Optimized design and analysis approach of user detection by non cooperative detection computing methods in CR networks

Kumar, Budati Anil; Rao, P. Trinatha

doi:10.1007/s10586-017-1523-y

Cited by 9 publications

(9 citation statements)

References 18 publications

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“…This aims to minimize the energy efficiency optimization complexity. The average power is determined in Equation (18).…”

Section: Generating Optimal Energy Efficiencymentioning

confidence: 99%

“…Here, the terms Λ 𝜂 and D TL (𝜃) shows the threshold of the transmission power allocated energy efficiency, and the effective capacity of the MIMO-OFDM model. Initially, the threshold for the power transmission allocated is formulated in Equation (18), which is tuned with the help of the designed HFF-SSO algorithm for obtaining the maximization of energy efficiency that lies between 0 and 3. Then, the energy efficiency 𝜂 OPT and mean effective capacity of the n th group sub-channel mean (D TL (𝜃)) are formulated using Equations ( 9) and ( 6), respectively.…”

Section: Objective Modelmentioning

confidence: 99%

“…In wireless communication systems, the DTX method is much more efficient while analyzing all the energy efficiency techniques because of transferring nature of radio signals without the data transmission, and thus, it shows its effectiveness in decreasing the consumption of system power. Recently in the base stations, the optimization [15][16][17][18] of power utilization has been determined to implement the DTX technique by some researchers. 19 When almost all the consequent cell users are not in the active state, the base station undergoes sleep mode in the network.…”

Section: Introductionmentioning

confidence: 99%

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An energy efficiency perceptive on MIMO‐OFDM systems using hybrid fruit fly‐based salp swarm optimization technique

Kumar

Rao

2022

Concurrency and Computation

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This paper is to plan for the development of energy‐efficient MIMO‐OFDM mobile multimedia communication systems using the intelligent meta‐heuristic approach. This model is concentrated on solving the multi‐channel optimization problem. Recent research work is focused on single‐channel optimization problems, and hence the conversion of multi‐channel optimization problems into single‐channel optimization problems is considered the challenging point in this research. In the proposed MIMO‐OFDM method for mobile multimedia communication systems, the channel‐matrix Singular Value Decomposition (SVD) method is used for implementing the sub‐channel grouping scheme, which has simplified the multichannel problem to a multi‐target single‐channel optimization problem. In this optimization problem, the transmission power‐allocation threshold is optimized for attaining the objective function of maximization of energy efficiency. A hybridized optimization algorithm named Hybrid Fruit Fly‐based Salp Swarm Optimization (HFF‐SSO) is utilized for energy optimization in the MIMO‐OFDM mobile multimedia communication system. This hybrid algorithm has reached an energy‐efficient system with a high convergence rate, which is proved by the comparison over the conventional systems.

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“…This aims to minimize the energy efficiency optimization complexity. The average power is determined in Equation (18).…”

Section: Generating Optimal Energy Efficiencymentioning

confidence: 99%

Section: Objective Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An energy efficiency perceptive on MIMO‐OFDM systems using hybrid fruit fly‐based salp swarm optimization technique

Kumar

Rao

2022

Concurrency and Computation

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“…The PU transmitted signal is assumed to be Gaussian random process with zero mean and variance. 22,23 𝜂(t) is the additive white Gaussian noise at the ith channel. h i is the channel gain and assumed to be constant over each sensing period.…”

Section: Ed Features and Dt Classifiermentioning

confidence: 99%

Adaptive cooperative sensing in cognitive radio networks with ensemble model for primary user detection

Prasad

Rao

2019

Int J Communication

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Opportunistic spectrum sharing ability enables higher spectrum utilization in cognitive radio networks. Detecting the presence of primary user in the network is the most important functionality in cognitive radio network as the cognitive users cannot use the spectrum with interference to primary users. Most solutions proposed for primary user detection suffer from hidden terminal problem resulting from multipath fading and shadow effects. The work focus on Rayleigh and Nakagami fading channel with comparable nonfading AWGN channel in cognitive radio. An ensemble model to detect the presence of primary user with high confidence is proposed in this work. The approach is based on training machine learning models with energy vectors in presence and absence of primary users. The trained model is then used to predict the primary user based on the energy vector.

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“…Therefore, optimum performance and efficient spectrum utilization can be achieved by the cognitive radio whose architecture (embed the physical layer and intelligence within a single device) and software provide interoperability. Applications of CRbased IoT [22] covers all the fields [5].…”

Section: Introductionmentioning

confidence: 99%

Preliminary Step for Implementing Cognitive Internet of Things Through Software-Defined Radio

Gummineni

Polipalli

2021

SN COMPUT. SCI.

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The exponential growth of Internet of Things (IoT) leads to spectrum-related issues such as Spectrum Allocation and Management. IoT devices are interconnected in heterogeneous networks, which have interference, and hardware-software interconnection problems. Cognitive radio (CR) that has connectivity to the Internet strengthens the concept of "Internet of Things". Due to the diversification of applications, embedding the IoT technology with cognitive capability aids intelligence and improves the overall performance of the system. The Cognitive Internet of Things (CIoT) has emerged as an equipping technology, which focuses on the functions of CR and its potential contribution to IoT. The reason for deploying CR as the central device is to manage and provide an interface to different users. CR has the capabilities to implement artificial intelligence schemes to provide learning features and automatic reconfiguration. An attempt is made to design, and implement a preliminary step called sensing control layer which is the first step out of the framework of CIoT. Software-defined radio (SDR) has been used as the central unit whose function is to sense the spectrum and connect the IoT devices. Experimental results show that SDR as the central unit extends the functions of the network, re-configurability, and also provides interoperability in the heterogeneous network. Keywords Software-defined radio (SDR) • Internet of Things (IoT) • Cognitive radio (CR) • Reconfigurable radio • Spectrum sensing • Spectrum hole • Universal Software Radio Peripheral (USRP) This article is part of the topical collection "Cyber Security and Privacy in Communication Networks" guest edited by

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Optimized design and analysis approach of user detection by non cooperative detection computing methods in CR networks

Cited by 9 publications

References 18 publications

An energy efficiency perceptive on MIMO‐OFDM systems using hybrid fruit fly‐based salp swarm optimization technique

An energy efficiency perceptive on MIMO‐OFDM systems using hybrid fruit fly‐based salp swarm optimization technique

Adaptive cooperative sensing in cognitive radio networks with ensemble model for primary user detection

Preliminary Step for Implementing Cognitive Internet of Things Through Software-Defined Radio

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