Spectrum Management Techniques Research Articles

Experimental Analysis Using USRP for Novel Wavelet-Based Spectrum Sensing for 2.2 GHZ Band Communication Using LabVIEW

Spectrum sensing allows cognitive radio systems to detect relevant signals even in the presence of interference for reliable communication. Most of the existing spectrum sensing techniques use a particular signal-to-noise ratio model with assumptions and provide certain detection performance. Dynamic spectrum management techniques enabled the efficient allocation of channels to increasing number of users. In cognitive radio system, the dynamic spectrum management is efficient for sensing the channel occupancy and mobilizing the secondary user towards the unused primary user channel. For spectrum sensing, wavelet-based spectrum sensing method is analyzed and effectively made spectrum decision. The performance analysis is made for various SNR values with enhanced false alarm and throughput in spectrum management cognitive radio system in 2.2 GHZ band communication.

Hybrid spectrum management using integrated fuzzy and femtocells in cognitive domain

In Today’s pandemic situation, ‘Spectrum accessing and smart usage’ is the sacred Mantra uttered by every individual citizen in the world. Work from home for techies, online classes for students, games for kids, webinar for teaching fraternity etc., are going almost on indoor coverage without any limit in pace because of the smart spectrum coverage by the network service providers. This paper provides an add-on facility to the existing wireless infrastructure to provide a better user experience in this highly regrettable routine. In this paper, a cognitive domain unused spectrum holes are efficiently handled by (i) adaptive spectrum management technique; (ii) Fuzzy Inference System based spectrum administration and (iii) Hybrid Cognitive Femtocell approaches based on the user demand and their applications. The proposed integrated cognitive femtocell and Fuzzy-based approach reduces the indoor coverage problems and enhances the throughput of the macrocell users by allowing adaptive spectrum management based on the demand, thereby eliminating spectrum underlay and overlay problems during critical conditions. In cognitive femtocell networks, the access points are prepared and installed with Cognitive Radio which can determine spectrum dynamically by macrocells and nearby Femto Access Points. It adjusts its radiating parameters to evade the macrocells’ interferences and the neighbouring femtocells, thereby maximising the spectrum band’s overall utility.

A Survey on the AI and Spectrum Management for Cache-Enabled Internet of Things in Smart Cities

In this paper, we provide a comprehensive survey for the artificial intelligence and spectrum management, which are used for cache-enabled Internet of Things (IoT) in smart cities. In smart cities, there emerge a lot of new applications such as data collection and communication, environment monitoring, and real-time processing, which cannot be supported by the conventional wireless transmission techniques. Hence, some new wireless transmission techniques should be developed to support the emerging applications in smart cities. In this survey, we focus on the artificial intelligence, spectrum management, and caching techniques, where the interference arises due to the limited spectrum resources. In particular, we first review the current research status of these new techniques and, then, give some challenges on the system design. We further provide several feasible solutions on these challenges, in order to implement the IoT networks in smart cities. Finally, we conclude the work in the part of conclusions and give some discussions on the future works.

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

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

4차 산업혁명을 위한 전파관리

Owing to the advent of the 4th industrial revolution, radio resources are facilitating the economic and social growth of technologies such as 5G mobile communication, self-driving cars, drones, and internet of things. However, although the demand for radio spectrum is growing exponentially, the supply of the available spectrum is limited. Therefore, governments must allocate new spectrums to new radio industries to maintain international competitiveness and re-deploy the spectrum of old declining services. To achieve these goals, major countries should aim to maximize the value of spectrums through statutory regulator and transparent decision-making processes. In this study, we analyze the radio spectrum management techniques of major countries and determine implications based on this analysis.

Spectrum Sharing and Dynamic Spectrum Management Techniques in 5G and Beyond Networks: A Survey

Advancing technologies and bandwidth-hungry applications have increased mobile data traffic in the radio spectrum. Utilizing spectrum is one of the indispensable performance metric seconded by techniques to increase the bandwidth. Spectrum efficient techniques have always been a part of all the generations of wireless communication. It has considered being of utmost criticality with 5G networks. The spectrum sharing and management demand contributions from technical research groups as well as regulatory bodies. Recently, many technologies proved their potentials to invoke efficient spectrum utilization. Different approaches have been considered including cognitive radio, machine learning for dynamic spectrum management, spectrum sharing, spectrum harmonization, spectrum identification strategies, etc. Efficient technology is very important in order to have high spectral as well as energy efficiency. It is also important from a cost-efficiency perspective. Therefore, this paper presents an overview of the various spectrum sharing and management aspects. This comparative study is motivated to provide a clear picture to design a spectrum efficient system for 5G and beyond the network.

Multi-Armed Bandits for Spectrum Allocation in Multi-Agent Channel Bonding WLANs

While dynamic channel bonding (DCB) is proven to boost the capacity of wireless local area networks (WLANs) by adapting the bandwidth on a per-frame basis, its performance is tied to the primary and secondary channel selection. Unfortunately, in uncoordinated high-density deployments where multiple basic service sets (BSSs) may potentially overlap, hand-crafted spectrum management techniques perform poorly given the complex hidden/exposed nodes interactions. To cope with such challenging Wi-Fi environments, in this paper, we first identify machine learning (ML) approaches applicable to the problem at hand and justify why model-free RL suits it the most. We then design a complete RL framework and call into question whether the use of complex RL algorithms helps the quest for rapid learning in realistic scenarios. Through extensive simulations, we derive that stateless RL in the form of lightweight multi-armed-bandits (MABs) is an efficient solution for rapid adaptation avoiding the definition of broad and/or meaningless states. In contrast to most current trends, we envision lightweight MABs as an appropriate alternative to the cumbersome and slowly convergent methods such as Q-learning, and especially, deep reinforcement learning.

Blockchain and extreme learning machine based spectrum management in cognitive radio networks

AbstractIn recent times, spectrum sensing and spectrum management become a crucial design issue in cognitive radio networks (CRN). To improve the spectrum utilization in CRN, the secondary users (SUs) will try to utilize the spectrum resource when it is unoccupied by the authorized primary users (PUs). At the same time, blockchain principle has been introduced to efficiently identify the legitimate SUs and allocate the spectrum resource as per the demand specified by the SUs. In this view, this article presents a new machine learning (ML) with blockchain‐based spectrum management technique in CRN. The proposed model undergoes three processes, namely spectrum sensing, blockchain‐based spectrum access, and malicious user (MU) identification. Initially, ML‐based extreme learning machine (ELM) technique is applied for spectrum sensing. Then, the presented blockchain approach provides secured spectrum allocation for SUs. Finally, the MUs are identified and to be blocked from accessing the available spectrum resource. An extensive simulation analysis is carried out to ensure the goodness of the proposed model. The obtained results indicated that the proposed model has offered better performance compared with other methods. The experimental outcome stated that under the presence of −20 dB SNR, the proposed method has attained a maximum detection rate of 0.68, whereas the KNN and OR rule methods have demonstrated a minimum detection rate of 0.58 and 0.5, respectively.

A comprehensive survey on machine learning approaches for dynamic spectrum access in cognitive radio networks

ABSTRACT Due to exponential growth in demand for radio spectrum for wireless communication networking, the radio spectrum has become over-crowded. The fixed spectrum allocation policy of the radio spectrum leads to inefficient utilisation of the available spectrum, which diverted the attention of researchers towards different intelligent techniques to access the spectrum dynamically and efficiently. The concept of Cognitive Radio (CR) has been considered as a promising technology to solve the problem of spectrum scarcity through the utilisation of various unutilised spectrum bands. In a future network deployment, multiple radio access networks may coexist having different characteristics. Hence, it becomes a challenge for CR networks to select the optimal network out of available networks. For efficient realisation, CRs requires intelligent spectrum management techniques for Dynamic Spectrum Management (DSM). Till now, there does not exist a literature survey that addresses the spectrum management with machine learning techniques in an intelligent manner. Hence, this paper presents the detailed classification and comprehensive survey of various machine learning techniques for intelligent spectrum management with their paradigms of optimisation for cognitive radio networks. The paper also provides new directions and open issues for the research community to work further in CR networks.

A hybrid spectrum sensing approach to select suitable spectrum band for cognitive users

In recent years, the usage of wireless devices and wireless service has been increased exponentially and it results in spectrum scarcity. The policies of regulatory authority employ static spectrum allocation methods and assign new spectrum band for offering new kind of services to the users. These approaches lead to poor utilization of available spectrum bands. The cognitive radio (CR) provides better solution to these problems and it mainly focuses on efficient utilization of available spectrum bands. CR network (CRN) has to adopt spectrum management techniques to assign the unused spectrum band to the CR users by following a sequence of actions such as spectrum sensing, decision and management. Spectrum sensing is a vital process in spectrum allocation. In the traditional approaches, sensing accuracy is brought down by the probabilities of misdetection and false alarm rate and hence, sensing accuracy becomes low. As a result, the Cognitive Users (CUs) face the challenge of prolonged time to complete perfect cognitive radio communication. To overcome this issue, a cooperative spectrum sensing technique with a characteristic based cluster classifier has been proposed. This classifier learns the states and their transitions in the radio frequency environment, as well as the primary user activities at regular time intervals to support the spectrum decision technique. The novelty of the work is to propose a hybrid approach which combines clustering with expected maximization (EM) algorithm and reinforcement learning (RL) techniques. This hybrid approach enhances the system performance with accurate sensing results and by identifying the optimum spectrum band through hierarchical access model using interweaving approach, energy consumption is minimized. The simulation results show that by decreasing the probabilities of error ratio, false alarm rate and missed detection, the accuracy of sensing results is improved. Further, this hybrid approach outperforms the traditional approaches in terms of probability of detection even in low SNR values.

Guest editorial: Artificial intelligence (AI)-driven spectrum management

Recent advances in communication and networking technologies are leading to a plethora of novel wireless services that range from unmanned aerial vehicle (UAV) communication to smart cognitive networks and massive Internet of Things (IoT) systems. Enabling these emerging applications over the fifth generation (5G) of wireless cellular systems requires meeting numerous challenges pertaining to spectrum sharing and management. In fact, most 5G applications will be highly reliant on intelligent spectrum management techniques, which should adapt to dynamic network environments while also guaranteeing high reliability and high quality-of-experience (QoE). In this context, the use of artificial intelligence (AI) techniques that include deep learning, convolutional neural networks, and reinforcement learning, among many others, is expected to play a very important role in paving the way towards truly AI-driven spectrum management, thus enabling tomorrow's smart city services. Therefore, it has become imperative to investigate and apply AI techniques to solve emerging spectrum management problems in various wireless networks. This includes leveraging AI to address a wide range of wireless networking challenges ranging from network management to dynamic spectrum sharing and resource management.

Machine Learning Empowered Spectrum Sharing in Intelligent Unmanned Swarm Communication Systems: Challenges, Requirements and Solutions

The unmanned swarm system (USS) has been seen as a promising technology, and will play an extremely important role in both the military and civilian fields such as military strikes, disaster relief and transportation business. As the “nerve center” of USS, the unmanned swarm communication system (USCS) provides the necessary information transmission medium so as to ensure the system stability and mission implementation. However, challenges caused by multiple tasks, distributed collaboration, high dynamics, ultra-dense and jamming threat make it hard for USCS to manage limited spectrum resources. To tackle with such problems, the machine learning (ML) empowered intelligent spectrum management technique is introduced in this paper. First, based on the challenges of the spectrum resource management in USCS, the requirement of spectrum sharing is analyzed from the perspective of spectrum collaboration and spectrum confrontation. We found that suitable multi-agent collaborative decision making is promising to realize effective spectrum sharing in both two perspectives. Therefore, a multi-agent learning framework is proposed which contains mobile-computing-assisted and distributed structures. Based on the framework, we provide case studies. Finally, future research directions are discussed.

Reducing Near–Far Problem in a VDSL Network with Modified IWF Algorithm by Using Adaptive Spectral Mask Yielding Improved Data Rate of Near End Users

This paper presents a new technique for reducing the ‘Near–Far’ problem with modified iterative water filling (IWF) algorithm by using the adaptive power spectral mask yielding improved data rate of near end users in a VDSL network. It is shown through graphical results that the proposed technique for spectrum management achieves a significant gain in data rate for ten users VDSL network compared to that of traditional IWF algorithm. However, data rate of the proposed algorithm approaches near to highly complex optimal spectrum balancing (OSB) algorithm. The complexity of the proposed algorithm is same as that of IWF algorithm, which is much lower than that of highly complex OSB algorithm.

Spectral Efficiency Improvement based on Small-cell Deployments Toward 5G Communications Technologies

This paper emphasizes a method for improving spectral efficiency based on small cells (SCs) deployed in an existing macrocell (MC), which ultimately results in overall enhancement of the network capacity and coverage area. In addition, it allows an unlicensed secondary network to deliver messages through opportunistic access to the licensed network spectrum. By deploying SCs, users either inside or outside high residential buildings and/or commercial buildings can acquire signals during communications without interruption. The connectivity in the alliance of the two-tier networks becomes abundant, resulting in spectral efficiency improvement. In this paper, we first recall performance based on the Vandermonde-subspace frequency division multiplexing (VFDM) spectrum-sharing technique for spectrum management under both conventional (single-user) and enhanced scenarios; subsequently, the proposed method is introduced for comparison purposes. The performance of both methods is manifested in simulation results for the achievable rate, which demonstrate that the proposed approach outperforms enhanced VFDM and is a potential candidate in next-generation cellular technology. The motivation for the preference for SC deployment in the proposed approach is due to worthy characteristics (low power, low cost, very light weight, and self-organization). Unlike MCs, which need a considerable budget, they are installed anywhere without a lot of expense, and hence, save the network infrastructure.

$\alpha$ -Fair Dynamic Spectrum Management for QRD-Based Precoding With User Encoding Ordering in Downstream G.Fast Transmission

In next-generation digital subscriber line networks such as G.fast, employing discrete multi-tone transmission in high frequencies up to 212 MHz, the crosstalk among lines reaches very high levels. To precompensate the crosstalk in downstream transmission, QRD-based precoding has been proposed as a near-optimal dynamic spectrum management (DSM) technique. However, the performance of QRD-based precoding is greatly affected by the user encoding ordering (UEO). Since current multi-tone UEO methods are rather heuristic in the way they approach fairness, we develop, in this paper, a set of novel DSM algorithms for joint power allocation and UEO that enforce a generalized $ \alpha $ -fairness policy. Since finding the globally optimal UEO entails a combinatorial optimization problem with excessive computational complexity, an iterative algorithm is proposed which uses per-tone exhaustive searches (PTESs) and provides near-optimal approximate solutions. To further reduce the computational complexity, two suboptimal methods are suggested to replace the expensive PTESs, leading to two additional $ \alpha $ -fair DSM algorithms that are tractable for large scenarios against little performance loss. Simulations of a G.fast cable binder show that the $ \alpha $ -fair DSM algorithms achieve an efficient trade-off between fairness and performance in contrast to current UEO methods.

Efficient Spectrum Management Techniques for Cognitive Radio Networks for Proximity Service

Cognitive radio networks improve spectrum efficiency by employing vigilant and accurate spectrum management techniques. This is done to enable an unlicensed user to use the underutilized spectrum. Spectrum sensing determines if a spectrum hole exists for the unlicensed user, this is accomplished using various techniques such as energy detection, minimum Eigen value detection, and matched filter technique. Conventional energy detection techniques do not achieve high values of detection; hence an improved adaptive energy detection technique has been proposed to improve the efficiency. The proposed methodology exhibits better numerical results than conventional techniques. Another important spectrum management procedure is spectrum handoff. The concept of multiple attributes for decision making was implemented, under which, the simple additive weights method and the technique for order preference by similarity to ideal solution method were compared based on a performance involving the triple play of services.

A Performance Comparison of Centralized and Distributed Spectrum Management Techniques in Elastic Optical Networks

Elastic optical networks (EONs) have emerged to provide higher spectrum efficiency than traditional Dense Wavelength-Division-Multiplexing (DWDM) by utilizing enabling technologies such as flexible spectrum grid, Orthogonal Frequency Division Multiplexing (OFDM), and distance adaptive rate and modulation. The choice of the control-plane is an important consideration when deploying any new technology, especially in optical networks. This paper considers generic distributed and centralized spectrum assignment policies in conjunction with the accompanying connection set-up signaling protocols in EONs. A network simulator for Generalized Multiprotocol Label Switching (GMPLS) was developed with Forward Reservation Protocol and Backward Reservation Protocol signaling methods. These signaling techniques are used with the First Fit (FF) and Random Fit (RF) Routing and Spectrum Allocation (RSA) algorithms. The paper discusses control elements (central and distributed architectures) decisions under busy hour and normal network conditions and presents a comprehensive performance analysis of key performance metrics such as connection success rate, connection establishment time, and capacity requirement.

Spectrum Management for MmWave Enabled UAV Swarm Networks: Challenges and Opportunities

In wireless communications, as the spatio-temporal distribution of the traffic is dynamic, the performance degradation of cellular networks becomes inevitable. Especially in catastrophic scenarios or hot-spot areas, terrestrial base stations may be poorly functioning and/or congested; thus, deploying ASCs that were carried by a UAV swarm is reasonable and cost-effective. Moreover, services such as disaster evaluation and live broadcasting require high-definition video streams, which undoubtedly need broad-band wireless transmission. To achieve this goal, the mmWave approach is introduced in the UAV swarm. However, wireless backhaul links, the mobility of UAVs and system coexistence hamper performance. In this article, we propose a novel spectrum management (SM) architecture for UAV-assisted cellular networks. Considering the special feature of mmWave, we also study SM techniques for opportunistic utilization of low-altitude UAV swarm using multi-mode radio access technologies (RATs). Both motivations and challenges of the proposed SM architecture for the UAV swarm are analyzed. To evaluate the performances of the proposed mmWave based wireless backhaul in UAV-assisted cellular networks, different SM schemes have been discussed and verified with numerical results in five typical scenarios.

Secondary users selection and sparse narrow-band interference mitigation in cognitive radio networks

Spectrum scarcity is a critical problem that may reduce the effectiveness of wireless technologies and services. To address this problem, different spectrum management techniques have been proposed in the literature such as overlay cognitive radio (CR) where the unlicensed users can share the same spectrum with the licensed users. The main challenges in overlay CR networks are the identification and detection of the Primary User (PU) signals in a multi-source narrow-band interference (NBI) scenario. Therefore, in this paper, we investigate the performance of an orthogonal frequency division multiplexing (OFDM) overlay CR network with Secondary Users (SUs) and subcarriers selection schemes. Three approaches for SUs and subcarriers Selection named Direct, Distributed and Incremental selection techniques are proposed in this paper to increase the expected signal to interference and noise ratio based on full or partial knowledge of the channel state information (CSI). We also show that Distributed selection techniques provide all the SUs equal chances to be selected without affecting the selection diversity gain. General as well as simplified outage probability expressions are derived and extensive simulations are conducted to evaluate the performance of the proposed techniques and support the theoretical derivations. To accommodate more SUs, a new approach for asynchronous NBI estimation and mitigation in CR networks is investigated. Without any prior knowledge of the NBI characteristics and based on sparse signal recovery theory, the proposed approach allows the PU to exploit the sparsity of the SUs interference to recover it and approach the interference-free limit over practical ranges of NBI power levels.

Joint virtual edge-clustering and spectrum allocation scheme for uplink interference mitigation in C-RAN

Fractional Frequency Reuse (FFR) and Coordinated MultiPoint (CoMP) processing are two of the conventional methods to mitigate the Inter-Cell Interference (ICI) and to improve the average Signal-to-Interference-plus-Noise Ratio (SINR). However, FFR is associated with low system spectral efficiency and CoMP does not take any action to mitigate the inter-cluster interference. In this article, we study the challenges and problems of the current interference management techniques and explain why the clustering and spectrum allocation must be studied jointly. Then, in the context of Cloud Radio Access Network (C-RAN), we propose a joint virtual clustering and spectrum allocation scheme, called Cloud-CFFR, to address such problems. With respect to both FFR and CoMP, Cloud-CFFR decreases the complexity, delay, and ICI while increasing the system spectral efficiency. Since the system performance in cell-edge regions relies on the cooperation of different Virtual Base Stations (VBSs), there is no service interruption in handling handovers; moreover, in order to address the unanticipated change in capacity demand, a flexible spectrum management technique is proposed which dynamically changes the subband boundaries based on the number of active users in the clusters. Simulation results confirm the validity of our analysis and show the benefits of this novel uplink solution compared to the traditional schemes.