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Next-generation wireless networks will need to support of very high data rates and low–latency communications, which will require a new wireless radio technology paradigm. The growing number of mobile users is causing spectrum scarcity; and hence, an efficient spectrum utilization method is required. Conventional scheduling-based resource allocation scheme in wireless networks under limited resources is a challenging due to the complex network situations, dynamic network environment, and diverse needs for future networks. To overcome resource scarcity in mobile networks, spectrum sharing among multiple operators may be an efficient solution. Traditional methods of dynamic spectrum sharing are model-dependent, and they are not robust to the changing wireless environments. To enable low-latency communications for complex future wireless networks, efficient machine learning algorithms can be used across the wireless network infrastructure. Integrating machine learning for resource allocation can leverage intelligent and efficient mechanisms for dynamic wireless networks. To efficiently and intelligently utilize the scarce resources of dynamic networks, this paper proposes an efficient machine learning-based spectrum sharing method among multiple mobile network operators (MNOs). A mobile network operator uses the idle slots of the another operator and transmits the information efficiently. Using the neural network model, each MNO learns the spectrum utilization of other MNOs and selects the idle slots of other MNOs. Simulation results have been computed and compared with the conventional scheme where resources are not shared. These simulation results show that the proposed neural network model can efficiently learn the network quickly, and spectrum sharing can lead to improved network performance in terms of the delay, user-perceived throughput, resource usage, packet drop, and sum throughput of the network. [ABSTRACT FROM AUTHOR]

Ambient backscatter communication (AmBC), a promising solution to future Internet of Things (IoT), has attracted increasing attention from both academia and industry in recent years. AmBC systems enable communications between RF-powered devices (e.g., tags and sensors) and AmBC receivers by utilizing ambient RF source signals from existing communication systems. Thus it offers a low-cost and energy-efficient means of communications for future IoT applications. In the current AmBC research, the impact of using channel coding on existing primary networks in terms of the bit-error-rate (BER) performance of the AmBC systems are mostly ignored. Low-density parity-check (LDPC) codes that offer fast encoding and decoding process have been proposed as promising channel codes in the fifth-generation (5 G) networks. In this paper, we investigate the BER performance of an AmBC system that uses LDPC-coded RF source signals. We have analytically derived the BER upper and lower bounds of both the RF source and tag signals for our proposed AmBC system. Simulation results show that the BERs of both signals decrease significantly, compared to conventional AmBC systems with uncoded RF source signals. [ABSTRACT FROM AUTHOR]

This paper proposes a new transmitter precoding approach for co-existence between multiple-input multiple-output (MIMO) radar and multi-user MIMO communication systems employing phase shift keying (PSK) modulation. To enable the satisfactions of radar communication functionalities, the common practice is to suppress the mutual interferences, while our proposed scheme is to use the known interferences existing already in radar and communications systems and make the most of them: selectively reverse the destructive interferences to the negative direction while retaining the interferences which are beneficial to the desired signal. The resulting interferences are always kept constructive and can contribute to the power of the useful signal. Numerical results validate that the effective signal to noise ratio (SNR) can be enhanced by 7 dB with no additional power-peruser investment. [ABSTRACT FROM AUTHOR]

A delay-Doppler robust spectrum sharing method is proposed for the unmanned aerial vehicle (UAV) and terrestrial systems by actively introducing the assistive slots (AS) in this work. With the aids of AS, the receiver in the UAV/terrestrial link can extract the delay-Doppler parameter of the signal, and hence further recover the desired signal. Inserting AS in frames introduces different possibilities of signals sampled at AS and non-AS points, and the UAV/terrestrial signal samples are differentiated from the compound signals by focusing on the energy gap among the samples. Since the signal sample differentiation and the delay-Doppler parameter extraction are receiver oriented, the effects of multipath and node mobility are included, and the signal recovery is free from the adverse impacts of delay and Doppler shifts. Analysis shows that although introducing AS is beneficial to recovering the UAV/terrestrial signal, the signal transmission efficiency suffers degradation. Therefore, the optimal AS ratio regarding the tradeoff between delay-Doppler parameter extraction accuracy and transmission efficiency is investigated. For the optimal AS ratio, the signal-to-interference ratio of the spectrum sharing system plays a key role under Rician/Rayleigh distributed terrestrial fading channels. The numerical results validate the effectiveness of the proposed scheme in comparison to the traditional methods. [ABSTRACT FROM AUTHOR]

We investigate the coexistence of an age optimizing network (AON) and a throughput optimizing network (TON) that share a common spectrum band. We consider two modes of long run coexistence: (a) networks compete with each other for spectrum access, causing them to interfere and (b) networks cooperate to achieve non-interfering access. To model competition, we define a non-cooperative stage game parameterized by the average age of the AON at the beginning of the stage, derive its mixed strategy Nash equilibrium (MSNE), and analyze the evolution of age and throughput over an infinitely repeated game in which each network plays the MSNE at every stage. Cooperation uses a coordination device that performs a coin toss during each stage to select the network that must access the medium. Networks use the grim trigger punishment strategy, reverting to playing the MSNE every stage forever if the other disobeys the device. We determine if there exists a subgame perfect equilibrium, i.e., the networks obey the device forever as they find cooperation beneficial. We show that networks choose to cooperate only when they consist of a sufficiently small number of nodes, otherwise they prefer to disobey the device and compete. [ABSTRACT FROM AUTHOR]

The increasing number of wireless devices operating in unlicensed spectrum motivates the development of intelligent adaptive approaches to spectrum access. We consider decentralized contention-based medium access for base stations (BSs) operating on unlicensed shared spectrum, where each BS autonomously decides whether or not to transmit on a given resource. The contention decision attempts to maximize not its own downlink throughput, but rather a network-wide objective. We formulate this problem as a decentralized partially observable Markov decision process with a novel reward structure that provides long term proportional fairness in terms of throughput. We then introduce a two-stage Markov decision process in each time slot that uses information from spectrum sensing and reception quality to make a medium access decision. Finally, we incorporate these features into a distributed reinforcement learning framework for contention-based spectrum access. Our formulation provides decentralized inference, online adaptability and also caters to partial observability of the environment through recurrent Q-learning. Empirically, we find its maximization of the proportional fairness metric to be competitive with a genie-aided adaptive energy detection threshold, while being robust to channel fading and small contention windows. [ABSTRACT FROM AUTHOR]

This paper describes some of the challenges that need to be addressed in order to develop collaborative spectrum-sharing systems. The importance of these challenges stems from the assumption that rules for spectrum sharing can change after the deployment of radio networks and that the whole system must be able to adapt to them. To address such a requirement, we used a policy-based approach in which transmissions are controlled by a policy interpreter system, and the policies can be modified during system operation. Our primary goal was to develop a prototype of such a system. In this paper, we outline the implementation of policy interpretation, automatic generation of transmission opportunities in case a request for transmission is denied by the policy reasoner, and the generation of rendezvous channels for the synchronization of otherwise asynchronously running software-defined radios. [ABSTRACT FROM AUTHOR]

Summary: Effective utilization of spectrum resources is an important factor in wireless communication which reduces spectrum scarcity. Over the years, communication systems use different frequency bands, and the users are categorized into licensed and unlicensed users. Most of the wireless bands are typically licensed; as a result, accommodation of new technologies such as Internet of Things and machine to machine communication becomes difficult. So it is essential to obtain a wireless spectrum to adopt new technologies. Cognitive radio technology is introduced to improve such spectrum utilization. Reports state that most of the licensed spectrums are underutilized, and few spectrums are overutilized. Cognitive radio networks help to exploit the licensed spectrum and access the spectrum without any interference to the licensed user. Through its spectrum sensing and spectrum sharing process, cognitive radio network gains more attention in wireless communication. This research work proposed an efficient optimized spectrum sensing technique for cognitive radio networks through dragonfly optimization algorithm along with the adaptive threshold process. Proposed work performs better in terms of detection accuracy and efficiency when compared to conventional spectrum sensing schemes such as linear support vector machine and particle swarm optimization models. [ABSTRACT FROM AUTHOR]

With the rapid development of wireless communication technology and the increasing number of terminal devices, in order to alleviate this phenomenon and increase the system network capacity, aiming at the problem of cellular D2D (device to device) communication resource allocation in the Internet of Vehicles, this paper proposed an allocation algorithm to maximize the utilization of spectrum resources. The algorithm took maximizing the utilization of spectrum resources as the optimization goal, and met the basic quality of service (QoS) for Internet of Vehicles communication, and used V2V (vehicle to vehicle ) and V2P (vehicle to people) share channel resources to improve spectrum resource utilization. Firstly, it used the link gain factor defined by the channel state information to find a set of potential communication links for the end user, then proved that the power allocation problem when end users reused link resources was a convex optimization problem, and used convex optimization theory to obtain the optimal transmiss ion power. Finally, this paper solved the optimal channel matching problem. This problem was a many-to-one weighted matching problem, this paper solved it by KM( Kuhn Munkres) algorithm to reduce the complexity of the algorithm. Simulation results show that, compared with other algorithms, the proposed algorithm can effectively improve system throughput, increase spectrum resource utilization, improve network performance, and optimize vehicle networking communication resource allocation. [ABSTRACT FROM AUTHOR]

Interference minimization in cellular network has and will always be top priority, whether in current or future generation of cellular technology. Therefore, cellular channel assignment problem (CAP) requires continuous study and research. This paper presents the study and comparison of Genetic Algorithm (GA) and Particle Swam Optimization (PSO) for CAP in minimizing interference. GA with three variants in term of population selection – roulette wheel selection (RWS), tournament selection (TS) and stochastic universal sampling (SUS) were studied, and then compared with classic PSO. Two CAPs were derived and used to comprehensively evaluate the performances of the PSO and GAs. It was found that GA-TS is ~11% and ~7% faster than GA-RWS and GA-SUS, respectively. Although the difference is small, but it allowed GA-TS to run for few more iterations and eventually achieved better interference minimization. Moreover, it was also found that GA-SUS has less noise and produce a more consistent result. On the other hand, PSO is slower than GA-TS, but has higher potential to converge on smaller minimum value. [ABSTRACT FROM AUTHOR]

$L$ -band Digital Aeronautical Communication System (LDACS) aims to exploit vacant spectrum in $L$ -band via spectrum sharing, and orthogonal frequency division multiplexing (OFDM) is the currently accepted LDACS waveform. Recently, various works dealing with improving the spectrum utilization of LDACS via filtering/windowing are being explored. In this direction, we propose an improved and low complexity reconfigurable filtered OFDM (LRef-OFDM) based LDACS using novel interpolation and masking based multi-stage digital filter. The proposed filter is designed to meet the stringent non-uniform spectral attenuation requirements of LDACS standard. It offers significantly lower complexity as well as higher transmission bandwidth than state-of-the-art approaches. We also integrate the proposed filter in our end-to-end LDACS testbed realized using Zynq System on Chip and analyze the performance in the presence of $L$ -band legacy user interference as well as LDACS specific wireless channels. Via extensive experimental results, we demonstrate the superiority of the proposed LRef-OFDM over OFDM and Filtered-OFDM based LDACS in terms of power spectral density, bit error rate, implementation complexity, and group delay parameters. [ABSTRACT FROM AUTHOR]

To realizing the network slice with spectrum sharing in cognitive capacity harvesting network, a spectrum sharing strategy was proposed based on 4D conflict graph and opportunistic capacity. Firstly, a 4D conflict graph model for mesh network was built and a method to achieve the node sets with conflict free was proposed. Then, the opportunistic capacity model of spectrum sharing was established and the opportunistic capacity of the unlicensed channel was derived. Finally, the spectrum sharing strategy based on 4D conflict graph and opportunistic capacity was proposed for cognitive capacity harvesting network. The simulation results show that compared with existing algorithms, the proposed algorithm can effectively utilize opportunistic spectrum resources and further improve channel utilization. [ABSTRACT FROM AUTHOR]

Summary: This paper describes a licensed shared access (LSA) testbed and field trials using a live network. The testbed includes real 4G base stations and up to 1000 virtual base stations, in the spectrum sharing scenario between satellite and cellular systems. The trials focus on 5G pioneer bands 3.4–3.8 GHz and 24.25–27.5 GHz where a satellite system is operating in the downlink direction and a cellular system is accessing the same band. The designed testbed supports both frequency bands. The performance evaluation concerns evacuation and frequency change times using different types of base stations in 3.6 GHz, that is, how fast the system relinquishes the shared band to the primary user and continues transmission using another band. We show that our LSA system is scalable and able to support large number of base stations. In addition, we investigate how satellite systems could reuse International Mobile Telecommunication (IMT) bands to offer enhanced satellite communication services for land, maritime, and aeronautical applications. Preliminary simulations and analysis confirm the possibility to reuse IMT spectrum for satellite systems without causing harmful interference to the terrestrial system. [ABSTRACT FROM AUTHOR]

Considering grant-free transmissions in low-power IoT networks with unknown time-frequency distribution of interference, we address the problem of Dynamic Resource Configuration (DRC), which amounts to a Markov decision process. Unfortunately, off-the-shelf methods based on single-objective reinforcement learning cannot guarantee energy-efficient transmission, especially when all frequency-domain channels in a time interval are interfered. Therefore, we propose a novel DRC scheme where configuration policies are optimized with a Multi-Objective Reinforcement Learning (MORL) framework. Numerical results show that the average decision error rate achieved by the MORL-based DRC can be even less than 12% of that yielded by the conventional R-learning-based approach. [ABSTRACT FROM AUTHOR]

To efficiently utilize nonexclusive underwater acoustic frequencies, we propose an Underwater Cooperative Spectrum Sharing (UCSS) protocol for a centralized underwater cognitive acoustic network that mainly consists of two parts. In the first part, to check the random occurrence of interferers periodically, the time domain is divided into frames that consist of a sensing and a non-sensing sub-frame. Then, we set the ratio of the two sub-frames to enhance the sensing rate via simulations. As a result, there exists the upper limit of the ratio, which can be used for determining the proportion of the sensing time within a frame. The second part is to design two heuristic resource allocation (RA) algorithms. One is a multiround RA (MRRA), where a central entity allocates a data channel (i.e., resource) to a CU each round so that multiple rounds are executed until no CUs need to be allocated or there is a lack of data channels. The other is a single-round RA (SRRA), where a CU is allocated to as many data channels as its QoS within a round. We also specify four rules to determine the allocation order of the CUs: random, fixed, high-QoS-based, and low-channel allocation-rate-based. In this study, we investigate the best RA allocation order pair supporting the highest channel allocation rate and fairness index via extensive simulations. It is shown that the MRRA outperformed the SRRA, regardless of allocation orders at any conditions, and the random and low-channel allocation-rate-based allocation orders with MRRA supported the best performance. In particular, even without the optimization process, the MRRA guarantees more than 95% fairness.

The aim of this paper is to obtain communication resource based on the overlay method for the secondary user (SU) in the primary communication environment operating in the Stop-and-Wait Hybrid Automatic Repeat Request (SW-HARQ) protocol. Unlike the overlay Cognitive Radio applications in the literature, in this work communication resources have been obtained for the SU in the time period in which the primary user (PU) is active in the channel. Our performance results have shown that a significant amount of sources of communication can be achieved for the SU by evaluating the waiting times in the primary SW HARQ periods, even when the PU is continuously running in the SW HARQ protocol without emptying the channel. [ABSTRACT FROM AUTHOR]

In underlay spectrum sharing, transmit antenna selection (TAS) improves the performance of a secondary system and helps it control the interference it causes to a primary system. TAS does so with a hardware complexity and cost comparable to a single antenna system. We present a novel and optimal joint TAS and continuous power adaptation rule for a practically relevant, less explored model in which the secondary transmitter knows only the statistics of channel gains from itself to one or more primary receivers. The rule minimizes the average symbol error probability (SEP) of the secondary system for an entire class of stochastic interference constraints. This general class subsumes the average interference constraint and its novel generalization, and the interference-outage constraint. We derive closed-form expressions for the transmit power and selected antenna. We then develop a general analysis of the optimal average SEP that applies to several widely-used fading models. We also present computationally-efficient approaches to determine the parameters that specify the optimal rule. Our comprehensive numerical results characterize the very different impacts of the interference constraint on both secondary and primary systems. They show that the optimal rule reduces the average SEP by two orders of magnitude compared to conventional approaches. [ABSTRACT FROM AUTHOR]

In this article, we investigate a spectral and energy-efficient wireless body area network (WBAN) for smart healthcare applications. Herein, we primarily focus on improving the spectrum utilization by intelligently sharing spectrum resources through cognitive radio (CR) technology, allowing various sensor nodes in the underlying network to co-exist without compromising on their quality-of-service (QoS). We prioritize the sensor nodes based on their applications (medical/non-medical) and thereby categorize them into primary and secondary users to implement an on-body CR-WBAN. Furthermore, two energy harvesting (EH) protocols namely time-switching cooperation (TSC) and power-splitting cooperation (PSC) are employed to facilitate the secondary node cooperation with the primary network in return for spectrum access. For both the primary and secondary networks, we derive useful expressions for the outage probability. To provide more insights into the proposed CR-WBAN, the throughput and energy efficiency performances assuming a delay-limited scenario are investigated. The impact of key parameters are demonstrated to provide guidelines for the practical design of spectral and energy-efficient WBANs. The accuracy of our proposed analytical framework is verified through Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

The objective of this paper is to estimate performance of a new approach for spectrum sharing and coordination between terrestrial base stations (BS) and On-board radio access nodes (UxNB) carried by Unmanned Aerial Vehicles (UAV). This approach employs an artificial intelligence (AI) based algorithm implemented in a centralized controller. According to the assessment based on the latest specifications of 3rd Generation Partnership Project (3GPP) the newly defined Unmanned Aerial System Traffic Management (UTM) is feasible to implement and utilize an algorithm for dynamic and efficient distribution of available radio resources between all radio nodes involved in process of optimization. An example of proprietary algorithm has been described, which is based on the principles of Kohonen neural networks. The algorithm has been used in simulation scenario to illustrate the performance of the novel approach of centralized radio channels allocation between terrestrial BSs and UxNBs deployed in 3GPP-defined rural macro (RMa) environment. Simulation results indicate that at least 85% of simulated downlink (DL) transmissions are gaining additional channel bandwidth if presented algorithm is used for spectrum distribution between terrestrial BSs and UxNBs instead of baseline soft frequency re-use (SFR) approach. [ABSTRACT FROM AUTHOR]

We consider an underlay coexistence scenario where secondary users (SUs) must keep their interference to the primary users (PUs) under control. However, the channel gains from the PUs to the SUs are uncertain due to a lack of cooperation between the PUs and the SUs. Under this circumstance, it is preferable to allow the interference threshold of each PU to be violated occasionally as long as such violation stays below a probability. In this article, we employ Chance-Constrained Programming (CCP) to exploit this idea of occasional interference threshold violation. We assume the uncertain channel gains are only known by their mean and covariance. These quantities are slow-changing and easy to estimate. Our main contribution is to introduce a novel and powerful mathematical tool called Exact Conic Reformulation (ECR), which reformulates the intractable chance constraints into tractable convex constraints. Further, ECR guarantees an equivalent reformulation from linear chance constraints into deterministic conic constraints without the limitations associated with Bernstein Approximation, on which our research community has been fixated on for years. Through extensive simulations, we show that our proposed solution offers a significant improvement over existing approaches in terms of performance and ability to handle channel correlations (where Bernstein Approximation is no longer applicable). [ABSTRACT FROM AUTHOR]

This letter analyzes the performance of a cellular system that shares spectrum with a nearby rotating radar. The analysis is based on stochastic geometry and combines opportunistic with concurrent spectrum sharing. We derive expressions for the cellular probability of coverage taking into consideration the radar guard zone and the operating parameters of both the radar and the cellular system. Results show that allowing eNBs inside the radar guard zone to operate when not in the radar beam improves the coverage and service of the cellular system compared to the case of a strictly silent guard zone. [ABSTRACT FROM AUTHOR]

The identification of the radio access technology (RAT) of the primary user by secondary users is important to avoid interference for spectrum-sharing techniques using cognitive radio (CR). RATs have become more diversified with the introduction of various services that use wireless communication. Therefore, it is desirable that a RAT identification system, which can easily cope with the diversification of RATs, is applied to CR. The purpose of this study is to use online learning to identify multiple RATs in the same frequency band. To improve the accuracy of identifying RATs with similar features, we evaluate a normalization of radio feature method proposed in our previous research for the features extracted from the signal's spectrogram. We evaluate the RAT classifier created using the proposed method by calculating the curve of receiver operating characteristics (ROC) and the identification accuracy. The results for the ROC curve show that the proposed method is effective for several supervised learning methods. Moreover, the results for the identification accuracy show that the proposed method improves the identification performance compared to the identification accuracy of conventional methods.

This paper introduces a performance analysis of a multi-user spectrum sharing based the effect of node positions in an MIMO cognitive radio (CR) network. The objective is to develop and enable reliableCR technology. This paper: 1) develops the performance analysis to support multi-user CR systems, 2) describes the significant effect of each node position and the distance between them, 3) combines decision results on both downlink and uplink operations,and, 4) presents a spectrum allocation method for all users in CR systems. The simulation results show the performance of secondary users in terms of thebit error rate inside their coverage areas together with the effect of GPS error. Finally, a completeself-evaluation guideline of the overlapping spectrum sharing for multi-user CR systems is presented. The outcome of this paper is very useful to enhance CR systems. Also, it can be easily implemented in practice for spectrum sharing. The users can realize by themselves whether their positions are in the available area or not.

A multi-tenant cellular network is a paradigm where the physical infrastructure of the network is leased by various big industries, e.g., power utilities and transportation. Hence, a major challenge in a multi-tenant cellular network is the efficient allocation of the physical spectrum to various tenants with broadly distinct quality-of-service (QoS) requirements and communications traffic characteristics. In this paper, we approach this issue by presenting a versatile spectrum sharing scheme, which may be deployed to model any spectrum sharing strategy between various tenants in a multi-tenant cellular network. The proposed spectrum sharing scheme is based upon a queuing system that considers the various communications traffic characteristics of the tenants. In addition, by using the developed queuing system, mathematical expressions for the blocking probability and spectrum utilization are derived. We then propose an optimal spectrum planning scheme, referred to as reservation-based sharing (RBS) policy that maximizes the spectrum utilization by allocating the spectrum resources to various tenants according to their traffic loads. The computational complexity of the optimal RBS policy is reduced by developing a learning automata technique, referred to as pursuit learning-based RBS policy. By using real traffic parameters for various tenants, the results show that the simulation and analytical results match well, ensuring the accuracy of the proposed analytical model. Moreover, the results indicate that the proposed pursuit learning-based RBS policy firmly matches the optimal solution and delivers a higher spectrum utilization that increases linearly with the number of tenants.

In this paper, we present a novel technique for sharing satellite spectrum with terrestrial-mobile 3-dimensional (3D) in-building small cells, namely femtocells. Both the space-satellite and the terrestrial-mobile systems (TMSs) operate at their respective licensed spectrums. However, due to the scarcity of the available spectrum, a huge amount of data generated indoors, and a high external wall penetration loss of a 3D building, we propose to reuse the satellite spectrum to small cells deployed only in 3D buildings of the TMS. The proposed spectrum sharing technique is detailed by identifying and addressing the major related issues. The co-channel interference generated because of the co-existence of the satellite user equipments (UEs) in the small-cell coverage is analyzed, and the handover procedures for a satellite UE toward and away from any small cell are discussed. The almost blank subframe (ABS)-based enhanced intercell interference coordination (eICIC) technique is employed to small cells in order to assure the quality-of-service (QoS) between small-cell UEs and satellite UEs. We derive the system-level capacity, spectral efficiency, and energy efficiency performance metrics for the proposed technique by varying the number of buildings over the coverage of a macrocell and deduce the condition for optimality for both the energy efficiency and the spectral efficiency. With an extensive system-level simulation and numerical analysis, we justify the impact of using the ABS-based eICIC technique toward provisioning QoS in terms of radio resource allocation. Furthermore, we show the outperformance of the proposed technique in terms of spectral efficiency and energy efficiency over the ones expected for the fifth generation (5G) network requirements as well as achieved by numerous existing techniques. Finally, we discuss the radio resource scheduler implementation as well as the significance, challenge, and future research perspective of the proposed spectrum sharing technique.

In this paper, new, exact expressions for the probability density functions and the cumulative distribution functions of the ratio of random envelopes involving the α-μ, η-μ, and κ-μ fading distributions are derived. The expressions are obtained in terms of easily computable infinite series and also in terms of the multivariable Fox H-function. Some special cases of these ratios, namely, Hoyt/Hoyt, η-μ/Nakagami-m, κ-μ/Nakagami-m, κ-μ/η-μ with an integer μ for the η-μ variate, η-μ/η-μ with an integer μ for only one of the η-μ, and their reciprocals are found in novel exact closed-form expressions. In addition, simple closed-form expressions for the asymptotes of the probability density functions and cumulative distribution functions of all ratios, both for the lower and upper tails of the distributions are derived. In the same way, asymptotes for the bit error rate on a binary signaling channel are obtained in closed-form expressions. To demonstrate the practical utility of these new formulations, an application example is provided. In particular, the secrecy capacity of a Gaussian wire-tap channel used for device-to-device and vehicle-to-vehicle communications is characterized using data obtained from field measurements conducted at 5.8 GHz.

This paper proposes a chance-constrained stochastic optimization technique that enables effective coexistence between LTE-Unlicensed base stations and radars in a shared spectrum. The optimization problem is formulated to guarantee the minimum performance criteria for radar operation, and at the same time, allows the LTE-Unlicensed base station to control its transmit power to maximize the performance for the serving LTE-Unlicensed device. The proposed power control mechanism results in significant reduction of the required protection distance (3.9% of the one imposed by regulations) between the radar and the LTE-Unlicensed network for the two to effectively coexist in a shared spectrum.

In this paper, we investigate the resource allocation problem for the symbiotic radio system, in which the passive backscatter transmission is parasitic in the primary transmission, and the cooperative receiver simultaneously detects the signals transmitted from the active primary transmitter (PT) and the backscatter device (BD). In particular, channel fading is considered, and the ergodic weighted sum rate of the primary and backscatter transmissions is maximized by jointly optimizing the transmit power at the PT and the reflection coefficient at the BD under either long-term or short-term transmit-power constraint over the fading states. Two practical transmission setups are considered, where the relationship between the primary and backscatter transmissions is either commensal or parasitic. For the commensal setup, we fix the reflection coefficient as the maximum value and derive the optimal power allocation scheme based on the standard convex optimization procedure. For the parasitic setup, the weighted sum-rate maximization problem is non-convex, and we resort to the concave-convex procedure to derive a suboptimal solution. Simulation results demonstrate that in the commensal setup, the BD may realize transmission while causing no harmful interference to the primary system; in the parasitic setup, a higher backscatter data rate can be achieved through slightly sacrificing the primary transmission rate.

We investigate the asymptotic behavior for an overlooked aspect of spectrum-sharing systems when the number of transmit antennas nt at the secondary transmitter (ST) grows to infinity. Considering imperfect channel state information (CSI), we apply the transmit antenna selection and the maximal-ratio combining techniques at the ST and the secondary receiver (SR), respectively. First, we obtain the signalto-noise ratio (SNR) distributions received by the SR under perfect and imperfect CSI conditions. Then we show that the SNR distributions are tail-equivalent in the sense that the right tails of the two distributions decay in the same rate as the number of transmit antennas nt grows to infinity. Based on the extreme value theory, when the transmit power of the ST is solely limited by the interference constraint, we show that the limiting SNR at the SR is Fréchet-distributed and the limiting rate scales as log(nt). When the transmit power of ST is determined by both the maximal transmit power and the interference power constraints, the limiting SNR is Gumbel-distributed and the limiting rate scales as log(log(nt)). We further show that the average rate can be estimated by the corresponding easier-to-obtain outage rate. Numerical results indicate that the derived asymptotic rate expressions represent accurate approximations even when nt is “not-solarge”. Finally, we study the robustness of the secondary transmissions by analyzing the corresponding average symbol error rates (SER) under general modulation and coding schemes. The findings indicate that the SER is Weibull distributed, when the maximal transmit power and interference power constraints are comparable.

In recent years, the various Internet of Things (IoT) communication technologies have been researched. The third generation partnership project (3GPP) is dedicated to building the IoT platforms, including NB-IoT, eMTC, and URLLC. However, these platforms cannot adequately meet all the needs of future IoT use cases which are usually broad and beyond cellular. In order to reduce the deployment cost, making the full use of the unlicensed spectrum is strongly appealing to the IoT scenarios. Transform domain communication system (TDCS) is a typical system with spectrum sensing and sharing, and it has unique advantages in supporting multiuser communication in a complex electromagnetic environment. Thus, TDCS is proposed for the IoT massive multiple access scenarios. Previous studies indicate that traditional TDCS has poor BER performance when encountering a large number of users, especially with the near-far effect. To address this, a hybrid clustering TDCS targeting at cognitive IoT applications is proposed to support massive multiple access with the near-far effect. The optimal construction method of the sequence set is also given for the hybrid clustering TDCS. Compared with traditional TDCS, the simulation results show that the proposed system achieves an improvement in multiple access ability, and the system performance can be improved by making a compromise on the number of clusters.

Geostationary orbit (GSO) satellite communication systems (SCSs) play an important role in nowadays satellite communication. Orbiting in the Clarke Belt, the GSO satellites provide voice, video, Internet access, and other data services to the users all over the world. The GSO SCSs are seeking for more spectrum resource to satisfy the growing demand of high downlink data rate, while the radio astronomy system (RAS) is suffering the inevitable radio-frequency interference (RFI) from the downlink of these GSO satellites. To solve the conflict between the GSO SCSs and the RAS in the spectrum allocation, we propose a spectrum sharing paradigm for the GSO SCS and the RAS with three potential RFI reduction methods. Depending on the radio astronomical observation (RAO) tasks of different observatories, the corresponding appropriate RFI reduction method combination can be chosen. Our analyses show that the proposed paradigm can guarantee most of the telescopes with the sample loss rate lower than 2% in both protected and unprotected RAS bands, while the SCS can obtain up to 13.88% extra average downlink throughput.

We propose a spectral and energy efficient cooperative spectrum sharing scheme with the primary user (PU) capable of harvesting wireless energy. Along with an access point (AP) transferring wireless power to PU, a secondary source (SS) and a secondary relay (SR) can cooperatively transmit data to a secondary destination (SD) using the amplify-and-forward protocol. The interference from secondary transmissions can boost the amount of harvested energy at PU. After the wireless power transfer phase, PU transmits data to AP using the harvested energy, and meanwhile SS transmits data to SD. The SR could simultaneously forward the data of both systems to improve their communication qualities. With the energy and data cooperations from SS and SR, more energy can be harvested by PU and the data reception at AP becomes more reliable, so the throughput performance of primary system can be satisfied more easily. As a result, a fraction of spectrum could be enough for the primary system and the remaining spectrum is released to the secondary system. The optimal time and bandwidth factors are determined to maximize the throughput of secondary system under the performance constraint of primary system. The numerical results show that our proposed scheme significantly outperforms the non-cooperative scheme in terms of system throughput.

Cognitive radio (CR)-based Internet of Things (IoT) system is an effective step toward a world of smart technology. Many frameworks have been proposed to build CR-based IoT systems. The CR-based IoT frameworks are the key points on which this survey focuses. Efficient spectrum sensing and sharing are the main functional components of the CR-based IoT. Reviews of recent SS and sharing approaches are presented in this survey. This survey classifies the SS and sharing approaches and discusses the merits and limitations of those approaches. Moreover, this survey discusses the design factors of the CR-based IoT and the criteria by which the proper SS and access approaches are selected. Furthermore, the survey explores the integration of newly emerging technologies with the CR-based IoT systems. Finally, the survey highlights some emerging challenges and concludes with suggesting future research directions and open issues.

This paper presents a stochastic geometry analysis of radio interference and a grid-based design of a primary exclusive region (PER) for spectrum sharing in the 3D unmanned aerial vehicle (UAV) networks. When a UAV network shares frequency bands with a primary system (e.g., a weather radar system), the UAVs must avoid harmful interference with the primary system. To facilitate the design of a complex-shaped PER according to a primary user's antenna pattern, spatial grid models, namely cylindrical and cubic grid models, are introduced. In the cylindrical grid model, to approximate the distribution of the interference at the radar, the cumulants of the interference are expressed by expressions with simple integrals or even closed-form expressions based on the assumption that the distributions of the UAVs in each grid cell follow an inhomogeneous 3D Poisson point process (PPP). In the cubic grid model, the shape of the grid cell is approximated by the cylindrical grid cell to derive the cumulants of the interference because they cannot be calculated in the same manner as in the cylindrical grid model. Using the derived interference cumulants to determine a PER to satisfy the radar's outage probability target, an optimization problem that minimizes the number of the UAVs forbidden from transmitting signals is formulated. The numerical results confirm that the approximated interference distribution using cumulants is in acceptable agreement with the simulation results and the PER obtained from the proposed optimization problem improves the number of the transmitting UAVs by reducing the volume of grid cells.

Abstract In this work, we study the cognitive decode-and-forward (DF) relaying, where a primary user (PU) communicates with an access point (AP), and in the same geographical region, a secondary source (SS) communicates to a secondary destination (SD) with assistance from a secondary relay (SR). Either SR or SD can decode the secondary data directly (DIR) or using the successive interference cancelation (SIC) technique. Based on the decoding methods of SR and SD, we proposed SR-DIR-SD-DIR, SR-DIR-SD-SIC, SR-SIC-SD-DIR, and SR-SIC-SD-SIC-based cognitive DF relaying schemes. The outage probabilities of both primary and secondary systems are analyzed for all the decoding manners. The transmit powers of SS and SR are further determined to minimize the outage probability of secondary system subject to the constraint that the outage performance loss of primary system should not exceed a certain percentage compared with the stand-alone primary network without spectrum sharing. Numerical results show that by using the SIC decoding, the outage probability of secondary system can be greatly decreased, especially when the interference from PU is strong or the transmission rate of primary data is low.