Search Results (154)

This review paper examines the integration of artificial intelligence (AI) in wireless communication, focusing on cognitive radio (CR), spectrum sensing, and dynamic spectrum access (DSA). As the demand for spectrum continues to rise with the expansion of mobile users and connected devices, cognitive radio networks (CRNs), leveraging AI-driven spectrum sensing and dynamic access, provide a promising solution to improve spectrum utilization. The paper reviews various deep learning (DL)-based spectrum-sensing methods, highlighting their advantages and challenges. It also explores the use of multi-agent reinforcement learning (MARL) for distributed DSA networks, where agents autonomously optimize power allocation (PA) to minimize interference and enhance quality of service. Additionally, the paper discusses the role of machine learning (ML) in predicting spectrum requirements, which is crucial for efficient frequency management in the fifth generation (5G) networks and beyond. Case studies show how ML can help self-optimize networks, reducing energy consumption while improving performance. The review also introduces the potential of generative AI (GenAI) for demand-planning and network optimization, enhancing spectrum efficiency and energy conservation in wireless networks (WNs). Finally, the paper highlights future research directions, including improving AI-driven network resilience, refining predictive models, and addressing ethical considerations. Overall, AI is poised to transform wireless communication, offering innovative solutions for spectrum management (SM), security, and network performance. Full article

Cognitive Radio (CR) is widely acknowledged as a pivotal technology for mitigating the scarcity of spectrum resources, with Transform Domain Communication Systems (TDCSs) regarded as one of the primary candidate technologies for CR. However, conventional Wideband Spectrum Sensing (WBSS) techniques utilized in TDCS exhibit limitations and are insufficient for adapting to the current complex electromagnetic environment. This paper tackles the time–frequency reconstruction challenge in WBSS by proposing a fast time–frequency reconstruction (FTFR) algorithm. The proposed algorithm acquires sub-Nyquist samples through the introduction of a Multi-Coset Sampling structure and reconstructs the autocorrelation of signals across various windows through a series of low-complexity operations. It captures the dynamic variations of signals by integrating spectra from adjacent time windows. In comparison to existing time–frequency reconstruction algorithms in WBSS, the proposed algorithm demonstrates reduced computational complexity. Simulation experiments indicate that the FTFR algorithm can effectively reconstruct the time–frequency characteristics of signals and significantly restore the primary temporal and frequency distributions, even in low Signal-to-Noise Ratio (SNR) environments. Full article

Cognitive radio vehicle ad hoc networks (CR-VANETs) can utilize spectrum resources flexibly and efficiently and mitigate the conflict between limited spectrum resources and the ever-increasing demand for vehicular communication services. However, in CR-VANETs, the mobility characteristics of vehicles as well as the dynamic topology changes and frequent disruptions of links can lead to large end-to-end delays. To address this issue, we propose the social-based minimum end-to-end delay routing (SMED) algorithm, which leverages the social attributes of both primary and secondary users to reduce end-to-end delay and packet loss. We analyze the influencing factors of vehicle communication in urban road segments and at intersections, formulate the end-to-end delay minimization problem as a nonlinear integer programming problem, and utilize two sub-algorithms to solve this problem. Simulation results show that, compared to the intersection delay-aware routing algorithm (IDRA) and the expected path duration maximization routing algorithm (EPDMR), our method demonstrates significant improvements in both end-to-end delay and packet loss rate. Specifically, the SMED routing algorithm achieved an average reduction of 11.7% in end-to-end delay compared to EPDMR and 25.0% compared to IDRA. Additionally, it lowered the packet loss rate by 24.9% on average compared to EPDMR and 32.5% compared to IDRA. Full article

Integrating Non-Orthogonal Multiple Access (NOMA) backscatter with Cognitive Radio (CR) can effectively alleviate the pressure of large-scale Internet of Things (IoT) device connections while reducing power consumption. In the downlink of NOMA backscatter-based CR networks (NB-CR), most researchers only consider the case of a single backscatter device (BD), ignoring the fact that multiple BDs can prolong device usage and enhance system robustness. The resource allocation (RA) problem is crucial in the downlink of NB-CR networks. Most existing RA algorithms focus on system throughput but rarely consider energy efficiency (EE). In this paper, we propose the RA problem for downlink communication (NBCR-RA) in NB-CR networks with multiple BDs. We jointly optimize power allocation coefficients and reflection coefficients (RCs) to maximize EE. We model the NBCR-RA problem as a non-convex problem and divide it into two subproblems: power allocation optimization and RC optimization. Firstly, we propose a Lagrange-based power allocation optimization (L-PA) algorithm to obtain the optimal power allocation coefficients. Secondly, we design an RC optimization algorithm, PS-RC, based on a particle swarm algorithm to determine the optimal RCs. Finally, we validate the superiority of L-PA and PS-RC algorithms in terms of EE. Through multiple experiments, we obtained a $95\%$ confidence interval of [10.5552, 10.6465]. Full article

In order to address the problems of low spectrum efficiency in current communication systems and extend the lifetime of energy-constrained relay devices, this paper proposes a novel dual-hop free-space optical (FSO) system that integrates cognitive radio (CR) and energy harvesting (EH). In this system, the source node communicates with two users at the terminal via FSO and terahertz (THz) hard-switching links, as well as a multi-antenna relay for non-orthogonal multiple access (NOMA). There is another link whose relay acts as both the power beacon (PB) in the EH system and the primary network (PN) in the CR system, achieving the double function of auxiliary transmission. In addition, based on the three possible practical working scenarios of the system, three different transmit powers of the relay are distinguished, thus enabling three different working modes of the system. Closed-form expressions are derived for the interruption outage probability per user for these three operating scenarios, considering the Gamma–Gamma distribution for the FSO link, the $\alpha -\mu$ distribution for the THz link, and the Rayleigh fading distribution for the radio frequency (RF) link. Finally, the numerical results show that this novel system can be adapted to various real-world scenarios and possesses unique advantages. Full article

In this paper, we propose an underlay cognitive radio (CR) system that includes subscribers, termed secondary users (SUs), which are designed to coexist with the spectrum owners, termed primary users (PUs). The suggested network includes the PUs system and the SUs system. The coexistence between them is achieved by using a novel dynamic spectrum co-access multicarrier-based cognitive radio (DSCA-MC-CR) technique. The proposal uses a quadrature phase shift keying (QPSK) modulation technique within the orthogonal frequency-division multiplexing (OFDM) scheme that maximizes the system data rate and prevents data inter-symbol interference (ISI). The proposed CR transmitter station (TX) and the CR receiver node (RX) can use an advanced smart antenna system, i.e., a multiple-input and multiple-output (MIMO) system that provides high immunity against channel impairments and provides a high data rate through its different combining techniques. The proposed CR system is applicable to coexist within different existing communication applications like fifth-generation (5G) applications, emergence applications like the Internet of Things (IoT), narrow-band (NB) applications, and wide-band (WB) applications. The coexistence between the PUs system and the SUs system is based on using power donation from the SUs system to improve the quality of the PU signal-to-interference-and-noise ratios (SINRs). The green communication concept achieved in this proposal is compared with similar DSCA proposals from the literature. The simulations of the proposed technique show enhancement in the PUs system throughput and data rate along with the better performance of the SUs system. Full article

This paper presents a cognitive radio (CR)-enabled symbiotic ambient backscatter communication (AmBC) system with the help of a zero-energy reconfigurable intelligent surface (zeRIS). An adaptive transmission (AT) strategy for the zeRIS is devised based on the amount of harvested energy. Specifically, when energy reserve is insufficient, the zeRIS merely reflects signals without any phase adjustments (PAs), whereas under sufficient energy conditions, it reflects signals following precise PAs. Moreover, a segmented zeRIS is adopted by taking primary transmission (PT) and backscatter transmission (BT) into account. Following this, the coexistence outage probability and ergodic capacity are derived to assess the reliability and effectiveness of the proposed model, respectively. Their asymptotic performance is analyzed to gain insightful observations. Finally, simulation results are provided to verify the accuracy of the theoretical analysis, confirming that AT offers improved reliability, system rate, and energy efficiency over non-adaptive transmission. Furthermore, CR-aided AT demonstrates superior energy efficiency compared to non-CR-assisted AT. It is also crucial to note that the allocation of reflective elements between PT and BT must be reasonably managed to satisfy specific system requirements. Full article

Nowadays, efficient spectrum usage is one of the most important design principles to take into account in wireless communications due to the exponential growth of mobile devices. In that sense, solutions such as Non-Orthogonal Multiple Access (NOMA) and cognitive radio (CR) have been proposed. In essence, NOMA allows some interference level by using non-orthogonal resource allocation with a tolerable increase in receiver complexity employing successive interference cancellation (SIC). In this work, a novel mathematical model of teletraffic for users performing accessment, simultaneously, by means of Orthogonal Multiple Access (OMA) and NOMA, is developed using a Markovian process that considers bursts of arrivals to model the access schemes. This novel procedure implies a closed-form solution of the proposed system compared to other works where these parameters are estimated assuming the moment generating function obtained with approximation models. The model is validated with a discrete event simulator, considering different scenarios and simulation conditions. The simulation results are in agreement with the mathematical solution proposed. Full article

The adoption of UAVs in defence and civilian sectors necessitates robust communication networks. This paper presents a routing protocol for Cognitive Radio Unmanned Aerial Vehicles (CR-UAVs) in Flying Ad-hoc Networks (FANETs). The protocol is engineered to optimize route selection by considering crucial parameters such as distance, speed, link quality, and energy consumption. A standout feature is the introduction of the Central Node Resolution Factor (CNRF), which enhances routing decisions. Leveraging the Received Signal Strength Indicator (RSSI) enables accurate distance estimation, crucial for effective routing. Moreover, predictive algorithms are integrated to tackle the challenges posed by high mobility scenarios. Security measures include the identification of malicious nodes, while the protocol ensures resilience by managing multiple routes. Furthermore, it addresses route maintenance and handles link failures efficiently, cluster formation, and re-clustering with joining and leaving new nodes along with the predictive algorithm. Simulation results showcase the protocol’s self-comparison under different packet sizes, particularly in terms of end-to-end delay, throughput, packet delivery ratio, and normalized routing load. However, superior performance compared to existing methods, particularly in terms of throughput and packet transmission delay, underscoring its potential for widespread adoption in both defence and civilian UAV applications. Full article

Security is an important consideration when delivering information-aware messages to vehicles that are far away from the current location of the information-sending vehicle. This information helps the receiver to save fuel and time by making wise decisions to avoid damaged or blocked roads. To ensure the safety and security of this type of information using blockchain technology, we propose a new cognitive vehicular communication scheme to transfer messages from source to destination. Due to spectrum scarcity in vehicular networks, there needs to be a wireless medium available for every communication link since vehicles require it to communicate. The primary user (PU) makes a public announcement about a free channel to all secondary users nearby and only gives it to authentic vehicles. The authenticity of vehicles is guaranteed by a roadside unit (RSU) that offers secure keys to any vehicle that joins this blockchain network. Those who participate in this network must pay a certain amount and receive rewards for their honesty that exceed the amount spent. To test the performance of various parameters, the proposed scheme utilizes the Ethereum smart contract and compares them to blockchain and non-blockchain methods. Our results show a minimum delivery time of 0.16 s and a minimum overhead of 350 bytes in such a dynamic vehicle environment. Full article

For a multiuser multiple-input–multiple-output (MIMO) overlay cognitive radio (CR) network, an opportunistic interference alignment (IA) technique has been proposed that allows spectrum sharing between primary users (PUs) and secondary users (SUs) while ensuring zero interference to the PU. The CR system consists of one PU and K SUs where the PU uses space-time water-filling (ST-WF) algorithm to optimize its transmission and in the process, frees up unused eigenmodes that can be exploited by the SU. The SUs make use of an optimal power allocation algorithm to align their transmitted signals in such a way their interference impairs only the PUs unused eigenmodes. Since the SUs optimal power allocation algorithm turns out to be an optimal beamformer with multiple eigen-beams, this work initially proposes combining the diversity gain property of space-time block codes, the zero-forcing function of IA and beamforming to optimize the SUs transmission rates. This proposed solution requires availability of channel state information (CSI), and to eliminate the need for CSI, this work then combines Differential Space-Time Block Coding (DSTBC) scheme with optimal IA precoders (consisting of beamforming and zero-forcing) to maximize the SUs data rates. Simulation results confirm the accuracy of the proposed solution. Full article

In this paper, we propose a novel system integrating reconfigurable intelligent surfaces (RISs) with cognitive radio (CR) technology, presenting a forward-looking solution aligned with the evolving standards of 6G and beyond networks. The proposed RIS-assisted CR networks operate with a base station (BS) transmitting signals to two users, the primary user (PU) and secondary user (SU), through direct and reflected signal paths, respectively. Our mathematical analysis focuses on deriving expressions for SU in the RIS-assisted CR system, validated through Monte Carlo simulations. The investigation covers diverse aspects, including the impact of the signal-to-noise ratio (SNR), power allocations, the number of reflected surfaces, and blocklength variations. The results provide nuanced insights into RIS-assisted CR system performance, highlighting its sensitivity to factors like the number of reflectors, fading severity, and correlation coefficient. Careful parameter selection, such as optimizing the configuration of reflectors, is shown to prevent a complete outage, showcasing the system’s robustness. Additionally, the work suggests that the optimization of reflectors configuration can significantly enhance overall system performance, and RIS-assisted CR systems outperform reference schemes. This work contributes a thorough analysis of the proposed system, offering valuable insights for efficient performance evaluation and parameter optimization in RIS-assisted CR networks. Full article

Automatic Modulation Recognition (AMR) is a key technology in the field of cognitive communication, playing a core role in many applications, especially in wireless security issues. Currently, deep learning (DL)-based AMR technology has achieved many research results, greatly promoting the development of AMR technology. However, the few-shot dilemma faced by DL-based AMR methods greatly limits their application in practical scenarios. Therefore, this paper endeavored to address the challenge of AMR with limited data and proposed a novel meta-learning method, the Multi-Level Comparison Relation Network with Class Reconstruction (MCRN-CR). Firstly, the method designs a structure of a multi-level comparison relation network, which involves embedding functions to output their feature maps hierarchically, comprehensively calculating the relation scores between query samples and support samples to determine the modulation category. Secondly, the embedding function integrates a reconstruction module, leveraging an autoencoder for support sample reconstruction, wherein the encoder serves dual purposes as the embedding mechanism. The training regimen incorporates a meta-learning paradigm, harmoniously combining classification and reconstruction losses to refine the model’s performance. The experimental results on the RadioML2018 dataset show that our designed method can greatly alleviate the small sample problem in AMR and is superior to existing methods. Full article

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. Full article

The advancement of underwater cognitive acoustic network (UCAN) technology aims to improve spectral efficiency and ensure coexistence with the underwater ecosystem. As the demand for short-term underwater applications operated under distributed topologies, like autonomous underwater vehicle cluster operations, continues to grow, this paper presents Underwater Multi-channel Medium Access Control with Cognitive Acoustics (UMMAC-CA) as a suitable channel access protocol for distributed UCANs. UMMAC-CA operates on a per-frame basis, similar to the Multi-channel Medium Access Control with Cognitive Radios (MMAC-CR) designed for distributed cognitive radio networks, but with notable differences. It employs a pre-determined data transmission matrix to allow all nodes to access the channel without contention, thus reducing the channel access overhead. In addition, to mitigate the communication failures caused by randomly occurring interferers, UMMAC-CA allocates at least 50% of frame time for interferer sensing. This is possible because of the fixed data transmission scheduling, which allows other nodes to sense for interferers simultaneously while a specific node is transmitting data. Simulation results demonstrate that UMMAC-CA outperforms MMAC-CR across various metrics, including those of the sensing time rate, controlling time rate, and throughput. In addition, except for in the case where the data transmission time coefficient equals 1, the message overhead performance of UMMAC-CA is also superior to that of MMAC-CR. These results underscore the suitability of UMMAC-CA for use in challenging underwater applications requiring multi-channel cognitive communication within a distributed network architecture. Full article