ADAPTIVE DIMENSIONAL SEARCH BASED IMPROVISED ENSEMBLE METHOD FOR COGNITIVE RADIO IN SOFTWARE-DEFINED NETWORKS

Recently, the increasing demand of radio spectrum for the next generation communication systems due to the explosive growth of applications appetite for band-widths has led to the problem of spectrum scarcity. The potential approaches among the proposed solutions to resolve this issue are well explored cognitive radio (CR) technology and recently introduced non-orthogonal multiple access (NOMA) techniques. Both the techniques are employed for efficient spectrum utilization and assure the significant improvement in the spectral efficiency. Further, the significant improvement in spectral efficiency can be achieved by combining both the techniques. Since the CR is well-explored technique as compared to that of the NOMA in the field of communication, therefore it is worth and wise to implement this technique over the CR. In this article, we have presented the frameworks of NOMA implementation over CR as well as the feasibility of proposed frameworks. Further, the differences between proposed CR-NOMA and conventional CR frameworks are discussed. Finally, the potential issues regarding the implementation of CR-NOMA are explored.

In the ever-evolving landscape of wireless communication, the demand for efficient spectrum utilization is paramount. The research begins by acknowledging the existing challenges in CR within SDNs, particularly the need for adaptive strategies to dynamically allocate spectrum resources. A critical research gap lies in the absence of an approach that seamlessly integrates Gated Dual-Path RNNs and Adaptive Dimensional Search to enhance the adaptability and efficiency of CR systems. The proposed methodology leverages the power of Gated Dual-Path RNNs for real-time learning and decision-making, coupled with an Adaptive Dimensional Search algorithm for dynamic spectrum allocation. This study introduces a novel approach, the Gated Dual-Path Recurrent Neural Network (RNN) Empowered Adaptive Dimensional Search, tailored for Cognitive Radio (CR) in Software-Defined Networks (SDNs). The escalating proliferation of wireless devices and applications has exacerbated the spectrum scarcity problem, necessitating intelligent solutions to optimize spectrum utilization. This dual-path architecture enables the CR system to capture temporal dependencies in the spectrum environment and adaptively adjust its parameters for optimal performance. The experimental results demonstrate the efficacy of the proposed approach, showcasing significant improvements in spectrum utilization efficiency, throughput, and adaptability compared to traditional methods. The Gated Dual-Path RNN Empowered Adaptive Dimensional Search proves to be a robust solution for enhancing CR capabilities in SDNs, paving the way for more intelligent and adaptive wireless communication systems.

SummaryThe integration of 5G networks with cognitive radio (CR) technology enables the software‐defined networking (SDN) infrastructure to support emergency applications. In future, CR can be integrated with 5G and many wireless networks like Wi‐Fi, WSN, and MANET for efficient spectrum utilization with higher data rate and lower latency. This CR technology allows unlicensed users to access the licensed spectrum, whenever it is free. In this paper, an efficient SDN architecture with cognitive ability for emergency network is proposed in which the SDN controller prolong communication between disaster victims and first responders and so the first responders can arrive at the spot directly and rescue the victims. The SDN controller has cognitive ability so that the victims can utilize the vacant licensed band to communicate with the first responders, thereby improving the spectrum utilization of the network. Another two main challenges during emergency are the occurrence of interference and link failure. The proposed dynamic handover algorithm with interference cancellation (DHAIC) cancels the interference between the nodes inside the network and performs dynamic handoff, whenever link failure occurs between the cluster head (CH) and the controller. An optimum throughput and minimal delay is achieved to ensure the network performance.

This paper considers a multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) cognitive radio (CR) system with an unmanned aerial vehicle relay (UR). In this system, a secondary transmitter (ST) uses licensed spectrum from the primary network to transmit signals to its secondary receivers (SRs) based on NOMA. The UR is used as a relay to forward the signals from the ST to the SRs. As a result, the system can achieve significant improvements in spectral efficiency and network capacity. However, such a MIMO NOMA CR system faces issues of interference and security, i.e., eavesdropping attacks, due to the shared spectrum use and the UR. Therefore, we aim to minimize the outage probability of the secondary network, subject to constraints on the outage probability of the primary network and the intercept probabilities of eavesdroppers. Then, we attempt to optimize the transmit power of the UR, the coordinates of the UR, and the power allocation factors for NOMA. We further derive closed-form expressions for the outage probabilities of the secondary and primary networks and the intercept probabilities at the eavesdroppers. We propose using a machine learning algorithm based on a constrained continuous genetic algorithm to solve the optimization problem.

In this paper, we present the opportunistic ambient backscatter communication (ABC) for improving the energy efficiency (EE) of a cognitive radio (CR) system, in the presence of a jamming attack. In particular, we consider an energy-harvesting- aided ABC CR system, that enables simultaneous improvements in spectral efficiency and energy utility. The expressions for sum rate, energy consumption and EE of the considered system are expressed in closed form to render convenience in numerical analysis. In addition, an optimization problem is formulated to maximize EE of the CR network, subject to the imperfect sensing constraints and interference from primary user and jammer. Simulation results presented for corroborating the analytical results and to demonstrate the improvement in performance of the proposed system in terms of data rate and EE.

Background/Objectives: The rapid growth of demand for ever-increasing data rate and wireless services results in spectrum scarcity. Cognitive radio technology is used as solution for this problem. Methods/Statistical Analysis: Spectrum sensing is the key mechanism used to share the spectrum in cognitive radio which will solve the spectrum scarcity problem. Conventional cognitive radio is operated in half-duplex mode which fist sense the spectrum hole and transmit if spectrum hole is found. This listen and talk approach has the constraint of losing transmission opportunities during sensing or has the Risk of undetectable collisions during transmission. This problem can be rectified by operating the cognitive radio in full duplex mode. Findings: Modern full-duplex wireless radio transceivers will be capable of simultaneous spectrum sensing and transmission. But one of the main issues in the full duplex simultaneous sensing and transmission is the residual self-interference.In this paper a full duplex cognitive radio with two antennas (2x2 MIMO) which will provide for higher physical isolation is proposed with the self-interference cancelling technique. The technique consists of a power control and interference cancellation filter or self-interference whitening filter. Since the full-duplex system, radios transmit and sense simultaneously in the same frequency band at the same time, providing improvement in spectral efficiency over a half-duplex system. Applications/Improvements: The result shows that the proposed full duplex cognitive radio provides spectral efficiency of 5.2 bits/Hz/sec at 11dB SNR but the conventional half duplex cognitive radio provides only 2 bits/Hz/sec. Keywords: Cognitive Radio, Full Duplex, MIMO, Power Control, Spectrum Sensing

Under the energy detection scheme based cognitive radio (CR) system, the process of spectrum sensing is of high importance. The sensing performance of CR primarily depends on two important parameters namely, the sensing time τ and the reference threshold λ. In order to achieve a goal where the CR system obtains a high value of throughput and simultaneously ensures a sufficient level of protection to the licensed users, the values of these parameters can neither be too high nor too low, so proper settings of their values is of prime concern. However, under these constraints on choosing a particular value of τ and λ, it is challenging for CR to fulfill this goal. In this paper we propose a CR system which operates under the scheme of double threshold to ensure a sufficient protection required by the licensed users and also makes an efficient utilization of the confusion region to improve its achievable throughput. It is observed that, under the proposed approach, the CR system achieves better throughput than the CR system based on the single threshold and also to the conventional double threshold based CR system where confusion region is used based on the results of sensing performed in the next sensing rounds. We further study the problem of optimizing the sensing duration to maximize the throughput of the proposed CR system. We formulate the sensing-throughput tradeoff problem mathematically and prove that, the formulated problem indeed has an optimal sensing duration where throughput of the CR system is maximized.

Higher requirements must be put forward about wireless signal transmission in harsh environments. Energy loss mitigation, channel estimation, noise interference reduction demands high quality of service in wireless communication. In the fifth generation wireless communication, massive multiple-input multiple-output (MIMO) networks have been used and play a significant role to fulfill the requirements. Cell-Free massive MIMO networks are recognized as possible solution in the future wireless communication. Spectral efficiency (SE) is a very important index in assessing massive MIMO networks. This article tries to optimize both SE and power control of cell-free massive MIMO networks. Both uplink and downlink transmission are included in the study. The optimization model contains both SE and power control of massive MIMO networks. To tackle the model, a novel ensemble method is developed inspired by ensemble learning. The ensemble method is built up on the neighborhood field optimization method. The goodness of the developed ensemble method is verified by comparing with genetic algorithm, gradient descent method and Newton method. Extensive simulations are performed to study the optimization of SE and power control. Different wireless sensors are taken into consideration to simulate different requirements of massive MIMO networks. The results demonstrate the effectiveness of the proposed method for applying in massive MIMO networks.

As one of the key enabling technologies of the fifth generation wireless network (5G), software defined network (SDN) offers a logically centralized control model, flexible programmability, and a flow-based paradigm that is ideally suited for highly scalable wireless networks, from access to core part. Following this paradigm, a novel software-defined radio access network (SDRAN) architecture and the function modules have been proposed in this paper. In particular, the motivation, challenge, and deployment roadmap of SDRAN framework are discussed. The relationships between alternative solutions (Cloud RAN, network function virtualization) and complementary technologies (cognitive radio, self-organizing network, big data analysis) are analyzed in detail. Taking interference management of heterogeneous mobile network as the example use case, scheme design and preliminary system evaluations are given to show the benefit of SDRAN architecture.

Coordination structures of various Multi -RATs in wireless 6G architectures enable the seamless integration of heterogeneous radio access technologies (Multi -RATs), such as sub-combination, MMWAE, and Wi-Fi 7. These return structures ensure resource efficiency, resource reliability, dynamic conference, low-latency experience, and intense efficiency. The objectives of the coordination structures of various Multi -RAT in 6G wireless architectures include obtaining perfect interoperability between various radio access technologies, optimizing spectrum use, minimizing latency during transfers, improving energy efficiency, and supporting low energy-related communications (URLLC). These structures aim to ensure intelligent connectivity on heterogeneous networks for next-generation applications. To implement coordination structures of various Multi - RAT in 6G, methods such as AI-oriented resource allocation, software-defined network (SDN), and network function virtualization (NFV) are employed. They allow real-time decision making, dynamic rat selection, and smart traffic direction. Simulation models and test shards integrating subTHz, MMWAE, and legacy systems are designed to validate performance. The results show a 35% improvement in spectral efficiency, 40% reduction in delivery latency, and higher service quality for high mobility scenarios. Coordinated architecture also demonstrated better adaptability to network conditions, ensuring perfect connectivity and reliability in various cases of use, including IoT, AR/VR, and autonomous systems.

Hybrid automatic repeat request (HARQ) is widely used in modern wireless communication systems to improve the spectral efficiency. However, the spectral efficiency improvement is at the cost of increased transmission delay. In this paper, we consider a HARQ protocol called rate adaptive HARQ (RA-HARQ), which allows dynamic resource allocation based on outdated channel state information (CSI) feedback information from the receiver. Under the assumption of the cumulative distribution function of the fading channel capacity being a power function, spectral efficiency of simple linear stationary policy converges to the ergodic channel capacity at order of 1#x002F;D^2 . For a wide class of non-linear policies, the spectral efficiency can converge to ergodic channel capacity at order of e^{-D}. The optimal policy can be found in a simple explicit form sequentially by a greedy algorithm. Simulation results show that it achieves significantly higher spectral efficiency than other existing schemes at any given average delay.

Technological advancements in wireless communications and electronics have enabled the rapid evolution of smart things connected to the Internet. Traditional networks face the challenges of scalability, real-time data delivery, programmability and mobility to support these smart objects collectively known as Internet of Things (IoT). To solve these issues, the integration of two emerging network technologies, namely, software defined networking (SDN) and Fog computing have gained a momentum as a novel model that support IoT architecture for manageability and low latency. SDN has a logically centralized network control plane, which is used for implementing sophisticated mechanisms of traffic control and resource management. On the other hand, Fog computing enables IoT devices’ data to be processed and managed at the network edge, thus providing support for applications that require very low and predictable latency. Though the communication latency is substantially reduced by the adoption of distributed fog layer closer to IoT ends, the latency overhead in the IoT/fog network is not only because of long distance between IoT devices and the cloud, but it is also caused by flow entry installation delay, which comes from limitations in data and control space designs. Traditional fog networks lack priority based fine-grain control over allocation of flows, and this incurs unnecessary delay for critical packets. The impact of packet blocking on QoS delivery could be reduced if the programmability power of SDN approach is employed in IoT applications for priority oriented flow space management in fog networks. In this paper, we propose a converged SDN and IoT/fog architecture which employs differential flow space allocation for heterogeneous IoT applications per flow classes to satisfy priority based quality of service requirements. Our analytical results demonstrate that urgent flow classes are served more efficiently than Naive approach without compromising fairness of allocation for normal flow classes.

The rising number and capacity requirements of wireless systems bring increasing demand for RF spectrum. Cognitive radio (CR) system is an emerging concept to increase the spectrum efficiency. CR system aims to enable opportunistic usage of the RF bands that are not occupied by their primary licensed users in spectrum overlay approach. In this approach, the major challenge in realizing the full potential of CR systems is to identify the spectrum opportunities in the wide band regime reliably and optimally. In the spectrum underlay approach, CR systems enable dynamic spectrum access by co-existing and transmitting simultaneously with licensed primary users without creating harmful interference to them. In this case, the challenge is to transmit with low power so as not to exceed the tolerable interference level to the primary users. Spectrum sensing and estimation is an integral part of the CR system, which is used to identify the spectrum opportunities in spectrum overlay and to identify the interference power to primary users in spectrum underlay approach. In this chapter, the authors present a comprehensive study of signal detection techniques for spectrum sensing proposed for CR systems. Specifically, they outline the state of the art research results, challenges, and future perspectives of spectrum sensing in CR systems, and also present a comparison of different methods. With this chapter, readers can have a comprehensive insight of signal processing methods of spectrum sensing for cognitive radio networks and the ongoing research and development in this area.

<span>Increase in data traffic, number of users and their requirements laid to a necessity of more bandwidth. Cognitive radio is one of the emerging technology which addresses the spectrum scarcity issue. In this work we study the advantage of having collaboration between cognitive enabled small cell network and primary macrocell. Different from the existing works at spectrum sensing stage we are applying enhanced spectrum sensing to avoid probability of false alarms and missed detections which has impact on spectral efficiency. Later power control optimization for secondary users known as Hybrid spectrum sharing is used for further improvement of spectral efficiency. Furthermore, the failed packets of Primary users are taken care by high ranked relays which in turn decreases the average Primary user packet delay by 20% when compared between assisted Secondary user method and non-assisted Secondary user method. </span>

Cognition added to RF/antenna systems has extended software defined radio (SDR) communication systems into cognitive radio systems. Software defined radio has been established as a key enabling technology to realize cognitive radio. Thus a cognitive radio is an SDR that is aware of its environment, and autonomously adjusts its operations to achieve the designated objectives. A cognitive radio system is able to sense, reason, learn and be aware of its environment. A dynamic communication application such as cognitive radio requires antenna researchers to design software controlled reconfigurable antennas. The tuning ability of such antennas and the switching time are important to satisfy the requirements of continuously changing communication channels. Neural Networks (NNs) arose as a perfect candidate to control these antennas through Field Programmable Gate Arrays (FPGAs). NNs represent a perfect solution to add learning and reasoning to the cognitive radio antenna systems. In this work, a NN is applied on a reconfigurable antenna where switches are used to connect and disconnect the different parts of its structure. Reconfigurable antennas are potential candidate for cognitive radio since they are able to change their operating characteristics based on the channel activity. Applying NNs to such antennas result in the association of different antenna configurations with the various frequency responses. This association allows training the NN to be able to configure the antenna and regenerate switch combinations/frequency responses on demand. The NN is built and trained in Matlab Simulink and a Xilinx system generator creates the NN VHDL code to be transferred to the FPGA. The FPGA now controls the switches that are incorporated within the reconfigurable antenna structure. The application of NN on cognitive radio antenna systems allows such systems to react swiftly to any change in their environment. The cognitive radio antennas will regenerate the appropriate switch combinations using NN previous training. This will allow communicating over the unoccupied parts of the spectrum which are called white spaces. The dynamic changes that occur in the spectrum require a robust and fast antenna software control. Thus NN prove to be a valid and necessary technique to employ on CR antennas.