Spectrum Allocation Research Articles

Demand-Side Relay Spectrum Allocation in Smart Grid Based on Bilateral Auction

Demand-Side Relay Spectrum Allocation in Smart Grid Based on Bilateral Auction

A Novel Spectrum Allocation and Resource Selection in Cognitive Radio using Multi-Antenna Spectrum Sharing Model

A Novel Spectrum Allocation and Resource Selection in Cognitive Radio using Multi-Antenna Spectrum Sharing Model

Novel Application of Quantum Computing for Routing and Spectrum Assignment in Flexi-Grid Optical Networks

Flexi-grid technology has revolutionized optical networking by enabling Elastic Optical Networks (EONs) that offer greater flexibility and dynamism compared to traditional fixed-grid systems. As data traffic continues to grow exponentially, the need for efficient and scalable solutions to the routing and spectrum assignment (RSA) problem in EONs becomes increasingly critical. The RSA problem, being NP-Hard, requires solutions that can simultaneously address both spatial routing and spectrum allocation. This paper proposes a novel quantum-based approach to solving the RSA problem. By formulating the problem as a Quadratic Unconstrained Binary Optimization (QUBO) model, we employ the Quantum Approximate Optimization Algorithm (QAOA) to effectively solve it. Our approach is specifically designed to minimize end-to-end delay while satisfying the continuity and contiguity constraints of frequency slots. Simulations conducted using the Qiskit framework and IBM-QASM simulator validate the effectiveness of our method. We applied the QAOA-based RSA approach to small network topology, where the number of nodes and frequency slots was constrained by the limited qubit count on current quantum simulator. In this small network, the algorithm successfully converged to an optimal solution in less than 30 iterations, with a total runtime of approximately 10.7 s with an accuracy of 78.8%. Additionally, we conducted a comparative analysis between QAOA, integer linear programming, and deep reinforcement learning methods to evaluate the performance of the quantum-based approach relative to classical techniques. This work lays the foundation for future exploration of quantum computing in solving large-scale RSA problems in EONs, with the prospect of achieving quantum advantage as quantum technology continues to advance.

Routing Algorithms for SDM Flexible Optical Networks

This paper considers the online routing, modulation, spectrum, and core allocation problem in elastic optical networks. Three algorithms are proposed to minimize the bandwidth blocking probability while taking into account the impact of interference from established lightpaths. These algorithms are based on a sequence of alternative paths, ordered in terms of increasing lengths, determined by Yen’s algorithm. Each of the algorithms for the selected single or dual lightpath enforces the constraints of spectrum continuity, contiguity of the slots, and non-overlapping of the spectra with the established lightpaths, maximizes the number of bits per symbol, and selects a specific core on each link of the path. Simulation studies performed for two sample networks showed that the crosstalk has a significant impact on the bandwidth blocking probability. The best results were obtained by iterating over all paths and all slots, selecting the lightpath with the lowest average crosstalk per slot, consisting of the most loaded cores.

A Novel Clinching Auction Mechanism for Edge Computing Resource Allocation With Budget Limits

ABSTRACTAn auction mechanism is an effective resource allocation method that can increase the revenue of resource providers in the field of edge computing. Existing auction mechanism designs mostly aim to maximize social welfare when allocating resources, but these schemes lead to low revenue. In contrast, clinching auctions have achieved good results in spectrum allocation and advertising due to their high revenue. Therefore, a clinching auction mechanism is a promising tool for allocating edge computing resources. However, clinching auctions have the drawback that they can only allocate homogeneous finitely divisible goods, meaning that they cannot be directly applied for resource allocation in edge computing. This article presents two new auction mechanisms that improve on the clinching auction. Specifically, based on the principle of increasing global prices and local competition, two mechanisms are designed, one from the perspective of resource providers (MDCAM‐ECS) and the other from the perspective of users (MDCAM‐User), to solve the problem of edge computing resource allocation and pricing with deployment constraints and user budget constraints. The mechanisms proposed in this article have the properties of individual rationality, truthfulness, and computational efficiency. In the experiments, in terms of social welfare and revenue, our algorithms can achieve a 20% improvement over existing algorithms, such as fixed‐price, Vickery–Clarke–Groves (VCG), and monotonic critical‐price mechanisms. Additionally, in most experiments, our algorithm can ensure resource utilization greater than 80%.

Time-Efficient RSA over Large-Scale Multi-Domain EON

The poor timeliness of routing has always been an urgent problem in practical operator networks, especially in situations with large-scale networks and multiple network domains. In this article, a pruning idea of routing integrated with Dijkstra’s shortest path searching is utilized to accelerate the process of routing in large-scale multi-domain elastic optical networks (EONs). The layered-graph approach is adopted in the spectrum allocation stage. To this end, an efficient heuristic algorithm is proposed, called “Branch-and-Bound based Routing and Layered Graph based Spectrum Allocation algorithm (BBR-LGSA)”, which is an integrated RSA algorithm. Notably, the significant reduction in algorithm time complexity is not only reflected in the pruning method used in the routing stage but also in the construction of auxiliary graphs during the spectrum allocation stage utilizing the Branch-and-Bound method. Simulation results show that the proposed BBR-LGSA significantly reduces the average running time by nearly 78% with higher spectrum utilization in large-scale multi-domain EONs, compared with benchmark algorithms. In addition, the impact of key parameters on performance comparisons of different algorithms is evaluated.

A RMSCA algorithm for space division multiplexing elastic optical networks with core switching

Space division multiplexing elastic optical networks (SDM-EONs) have attracted much attention due to the advantages of high capacity, flexibility, and spectrum utilization. Space division multiplexing technology enhances the capacity of a single fiber link by using multi-core fiber (MCF). With the support of MCFs, SDM-EONs will become an important form of future optical transmission networks.In order to reduce the blocking rate of the network, this paper proposes a routing, modulation format, spectrum and core allocation (RMSCA) algorithm based on the maximum number of available cores with core switching. The algorithm selects the spectrum block with the most available cores in the entire path for spectrum allocation by using core switching. We perform simulations on two networks, NSF-Net and UBN24, and compare them with the spectrum allocation algorithm with core switching (FF-ISC-RMSCA), which uses first-time adaptation, and the spectrum allocation algorithm with maximum number of cores available (N-ISC-RMSCA), which does not perform core switching. Simulation results show that the proposed algorithm significantly reduces the bandwidth blocking rate and has high spectrum utilization.

Advancements in Wireless Communication Technology: A Comprehensive Analysis of 4G to 7G Systems

This paper presents a comprehensive analysis of the advancements in wireless communication technology from 4G to 7G systems, focusing on critical aspects such as network architecture, spectrum allocation, data rates, security, and coverage. The evolution of wireless communication technology has been remarkable, with each generation introducing significant improvements to meet the growing demands of users and applications in an increasingly connected world. From the early adoption of 4G technology to the anticipated rise of 7G systems, sweeping changes have reshaped the landscape of wireless communications and changed the way we communicate, work and interact with our surroundings we are surrounded by communication. These changes signal a shift towards more efficient, high-speed and high-capacity systems designed to meet the needs of a variety of industries. Furthermore, the need to strengthen security measures and expand network services highlights the importance of ensuring reliability and security in networks in the face of competition and threats Continued research and innovation are essential and for further developments in this area and to realize and enable new work capabilities and services to benefit people, businesses and businesses This article examines the proposed mobile communication features from 4G to 7G in. Each generation brings unique spectrum allocation, usage and operational challenges. Each generation presents unique spectrum allocation, utilization, and efficiency challenges. Furthermore, this paper investigates network architecture aspects, data rates, and throughput, emphasizing latency and reliability improvements. Advancements in spectral efficiency, MIMO scheme usage, cell size, used frequency bands, and coverage enhancement strategies are considered. Additionally, the integration of energy efficiency with economic and social sustainability is analyzed. Security challenges, including privacy protection, new services, access control models, and security monitoring, are classified for each generation. One of the main objectives of this study is to identify the primary applications, impact areas, and overall impact of 4G to 7G technologies.

Comprehensive Brand Analysis of the Telecommunication Industry in Delhi and Delhi NCR

The telecommunication sector in Delhi and the National Capital Region (NCR) is a crucial component of India's digital landscape, catering to a population exceeding 30 million and driving technological advancements in one of the country's most economically vibrant regions. In this dynamic environment, understanding the intricacies of brand strategies, market dynamics, and consumer behavior is paramount for industry stakeholders seeking to thrive amidst fierce competition and rapid technological evolution. This in-depth brand analysis embarks on a journey to unravel the complexities of the telecommunication sector in Delhi and the NCR. Through a meticulous blend of qualitative and quantitative methodologies, the research endeavors to shed light on the diverse array of factors shaping the regional telecommunications landscape. From the strategies adopted by industry contenders to the regulatory frameworks governing operations, this exploration aims to provide a comprehensive understanding of the opportunities and challenges inherent in the Delhi-NCR telecommunication market. The study delves into the strategies deployed by key players to navigate the competitive landscape. It illuminates how brands position themselves to capture market share, differentiate their offerings, and respond to evolving consumer demands. Additionally, the analysis dissects the regulatory context shaping operations within Delhi and the NCR, including licensing requirements, spectrum allocation policies, and consumer protection regulations. By examining the interplay between industry dynamics and regulatory frameworks, the research provides insights into the governance of the telecommunication sector in the region. As the telecommunication sector in Delhi-NCR continues to evolve, informed decision-making becomes imperative for industry participants, policymakers, and investors alike. By synthesizing insights gleaned from the analysis of market dynamics, consumer preferences, and regulatory frameworks, this examination serves as a strategic resource for navigating the complexities of the regional telecommunication market. Armed with actionable intelligence and a nuanced understanding of the telecommunication landscape, stakeholders can capitalize on emerging opportunities, address challenges, and contribute to the continued growth and innovation of the sector in Delhi and the NCR.

Joint spectrum and power allocation scheme based on value decomposition networks in D2D communication networks

Device-to-device (D2D) communications allow short-range communication devices to multiplex cellular-licensed spectrum to directly establish local connections for ultra-high number of terminal connections and greater system throughput. However, spectrum sharing also brings serious interference to the network. Therefore, a reliable and efficient resource allocation strategy is important to mitigate the interference and improve the system spectral efficiency. In this paper, we investigated spectrum access and power allocation in D2D communications underlay cellular networks based on deep reinforcement learning with the aim of finding a feasible resource allocation strategy to maximize data rate and system fairness. We proposed a value decomposition network-based resource allocation scheme for D2D communication networks. Our proposed scheme avoids frequent information exchanges among D2D users by centralized training, while allowing D2D users to make distributed joint resource allocation decisions. Simulation results show that the proposed scheme has stable convergence and good scalability, and can effectively improve the system capacity.

Enhancing IoT connectivity through spectrum sharing in 5G networks

The integration of the Internet of Things (IoT) into high-performance devices and monitoring systems, spanning domains such as smart building, e-health care, and smart agriculture, necessitates a critical emphasis on advancing mobile communications through efficient spectrum utilization. This research addresses pivotal challenges within agricultural IoT applications, specifically focusing on the substantial decline in spectrum efficiency observed with the increasing escalation of network bandwidth. Acknowledging the absence of comprehensive reviews on 5G resource allocation strategies in the existing literature, our study aims to contribute to a nuanced understanding of their implications for service quality. The identified research gaps underscore an urgent need for heightened efforts to optimize resource allocation in 5G networks. This investigation delves into the intricacies of spectrum sharing and real-time analysis techniques within the 5G and beyond network, with a targeted focus on augmenting agricultural IoT services. Three distinct models, namely (i) Non-Priority Algorithm (NPA), (ii) Reserved Channel Algorithm (RCA) - No Permanent Channels, and (iii) Reserved Channel Algorithm (RCA) - Permanent Channels, were meticulously designed and simulated for Agricultural IoT application scenarios. The methodology encompasses the comprehensive evaluation of performance metrics, including call blocking, termination, and handover, to strategically identify and allocate spectrum resources effectively. The research endeavors to address ongoing challenges pertaining to effective communication, standardization, and data management for diverse 5G IoT devices. In light of these persisting concerns, the study not only seeks to enhance the overall efficiency of 5G IoT networks but also proposes innovative perspectives on intelligent and ingenious spectrum allocation techniques. The anticipated outcomes pledge to optimize the utilization of limited spectrum through novel spectrum-sharing strategies, thereby contributing to the advancement of 5G networks and bolstering agricultural IoT devices and services.

Exploring the Role of Wireless Networking in Uganda’s Socio-Economic Development: Deployment, Impact, and Challenges

In Uganda, wireless networking technologies are considered an indispensable destination for advancement in connectivity, digital inclusion, and socio-economic progress. Wi-Fi connectivity is the technology that uses radio waves to send data without cables, permitting devices to share files wirelessly. Resolving the linkage problem and creating a chance for a digital economy are what matter most in Uganda’s economic development. Because of the high cost of setting up and operating wireless infrastructure in rural areas, we could use cellular networks, Wi-Fi, and satellite internet in tandem to bridge the digital divide. Such an infrastructure also provides an enabling environment for innovations, start-ups, job creation, and education. This paper will examine the deployment of wireless networks in Uganda, specifically focusing on sectors like telecommunications, education, healthcare, and agriculture. It emphasized the importance of Wi-Fi and its various benefits, including bridging the digital divide, promoting digital inclusion, and fostering innovation. Nevertheless, these problems of infrastructure constraints, spectrum allocation problems, interference issues, and socioeconomic hurdles are still there and remain. The content also includes sections on power supply, safety, staffing deficiencies, and regulatory compliance. It is capable of demonstrating the current level of cellular networking as well as its future growth prospects. We will employ pertinent recently published data (2014–2024) from multiple reliable databases. Uganda’s wireless technology future is promising for growth, innovation, and social-economic development. Accelerating 5G deployment, developing rural connectivity, and investing in IoT and smart infrastructure are essential. The government should provide financial incentives, strengthen cybersecurity, raise public awareness, and collaborate on modernised curriculum and certifications. Keywords: wireless networking, Uganda, telecommunications, digital inclusion, innovation, challenges, opportunities.

Efficient spectrum allocation by heterogeneous automated frequency coordination network within 6 GHz band

This work proposes a heterogeneous automated frequency coordination network (HAFCN) to enhance reliability and enable dynamic spectrum allocation for unlicensed Wi-Fi devices (UWDs) within the 6 GHz band. This process relies heavily on spectrum sensing within the HAFCN. However, the widespread implementation of spectrum sensing by multiple UWDs in an automated frequency coordination (AFC) network using conventional fusion schemes poses computational challenges at the AFC provider. In response to this challenge, we present a selective soft-information (SSI) fusion scheme for the proposed HAFCN. First, we present generic mathematical expressions of false-alarm and missed detection probabilities for the HAFCN using an SSI fusion scheme. Second, a generalized AFC SSI fusion problem (GASFP) is formulated to minimize the system’s total error probability. Further, to mitigate the AFC provider’s overhead in solving the GASFP, this work presents the swift-sensing problem, determining the minimum antennas at UWD required to achieve a desired total error probability. Finally, comparative numerical results demonstrate that the HAFCN with the SSI fusion scheme shows a significant performance improvement over conventional fusion schemes in terms of the total error probability.

Enhanced IoT Spectrum Utilization: Integrating Geospatial and Environmental Data for Advanced Mid-Band Spectrum Sharing.

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.

Politeconometrics: The regulatory confluence theory as a mathematical framework for measuring the impact of regulations relative to other factors and externalities in the space industry

This paper introduces “Politeconometrics: The Regulatory Confluence Theory,” a novel mathematical framework designed to measure the impact of regulations in the burgeoning space industry. Traditional approaches to regulatory evaluation are inadequate in capturing the complexities of modern space activities. This paper addresses these limitations by proposing Politeconometrics, which combines econometrics, mathematical modeling, and regulatory theory to provide a comprehensive assessment of space regulations.The framework evaluates the efficacy of space regulations in advancing objectives across various jurisdictions, translating legal structures into quantifiable impacts, and identifying regulatory gaps. By incorporating factors like market entry feasibility, regulatory resistance, and industry dynamics, Politeconometrics offers a refined understanding of regulatory outcomes.Through case studies, including New Zealand’s space industry and the challenges of International Telecommunication Union (ITU) Spectrum Allocation, the paper demonstrates the practical application of Politeconometrics. The methodology involves data collection, assumption management, application of structural formulae, sensitivity analysis, and scenario simulations to ensure robust and actionable insights. The paper also explores the Regulatory Confluence Theory, which assesses the interconnectedness of regulatory frameworks and their cumulative effects on industry dynamics. This theory, combined with Politeconometrics, addresses the limitations of international space law by offering a dynamic, evidence-based approach to regulatory governance.Despite challenges like data availability and interdisciplinary integration, Politeconometrics and the Regulatory Confluence Theory provide significant advantages over existing methods. They offer a nuanced evaluation of the space industry’s regulatory environment, fostering innovation and sustainable development in space activities.

Intelligent Transition Mechanisms in 5G DSA: A Logistic Regression Approach to Reuse Factor Adaptation

This research work use Dynamic Spectrum Allocation (DSA) embedded in 5G technology and logistic regression as Machine learning tool to alter the reuse factor of assigned telecommunication frequency to a more robust and reliable frequency. The switch from a frequency reuse of 3 (FR3) to a frequency reuse of 7 (FR7) was considered. Historical data generated from a Telecommunications Company in Nigeria was utilised. This data consist of User throughput and the number of user equipment to assess traffic load and congestion from user equipment (UE).The ideal was meant to determine the probability at which a switch or transition between reuse factors will occur. With a resolution threshold of 50 users which was set to commence the shift from reuse factor of 3 when the number of users have reached its threshold of 50 to reuse factor of 7 when the number of users exceeds 50 and above. The objective of this combination is to increase spectrum utilisation efficiency and network flexibility, by optimising spectrum resources in densely populated urban areas. This study explains the mechanisms and also demonstrate how logistic regression with DSA will enhance network capacity of wireless communication systems. The logistic regression model proved effective in making intelligent decisions regarding the transition from reuse factor 3 to reuse factor 7 as it was able to accommodate up to 79 users as against the threshold value of 50 users when running under the reuse factor of 3. With a high accuracy of 91.9% and balanced precision-recall values, it showcased its ability to optimize spectral efficiency.

A Survey of Resource Allocation in V2V Communications

In this paper, a comprehensive analysis of resource allocation in vehicle-to-vehicle (V2V) communication systems is presented, which is crucial for enhancing traffic control and improving road safety and driving enjoyment. A variety of resource allocation strategies are classified and examined in this paper, including game theory-based approaches, spectrum allocation, energy allocation, location-based allocation, and machine learning-based procedures. Open research questions and areas that need further study in the field of V2V communication are highlighted for future benefit. This survey helps readers make informed decisions about system design, implementation, and improvement by providing a comprehensive overview of the latest developments in V2V resource allocation. It also indicates areas that need further research and possible directions to enhance the security, flexibility, and scalability of V2V communication networks.

Regulatory challenges and policy gaps for in-space servicing, assembly, and manufacturing technologies

In-Space Servicing, Assembly, and Manufacturing (ISAM) technologies provide incredible opportunities to improve space exploration by giving space assets longer lives, enabling the creation of space objects that are too large to launch from Earth, and allowing space technology to be built using materials sourced from other celestial bodies. However, ISAM technologies also pose legal and regulatory challenges. There are some ongoing challenges with satellite regulations that also apply to ISAM technologies, like spectrum allocation and enforcement, and some difficulties that are unique to ISAM, like mission authorization and continuing supervision. This paper explores how ISAM technologies fit within current space policy framework and the current efforts to regulate ISAM. Additionally, it identifies policy gaps and future work to be done to ensure the safe and sustainable use of ISAM technology.

Deep learning‐based spectrum sharing in next generation multi‐operator cellular networks

SummaryOwing to the exponential increase in wireless network services and bandwidth requirements, sharing the radio spectrum among multiple network operators seems inevitable. In wireless networks, enabling efficient spectrum sharing for resource allocation is quite challenging due to several random factors, especially in multi‐operator spectrum sharing. While spectrum sensing can be useful in spectrum‐sharing networks, the chance of collision exists due to the inherent unreliability of wireless networks, making operators reluctant to use sensing‐based mechanisms for spectrum sharing. To circumvent these issues, we utilize an alternative approach, whereby we propose an efficient spectrum‐sharing mechanism leveraging a spectrum coordinator (SC) in a multi‐operator spectrum‐sharing scenario assisted by deep learning (DL). In our proposed scheme, before the beginning of each timeslot, the base station of each operator transmits the number of required resources based on the number of packets in the base station's queue to SC. In addition, base stations also transmit the list of available channels to SC. After gathering information from all base stations, SC distributes this collected information to all the base stations. Each base station then utilizes the DL‐based spectrum‐sharing algorithm and computes the number of resources it can use based on the number of packets in its queue and the number of packets in the queues of other operators. Furthermore, by leveraging DL, each operator also computes the cost it must pay to other operators for using their resources. We evaluate the performance of the proposed network through extensive simulations. It is shown that the proposed DL‐based spectrum‐sharing mechanism outperforms the conventional spectrum allocation scheme, thus paving the way for more dynamic and efficient multi‐operator spectrum sharing.

A low-complexity ADMM-based secure beamforming for IRS-assisted uplink cognitive radio networks with NOMA

Intelligent reflecting surface (IRS) and non-orthogonal multiple access (NOMA) are both powerful means to improve the spectrum efficiency and can assist the cognitive radio network (CRN) in achieving more effective spectrum allocation. However, spectrum sharing in CRNs and the utilization of the scarce bandwidth in NOMA make it vulnerable to security threats. The secure beamforming design in IRS-assisted CRNs with NOMA is complicated and non-convex. Traditional semidefinite relaxation along with Gaussian randomization (SDR-GR) algorithm can solve the non-convex problem, however a high computational complexity. This paper proposes an alternating direction method of multipliers (ADMM)-based sum secrecy rate maximization (A-SSRM) scheme to achieve secure transmission with low computation complexity in IRS-assisted uplink CRNs with NOMA. Specifically, we derive an appropriate transformation of the original problem based on the ADMM framework and decompose it into multiple sub-problems, which are carefully designed to have closed-form solutions. Furthermore, an eavesdropper zero forcing (EZF)-based sub-optimal scheme is presented. Simulation results reveal that our proposed A-SSRM scheme outperforms other benchmarks in terms of sum secrecy rate and reduces the average running time by 6 times compared with the traditional SDR-GR algorithm. This is attributed to our effective decoupling and decomposing of the original problem, which enables each sub-problem to obtain a simple closed-form solution through derivation.