Energy Efficient Spectrum Research Articles

Design Problems in Cognitive Radio Networks for Spectrum Sensing

Cognitive radio is regarded as an innovative approach aimed at enhancing the efficient utilization of the valuable natural resource, namely the electromagnetic spectrum for radio communication. Based on principles of SDR, cognitive radio is wireless communication system with intelligence to interpret its environment. It uses "understanding-by-building" approach to learn about its surroundings and adjust to statistical variations in input signals.Providing rock-solid connectivity whenever and wherever it's needed.aiding in effective use of radio spectrum.Spectrum sensing methods are discussed in this abstract. Spectrum sensing requires energy, so keep that in mind. Therefore, our focus centers on energy-efficient spectrum sensing in order to conserve energy during transmissions, taking into account relevant factors. In this context, an efficient model will be proposed for cognitive radio, encompassing both analytical and simulation aspects.

An incentive-based dynamic energy efficient spectrum allocation for cognitive radio networks

Cognitive radio is a successful technique for utilizing the unused and under-used spectrum, and dynamic spectrum access is one of the major facilitators in making this happen. When a secondary user (an unlicensed user) interferes with the licensed user, the idea of using unused or under-utilized spectrum offers a challenge. Therefore, effective spectrum sensing is necessary to ensure the primary user’s protection and the successful transmission of data by the secondary user. An Optimal Incentive algorithm is suggested to meet this need. It effectively uses the available idle channel based on the joint optimization of sensing time and transmission time without interfering with the primary user. The proposed work also contributes to a significant increase in energy efficiency with minimal interference. Simulation results show an increase in efficiency when compared with the algorithms, namely, exhaustive search and sub-optimal algorithms.

Spectrum and energy efficient multi-antenna spectrum sensing for green UAV communication

Unmanned Aerial Vehicle (UAV) communication is a promising technology that provides swift and flexible on-demand wireless connectivity for devices without infrastructure support. With recent developments in UAVs, spectrum and energy efficient green UAV communication has become crucial. To deal with this issue, Spectrum Sharing Policy (SSP) is introduced to support green UAV communication. Spectrum sensing in SSP must be carefully formulated to control interference to the primary users and ground communications. In this paper, we propose spectrum sensing for opportunistic spectrum access in green UAV communication to improve the spectrum utilization efficiency. Different from most existing works, we focus on the problem of spectrum sensing of randomly arriving primary signals in the presence of non-Gaussian noise/interference. We propose a novel and improved p-norm-based spectrum sensing scheme to improve the spectrum utilization efficiency in green UAV communication. Firstly, we construct the p-norm decision statistic based on the assumption that the random arrivals of signals follow a Poisson process. Then, we analyze and derive the approximate analytical expressions of the false-alarm and detection probabilities by utilizing the central limit theorem. Simulation results illustrate the validity and superiority of the proposed scheme when the primary signals are corrupted by additive non-Gaussian noise and arrive randomly during spectrum sensing in the green UAV communication.

Improvement of energy conservation using blockchain-enabled cognitive wireless networks for smart cities

In Smart Cities’ applications, Multi-node cooperative spectrum sensing (CSS) can boost spectrum sensing efficiency in cognitive wireless networks (CWN), although there is a non-linear interaction among number of nodes and sensing efficiency. Cooperative sensing by nodes with low computational cost is not favorable to improving sensing reliability and diminishes spectrum sensing energy efficiency, which poses obstacles to the regular operation of CWN. To enhance the evaluation and interpretation of nodes and resolves the difficulty of sensor selection in cognitive sensor networks for energy-efficient spectrum sensing. We examined reducing energy usage in smart cities while substantially boosting spectrum detecting accuracy. In optimizing energy effectiveness in spectrum sensing while minimizing complexity, we use the energy detection for spectrum sensing and describe the challenge of sensor selection. This article proposed the algorithm for choosing the sensing nodes while reducing the energy utilization and improving the sensing efficiency. All the information regarding nodes is saved in the fusion center (FC) through which blockchain encrypts the information of nodes ensuring that a node’s trust value conforms to its own without any ambiguity, CWN-FC pick high-performance nodes to engage in CSS. The performance evaluation and computation results shows the comparison between various algorithms with the proposed approach which achieves 10% sensing efficiency in finding the solution for identification and triggering possibilities with the value of alpha = 1.5 and gamma = 2.5 with the varying number of nodes.

Energy Aware Multipath Routing Protocol for Cognitive Radio Ad Hoc Networks

Cognitive radio networks (CRNs) emerged as a paradigm to solve the problem of limited spectrum availability and the spectrum underutilization in wireless networks by opportunistically exploiting portions of the spectrum temporarily vacated by licensed primary users (PUs). Routing in CRNs is a challenging problem due to the PU activities and mobility. On the other hand, energy aware routing is very important in energy-constraint CRNs. In addition, it is crucial that CR users efficiently exchange data with each other before the appearance of PUs. To design a robust routing scheme for mobile CR ad hoc networks (CRANs), the constraints on residual energy of each CR user, reliability, and the protection of PUs must additionally be taken into account. Moreover, multipath routing has great potential for improving the end-to-end performance of ad hoc networks. Considering all these evidences, in this paper, we propose an energy aware on-demand multipath routing (EOMR) protocol for mobile CRANs to ensure the robustness and to improve the throughput. The proposed routing scheme involves energy efficient multipath route selection and spectrum allocation jointly. The simulation results show that our approach improves the overall performance of the network.

Game theory based spectrum sensing and transmission in energy harvesting hybrid cognitive radio networks (EH‐HCRN)

SummaryIn an energy harvesting cognitive radio network (EH‐CRN), the correspondence among the primary transmitter (PT) and primary receiver (PR) for spectrum sensing and transmission becomes ineffective as a result of insufficient SNR, finite sensing duration and interferences. We recommend designing a suitable spectrum sensing structure based on game theory for energy harvesting hybrid CRN to resolve these complications. This comprises three phases: (i) co‐operative relay approach for primary user (PU) transmission; (ii) optimal sensing interval estimation; and (iii) co‐operative energy efficient spectrum sensing and transmission. Initially, a batch of suitable secondary transmitters (STs) are identified as relay nodes for the PTs in the first phase. Next, in the second phase, a sensing interval that maximizes a utility function devised concerning average throughput, collision probability regarding secondary user (SU) and energy detector threshold is determined by applying the game theory model. Finally, co‐operative transmission is utilized in the third phase if the PR could not decode the signal from PT via STs. The presented framework is simulated using NS2, and its efficiency is estimated in terms of the parameters like transmission latency, packet loss, residual energy and possibilities of successful data delivery.

Broad spectrum artificial light at night increases the conspicuousness of camouflaged prey

Abstract The growing global prevalence of energy efficient broad spectrum lighting threatens to disrupt an array of visually guided ecological processes. Broad spectrum lighting likely better enables the discrimination of colour, yet it is potential to increase the conspicuousness of camouflaged prey at night remains little explored. Using a well‐established visual model, we quantified the impacts of four spectrally distinct narrow and broad spectrum lighting technologies on the conspicuousness of three different polymorphic colour variations of intertidal littorinid snail, as viewed by three model predators. Modern broad spectrum lighting technologies increased the conspicuousness of prey compared to 20th‐century narrow spectrum lighting. This effect was most prominent in the yellow colour morphs due to greater contrast with their natural fucoid seaweed background. Synthesis and applications. Our results provide evidence that the global transition to broad spectrum lighting will decrease the efficacy of camouflage at night in nature, potentially altering selective predation, population dynamics and the genetic structure of polymorphic populations. These findings highlight the need for further consideration in environmental management and planning, to ensure habitats are protected from unnecessary exposure to artificial light.

An energy efficient NOMA-based spectrum sharing techniques for cell-free massive MIMO

An energy efficient NOMA-based spectrum sharing techniques for cell-free massive MIMO

A throughput analysis of an energy-efficient spectrum sensing scheme for the cognitive radio-based Internet of things

Spectrum sensing in a cognitive radio network involves detecting when a primary user vacates their licensed spectrum, to enable secondary users to broadcast on the same band. Accurately sensing the absence of the primary user ensures maximum utilization of the licensed spectrum and is fundamental to building effective cognitive radio networks. In this paper, we address the issues of enhancing sensing gain, average throughput, energy consumption, and network lifetime in a cognitive radio-based Internet of things (CR-IoT) network using the non-sequential approach. As a solution, we propose a Dempster–Shafer theory-based throughput analysis of an energy-efficient spectrum sensing scheme for a heterogeneous CR-IoT network using the sequential approach, which utilizes firstly the signal-to-noise ratio (SNR) to evaluate the degree of reliability and secondly the time slot of reporting to merge as a flexible time slot of sensing to more efficiently assess spectrum sensing. Before a global decision is made on the basis of both the soft decision fusion rule like the Dempster–Shafer theory and hard decision fusion rule like the “n-out-of-k” rule at the fusion center, a flexible time slot of sensing is added to adjust its measuring result. Using the proposed Dempster–Shafer theory, evidence is aggregated during the time slot of reporting and then a global decision is made at the fusion center. In addition, the throughput of the proposed scheme using the sequential approach is analyzed based on both the soft decision fusion rule and hard decision fusion rule. Simulation results indicate that the new approach improves primary user sensing accuracy by 13% over previous approaches, while concurrently increasing detection probability and decreasing false alarm probability. It also improves overall throughput, reduces energy consumption, prolongs expected lifetime, and reduces global error probability compared to the previous approaches under any condition [part of this paper was presented at the EuCAP2018 conference (Md. Sipon Miah et al. 2018)].

Efficient Channel Allocation for Cognitive Radio Internet of Things

Recently, multiple wireless technology has arisen to provide effective and flexible IoT networking. Cognitive radio (CR) technology that facilitates software-defined radio is one of the key technology that gives IoT connectivity to a multitude of linked IoT devices in an opportunistic way. Unlicensed bandwidth has congested latest creation and study in the area of networking technology. The effect is unchecked and unregulated intrusion in the Internet of Things with low-powered wireless sensors (IoT). On the other hand, these technologies allowed low-energy IoT architecture at low cost, low energy usage and efficient spectrum use. Cognitive radio (CR) network, a low-cost approach for effective spectrum use, discusses the issue of spectrum use. Unlicensed users use underused bandwidth in CR networks. Due to its opportunistic existence, these networks' output relies on the observed spectrum pattern of the primary consumer. In these networks, frequency detection and usage must be accurately modelled. In this article, we propose the main Cognitive Radio Internet of Things (CR IoT) user identity mechanism using Markov's secret model. We implemented two algorithms: one for free channel identification and one for successful channel allocation. Simulation results suggest that CR-IoT surpassed traditional networking systems.

Hybrid NN-based green cognitive radio sensor networks for next-generation IoT

In any cognitive radio sensor networks (CRSNs), the secondary users (SUs) and primary users (PUs) share the opportunity to use the authorized frequency band. Here, the SU nodes can only transmit in the temporarily idle spectrum when it is not in use by any PU nodes. The proper estimation and detection of primary nodes are important for the energy-efficient spectrum access. The work basically presents a state of the art in implementing Bayesian-based convolutional neural network (CNN) for addressing the issue of energy constraints in next-generation Internet of things (Nx-IoT) using CRSN. Initially, we use blind source separation to extract the energy features and cyclic spectrum features of the signal and carry on the asymptotic autocorrelation calculation to the extracted signal. Finally, we construct the corresponding training set for CNN training and establish a suitable spectrum sensing model for Nx-IoT. Theoretical analysis and simulation results validate a suitable B-CNN spectrum sensing model along with energy-efficient cooperative communication between the SU nodes.

An energy efficient NOMA-based spectrum sharing techniques for cell-free massive MIMO

An energy efficient NOMA-based spectrum sharing techniques for cell-free massive MIMO

Energy Efficient MIMO-OFDM Spectrum Sensing Using Deep Stacked Spiking Delayed Feedback Reservoir Computing

This article introduces a novel energy efficient spectrum sensing method for multiple-input multiple-output and orthogonal frequency division multiplexing (MIMO-OFDM) systems in dynamic spectrum sharing (DSS) environments. To be specific, a spiking reservoir computing (RC) based approach is introduced for spectrum sensing of MIMO-OFDM systems to take advantage of the spatial and temporal correlations of the environment. In particular, we develop a spiking delayed feedback reservoir (DFR). DFRs are a new class of recurrent neural networks (RNNs) which tackle the vanishing gradient problem of traditional RNNs while spiking neurons are energy efficient and biologically plausible neuron models. We extend our introduced model in time and space domains to capture the spatial and temporal correlations in DSS environments. Conditional generative adversarial networks (cGANs) are introduced to tackle the data scarcity issue arose from applying machine learning techniques to MIMO-OFDM systems where training data is limited. Simulation results suggest that the probability of detection of the introduced RC based spectrum sensing at the low signal-to-noise (SNR) regime is significantly higher than the state-of-the-art techniques and the computational complexity is also reduced compared with traditional RNNs.

Energy Efficient and Interference-aware Spectrum Sensing Technique for Improving the Throughput in Cognitive Radio Networks

Cognitive Radio network (CRN) depends on opportunistic spectrum access and spectrum sensing for improving the wireless networks’ spectrum efficiency. Since throughput maximization can result in high energy consumption, the spectrum sensing technique should address the energy-throughput trade off. The spectrum sensing time has to be determined by the considering the residual battery energies of each secondary user (SU). The Primary User (PU) interference degrades the throughput of the entire network. Hence the transmit power level should be determined considering the PU interference and SU battery energy. This paper proposes Energy Efficient and Interference-aware Spectrum Sensing (EEISS) technique for improving the throughput in CRN. In this work, the sensing time is dynamically estimated based on the battery energy levels of SUs and the transmit power is determined depending on PU signal and battery energy levels of SUs. A Game theory model is formulated to maximize the throughput based on these constraints. Experimental results show that EEISS attains improved throughput, higher probability of detection and higher residual energy.

Withdrawal Notice: An Energy Efficient Spectrum Aware Routing for Cognitive Radio Networks (CRNs)

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A Paradigm Shift Toward Satisfaction, Realism and Efficiency in Wireless Networks Resource Sharing

The sheer growth of data traffic and the rising number of connected devices have highlighted and elevated the need of addressing spectral scarcity and enhancing resource utilization in 5G wireless networks. Toward this direction, the overwhelming majority of existing wireless resource management approaches and methodologies aim directly at energy efficiency maximization. In this article, we argue and demonstrate that not only the stability of these solutions depends on whether or not each user achieves the highest performance possible, but they do not properly reflect in reality the most desirable operation points from both the user and system point of view. In contrast, it is eventually more rewarding to opt for energy-awareness while targeting satisfaction of user Quality of Service (QoS) requirements, rather than targeting energy efficiency maximization itself. The proposed paradigm shift is further complemented with the introduction of a pragmatic approach in the different resource allocation paradigms in wireless networks, by integrating risk preferences in the user transmission decision making process, that traditional models fail to capture. This facilitates the inclusion and study of realistic and dynamic user behavior, under potential risks, gains and uncertainties, characteristics that are commonly present in wireless communications and networking environments. Proof of concept use cases and numerical results are presented that stress the benefits that can be obtained by this paradigm, both in terms of energy efficiency and efficient spectrum utilization, under different realizations of unlicensed and licensed spectrum sharing.

Hybrid Neural Network Based Wideband Spectrum Behavior Sensing Predictor for Cognitive Radio Application

The work behind research is energy and time efficient spectrum occupancy analysis for the deployment of cognitive radio (CR) in India. The measurement campaign is initial step, which provides data for analysis. The campaigns conducted worldwide lead to a complex measurement set up. So, first aim was to provide solution for simple and compact measurement set up. A new wideband circularly polarized microstrip antenna is proposed instead of existing commercially available antennas; which show better spectrum sensing ability. The conventional spectrum occupancy analysis consumes more sensing time as well as network resources because it senses whole spectrum every time. This problem can be overcome by adopting predictive method to predict the behavior of spectrum. A new hybrid neural network (HNN) model is proposed as a predictor for spectrum behavior sensing through which the status of different channels can be predicted by proper learning. The required database for training and validation of HNN predictor was collected from the measurement campaign conducted first time for seven days at Solapur city, India. The HNN model performance was examined for popular bands, and different days (weekdays and weekend) using root mean square error (RMSE) performance metric. The result shows, it has greater ability as spectrum behavior sensing predictor, and would be better choice for energy as well as time efficient spectrum occupancy analysis for cognitive radio application.

An Energy Efficient Network Architecture and Spectrum Sharing Technique for Cognitive Radio Based Smart Grid Communications

With the introduction of an automated electric power system known as smart grid, issues relating to power quality, shortages and inefficiency have been solved. Two vital foundations necessary for the sustainability and the stability of Smart Grids (SG) are a resourceful and reliable communication between users in a smart grid network and an energy efficient Smart Grid Communication Network (SGCN) architecture. On the other hand, Cognitive radios (CR) are equipped with the capability of exploiting unused licensed spectrum resources for effective spectrum utilization. The objective of this paper is to integrate cognitive radio into smart grid communication network in order to make the network a lot smarter and provide spectrum sharing solutions in SGCN. In this paper, an energy efficient SGCN architecture that is layered into the Home Area Network (HAN), Neighbourhood Area Network (HAN) and the Wide Area Network (WAN) has been proposed. Furthermore, in order to achieve a fair spectrum sharing amongst users in the network, CR users in the HANs estimate the need of its neighbouring users to transmit data. Then it uses this information to predict the vacant channels that will be utilized by any node in the network using a Markov chain process. In the NANs, a model is also proposed where every user sensor node access the spectrum based on the buffer occupancy estimates of its neighboring nodes. Simulation results show that there is a higher throughput in the network and also an increase in the quantity of data packets successfully transmitted using the proposed model.

Energy Efficient Spectrum Allocation and Mode Selection for D2D Communications in Heterogeneous Networks

In this paper, we consider a heterogeneous network consisting of both macro Base Station (MBS) and pico Base Stations (PBSs) in order to provide a spectrum allocation and mode selection in device-to-device (D2D) communications. A number of Component Carriers (CC) are considered available for allocation to the MBS and PBSs that are being utilized through carrier aggregation (CA) while mode selection decisions are made by each BS in order to balance between power consumption minimization and UE data rate requirements. A power minimization (energy-efficient) problem is formulated in order to provide a joint spectrum allocation and mode selection solution. This problem is solved using a state of the art optimization method known as proximal algorithm. First, a non-cooperative (centralized) solution is provided and second, a cooperative (distributed) employing distributed proximal algorithm is devised reducing the induced complexity. The cooperative solution is achieved by implementing distributed alternating direction method of multipliers (D-ADMM). Simulation results are carried out for all cases that reveal the energy efficient spectrum allocation and mode selection according under certain channels’ conditions that can balance between achieving high data rate requirements and power minimization. Finally, useful insights are presented such as complexity, convergence, delay and actual implementation of such a solution for the future wireless networks.

AN ENERGY EFFICIENT SPECTRUM AWARE ROUTING FOR COGNITIVE RADIO NETWORKS

Background: Now a days Due to rapid increase in the internet the Spectrum available is been overcrowded and thus the spectrum Sensing can be achieved by using the predominant technology i.e., Cognitive Radio as the spectrum is been under-utilized by the wireless communication technologies. Objective: The prime objective of the research work carried out is to achieve the energy efficiency and to use the spectrum effectively by using the spectrum management concept and achieve better throughput, end to end delay etc., Methods: The detection of the spectrum hole plays a vital role in the routing of the cognitive radio networks. As the hidden node problems and the multipath fading and the shadowing are considered to be impacting the sensing of the spectrum to overcome these issues Cooperative spectrum Sensing Mechanism is been adopted to sense the spectrum in an optimal manner and over cum the issues. Along with these issues the spectrum resources changes time to time in the routing. Result: An Energy Efficient Spectrum aware Routing (ESR) is been proposed which involves the timeslot and the routing schemes were been discussed. The simulation parameters have been proposed and the improvement in the performance of network through and a decrease in the end-to end delay have been observed and analyzed. Conclusion: An Energy Efficient spectrum management and the routing is successfully established to communication in the network and further traffic load is observed to be balanced in the protocol based on the residual energy in a node and further it prologs the Network Lifetime of the Overall Network and the Individual CR user. Further the end-to-end Delay has been observed to be lower which ensure higher throughput in the network and improves the network life time.