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Smart mobile device usage is increasing rapidly; hence, cellular operators face the challenge of spectrum resource shortage. To address this issue, researchers have explored several approaches to achieving a highly efficient utilization of wireless communication network resources. One promising solution lies in the fair coexistence of 5G/Wi-Fi 6 in the unlicensed 5 GHz band. This research investigates a duty cycle mechanism to perform fair spectrum sharing between these two wireless technologies, intending to optimize performance metrics such as throughput, capacity, bit error rate (BER), and latency. The results of this study demonstrate a significant improvement in system performance when employing the proposed coexistence method compared to using 5G alone in a single cell. Specifically, a 40% increase in throughput and a 14% improvement in capacity are reported. Moreover, for a single cell using Wi-Fi 6 only, the BER was reduced by 19%, and the latency was less than one millisecond. Additionally, the duty cycle mechanism reported here is used to prioritize call services, with the blocking probability for voice-over internet protocol (VoIP) and video stream calls being improved. Furthermore, the adaptive bandwidth reservation reduced the blocking probability of video calls from 21.8% to 0.9% compared to the fixed method; no VoIP calls were blocked. [ABSTRACT FROM AUTHOR]

Spectral sharing with in-band full-duplex (FD) primary users (PUs) in cognitive radio (CR) systems is a difficult and challenging task. The ability of the secondary user (SU) to access the spectrum could be substantially impeded by the primary network's self-interference. We propose a novel dynamic spectrum sharing (DSS) scheme while satisfying In-band FD technology as the mitigation of strong self-interference in the 5G cellular networks platform. In particular, the in-band FD-PU pair implements the normal proper Gaussian signal, although it is intended for SU downlink transmission with an inappropriate Gaussian signal. First, we derive the closed-form expression and the upper bounds of the default probabilities for SU and PU, respectively. While discussing major concerns in the 5G cellular networks system, the second stage optimization is carried out utilizing the optimal spectrum sharing approach, and a self-interference game methodology based on the proportional enthalpy-based sigmoid solution is applied. This method is primarily employed to reduce computational complexity and overheads. In terms of SINR, Spectral efficiency, and other metrics, the computational studies show that the provided method outperforms existing methods. [ABSTRACT FROM AUTHOR]

In the realm of vehicular networks, ensuring top-notch services for high-speed mobile vehicles remains a formidable challenge. This study squarely addresses the spectrum-sharing predicament within the Internet of Vehicles (IoV) by scrutinizing the spectrum shared by V2I links serving multiple V2V links. The swiftness of vehicular movement ushers in challenges for centralized resource management, necessitating an innovative approach. To surmount this obstacle, the resource allocation is casted as a Multi-agent Deep Reinforcement Learning (RL) and a distributed Multi-Agent Deep Deterministic Policy Algorithm (MADDPG) algorithm is introduced. Each agent autonomously interacts with the vehicular network, assessing its local state and receiving a shared reward. Through collaboration, the Critic network refines power control for each agent. The reward function and training mechanism empower the multi-agent algorithm to attain distributed resource allocation, markedly enhancing both V2I link capacity and V2V transmission rates. This pioneering approach optimizes spectrum utilization, ushering in superior service quality for high-speed mobile vehicles in vehicular networks. [ABSTRACT FROM AUTHOR]

The improvement of energy efficiency and spectral efficiency in networks is achieved by seamlessly integrating energy harvesting, cognitive radio technologies, and Non-Orthogonal Multiple Access (NOMA) techniques. These complementary strategies optimize resource usage and address challenges related to energy consumption. Additionally, the adaptability and versatility of Unmanned Aerial Vehicles (UAVs) offer innovative solutions for enhancing coverage performance, thereby improving connectivity, efficiency, and reliability. We introduce a novel approach called the Deep Reinforcement Learning-Random Walrus (DRL-RW) algorithm, which combines Deep Reinforcement Learning (DRL) with the Random Walrus optimization (RWO) technique for efficient spectrum resource allocation and energy harvesting management in dynamic environments. The DRL-RW algorithm enables UAVs to learn optimal spectrum-sharing strategies and energy harvesting policies, while the RWO enhances the algorithm's adaptability and speed in exploring diverse solutions. Simulation results demonstrate the effectiveness of the DRL-RW algorithm, showing significant improvements in several performance metrics, including reduced energy consumption, enhanced computation time, improved convergence, increased signal-to-noise ratio, higher throughput, extended network lifetime, and increased harvested energy. These findings underscore the efficacy of the DRL-RW approach in addressing challenges associated with energy management in cognitive radio networks. The integration of UAVs, NOMA networks, and this novel algorithm represents a promising direction for developing energy-efficient communication systems. [ABSTRACT FROM AUTHOR]

Higher energy consumption and poor spectrum sharing were identified as the common problems in spectrum sharing at cognitive radio networks. So to overcome these issues in the present research, a novel Horse herd-based Elman spectrum sharing model is developed. The current study found lower energy consumption and a higher spectrum sharing rate in less time. Subsequently, a channel was created for sharing the spectrum over the primary and secondary users. After that, through the proposed model, the sharing system is monitored. Moreover, the spectrum is transmitted until the false alarm is detected. The system stops the users' spectrum sharing function if a false alarm is detected. However, the spectrum is shared with every user in the designed channel. Then a case study is developed to explain the working process of the model. After that, the performance of the proposed design is detailed, and in the comparison analysis, the performance of the proposed model is compared with the existing models. Consequently, the performance of the proposed model is validated based on the throughput, outage probability, bit error rate, energy consumption, data transmission, and spectral efficiency. [ABSTRACT FROM AUTHOR]

The anticipated surge of Internet of Things (IoT) devices is expected to intensify the demand for mid-band spectrum resources, posing challenges to traditional spectrum sharing methods. This paper addresses the limitations of static database-assisted spectrum management frameworks and proposes a novel approach integrating high-resolution geospatial and real-time environmental data. Leveraging these inputs, the proposed framework enhances spectrum allocation accuracy, and mitigates interference more effectively, thereby increasing the access opportunities for IoT deployments. A detailed example scenario illustrates the efficacy of the proposed approach, demonstrating significant gains in spectrum sharing efficiency. It shows gains in the number of new entrants accessing the spectrum, ranging from 77% to 140%. These gains occur when moving to less conservative interference conditions and including more complex geospatial information in the propagation environment. These findings underscore the critical role of advanced spectrum sharing techniques in optimizing spectrum utilization for future IoT networks. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are promising tools in mobile communication due to their flexibility. However, the rapid development of mobile communications further intensifies the challenge of spectrum scarcity, necessitating spectrum sharing with other systems. We suggest a Spectrum Sharing Algorithm for a UAV-Assisted Relay System. The utility function of secondary users (SUs) is defined by their communication rate, rewards from relay primary users (PUs), and spectrum leasing expenses. The utility function of PUs consists of their communication rate and revenue from spectrum leasing. We propose a joint optimization algorithm for the positioning and power allocation of UAVs, maximizing the frequency spectrum utilization rate of users. Spectrum trading between PUs and SUs is modeled as a Stackelberg game, and the problem is solved by using Lagrange multipliers and KKT conditions. To enhance the security of spectrum trading, a reputation-based spectrum sharing blockchain consensus algorithm is designed. We utilize Shamir's secret sharing method to reduce computational complexity. Additionally, we design a smart contract to optimize the functionality of transaction transfers. The findings demonstrate that the proposed algorithm enhances the benefits for both participants in spectrum sharing, while safeguarding the security of spectrum transactions. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of enhancing spectrum sharing in Cognitive Radio Networks (CRNs) while maintaining optimal data rates, energy efficiency, and interference thresholds. We introduce an innovative approach that integrates Non-Orthogonal Multiple Access (NOMA) into CRNs to accommodate a higher number of secondary users effectively. The core of this methodology is the development of a Joint User Cluster Pairing (J-UCP) scheme, which plays a pivotal role in balancing energy constraints against average interference power in the CRN-NOMA system. The J-UCP scheme operates by first arranging secondary users in ascending order of their channel gains relative to the secondary base station. These users are then divided into two groups: a 'strong user group' comprising the upper half and a 'weak user group' forming the lower half. In an innovative step, the weak user group is further split and reordered. Pairing within these groups follows a strategic protocol: a strong and a weak user are paired only if their channel gain difference exceeds a predefined threshold. This process effectively mitigates the formation of low-gain (near-gain) user pairs, which can impede system performance. Our research primarily investigates the energy efficiency of these user pairs in relation to average interference power, considering both peak and average energy efficiencies across Rayleigh and Rician channels. Additionally, we explore the ergodic capacity of user pairs under these parameters. The findings demonstrate the efficacy of the J-UCP scheme in optimizing the performance of CRN-NOMA systems, presenting a significant advancement in the field of wireless communication. [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).

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]

Cognitive Radio Networks (CRNs) have the potential to improve energy efficiency and spectrum usage through the integration of Wireless Energy Harvesting (WEH) with spectrum sharing. According to the findings of this research, a unique Deep Reinforcement Learning (DRL) approach has been developed with the intention of increasing the availability of spectrum and making it possible for secondary users to effectively harvest energy. By dynamically adjusting spectrum sharing decisions in accordance with the energy availability of secondary users, the DRL model enhances network performance. This model places an emphasis on the essential aspect of "harvested energy." The findings of the simulation indicate that the DRL-based technique that was proposed greatly enhances the efficacy and throughput of CRNs, which highlights the potential of this approach for conducting wireless communication over extended periods of time. [ABSTRACT FROM AUTHOR]

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.

To deal with the spectrum demand of new low earth orbit satellite (LEO) communication component and the aggregate interference generated by incumbent terrestrial component in future integrated satellite-terrestrial system, a novel space-borne spectrum-sensing (SS) and spectrum-database assisted spectrum-sharing scheme with interweave model for LEO cognitive system was proposed.Firstly, considering the adverse impact of the sparse distribution of LEO cognitive node (CN) on SS and interference cartography (IC), the concept of cognitive node position was put forward.Secondly, the spectrum-DB was built based on dual-threshold hybrid SS and IC with LEO cognitive system.Finally, a spectrum-sharing scheme with multi-state virtual network (MSVN) was designed, which could make the coexistence between terrestrial component and uplink of LEO component available.Simulation results indicate that the spectrum-database approach based on the proposed dual-threshold hybrid SS and IC can support the spectrum awareness of high reliability with relatively low-density LEO CN, and that the proposed cooperative access scheme based on spectrum-database and interweave model for LEO component can share spectrum with terrestrial one, but not significantly increase the end-to-end delay of LEO component.

[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]

Copyright of Journal on Communication / Tongxin Xuebao is the property of Journal on Communications Editorial Office 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.)

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.

To meet the communications demands of connected vehicles, the wireless devices deployed in vehicles and on roadside infrastructure may need access to more spectrum than is available today. This paper proposes a novel approach that allows connected vehicle devices using V2X technology (e.g., C-V2X or NR-V2X) to share spectrum with Wi-Fi and other unlicensed devices, thereby gaining access to more spectrum. The proposed approach requires no change to Wi-Fi technology so there is no need to replace Wi-Fi devices that have been deployed, and only modest modifications to V2X which reduces cost and complexity. It uses a backward-compatible form of beaconing. Unlike previous work, the resources allocated to V2X are dynamically adjusted for greater efficiency. The approach also does not require involvement from a cellular operator or other centralized controller. One spectrum band where this approach could be especially beneficial is adjacent to the Intelligent Transportation System (ITS) band, where this approach could help meet the needs of both connected vehicles and Wi-Fi 6. Simulation results show that it is possible to protect quality of service for both V2X and Wi-Fi communications in a shared band, while greatly improving spectrum efficiency. This paper also describes steps that standards bodies (IEEE 802.11 and 3GPP) and spectrum regulators could take to advance this spectrum-sharing approach.

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%.

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.

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.

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.

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.

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 advent of the next generation of wireless communication (NextGen) demands substantial investments and collaborative research for the fundamental need of wireless communications. Therefore, cooperative communication plays a vital role in overcoming challenges such as reliability, throughput, and outage trade-offs. We propose a two-transmission phase spectrum-sharing protocol, employing cooperative decode-and-forward relaying to grant spectrum access for both primary and secondary users. The system consists of a primary sender (PS), primary recipient (PR), secondary sender (SS), and secondary recipient (SR), linked as PS-SS, PS-PR, PS-SR, SS-SR, and SS-PR. In the first transmission phase, PS broadcasts the primary signal, received by PR, SS, and SR. SS regenerates the primary signal after successful reception, combining it linearly with the secondary signal, and allocating power fractions $\epsilon $ and ( $1 - \epsilon $ ) to the primary and secondary signals. SS then broadcasts the combined signal in the second transmission phase. Analysis reveals a threshold for optimal performance when SS is within range of PS. Beyond this threshold, the outage performance for the primary user equals or exceeds the case without spectrum sharing. The outage performance for the secondary system is also quantified. The performance of both the primary and secondary users is assessed by deriving a closed-form expression for the outage probability using the Nakagami-m distribution. The effectiveness of the scheme is affirmed through analytical and simulation results.

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing 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 the low earth orbit (LEO) uplink communication scenario, aiming at the different needs of different users for high capacity transmission (HCT) and high reliability transmission (HRT), the spectrum of two users with different needs was shared, a resource allocation model for maximizing the total capacity of HCT users under the constraint of HRT users was established, and the user power and channel resources were optimized.Based on the statistical characteristics of channel fading, the power allocation was carried out to deal with the uncertainty challenge caused by the randomness of channel fading.On the basis of power allocation, the Hungarian algorithm was used to pair users to share the same channel.In order to improve the fairness of HCT users, the maximization of minimum ergodic capacity was included in the optimization goal, and a balance matrix was introduced to solve the problem based on the existing algorithms.Simulation results show that the total capacity of HCT users of the proposed algorithm is higher than that of other algorithms under the same HRT user outage probability, and the improved algorithm also has a significant effect on improving the fairness and robustness of HCT users.

To alleviate the shortage of 5G licensed spectrum resources, using unlicensed spectrum has become an important solution.With the large-scale access of power terminals, NR-U and Wi-Fi spectrum sharing for power services quality guaranteeing has become an important research hotspot.Firstly, an NR-U uplink transmission mechanism was proposed, which ensured the average throughput of Wi-Fi users and realized the data uplink transmission of power service terminals.In addition, a resource optimization algorithm for joint transmission time and subcarrier allocation (TTSA) was proposed to ensure the quality of service (QoS) of various types of power services and maximize the total throughput of terminals.The optimization problem was decoupled, and proximal policy optimization (PPO) was used to allocate subcarriers to terminals.The simulation results show that compared with the existing algorithms, the proposed resource optimization algorithm for TTSA has superior performance in guaranteeing the service quality of power services and maximizing the total terminals throughput.

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]

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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]