scholarly journals Optimal Power Allocation for MIMO-MAC in Cognitive Radio Networks

2021 ◽  
Author(s):  
Peter He ◽  
Guangming (Minco) He ◽  
Lian Zhao
Keyword(s):  
Cognitive Radio ◽  
Communication Systems ◽  
Multiple Input Multiple Output ◽  
Mobile User ◽  
Upper And Lower Bounds ◽  
Maximization Problem ◽  
Dual Decomposition ◽  
Secondary Users ◽  
Primary Users ◽  
Sum Rate

This paper considers a cognitive radio (CR) network, in which the unlicensed (secondary) users (SUs) are allowed to concurrently access the spectrum allocated to the licensed (primary) users, provided that the interference of SUs with the primary users (PUs) satisfies certain constraints. It is more general and owns a stronger challenge to ensure the quality of service (QoS) of PUs, as well as to maximize the sum-rate of SUs. On the other hand, the multiple-antenna mobile user case has not been well investigated for the target problem in the open literature. We refer to this setting as multiple input multiple output multiple access channels (MIMO-MAC) in the CR network. Subject to the interference constraints of SUs and the peak power constraints of SUs, the sum-rate maximization problem is solved. To efficiently maximize the achievable sum-rate of SUs, a tight pair of upper and lower bounds, as an interval, of the optimal Lagrange multiplier is proposed. It can avoid ineffectiveness or inefficiency when the dual decomposition is used. Furthermore, a novel water-filling-like algorithm is proposed for the inner loop computation of the proposed problem. It is shown that this algorithm used in the inner loop computation can obtain the exact solution with a few finite computations, to avoid one more loop, which would be embedded in the inner loop. In addition, the proposed approach overcomes the limitation of Hermitian matrices, as optimization variables. This limitation to the optimization problem in several complex variables has not been well investigated so far. As a result, our analysis and results are solidly extended to the field of complex numbers, which are more compatible with practical communication systems.

scholarly journals Optimal Power Allocation for MIMO-MAC in Cognitive Radio Networks

2021 ◽  
Author(s):  
Peter He ◽  
Guangming (Minco) He ◽  
Lian Zhao
Keyword(s):  
Cognitive Radio ◽  
Communication Systems ◽  
Multiple Input Multiple Output ◽  
Mobile User ◽  
Upper And Lower Bounds ◽  
Maximization Problem ◽  
Dual Decomposition ◽  
Secondary Users ◽  
Primary Users ◽  
Sum Rate

This paper considers a cognitive radio (CR) network, in which the unlicensed (secondary) users (SUs) are allowed to concurrently access the spectrum allocated to the licensed (primary) users, provided that the interference of SUs with the primary users (PUs) satisfies certain constraints. It is more general and owns a stronger challenge to ensure the quality of service (QoS) of PUs, as well as to maximize the sum-rate of SUs. On the other hand, the multiple-antenna mobile user case has not been well investigated for the target problem in the open literature. We refer to this setting as multiple input multiple output multiple access channels (MIMO-MAC) in the CR network. Subject to the interference constraints of SUs and the peak power constraints of SUs, the sum-rate maximization problem is solved. To efficiently maximize the achievable sum-rate of SUs, a tight pair of upper and lower bounds, as an interval, of the optimal Lagrange multiplier is proposed. It can avoid ineffectiveness or inefficiency when the dual decomposition is used. Furthermore, a novel water-filling-like algorithm is proposed for the inner loop computation of the proposed problem. It is shown that this algorithm used in the inner loop computation can obtain the exact solution with a few finite computations, to avoid one more loop, which would be embedded in the inner loop. In addition, the proposed approach overcomes the limitation of Hermitian matrices, as optimization variables. This limitation to the optimization problem in several complex variables has not been well investigated so far. As a result, our analysis and results are solidly extended to the field of complex numbers, which are more compatible with practical communication systems.

scholarly journals Deep Learning Predictive Models for Cognitive Radio System

2019 ◽  
Vol 9 (2S) ◽  
pp. 491-496
Keyword(s):  
Deep Learning ◽  
Cognitive Radio ◽  
Frequency Band ◽  
Communication Systems ◽  
Geographical Area ◽  
Data Sets ◽  
Secondary Users ◽  
Radio System ◽  
Primary Users ◽  
Accuracy Of Prediction

Cognitive Radio (CR) was introduced to improve the utilization of Radio Frequencies (RF) that remain under-utilized by the primary users (licensee). The main idea behind CR is to allow un-licensed (secondary) users to occupy vacancies in licensed bands. However, CR mandates the secondary user to vacate the frequency band within a specified time after the primary user attempts to use the frequency band. CR does not expect the primary users to share their frequency usage schedules and hence the secondary users have to scan and predict the vacancy. The advantage for the secondary users is that they do not pay for utilization of band, if they are conformal to the CR specifications. CR is the next generation of smart communication systems. CR requires continuous monitoring of the intended RF band in the intended geographical area. This information may be used to predict spectral vacancies (white spaces). Certain bands, e.g. Analog TV bands, will have pre declared utilization schedules but in general, spectrum utilization is a random process and hence prediction can be difficult. However, Deep Learning (DL) techniques can improve the accuracy of prediction. Deep Learning techniques require large and clean data sets to work correctly. Such data sets are also necessary to compare achievable accuracy of prediction algorithms. Towards this end, we have created data sets that can be used for simulation, training and testing of CR over GSM band (890-960MHz). A typical file with two hour of observations will have about 1.2 million samples. More than 1000 sets of data samples have been captured from urban and rural areas in India. All the data sets have been cleaned to avoid instrument errors and statistical outliers. In this paper we have used these standardized data sets to perform a comparative analysis of three DL methods for CR, viz. Auto-encoder (AE), Long Short-Term Memory (LSTM) and Multi Layer Perceptron (MLP). Results of the comparison are discussed.

scholarly journals An Energy-Efficient Unselfish Spectrum Leasing Scheme for Cognitive Radio Networks

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6161
Author(s):  
Denis Bilibashi ◽  
Enrico M. Vitucci ◽  
Vittorio Degli-Esposti ◽  
Andrea Giorgetti
Keyword(s):  
Energy Efficiency ◽  
Cognitive Radio ◽  
Energy Efficient ◽  
Cooperative Communications ◽  
Cooperative Behavior ◽  
Maximization Problem ◽  
Secondary Users ◽  
Bipartite Matching ◽  
Spectrum Leasing ◽  
Primary Users

Cooperative Communications in Cognitive Radio (CR) have been introduced as an essential and efficient technique to improve the transmission performance of primary users and offer transmission opportunities for secondary users. In a typical multiuser Cooperative Communication in CR, each primary user can choose one secondary user as a relay node. To encourage the cooperative behavior of the secondary users, primary users lease a fraction of their allocated spectrum to the relay secondary users to transmit their data packets. In this work, a novel unselfish spectrum leasing scheme in CR networks is proposed that offers an energy-efficient solution minimizing the environmental impact of our network. A network management architecture is introduced, and resource allocation is proposed as a constrained sum energy efficiency maximization problem. The optimization problem is formulated and solved using non-linear programming methods and based on a modified Kuhn-Munkres bipartite matching algorithm. System simulations demonstrate an increment in the energy efficiency of the primary users’ network compared with previously proposed algorithms.

Interference Modeling and Analysis in Cognitive Radio Networks

2013 ◽  
Vol 4 (4) ◽  
pp. 1-15
Author(s):  
Yanxiao Zhao ◽  
Bighnaraj Panigrahi ◽  
Kazem Sohraby ◽  
Wei Wang
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Critical Role ◽  
Radio Networks ◽  
Secondary Users ◽  
Idle State ◽  
Modeling And Analysis ◽  
Interference Modeling ◽  
Primary Users

Cognitive radio networks (CRNs) have received considerable attention and viewed as a promising paradigm for future wireless networking. Its major difference from the traditional wireless networks is that secondary users are allowed to access the channel if they pose no harmful interference to primary users. This distinct feature of CRNs has raised an essential and challenging question, i.e., how to accurately estimate interference to the primary users from the secondary users? In addition, spectrum sensing plays a critical role in CRNs. Secondary users have to sense the channel before they transmit. A two-state sensing model is commonly used, which classifies a channel into either busy or idle state. Secondary users can only utilize a channel when it is detected to be in idle state. In this paper, we tackle the estimation of interference at the primary receiver due to concurrently active secondary users. With the spectrum sensing, secondary users are refrained from transmitting once an active user falls into their sensing range. As a result, the maximum number of simultaneously interfering secondary users is bounded, typically ranging from 1 to 4. This significant conclusion considerably simplifies interference modeling in CRNs. The authors present all the cases with possible simultaneously interfering secondary users. Moreover, the authors derive the probability for each case. Extensive simulations are conducted and results validate the effectiveness and accuracy of the proposed approach.

QoS Maintenance in Cognitive Radio Networks with Priority-Supporting Novel Channel Allocation Method

2019 ◽  
Vol 15 (1) ◽  
pp. 1-16
Author(s):  
K. Annapurna ◽  
B. Seetha Ramanjaneyulu
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Channel Allocation ◽  
Radio Networks ◽  
Secondary Users ◽  
Primary Users ◽  
Maximum Value ◽  
Blocking Probabilities ◽  
Bid Price

Satisfying the Quality of Service (QoS) is often a challenge in cognitive radio networks, because they depend on opportunistic channel accessing. In this context, appropriate pricing of vacant channels that is linked to the preference in their allocation, is found to be useful. However, ambiguity on the possible price at which the channel would be allotted is still a concern. In this work, an auction mechanism in which maximum value of the bid is predefined is proposed. With this, users quote their bid values as per their needs of getting the channels, up to the predefined maximum allowed bid price. However, final price of allocation is decided based on the sum total demand from all the users and the availability of vacant channels. Performance of the system is found in terms of blocking probabilities of secondary users and revenues to primary users. The proposed system is found to yield similar quantum of revenues as that of the Generalized Second Price (GSP) auction, while offering much lesser blocking probabilities to high-priority users to satisfy their QoS requirements.

scholarly journals Secure Beamforming in 5G-Based Cognitive Radio Network

Symmetry ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1260
Author(s):  
Hyils Sharon Magdalene Antony ◽  
Thulasimani Lakshmanan
Keyword(s):  
Cognitive Radio ◽  
Information Exchange ◽  
Cognitive Radio Network ◽  
Multiple Input Multiple Output ◽  
Primary User ◽  
Radio Network ◽  
Secrecy Rate ◽  
Secondary Users ◽  
Input Multiple Output ◽  
The Impact

Cognitive radio network (CRN) and non-orthogonal multiple-access (NOMA) is a significant system in the 5G wireless communication system. However, the system is an exceptional way for the cognitive users to secure a communication from the interferences in multiple-input multiple-output (MIMO)-NOMA-based cognitive radio network. In this article, a new beamforming technique is proposed to secure an information exchange within the same cells and neighboring cells from all intervened users. The interference is caused by an imperfect spectrum sensing of the secondary users (SUs). The SUs are intended to access the primary channels. At the same time, the primary user also returns to the channel before the SUs access ends. This similar way of accessing the primary channel will cause interference between the users. Thus, we predicted that the impact of interferences would be greatly reduced by the proposed technique, and that the proposed technique would maximize the entire secrecy rate in the 5G-based cognitive radio network. The simulation result provides better evidence for the performance of the proposed technique.

scholarly journals Bio-inspired route estimation in cognitive radio networks

2020 ◽  
Vol 10 (3) ◽  
pp. 3095
Author(s):  
Miguel Tuberquia ◽  
Hans Lopez-Chavez ◽  
Cesar Hernandez
Keyword(s):  
Cognitive Radio ◽  
Ant Colony Optimization ◽  
Cognitive Radio Networks ◽  
Radio Networks ◽  
Ant Colony ◽  
Secondary Users ◽  
Mobility Spectrum ◽  
Hybrid Strategy ◽  
Primary Users ◽  
Specific Radio

Cognitive radio is a technique that was originally created for the proper use of the radio electric spectrum due its underuse. A few methods were used to predict the network traffic to determine the occupancy of the spectrum and then use the ‘holes’ between the transmissions of primary users. The goal is to guarantee a complete transmission for the second user while not interrupting the trans-mission of primary users. This study seeks the multifractal generation of traffic for a specific radio electric spectrum as well as a bio-inspired route estimation for secondary users. It uses the MFHW algorithm to generate multifractal traces and two bio-inspired algo-rithms: Ant Colony Optimization and Max Feeding to calculate the secondary user’s path. Multifractal characteristics offer a predic-tion, which is 10% lower in comparison with the original traffic values and a complete transmission for secondary users. In fact, a hybrid strategy combining both bio-inspired algorithms promise a reduction in handoff. The purpose of this research consists on deriving future investigation in the generation of multifractal traffic and a mobility spectrum using bio-inspired algorithms.

scholarly journals Intelligent Process of Spectrum Handoff/Mobility in Cognitive Radio Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Prince Semba Yawada ◽  
Mai Trung Dong
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Communication Systems ◽  
Spectral Band ◽  
Spectrum Management ◽  
Radio Networks ◽  
Innovative Technology ◽  
Main Task ◽  
Secondary Users ◽  
Spectrum Handoff

Cognitive radio is an innovative technology in the field of wireless communication systems, aimed at significantly improving the use of the radio spectrum while allowing secondary users to access the spectral band opportunistically. Spectrum management mechanism ensures the transmission of data by controlling the efficiency of operation between the primary and secondary networks. The main task of spectrum management is to ensure that secondary users benefit from the spectrum without interfering with primary users. This paper deals with some of the important characteristics of spectrum mobility in the cognitive radio networks. The new management approaches of the mobility and the connection are designed to reduce the latency and loss of information during spectrum handoff, a list of channel safeguard is maintained in this effect, but the maintenance and update are a challenge. In this paper, we describe the reasons and mechanisms of spectrum handoff. Protocols have been developed to illustrate this handoff mechanism. We also make a comparison between the different methods of spectrum handoff. The simulation results obtained confirm that the protocols developed and the proposed method performed better than the pure reactive handoff method.

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