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The paper aims to enhance wireless networks by improving cost and fairness with the assistance of reconfigurable intelligent surfaces (RISs) and game theory. Trusted RIS holders could purchase the copyrights of RIS technology from wireless service providers, their competitive behaviors were simulated using a non-cooperative pricing game to optimize the utility of the pricing structure. RIS holders were supposed to be bounded rational and base their decisions on past strategies. This selfish action was framed as a duopoly game of bounded rationality with history. To ensure fairness, the solution to this game was defined as the Nash equilibrium, but maintaining the stability of this solution was difficult. The local stability of the solution was analyzed. Numerical simulations showed that RIS holders who possessed story-awareness were more likely to attain Nash equilibrium, and its stability region became larger for specific story weights and RIS size values. Next, a basin of attraction graph simulated the effect of history on the global stability of Nash equilibrium. The study provided information to RIS holders to minimize the cost of installing RIS on their own or rented properties while transmitting more radio spectrum to users. [ABSTRACT FROM AUTHOR]

The integrated satellite-terrestrial network (ISTN) provides a promising solution to achieve high-data-rate and ubiquitous connectivity in next-generation communication networks. Considering the scarce spectrum resources and unevenly distributed traffic demands, we investigate the resource allocation algorithms for ISTNs, where the beam-hopping (BH)-based satellite system and terrestrial systems share the same frequency band. Taking advantage of the scheduling flexibility of BH technology, the dynamical protection zones are constructed to avoid co-channel interference and improve the spectrum efficiency. Since both spectrum efficiency and user fairness are the key optimization indexes in practical systems, two resource allocation problems are formulated to maximize the weighted sum of capacity (MWSC) and maximize the minimum capacity-to-demand ratio (MMCDR) of ISTNs, respectively. By reformulating the problems as mixed-integer linear programming problems, optimal solutions are obtained. To reduce the computational complexity, two greedy suboptimal algorithms are proposed for the MWSC and MMCDR, respectively. The simulation results show that the proposed algorithms achieve higher spectrum efficiency and guarantee fairness between the satellite and terrestrial systems. It is also shown that both the greedy algorithms perform similarly to the optimal algorithms while having much lower complexity. [ABSTRACT FROM AUTHOR]

The integration of both sensing and communication functions is a crucial feature for future communication systems. This paper considers a novel scenario where a radar covers multiple small-cell base stations which operate in different spectra. Due to the limited spectra resource, the radar needs to reuse the spectra of one BS for tracking. To suppress interference and improve performance, we propose a framework including two stages and the corresponding communication and sensing algorithms. Specifically, at the detection stage, we aim to minimize the system transmit power while maintaining the performance of both radar and communication. At the tracking stage, our goal is to maximize the radar sensing performance under a given power budget, while ensuring communication quality. Due to the complexity of the original problem, we address it by introducing auxiliary variables and proposing a penalty dual decomposition-based algorithm, enabling us to solve a more tractable form of the problem. In the inner loop, we propose a concave–convex procedure-based algorithm to handle the optimization problem, while adopting the block coordinate descent algorithm to iteratively update the variables. In the outer loop, we update the penalty term or Lagrange multipliers. Furthermore, we propose a radar target parameter estimation algorithm based on successive interference cancellation, which aims to mitigate communication interference. Finally, numerical simulations demonstrate the effectiveness of the proposed system, and the superior performance of our proposed algorithms over benchmark algorithms. [ABSTRACT FROM AUTHOR]

Abstract The integration of both sensing and communication functions is a crucial feature for future communication systems. This paper considers a novel scenario where a radar covers multiple small-cell base stations which operate in different spectra. Due to the limited spectra resource, the radar needs to reuse the spectra of one BS for tracking. To suppress interference and improve performance, we propose a framework including two stages and the corresponding communication and sensing algorithms. Specifically, at the detection stage, we aim to minimize the system transmit power while maintaining the performance of both radar and communication. At the tracking stage, our goal is to maximize the radar sensing performance under a given power budget, while ensuring communication quality. Due to the complexity of the original problem, we address it by introducing auxiliary variables and proposing a penalty dual decomposition-based algorithm, enabling us to solve a more tractable form of the problem. In the inner loop, we propose a concave–convex procedure-based algorithm to handle the optimization problem, while adopting the block coordinate descent algorithm to iteratively update the variables. In the outer loop, we update the penalty term or Lagrange multipliers. Furthermore, we propose a radar target parameter estimation algorithm based on successive interference cancellation, which aims to mitigate communication interference. Finally, numerical simulations demonstrate the effectiveness of the proposed system, and the superior performance of our proposed algorithms over benchmark algorithms.

Abstract Semantic communication and spectrum sharing are pivotal technologies in addressing the perennial challenge of scarce spectrum resources for the sixth-generation (6G) communication networks. Notably, scant attention has been devoted to investigating semantic resource allocation within spectrum sharing semantic communication networks, thereby constraining the full exploitation of spectrum efficiency. To mitigate interference issues between primary users and secondary users while augmenting legitimate signal strength, the introduction of Intelligent Reflective Surfaces (IRS) emerges as a salient solution. In this study, we delve into the intricacies of resource allocation for IRS-enhanced semantic spectrum sharing networks. Our focal point is the maximization of semantic spectral efficiency (S-SE) for the secondary semantic network while upholding the minimum quality of service standards for the primary semantic network. This entails the joint optimization of parameters such as semantic symbol allocation, subchannel allocation, reflective coefficients of IRS elements, and beamforming adjustment of secondary base station. Recognizing computational intricacies and interdependence of variables in the non-convex optimization problem formulated, we present a judicious approach: a hybrid intelligent resource allocation approach leveraging dueling double-deep Q networks coupled with the twin-delayed deep deterministic policy. Simulation results unequivocally affirm the efficacy of our proposed resource allocation approach, showcasing its superior performance relative to baseline schemes. Our approach markedly enhances the S-SE of the secondary network, thereby establishing its prowess in advancing the frontiers of semantic spectrum sharing (S-SE).

Semantic communication and spectrum sharing are pivotal technologies in addressing the perennial challenge of scarce spectrum resources for the sixth-generation (6G) communication networks. Notably, scant attention has been devoted to investigating semantic resource allocation within spectrum sharing semantic communication networks, thereby constraining the full exploitation of spectrum efficiency. To mitigate interference issues between primary users and secondary users while augmenting legitimate signal strength, the introduction of Intelligent Reflective Surfaces (IRS) emerges as a salient solution. In this study, we delve into the intricacies of resource allocation for IRS-enhanced semantic spectrum sharing networks. Our focal point is the maximization of semantic spectral efficiency (S-SE) for the secondary semantic network while upholding the minimum quality of service standards for the primary semantic network. This entails the joint optimization of parameters such as semantic symbol allocation, subchannel allocation, reflective coefficients of IRS elements, and beamforming adjustment of secondary base station. Recognizing computational intricacies and interdependence of variables in the non-convex optimization problem formulated, we present a judicious approach: a hybrid intelligent resource allocation approach leveraging dueling double-deep Q networks coupled with the twin-delayed deep deterministic policy. Simulation results unequivocally affirm the efficacy of our proposed resource allocation approach, showcasing its superior performance relative to baseline schemes. Our approach markedly enhances the S-SE of the secondary network, thereby establishing its prowess in advancing the frontiers of semantic spectrum sharing (S-SE). [ABSTRACT FROM AUTHOR]

[Purpose] Spectrum is a scarce and valuable national resource. With 5G, it is predicted that the demand for spectrum will increase gradually. In order to overcome the increasing data traffic problem and connection needs, the spectrum should be used as efficiently as possible. This paper aims to develop a new perspective on spectrum management in Turkey, and the applicability of spectrum sharing applications in Turkey has been investigated. Suggestions were made regarding the changes that can be made in the current legislation. [Methodology/Approach/Design] Within this study, interviews were held with the three leading mobile operators in Turkey through focus group discussion, and evaluations were made about the approach of Turkey's mobile communication sector to the subject. In addition, innovative spectrum management practices in Europe and United States (US) regarding the emerging spectrum sharing approach were examined. The current practices of European countries were presented in the semi-structured interview via e-mail. [Findings] This paper provides opinions of Turkish leading mobile operators about spectrum sharing, current EU and US practices, and necessary changes in Turkish legislation to enable spectrum sharing. A new perspective on spectrum management in Turkey has been developed, and the applicability of spectrum sharing applications in Turkey has been investigated. Suggestions were made regarding the changes that can be made in the current legislation. [Practical Implications] What outcomes and implications for practice, applications and consequences are identified? How will the article impact on society, business, or enterprise? What changes to practice should be made as a result of this research? What is the legal or economic impact? [Originality/Value] This paper fulfills the need for originality and presents new knowledge about spectrum sharing in Turkey's telecommunication sector. [ABSTRACT FROM AUTHOR]

Spectrum sharing provides a rapid migration pathway toward 5G by enabling the coexistence of 4G LTE and 5G new radio (NR) that share the same spectrum. Due to significant differences in the LTE and 5G-NR air interfaces, several enablers are required to facilitate the spectrum sharing. In this study, we explore the coexistence features and investigate their impacts on network performance. For static and dynamic spectrum sharing scenarios, we assess the impacts of different spectrum sharing ratios, user ratios, MIMO configurations, mixed numerology profiles and traffic patterns on the user throughput and network capacities of spectrum sharing networks, compared with the LTE only and 5G-NR only networks with exclusive spectrum access. The key results show that spectrum sharing leads to a marginal capacity gain over LTE only network and achieves considerably lower capacity than the 5G-NR only network. Also, the results show that mixed numerology profiles between the LTE and 5G-NR lead to capacity losses due to inter-numerology interference. In addition, user and spectrum sharing ratios between LTE and 5G-NR have critical impacts on performance. Reduced spectrum per device as the number of 5G devices increases, higher signaling overhead and higher scheduling complexity are other limiting factors for spectrum sharing networks. The results show limited capacity benefits and reinforce spectrum sharing between LTE and 5G-NR as mainly an evolutionary path to accommodate 5G users in the same LTE spectrum while migrating to the fully-fledged 5G networks. For significant capacity increase, other features such as carrier aggregation, overlay of small cells and higher order MIMO would need to be incorporated into the network. [ABSTRACT FROM AUTHOR]

Cell-free massive multiple-input-multiple-output (CF-mMIMO) networks incorporate a cell-free architecture with distributed antennas in a geographical area, and aim to deliver high data rates and support large numbers of users. It is crucial that such networks operate in an energy-efficient manner within the available spectrum. Thus, we focus on maximizing the energy efficiency (EE) of a CF-mMIMO network, which coexists with a collocated primary network in underlay mode. The EE maximization is a non-convex problem and to compute a power allocation policy efficiently, we propose a weighted minimum-mean-square-error (WMMSE) based Dinkelbach’s algorithm. Besides this, we also provide a simplified algorithm for maximum-ratio precoding in which we approximate the non-convex EE objective function with a lower bound, transforming the non-convex EE problem into a convex problem. Subsequently, we propose a policy for downlink power allocation that maximizes the EE of the secondary CF-mMIMO network while adhering to power constraints at each access point and interference constraints at each primary user. We also compare with some heuristic power allocation policies. The results demonstrate that the proposed WMMSE based power allocation scheme outperforms the heuristic power allocation schemes to a significant degree.

The sixth-generation (6G) network is expected to provide both communication and sensing (C&S) services. However, spectrum scarcity poses a major challenge to the harmonious coexistence of C&S systems. Without effective cooperation, the interference resulting from spectrum sharing impairs the performance of both systems. This paper addresses C&S interference within a distributed network. Different from traditional schemes that require pilot-based high-frequency interactions between C&S systems, we introduce a third party named the radio map to provide the large-scale channel state information (CSI). With large-scale CSI, we optimize the transmit power of C&S systems to maximize the signal-to-interference-plus-noise ratio (SINR) for the radar detection, while meeting the ergodic rate requirement of the interfered user. Given the non-convexity of both the objective and constraint, we employ the techniques of auxiliary-function-based scaling and fractional programming for simplification. Subsequently, we propose an iterative algorithm to solve this problem. Simulation results corroborate our idea that the extrinsic information, i.e., positions and surroundings, is effective to decouple C&S interference.

With the dense deployment of the satellite constellations, spectrum sharing among multiple satellite communication systems is a feasible way to alleviate the scarcity of spectrum resources. In order to improve the spectrum efficiency, we investigate the resource allocation in the spectrum sharing scenario of multiple beam-hopping (BH)-based satellite systems. The BH technology enhances the scheduling flexibility and the reusability of the transmission resources, but it also makes the inter-system interference more complicated. Under the interference constraint, the resource allocation problem is formulated to maximize the throughput of the satellite system. Based on the set of feasible beam allocation patterns, the optimization problem is reformulated as a mixed integer linear programming problem to obtain the optimal solution. A greedy suboptimal algorithm is also proposed to solve the problem with low complexity. To further accelerate the resource allocation process, the inter-beam distance threshold is introduced to reduce the number of feasible beam allocation patterns. Simulation results show that the proposed resource allocation algorithms achieve higher system throughput and better adaptability to the uneven distribution of traffic demands. It is also shown that the suboptimal algorithm achieves desirable performance with lower computational complexity, which is more attractive for the real-time resource allocation in the satellite systems.

Next-generation cellular networks offer enhanced-mobile broadband, ultra-reliable low latency, and massive machine-type communications. Conventional technology may not meet these demands due to complexity and dynamicity of the network and diverse traffic requirements. To overcome these limitations, resource sharing among network operators is widely studied. The service performance can be improved by leveraging multi-access edge computing (MEC) technology. A mobile user receiving service from virtual network function at the MEC, may experience performance degradation due to lack of resources. To meet the quality of service requirements of users, this paper proposes a multi-operator spectrum and MEC resource sharing scheme. We introduce a user plane function agent at main cloud of the mobile network operator (MNO) that enables inter-operator communications and manages resource sharing requests. Service continuity is enabled by relocating users’ associated VNFs considering current resources at the edge network. The proposed scheme has been evaluated using simulations and an experimental testbed. The results show that the proposed scheme reduces network delay, improves network throughput, increases spectrum utilization, increases successful VNF placement ratio, reduces the packet drop ratio, reduces load on edge nodes, and increases revenue for the operator, compared to that of the conventional scheme.

The number of users in wireless networks, such as mobile and Internet-of-Things networks, is witnessing a tremendous increase, turning the available frequency spectrum into a scarce resource that needs to be efficiently utilized. Cognitive radio (CR) is a key technology for achieving spectrum efficiency by continuously sensing and detecting frequency bands that are not used by licensed primary users (PU) and allowing unlicensed secondary users (SUs) to use them. One of the main challenges in CR is the design of a medium access control (MAC) protocol that ensures efficient spectrum sharing by SUs without disrupting the connectivity of PUs. To achieve that, many of the existing MAC protocols in the literature allow multiple SU transmissions to proceed simultaneously by performing batch-based power control decisions to limit mutual interference between them. Interestingly, the majority of such protocols are demand-rate unaware; i.e., all SUs are granted the same data rate, regardless of their data rate demand. In this paper, we highlight the severe drawbacks of demand-rate unawareness and propose the rate-aware power-controlled channel assignment (RPCCA) MAC protocol, which performs batch-based simultaneous channel assignment decisions to competing SUs along with power control to limit mutual interference, while taking into account the variable demand-rate across SUs. Simulation experiments have demonstrated that the RPCCA protocol offers substantial performance improvements over existing demand-rate unaware CR-based MAC protocols.

Wireless networks operate in a resource-constrained setting, of which spectrum is an important example. Here, we attack the scarcity problem by developing a market for virtual resources based on a common ownership self-assessed tax. We propose a utilization-based penalty mechanism to incentivize efficient use of resources and discourage hoarding. Our analysis and simulations show that the resulting market behaviour under various scenarios achieves this goal.

Shared spectrum systems facilitate spectrum allocation to unlicensed users without harming the licensed users; they offer great promise in optimizing spectrum utility, but their management (in particular, efficient spectrum allocation to unlicensed users) is challenging. To allocate spectrum efficiently to secondary users (SUs) in general scenarios, we fundamentally need to have knowledge of the signal path-loss function. In practice, however, even the best-known path-loss models have unsatisfactory accuracy, and conducting extensive surveys to gather path-loss values is infeasible. Thus, the current allocation methods are either (i) too conservative in preventing interference that they sacrifice performance, or (ii) are based on imperfect propagation models and/or spectrum sensing with insufficient spatial granularity. This leads to poor spectrum utilization, the fundamental objective of shared spectrum systems. In this work, we thus propose to learn the spectrum allocation function directly using supervised learning techniques. Such an approach has the potential to deliver near-optimal performance with sufficient and effective training data. In addition, it has the advantage of being viable even when certain information is unavailable; e.g., in settings where PUs’ information is not available, we make use of a crowdsourced sensing architecture and use the spectrum sensor readings as features. In general, for spectrum allocation to a single SU, we develop a CNN-based approach (called DeepAlloc) and address various challenges that arise in our context; to handle multiple SU requests simultaneously, we extend our approach based on recurrent neural networks (RNNs). Via extensive large-scale simulation and a small testbed, we demonstrate the effectiveness of our developed techniques; in particular, we observe that our approach improves the accuracy of standard learning techniques and prior work by up to 60%.

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The emergence of the Internet of Things (IoT) has triggered a massive digital transformation across numerous sectors. This transformation requires efficient wireless communication and connectivity, which depend on the optimal utilization of the available spectrum resource. Given the limited availability of spectrum resources, spectrum sharing has emerged as a favored solution to empower IoT deployment and connectivity, so adequate planning of the spectrum resource utilization is thus essential to pave the way for the next generation of IoT applications, including 5G and beyond. This article presents a comprehensive study of prevalent wireless technologies employed in the field of the spectrum, with a primary focus on spectrum-sharing solutions, including shared spectrum. It highlights the associated security and privacy concerns when the IoT devices access the shared spectrum. This survey examines the benefits and drawbacks of various spectrum-sharing technologies and their solutions for various IoT applications. Lastly, it identifies future IoT obstacles and suggests potential research directions to address them.

The integrated satellite-terrestrial network (ISTN) provides a promising solution to achieve high-data-rate and ubiquitous connectivity in next-generation communication networks. Considering the scarce spectrum resources and unevenly distributed traffic demands, we investigate the resource allocation algorithms for ISTNs, where the beam-hopping (BH)-based satellite system and terrestrial systems share the same frequency band. Taking advantage of the scheduling flexibility of BH technology, the dynamical protection zones are constructed to avoid co-channel interference and improve the spectrum efficiency. Since both spectrum efficiency and user fairness are the key optimization indexes in practical systems, two resource allocation problems are formulated to maximize the weighted sum of capacity (MWSC) and maximize the minimum capacity-to-demand ratio (MMCDR) of ISTNs, respectively. By reformulating the problems as mixed-integer linear programming problems, optimal solutions are obtained. To reduce the computational complexity, two greedy suboptimal algorithms are proposed for the MWSC and MMCDR, respectively. The simulation results show that the proposed algorithms achieve higher spectrum efficiency and guarantee fairness between the satellite and terrestrial systems. It is also shown that both the greedy algorithms perform similarly to the optimal algorithms while having much lower complexity.

Cognitive radio technology is aimed at utilizing the maximum available licensed spectrum for secondary users. However, the performance of a cognitive radio mobile ad hoc network can be degraded significantly due to selfish nodes that preoccupy idle licensed spectrum. Solving selfish attacks has become an important task in cognitive radio mobile ad hoc networks. We propose a selfish node detection algorithm that can solve the miss detection problem that happened with existing selfish node detection methods. The proposed algorithm reflects network errors to increase detection performance. In the simulation, our proposed algorithm is efficient and reliable for selfish node detection in various network conditions. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, ergodic capacity performance for the secondary user is analyzed under the average received power constraint taking into account the effect of interference from the primary user in the spectrum sharing model for the cognitive radio network. With the capacity-maximizing water-filling solution and power allocation approach, the theoretical derivations for ergodic capacity are provided on Nakagami-m and lognormally distributed fading channels. The numerical results show that interference channels originating from the primary network have a significant impact on the capacity of the secondary user. Ergodic capacity performance for the secondary user is verified by Monte Carlo simulations, taking into account the fading parameter of the Nakagami distribution, the standard deviation of the log-normal distribution, and the Lagrange multiplier parameters that determine the optimum power of the secondary user. [ABSTRACT FROM AUTHOR]

With the high demand for advanced services and the increase in the number of connected devices, current wireless communication systems are required to expand to meet the users' needs in terms of quality of service, throughput, latency, connectivity, and security. 5G, 6G, and Beyond (xG) aim at bringing new radical changes to shake the wireless communication networks where everything will be fully connected fulfilling the requirements of ubiquitous connectivity over the wireless networks. This rapid revolution will transform the world of communication with more intelligent and sophisticated services and devices leading to new technologies operating over very high frequencies and broader bands. To achieve the objectives of the xG networks, several key technology enablers need to be performed, including massive MIMO, software-defined networking, network function virtualization, vehicular to everything, mobile edge computing, network slicing, terahertz, visible light communication, virtualization of the network infrastructure, and intelligent communication environment. In this paper, we investigated the recent advancements in the 5G/6G and Beyond systems. We highlighted and analyzed their different key technology enablers and use cases. We also discussed potential issues and future challenges facing the new wireless networks. [ABSTRACT FROM AUTHOR]

With the widespread application of 5G technology, there has been a significant surge in wireless video service demand and video traffic due to the proliferation of smart terminal devices and multimedia applications. However, the complexity of terminal devices, heterogeneous transmission channels, and the rapid growth of video traffic present new challenges for wireless network-based video applications. Although scalable video coding technology effectively improves video transmission efficiency in complex networks, traditional cellular base stations may struggle to handle video transmissions for all users simultaneously, particularly in large-scale networks. To tackle this issue, we propose a scalable video multicast scheme based on user demand perception and Device-to-Device (D2D) communication, aiming to enhance the D2D multicast network transmission performance of scalable videos in cellular D2D hybrid networks. Firstly, we analyze user interests by considering their video viewing history and factors like video popularity to determine their willingness for video pushing, thereby increasing the number of users receiving multicast clusters. Secondly, we design a cluster head selection algorithm that considers users' channel quality, social parameters, and video quality requirements. Performance results demonstrate that the proposed scheme effectively attracts potential request users to join multicast clusters, increases the number of users in the clusters, and meets diverse user demands for video quality. [ABSTRACT FROM AUTHOR]

Satellite Internet-of-Things (IoT) is a critical part to build information infrastructure for environmental and ecological monitoring in remote areas where the cellular networks are uneasy to cover. When massive IoT terminals pour in the crowded satellite channels, how to enhance the spectrum utility of satellite band and guarantee the system capacity calls for full investigations. In this paper, a secure spectrum optimization scheme is proposed in satellite IoT by introducing blockchain to hamper malicious users to involve in the spectrum sharing. A market-oriented spectrum optimization strategy is designed to improve the satellite systems' benefits wherein the heterogeneous satellite channels are fully be considered and priced. In this circumstance, differentiated pricing is adopted to satisfy the various spectrum users' demands. When applying blockchain to secure the spectrum trading, the edge computing along with the mechanism of user reputation are devised to address the concerns of limited computation power for general IoT devices. Besides, the consensus algorithm of Delegated Proof of Stake is used to improve the computing efficiency so as to better fit in the application circumstance of satellite IoT. Numerical results are provided to evaluate the performances of our proposed solution on satellite channel pricing and system benefits. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunications Science is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To help meet the growing demand for wireless data capacity, the United States Federal Communications Commission (FCC) and National Telecommunications and Information Administration (NTIA) define and regulate a spectrum-sharing approach in several radio frequency (RF) bands, with sharing in additional bands expected in the near future. This paper examines the case in which some of the users in the shared bands are government radars, as is the case in the 3.5 GHz Citizens Broadband Radio Service (CBRS) band. This paper surveys topics related to dynamic spectrum sharing (DSS) of RF bands between government radars and communication systems, including spectrum policy, dynamic spectrum access (DSA), dynamic frequency selection (DFS) and frequency assignment, Primary User (PU) protection and privacy, radar-communications interference mitigation, and joint radar-communications system design. The purpose of this paper is to provide readers with a summary of current knowledge of these aforementioned concepts while also providing references so that topics of interest may be studied deeper. The structure of this paper is broken into several topics, where within each topic table(s) provide a survey of the relevant topic. The contribution made includes over 200 references. Tables include summaries of approaches described in multiple references, as well as descriptions and summaries of individual references used throughout the survey. A taxonomy of approaches applicable to spectrum sharing between radar and communication systems is provided, as are conclusions and recommendations. Findings include existing technology, systems, and regulations utilizing the CBRS in its current structure. Therefore, all of the advancements will most likely be derived using a combination, derivative, or adapted methodology defined in the different sections throughout this survey.

The upcoming sixth generation (6G) mobile communication system is expected to operate across a wide range of spectrum that includes not only the bands used by previous generations but also higher frequency bands such as millimeter wave (mmWave), which are currently assigned to fifth generation (5G) networks, terahertz (THz), and optical spectrum. By utilizing a broader range of frequencies, it will be possible to support 6G applications with faster data rates, higher capacity, and lower latency. However, the higher frequency bands pose unique challenges such as higher path loss, absorption loss, and engineering difficulties for antennas and radio frequency (RF) circuitry design, which require advanced technologies and innovative solutions. Given that the spectrum is a scarce resource, efficient management is crucial to ensure the most effective exploitation of frequency bands. The spectrum management has evolved over the years, with different approaches being used to assign and utilize frequency bands. In this paper, we provide a review of spectrum management approaches, including their use in awarding 5G spectrum, and explore their expected use in 6G. We then offer a brief overview of spectrum sharing and its role in enabling the efficient use of spectrum resources. The regulations, standardization, features, limitations, and potential use cases of higher frequency bands such as, mmWave, THz, and visible light (VL) are analyzed to provide a comprehensive understanding of the spectrum options available for the upcoming 6G technology.

Spectrum sharing disruption in cognitive radio networks (CRNs) can significantly degrade network performance. Most sharing disruption attacks in the literature focus on orthogonal multiple access (OMA) or higher layers, such as medium access control (MAC). However, this paper focuses on multi-carrier cognitive radio non-orthogonal multiple access (MC CR-NOMA). The sharing disruption mechanism is established by jamming the channel estimation phase. This is shown to cause a denial-of-service (DoS) for secondary users. We derive the optimal power allocation to disrupt spectrum sharing for a number of secondary users. This is demonstrated by deriving the maximum average number of DoS bands under a constraint on power of the adversary. Furthermore, a comparison between optimal power allocation and uniform power allocation is provided. Both the analytical and numerical results of the optimal sharing disruption are presented. Overall, this study highlights the vulnerabilities in spectrum sharing for MC CR-NOMA and presents a new type of attack.

The highly dynamic nature of cognitive radio (CR) systems and their stringent latency requirements pose a significant challenge in the realization of efficient intelligent transport systems (ITS). In this paper, we investigate relay selection and opportunistic spectrum access in conjunction with blockchain technology in a secure manner. Specifically, we propose a cross-layer method for secure relay selection, where secondary relays (SRs) are granted access to available spectrum bands based on the balance of their respective virtual wallets. These virtual wallets, which are built based on the SRs' secrecy capacity and their behavior in the network, are the predominant factors that allow SRs to participate in an auction model. To quantify the trustworthiness of the SRs, we formulate a mathematical framework to evaluate the trust value of each SR, which is then leveraged for rewarding or penalizing the SR. Also, we develop an offline blockchain framework to store the information of participating relays and make it available for future operations, to detect reputable and non-reputable relays in the presence of multiple eavesdroppers. The stored reputations of the participating relays are used to develop a self-learning algorithm to exclude the non-reputable relays from the selection group. Finally, we present thorough numerical results to demonstrate the superiority of the proposed system in terms of security, credibility, and integrity.

In the $\mathrm {6~GHz}$ spectrum sharing band, unlicensed devices are managed by automated frequency coordination (AFC) systems to protect incumbent services from interference. Thus, it is important to select accurate propagation models for interference calculation and analysis. This paper utilizes a model-aided deep learning technique for path loss prediction at $\mathrm {7~GHz}$ , as a representative frequency within the $\mathrm {6~GHz}$ band, in an urban environment. The proposed model is a hybrid model, which leverages both domain expert knowledge from a physics-based general-purposed channel model as well as the learning-based capability from a neural network, for path loss prediction. The model is trained and tested using sufficient-quantity and high-quality real propagation measurement data collected in four locations in an urban environment. Numerical results show that the deep learning model provides a better prediction performance than most empirical models. Furthermore, the feasibility of proposed model generalization to new locations after fine-tuning is examined.

We investigate effective interference management for Low Earth Orbit (LEO) satellite networks that provide downlink services to ground users and share the same frequency spectrum range. Since there are multi-group LEO satellites with different constellation orbits, the ground users will experience time-varying interference due to the overlapping of main/side lobes of the satellite beams, which becomes even more challenging when the interfering satellites cannot communicate directly. To address the problem, we consider two LEO satellite groups that provide communication service in the same ground area, while competing for communication resources. We develop solutions that maximize the throughput and manage the time-varying interference under a certain level, without explicit message exchanges between the satellite groups. By exploiting statistical learning and deep reinforcement learning techniques, we develop learning-based resource allocation schemes and evaluate their performance through extensive simulations. We show their effectiveness under different reward settings and different interference managements, and demonstrate that a Deep Q-Network (DQN)-based scheme can achieve the close-to-optimal performance.

As the services and requirements of next-generation wireless networks become increasingly diversified, it is estimated that the current frequency bands of mobile network operators (MNOs) will be unable to cope with the immensity of anticipated demands. Due to spectrum scarcity, there has been a growing trend among stakeholders toward identifying practical solutions to make the most productive use of the exclusively allocated bands on a shared basis through spectrum sharing mechanisms. However, due to the technical complexities of these mechanisms, their design presents challenges, as it requires coordination among multiple entities. To address this challenge, in this paper, we begin with a detailed review of the recent literature on spectrum sharing methods, classifying them on the basis of their operational frequency regime—that is, whether they are implemented to operate in licensed bands (e.g., licensed shared access (LSA), spectrum access system (SAS), and dynamic spectrum sharing (DSS)) or unlicensed bands (e.g., LTE-unlicensed (LTE-U), licensed assisted access (LAA), MulteFire, and new radio-unlicensed (NR-U)). Then, in order to narrow the gap between the standardization and vendor-specific implementations, we provide a detailed review of the potential implementation scenarios and necessary amendments to legacy cellular networks from the perspective of telecommunication vendors and regulatory bodies. Next, we analyze applications of artificial intelligence (AI) and machine learning (ML) techniques for facilitating spectrum sharing mechanisms and leveraging the full potential of autonomous sharing scenarios. Finally, we conclude the paper by presenting open research challenges, which aim to provide insights into prospective research endeavors.

The fifth-generation (5G) system utilizing millimeter wave is available in Japan, but more bandwidth is demanded by the mobile phone operators to accommodate the demand for high data rate. Allocation of the vacant spectrum is impossible due to the past spectrum allocation to other radio systems. For example, 24.25-27.5 GHz has been allocated for the fixed wireless access (FWA) and license exempt small power data communication system in Japan. Although Japanese government is considering to license a part of the band for 5G under the spectrum sharing scheme, the coexistence design is quite challenging due to unavailable operation data of such systems in a form of database for the coexistence study. For this purpose, this paper introduces a maximum-likelihood estimator (MLE), which utilizes the angle-of-arrival (AoA) and received power of the line-of-sight (LOS)/diffraction paths as well as the map database to simultaneously estimate these unknown parameters via the spectrum sensing. It was applied to the measurement data obtained from a sub-urban environment where a commercial fixed wireless access system with similar operational parameters is under operation. Its performance was evaluated for a different number of receiver (Rx) points. The results showed that the parameter estimation accuracy is proportional to the number of receiver points. The distance and orientation errors of 45 m and 5 deg were achieved at 90th percentile with 40 Rx points. Furthermore, the prediction performance of the effective isotropic radiated power (EIRP) was decent, albeit a slight underestimation of the beamwidth.

This paper proposes a radio-protected area (RPA) estimation model that achieves full protection and avoids overprotection by adding an appropriate margin to the extended-Hata (EH) model, for realizing efficient spectrum sharing in the V-High-band. This additional margin, called location gain, is a location-dependent value that accounts for knife-edge losses calculated from topographic information. This study experimentally obtains the location gain by conducting VHF-band-based propagation experiments in urban and suburban areas. Subsequently, we developed an estimation formula for the location gain using topographic data, which include correction terms based on the experimental data. As a result, RPA can be estimated using only the topographic data, excluding the need for propagation experiments. The proposed model resolved the problems of the EH model (incomplete protection) and free-space (FS) model (overprotection). In urban and suburban areas, the proposed model achieved full protection and over 99.7% reduction in the RPA size of the FS model when the threshold of the received power for determining the RPA was set to –80 dBm.

The integration of non-orthogonal multiple access (NOMA) technology and cognitive radio networks (CRNs) promises to enhance the spectrum utilization efficiency of 5G and beyond-5G (B5G) mobile communication systems. In this article, a NOMA-based spectrum-sharing scheme is proposed for dual-hop CRNs in which a primary transmitter separated by a long distance from the primary receiver communicates via NOMA-based CRN. In this scenario, we mathematically formulate a constrained optimization problem to maximize the sum rate of all secondary users (SUs) while maintaining the total transmit power of the system. Inspired by the effectiveness of computational intelligence (CI) tools in solving non-linear optimization problems, this article proposes three CI-based solutions to the given problem aiming to guarantee quality of service (QoS) for all users. In addition, an enhanced version of the classic artificial bee colony (ABC) algorithm, referred to here as the enhanced-artificial-bee-colony (EABC)-based power allocation scheme, is proposed to overcome the limitations of classic ABC. The comparison of different CI approaches illustrates that the minimum power required by the secondary NOMA relay to satisfy the primary rate threshold of 5 bit/s/Hz is 20 mW for EABC, while ABC, PSO and GA achieve the same target at 23 mW, 27 mW and 32 mW, respectively. Thus, EABC reduces power consumption by 13.95% compared to ABC, while 29.78% and 46.15% power-saving is achieved compared to PSO and GA, respectively. [ABSTRACT FROM AUTHOR]

The relentless expansion of communications services and applications in 5G networks and their further projected growth bring the challenge of necessary spectrum scarcity, a challenge which might be overcome using the concept of cognitive radio. Furthermore, an extremely high number of low-power devices are introduced by the concept of the Internet of Things (IoT), which also requires efficient energy usage and practically applicable device powering. Motivated by these facts, in this paper, we analyze a wirelessly powered underlay cognitive system based on a realistic case in which statistical channel state information (CSI) is available. In the system considered, the primary and the cognitive networks share the same spectrum band under the constraint of an interference threshold and a maximal tolerable outage permitted by the primary user. To adopt the system model in realistic IoT application scenarios in which network nodes are mobile, we consider the randomly moving cognitive user receiver. For the analyzed system, we derive the closed-form expressions for the outage probability, the outage capacity, and the ergodic capacity. The obtained analytical results are corroborated by an independent simulation method. [ABSTRACT FROM AUTHOR]

Cognitive radio (CR) is a candidate for opportunistic spectrum implementation in wireless communications, allowing secondary users (SUs) to share the spectrum with primary users (PUs). In this paper, a robust adaptive target power allocation strategy for cognitive nonorthogonal multiple access (NOMA) networks is proposed, which involves the maximum transmission power of each SU and interference power threshold under PU constraints. By introducing the signal-to-interference-plus-noise ratio (SINR) adjustment factor, the strategy enables single-station communication to achieve energy efficiency (EE) or high throughput (HT), thus making the target function more flexible. In the same communication scenario, different cognitive users can choose different communication targets that meet their needs. Different QoS can be selected by the same cognitive user at different times. In the case of imperfect channel state information (CSI), semi-infinite (SI) constraints with bounded uncertainty sets are transformed into an optimization problem under the worst case, which is solved by the dual decomposition method. Simulation results show that this strategy has good adaptive selectivity and robustness. [ABSTRACT FROM AUTHOR]