Spectrum Shortage Research Articles

Enhancing Cognitive Radio Network Through the Novel Optimization Process and Deep Learning Model in the Transverse System

The dynamic distribution of spectrum, made possible by next-generation networks that utilise Cognitive Radio technology, helps to alleviate spectrum shortages. Additionally, these networks may be dynamically operated to save energy. The innovative cloud-sharing-decision-based method for optimising cognitive radio networks' wireless networks is introduced in this research. Optimisation was achieved via 3 key performance indicators: spectrum utilisation, power utilisation, and human revelation. Researcher find the best key performance indicators for a realistic India’s suburban situation. An optimisation technique in the cloud-based design for architecture simultaneously decreases networks power usage for about 27.6%, mean worldwide revelation by 34.4%, & spectrum utilisation for about 34.6% when related to a standard Cognitive Radio net. For the areas of networks power consumption (4.9%), spectrum utilisation (7.4%), and global exposure (4.4%), method outperforms the old design, even in the worst-case optimisation scenario.

A review of deep learning techniques for enhancing spectrum sensing and prediction in cognitive radio systems: approaches, datasets, and challenges

Cognitive radio (CR) is an emerging wireless technology designed to optimize frequency band usage and address spectrum shortages. Spectrum sensing and prediction are crucial for cognitive radios to make intelligent spectrum access decisions and dynamically alter transmission parameters based on real-time spectrum conditions. These techniques contribute to the efficient use of the spectrum. However, they encounter significant obstacles such as noise uncertainty, low signal-to-noise ratios, channel fading, and more, necessitating robust and intelligent solutions. Deep learning has attracted much attention and displayed great potential in various fields in recent years. This review paper provides a thorough examination of the use of deep learning techniques in spectrum sensing and prediction in the context of cognitive radio. It examines the available literature in depth, highlighting the techniques, the assessment keys, datasets, and limitations of the investigations. The article seeks to provide significant insights and guidance for future developments in harnessing deep learning for better cognitive radio spectrum management by critically assessing the present state of research.

Radio Frequency Interference Detection and Automatic Modulation Recognition Based on Mask RCNN

The increasing complexity of wireless communication conditions presents noteworthy obstacles to the adaptability and flexibility of cognitive radio systems. The latter can alleviate the problem of spectrum shortages through proper spectrum management. However, cognitive radio has difficulty distinguishing between signals of interest and interference, which can be a general enemy since it is undesirable because it influences the signal's quality. Efficient detection and characterization of interference in wireless communication networks is critical for ensuring strong security. However, in this research, we use the Mask R-CNN methodology to present a new concept for automatic modulation recognition and radio frequency interference detection. Moreover, this algorithm can segment, detect, and recognize several types of interference that can affect wireless communication systems, such as chirp interference (CI), continuous wave interference (CWI), and multiple continuous wave interference (MCWI) within the signal of the interest (SOI), as well as the modulation kind present in the SoI. Overall, the combination of these distinct techniques can be very valuable in the field of signal processing, especially in anti-jamming strategies in wireless networks. Moreover, the proposed approach showcases exceptional performance in the validation dataset. For radio frequency interference detection, it achieves a mean average precision (mAP) of 0.946 and a mean average recall (mAR) of 0.954. For automatic modulation recognition, it attains a mAP of 0.898 and a mAR of 0.916.

An efficient beam sweeping scheme with backup paging occasions in NR-Unlicensed Spectrum

With the increasing demand of user applications and the shortage of cellular spectrum, New Radio (NR) access to Unlicensed Spectrum (NR-U) is getting significant attention. Listen Before Talk (LBT) procedure is used to sense unlicensed channel availability prior to transmission. The transmissions are subjected to LBT procedure failure, which brings extra delay as they are postponed to the next available transmission opportunity. This excess delay also affects time-critical procedures, such as paging. The directional communication in NR requires beamforming, which further complicates paging procedure as additional paging message transmissions are needed to cover all the beams. In this work, we propose an NR-U paging mechanism (called FF_BP) consisting of Full and Fast Paging Cycles with Backup Paging Occasions (POs). Full Paging uses a normal NR-U paging cycle and broadcasts a paging message on all the beams. On the other hand, Fast Paging, which targets delay-sensitive mobile users, uses a shorter paging cycle and broadcasts a paging message on selected beams only. The Backup POs deal with LBT procedure failure impairments while paging broadcast on selective beams reduces paging resource usage. We analyze FF_BP using a discrete-time semi-Markov chain model and validate the model by extensive simulations. The simulation results show that FF_BP outperforms several baseline mechanisms in terms of average paging delay and paging resource usage.

LSTM-BIGRU based Spectrum Sensing for Cognitive Radio

There is a shortage of wireless spectrum due to developments in the area of wireless communications as well as the number of users that are using resources. Spectrum sensing is a method that solves the issue of shortage. Deep learning surpasses classical methods in spectrum sensing by enabling autonomous feature learning, which enables the adaptive identification of complicated patterns in radio frequency data for cognitive radio in wireless sensor networks. This innovation increases the system's capacity to manage dynamic, real-time circumstances, resulting in increased accuracy over traditional approaches. Spectrum sensing (SS) using LSTM-BIGRU with gaussian noise has been suggested in this article. Long-term dependencies in sequential data are well- preserved by LSTM due to its dedicated memory cells. In addressing and man- aging long-term dependencies in sequential data, BIGRU's integration enhances the efficacy of the model as a whole. To conduct the investigation, RadioML2016.04C.multisnr open-source dataset was utilized. Whereas, by using RadioML2016.10b open-source dataset, QAM64, QPSK and QAM16 performance evaluation has been investigated. The experimental findings demonstrate that the suggested Spectrum Sensing has better accuracy on the dataset particularly at lower SNRs. The improved spectrum sensing (SS) performance of our suggested model is shown by the evaluation of performance indicators, such as the F1 Score, CKC and Matthew's correlation coefficient, highlighting its potency in the field of spectrum sensing applications.

Optimizing Priority Queuing Systems with Server Reservation and Temporal Blocking for Cognitive Radio Networks

In the domain of cognitive radio (CR), unlicensed users have the opportunity to efficiently use available spectrum bands without interfering with licensed primary users (PUs). Our study addresses the challenge of secondary user (SU) spectrum shortage due to high arrival rates of licensed users. We propose two models aimed at improving the average total waiting time for SUs in such scenarios. These models incorporate non-acquired and preemptive priority mechanisms within the M/D/1 model of a PU delay system. Through quantitative evaluations and Monte Carlo simulations, we evaluate the performance of these models. Our findings show significant improvements in average waiting time for both PUs and SUs, especially under the priority scheme. Furthermore, we explore these models in the context of real-time systems, considering the limited buffer capacity for both user types. This further improves the average waiting time for PUs and SUs in both priority schemes. Our contribution lies in providing effective solutions to mitigate SU shortages in CR networks, providing insight into priority-based approaches and real-time system considerations.

EFFICIENTLY UTILIZE THE SPECTRUM AND LOAD BALANCING IN COGNITIVE RADIO NETWORK USING GENETIC ALGORITHM WITH COVERSET PREDICTION

Wireless communication is one of the most in-demand fields of communication nowadays. Due to people's desire to transfer data as fast and broadly as possible, most communication processes are impacted by overloading and spectrum shortage when there is significant network usage. Make use of a cognitive radio network to keep an eye on things automatically, and use the spectrum dynamically to avoid overloading. Instead of evaluating the node's fitness, the CRN will alert the secondary user (SU) of the unused spectrum. To choose the fittest node in an environment with a coverage zone, a cognitive radio network in the proposed work uses a genetic algorithm plus coverset prediction. To address these issues, this paper offers a CRN calculation strategy.

Bayesian detection with feedback for cooperative spectrum sensing in cognitive UAV networks

AbstractUnmanned aerial vehicles (UAVs) are becoming a popular research topic in applications that do not require human intervention. A variety of applications and devices coexist in the environment where UAVs operate, resulting in a serious spectrum shortage. Therefore, cognitive radio (CR) is a promising solution for opportunistic access to underutilized spectrum bands by the primary user (PU) through cooperative spectrum sensing (CSS) technique. However, the flexible location of UAVs makes CSS inefficient and even difficult to be implemented. In view of this, a cognitive UAV network model consisting of a pair of UAVs which follows a circular flight trajectory to participate in CSS is proposed in a spectrum sensing frame structure. According to the local energy detection, we further propose an optimization problem about the stopping time in a quickest detection paradigm and conduct out Bayesian detection method with feedback to minimize the sensing delay and the false alarm probability by optimizing the stopping time. Moreover, we theoretically derive the optimal threshold pair and prove the optimal stopping time by means of Markov process. At last, a series of numerical simulations are shown to corroborate the proposed Bayesian detection method with feedback, in terms of the false alarm probability, the sensing delay, and achievable throughput. In contrast to the classic Neyman‐Pearson and Bayesian detection methods, the advantage of Bayesian detection method with feedback sensing is presented.

Efficient resource allocation for 5G/6G cognitive radio networks using probabilistic interference models

The Cognitive Radio Network, incorporating Device-to-Device communication and a heterogeneous network, has garnered significant attention due to its ability to address spectrum shortage issues and optimize the efficient utilization of spectrum resources. However, resource allocation considering stochastic behavior has not been considered in existing studies. In this paper, our work aimed to maximize the throughput of the overall network considering multiple users under the umbrella of the Cognitive radio network assisted by amplify and forward relay. The constraints are treated as chance constraints with a probability of satisfaction in them, which leads to a non-convex mixed integer nonlinear problem which is an NP-Hard problem. To solve this problem an exhaustive search solution for optimal results is required. However, the computational burden always increases with the user equipment. Therefore, to obtain an optimal solution with having low computational burden, the Outer Approximation Algorithm is utilized in this research. To evaluate the desired results, extensive simulations have been carried out. The effectiveness of the proposed algorithm is verified by results in terms of throughput maximization under the impact of chance constraint formulation in the cognitive radio networks.

Exploiting Successive Interference Cancellation for Spectrum Sharing Over Unlicensed Bands

Harmonious coexistence among different unlicensed wireless technologies has became increasingly important due to the spectrum shortage problem. As a representative case, we focus on addressing LTE-LAA and WiFi coexistence in unlicensed bands. Traditionally, collision avoidance-based medium access control (MAC) protocols adopted by both LAA and WiFi have led to low channel utilization and fairness. In this paper, we explore physical-layer interference suppression techniques (e.g., successive interference cancellation, SIC) to enhance the spectrum utilization of coexisting LAA and WiFi networks. We propose a SIC-aware MAC protocol that embraces concurrent transmissions and optimizes the channel access strategy at the MAC layer, so as to mitigate the interference (same-technology or cross-technology) due to excess channel contentions. We theoretically analyze the network throughput by extending Bianchi's Markov model, considering the impact of concurrent transmissions and SIC. We also extend the analysis to consider MIMO and MU-MIMO links with SIC. We validate our theoretical analysis and the effectiveness of the proposed MAC protocol via extensive simulations. We also implement a prototype LAA/WiFi SIC receiver on USRP devices to demonstrate the feasibility of cross-technology SIC and the proposed MAC protocol.

Improved spectrum prediction model for cognitive radio networks using hybrid deep learning technique

Cognitive Radio (CR) technology has been highlighted as one of the most likely answers to the issue of spectrum shortage with the rise of fifth generation and beyond communication. Secondary users (SUs) in cognitive radio networks (CRN) must continuously monitor the spectrum to forecast channel occupancy by primary users (PUs) based on fundamental factors, such as location, time, and RF band. A hybrid deep learning model called LSTM-MLP (Long Short-Term Memory-Multilayer Perceptron) is proposed to improve idle channel prediction probability thus reducing the overall sensing time by cognitive users during spectrum sensing. Performance evaluation for the proposed model is done in terms of prediction error and efficiency, the GSM-900 spectrum dataset demonstrates that LSTM-MLP performs better in terms of improved prediction accuracy compared to existing state-of-art prediction techniques.

Enhanced Wireless Network Usage and Loading on Network Delay using Cognitive Radio Network

Wireless communication is one of the fastest-growing fields of communication. Because the user can access and share information anywhere at any time. The communication process will suffer from an overload of traffic. If the node gets overloaded because of heavy traffic, then the performance of the whole network is degraded. So, the usage of the network will be delayed. The proposed work uses the intellectual load balancing approach for Cognitive Radio Network to focus on automatic monitoring of network activity, and the node will continuously balance among numerous nodes. In Cognitive Radio Network the Dynamic Spectrum Access techniques are used to avoid spectrum scarcity and underutilization. The CR base station collect the surrounding about the active primary user and waiting secondary user. To determine the principal user's on/off status of availability, the Markov chain model is employed. The waiting secondary user utilize the spectrum when the primary user is not using it. The secondary user leaves the spectrum and uses the spectrum of another primary user if the primary user is on status. It will assist with dynamic spectrum access method and it will help increase the overall performance of the network. The suggested Load balancing Cognitive Radio Network (LBCRN) sorts the nodes and uses spectrum identification before allocating the node to the secondary user. So that minimise transaction delays, prevent overcrowding of a specific node, automatically lower the level of packet drops, and enhance packet delivery radio levels. As a result, this strategy has helped to lessen the issue of spectrum shortages in the wireless communication industry.

AI-Enabled Compressive Spectrum Classification for Wideband Radios

Cognitive radio is a promising technology that emerged as a potential solution to the spectrum shortage problem by enabling opportunistic spectrum access. In many cases, cognitive radios are required to sense a wide range of frequencies to locate the spectrum white spaces; hence, wideband spectrum comes into play, which is also an essential step in future wireless systems to boost the throughput. Cognitive radios are intelligent devices and therefore can be opted for the development of modern jamming and anti-jamming solutions. To this end, our article introduces a novel AI-enabled energy-efficient and robust technique for wideband radio spectrum characterization. Our work considers a wideband radio spectrum made up of numerous narrowband signals, which could be normal communications or signals disrupted by a stealthy jammer. First, the receiver recovers the wideband from significantly low sub-Nyquist rate samples by exploiting compressive sensing technique to decrease the overhead caused by the high complexity analog-to-digital conversion process. Once the wideband is recovered, each available narrowband signal is given to a cyclostationary feature detector that computes the corresponding spectral correlation function and extracts the feature vectors in the form of cycle and frequency profiles. Then profiles are concatenated and given as input features set to an artificial neural network which in turn classifies each NB signal as legitimate communication with a specific modulation or disrupted by a stealthy jammer. The results show a classification accuracy of about 0.99 is achieved. Moreover, the algorithm highlights significantly high performances in comparison to recently reported spectrum classification techniques. The proposed technique can be used to design anti-jamming systems for military communication systems.

Multi-Agent Reinforcement Learning Resources Allocation Method Using Dueling Double Deep Q-Network in Vehicular Networks

The communication between vehicle-to-vehicle (V2V) with high frequency, group sending, group receiving and periodic lead to serious collision of wireless resources and limited system capacity, and the rapid channel changes in high mobility vehicular environments preclude the possibility of collecting accurate instantaneous channel state information at the base station for centralized resource management. In addition, road and traffic safety have very strict requirements for low-latency, high-reliability communication, and information transmission is only valuable within a certain range, and requires timeliness. For the Internet of Vehicles (IoV), it is a fundamental challenge to achieve low latency and high reliability communication for real-time data interaction over short distances in a complex wireless propagation environment, as well as to attenuate and avoid inter-vehicle interference through a reasonable spectrum allocation. To solve the above problems, this paper proposes a resource allocation (RA) method using dueling double deep-Q network reinforcement learning (RL) with low-latitude fingerprints and soft-update architecture (D3QN-LS) while constructing a multi-agent model based on a Manhattan grid layout urban virtual environment, with communication links between V2V links acting as agents to reuse vehicle-to-infrastructure (V2I) spectrum resources. These agents work in concert, interact with the environment, receive appropriate observations, rewards, and ultimately learn to improve power and spectrum allocation to provide a better entertainment experience and safer driving environment for users. We also address the shortcomings in existing literature studies. Firstly, the low upper limit of transmitted data volume; Secondly, the artificial assumption that the spectrum is consistent with the number of V2V links, which is insufficient for future applications in spectrum shortage sections. We accordingly extend the amount of transmitted data while adding a scenario where the spectrum resources are relatively short, i.e., the number of V2V links is significantly larger than the amount of spectrum. Experimental results show that with proper training mechanism and reward function construction, multiple intelligence can cooperate effectively. Therefore, the total link capacity of the V2I link and the success rate of periodic security information transmission are further improved.

Novel Energy Optimized LDPC Codes for Next-Generation MIMO OFDM Systems

With the increasing prevalence of internet services in our daily lives, there is a growing demand for spectrum, resulting in a shortage of available frequencies. This is primarily due to the recent surge in subscribers seeking faster and more dependable data rates. Researchers globally have suggested various solutions to combat the spectrum shortage, such as technology, modulation schemes, beamforming, intelligent reflective surfaces, and channel coding schemes. With respect to channel coding, multiple codes have been proposed to overcome challenging and uncertain channel conditions. The paper introduces a DWT-incorporated LDPC STBC system as a solution to meet the rigorous demands of 5G deployment and use cases. A comparative analysis of the proposed codes is conducted to assess their suitability for next-generation communication networks, considering SNR and BER. Results indicate that the suggested code performs optimally, achieving a BER of 10-4 at 7dB SNR for Rayleigh Faded wireless channel. Therefore, the suggested code is deemed the most suitable option for the deployment of 5G and beyond systems.

Deep Reinforcement Learning and NOMA-Based Multi-Objective RIS-Assisted IS-UAV-TNs: Trajectory Optimization and Beamforming Design

In this paper, we discuss the co-optimized performance of multi-reconfigurable intelligent surface (RIS)-assisted integrated satellite-unmanned aerial vehicle-terrestrial network (IS-UAV-TN), where the multiple vehicle users are applied to the network under consideration. The performance optimization of IS-UAV-TNs faces two major challenges: one is the obstacles in the transmission path and the other is the highly dynamic communication environment caused by the UAV movement for the multiple ground vehicle users. To tackle these above issues efficiently, we will install RIS on the UAV for the purpose of reshaping the wireless transmission path. In addition, non-orthogonal multiple access (NOMA) protocols are considered as a new paradigm to address spectrum shortage and enhance connection quality. Considering the UAV energy consumption, the satellite transmission beamforming matrix and RIS phase shift configuration, a multi-objective optimization problem is proposed to maximize the system achievable rate and minimize the UAV energy consumption during a specific mission. On this foundation, to facilitate the online decision problem, the deep reinforcement learning (DRL) algorithm is utilized to achieve real-time interaction with the communication environment. A multi-objective deep deterministic policy gradient (MO-DDPG) algorithm is proposed to search for sub-optimal solutions about the learning problem of multi-objective control policies in IS-UAV-TNs. Experimental results show that the method can simultaneously consider three optimization objectives and effectively adjust the optimal update policy according to the settings of different weight parameters.

Hybrid VLC-RF Systems With Multi-Users for Achievable Rate and Energy Efficiency Maximization

With the explosive growth of the data traffic and the access number of intelligent devices, the wireless communication systems based on the radio frequency (RF) is suffering from the pressure of spectrum shortage and access congestion constantly, which can be alleviated with the help of visible light communication (VLC). However, due to the limitations of VLC such as finite coverage and inconvenient uplinks, it is imperative to form the hybrid systems and realize the complementary advantages of VLC and RF. In this paper, the hybrid VLC-RF system with multi-users is investigated, where the sum achievable rate and energy efficiency (EE) are maximized via the alternate optimization of transceiver association and power allocation. In addition, based on various types of multiple access points, EE is optimized under different QoS requirements for green communications. With the assistance of proper approximations, the overall problem is decomposed into several convex sub-problems, which can be solved analytically and avoid traversal permutation. The numerical simulations show the effectiveness and scalability of the proposed algorithm in the hybrid VLC-RF system, which can achieve superior performances than the conventional methods and single systems in multifarious scenarios.

Cooperative Spectrum Sensing for IoT System

This paper it mainly writes how to make better use of spectrum and propose different algorithms to reduce excess spectrum waste in the case of spectrum shortage. Using Randomly distribute PU and SU’s location in certain area, with the help of graphs mark the PU and SU’s, we calculate the SNR considering channel fading, using formula to find the path loss. To perform energy detection, we generated an observation period of H1 signals and run simulations and evaluated it based on the graph of probability of detection (Pd) vs probability of false alarm (Pf). We also made some improvement on the algorithm to deal with sudden impulsive change in the environment variables. We subsequently used OR rule to decide presence of PUs. To get good performance and affordable work, we divided PU sensors into groups to detect energy of different PUs. When grouping according to SNR between PU and SU pairs, we achieved stable performance and less burden.

Improved Q-Reinforcement Learning based Optimal Channel Selection in Cognitive Radio Networks

Cognitive Radio Networks are an emerging technologyin forwireless communication. With increasing number of wireless devices in wireless communication, there is a shortage of spectrum. Also, due to thestatic allocation of channels in wireless networks, there is a scarcity of spectrum underutilization. For efficient spectrum utilization, secondary users dynamically select the free channel of primary users for the transmission of packets. In this work, the performance of routing in a cognitive radio network is improved by the decision of optimal channel selection. The aim of this work is to maximize the throughput and reduce the end-to-end delay. Therefore, an Improved Q-Reinforcement learning algorithm is proposed for the optimal channel selection during the packet routing between source and destination. The performance of this work is compared with the existing routing protocols. It is simulated in network simulator-2 (NS2) with Cognitive Radio Cognitive Network (CRCN) simulation. After performance evaluation, it is observed thatthe proposed work performs better than existing work with respect to packet delivery ratio, throughput, delay, jitter, control overhead, call blocking probability, packet dropping ratio, goodput and normalized routing overhead.

To mitigate primary user emulation attack trajectory using cognitive single carrier frequency division multiple access approaches: Towards next generation green IoT

AbstractThe growing cognizance of spectrum scarcity has become a more significant concern in wireless radio communications. Due to the exponential growth of data transmission in intelligent wireless sensor networks, energy spectrum detection has become a promising solution for resolving spectrum shortages. Primary user emulation attack (PUEA) has been identified as a significant attack vector in the cognitive radio (CR) domain's physical layer. In comparison, the CR is a promising method to increase spectrum efficiency by allowing unlicensed secondary users (SUs) to access licensed frequency bands without interfering with approved primary users (PUs). The study's primary findings are the methodology for preventing PUEA using authentication tags, which are unique sequences. This research blends SC‐FDMA with CR to protect CR networks from PUEA attacks, a Latin square (LS) matrix tag generation system is proposed to mitigate the PUEA effect. The technology is meant to provide effective authentication and protection against malicious users. In a secured environment, the LS tag technique is utilized to track and estimate the PU. For example, the BER of both techniques is virtually identical between 0 and 4 dB, while the BER performance of the suggested LS tag generation improves with increasing signal‐to‐noise ratio (SNR). As a result, the suggested LS tag generation is less susceptible to PUEA. To diminish the influence of PUEA in CR networks, an efficient enlightening approach for making the future Green Cognitive Radio Wireless networks structure is proposed. The simulation results also demonstrate the resilience of the proposed CR spectrum sensing techniques for energy‐efficient knowledge at varying degrees to reduce the adverse effects of environmental technologies.