing, Signal to noise ratio, Detection and tracking algorithms, Modulation, Genetics, Quadrature amplitude modulation, Chemical elements, Computing systems, Telecommunications
In this paper, a novel genetic algorithm application is proposed for adaptive power and subcarrier allocation in multi-user
Orthogonal Frequency Division Multiplexing (OFDM) systems. To test the application, a simple genetic algorithm
was implemented in MATLAB language. With the goal of minimizing the overall transmit power while ensuring the
fulfillment of each user's rate and bit error rate (BER) requirements, the proposed algorithm acquires the needed
allocation through genetic search. The simulations were tested for BER 0.1 to 0.00001, data rate of 256 bit per OFDM
block and chromosome length of 128. The results show that genetic algorithm outperforms the results in [3] in
subcarrier allocation. The convergence of GA model with 8 users and 128 subcarriers performs better in power
requirement compared to that in [4] but converges more slowly.
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