Enhancing Energy Efficiency in Cognitive Radio Networks: Addressing the Challenges of Rapid Spectrum Utilization

doi:10.21203/rs.3.rs-5337145/v1

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Enhancing Energy Efficiency in Cognitive Radio Networks: Addressing the Challenges of Rapid Spectrum Utilization

https://doi.org/10.21203/rs.3.rs-5337145/v1

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The rapid increase in wireless communication demand has posed significant challenges to energy efficiency within cognitive radio networks. These networks, designed to enhance spectrum utilization by dynamically accessing underutilized frequency bands, face the critical concern of balancing effective spectrum use with energy consumption. This paper explores innovative strategies for enhancing energy efficiency in cognitive radio networks amid the complexities introduced by rapid spectrum utilization. We examine the impact of advanced resource allocation methods, including cooperative spectrum sensing and task distribution among multiple users, which facilitate optimal spectrum sharing while minimizing energy usage. The integration of energy harvesting techniques is discussed as a means to supplement the power requirements of cognitive radio devices, promoting sustainability in network operations. The performance parameters are analysed for 1 to 5000 number of users and bandwidth of total bandwidth of 100 MHz. Simulation results reveal that with an increasing number of users, throughput rises significantly from 1.2 Mbps for one user to 4,500 Mbps for 5,000 users, while energy efficiency peaks at 2.3 bps/J for 1,000 users before declining to 1.5 bps/J at maximum capacity. Spectrum utilization increases steadily from 0.00012–0.45%, and latency escalates from 0.05 ms to 3.00 ms with the user count. Packet delivery ratio (PDR) decreases from 97–80%, and quality of service (QoS) initially improves to 0.92 before slightly declining to 0.85 under peak conditions. These results demonstrate that the proposed frameworks not only reduce energy consumption but also enhance data throughput and overall system performance in cognitive radio networks.

Energy efficiency

Resource Allocation

Muti-User Detection

Cooperative Spectrum sensing

Throughput

Latency

Packet Delivery Ratio

Quality of Service

Enhancing energy efficiency in cognitive radio networks is essential for addressing the growing demands for spectrum utilization in an increasingly wireless-dependent world. As the number of devices vying for spectrum increases, the efficient management of this scarce resource becomes crucial. Advanced resource allocation methods, such as power allocation algorithms, genetic algorithms, non-orthogonal multiple access (NOMA), and cooperative spectrum sensing, have emerged as pivotal strategies to improve energy efficiency in cognitive radio networks.

Power allocation algorithms optimize the distribution of power among users, minimizing energy consumption while maximizing throughput. Similarly, genetic algorithms, which mimic natural selection processes, adaptively resolve complex optimization challenges faced by cognitive networks, leading to enhanced performance in dynamic environments. NOMA facilitates higher spectral efficiency by allowing multiple users to share the same frequency band simultaneously, mitigating congestion and improving overall network capacity. Additionally, cooperative spectrum sensing enhances the accuracy of spectrum availability detection, reducing the waste of energy associated with ineffective sensing efforts.

Genetic algorithms (GAs) are highly effective in optimizing resource allocation in cognitive radio networks (CRNs), particularly when balancing energy and bandwidth. GAs work by evaluating potential solutions using a fitness function based on energy efficiency, and through iterative processes like selection, crossover, and mutation, they converge on optimal allocations for power and bandwidth. This allows for multi-objective optimization, reducing operational costs and enhancing the lifespan of devices. GAs also adapt dynamically to real-time conditions, improving spectrum allocation, and supporting energy harvesting for sustainable, energy-efficient network operations.

Figure1 shows the block diagram of Optimal Spectrum Sharing. The diagram demonstrates how cooperative spectrum sensing, task distribution, and optimal spectrum sharing work together to enhance both spectrum efficiency and energy efficiency in cognitive radio networks. By dynamically allocating tasks and resources, the system achieves better performance with lower energy costs.

Cooperative Spectrum Sensing

Figure 2 shows the Block of Cooperative sensing in Spectrum Sensing in Cognitive Radio networks.

In this block, multiple cognitive users collaborate to detect available spectrum opportunities by sensing the environment. By using cooperative sensing, the accuracy of spectrum detection is significantly improved, reducing false alarms and missed detections. The gathered information is shared with the central resource manager (e.g., a base station) to make informed decisions about spectrum usage. This helps in better identifying underutilized spectrum, thus improving spectrum efficiency.

Task Distribution Among Users:

Figure 3 shows the Block of and Task Distribution Among Users in Cognitive Radio networks. Once the available spectrum is identified, tasks (such as data transmission or sensing duties) are distributed among the cognitive users. This ensures that no single user is overloaded, leading to a more balanced and efficient use of spectrum and computational resources. Task distribution helps avoid contention and interference, ensuring that multiple users can share the spectrum without degrading overall network performance.

Optimal Spectrum Sharing:

Figure 4 shows the Block of Optimal Spectrum Sharing in Cognitive Radio networks and Energy Minimization in Spectrum Sensing.

This block represents the central decision-making process where the resource manager allocates spectrum resources among the cognitive users based on the data received from cooperative sensing and task distribution. The goal here is to assign spectrum in a way that maximizes spectral efficiency while minimizing interference between users. Techniques like Orthogonal Frequency Division Multiple Access (OFDMA) can be used to enable simultaneous spectrum usage by multiple users without interference. Optimal spectrum sharing ensures that users dynamically access and utilize the spectrum only when it's available, preventing wastage of resources.

Energy Minimization:

Figure 4 shows the Block of Energy Minimization in Cognitive Radio networks and Energy Minimization in Spectrum Sensing Energy consumption is a critical factor in CRNs. This block highlights how optimal spectrum sharing is closely tied to energy efficiency. By minimizing idle listening, reducing redundant sensing, and avoiding unnecessary transmissions, the system conserves energy. Through cooperative spectrum sensing and task distribution, users can reduce power consumption while achieving better spectrum utilization, extending the battery life of mobile devices in the network.

These advanced methodologies not only promise significant improvements in energy efficiency but also ensure that cognitive radio networks can effectively respond to the challenges of rapidly increasing spectrum utilization.

Cognitive Radio Networks (CRNs) have emerged as a vital solution for addressing the increasing demand for spectrum utilization in wireless communication systems. With the rise in mobile devices and services, enhancing energy efficiency within CRNs is crucial. This literature review explores recent advancements in the field, focusing on energy efficiency strategies, resource allocation methods, and the challenges posed by rapid spectrum utilization.

Yao et al. (2023) explored the synergy between DSA and machine learning, emphasizing that adaptive algorithms can optimize spectrum allocation while minimizing energy consumption. This aligns with the findings of Chen et al. (2023), who demonstrated that integrating artificial intelligence into CRNs significantly enhances energy efficiency and reduces latency.

Wang et al. (2023) provided a comprehensive review of EH methods, particularly focusing on RF energy harvesting, which allows CRNs to leverage ambient energy sources. Their work highlighted how these techniques can reduce dependency on traditional power supplies and enhance overall network sustainability.

Ahmed et al. (2023) demonstrated that GAs could dynamically adjust power and bandwidth allocations in response to changing network conditions, thus enhancing energy efficiency. Similarly, Zhang et al. (2023) highlighted the role of non-orthogonal multiple access (NOMA) in improving spectral efficiency while addressing energy consumption challenges.

As discussed by Nasr et al. (2023), implementing cooperative sensing allows multiple users to share their sensing information, which enhances the overall performance of the network and reduces the energy spent on ineffective sensing.

Liu et al. (2023) examined the integration of DSA with machine learning algorithms to optimize spectrum allocation and minimize energy consumption. Their findings indicate that adaptive DSA approaches can significantly enhance the performance of CRNs while addressing energy constraints. Similarly, Zhang and Zhao (2023) highlighted the role of DSA in mitigating spectrum congestion, ultimately leading to improved energy efficiency.

Wang et al. (2023) discussed various EH methods, including solar and RF energy harvesting, emphasizing their impact on enhancing the sustainability of CRNs. Their research suggests that incorporating EH can significantly reduce reliance on conventional power sources. Moreover, Gupta et al. (2023) explored the synergies between EH and CRN design, highlighting how energy-harvesting techniques can effectively support real-time network demands.

Ahmed et al. (2023) demonstrated that GAs could effectively manage bandwidth and power allocations in response to varying network conditions, thereby enhancing energy efficiency. In parallel, Kaur and Singh (2023) explored the use of non-orthogonal multiple access (NOMA) in CRNs, indicating that NOMA can improve spectral efficiency while also addressing energy consumption issues.

In 2019, Zhao et al. introduced a model for dynamic spectrum access (DSA) integrated with power allocation to reduce energy consumption in CRNs. Their study, demonstrated how adapting spectrum access dynamically in response to network conditions can substantially minimize energy usage. This research set the stage for more complex DSA models that followed in the coming years.

A year later, Xu et al. (2020) expanded on this by introducing a hybrid DSA and energy-efficient power control framework. Their work combined adaptive spectrum access with real-time energy optimization strategies, showing a marked improvement in both network throughput and energy savings.

In 2021, Pan et al. explored the concept of ambient energy harvesting in CRNs in their paper published in Sensors. Their work emphasized the importance of harvesting energy from natural sources such as solar and RF signals, providing a sustainable power source for CRN operations. This research illustrated how energy harvesting could reduce the dependence on traditional energy sources, making the CRN framework more eco-friendly.

In 2022, Kim and Lee expanded this concept by integrating energy harvesting into a cooperative CRN framework. Their research, demonstrated that cooperative spectrum sensing, coupled with energy harvesting, could further reduce the energy expenditure required for effective spectrum management.

In 2018, Sharma et al detailing how GAs could optimize bandwidth and power allocation in dynamic CRN environments. Their findings showed that GAs could significantly reduce energy consumption while maximizing the quality of service (QoS).

Ahmed et al. (2020) introduced a multi-objective genetic algorithm for resource optimization, which balanced energy efficiency with spectrum utilization in CRNs. Their study revealed how genetic algorithms could enable CRNs to allocate resources more efficiently in rapidly changing network conditions, achieving both energy savings and enhanced network performance.

In 2020, Liu et al. detailing how cooperative spectrum sensing techniques could minimize energy waste by allowing multiple CRN nodes to share their sensing results. By aggregating this data, networks could more efficiently detect available spectrum, thereby saving energy spent on redundant sensing.

In 2022, Nasr et al. further refined this approach, introducing an AI-based spectrum sensing algorithm that leveraged machine learning to predict spectrum availability in real time. Their work showed that incorporating AI into cooperative sensing could further optimize energy consumption while improving spectrum detection accuracy

In 2021, Li et al. explored NOMA's application in CRNs, highlighting how NOMA could allow multiple users to share the same frequency band, thus reducing spectrum congestion while conserving energy. Their findings demonstrated a significant increase in energy efficiency when NOMA was paired with advanced resource management techniques.

The block diagram for Enhancing Energy Efficiency in Cognitive Radio Networks is as shown in Fig. 6, which represents a structured methodology to optimize both energy efficiency and spectrum utilization in cognitive radio networks.

Spectrum Sensing block monitors the radio spectrum to detect idle frequency bands that secondary users (SUs) can access without interfering with primary users (PUs). Various sensing techniques (like energy detection, matched filtering) are applied to ensure accurate detection of spectrum availability. Spectrum Utilization Monitoring module continuously tracks how spectrum resources are being used by both primary and secondary users. It identifies patterns of usage, including congestion or idle periods, providing data for optimizing spectrum sharing. Energy Efficiency Analysis component evaluates the energy consumption of the system during spectrum sensing and communication. It aims to identify inefficiencies and opportunities to reduce energy consumption while maintaining high sensing accuracy and effective communication. Dynamic Resource Allocation is based on the data from spectrum monitoring and energy analysis, this module dynamically adjusts the allocation of spectrum and power resources to the secondary users. The goal is to ensure optimal use of available spectrum with minimal energy wastage, adapting in real time to network conditions. Optimization Module for Spectrum and Energy Trade-offs block implements optimization algorithms (e.g., game theory, machine learning) to find the best trade-off between energy consumption and spectrum efficiency. It helps to balance the need for rapid spectrum utilization and energy savings, improving overall network performance. After spectrum and power resources are allocated, the secondary users begin transmitting data on the assigned channels. This transmission is carefully managed to avoid interference with primary users and to minimize energy consumption. Feedback loops are essential for continuous real-time adaptation of the network. This mechanism takes input from ongoing transmission and sensing results, feeding it back into the optimization and dynamic resource allocation modules to adjust strategies based on current conditions.

Cooperative Spectrum Sensing

The Fig. 7 presents various performance metrics of Cognitive Radio Networks (CRN) plotted against Signal-to-Noise Ratio (SNR) in decibels (dB). The throughput (in bits per second) increases as SNR increases. At SNR = -10 dB, throughput is approximately 0.5 bps. At SNR = 0 dB, throughput increases to around 1 bps. At SNR = 10 dB, throughput reaches approximately 2 bps. Higher SNR leads to significantly improved throughput, indicating that better signal quality allows for higher data rates. The Latency (in seconds) decreases as SNR increases. At SNR = -10 dB, latency is around 0.008 seconds. At SNR = 0 dB, latency reduces to approximately 0.005 seconds. At SNR = 10 dB, latency further decreases to about 0.002 seconds. Improved SNR results in lower latency, suggesting that better signal conditions facilitate faster communication.

Spectrum utilization remains constant across all SNR levels. Spectrum utilization is approximately 10 for all measured SNR values. The system maintains consistent spectrum usage regardless of changes in SNR, indicating efficient spectrum management. PDR stays constant irrespective of changes in SNR. PDR is consistently around 1 (or 100%) for all SNR levels. The cognitive radio network exhibits robust delivery performance, ensuring high packet delivery rates regardless of signal quality. QoS remains stable across the SNR range. QoS hovers around 0.9 (90%) for the entire SNR range. This stable QoS indicates that the system effectively manages network performance even with varying SNR conditions. Energy efficiency (measured in bits per joule) increases with rising SNR. At SNR = -10 dB, energy efficiency is approximately 1 bit/J. At SNR = 0 dB, it rises to about 2 bits/J. At SNR = 10 dB, energy efficiency reaches around 5 bits/J. Better signal quality enhances energy efficiency, allowing for more effective energy use in data transmission. Cooperative Spectrum Sensing shows that higher SNR leads to better throughput, reduced latency, improved energy efficiency, and stable delivery and quality of service. The consistent performance metrics for spectrum utilization and PDR demonstrate the network's robustness and efficiency in resource management.

Task distribution among Users

The Fig. 8 presents various Task Distribution Performance Metrics in Cognitive Radio Networks (CRNs).

Task distribution among Users in spectrum sensing shows that Higher SNR leads to better throughput and energy efficiency while reducing latency. The consistent performance of spectrum utilization, PDR, and QoS indicates robust and efficient resource management in cognitive radio networks, ensuring reliable communication even in varying signal conditions.

Optimal Spectrum sharing

The Fig. 9 presents various Optimal Spectrum sharing in Cognitive Radio Networks (CRNs). It presents a comprehensive overview of the performance metrics for cognitive radio networks as a function of SNR. The trends show that higher SNR leads to better throughput and energy efficiency while reducing latency. The consistent performance of spectrum utilization, PDR, and QoS highlights the robustness of cognitive radio networks in maintaining reliable communication even in varying signal conditions. These results emphasize the importance of improving SNR in enhancing the overall performance of cognitive radio networks.

Energy Minimization

The Fig. 10 presents Energy Minimization in Cognitive Radio Networks.

The Fig. 10 clearly illustrates the effectiveness of energy minimization strategies in CRNs. The quantitative results demonstrate that, through dynamic spectrum access and optimized power allocation, it is possible to significantly enhance both energy efficiency and spectrum utilization. Future work should focus on real-world implementations and further optimizations in energy management strategies.

Performance Parameters:

Throughput (T)

Throughput measures the rate of successful data transmission over the network. In CRNs, it is affected by spectrum sensing, channel switching, and the presence of primary users (PUs).

Throughput _CRN= Number of Successfully Transmitted Bits/ Total Transmission Time – (1)

Throughput _CRN = R × Utilization Factor × (1 − P_PU)

Where:

RRR is the data transmission rate (bits per second).
Utilization Factor is the fraction of time the channel is used for data transmission.
P _PU  is the probability of primary user activity.

(2) Energy Consumption (E)

Energy consumption in CRNs includes the energy used for data transmission, spectrum sensing, and channel switching. The total energy consumption can be represented as:

E _CRN = E _Transmission+ E _Sensing+ E _Switching - (2)

_Where:

_{E Transmission is the energy used for data transmission}.
_{E Sensing is the energy spent in spectrum sensing}.
_{E Switching is the energy used when switching channels}.

_{To calculate these components}:

_{Energy for Transmission}:

_{E Transmission=P Transmit ×T Transmit}

_{Where P Transmit is the transmission power, and T Transmit is the transmission time}.

_{Energy for Sensing}:

_{E Sensing=P Sensing × T Sensing}

_{Where P Sensing is the power consumed during sensing, and T Sensing is the time spent on spectrum sensing}.

_{Energy for Switching}:

_{E Switching= P Switch × T Switch}

_{Where P Switch is the power used during channel switching, and T Switch is the time for switching}.

3. Spectral Efficiency (SE)

Spectral efficiency measures how efficiently the available spectrum is utilized. It is defined as the throughput per unit bandwidth and can be influenced by spectrum sensing accuracy, channel availability, and interference.

Spectral Efficiency _CRN = (Throughput _CRN/ Bandwidth) -(3)

In a dynamic environment, accounting for sensing accuracy and channel availability:

Spectral Efficiency _CRN= ((R× Utilization Factor × (1 − P_PU))/Bandwidth)

Where:

RRR is the achievable data rate.
Bandwidth is the frequency range over which communication occurs.

These equations reflect the unique characteristics of CRNs, including dynamic spectrum management, energy efficiency considerations, and adaptive spectrum usage.

4.Latency

In CRNs, latency includes additional delays due to spectrum sensing and channel switching. The total latency can be expressed as:

Latency _CRN =Transmission Delay + Propagation Delay + Queuing Delay + Processing Delay + Sensing Delay + Switching Delay − (4)

Sensing Delay: Time taken to detect an available channel for communication.
Switching Delay: Time taken to switch to a new channel after detecting an idle channel

5.Packet delivery ratio:

In CRNs, the PDR is affected by spectrum availability and interference from primary users (PUs). It can be expressed as:

PDR _CRN = (Number of Packets Successfully Delivered/Number of Packets Sent) ×100% -(5)

Factors that can reduce PDR in CRNs include:

Interference from PUs, which can cause packet loss.
Frequent channel switching due to dynamic spectrum changes.

6.QOS

QoS in CRNs takes into account metrics like latency, PDR, spectrum availability, interference levels, and channel utilization. A comprehensive QoS score can be calculated by considering these parameters, typically using a weighted sum approach:

QoS CRN=w1(1/(Latency _CRN)) + w2 (PDR_CRN) + w3 (Spectrum Availability) − w4(Interference Level)

− (6)

Performance Parameters Table

Table 1 summarizing the performance parameters as the number of users varies from 1 to 200, with a total bandwidth of 100 MHz

Table 1

**The performance parameters for 1 to 200 number of users and bandwidth of total bandwidth of** 100 MHz.
Number of Users	Throughput (Mbps)	Energy Efficiency (bps/J)	Spectrum Utilization (%)	Latency (ms)	Packet Delivery ratio (%)	Quality of Service
1	1	0.5	1	0.01	95	0.8
10	10	0.8	10	0.1	94	0.85
50	50	1.2	50	0.5	92	0.9
100	100	1.5	100	1.0	90	0.95
150	150	1.1	150	1.5	88	0.9
200	200	0.9	200	2.0	85	0.87

Figure 11 (a): Energy Minimization in Cognitive Radio Networks for 1 to 200 number of users and bandwidth of total bandwidth of 100 MHz

Tables 1 and 2 summarizing the performance parameters as the number of users varies from 1 to 5000, with a total bandwidth of 100 MHz

Table 2

The performance parameters for 1 to 5000 number of users and bandwidth of total bandwidth of 100 MHz.
Number of Users	Throughput (Mbps)	Energy Efficiency (bps/J)	Spectrum Utilization (%)	Latency (ms)	Packet Delivery ratio (%)	Quality of Service
1	1.2	0.6	0.00012	0.05	97	0.85
100	120	1.5	0.012	0.30	95	0.88
500	500	2.0	0.05	0.50	93	0.90
1000	950	2.3	0.095	0.75	90	0.92
2500	2300	1.9	0.23	1.50	85	0.87
5000	4500	1.5	0.45	3.00	80	0.85

5.Analysis of Results

The throughput of the cognitive radio network exhibits a remarkable growth trend as the number of users increases, escalating from 1.2 Mbps for a single user to an impressive 4,500 Mbps when accommodating 5,000 users. This significant increase indicates that the network is adept at leveraging its resources to facilitate a high volume of data transmission. As more users connect, the cognitive radio network efficiently allocates available bandwidth, ensuring that each user experiences enhanced connectivity and speed. This capability is particularly important in an era where data demands are surging due to applications such as video streaming, online gaming, and cloud computing. The ability to maintain high throughput in a multi-user environment demonstrates the robustness of cognitive radio technologies in managing spectrum dynamically and effectively, making it a promising solution for meeting the growing demands of wireless communication.

The analysis of energy efficiency reveals an intriguing pattern: energy efficiency initially climbs with the increase in users, reaching a peak of 2.3 bps/J for 1,000 users before experiencing a decline to 1.5 bps/J for 5,000 users. This initial increase suggests that the cognitive radio network employs optimal strategies for power allocation, allowing it to handle user requests efficiently while conserving energy. However, the subsequent decrease in energy efficiency indicates that as the user base expands, the network may face challenges related to congestion and interference. Increased demand can lead to suboptimal resource allocation, necessitating higher energy consumption to maintain performance levels. This trade-off highlights the importance of developing adaptive energy management strategies that can sustain efficiency even as user demands rise, ultimately contributing to a more sustainable and cost-effective communication system.

The gradual increase in spectrum utilization, from 0.00012% with one user to 0.45% at the maximum user capacity, illustrates the effective deployment of spectrum resources within the cognitive radio network. This steady rise signifies that the network is optimizing the use of available spectrum bands as more users connect. Efficient spectrum utilization is crucial in cognitive radio networks, which are designed to dynamically access underutilized frequency bands to alleviate congestion in heavily loaded channels. By maximizing spectrum usage, the network not only enhances its capacity but also contributes to the overall goal of spectrum efficiency, thereby supporting diverse applications that require varying bandwidth levels.

The increase in latency from 0.05 ms for one user to 3.00 ms for 5,000 users reflects a critical aspect of network performance. As user numbers rise, the network's response time becomes progressively longer, which can lead to noticeable delays in data transmission. This increase in latency is a common challenge in multi-user environments, where shared resources can create bottlenecks. The implications of higher latency are particularly significant for applications requiring real-time communication, such as VoIP and online gaming, where delays can detract from the user experience. Understanding and managing latency is essential for maintaining user satisfaction, and future enhancements in the cognitive radio network may need to focus on minimizing delays, especially during peak usage times.

The packet delivery ratio reveals a concerning trend, decreasing from 97% with one user to 80% when the network is saturated with 5,000 users. This decline indicates potential difficulties in maintaining reliable communication as network congestion grows. A lower PDR can lead to increased packet loss, which adversely affects the quality of services provided. This finding underscores the necessity of implementing robust error correction and retransmission mechanisms to ensure that data packets reach their intended destinations even under high traffic conditions. Maintaining a high PDR is crucial for sustaining user trust and satisfaction in network services.

The analysis of QoS presents a nuanced view: it initially improves from 0.85 to 0.92 as the number of users rises to 1,000, suggesting that the network can effectively accommodate moderate user loads while delivering high-quality services. However, as user numbers swell to 5,000, the QoS slightly declines to 0.85. This fluctuation indicates that while the cognitive radio network excels in providing quality service at lower to moderate loads, it may struggle to uphold the same standards under maximum capacity. This finding highlights the importance of continuously monitoring QoS metrics and adapting strategies to enhance performance, particularly as demand continues to grow.

The performance parameter results highlight both the strengths and weaknesses of cognitive radio networks in meeting user demands. The network exhibits impressive throughput, increasing from 1.2 Mbps for one user to 4,500 Mbps for 5,000 users, alongside a steady rise in spectrum utilization from 0.00012–0.45%. However, challenges arise as user numbers grow, particularly concerning energy efficiency, which peaks at 2.3 bps/J for 1,000 users but declines to 1.5 bps/J at maximum capacity. Additionally, latency escalates from 0.05 ms to 3.00 ms, and the packet delivery ratio drops from 97–80% with increased congestion. Quality of Service (QoS) initially improves to 0.92 before slightly declining to 0.85 under peak conditions.

Future research should prioritize several key areas to improve the performance and sustainability of cognitive radio networks. First, developing advanced algorithms for dynamic resource allocation that utilize real-time user demand data is essential for optimizing energy efficiency, throughput, and latency. Second, integrating machine learning techniques to predict user behaviour can enhance spectrum utilization and improve packet delivery ratios by enabling adaptive resource management

Acknowledgements: I would like to express my sincere gratitude to Department of Science and Technology, Govt. of India, New Delhi, India “DST-CURIE-PG/2022/71” of GSSS IETW, Mysore, for providing the necessary simulation environment in MATLAB 2022, which plays a vital role for the analysis of the Energy Efficiency, Throughput, Spectrum Utilization, Latency, Packet Delivery Ratio and Quality of Service.

Author contributions: All the authors contributed to prepare the manuscript. Both read and approved the final manuscript.

Funding: The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Data Availability: The data sets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Code Availability: The code is available with corresponding Author.

Competing interests: The authors declare that there are no relevant financial or non-financial competing interests to report.

Ethics approval: The manuscript in part or in full has not been submitted or published anywhere.

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No competing interests reported.

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Enhancing Energy Efficiency in Cognitive Radio Networks: Addressing the Challenges of Rapid Spectrum Utilization

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