Number Of Secondary Users Research Articles

Atkinson Index Detector for Spectrum Sensing

Recently, a new approach for designing detectors for cooperative spectrum sensing has been coined. It applies an incoming inequality index, which is normally used in economic and social sciences, to build a test statistic that operates on the entries of the received signal sample covariance matrix. The first detector designed using this approach was the Gini index detector (GID). This letter devises the Atkinson index detector (AID), which is shown to outperform the GID in many situations, with the special attribute of attaining a time complexity that grows linearly with the number of secondary users.

Adaptive coalition with Kullback‐Leibler divergence for cooperative spectrum sensing (KLDCSS) in cognitive radio networks

SummaryEffective, reliable, and rapid spectrum sensing in concordance with maximum throughput efficiency is necessary at the cognitive radio network to maximize the network's utilization. An adaptive coalition using Kullback–Leibler divergence (KLD)‐based cooperative spectrum sensing scheme (KLDCSS) with a nominal sensing time to optimize the spectral efficiency is presented in this paper. In a coalition‐based spectrum sensing scheme, cooperating secondary users (SUs) are allocated to different coalitions to sense different Primary User (PU) channels. The same cognitive users allocated to sense the primary user channels are allotted to detect in different sensing phases. In the proposed sensing scheme, the cooperating SUs are adaptively allocated to different coalitions to maximize the throughput efficiency. Collaborating cognitive users with a standard signal‐to‐noise ratio and distinct energy levels that are characterized by the localized probabilities of detection and false alarm is considered. An adaptive coalition and user allocation algorithm are proposed to optimize the average opportunistic throughput and diminish the sensing overhead. The expression for throughput efficiency in terms of average throughput and sensing accuracy is derived. In this scheme, the SU computes the log‐likelihood ratio (LLR) from the acquired sample and forwards the same to the fusion center. The fusion center then accumulates the LLR values computed by the SU to estimate the likelihood of spectrum availability. Simulation results highlighting the competitive edge of the proposed scheme, with higher throughput efficiency over various numbers of SUs, coalitions, and signal‐to‐noise ratios, are presented.

Communication spectrum prediction method based on convolutional gated recurrent unit network

In modern wireless communication systems, the scarcity of spectrum resources poses challenges to the performance and efficiency of the system. Spectrum prediction technology can help systems better plan and schedule resources to respond to the dynamic changes in spectrum. Dynamic change in the spectrum refers to the changes in the radio spectrum in a wireless communication system. It means that the available spectrum resources may change at different times and locations. In response to this current situation, this study first constructs a communication collaborative spectrum sensing model using channel aliasing dense connection networks. Then, combining convolutional neural network and gated cyclic unit network in deep learning technology, a communication spectrum prediction model is built. It aims to achieve accurate perception and prediction of spectrum resources through the aforementioned spectrum sensing and prediction models. The results confirm that the proposed perception model has inconsistent perception accuracy under different number of secondary users, with a maximum of 0.99. It is verified that the proposed spectrum prediction model achieves a high prediction accuracy of 0.95 within 208 s and its performance outperforms current similar models. The results are based on the model's deep learning analysis of massive historical communication data, in which the optimized shuffle dense net model plus convolutional gated recurrent unit model is the key to achieve fast and accurate prediction. On the contrary, the highest spectrum prediction accuracy of Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Convolutional Neural Networks-Long Short-Term Memory (ConvLSTM) models are 0.86, 0.90, and 0.85, respectively. And the model needs to run for a longer period of time, up to 324, for ConvLSTM to reach the prediction accuracy value of 0.95. In summary, the perception and prediction model built by this research has good performance, and its application in the field of wireless communication can assist staff in better monitoring spectral changes, thereby making more efficient use of spectral resources.

Resource Allocation for Cognitive Satellite-HAP-Terrestrial Networks With Non-Orthogonal Multiple Access

Cognitive radio and non-orthogonal multiple access are promising to improve the spectrum efficiency for the satellite-high altitude platform (HAP)-terrestrial networks. To achieve the optimal system performance and guarantee the quality of service, we formulate a constrained mixed integer nonlinear programming problem to maximize the sum rate of secondary network for the considered NOMA-enabled cognitive satellite-HAP-terrestrial network, while considering the constraints of the quality of service of primary users, the maximum power of HAP, and the number of secondary users in a NOMA group. To tackle the non-convex optimization problem, we decouple it into subchannel assignment and power assignment subproblems. Then, a greedy heuristic algorithm is proposed to assign subchannels to secondary users. Moreover, to solve the power allocation subproblem, we further design a power allocation algorithm by utilizing successive convex approximation, dual decomposition, and subgradient methods. On this basis, an iterative joint resource allocation algorithm is designed. Numerical simulations and comparisons with the orthogonal multiple access scheme are provided to verify the effectiveness of the proposed scheme.

Directional-antenna-based spatial and energy-efficient semi-distributed spectrum sensing in cognitive internet-of-things networks

Recently, mmWave-based massive antenna transmission and cognitive networks have emerged as promising approaches for achieving high performances in dense, complicated 6G networks. However, accurate and efficient spectrum sensing that can provide the primary users (PUs) and secondary users (SUs) with satisfactory performance levels remains to be realized. Therefore, this paper proposes a novel semi-distributed and cooperative spectrum sensing approach for directional-antenna-based cognitive radio networks, which are considered key technologies for the next internet-of-things systems. The proposed approach finds the optimal directional sensing beams and spectrum detection energy thresholds to maximizes the system utilization, in terms of the spectrum sensing probability and spatial efficiency. To determine the optimal controls in a semi-distributed manner, a modified elimination method and low-complex coordination algorithm for localized user groups are developed. Simulation results indicate that the proposed control strategy enables enhanced spectrum sensing in terms of the sensing accuracy, spatial efficiency, and energy consumption compared with those achievable using the legacy omnidirectional antenna-based sensing method and directional-antenna-based semi-distributed and fully distributed sensing methods. Notably, the performance gain increases with increase in the number of SUs. Moreover, the proposed scheme achieves near-optimal performance within 1% of that of a directional-antenna-based centralized sensing approach.

CNN-SVM Spectrum Sensing in Cognitive Radio Based on Signal Covariance Matrix

Among the basic components of cognitive radio technology is spectrum sensing technology. Cooperative spectrum sensing is also a crucial factor research directions of spectrum sensing. Due to the problems of channel fading and hidden nodes, the spectrum sensing based on a single node cannot meet the requirements of the cognitive radio system for the primary user (PU) detection performance. A spectrum sensing algorithm is based on CNN-SVM and covariance matrix. In this algorithm, the covariance matrix of the signal is input into the convolutional neural network for feature extraction. Secondly, the result of the fully connected layer is taken as the input of the support vector machine for training to obtain a classifier, and finally, spectrum sensing is performed. The simulation outcomes demonstrate that the algorithm suggested in this research works outperform other algorithms in this paper. When the signal-to-noise ratio is -14 dB, the false alarm probability is 0.1, the number of secondary users is 32, and the detection probability reaches 0.81.

Enhancement of spectrum sensing technique with energy harvesting for cognitive radio network

Spectrum Sensing (SS) is cognitive radio's (CR’s) fundamental and essential mechanism to locate idle spectrum. Multipath effects in wireless channels, on the other hand, reduce CR's sensing accuracy, resulting in a significant risk of missing data. Optimal Spectrum Sensing Technique with Energy Harvesting (OSSTEH) used to solve this problem suffers from poor performance due to only one Primary User (PU) branch used. Hence, in this paper, an enhanced SS with energy harvesting technique is proposed using Maximal Ratio Combiner (MRC). The multiple copies of the PU signal were received using 'N' number of secondary users (SU) antennas and divided into two equal fractions. The first fraction was used for spectrum sensing, while the latter was used for energy harvesting. The first set of SU antennas received a PU signal, which was combined using MRC, and the output of the combiner was used as an input to the energy detector to calculate the received signal's energy. To assess the existence or absence of the PU signal, the acquired energy was compared to the threshold limit. The enhanced technique was evaluated using the probability of detection and signal strength compared with conventional OSSTEH. The enhanced technique gave a high detection rate with an increase in charging rate than the conventional OSSTEH.

Adaptive fusion based clustering approach in cognitive radio networks

Recently, the cognitive radio (CR), which is a technology to effectively use the radio band, has been introduced to allow unlicensed users to use the spectrum efficiently. The key problem of the CR system is the overhead issue in control channel as the number of secondary users (SUs) is very high in the cooperative spectrum sensing (CSS) scenario. To alleviate this problem, the Cluster-based CSS solution is useful to prevent crowding on the control channel. Therefore, this paper uses a neural network (NN) approach based on a supervised learning algorithm at the fusion center (FC) for the suggested clustering technique. Scheme suggests a combination of hard decisions, using NN approach to increase the efficiency of CRNs with multiple clusters and to decrease the overhead issue between the FC to cluster head (CH). Also, under the Rayleigh fading channel, the proposed scheme is examined and simulated to test their detection efficiency and compare their performance with conventional schemes. The simulation results prove that proposed clustering approach enhances the accuracy of the system. Also, it is more robust than other conventional schemes.

A branch-and-price approach to a variant of the cognitive radio resource allocation problem

Radio-frequency portion of the electromagnetic spectrum is a scarce resource. Cognitive radio (CR) technology has emerged as a promising solution to overcome the spectrum scarcity bottleneck. Through this technology, secondary users (SUs) sense the spectrum opportunities free from primary users (PUs), and opportunistically take advantage of these (temporarily) idle portions, known as spectrum holes. In this correspondence, we consider a variant of the cognitive radio resource allocation problem posed by Martinovic et al. in 2017. The distinguishing feature of this version of the problem is that each SU, due to its hardware limitations, imposes the requirement that the to-be-aggregated spectrum holes cannot be arbitrarily far from each other. We call this restriction as the Maximal Aggregation Range (MAR) constraint, and refer to this variant of the problem as the MAR-constrained hole assignment problem. The problem can be formalized as an NP-hard combinatorial optimization problem. We propose a novel binary integer linear programming (ILP) formulation to the problem. The number of constraints in this formulation is the number of spectrum holes plus the number of SUs. On the other hand, the number of binary decision variables in the formulation can be prohibitively large, as for each legitimate spectrum allocation to each SU, one variable is needed. Due to this difficulty, the resulting 0-1 ILP instances may not even be explicitly describable, let alone be solvable by an ILP solver. We propose a branch-and-price (B&P) framework to tackle this challenge. This framework is in fact a branch-and-bound procedure in which at each node of the search tree, we utilize the so-called (delayed) column generation technique for solving the LP relaxation of the corresponding subproblem. As evidenced by the numerical results, the LP relaxation bounds are very tight. This allows for a very effective pruning of the search space. Compared to the previously suggested formulations, the proposed technique can require much less computational effort.

CM-LSTM Based Spectrum Sensing.

This paper presents spectrum sensing as a classification problem, and uses a spectrum-sensing algorithm based on a signal covariance matrix and long short-term memory network (CM-LSTM). We jointly exploited the spatial cross-correlation of multiple signals received by the antenna array and the temporal autocorrelation of single signals; we used the long short-term memory network (LSTM), which is good at extracting temporal correlation features, as the classification model; we then input the covariance matrix of the signals received by the array into the LSTM classification model to achieve the fusion learning of spatial correlation features and temporal correlation features of the signals, thus significantly improving the performance of spectrum sensing. Simulation analysis shows that the CM-LSTM-based spectrum-sensing algorithm shows better performance compared with support vector machine (SVM), gradient boosting machine (GBM), random forest (RF), and energy detection (ED) algorithm-based spectrum-sensing algorithms for different signal-to-noise ratios (SNRs) and different numbers of secondary users (SUs). Among them, SVM is a classical machine-learning algorithm, GBM and RF are two integrated learning methods with better generalization capability, and ED is a classical, traditional, and spectrum-sensing algorithm.

Price- and rate-aware multi-channel spectrum access for profit enhancement in opportunistic networks with QoS guarantees

Cognitive radio (CR) is a key technology that can enable opportunistic spectrum access, which enables secondary users (SUs) to dynamically exploit the under-utilized channels in the licensed spectrum, owned by primary radio networks (PRNs), referred to as dominant firms . Such sharing is subject to interference, SU QoS and cost constraints, in which SUs should not introduce harmful interference to PR users, achieve QoS rate demand and pay a price for using the licensed PR spectrum. The price of accessing idle PR channels depends on the level of channel utilization and price paid by PRs to access the channels, while the amount of needed spectrum to serve the rate demand of each SU heavily depends on the link-quality of the various channels. In this paper, the spectrum assignment problem in a CR network (CRN), referred to as follower firm , is investigated with the target of serving the largest possible number of SUs with the least possible total price paid to the PRNs (highest CRN profit) while being aware of the time-varying achieved transmission rate and level of utilization of the various PR channels. The problem is mathematically expressed as an optimization problem with the goal of maximizing the number of served SUs and the profit made by the CRN, which has been shown to be a binary linear programming (BLP) problem. Due to the high complexity of solving such optimization, we use the well-known sequential-fixing optimization method to obtain sub-optimal solutions. Simulation results indicate that our channel-assignment optimization significantly increases the CRN profit by reducing the price paid to the PRNs while achieving comparable performance offered by previous price-unaware protocols.

Transmission State Prediction from MAC in Cognitive Radio via Optimized Deep Learning Architecture

Cognitive Radio (CR) is the hottest network paradigm, which permits the Secondary Users (SUs) like wireless devices/users for intelligent accessing of unallocated radio spectrum. Such accessing happens by enabling interference-free transmission of Primary Users (PUs), who are allotted with some deserved radio spectrum portions. This radio communication paradigm has effective usage in vehicular networks, where communication should be established from vehicles to static stations (vehicle-to-infrastructure) or within vehicles (vehicle-to-vehicle), without allotting dedicated frequencies. Nevertheless, the major issue in designing CR is that it must be built to aid in efficient transmitting and sensing of data through the available radio spectrum channels. This paper proposes a Model Predictive Control (MPC)-based prediction model, via a Deep Learning approach. Here, a Deep Belief Network (DBN) allows in predicting the PU transmission state as idle or busy. Moreover, this paper comes out with a new optimization concept that achieves more accurate and precise prediction. The weight of DBN is optimally selected to pave way for effective performance. Further, a new hybrid algorithm named as Cuckoo Search-Grasshopper Optimization Algorithm (CS-GOA) is proposed. The performance of the proposed model is compared over the other conventional models, in terms of channel utilization and back off, and proved for supremacy. The throughput of the proposed model, even at 50 SUs is better, when compared to other methods. CS-GOA achieved better channel utilization and backoff rate, as compared to ProMAC and NN, when the numbers of SUs and PUs in the architecture are varied with time.

Energy-Efficient Resource Allocation in Radio-Frequency-Powered Cognitive Radio Network for Connected Vehicles

Radio-frequency-energy-powered cognitive radio network (RF-CRN) is being taken seriously in Connected Vehicles, especially in 5G network, which can better address the challenges of energy limitation and spectrum scarcity. However, the energy efficiency (EE) of the RF-CRN wherein multiple secondary users (SUs) share the same channel is rarely presented. In this article, we consider a RF-CRN in which SUs first harvest energy from RF signals originating from a primary network (PN) and then utilize the available energy in the battery to transmit data. Since all SUs can access the authorized spectrum for transmission simultaneously, co-frequency interference (Co-FI) occurs among SUs. Given the quality of service (QoS) requirement, our goal is to achieve the maximum EE of the RF-CRN by jointly optimizing transmission time and power control. To this end, a resource allocation scheme referred to as approximate convex policy for co-frequency interference (CO-ACP) is proposed. Specifically, the EE problem is firstly converted into a convex one by CO-ACP. Then, we utilize Frank-Wolfe (FW) and one-dimensional linear programming to obtain the optimal solution. Simulation results demonstrate that a tight lower-bound optimum solution for the non-convex EE maximization can be achieved by CO-ACP. Moreover, the CO-ACP provides meaningful system features, such as the number of SUs, energy harvesting efficiency, and the battery energy state of the SUs under different RF-CRN scenarios, providing a clear reference for future deployment of RF-CRN.

Sum‐throughput maximization for overlay cognitive wireless powered network with energy harvesting capability

AbstractIn this article, a novel time division multiple access (TDMA)‐based approach for optimal resource allocation in an overlay cognitive wireless powered communication network is proposed. In this network, power in the secondary network (SN) is provided by the primary network (PN) in addition to its own wireless network. Using the TDMA and a harvest‐then‐transmit protocol, the time and power allocation for downlink wireless energy transfer (WET) and uplink wireless information transmission (WIT) are jointly optimized to maximize the sum‐throughput in SN. Quality of service in PN and power and time in SN, as the main constrains are considered for sum‐throughput maximization in this network. This is a non‐convex problem which gets converted to a convex problem using active interference‐temperature control technique. Moreover, a new closed‐form expression which results in sub‐optimal power and time‐allocation (SO‐PT‐A) is addressed. The performance of proposed algorithm is compared with the equal time allocation (ETA) and fixed TDMA‐allocation (FTDMA‐A) algorithms which impose some special constraints for optimal power and time allocation. The simulation results depict that the SO‐PT‐A algorithm outperforms the ETA and FTDMA‐A algorithms. Eventually, the performance of the SO‐PT‐A algorithm is evaluated with other well‐known algorithms considering different values of the average and maximum transmit power of the hybrid‐access point (H‐AP), the number of secondary users (SUs) and path loss exponent.

MACBHA: Modified Adaptive Cluster-Based Heuristic Approach with Co-operative Spectrum Sensing in Wireless Sensor Networks

In this paper, a Modified Adaptive Cluster-Based Heuristic Approach (MACHBA) has been proposed for wireless sensor networks (WSNs) to perform the cooperative spectrum sensing (CSS) in the shopping mall, weather forecasting, military area and audio, video transmission applications. A Secure CSS based MACBHA has been proposed for secondary spectrum usage. Unlicensed Secondary Users (SUs) utilize parts of the spectrum, which are not used by the licensed primary users (PUs) in cognitive radio WSNs. The unused spectrum of the PUs is utilized by the secondary user cluster. The performance of the MACBHA in WSNs is evaluated using the network simulator tool NS-2.35 in Ubuntu 16.04.6 LTS (Xenial Xerus) operating system. The simulation result shows the performance improvement in network utility. Even though, the number of SUs increases, a minimum latency is achieved.

Discrete Exclusion Zone for Dynamic Spectrum Access Wireless Networks

The implementation of a geographic exclusion zone (GEZ) has been a scheme in regulations developed to protect a primary user (PU) in dynamic spectrum access wireless networks, where secondary users (SUs) can transmit only outside the exclusion zone region centered at the PU receiver. After determining the radius of the GEZ, the number of operable nodes in actual deployment is quite uncertain due to the random location of nodes. This poses certain difficulty for SU spectrum sharing planning. In this paper, we propose an alternative PU protection scheme called the discrete exclusion zone (DEZ), which is shapeless. The PU protection is achieved by switching off the first <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k-1$ </tex-math></inline-formula> nearest neighboring SUs surrounding the PU. Building on the stochastic geometry of wireless node locations, the conditions under which the mean and the variance of the aggregate interference from SUs to the PU exist are obtained. These conditions define the minimum size of the DEZ. Then, we obtain the closed-form expressions for the mean and the variance as a function of the DEZ size <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> for a given number of SUs <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> , including <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N\rightarrow \infty $ </tex-math></inline-formula> . Since it is challenging to obtain a closed-form expression of the density function, we resort to the Gamma distribution to approximate the distribution of the aggregate interference, which is validated by simulations. Finally, the performances of the GEZ and DEZ are investigated in terms of the number of operable SUs outside the GEZ and DEZ, respectively, for achieving a given PU protection requirement. The results show that the DEZ gives a fixed number of operable nodes in the presence of topology randomness associated with the actual SU network deployment.

Dynamic Decision-Making Process in the Opportunistic Spectrum Access

We investigate in this paper many problems related to the decision-making process in the Cognitive Radio (CR), where a Secondary User (SU) tries to maximize its opportunities by finding the most vacant channel. Recently, Multi-Armed Bandit (MAB) problems attracted the attention to help a single SU, in the context of CR, makes an optimal decision using the well-known MAB algorithms, such as: Thompson Sampling, Upper Confidence Bound, e-greedy, etc. However, the big challenge for multiple SUs remains to learn collectively or separately the vacancy of channels and decrease the number of collisions among users. To solve the latter issue for multiple users, the All-Powerful Learning (APL) policy is proposed; this new policy considers the priority access and the dynamic multi-user access, where the number of SUs may change over time. Based on our APL policy, we consider as well as the Quality of Service (QoS), where SUs should estimate and then access best channels in terms of both quality and availability. The experimental results show the superiority of APL compared to existing algorithms, and it has also been shown that the SUs are able to learn channels qualities and availabilities and further enhance the QoS. © 2020 ASTES Publishers. All rights reserved.

Cooperative Spectrum Sensing in Cognitive Radio Using Bayesian Updating with Multiple Observations

We study cooperative spectrum sensing in cognitive radio (CR) networks using the hidden Markov model (HMM) for opportunistic spectrum access (OSA). We assume that the primary channel operates in a time division multiple address (TDMA) manner. Thus, spectrum sensing is operating in a slot-by-slot basis. In contrast to the conventional Bayesian updating using only one observation, in this work, we propose to perform the update in a concatenated fashion with all the observations available from the secondary users (SUs). In the proposed scheme, a predefined threshold on the belief is used for determining the channel activity. With the threshold, the proposed scheme is more flexible in the system operation than the simple majority vote scheme, in which no such threshold is available. We compare, by simulations, the performance of the proposed concatenated update scheme with that of the majority vote scheme and show that the probabilities of correctly detecting a busy state and an idle state are about 1 as the number of SUs is as large as 15, so the effects of the further increase in the number of SUs are limited.

Decentralized Learning for Opportunistic Spectrum Access: Multiuser Restless Multiarmed Bandit Formulation

In opportunistic spectrum access, each secondary user selects a channel from a pool of multiple channels based on their local observations. The challenge here is to learn the best channel in terms of availability, as the channel availability statistics are unknown. In order to learn these unknown statistics, a novel decentralized multiuser learning technique termed as DSEE for channel selection in dynamic networks (DSEE-CSDN) has been proposed. DSEE-CSDN allows secondary users to enter the network during different time slots. Thus, the number of secondary users is not known beforehand. Moreover, the availability status of different independent channels is considered to be changing according to the two-state restless Markov chain model, which, in practice, is more realistic as compared to independent and identically distributed channel state model. Thus, the problem is formulated as a stochastic multiuser restless multiarmed bandit. The proposed algorithm achieves system-wide order-optimal performance under self-play. Results indicate that DSEE-CSDN is able to achieve a logarithmic order of regret. Furthermore, collisions and switching cost are just around 5% and 2% of total time slots, respectively. Also, DSEE-CSDN can achieve probabilistic fairness in channel selection without any preagreement among users.

On the Stochastic Modeling of the Holding Time of SUs to PU Channels in Cognitive Radio Networks

To efficiently utilize the idle periods on primary channels in cognitive radio networks; unlicensed secondary users (SUs) should have prior knowledge of the periods of time over which they can use the channel(s). Therefore, we focus on the problem concerned with the amount of time with which a SU holds a primary channel or more as well as the average amount of data that can be transmitted during this time. We start by deriving closed-form probabilistic expressions for the distribution of that time for different cases that model realistic scenarios. This is done based on the activity levels of primary users (PUs) for cases that include identical and non-identical primary channels for different numbers of SUs. To reduce the computational complexity, we derive closed-form expressions to approximate the obtained expressions for all considered scenarios. Numerical investigations and simulations are carried out to study the correctness of the exact expressions as well as the approximations. The obtained results demonstrate full agreement with the derived expressions and highlight the fact that the gamma distribution is a better approximation than the normal distribution. Finally, two simple channel allocation strategies are implemented to demonstrate the simplicity with which the obtained expressions can be used.