Dynamic Spectrum Access Scheme Research Articles

Joint Collaborative Big Spectrum Data Sensing and Reinforcement Learning Based Dynamic Spectrum Access for Cognitive Internet of Vehicles

Cognitive Internet of Vehicles (CIoV) is an intelligent vehicle network envisioned to opportunistically access spectrum licensed to primary users (PUs) on the premise of not interrupting their normal communications. Dynamic spectrum access enables the CIoV to choose the best possible spectrum for communications based on the outcomes of spectrum data sensing, which can improve the spectrum access performance effectively. In this paper, we enable the CIoV to adapt to various spectrum states through: a) a collaborative big spectrum data sensing scheme to sense a massive amount of spectrum data; and b) a reinforcement learning (RL) based dynamic spectrum access scheme to optimize spectrum selection strategies. Q-learning, which is a popular RL approach, is proposed for underlay, overlay, and collaborative spectrum access modes to allocate spectrum resources to the CIoV intelligently. The Q-learning models, which include the spectrum state vector, the action vector of CIoV, and the spectrum access reward received in different spectrum situations, are defined for the spectrum access modes. A Q-learning based spectrum access algorithm is proposed to improve the communication performance of the CIoV in different spectrum access modes. Simulation results indicate that the collaborative spectrum access mode can achieve higher average throughput, lower interference power and lower communication outage compared with the underlay and overlay spectrum access modes.

Federated Multi-Agent Deep Reinforcement Learning (Fed-MADRL) for Dynamic Spectrum Access

Dynamic spectrum access (DSA) has been introduced as a promising technology that allows a secondary system to access the licensed spectrum of the primary system to improve spectrum utilization. In this paper, we introduce Fed-MADRL by incorporating federated learning (FL) and multi-agent deep reinforcement learning (MADRL) to design a collaborative DSA strategy. Our Fed-MADRL scheme employs FL to enable multiple users to collaboratively optimize the system goal without sharing their training data. By keeping all the training data at the user end, FL improves the communication efficiency and strengthens user data privacy. To further reduce the communication overheads, each user only shares quantized information. We provide the convergence analysis to characterize the trade-off between the communication efficiency and the system performance. In particular, we show that the introduced method converges at a rate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (1/ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/4</sup> ), where <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> is the number of FL iterations. To the best of our knowledge, Fed-MADRL is the first work that utilizes FL in DSA networks under quantized communication. Performance evaluation results show that the introduced Fed-MADRL method outperforms the independent learning method and achieves comparable performance with the centralized MADRL method, which requires much higher communication overheads.

Metasurface-Based Modulation With Enhanced Interference Resilience

Reconfigurable Intelligent Surfaces (RISs) have great potential for Internet of Things (IoT) applications due to their cost-effectiveness and energy-efficiency. However, under the application of RIS, current RIS systems cannot actively predict and avoid interference, as evolving interference signals can dynamically change their frequency bands. To address this issue, we develop a learning-based dynamic RIS spectrum access scheme to avoid interference and enhance wireless transmission reliability. An intelligent metasurface-based modulation (MM) is designed for generating and reflecting anti-interference radio-frequency signals. Specifically, a Dyna-Q algorithm is employed at the RIS controller to learn the frequency of interference signals, which is used to determine the frequency of the carrier signals. Experiments are conducted on the proposed RIS demo system to evaluate its anti-interference capability, and the results show that our scheme leads to an increase of at least 50% in the system throughput. These numerical results demonstrate the effectiveness of proposed scheme.

A Usage Aware Dynamic Spectrum Access Scheme for Interweave Cognitive Radio Network by Exploiting Deep Reinforcement Learning.

Future-generation wireless networks should accommodate surging growth in mobile data traffic and support an increasingly high density of wireless devices. Consequently, as the demand for spectrum continues to skyrocket, a severe shortage of spectrum resources for wireless networks will reach unprecedented levels of challenge in the near future. To deal with the emerging spectrum-shortage problem, dynamic spectrum access techniques have attracted a great deal of attention in both academia and industry. By exploiting the cognitive radio techniques, secondary users (SUs) are capable of accessing the underutilized spectrum holes of the primary users (PUs) to increase the whole system’s spectral efficiency with minimum interference violations. In this paper, we mathematically formulate the spectrum access problem for interweave cognitive radio networks, and propose a usage-aware deep reinforcement learning based scheme to solve it, which exploits the historical channel usage data to learn the time correlation and channel correlation of the PU channels. We evaluated the performance of the proposed approach by extensive simulations in both uncorrelated and correlated PU channel usage cases. The evaluation results validate the superiority of the proposed scheme in terms of channel access success probability and SU-PU interference probability, by comparing it with ideal results and existing methods.

Dynamic spectrum access based on deep reinforcement learning for multiple access in cognitive radio

With the increasing shortage of spectrum resources, dynamic spectrum access (DSA) technology is proposed to maximize the spectrum resources utilization. Traditional DSA solutions can no longer meet the requirements of high throughput and low interference in large-scale access scenarios of cognitive radio (CR). Therefore, in this paper, we propose a DSA scheme based on deep reinforcement learning (DRL) combined with multiple access methods to maximize the system throughput. In the DSA network, the access strategy adopted by the secondary user (SU) will directly affect the performance of the entire system, so we introduce DRL to help the SU learn the best access strategy in a dynamic environment. The trained SU can intelligently access the appropriate channel to avoid interference to the primary user (PU) and other SUs. By combining deep Q-network (DQN) into two multiple access methods: Frequency Division Multiple Access (FDMA) and Non-orthogonal Multiple Access (NOMA), DQN-based FDMA scheme and DQN-based NOMA scheme are designed, respectively, which can find the best DSA strategy to avoid collisions with PU or other SU and improve system throughput. Simulation results show that the DQN-based NOMA scheme has better performance than the DQN-based FDMA scheme.

Joint bandwidth and power resource allocation technique in multi‐carrier non‐orthogonal multiple access‐based cognitive Internet of Things networks

AbstractThe recent development of the Internet of Things (IoT) devices offers an intelligent concept for integrating massive number of interconnected wireless devices. However, supporting such massive number of IoT connections (hundreds of billions) requires an efficient and dynamic spectrum access strategy. Accordingly, the integration of non‐orthogonal multiple access (NOMA) in the cognitive radio (CR) systems, referred to as a multi‐carrier NOMA CR‐enabled system, is envisioned as a potential spectrum‐efficient networking paradigm that can support massive energy‐efficient IoT connectivity in B5G networks while maintaining the quality of services (QoS) of the IoT devices. However, the power consumption issue of the multi‐carrier NOMA CR‐based system becomes as one of key challenges, especially when considering the expected massive connectivity. Accordingly, several power‐aware optimization frameworks have been considered to address this issue. While most of the existing CR‐based multi‐carrier NOMA power allocation mechanisms only optimize the allocated per‐user power, this article proposes a novel joint bandwidth and power allocation problem over each available idle channel with the aim of minimizing the overall transmission power under a set of QoS constrains. Specifically, the bandwidth of each channel is adaptively subdivided into two variable‐width sub‐channels, each is capable to serve a group of CR users using power‐domain NOMA. This novel design is formulated as a joint optimization problem that is shown to be a non‐convex. Therefore, an iterative algorithm with a second‐order cone programming is deployed to handle the non‐convexity of the problem, and thus, determine the solution of it. Simulation results reveal that the developed variable bandwidth adaptive power allocation mechanism outperforms the conventional equal sub‐channel allocation in terms of the required transmission power, which significantly improves the energy efficiency in the system.

Dynamic Spectrum Access for Internet-of-Things Based on Federated Deep Reinforcement Learning

The explosive growth of Internet-of-Things (IoT) applications such as smart cities and Industry 4.0 have led to drastic increase in demand for wireless bandwidth, hence motivating the rapid development of new techniques for enhancing spectrum utilization needed by new generation wireless communication technologies. Among others, dynamic spectrum access (DSA) is one of the most widely accepted approaches. In this paper, as an enhancement of existing works, we take into consideration of inter-node collaborations in a dynamic spectrum environment. Typically, in such distributed circumstances, intelligent dynamic spectrum access almost invariably relies on self-learning to achieve dynamic spectrum access improvement. Whereas, this paper proposes a DSA scheme based on deep reinforcement learning to enhance spectrum and access efficiency. Unlike traditional Q-learning-based DSA, we introduce the following to enhance the spectrum efficiency in dynamic IoT spectrum environments. First, deep double Q-learning is adopted to perform local self-spectrum-learning for IoT terminals in order to achieve better dynamic access accuracy. Second, to accelerate learning convergence, federated learning (FL) in edge nodes is used to improve the self-learning. Third, multiple secondary users, who do not interfere with each other and have similar operation condition, are clustered for federated learning to enhance the efficiency of deep reinforcement learning. Comparing with the traditional distributed DSA with deep learning, the proposed scheme has faster access convergence speed due to the characteristic of global optimization for federated learning. Based on this, a framework of federated deep reinforcement learning (FDRL) for DSA is proposed. Furthermore, this scheme preserves privacy of IoT users in that FDRL only requires model parameters to be uploaded to edge servers. Simulations are performed to show the effectiveness of theproposed FDRL-based DSA framework.

Reinforcement learning based dynamic spectrum access in cognitive Internet of Vehicles

Cognitive Internet of Vehicles (CIoV) can improve spectrum utilization by accessing the spectrum licensed to primary user (PU) under the premise of not disturbing the PU's transmissions. However, the traditional static spectrum access makes the CIoV unable to adapt to the various spectrum environments. In this paper, a reinforcement learning based dynamic spectrum access scheme is proposed to improve the transmission performance of the CIoV in the licensed spectrum, and avoid causing harmful interference to the PU. The frame structure of the CIoV is separated into sensing period and access period, whereby the CIoV can optimize the transmission parameters in the access period according to the spectrum decisions in the sensing period. Considering both detection probability and false alarm probability, a Q-learning based spectrum access algorithm is proposed for the CIoV to intelligently select the optimal channel, bandwidth and transmit power under the dynamic spectrum states and various spectrum sensing performance. The simulations have shown that compared with the traditional non-learning spectrum access algorithm, the proposed Q-learning algorithm can effectively improve the spectral efficiency and throughput of the CIoV as well as decrease the interference power to the PU.

Detection and Defense of PUEA in Cognitive Radio Network

In wireless communication, there is an enormous increase in the number of users which results in spectrum shortage. This can be defeated through dynamic spectrum access scheme and cognitive radio is the best solution to achieve this. A cognitive radio (CR) is programmable, configurable and can dynamically use the spectrum in the wireless environment. One of the important tasks of CR is to perform spectrum sensing. Sensing done on spectrum facilitates the cognitive radio user to find the unoccupied area of the spectrum. This CR is prone to many attacks in the wireless environment like primary user emulation attack (PUEA), denial of service attack, replay attack, etc. Among them, the most important attack is PUEA. When a user mimics the primary user and acquires priority over other users to access the spectrum then it is termed as PUEA. In this work, PUEA detection and its defense techniques have been modeled to avoid the degradation of spectrum. To detect the malicious users feature detection-based sensing with double threshold has been proposed. If the detected signal falls above the upper threshold or below the lower threshold then it is considered as primary user signal, whereas if the signal falls in between the thresholds then that signal is identified as malicious user signal. The detection of PUEA cannot be accurate due to factors like false alarm and miss detection. Hence, a game model has been designed for the legitimate nodes to reach strategic defense decisions in the presence of multiple attackers. Based on the actions of the players, the defenders’ payoff is determined to obtain optimal defense decisions.

Neural Network Cognitive Engine for Autonomous and Distributed Underlay Dynamic Spectrum Access

Two key challenges in underlay dynamic spectrum access (DSA) are how to establish an interference limit from the primary network (PN) and how cognitive radios (CRs) in the secondary network (SN) become aware of the interference they create on the PN, especially when there is no exchange of information between the two networks. These challenges are addressed in this paper by presenting a fully autonomous and distributed underlay DSA scheme where each CR operates based on predicting its transmission effect on the PN. The scheme is based on a cognitive engine with an artificial neural network that predicts, without exchanging information between the networks, the full adaptive modulation and channel coding configuration for the primary link that is received with highest power by a transmitting CR. By managing the effect of the SN on the PN, the presented technique maintains the relative average throughput change in the PN within a prescribed maximum value, while also finding transmit settings for the CRs that result in throughput as large as allowed by the PN interference limit. Simulation results show that the ability of the cognitive engine in estimating the effect of a CR transmission on the full adaptive modulation and coding (AMC) mode leads to a very fine resolution underlay transmit power control. This ability also provides higher transmission opportunities for the CRs, compared to a scheme that can only estimate the modulation scheme used at the PN link.

Offshore Electromagnetic Spectrum Detection System Based on Self-Organizing Network

The offshore electromagnetic spectrum resource is one of the important marine resources. It is the physical channel for wireless communication between ships, ships and islands, waterways, and bays. The traditional discrete electromagnetic spectrum energy detection method extracts few signal features, and the data analysis is of little significance. This method is not suitable for offshore electromagnetic spectrum detection activities. We propose a distributed detection system based on self-organizing network to meet real-time large-scale offshore electromagnetic spectrum detection activities. Combining space energy spectrum, phase information, radio frequency (RF) I/Q multiple signal characteristics to study offshore electromagnetic spectrum sensing methods. We propose to replace the traditional electromagnetic spectrum detection method with signal feature description method. Combined with the attitude of the antenna, we introduced the wave spectrum to fit the complex sea surface under different sea conditions, and realized the offshore distributed electromagnetic spectrum prediction system. The research of offshore distributed electromagnetic frequency detection system is of great significance to improve the accuracy of offshore electromagnetic spectrum detection. It has theoretical guiding significance in analyzing the utilization of offshore electromagnetic spectrum resources, managing electromagnetic spectrum resources, and formulating dynamic spectrum access strategies.

WITHDRAWN: An extensive survey on cognitive radio based spectrum sensing using different algorithms

Abstract Latest technical developments also emerged in the implementation of wireless networks which consisting of self-organizing devices which can be distributed without finding for infrastructure. Usually these devices having small form factor and also stored with embedded devices, ability of communication and processing. The licensing of wireless spectrum is highly developed over large global locations on a basis of long-term. The FFC was recently implemented the usage of unlicensed devices in licensed bands in order to overcome the significant problems of spectrum scarcity. Dynamic spectrum access (DSA) strategies are therefore suggested to address the existing issues of spectrum inefficiency. In order to further boost spectrum quality; this new research area has recognized the creative network called Cognitive Radio (CR). The fundamental principle of CR network is that the unlicensed network (secondary users) must vacate the band until the licensed network (primary user) is sensed. However, due to the high variations which are available in the spectrum and (QoS) criteria, the CR network will able to face different challenges.

Channel allocation and ultra-reliable communication in CRNs with heterogeneous traffic and retrials: A dependability theory-based analysis

The current research efforts on Fifth Generation (5G) of wireless communication systems have identified the need for large extent improvements in accessibility and reliability of communication services. In this respect, Cognitive Radio (CR) has been envisioned as a key 5G enabler that allows dynamic spectrum access without causing interference to licensed (primary) users and can tackle the challenge of ultra reliable communication. Channel failures, which are generally caused due to hardware and software failure and/or due to intrinsic features such as fading and shadowing, can easily result in network performance degradation. In cognitive radio networks (CRNs), the connections of unlicensed (secondary) users are inherently vulnerable to breaks due to channel failures as well as licensed users’ arrivals. To explore the advantages of channel reservation and retrial phenomenon on performance improvement in error-prone channels, we propose and analyze dynamic spectrum access (DSA) scheme by also taking balking and reneging behavior into account. Moreover, since 5G networks should comprise heterogeneous applications that may have different Quality of Service (QoS), thus the present study facilitates the arrival of heterogeneous secondary users with access privilege variations. In addition, most previous works have studied the stationary performance of CRNs, however, those may not be adequate in practice, notably when the time horizon of operations is finite. This paper investigates the transient dynamics from the perspectives of dependability theory in CRNs. Furthermore, the whole system is modeled using a multi-dimensional continuous time Markov chain (CTMC) and numerical results illustrate the potential of the proposed scheme to achieve major gains in the performance of error-prone CRNs.

Dynamic Spectrum Access for Multimedia Transmission Over Multi-User, Multi-Channel Cognitive Radio Networks

The optimal spectrum access strategy is investigated for multi-user multi-channel scenario in cognitive radio networks. At first, an online learning method based on Dirichlet Process is adopted to predict the channel usage based on ACK/NACK feedbacks, which can avoid frequent information exchange among users. Based on the prediction result, the delay performance can be computed when a user transmits certain percentage of multimedia packets on a specific channel. Second, the packet delivery ratio (PDR) is derived from the prediction result of channel usage to reflect the accessing competition among multiple users. Finally, the quality of service (QoS) of multimedia applications is defined as the joint delay and throughput performances. Moreover, a dynamic spectrum access scheme is proposed to optimize the QoS metrics. The simulation results demonstrate that the QoS and the peak-signal-to-noise ratio (PSNR) of the proposed spectrum access algorithm outperform the three existing spectrum access algorithms, i.e., cognitive cross-layer algorithm, dynamic learning algorithm, and dynamic least interference algorithm. The proposed algorithm achieves more than 21.8%, 5.4%, and 3.9% PDR enhancement and over 3.23 dB, 0.82 dB, and 0.50 dB PSNR gains, compared with those three algorithms, given the transmission power as 10, 20, and 30 units, respectively.

Dynamic spectrum access strategy for cognitive wireless networks based on two channel sensing

A dynamic spectrum access strategy based on two channel sensing is proposed for the spectrum access model in multi-channel multi-user scenarios. The strategy reduces the contention of cognitive users to the channel by sensing the dynamic allocation of spectrum channels. The number of the perceptual access channels is adaptively allocated according to the number of access users, and the collision interference to the authorized channel is reduced. Then based on the above model, a hybrid dynamic spectrum access strategy is proposed, and the Overlay/Underlay hybrid access mode is adopted instead of the original single Overlay access mode. By setting the probability threshold to reduce the perception of the channel with lower credibility, the cognitive user adopts the Underlay access mode shared transmission at a lower transmission rate. The simulation results show that the proposed two dynamic spectrum access strategies can effectively reduce the collision probability and improve the network service quality.

A multiple description scalable coding scheme for adaptive video streaming over next generation cellular networks

Nowadays, billions of users are connected to the mobile infrastructure offering a plethora of emerging services, and video streams are among the main contributors to this unprecedented traffic explosion. The paradigm of dynamic spectrum access (DSA) was coined and has become a widely accepted technology to boost the capacity of next generation networks. In this work, we propose an enhanced video coding implementation jointly with a DSA protocol wherein the sender actively picks up the most appropriate access strategy contingent on the instantaneous spectrum observations. The legacy frequencies pool has been extended to include the spectral ranges left unused in the television (TV) bands. A specific coding framework using a concatenation of multiple description coding and layered coding is investigated. The video is decomposed into two sub-streams through even and odd frames separation, then the resulting sub-streams are encoded into a scalable structure producing two independent and cumulative descriptions. The sender chooses to make use of the extended spectrum pool exclusively when the default frequencies are no more advantageous in terms of network capacity. Through extensive simulations, we compare the performance of the cellular network with and without the proposed DSA strategy. It has been proven that the proposed video transmission scheme using hybrid spectrum access provides a significant enhancement in network capacity up to 6.3Mbps and the capacity gain outweighs the disadvantage of interference incidents due to licensed-cognitive, co-channel and adjacent channel coexistence, besides retrieving an improved perceived video quality at the customer side illustrated by an increment of up to 6.8dB (0.176, respectively) in terms of the achieved PSNR-HVS-M (MS-SSIM, respectively).

Performance evaluation and system optimization of Green cognitive radio networks with a multiple-sleep mode

Cognitive radio networks (CRNs) have been emerged as a solution for realizing dynamic spectrum allocation. Green communication in CRNs will contribute to reducing emission pollution, minimizing operation cost and decreasing energy consumption. The Green CRNs would help in realizing “green spectrum management”. In this paper, we examine the key issue to show how to conserve the energy of base stations in the Green CRNs. In order to meet the demand for more sustainable green communication, we propose a multiple-sleep mode for licensed channels in CRNs. Based on a dynamic spectrum access strategy with the proposed multiple-sleep mode, we establish a continuous-time Markov chain model to capture the stochastic behavior of secondary user (SU) and primary user packets. By using the matrix geometric solution method, we obtain the steady-state probability distribution for the system model. This paper further presents analysis for performance measures in terms of the throughput of SU packets, the average latency of SU packets, the energy saving rate of the system and the channel utilization. We also provide statistical experiments with analysis and simulation to investigate the influences of the service rate of one channel and the sleep timer parameter on the system performance measures. In order to get the utmost out of the spectrum resource and meet the demands for the quality of service requirements of SUs, we construct a system cost function, and improve a Jaya algorithm employing an insect-population model to optimize the proposed energy saving strategy. We also show the optimal combination and global minimum of the system cost by numerical results.

Market Mechanisms for Dynamic Spectrum Access (DSA)

This paper applies fundamental market-based approaches to the problem of (wireless) spectrum sharing between a licensed primary user (PU) and an unlicensed secondary user (SU). We consider both orthogonal and non-orthogonal modes of dynamic spectrum access (DSA), and explore rates and profits at market equilibrium. Our analytical formulation is distinct from prior art: we assume that 1) PU increases its transmit power (within bounds) to avoid any rate loss from DSA and 2) the spectrum owner provides compensation to exactly offset PU’s increased power costs, leaving PU’s overall economic well-being unaffected by DSA. This allows quantification of the marginal costs for various DSA schemes for fair comparison and insights into corresponding market behavior. Our analysis suggests that non-orthogonal DSA is more profitable when 1) spectrum owners face low competition; 2) SU is very sensitive to power consumption but not very sensitive to data rates (low or moderate rate applications); and 3) channel conditions imply low mutual interference between PU and SU; otherwise, orthogonal DSA is more profitable. Market competition has a very large impact on profits and rates under non-orthogonal DSA but not for orthogonal DSA. Finally, we explore a novel hybrid approach where some portion of the licensed bandwidth is reserved for PU’s exclusive use and non-orthogonal DSA is permitted over the rest, we determine bounds on how much bandwidth should be reserved for PU in such cases.

Dynamic Spectrum Reservation for CR Networks in the Presence of Channel Failures: Channel Allocation and Reliability Analysis

Providing channel access opportunities for new service requests and guaranteeing continuous connections for ongoing flows until service completion are two challenges for service provisioning in wireless networks. Channel failures, which are typically caused by hardware and software failures or/and by intrinsic instability in radio transmissions, can easily result in network performance degradation. In cognitive radio networks (CRNs), secondary transmissions are inherently vulnerable to connection breaks due to licensed users’ arrivals as well as channel failures. To explore the advantages of channel reservation on performance improvement in error-prone channels, we propose and analyze a dynamic channel reservation (DCR) algorithm and a dynamic spectrum access (DSA) scheme with three access privilege variations. The key idea of the DCR algorithm is to reserve a dynamically adjustable number of channels for the interrupted services to maintain service retainability for ongoing users or to enhance channel availability for new users. Furthermore, the DCR algorithm is embedded in the DSA scheme enabling spectrum access of primary and secondary users with different access privileges based on access flexibility for licensed shared access. The performance of such a CRN in the presence of homogeneous and heterogeneous channel failures is investigated considering different channel failure and repair rates.

Dynamic Spectrum Access for Internet of Things Service in Cognitive Radio-Enabled LPWANs.

In this paper, we focus on a dynamic spectrum access strategy for Internet of Things (IoT) applications in two types of radio systems: cellular networks and cognitive radio-enabled low power wide area networks (CR-LPWANs). The spectrum channel contention between the licensed cellular networks and the unlicensed CR-LPWANs, which work with them, only takes place within the cellular radio spectrum range. Our aim is to maximize the spectrum capacity for the unlicensed users while ensuring that it never interferes with the licensed network. Therefore, in this paper we propose a dynamic spectrum access strategy for CR-LPWANs operating in both licensed and unlicensed bands. The simulation and the numerical analysis by using a matrix geometric approach for the strategy are presented. Finally, we obtain the blocking probability of the licensed users, the mean dwell time of the unlicensed user, and the total carried traffic and combined service quality for the licensed and unlicensed users. The results show that the proposed strategy can maximize the spectrum capacity for the unlicensed users using IoT applications as well as keep the service quality of the licensed users independent of them.