Efficient Spectrum Management Research Articles

A Hybrid Multi-Agent Reinforcement Learning Approach for Spectrum Sharing in Vehicular Networks

Efficient spectrum sharing is essential for maximizing data communication performance in Vehicular Networks (VNs). In this article, we propose a novel hybrid framework that leverages Multi-Agent Reinforcement Learning (MARL), thereby combining both centralized and decentralized learning approaches. This framework addresses scenarios where multiple vehicle-to-vehicle (V2V) links reuse the frequency spectrum preoccupied by vehicle-to-infrastructure (V2I) links. We introduce the QMIX technique with the Deep Q Networks (DQNs) algorithm to facilitate collaborative learning and efficient spectrum management. The DQN technique uses a neural network to approximate the Q value function in high-dimensional state spaces, thus mapping input states to (action, Q value) tables that facilitate self-learning across diverse scenarios. Similarly, the QMIX is a value-based technique for multi-agent environments. In the proposed model, each V2V agent having its own DQN observes the environment, receives observation, and obtains a common reward. The QMIX network receives Q values from all agents considering individual benefits and collective objectives. This mechanism leads to collective learning while V2V agents dynamically adapt to real-time conditions, thus improving VNs performance. Our research finding highlights the potential of hybrid MARL models for dynamic spectrum sharing in VNs and paves the way for advanced cooperative learning strategies in vehicular communication environments. Furthermore, we conducted an in-depth exploration of the simulation environment and performance evaluation criteria, thus concluding in a comprehensive comparative analysis with cutting-edge solutions in the field. Simulation results show that the proposed framework efficiently performs against the benchmark architecture in terms of V2V transmission probability and V2I peak data transfer.

Fully Reconfigurable Photonic Filter for Flexible Payloads

Reconfigurable photonic filters represent cutting-edge technology that enhances the capabilities of space payloads. These advanced devices harness the unique properties of light to deliver superior performance in signal processing, filtering, and frequency selection. They offer broad filtering capabilities, allowing for the selection of specific frequency ranges while significantly reducing Size, Weight, and Power (SWaP). In scenarios where satellite communication channels are crowded with various signals sharing the same bandwidth, reconfigurable photonic filters enable efficient spectrum management and interference mitigation, ensuring reliable signal transmission. Furthermore, reconfigurable photonic filters demonstrate their ability to adapt to the dynamic space environment, withstanding extreme temperatures, radiation exposure, and mechanical stress while maintaining stable and reliable performance. Leveraging the inherent speed of light, these filters enable high-speed signal processing operations, paving the way to various space payload applications, such as agile frequency channelization. This capability allows for the simultaneous processing and analysis of different frequency bands. In this theoretical study, we introduce a fully reconfigurable filter comprising two decoupled ring resonators, each with the same radius. Each resonator can be independently thermally tuned to achieve reconfigurability in both central frequency and bandwidth. The precise reconfiguration of both central frequency and bandwidth is achieved by using the thermo-optic effect along the whole ring resonator path. A stopband rejection of 45 dB, with a reconfigurable bandwidth and central frequency of 20 MHz and 180 MHz, respectively, has been numerically achieved, with a maximum electrical power of 11.50 mW and a reconfiguration time of 9.20 µs, by using the scattering matrix approach, where the elements have been calculated through Finite Element Method-based and Beam Propagation Method-based simulations. This performance makes the proposed device suitable as key building block of RF optical filters, useful in the next-generation telecommunication payload domain.

Analysis of Secure Cognitive Radio Network with Optimal Resource Management

During the last decade, Cognitive Radio Network (CRN) technology has been a significant advance in addressing the ever-increasing spectrum demand. As the number of licensed and unlicensed users in a network rises to complete a certain activity, the information exchange between various types of traffic becomes more complex and difficult. Congestion in CRN is also caused by the conflict among several users for channel access (PUs and SUs). In a very crowded network, many applications perform badly owing to packet collisions and, as a result, packet loss before significant buffer queue building. This circumstance is aggravated by an increase in network users. Congestion control is a vital and essential aspect of the present research issue in communication networks. Several recent reviews in the literature indicate that the congestion issue in the CRN has not been thoroughly studied. Thus, effective and efficient congestion control strategies are sought to optimize network resource usage and management. To prevent congestion, it is crucial for CRN to do research on the creation of an efficient congestion management system. This will improve the network's resource consumption and performance. "Performance improvement via efficient spectrum management through optimum resource management and congestion control in the CRN by mitigating different threats" is the primary target of this project. This research also focused on enhancing performance by addressing security issues in an IoT-based CRN environment. This study provides a comprehensive review of several similar studies and their limitations, which may be used to formulate a new research target.

Accurate Spectrum Map Construction for Spectrum Management Through Intelligent Frequency-Spatial Reasoning

Spectrum maps are of crucial importance for realizing efficient spectrum management in the sixth-generation (6G) wireless communication networks. However, existing spectrum map construction schemes mainly depend on spatial interpolation or just simply exploit the frequency correlation and cannot accurately construct the spectrum map when measurement data at the target frequency are not available. To overcome this challenge, we propose two accurate spectrum map construction schemes using different intelligent frequency-spatial reasoning methods. The frequency correlation among different spectrum maps at different frequencies is fully exploited to construct highly accurate spectrum maps of the frequencies without spectrum data by combining the joint frequency-spatial spectrum representation method with deep learning data-driven techniques. Specifically, a joint three-dimensional spectrum representation model is established and both a novel autoencoder network and a novel conditional generative adversarial network suitable for processing the three-dimensional spectrum data are proposed to realize the intelligent frequency-spatial reasoning. Simulation results demonstrate that our proposed schemes are superior to the benchmark schemes in terms of the spectrum map construction accuracy. Moreover, simulation results demonstrate that our proposed neural networks have a fast convergence speed, which achieves a better tradeoff between the computation efficiency and the construction accuracy.

Technology recognition and traffic characterization for wireless technologies in ITS band

The rapid advancement of wireless technologies requires efficient spectrum management considering issues such as interference management and fair coexistence between different technologies. Wireless technology recognition is one of the approaches used to enable intelligent spectrum management. This work proposes a technology classification and traffic characterization system that can recognize and characterize a wide range of wireless technologies that may coexist in the ITS 5.9 GHz band, namely LTE, Wi-Fi, 5G NR, C-V2X PC5, and ITS-G5 technologies. Compared to current state-of-the-art technology recognition solutions, a short time resolution window is selected based on the shortest possible frame duration of the considered technologies. We carried out a “complexity and accuracy trade-off” analysis for six distinct technology recognition models trained and validated at different sampling rates, including 1, 5, 10, 15, 20, and 25 Msps. In addition, the performance of the technology recognition models was evaluated under different channel conditions. For average to high SNR, a less complex CNN model with lower sampling rates (e.g., 5 Msps) can effectively distinguish the signal with 96% classification accuracy. On the other hand, high classification accuracy is obtained using complex, high sampling rate-based CNN models (e.g., 20 Msps) for low (less than 0 dB) SNR channels. A traffic characterization process is also proposed, where the output of the technology recognition is used to identify the traffic characteristics of the technologies in terms of channel occupancy time, transmission pattern, and frame count. The obtained results show that the proposed solution can be used to effectively characterize the identified traffic.

Metasurface-Based Filters for High Data Rate THz Wireless Communication: Experimental Validation of a 14 Gbps OOK and 104 Gbps QAM-16 Wireless Link in the 300 GHz Band

We demonstrate the effectiveness of frequency selective surface filters in wireless communications at low terahertz (THz) frequencies. Full-wave simulations of pass-band filters designed at 270 GHz and 330 GHz are compared with measurements over 220–360 GHz, showing remarkable agreement. The filter spectral response is used to analytically model a THz filter-based wireless channel for modulated signals. In particular, numerical results and measurements for an OOK modulated signal are in good agreement for both free-space and filtered transmission at 14 Gb/s. In both cases, bit error rates (BER) as low as 10-10 are measured. This result demonstrates that the filters marginally affect the BER with respect to free-space, interference-free transmission, whereas interfering signals are strongly rejected. This result is demonstrated through a systematic evaluation of the BER in presence of an interfering signal with different carriers and amplitudes. Results confirm a strong filter rejection to interference carriers close to the filter central frequency. Conversely, without the filters the BER performance is fully compromised. Finally, we demonstrate numerically and experimentally that the constellation diagram for 104 Gb/s QAM-16 communication is not significantly affected by the filter. The investigated filters may provide a robust approach towards efficient spectrum management for future 6G wireless applications.

Building trust via blockchain in UAV‐assisted ultra‐dense 6G cellular networks

AbstractThe ultra‐dense deployment of unmanned aerial vehicles (UAVs) as mobile small cell base stations (SBS) is expected to support ultra‐high‐speed, ultra‐reliable, and ultra‐low‐latency wireless connections in sixth generation (6G) wireless cellular networks. Inevitably, the explosively increasing number of SBSs each with ever‐shrinking cell size will result in the spectrum scarcity and pose critical challenges to the spectrum management. Towards the centralized spectrum management system, a third‐party authority is employed to coordinate the spectrum sensing, sharing, and allocation among participants. However, a centralized authority is vulnerable to numerous security threats, such as single point of failure, denial of service attacks, and privacy disclosure, and cannot guarantee fair and efficient spectrum management without mutual trusts among participants. To address these problems, a novel framework of blockchain‐aided spectrum management is designed to securely record the spectrum auction data and spectrum allocation data in the decentralized spectrum sharing. Moreover, a trust management scheme is proposed to evaluate the trusts of all UAVs participating in the decentralized spectrum sharing, where the trust increase depends on legal use of spectrum allocation and honest report of spectrum sensing. In particular, it is shown that the proposed trust management can not only incentivize the UAVs to comply with the legal rule of spectrum sharing in blockchain, but also punish the malicious behaviour that either violates the spectrum sharing rule or provides misleading spectrum sensing results.

A DAG Blockchain-Enhanced User-Autonomy Spectrum Sharing Framework for 6G-Enabled IoT

The rapidly growing number of Internet-of-Things (IoT) devices poses new challenges for spectrum management in future wireless communication networks. It is critical to achieve efficient and dynamic spectrum management in the sixth-generation (6G) wireless communication networks era. To tackle the challenges of managing a large-scale IoT network with heterogeneous devices, we propose a directed acyclic graph (DAG) blockchain-enhanced user-autonomy spectrum sharing model. As the proposed consensus rule is closely related to system utility, the swarm intelligence of users gradually reaches the point of convergence in the process of blockchain consensus. We analyze the effect of the tip selection method of the DAG blockchain on spectrum allocation utility. A dynamic tip selection method is proposed to enhance the global utility, which is related to the spectrum supply–demand. In addition, the ring signature technique is utilized to realize privacy protection during the sharing process. Simulation indicates that the proposed tip selection method achieves a 10% enhancement in terms of the global utility. Furthermore, significant reductions in administrative expense and reliability improvement are demonstrated by simulation results. The stability of the tip number in the proposed model has been proved theoretically, which is also validated by simulation experiments.

Cognitive radio based spectrum sharing models for multicasting in 5G cellular networks: A survey

The dramatic growth of smart devices catering content-centric data traffic has fueled the need for group communication services in cellular networks, among which multicasting is of particular interest. The unique potential of multicasting to fulfill the vision of a hyper-connected society in 5G networks is underlined by its ability to manage resources efficiently, while supporting concurrent transmission of common data to a large number of users. The bandwidth intensive communication requirements of multimedia multicast applications demand optimal usage of the spectral resources. Moreover, massive capacity, connectivity and ultra low latency requirements of the content centric applications have triggered the urge to get the content nearer to the users and employ short range communication, thus leading to densification of cellular networks. However, the limited availability of radio resources coupled with elevated levels of interference pose a major hindrance towards enabling spectrum reuse in dense networks. Cognitive radio (CR) is a viable technology to enhance the spectral efficiency, by allowing low priority unlicensed users to share the spectrum of high priority licensed users. The ability of CR to adapt its transmission parameters according to the application demands makes it a promising candidate for efficient spectrum management and interference mitigation in 5G cellular networks. This paper presents a survey on the state-of-the art techniques proposed for catering multicast services in CR networks. The study provides a detailed analysis on the utility of various spectrum sharing models of CR networks in fulfilling the end-to-end communication goals of multicast services. We particularly emphasize on the nature of multicast traffic, the key enabling techniques and methodologies employed for enhancing the efficacy of such services and approaches for mitigating interference experienced by the licensed users. By carefully analyzing the existing works, we provide possible design guidelines for multicast services in future 5G networks. Finally, we identify few open issues and discuss the future research directions.

Spectrum Sharing for 6G Integrated Satellite-Terrestrial Communication Networks Based on NOMA and CR

The explosive growth of bandwidth-hungry Internet applications have led to the rapid development of new generation mobile network technologies that are expected to provide broadband access to the Internet in a pervasive manner. For example, 6G networks are capable of providing high-speed network access by exploiting higher frequency spectrum; high-throughout satellite communication services are also adopted to achieve pervasive coverage in remote and isolated areas. In order to enable seamless access, integrated satellite-terrestrial communication networks (ISTCNs) have emerged as an important research topic, which aim to provide high-speed and pervasive network access by integrating broadband terrestrial mobile networks with satellite communication systems. As the terrestrial networks have begun to use higher frequency bands that overlap with those of the satellite networks, the satellite terminals can potentially access the terrestrial networks in an integrated manner. However, more efficient spectrum management techniques are needed to reduce the congestion and interference caused by spectrum sharing. In this article, we seek to improve satellite-terrestri-al spectrum sharing performance by introducing non-orthogonal multiple access (NOMA) and cognitive radio (CR) in the spectrum access of the ISTCN. Through fusing NOMA and CR, CR-NO-MA allows multiple satellite terminals to access both the idle and busy spectrum simultaneously, which will achieve high-efficiency full spectrum access. Furthermore, some open research and challenges for the spectrum sharing of the ISTCN are discussed.

Agent-Based Spectrum Management Scheme in Satellite Communication Systems

Efficient spectrum management has always been an important issue due to the scarcity of satellite spectral resource, especially with the ever-increasing broadband demand. This paper proposes a market-driven technique to improve spectrum efficiency. In practice, spectrum resources are typically allocated in bulk to terrestrial agents which in turn will resell the bandwidth to end users of satellite communications. This paper explores the important role of terrestrial agents which serve as spectrum sales agents in satellite communications systems. The proposed approach aims to provide an incentive scheme for the agents to participate in the spectrum optimization process so as to result in maximizing the benefit of the agents as well as the satellite systems. We propose a dynamically optimal cooperation scheme between terrestrial agents and satellite systems, which is based on a stochastic process and optimal contract principle. By taking into consideration the satellite system's marginal cost related to transmission capacity in the given bandwidth, terrestrial agent's effort and the impact of market volatility, we designed an optimal incentive model which allows the satellite systems to determine a threshold value for paying sales commission to terrestrial agents or triggering contract termination if spectrum utilization is inefficient. Numerical results are presented to evaluate the performances of satellite systems’ profits in changing spectrum market and agency cost undertaken by satellite systems.

A Survey on Joint Communication-Radar Systems

Concerns for spectrum congestion have spurred extensive research efforts on efficient spectrum management. Therefore, devising schemes for spectrum sharing between radar and wireless communication systems has become an important area of research. Joint communications-radar (JCR) systems are among the several approaches proposed to achieve this objective. In JCR systems, additional components and processes are added to an existing standardized communication platform to enable radar functions. Moreover, the communication waveform is used as an integrated JCR waveform, i.e., the same signal is used to communicate information to a receiver and to perform radar detection and estimation operations for a nearby target. The most common application of JCR systems is found in vehicle-to-vehicle (V2V) communication scenarios. In this article, an overview of the spectrum sharing methods is presented, with a focus on JCR systems in automotive and other applications. We first review the recent works on IEEE 802.11p- and IEEE 802.11ad-based radars. A basic description of the modeling of a JCR system and channels is presented, followed by discussions on the main components and processes employed in various JCR systems. We are mainly interested in how radar detection and estimation functions are performed in conjunction with the communication receiver functions with minimal alterations to the existing system. At the end of the paper, some performance trade-offs between the communication and radar sub-systems are also discussed.

Self‐Organized Efficient Spectrum Management through Parallel Sensing in Cognitive Radio Network

In this paper, we propose an innovative self‐organizing medium access control mechanism for a distributed cognitive radio network (CRN) in which utilization is maximized by minimizing the collisions and missed opportunities. This is achieved by organizing the users of the CRN in a queue through a timer and user ID and providing channel access in an orderly fashion. To efficiently organize the users in a distributed, ad hoc network with less overhead, we reduce the sensing period through parallel sensing wherein the users are divided into different groups and each group is assigned a different portion of the primary spectrum band. This consequently augments the number of discovered spectrum holes which then are maximally utilized through the self‐organizing access scheme. The combination of two schemes augments the effective utilization of primary holes to above 95%, even in impasse situations due to heavy primary network loading, thereby achieving higher network throughput than that achieved when each of the two approaches are used in isolation. By efficiently combining parallel sensing with the self‐organizing MAC (PSO‐MAC), a synergy has been achieved that affords the gains which are more than the sum of the gains achieved through each one of these techniques individually. In an experimental scenario with 50% primary load, the network throughput achieved with combined parallel sensing and self‐organizing MAC is 50% higher compared to that of parallel sensing and 37% better than that of self‐organizing MAC. These results clearly demonstrate the efficacy of the combined approach in achieving optimum performance in a CRN.

Towards Enhancing Spectrum Sensing: Signal Classification Using Autoencoders

The demand for technologies relying on the radio spectrum, such as mobile communications and IoT, has been growing exponentially. As a consequence, providing access to the radio spectrum is becoming increasingly more important. The ever-growing wireless traffic and the increasing scarcity of available spectrum warrants efficient management of the radio spectrum. At the same time, machine learning (ML) is becoming ubiquitous and has found applications in many fields for its ability to identify patterns and assist with decision-making processes. Recently, machine learning algorithms have been used to address challenges in the wireless communications domain, such as radio spectrum sensing, and have shown better performance than traditional sensing methods, such as energy detection. Spectrum sensing, a method for detecting and identifying different wireless signals being transmitted in the same band of the radio spectrum, is crucial for improving dynamic spectrum sharing, which has the potential to enhance sharing and coexistence of different wireless technologies in the same frequency band and ultimately improve spectrum efficiency. To this end, this research evaluates different types of autoencoders, such as deep, variational and Long Short-Term Memory (LSTM) autoencoders, to identify and differentiate between LTE and Wi-Fi transmissions. The goal is to investigate the performance of the different types of autoencoders on an I/Q dataset consisting of LTE and a combination of Wi-Fi signals (IEEE 802.11ax and IEEE 802.11ac) for the classification task in terms of complexity, precision, and recall to identify the best algorithm. Our models have achieved up to 99.9% precision and 88.1% recall for this classification task. Additionally, with a shortest training time of approximately 47 seconds, the models are suitable for online learning and deployment in a dynamic RF environment.

Efficient‐spectrum management based on localisation of primary user position towards 5G

Recent emergent technologies with the advent of 5G use cases have made it imperative for high demands in broadband radio spectrum as compared to the current 4G technologies and other outdated ones. This study introduces an efficient spectrum management based on localisation of the primary user (PU) position in interweave cognitive radio networks (CRNs) towards 5G. In a typical network consisting of licensed system with PUs; and unlicensed system with secondary users (SUs), the SU opportunistically exploits the unused frequency bands of the licensed network provided there is no harmful interference to the PUs. Since the network is dynamic and the PUs are moving targets, the unused bands become unstable; hence, the SUs meet with limitations of detecting white spaces; finally resulting the interference. To this end, both Kriging interpolation and Kalman filter (KF) techniques for tracking and estimating the PUs position are proposed. The performance work was evaluated with respect to received signal strength indicator (RSSI) scheme. Both KF and Kriging interpolation outperformed RSSI. Moreover, the KF leads to the best performance compared to the other two as per localisation error prediction rate and system throughput; which makes it as the preferable technique to tackle mentioned challenges encountered by the SUs in such environment.

Techniques for Achieving High Isolation in RF Domain for Simultaneous Transmit and Receive

With the growth of wireless data traffic, additional spectrum is required to meet consumer demands. Consequently, innovative approaches are needed for efficient management of the available limited spectrum. To double the achievable spectral efficiency, a transceiver can be designed to receive and transmit signals simultaneously (STAR) across the same frequency band. However, due to the coupling of the high power transmitted signal into the collocated receiver, the receiver’s performance is degraded. For successful STAR realization, the coupled high-power transmit (Tx) signal should be suppressed by 100-120 dB over the entire operational bandwidth. So far, most STAR implementations are narrowband, and not useful for ultra wideband (UWB) communications. In this paper, we present a review of novel approaches employed to achieve improved cancellation across wide bandwidths in RF and propagation domains. Both single and multi-antenna systems are considered. Measurements show an average cancellation of 50 dB using two stages of RF signal cancellation.

AN ENERGY EFFICIENT SPECTRUM AWARE ROUTING FOR COGNITIVE RADIO NETWORKS

Background: Now a days Due to rapid increase in the internet the Spectrum available is been overcrowded and thus the spectrum Sensing can be achieved by using the predominant technology i.e., Cognitive Radio as the spectrum is been under-utilized by the wireless communication technologies. Objective: The prime objective of the research work carried out is to achieve the energy efficiency and to use the spectrum effectively by using the spectrum management concept and achieve better throughput, end to end delay etc., Methods: The detection of the spectrum hole plays a vital role in the routing of the cognitive radio networks. As the hidden node problems and the multipath fading and the shadowing are considered to be impacting the sensing of the spectrum to overcome these issues Cooperative spectrum Sensing Mechanism is been adopted to sense the spectrum in an optimal manner and over cum the issues. Along with these issues the spectrum resources changes time to time in the routing. Result: An Energy Efficient Spectrum aware Routing (ESR) is been proposed which involves the timeslot and the routing schemes were been discussed. The simulation parameters have been proposed and the improvement in the performance of network through and a decrease in the end-to end delay have been observed and analyzed. Conclusion: An Energy Efficient spectrum management and the routing is successfully established to communication in the network and further traffic load is observed to be balanced in the protocol based on the residual energy in a node and further it prologs the Network Lifetime of the Overall Network and the Individual CR user. Further the end-to-end Delay has been observed to be lower which ensure higher throughput in the network and improves the network life time.

Matching Theory as Enabler of Efficient Spectrum Management in 5G Networks

Matching Theory as Enabler of Efficient Spectrum Management in 5G Networks

Automatic Wireless Signal Classification: A Neural-Induced Support Vector Machine-Based Approach

Automatic Classification of Wireless Signals (ACWS), which is an intermediate step between signal detection and demodulation, is investigated in this paper. ACWS plays a crucial role in several military and non-military applications, by identifying interference sources and adversary attacks, to achieve efficient radio spectrum management. The performance of traditional feature-based (FB) classification approaches is limited due to their specific input feature set, which in turn results in poor generalization under unknown conditions. Therefore, in this paper, a novel feature-based classifier Neural-Induced Support Vector Machine (NSVM) is proposed, in which the features are learned automatically from raw input signals using Convolutional Neural Networks (CNN). The output of NSVM is given by a Gaussian Support Vector Machine (SVM), which takes the features learned by CNN as its input. The proposed scheme NSVM is trained as a single architecture, and in this way, it learns to minimize a margin-based loss instead of cross-entropy loss. The proposed scheme NSVM outperforms the traditional softmax-based CNN modulation classifier by managing faster convergence of accuracy and loss curves during training. Furthermore, the robustness of the NSVM classifier is verified by extensive simulation experiments under the presence of several non-ideal real-world channel impairments over a range of signal-to-noise ratio (SNR) values. The performance of NSVM is remarkable in classifying wireless signals, such as at low signal-to-noise ratio (SNR), the overall averaged classification accuracy is > 97% at SNR = −2 dB and at higher SNR it achieves overall classification accuracy at > 99%, when SNR = 10 dB. In addition to that, the analytical comparison with other studies shows the performance of NSVM is superior over a range of settings.

Matching theory as enabler of efficient spectrum management in 5G networks

AbstractThis paper analyzes the spectrum trading problem in virtualized fifth generation networks to enhance the network performance with respect to the spectrum utilization. The problem is modeled as a many‐to‐many matching game with utility‐based preferences and determines the matching between mobile network operators and mobile virtual network operators. The two proposed versions of utility functions for each set aim at maximizing the satisfaction of both sets with conflicting interests and improving the overall spectrum efficiency. In the simulation evaluation, the proposed scheme is compared with three different schemes in terms of the system utility, individual, and pair matching satisfaction. We also investigate the scalability aspects, the strategy plan impact on the matching performance of our proposed scheme, and at the same time, we attempt to make appropriate assumptions closer to reality. Our proposed scheme shows much better performance than the other schemes achieving a quite high level of satisfaction for the matching result on both sets.