Spectrum Management Framework Research Articles

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.

A Multi-Blockchain Scheme for Distributed Spectrum Sharing in CBRS System

To overcome the spectrum scarcity issues, the citizens broadband radio service (CBRS) presents a centralized spectrum management solution. The efficiency of spectrum utilization could be further improved by introducing spectrum trading. Blockchain-based spectrum trading has been considered as a decentralized, flexible, and secure approach. However, current studies rarely investigate the interference to incumbent users caused by spectrum trading between CBRS devices (CBSDs), and the scalability issues in blockchain-based spectrum trading are rarely discussed. To address the problems above, this paper develops the blockchain-based spectrum trading mechanisms for CBRS. Particularly, we propose a queuing mechanism for intra-coexistence group (CxG) trading, in which spectrum trading is leveraged to reduce the aggregated interference to incumbents. A new parameter termed “network feature” is proposed to prioritize spectrum transactions in different queues, which helps to achieve the trade-off between the interference to incumbents and resource requirements in spectrum transactions. Furthermore, we propose a multi-blockchain architecture and a corresponding cross-chain mechanism to improve the speed of inter-CxG spectrum trading. Simulation results show that the aggregated interference to incumbents can be reduced by adopting the proposed method. Meanwhile, when adopting the proposed cross-chain spectrum trading mechanism, the network throughput can be improved by up to 24% compared with the traditional CBRS spectrum management framework.

Blockchain‐Empowered Dynamic Spectrum Management for Space‐Air‐Ground Integrated Network

Space-air-ground integrated network is capable of providing seamless and ubiquitous services to cater for the increasing wireless communication demands of emerging applications. However, how to efficiently manage the heterogeneous resources and protect the privacy of connected devices is a very challenging issue, especially under the highly dynamic network topology and multiple trustless network operators. In this paper, we investigate blockchain-empowered dynamic spectrum management by reaping the advantages of blockchain and software defined network (SDN), where operators are incentive to share their resources in a common resourced pool. We first propose a blockchain enabled spectrum management framework for space-air-ground integrated network, with inter-slice spectrum sharing and intra-slice spectrum allocation. Specifically, the inter-slice spectrum sharing is realized through a consortium blockchain formed by the upper-tier SDN controllers, and then a graph coloring based channel assignment algorithm is proposed to manage the intra-slice spectrum assignment. A bilateral confirmation protocol and a consensus mechanism are also proposed for the consortium blockchain. The simulation results prove that our proposed consensus algorithm takes less time than practical Byzantine fault tolerance algorithm to reach a consensus, and the proposed channel assignment algorithm significantly improves the spectrum utilization and outperforms the baseline algorithm in both simulation and real-world scenarios.

Resource Allocation in Spectrum Access System Using Multi-Objective Optimization Methods.

The paradigm of dynamic shared access aims to provide flexible spectrum usage. Recently, Federal Communications Commission (FCC) has proposed a new dynamic spectrum management framework for the sharing of a 3.5 GHz (3550–3700 MHz) federal band, called a citizen broadband radio service (CBRS) band, which is governed by spectrum access system (SAS). It is the responsibility of SAS to manage the set of CBRS-SAS users. The set of users are classified in three tiers: incumbent access (IA) users, primary access license (PAL) users and the general authorized access (GAA) users. In this article, dynamic channel assignment algorithm for PAL and GAA users is designed with the goal of maximizing the transmission rate and minimizing the total cost of GAA users accessing PAL reserved channels. We proposed a new mathematical model based on multi-objective optimization for the selection of PAL operators and idle PAL reserved channels allocation to GAA users considering the diversity of PAL reserved channels’ attributes and the diversification of GAA users’ business needs. The proposed model is estimated and validated on various performance metrics through extensive simulations and compared with existing algorithms such as Hungarian algorithm, auction algorithm and Gale–Shapley algorithm. The proposed model results indicate that overall transmission rate, net cost and data-rate per unit cost remain the same in comparison to the classical Hungarian method and auction algorithm. However, the improved model solves the resource allocation problem approximately up to four times faster with better load management, which validates the efficiency of our model.

Enabling 5G and IoT

The internet of things (IoT) is the notion of all devices and products being connected to the internet rather than only computers and mobile devices, as has been the case until the recent past. The IoT is already booming, and its adoption will only accelerate in coming years. At the same time, the world continues to go wireless. Wired internet and telephone connections are becoming rarer with each passing year, and consumers expect and demand wireless communications more and more. One critical enabling technology for the IoT is wireless communications: IP-cameras, printers, kitchen appliances, and more deliver greater flexibility in their design when they do not need a wired connection to access the internet. In this paper, a flexible spectrum management framework to enable greater innovation, investment, and flexibility in IoT products, as well as the necessary architecture that will enable a wider deployment of IoT is proposed. An illustration and analogy from the success of the unlicensed bands used for Wi-Fi technology, and the accompanying supporting organizations that enable Wi-Fi's success are drawn.

Realizing Intelligent Spectrum Management for Integrated Satellite and Terrestrial Networks

Nowadays both satellite and terrestrial networks are expanding rapidly to meet the ever-increasing demands for higher throughput, lower latency, and wider coverage. However, spectrum scarcity places obstacles in the sustainable development. To accommodate the expanding network within a limited spectrum, spectrum sharing is deemed as a promising candidate. Particularly, cognitive radio (CR) has been proposed in the literature to allow satellite and terrestrial networks to share their spectrum dynamically. However, the existing CR-based schemes are found to be impractical and inefficient because they neglect the difficulty in obtaining the accurate and timely environment perception in satellite communications and only focus on link-level coexistence with limited interoperability. In this paper, we propose an intelligent spectrum management framework based on software defined network (SDN) and artificial intelligence (AI). Specifically, SDN transforms the heterogenous satellite and terrestrial networks into an integrated satellite and terrestrial network (ISTN) with reconfigurability and interoperability. AI is further used to make predictive environment perception and to configure the network for optimal resource allocation. Briefly, the proposed framework provides a new paradigm to integrate and exploit the spectrum of satellite and terrestrial networks.

A Deep Reinforcement Learning Framework for Spectrum Management in Dynamic Spectrum Access

Dynamic spectrum access (DSA) has the great potential to alleviate spectrum shortage and promote network capacity. However, two fundamental technical issues have to be addressed, namely, interference coordination between DSA users and interference suppression for primary users (PUs). These two issues are very challenging since generally there is no powerful infrastructures in DSA networks to support centralized control. As a result, DSA users have to perform spectrum management individually, including spectrum access and power allocation, without accurate channel state information and centralized control. In this article, a novel spectrum management framework is proposed, in which Q-learning, a type of reinforcement learning, is utilized to enable DSA users to carry out effective spectrum management individually and intelligently. For more efficient process, neural networks (NNs) are employed to implement Q-learning processes, so-called deep Q-network (DQN). Furthermore, we also investigate the optimal way to construct DQN considering both the performance of wireless communications and the difficulty of NN training. Finally, extensive simulation studies are conducted to demonstrate the effectiveness of the proposed spectrum management framework.

Security and channel noise management in cognitive radio networks

The Spectrum Channel Noise is a pseudorandom or random computational process in a manner that allows the security competence of the available spectrum management frameworks for cognitive radio networks. To mitigate the cognitive spectrum and its security issues, we recommend a central primary spectrum organization structure that is dynamically balanced, and that applies the Primary Key Cryptosystem (PKC). The node identity applicable in this PKC is utilized as the framework to produce the primary user identification structure. In that case, the authentication is rooted in the secondary user for necessary verification. The dynamic, secure key is provided based on the security aspect of the initial framework. Apart from that, the PKC-based McEliece secondary key provides an error correction capacity, which can remove the noise during secondary user allocation and enhance the effectiveness of the spectrum management, which collaborate effectively over the noise channel management.

Will 5G lead to more spectrum sharing? Discussing recent developments of the LSA and the CBRS spectrum sharing frameworks

This paper discusses two recent spectrum management frameworks, the Licensed Shared Access (LSA) developed in Europe and the Citizens Broadband Radio Services (CBRS) developed in the United States (US), which build their management approach on spectrum sharing. The importance of these two frameworks, besides their leading normative roles, is that recent debates have shaped them as cases to consider in the adoption of the upcoming fifth generation (5G) of mobile communications technology, in particular in the C-band. A discussion on these two frameworks is organised by following the four-step decision-making guide for spectrum management developed by Pogorel (2007), which requires spectrum authorities to make decisions in four areas of spectrum management: frequency harmonization, technology standardization, type of usage rights and assignment procedures.Notwithstanding the similarities with respect to the four areas of spectrum management considered, the two frameworks differ on their implementation schedules. CBRS leads the way, with a handful of providers receiving government approval to manage spectrum access controllers, and as of mid 2020, scheduled to have allocated spectrum licenses on half of its available spectrum. On the contrary, European countries have shown scarce interest towards implementing the LSA, despite the extensive work carried out by regulatory and standardization bodies.This may suggest that there are external contextual factors which influence the successful implementation of spectrum sharing frameworks. An interesting aspect which deserves further investigation is the institutional context in which decisions related to radio spectrum management are taken. Unlike the US authorities, European institutions do not possess coercive enforcement powers with respect to spectrum sharing. This key difference may contribute to explaining the different speed at which LSA and CBRS are implemented.

Spectrum Knowledge and Real-Time Observing Enabled Smart Spectrum Management

Spectrum is the indispensable resource for 5G wireless systems and beyond. The dynamic spectrum management (DSM) framework with spectrum sharing among different kinds of users is essential to satisfy the sustainable rapid increase of the bandwidth requirement. Based on the analysis of the DSM as well as the latent applications of the machine learning and artificial intelligence (AI) in the spectrum access, this paper will address the problem of incorporating intelligence into the spectrum management. Firstly, the spectrum opportunity (SOP) related information is layered into the spectrum data, the spectrum information and the spectrum knowledge based on the processing and abstractive level. The spectrum knowledge is formally defined as the extendible and scalable information to reason and predict the SOP usability as well as the outcome of the SOP occupancy. Then the smart spectrum model (SSM) is proposed with the spectrum knowledge as the key enabler and the coupled SOP exploration and exploitation as the core feature. Under the SSM framework, the spectrum knowledge and real-time observing (SKRO) enabled SOP exploration scheme is designed, which can make use of both the historical information and the real-time sensing information for smart SOP exploration. Extensive simulations are provided which demonstrate that the SKRO enabled SSM can achieve much better SOP utilization while satisfying the required legacy assurance. Specifically, in the low signal to noise environment, at least 16.55% gains on the SOP utilization ratio can be obtained compared with the DSM.

Spectrum Management for Multi-Access Edge Computing in Autonomous Vehicular Networks

In this paper, a dynamic spectrum management framework is proposed to improve spectrum resource utilization in a multi-access edge computing (MEC) in autonomous vehicular network (AVNET). To support the increasing communication data traffic and guarantee quality-of-service (QoS), spectrum slicing, spectrum allocating, and transmit power controlling are jointly considered. Accordingly, three non-convex network utility maximization problems are formulated to slice spectrum among base stations (BSs), allocate spectrum among autonomous vehicles (AVs) associated with a BS, and control transmit powers of BSs, respectively. Through linear programming relaxation and first-order Taylor series approximation, these problems are transformed into tractable forms and then are jointly solved through an alternate concave search (ACS) algorithm. As a result, the optimal spectrum slicing ratios among BSs, optimal BS-vehicle association patterns, optimal fractions of spectrum resources allocated to AVs, and optimal transmit powers of BSs are obtained. Based on our simulation, a high aggregate network utility is achieved by the proposed spectrum management scheme compared with two existing schemes.

Incentivizing spectrum sharing via subsidy regulations for future wireless networks

Traditional regulatory methods for spectrum licensing have been recently identified as one of the causes for under-utilization of the valuable radio spectrum. Governmental regulatory agencies such as the Federal Communications Commission (FCC) are seeking ways to remove stringent regulatory barriers and facilitate broader access to the spectrum resources. The goal of such new FCC-backed efforts is to allow for an improved and ubiquitous sharing of the precious radio spectrum between commercial service providers. In this paper, an interdisciplinary framework for spectrum management is proposed in which the government, using its regulatory power, can motivate spectrum sharing among the service providers in order to gain a net social benefit. In this framework, a noncooperative game is used to analyze how to foster more sharing of the radio spectrum via the use of regulatory power. The providers are incentivized with subsidized spectrum bands from the regulators. In return, the providers will be asked to provide coverage to the users that are not subscribed to them so as to maintain their subsidy incentives from the government. In a simplification of the model, the providers’ perfect equilibrium strategies are found numerically, and the existence of perfect equilibrium for the government’s strategy is discussed. Our numerical results using real base station locations from two cellular providers show that through subsidization, the government can provide small service providers a fair chance to compete with the large providers, thereby avoiding monopolization in the market.

Spectrum consumption model builder and analysis tool: a software tool to enhance spectrum sharing

Regulatory changes to spectrum management frameworks in the U.S. and worldwide are promoting the use of dynamic spectrum access (DSA) and spectrum sharing mechanisms. Wireless service environments that use these mechanisms will rely heavily on radio interference control and management techniques to coordinate the boundaries of spectrum use for devices owned/managed by different entities. To communicate the characteristics and limits of spectrum use of an RF transmitter, receiver, system or collection of systems, a common data structure to represent this information must be in place. Spectrum consumption models (SCMs) attempt to capture this information and support a set of computations that allow for the determination of the compatibility of the use of spectrum between devices and/or systems that communicate the characteristics of their use of spectrum with these models. SCMs are currently being standardized within the IEEE DySPAN-SC 1900.5.2 workgroup. This paper presents our results and ongoing efforts in developing an open source tool for the construction and analysis of SCMs as a means to promote their use and showcase their potential for enhancing spectrum sharing and DSA interactions.

Multicriteria Decision Analysis of Spectrum Management Frameworks for Futuristic Wireless Networks: The Context of Developing Countries

National regulatory authorities (NRAs) in developing countries need an accelerated means to formulate technical regulations for the telecommunications sector. This will enable countries to gain maximum benefits from the rapid advances in technology. The existing regulation-making processes are time-consuming and do not cope with the emergence of global technological changes. This article presents an efficient and quicker approach to formulate regulatory framework to govern the wireless technologies that are based on the Dynamic Spectrum Access (DSA) technique. The approach utilises Multicriteria Decision Analysis (MCDA) tools. The article uses a case study to evaluate dominant Dynamic Spectrum Management (DSM) frameworks. Finally, a sustainable DSM framework that has potential to address the digital divide challenges in the context of developing countries is proposed.

Scalable and Reliable IoT Enabled by Dynamic Spectrum Management for M2M in LTE-A

To underpin the predicted growth of the Internet of Things (IoT), a highly scalable, reliable and available connectivity technology will be required. Whilst numerous technologies are available today, the industry trend suggests that cellular systems will play a central role in ensuring IoT connectivity globally. With spectrum generally a bottleneck for 3GPP technologies, TV white space (TVWS) approaches are a very promising means to handle the billions of connected devices in a highly flexible, reliable and scalable way. To this end, we propose a cognitive radio enabled TD-LET test-bed to realize the dynamic spectrum management over TVWS. In order to reduce the data acquisition and improve the detection performance, we propose a hybrid framework for the dynamic spectrum management of machine-to-machine networks. In the proposed framework, compressed sensing is implemented with the aim to reduce the sampling rates for wideband spectrum sensing. A noniterative reweighed compressive spectrum sensing algorithm is proposed with the weights being constructed by data from geolocation databases. Finally, the proposed hybrid framework is tested by means of simulated as well as real-world data.

Wireless access policy for electronic communications services (WAPECS): Collision between theory and practice

One of the first practical implementations of flexible radio spectrum property rights was initiated in Europe in 2005 under the name ‘Wireless Access Policy for Electronic Communication Services (WAPECS)’. WAPECS aimed to introduce more flexibility into the European spectrum management framework by allowing technology and service neutrality. Internationally, the European Conference of Postal and Telecommunications Administrations (CEPT) have been keen to introduce more flexibility into the international service allocation framework.One of the main findings of this paper is the identification of different negative perceptions and disagreement about WAPECS among industry and regulatory stakeholders. Moreover, it is shown that concerns over WAPECS include that the flexibility associated with it may contradict with spectrum use efficiency and that the responsibility of resolving interference issues moves from the regulator to operators.The assessment of the influence of WAPECS on the market shows that while the concept was aimed mainly towards removing the spectrum usage restriction for industry, they have not appreciated this mainly due to the high cost and uncertainty associated with flexibility. On the other hand, several positive impacts of WAPECS are revealed including removing technology designation from CEPT decisions. The paper shows also that the international Radio Regulations (RR) can, to a certain extent, accommodate WAPECS.

Spectrum access system for the citizen broadband radio service

As a part of the global effort to address the overwhelming demand for wireless broadband capacity, the wireless communities in the United States have undertaken innovative initiatives such as tiered-access to shared spectrum. The Federal Communications Commission has proposed a dynamic spectrum management framework for a Citizen Broadband Radio Service (CBRS) governed by a spectrum access system (SAS). The implementation of a SAS capable of dynamic frequency assignment and interference management is critical for the success of the proposed framework. In this paper we present the efforts toward a spectrum sharing system in the U.S. context by summarizing different interest groups' standpoint on the FCC proposed framework. We also present an example SAS architecture that accommodates the tiered access to shared spectrum and example approaches to achieve important SAS capabilities.

Spectrum handoff model based on preemptive queuing theory in cognitive radio networks

Cognitive radio can significantly improve spectrum efficiency by temporarily sharing under-utilized licensed frequency with primary users. Its spectrum management framework consists of four parts: spectrum sensing, spectrum decision, spectrum sharing and spectrum handoff. The last part is what we focus on in this paper. Spectrum handoff, which aims at guaranteeing requirement for service of secondary users and shortening time delay produced by interruption from primary users, is an important functionality of cognitive radio networks. For solving the problem of optimizing the extended data delivery time, a spectrum handoff model is proposed based on the preemptive resume priority M/G/m queuing theory. In order to minimize the extended data delivery time, the queuing method with mixed queuing and parallel service is adopted. In this model, each channel has its own high-priority queue and there is only one low-priority queue for all secondary users. The primary and secondary users respectively enter into the high-priority and low-priority queue to establish corresponding primary connections and secondary connections and execute corresponding data transmission. On the above basis, secondary users’ channel usage behaviors are thoroughly analyzed in the cases of multiple secondary users, multiple licensed channels and multiple spectrum handoffs. In this process, when multiple interruptions occur, the secondary user will stay on the current channel and suspend data transmission until primary users finish their data transmission, otherwise the secondary user will switch from the current channel to the predetermined target channel to resume his unfinished data transmission. The target channel is sequentially obtained from the target channel sequence, which is determined by channel parameter estimation algorithm. Based on the analysis of channel usage behaviors for secondary users, the total time delay caused by spectrum handoffs within the whole data transmission process is derived first. The total time delay can be deduced from two scenarios. One is that the target channel is the current channel. For this reason, the total time delay equals transmission time of primary users in high-priority queue. Obviously, the other is that the target channel is not the current channel. Thus, the total time delay equals the sum of transmission times of primary users in high-priority and secondary users ahead in low-priority. In addition, appearance of new primary users should also be considered in the data transmission process. Then, expressions of the extended data delivery time in two different cases (i. e. always-staying strategy and always-changing strategy) are respectively derived. Furthermore, the adaptive spectrum handoff strategy is finally discussed, which is to choose the optimal scheme from always-staying and always-changing strategy when a spectrum handoff happens. Simulation results verify that this model can not only describe handoff behaviors of secondary users more perfectly, but also can make the transmission time delay smaller and make the extended data delivery time shorter than the existing spectrum handoff model. Especially, with the increase of service intensity of primary users, the advantages of the proposed spectrum handoff model are more outstanding. In addition, the allowable secondary user service intensity is improved and the receptive number of secondary user is increased in cognitive radio networks. All in all, the proposed spectrum handoff model improves the performance of spectrum handoff, increases the capacity of cognitive radio networks and optimally realizes spectrum sharing between secondary users and primary users.

Dynamic Spectrum Management in Multi-Radio Access Technology (RAT) Cellular Systems

Fixed Spectrum Allocation (FSA) results in suboptimal spectrum utilization, unbalanced system loading and the inability to adapt to system traffic variations when employed in multi-Radio Access Technology (RAT) systems. This paper proposes the use of flexible spectrum management techniques in multi-RAT systems. A Dynamic Spectrum Management (DSM) framework that addresses the shortcomings of FSA, while ensuring the reliable operation of all co-deployed RATs, is presented. Simulations for a system co-deploying HSPA and LTE show that the proposed framework outperforms FSA schemes and is capable of adapting to system traffic variations.

RAP Framework for Spectrum Management in Cognitive Radio

The Spectrum Management schemes currently in use overlook the need for interference measurements at the receiver end. The negligence culminates in an underwhelming performance of the Cognitive Radio Network (CRN) due to the presence of hidden/exposed primary senders. Our aim was to implement the RAP framework for spectrum management in a cognitive Radio. The scheme chosen deems it a necessity to take into consideration the inaccuracy of spectrum sensing in order to achieve the desired optimal performance. Unlike the widely used schemes which solely depend on local interference measurements, the proposed probabilistic framework aims at enhancing the CRN good put by incorporating interference measurements performed at the receiver into the protocol. Moreover, the spectrum management scheme, discussed in the study, makes most of the least exploitable spectral opportunities by relying on low power rate communication robust to interference. The issued spectrum access policy ensures fair distribution of spectral opportunity to its multiple secondary users. It, in contrast to the existing greedy spectrum access policies, prevents any user from monopolizing the available spectral opportunity as it is regulated and operated in a strictly probabilistic framework. It also diligently makes useful, provisions in the policy to ensure that every spectral opportunity, even while being exploited, appear available at all times to see to it that no Secondary User (SU) misidentifies the spectral opportunity as unavailable.