Showing papers on "Cognitive radio published in 2016"
TL;DR: Both analytical and numerical results are provided to demonstrate that F-NOMA can offer a larger sum rate than orthogonal MA, and the performance gain of F- NOMA over conventional MA can be further enlarged by selecting users whose channel conditions are more distinctive.
Abstract: Nonorthogonal multiple access (NOMA) represents a paradigm shift from conventional orthogonal multiple-access (MA) concepts and has been recognized as one of the key enabling technologies for fifth-generation mobile networks. In this paper, the impact of user pairing on the performance of two NOMA systems, i.e., NOMA with fixed power allocation (F-NOMA) and cognitive-radio-inspired NOMA (CR-NOMA), is characterized. For F-NOMA, both analytical and numerical results are provided to demonstrate that F-NOMA can offer a larger sum rate than orthogonal MA, and the performance gain of F-NOMA over conventional MA can be further enlarged by selecting users whose channel conditions are more distinctive. For CR-NOMA, the quality of service (QoS) for users with poorer channel conditions can be guaranteed since the transmit power allocated to other users is constrained following the concept of cognitive radio networks. Because of this constraint, CR-NOMA exhibits a different behavior compared with F-NOMA. For example, for the user with the best channel condition, CR-NOMA prefers to pair it with the user with the second best channel condition, whereas the user with the worst channel condition is preferred by F-NOMA.
1,391 citations
02 Sep 2016
TL;DR: It is shown that blind temporal learning on large and densely encoded time series using deep convolutional neural networks is viable and a strong candidate approach for this task especially at low signal to noise ratio.
Abstract: We study the adaptation of convolutional neural networks to the complex-valued temporal radio signal domain. We compare the efficacy of radio modulation classification using naively learned features against using expert feature based methods which are widely used today and e show significant performance improvements. We show that blind temporal learning on large and densely encoded time series using deep convolutional neural networks is viable and a strong candidate approach for this task especially at low signal to noise ratio.
737 citations
TL;DR: An F-RAN is presented as a promising paradigm for the fifth generation wireless communication system to provide high spectral and energy efficiency and key techniques and their corresponding solutions, including transmission mode selection and interference suppression, are discussed.
Abstract: An F-RAN is presented in this article as a promising paradigm for the fifth generation wireless communication system to provide high spectral and energy efficiency. The core idea is to take full advantage of local radio signal processing, cooperative radio resource management, and distributed storing capabilities in edge devices, which can decrease the heavy burden on fronthaul and avoid large-scale radio signal processing in the centralized baseband unit pool. This article comprehensively presents the system architecture and key techniques of F-RANs. In particular, key techniques and their corresponding solutions, including transmission mode selection and interference suppression, are discussed. Open issues in terms of edge caching, software-defined networking, and network function virtualization are also identified.
661 citations
TL;DR: A novel MIMO-NOMA framework for downlink and uplink transmission is proposed by applying the concept of signal alignment and closed-form analytical results are developed to facilitate the performance evaluation of the proposed framework for randomly deployed users and interferers.
Abstract: The application of multiple-input multiple-output (MIMO) techniques to nonorthogonal multiple access (NOMA) systems is important to enhance the performance gains of NOMA. In this paper, a novel MIMO-NOMA framework for downlink and uplink transmission is proposed by applying the concept of signal alignment. By using stochastic geometry, closed-form analytical results are developed to facilitate the performance evaluation of the proposed framework for randomly deployed users and interferers. The impact of different power allocation strategies, namely fixed power allocation and cognitive radio inspired power allocation, on the performance of MIMO-NOMA is also investigated. Computer simulation results are provided to demonstrate the performance of the proposed framework and the accuracy of the developed analytical results.
564 citations
TL;DR: A novel dynamic power allocation scheme is proposed to downlink and uplink non-orthogonal multiple access (NOMA) scenarios with two users for more flexibly meeting various quality of service requirements.
Abstract: In this paper, a novel dynamic power allocation scheme is proposed to downlink and uplink non-orthogonal multiple access (NOMA) scenarios with two users for more flexibly meeting various quality of service requirements. The exact expressions for the outage probability and the average rate achieved by the proposed scheme, as well as their high signal-to-noise ratio approximations, are established. Compared with the existing works, such as NOMA with fixed power allocation and cognitive radio inspired NOMA, the proposed scheme can: 1) strictly guarantee a performance gain over conventional orthogonal multiple access; and 2) offer more flexibility to realize different tradeoffs between the user fairness and system throughput. Monte Carlo simulation results are provided to demonstrate the accuracy of the developed analytical results and the performance gain of the proposed power allocation scheme.
523 citations
TL;DR: In this article, the authors comprehensively survey the recent advances of C-RANs, including system architectures, key techniques, and open issues, and discuss the system architectures with different functional splits and the corresponding characteristics.
Abstract: As a promising paradigm to reduce both capital and operating expenditures, the cloud radio access network (C-RAN) has been shown to provide high spectral efficiency and energy efficiency. Motivated by its significant theoretical performance gains and potential advantages, C-RANs have been advocated by both the industry and research community. This paper comprehensively surveys the recent advances of C-RANs, including system architectures, key techniques, and open issues. The system architectures with different functional splits and the corresponding characteristics are comprehensively summarized and discussed. The state-of-the-art key techniques in C-RANs are classified as: the fronthaul compression, large-scale collaborative processing, and channel estimation in the physical layer; and the radio resource allocation and optimization in the upper layer. Additionally, given the extensiveness of the research area, open issues, and challenges are presented to spur future investigations, in which the involvement of edge cache, big data mining, social-aware device-to-device, cognitive radio, software defined network, and physical layer security for C-RANs are discussed, and the progress of testbed development and trial test is introduced as well.
364 citations
TL;DR: A pivotal conclusion is reached that by carefully designing target data rates and power allocation coefficients of users, NOMA can outperform conventional orthogonal multiple access in underlay CR networks.
Abstract: In this paper, nonorthogonal multiple access (NOMA) is applied to large-scale underlay cognitive radio (CR) networks with randomly deployed users. To characterize the performance of the considered network, new closed-form expressions of the outage probability are derived using stochastic geometry. More importantly, by carrying out the diversity analysis, new insights are obtained under the two scenarios with different power constraints: 1) fixed transmit power of the primary transmitters (PTs); and 2) transmit power of the PTs being proportional to that of the secondary base station. For the first scenario, a diversity order of m is experienced at the mth-ordered NOMA user. For the second scenario, there is an asymptotic error floor for the outage probability. Simulation results are provided to verify the accuracy of the derived results. A pivotal conclusion is reached that by carefully designing target data rates and power allocation coefficients of users, NOMA can outperform conventional orthogonal multiple access in underlay CR networks.
358 citations
TL;DR: A comprehensive survey on the CRN communication paradigm in SGs, including the system architecture, communication network compositions, applications, and CR-based communication technologies is provided.
Abstract: Traditional power grids are currently being transformed into smart grids (SGs). SGs feature multi-way communication among energy generation, transmission, distribution, and usage facilities. The reliable, efficient, and intelligent management of complex power systems requires integration of high-speed, reliable, and secure data information and communication technology into the SGs to monitor and regulate power generation and usage. Despite several challenges, such as trade-offs between wireless coverage and capacity as well as limited spectral resources in SGs, wireless communication is a promising SG communications technology. Cognitive radio networks (CRNs) in particular are highly promising for providing timely SG wireless communications by utilizing all available spectrum resources. We provide in this paper a comprehensive survey on the CRN communication paradigm in SGs, including the system architecture, communication network compositions, applications, and CR-based communication technologies. We highlight potential applications of CR-based SG systems. We survey CR-based spectrum sensing approaches with their major classifications. We also provide a survey on CR-based routing and MAC protocols, and describe interference mitigation schemes. We furthermore present open issues and research challenges faced by CR-based SG networks along with future directions.
336 citations
TL;DR: In this article, the authors proposed a coexistence scheme for cognitive radio and cognitive radar systems in the same type of service, which alleviates the competition for spectrum resources, especially for radar and wireless communication systems.
Abstract: The last decade witnessed a growing demand on radio frequency that is driven by technological advances benefiting the end consumer but requiring new allocations of frequency bandwidths. Further, higher data rates for faster communications and wireless connections have called for an expanded share of existing frequency allocations. Concerns for spectrum congestion and frequency unavailability have spurred extensive research efforts on spectrum management and efficiency [1]-[4] within the same type of service and have led to cognitive radio [5] and cognitive radar [6]. On the other hand, devising schemes for coexistence among different services have eased the competition for spectrum resources, especially for radar and wireless communication systems [7]-[14]. Both systems have been recently given a common portion of the spectrum by the Federal Communications Commission.
272 citations
TL;DR: Numerical results not only demonstrate the close-to-optimal performance of the proposed suboptimal schemes but unveil an interesting tradeoff among the considered conflicting system design objectives as well.
Abstract: In this paper, we study resource allocation for multiuser multiple-input–single-output secondary communication systems with multiple system design objectives. We consider cognitive radio (CR) networks where the secondary receivers are able to harvest energy from the radio frequency when they are idle. The secondary system provides simultaneous wireless power and secure information transfer to the secondary receivers. We propose a multiobjective optimization framework for the design of a Pareto-optimal resource allocation algorithm based on the weighted Tchebycheff approach. In particular, the algorithm design incorporates three important system design objectives: total transmit power minimization, energy harvesting efficiency maximization, and interference-power-leakage-to-transmit-power ratio minimization. The proposed framework takes into account a quality-of-service (QoS) requirement regarding communication secrecy in the secondary system and the imperfection of the channel state information (CSI) of potential eavesdroppers (idle secondary receivers and primary receivers) at the secondary transmitter. The proposed framework includes total harvested power maximization and interference power leakage minimization as special cases. The adopted multiobjective optimization problem is nonconvex and is recast as a convex optimization problem via semidefinite programming (SDP) relaxation. It is shown that the global optimal solution of the original problem can be constructed by exploiting both the primal and the dual optimal solutions of the SDP-relaxed problem. Moreover, two suboptimal resource allocation schemes for the case when the solution of the dual problem is unavailable for constructing the optimal solution are proposed. Numerical results not only demonstrate the close-to-optimal performance of the proposed suboptimal schemes but unveil an interesting tradeoff among the considered conflicting system design objectives as well.
251 citations
TL;DR: A systematic review of high mobility communications, which focuses primarily on physical layer operations, which are affected the most by the mobile environment, and comprehensive reviews of techniques that can address these challenges and utilize the unique opportunities.
Abstract: Providing reliable broadband wireless communications in high mobility environments, such as high-speed railway systems, remains one of the main challenges faced by the development of the next generation wireless systems. This paper provides a systematic review of high mobility communications. We first summarize a list of key challenges and opportunities in high mobility communication systems, then provide comprehensive reviews of techniques that can address these challenges and utilize the unique opportunities. The review covers a wide spectrum of communication operations, including the accurate modeling of high mobility channels, the transceiver structures that can exploit the properties of high mobility environments, the signal processing techniques that can harvest the benefits (e.g., Doppler diversity) and mitigate the impairments (e.g., carrier frequency offset, intercarrier interference, channel estimation errors) in high mobility systems, and the mobility management and network architectures that are designed specifically for high mobility systems. The survey focuses primarily on physical layer operations, which are affected the most by the mobile environment, with some additional discussions on higher layer operations, such as handover management and control-plane/user-plane decoupling, which are essential to high mobility operations. Future research directions on high mobility communications are summarized at the end of this paper.
TL;DR: This paper proposes a resource allocation scheme for orthogonal frequency division multiple access (OFDMA)-based cognitive femtocells to maximize the total capacity of all femtocell users (FUs) under given quality-of-service and cotier/cross-tier interference constraints with imperfect channel sensing.
Abstract: The use of cognitive-radio(CR)-enabled femtocell is regarded as a promising technique in wireless communications, and many studies have been reported on its resource allocation and interference management. However, fairness and spectrum sensing errors were ignored in most of the existing studies. In this paper, we propose a resource allocation scheme for orthogonal frequency division multiple access (OFDMA)-based cognitive femtocells. The target is to maximize the total capacity of all femtocell users (FUs) under given quality-of-service (QoS) and cotier/cross-tier interference constraints with imperfect channel sensing. To achieve the fairness among FUs, the minimum and maximum numbers of subchannels occupied by each user are considered. First, the subchannel and power allocation problem is modeled as a mixed-integer programming problem, and then, it is transformed into a convex optimization problem by relaxing subchannel sharing and applying cotier interference constraints, which is finally solved using a dual decomposition method. Based on the obtained solution, an iterative subchannel and power allocation algorithm is proposed. The effectiveness of the proposed algorithm in terms of capacity and fairness compared with the existing schemes is verified by simulations.
TL;DR: In this survey, various spectrum occupancy models from measurement campaigns taken around the world are investigated and spectrum occupancy prediction is also discussed, where autoregressive and/or moving-average models are used to predict the channel status at future time instants.
Abstract: Spectrum occupancy models are very useful in cognitive radio designs. They can be used to increase spectrum sensing accuracy for more reliable operation, to remove spectrum sensing for higher resource usage efficiency, or to select channels for better opportunistic access, among other applications. In this survey, various spectrum occupancy models from measurement campaigns taken around the world are investigated. These models extract different statistical properties of the spectrum occupancy from the measured data. In addition to these models, spectrum occupancy prediction is also discussed, where autoregressive and/or moving-average models are used to predict the channel status at future time instants. After comparing these different methods and models, several challenges are also summarized based on this survey.
TL;DR: Current efforts to implement database-driven approaches for managing the shared co-existence of users with heterogeneous access and interference protection rights are focused on, and open research challenges are discussed.
Abstract: We are in the midst of a major paradigm shift in how we manage radio spectrum. This paradigm shift is necessitated by the growth of wireless services of all types and the demand pressure imposed on limited spectrum resources under legacy management regimes. The shift is feasible because of advances in radio and networking technologies that make it possible to share spectrum dynamically in all possible dimensions—i.e., across frequencies, time, location, users, uses, and networks. Realizing the full potential of this shift to Dynamic Spectrum Sharing will require the co-evolution of wireless technologies, markets, and regulatory policies; a process which is occurring on a global scale. This paper provides a current overview of major technological and regulatory reforms that are leading the way toward a global paradigm shift to more flexible, dynamic, market-based ways to manage and share radio spectrum resources. We focus on current efforts to implement database-driven approaches for managing the shared co-existence of users with heterogeneous access and interference protection rights, and discuss open research challenges.
TL;DR: This survey paper provides a detailed review of the state of the art related to the application of CS in CR communications and provides a classification of the main usage areas based on the radio parameter to be acquired by a wideband CR.
Abstract: Compressive sensing (CS) has received much attention in several fields such as digital image processing, wireless channel estimation, radar imaging, and cognitive radio (CR) communications. Out of these areas, this survey paper focuses on the application of CS in CR communications. Due to the under-utilization of the allocated radio spectrum, spectrum occupancy is usually sparse in different domains such as time, frequency, and space. Such a sparse nature of the spectrum occupancy has inspired the application of CS in CR communications. In this regard, several researchers have already applied the CS theory in various settings considering the sparsity in different domains. In this direction, this survey paper provides a detailed review of the state of the art related to the application of CS in CR communications. Starting with the basic principles and the main features of CS, it provides a classification of the main usage areas based on the radio parameter to be acquired by a wideband CR. Subsequently, we review the existing CS-related works applied to different categories such as wideband sensing, signal parameter estimation and radio environment map (REM) construction, highlighting the main benefits and the related issues. Furthermore, we present a generalized framework for constructing the REM in compressive settings. Finally, we conclude this survey paper with some suggested open research challenges and future directions.
TL;DR: Three PA algorithms to maximize the throughput of secondary users (SUs), the energy efficiency of the network, and the requirements of SUs, respectively, while guaranteeing the quality of service (QoS) of the primary user (PU) are proposed.
Abstract: Interference alignment (IA) is a promising technique for interference management and can be applied to spectrum sharing in cognitive radio (CR) networks. However, the sum rate may fall short of the theoretical maximum, particularly at low signal-to-noise ratio (SNR), and the quality of service (QoS) of the primary user (PU) may not be guaranteed. In addition, power allocation (PA) in IA-based CR networks is largely ignored, which can further improve its performance. Thus, in this paper, PA in IA-based CR networks is studied. To guarantee the QoS requirement of the PU, its minimal transmitted power is derived. Then, we propose three PA algorithms to maximize the throughput of secondary users (SUs), the energy efficiency (EE) of the network, and the requirements of SUs, respectively, while guaranteeing the QoS of the PU. To reduce the complexity, the closed-form solutions of these algorithms are further studied in detail. The outage probability of the PU according to its rate threshold is also derived to analyze the performance of these algorithms. Moreover, we propose a transmission-mode adaptation scheme to further improve the PU's performance when its QoS requirement cannot be guaranteed at low SNR, and it can be easily combined with the proposed PA algorithms. Simulation results are presented to show the effectiveness of the proposed adaptive PA algorithms for IA-based CR networks.
TL;DR: This paper provides an overview of cognitive radio (CR) networks, with focus on the recent advances in resource allocation techniques and the CR networks architectural design.
Abstract: This paper provides an overview of cognitive radio (CR) networks, with focus on the recent advances in resource allocation techniques and the CR networks architectural design. The contribution of this work is threefold. First, a systematic way to study the resource allocation problem is presented; various design approaches are introduced, such as signal-to-interference-and-noise ratio (SINR) or transmission power-based, and centralized or distributed methods. Second, CR optimization methods are presented, accompanied by a comprehensive study of the resource allocation problem formulations. Furthermore, quality of service criteria of the physical or/and the medium access control layers are investigated. Third, challenges in spectrum assignment are discussed, focusing on dynamic spectrum allocation, spectrum aggregation and frequency mobility. Such approaches constitute an emerging trend in efficient spectrum sharing and affect the performance of resource allocation techniques. The open issues for future research in this area are finally discussed, including adaptability-reconfigurability, dual accessibility, and energy efficiency.
TL;DR: A new spatial spectrum-sharing strategy for massive multiple-input multiple-output (MIMO) cognitive radio (CR) systems and a full-space coverage concept by employing two CBSs at the adjacent sides of each cell, which diminishes the sheltering effect from the primary radio.
Abstract: In this paper, we introduce a new spatial spectrum-sharing strategy for massive multiple-input multiple-output (MIMO) cognitive radio (CR) systems. Different from the conventional MIMO CR system, CR terminals can be discriminated by their angular information with the help of high spatial resolution of massive antennas at CR base station (CBS). Moreover, the discrete Fourier transform can be applied to efficiently obtain such angular information thanks to the massive antennas, again. We then formulate a 2-D spatial basis expansion model to represent the uplink/downlink channels of CRs with reduced parameter dimensions, which immediately alleviates the general headaches of massive MIMO systems, such as uplink pilot contamination and downlink training overhead. Moreover, we present a full-space coverage concept by employing two CBSs at the adjacent sides of each cell, which diminishes the sheltering effect from the primary radio. We also design two greedy CR scheduling algorithms for the dual CBSs to improve the spectral efficiency and enhance the scheduling probability of CRs. Since the proposed strategy exploits angular information and since the angle reciprocity holds for two frequency carriers with moderate distance, the proposed strategy is applied for both time division duplex and frequency division duplex systems.
TL;DR: Two dynamic channel accessing schemes are proposed to identify the channel sensing and switching sequences for intra-clusters and inter-cluster data transmission, respectively and can significantly reduce the energy consumption in CRSNs.
Abstract: Wireless sensor networks operating in the license-free spectrum suffer from uncontrolled interference as those spectrum bands become increasingly crowded. The emerging cognitive radio sensor networks (CRSNs) provide a promising solution to address this challenge by enabling sensor nodes to opportunistically access licensed channels. However, since sensor nodes have to consume considerable energy to support CR functionalities, such as channel sensing and switching, the opportunistic channel accessing should be carefully devised for improving the energy efficiency in CRSN. To this end, we investigate the dynamic channel accessing problem to improve the energy efficiency for a clustered CRSN. Under the primary users’ protection requirement, we study the resource allocation issues to maximize the energy efficiency of utilizing a licensed channel for intra-cluster and inter-cluster data transmission, respectively. Moreover, with the consideration of the energy consumption in channel sensing and switching, we further determine the condition when sensor nodes should sense and switch to a licensed channel for improving the energy efficiency, according to the packet loss rate of the license-free channel. In addition, two dynamic channel accessing schemes are proposed to identify the channel sensing and switching sequences for intra-cluster and inter-cluster data transmission, respectively. Extensive simulation results demonstrate that the proposed channel accessing schemes can significantly reduce the energy consumption in CRSNs.
TL;DR: This paper surveys novel approaches and discusses research challenges related to the use of cognitive radio technology for Internet of things, and intends to help new researchers entering the domain of CR and IoT by providing a comprehensive survey on recent advances.
TL;DR: The closed-form analytical results are developed to show that the cooperative transmission scheme gives better performance when more secondary users participate in relaying, which helps achieve the maximum diversity order at secondary user and a diversity order of two at primary user.
Abstract: This letter studies the application of non-orthogonal multiple access to a downlink cognitive radio (termed CR-NOMA) system. A new cooperative transmission scheme is proposed aimed at exploiting the inherent spatial diversity offered by the CR-NOMA system. The closed-form analytical results are developed to show that the cooperative transmission scheme gives better performance when more secondary users participate in relaying, which helps achieve the maximum diversity order at secondary user and a diversity order of two at primary user. The simulations are performed to validate the performance of the proposed scheme and the accuracy of the analytical results.
TL;DR: This paper presents a comprehensive survey on the state-of-the-art channel assignment algorithms in cognitive radio networks, and classify the algorithms by presenting a thematic taxonomy of the current channel assignments algorithms in Cognitive radio networks.
Abstract: The cognitive radio is an emerging technology that enables dynamic spectrum access in wireless networks. The cognitive radio is capable of opportunistically using the available portions of a licensed spectrum to improve the application performance for unlicensed users. The opportunistic use of the available channels in the wireless environment requires dynamic channel assignment to efficiently utilize the available resources while minimizing the interference in the network. A challenging aspect of such algorithms is the incorporation of the channels' diverse characteristics, highly dynamic network conditions with respect to primary users' activity, and different fragmented sizes of the available channels. This paper presents a comprehensive survey on the state-of-the-art channel assignment algorithms in cognitive radio networks. We also classify the algorithms by presenting a thematic taxonomy of the current channel assignment algorithms in cognitive radio networks. Moreover, the critical aspects of the current channel assignment algorithms in cognitive radio networks are analyzed to determine the strengths and weaknesses of such algorithms. The similarities and differences of the algorithms based on the important parameters, such as routing dependencies, channel models, assignment methods, execution model, and optimization objectives, are also investigated. We also discuss open research issues and challenges of channel assignment in the cognitive radio networks.
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TL;DR: In this article, the problem of robust secure artificial noise-aided beamforming and power splitting design under imperfect channel state information (CSI) was investigated in a multiple-input single-output cognitive radio downlink network with simultaneous wireless information and power transfer.
Abstract: A multiple-input single-output cognitive radio downlink network is studied with simultaneous wireless information and power transfer. In this network, a secondary user coexists with multiple primary users and multiple energy harvesting receivers. In order to guarantee secure communication and energy harvesting, the problem of robust secure artificial noise-aided beamforming and power splitting design is investigated under imperfect channel state information (CSI). Specifically, the transmit power minimization problem and the max-min fairness energy harvesting problem are formulated for both the bounded CSI error model and the probabilistic CSI error model. These problems are non-convex and challenging to solve. A one-dimensional search algorithm is proposed to solve these problems based on ${\cal S}\text{-Procedure} $ under the bounded CSI error model and based on Bernstein-type inequalities under the probabilistic CSI error model. It is shown that the optimal robust secure beamforming can be achieved under the bounded CSI error model, whereas a suboptimal beamforming solution can be obtained under the probabilistic CSI error model. A tradeoff is elucidated between the secrecy rate of the secondary user receiver and the energy harvested by the energy harvesting receivers under a max-min fairness criterion.
TL;DR: In this paper, an approach to exploit TV white space (TVWS) for device-to-device (D2D) communications with the aid of the existing cellular infrastructure is presented.
Abstract: This paper presents a systematic approach to exploiting TV white space (TVWS) for device-to-device (D2D) communications with the aid of the existing cellular infrastructure. The goal is to build a location-specific TVWS database, which provides a lookup table service for any D2D link to determine its maximum permitted emission power (MPEP) in an unlicensed digital TV (DTV) band. To achieve this goal, the idea of mobile crowd sensing is first introduced to collect active spectrum measurements from massive personal mobile devices. Considering the incompleteness of crowd measurements, we formulate the problem of unknown measurements recovery as a matrix completion problem and apply a powerful fixed point continuation algorithm to reconstruct the unknown elements from the known elements. By joint exploitation of the big spectrum data in its vicinity, each cellular base station further implements a nonlinear support vector machine algorithm to perform irregular coverage boundary detection of a licensed DTV transmitter. With the knowledge of the detected coverage boundary, an opportunistic spatial reuse algorithm is developed for each D2D link to determine its MPEP. Simulation results show that the proposed approach can successfully enable D2D communications in TVWS while satisfying the interference constraint from the licensed DTV services. In addition, to our best knowledge, this is the first try to explore and exploit TVWS inside the DTV protection region resulted from the shadowing effect. Potential application scenarios include communications between internet of vehicles in the underground parking and D2D communications in hotspots such as subway, game stadiums, and airports.
TL;DR: An aggregate network utility optimization framework is developed for the design of an online energy management, spectrum management, and resource allocation algorithm based on Lyapunov optimization to achieve two major goals: first, balancing sensors' energy consumption and energy harvesting while stabilizing their data and energy queues.
Abstract: In this paper, we study resource management and allocation for energy harvesting cognitive radio sensor networks (EHCRSNs). In these networks, energy harvesting supplies the network with a continual source of energy to facilitate the self-sustainability of the power-limited sensors. Furthermore, cognitive radio enables access to the underutilized licensed spectrum to mitigate the spectrum-scarcity problem in the unlicensed band. We develop an aggregate network utility optimization framework for the design of an online energy management, spectrum management, and resource allocation algorithm based on Lyapunov optimization. The framework captures three stochastic processes: energy harvesting dynamics, inaccuracy of channel occupancy information, and channel fading. However, a priori knowledge of any of these processes statistics is not required. Based on the framework, we propose an online algorithm to achieve two major goals: first, balancing sensors’ energy consumption and energy harvesting while stabilizing their data and energy queues; second, optimizing the utilization of the licensed spectrum while maintaining a tolerable collision rate between the licensed subscriber and unlicensed sensors. The performance analysis shows that the proposed algorithm achieves a close-to-optimal aggregate network utility while guaranteeing bounded data and energy queue occupancy. The extensive simulations are conducted to verify the effectiveness of the proposed algorithm and the impact of various network parameters on its performance.
TL;DR: This work tries to classify the possible directions in energy efficientCSS and presents a limited set of works introducing new ideas to an energy efficient CSS algorithm.
Abstract: The article analyzes the problem of energy efficient techniques in cooperative spectrum sensing (CSS). Although it was proven that single-device sensing is not sufficient for reliable sensing, cooperative spectrum sensing was proposed, burdened, however, with great overhead. Thus, work on the topic of energy efficient cooperative schemes gained more interest, which resulted in a number of energy efficient cooperative algorithm proposals. In this work, we try to classify the possible directions in energy efficient CSS and present a limited set of works introducing new ideas to an energy efficient CSS algorithm.
TL;DR: This paper investigates the performance of a cognitive hybrid satellite-terrestrial network, where the primary satellite communication network and the secondary terrestrial mobile network coexist, provided that the interference temperature constraint is satisfied.
Abstract: This paper investigates the performance of a cognitive hybrid satellite–terrestrial network, where the primary satellite communication network and the secondary terrestrial mobile network coexist, provided that the interference temperature constraint is satisfied. By using the Meijer-G functions, the exact closed-form expression of the outage probability (OP) for the secondary network is first derived. Then, the asymptotic result in a high-signal-to-noise-ratio (SNR) regime is presented to reveal the diversity order and coding gain of the considered system. Finally, computer simulations are carried out to confirm the theoretical results and reveal that a more loose interference constraint or heavier shadowing severity of a satellite interference link leads to a reduced OP, whereas stronger satellite interference power poses a detrimental effect on the outage performance.
TL;DR: A detailed classification and comprehensive survey of existing spectrum handoff schemes for cognitive radio networks is presented and various research issues and challenges are presented which require the attention of the researchers.
TL;DR: A survey of CB schemes for traditional wireless networks such as cellular networks, wireless local area networks, and wireless sensor networks is provided, and a detailed discussion on the CB schemes proposed for cognitive radio networks are provided.
Abstract: Channel bonding (CB) is a proven technique to increase bandwidth and reduce delays in wireless networks. It has been applied in traditional wireless networks such as cellular networks and wireless local area networks along with the emerging cognitive radio networks. This paper first focuses on providing a survey of CB schemes for traditional wireless networks such as cellular networks, wireless local area networks, and wireless sensor networks, and then provides a detailed discussion on the CB schemes proposed for cognitive radio networks. Finally, we highlight a number of issues and challenges regarding CB in cognitive radio sensor networks and also provide some guidelines on using CB schemes in these futuristic networks.
01 Dec 2016
TL;DR: A deep learning-based AMC method that employs Spectral Correlation Function (SCF) and Deep Belief Network (DBN) is proposed for pattern recognition and classification that achieves high accuracy in modulation detection and classification even in the presence of environment noise.
Abstract: Automated Modulation Classification (AMC) has been applied in various emerging areas such as cognitive radio (CR). In our paper, we propose a deep learning-based AMC method that employs Spectral Correlation Function (SCF). In our proposed method, one deep learning technology, Deep Belief Network (DBN), is applied for pattern recognition and classification. By using noise-resilient SCF signatures and DBN that is effective in learning complex patterns, we achieve high accuracy in modulation detection and classification even in the presence of environment noise. Our simulation results illustrate the efficiency of our proposed method in classifying 4FSK, 16QAM, BPSK, QPSK, and OFDM modulation techniques in various environments.