Spectrum Access Techniques Research Articles

Dynamic Spectrum Sharing in the Age of Millimeter-Wave Spectrum Access

Next-generation wireless networks are facing spectrum shortage challenges, mainly due to, among other factors, the projected massive numbers of IoT connections and the emerging bandwidth-hungry applications that such networks ought to serve. Spectrum is scarce and expensive, and therefore, it is of crucial importance to devise dynamic and flexible spectrum access policies and techniques that yield optimal usage of such a precious resource. A new trend recently being adopted as a key solution to this spectrum scarcity challenge is to exploit higher frequency bands, namely mmWave bands, that were considered impractical few years ago, but are now becoming feasible due to recent advances in electronics. Though, fortunately, spectrum regulatory bodies have responded by allowing the use of new bands in the mmWave frequencies, much work still needs to be done to benefit from such new spectra. In this paper, we discuss some key spectrum management challenges that pertain to dynamic spectrum access at the mmWave frequencies, which need to be overcome in order to promote dynamic spectrum sharing at these mmWave bands. We also propose new techniques that enable efficient dynamic spectrum sharing at the mmWave bands by addressing some of the discussed challenges, and highlight open research challenges that still need to be addressed to fully unleash the potential of dynamic spectrum sharing at mmWave bands.

Comparative Assessment of the LoRaWAN Medium Access Control Protocols for IoT: Does Listen before Talk Perform Better than ALOHA?

Low-Power Wide-Area Networks (LPWANs) are emerging as appealing solutions for several Internet of Things (IoT) applications, such as healthcare, smart cities and Industry 4.0, thanks to their ease of deployment, low energy consumption and large coverage range. LoRaWAN is one of the most successful LPWAN standards, as it supports robust long-distance communications using low-cost devices. To comply with the ETSI regulations, LoRaWAN can adopt as medium access control (MAC) layer either a pure ALOHA approach with duty-cycle limitations or a polite spectrum access technique, such as Listen Before Talk (LBT). The two approaches have their pros and cons that need to be carefully evaluated. The studies in the literature that so far have addressed an evaluation of MAC protocols for LoRaWAN refer to a previous and now obsolete version of the ETSI regulations, therefore they do not take into account the current limits on the timing parameters for polite spectrum access, such as that maximum time an end-node is allowed to be transmitting per hour. For this reason, the contribution of this work is two-fold. First, the paper discusses the restrictions that the current ETSI regulations impose on some timing parameters of the two kinds of MAC protocols for LoRaWAN. Second, the paper provides comparative performance assessments of the two protocols through simulations in realistic scenarios under different workload conditions.

Orthogonal codes-based dynamic spectrum access in cognitive radio networks

Dynamic spectrum access (DSA) in cognitive radio (CR) networks became a challenging research area recently. In CR technology, the DSA between primary users (PUs) and secondary users (SUs) simultaneously can be achieved without degrading the performance of the PUs by SUs interference. This can be achieved by donating incentive power to the PUs in order to compensate the interference caused by the SUs. Consequently, PUs allow SUs to share the spectrum. In this paper, orthogonal codes-based dynamic spectrum access (OC-DSA) technique is proposed. OC-DSA technique employs orthogonality between PUs and SUs transmitted data symbols in addition to the incentive power donation. Compared to other techniques, the proposed technique uses a simple encoder at the SU network for the same PU network infrastructure. By applying orthogonal codes, the interference caused by SUs is canceled and hence the donated power to incentivize the PUs is reduced. Also, the SU packet rate is increased significantly. The simulation results show that the proposed technique provides effective improvements over other existing techniques in the signal strength and the bit error rate performance of both the PU network and the SU network at the receiver side. Moreover, the proposed technique requires less donated power to incentivize the PU and has higher packet rate.

Collaborative and Passive Channel Gain Estimation in Fading Environments

Dynamic spectrum access techniques are typically aided by knowledge of the wireless channel gains among participating radios, as this knowledge allows the potential interference impact of any radio’s transmissions on its neighbors to be quantified. We present a technique for collaborative inference of the channel gains which relies solely on the radios monitoring their aggregate transmitted and received energies as they transmit their data packets. We demonstrate that through low data-rate exchange of these energy metrics among bursty networks, the gains can be jointly estimated within a dB and with low latency on the order of seconds. In particular, we derive the best linear unbiased estimator (BLUE) for the gains. While this estimator relies on knowledge of fading parameters not known in practice, we propose a practical variant which achieves performance comparable to the BLUE in the realistic fading setting used in our simulations.

Cooperative communication based access technique for sensor networks

ABSTRACTThe collaboration of users in communication systems is defined as cooperative communication. The cognitive radio, i.e. dynamic spectrum access technique, is a wireless communication technology that provides a great chance for unlicensed users to exploit the frequency bands in an opportunistic way. A wireless sensor network is a widely used communication technology composing of spatially distributed independent sensors in order to monitor physical or environmental circumstances. In this work, a new technique that unlicensed users become a cooperative relay when they are in idle mode is proposed. Along with the proposed technique, unlicensed users help sensor nodes as a cooperative relay when they are in idle mode. Similarly, sensor nodes help unlicensed users for detecting idle frequency bands while in sleep mode. By preventing any disruption that remote users may be exposed owing to signal attenuation, the proposed cooperative relay utilises amplify and forward based cooperative communication protocol. Thanks to this approach; the overall network has greater performance than wireless sensor network that does not use cooperative communication based dynamic access technique in terms of throughput, energy, and delay.

An Evolutionary Scheme for Secondary Virtual Networks Mapping onto Cognitive Radio Substrate

The Fifth Generation (5G) of wireless communication is envisioned to comprise heterogeneous applications, different radio access technologies (RATs), and a large demand for mobile traffic. In this respect, Wireless Virtualization (WV) and Cognitive Radio (CR) are put forward as 5G enablers for providing additional spectrum resources through dynamic spectrum access (DSA) techniques, besides dealing with heterogeneity with no hardware modification. By empowering the synergy between CR and WV, we visualize an environment denoted as Cognitive Radio Virtual Networks Environment (CRVNE) that encompasses VWNs with different access priorities, called Primary Virtual Networks (PVNs) and Secondary Virtual Networks (SVNs) that may be deployed in an overlay manner. In this scenario, the SVNs users (SUs) access the resources opportunistically, which naturally raises challenges towards the SVN mapping. In this paper, we revisit our previous letter that models the interactions between PUs and SUs in a CRVNE and analyzes a proposed formulation for collision probability during the SVN mapping process. The current work is pioneer as it presents a comprehensive approach to the SVNs mapping problem; models, validates, and analyzes additional performance metrics such as SU blocking and SU dropping probabilities and joint utilization; formulates the SVNs mapping as a multiobjective problem; and proposes an evolutionary scheme based on Genetic Algorithms (GAs) to solve it. The results show that the proposed scheme outperforms the alternative method in terms of collision, SU dropping, SU blocking probabilities, and joint utilization under different primary and secondary loads.

An Effective Spectrum Handoff Based on Reinforcement Learning for Target Channel Selection in the Industrial Internet of Things.

The overcrowding of the wireless space has triggered a strict competition for scare network resources. Therefore, there is a need for a dynamic spectrum access (DSA) technique that will ensure fair allocation of the available network resources for diverse network elements competing for the network resources. Spectrum handoff (SH) is a DSA technique through which cognitive radio (CR) promises to provide effective channel utilization, fair resource allocation, as well as reliable and uninterrupted real-time connection. However, SH may consume extra network resources, increase latency, and degrade network performance if the spectrum sensing technique used is ineffective and the channel selection strategy (CSS) is poorly implemented. Therefore, it is necessary to develop an SH policy that holistically considers the implementation of effective CSS, and spectrum sensing technique, as well as minimizes communication delays. In this work, two reinforcement learning (RL) algorithms are integrated into the CSS to perform channel selection. The first algorithm is used to evaluate the channel future occupancy, whereas the second algorithm is used to determine the channel quality in order to sort and rank the channels in candidate channel list (CCL). A method of masking linearly dependent and useless state elements is implemented to improve the convergence of the learning. Our approach showed a significant reduction in terms of latency and a remarkable improvement in throughput performance in comparison to conventional approaches.

Novel QoS-Aware Proactive Spectrum Access Techniques for Cognitive Radio Using Machine Learning

Traditional cognitive radio (CR) spectrum access techniques have been primitive and inefficient due to being blind to the occupancy conditions of the spectrum bands to be sensed. In addition, current spectrum access techniques are also unable to detect network changes or even consider the requirements of unlicensed users, leading to a poorer quality of service (QoS) and excessive latency. As user-specific approaches will play a key role in future wireless communication networks, the conventional CR spectrum access should also be updated in order to be more effective and agile. In this paper, a comprehensive and novel solution is proposed to decrease the sensing latency and to make the CR networks (CRNs) aware of unlicensed user requirements. As such, a proactive process with a novel QoS-based optimization phase is proposed, consisting of two different decision strategies. Initially, future traffic loads of the different radio access technologies (RATs), occupying different bands of the spectrum, are predicted using the artificial neural networks (ANNs). Based on these predictions, two strategies are proposed. In the first one, which solely focuses on latency, a virtual wideband (WB) sensing approach is developed, where predicted relative traffic loads in WB are exploited to enable narrowband (NB) sensing. The second one, based on Q -learning, focuses not only on minimizing the sensing latency but also on satisfying other user requirements. The results reveal that the first strategy manages to significantly reduce the sensing latency of the random selection process by 59.6%, while the Q -learning assisted second strategy enhanced the full-satisfaction by up to 95.7%.

Achievable Rates With Cooperative Spectrum Sharing in the Modulation-Based Dimension

Cooperative spectrum sharing (CSS) is a joint spectrum access technique that involves collaboration between primary user (PU) and secondary user (SU) systems. An instance of CSS occurs when the SU transmitter acts as a relay for the PU system. In forwarding the PU signal, the SU transmitter deliberately superimposes the signal intended for the SU receiver. This process unfortunately causes mutual interferences at both PU and SU receivers. Achievable rates for both systems are typically analyzed by assuming Gaussian signals and interferences. Unlike most of previous work, this paper studies the use of practical digital modulation schemes in CSS. The novelty of the proposed CSS approach lies on the smart alignment and superimposition of the SU's pulse amplitude modulated signal to the PU's phase-shift keying signal. It is shown that the SU signal does not interfere with the PU signal, it actually improves the PU signal strength. This paper then focuses on deriving the approximated theoretical transmission rates for both the PU and SU. Interestingly, the analysis shows superior transmission rates for both systems, compared to that achieved by conventional signal superimposition approaches. Simulation results then confirm the matching with the analytical results and validate the performance advantages of the proposed CSS approach.

MEETS: Maximal Energy Efficient Task Scheduling in Homogeneous Fog Networks

A homogeneous fog network is defined as a group of peer nodes with sharable computing and storage resources, as well as spare spectrum for node-to-node/device-to-device communications and task scheduling. It promotes more intelligent applications and services in different Internet of Things (IoT) scenarios, thanks to effective collaborations among neighboring fog nodes via cognitive spectrum access techniques. In this paper, a comprehensive analytical model that considers circuit, computation, offloading energy consumptions is developed for accurately evaluating the overall energy efficiency (EE) in homogeneous fog networks. With this model, the tradeoff relationship between performance gains and energy costs in collaborative task offloading is investigated, thus enabling us to formulate the EE optimization problem for future intelligent IoT applications with practical constraints in available computing resources at helper nodes and unused spectrum in neighboring environments. Based on rigorous mathematical analysis, a maximal energy-efficient task scheduling (MEETS) algorithm is proposed to derive the optimal scheduling decision for a task node and multiple neighboring helper nodes under feasible modulation schemes and time allocations. Extensive simulation results demonstrate the tradeoff relationship between EE and task scheduling performance in homogeneous fog networks. Compared with traditional task scheduling strategies, the proposed MEETS algorithm can achieve much better EE performance under different network parameters and service conditions.

Stochastic-Geometry Based Characterization of Aggregate Interference in TVWS Cognitive Radio Networks

In this paper, we characterize the worst-case interference for a finite-area TV white space heterogeneous network using the tools of stochastic geometry. We derive closed-form expressions on the probability distribution function (PDF) and an average value of the aggregate interference for various values of path loss exponent. The proposed characterization of the interference is simple and can be used in improving the spectrum access techniques. Using the derived PDF, we demonstrate the performance gain in the spectrum detection of an eigenvalue-based detector for cognitive radio networks.

Spectrum aware fuzzy clustering algorithm for cognative radio sensor networks

This paper proposes a SAFCA for a self-organized CH selection within a CRSN. The algorithm caters CR and WSN constraints by exploiting the dynamic spectrum access and fuzzy inference technique for an energy efficient CRSN. It utilizes channel availability and fuzzy parameters of residual energy, communication cost and node distribution. The simulation measure the significant of the selected parameters. The first stage investigates the impact of inclusion of fuzzy inference towards the channel availability in the clustering algorithm, meanwhile the second stage explores the significant of the fuzzy parameters. The performance metric of network stability i.e. FND and network lifetime i.e.HNA are measured to determine the energy efficiency of the clustering algorithm. The results show the algorithm outperforms SACA, SAFEC, SAFEN and SAFCN of FND, HNA, number of alive nodes and energy consumption. Keywords : cognitive radio; wireless sensor node; clustering; channel availability; fuzzy.

Spectrum Sensing Using Hybrid Gravitational Search Genetic Algorithm Based Predictor Model in Cognitive Radio Networks

The exponential growth of wireless users and devices increases the demand for frequency spectrum. The radio spectrum is considered as a precious natural resource that is available in a limited range for the use of wireless services. Hence the primary challenge is to design efficient spectrum allotment policies that utilize the spectrum in a better way. The frequency spectrum has been regulated and assigned to the service provider by ITU. Only 70% of the spectrum bands are utilized efficiently while 30% are underutilized by the licensed users. These spectrum holes can be used by the users in an opportunistic manner through Dynamic Spectrum Access (DSA) technique. Spectrum holes of the licensed users have to be utilized by an unlicensed user without causing interference to the licensed user. Among the various functionalities of cognitive radio, spectrum sensing plays a significant role in determining the availability of spectrum holes. The Time and energy consumed by the spectrum sensing phase can be reduced by incorporating prediction before sensing the entire spectrum range. The prediction helps in predicting holes in near future based on the current and the past history. By employing an accurate and reliable prediction model, the secondary user’s energy can be conserved, so that only idle channels which are predicted by the model be sensed by the sensing module. The proposed work presents a hybrid predictor scheme that uses Gravitational Search Genetic Algorithm (GSGA) in Backpropagation Neural Network to forecast the Primary User (PU) channel occupancy status. The proposed approach is simulated using MATLAB and the results show high accuracy when compared with Neural Network model based on Backpropagation.

Optimal sensing performance for cooperative and non-cooperative cognitive radio networks

Spectrum sensing has gained new aspects with the advent of cognitive radio and opportunistic spectrum access techniques. Spectrum sensing plays a vital role in cognitive radio networks, and its rel...

Performance analysis of cognitive radio networks using channel-prediction-probabilities and improved frame structure

This paper focuses on potential issues related to the random selection of a sensing channel that occurs after the prediction phase in a Cognitive Radio Network (CRN). A novel approach (Approach-1) for improved selection is proposed, which relies on the probabilities of channels by which they are predicted idle. Further, closed-form expressions are derived for the throughput of Cognitive Users (CUs) in the conventional and proposed approaches. In addition to this, a fundamental approach for computing the prediction probabilities is also proposed. Moreover, a new challenging issue named “sense and stuck” was observed in the conventional approach. The proposed approach is validated by comparing the results achieved with the results of the conventional approach. However, to achieve the prediction probabilities, the pre-channel-state-information is a prerequisite, but it may be unavailable for particular scenarios; therefore, a modified selection method is introduced to avoid the sense and stuck problem. An algorithm to evaluate the throughput using the random, improved, and modified selection methods is presented with its space and time complexities. Furthermore, for additional improvement in the throughput of the CU, a new frame structure is introduced, in which the spectrum prediction and sensing periods are exploited for simultaneous transmission of data via the underlay spectrum access technique (Approach-2). The simulated results of Approach-2 are compared with our pre-obtained results of Approach-1, which confirm significant improvement in the throughput.

Pricing‐based distributed spectrum access for cognitive radio networks with geolocation database

A pricing-based distributed spectrum access technique for cognitive radio (CR) networks which adopt the geolocation database (GD) is proposed. The GD contains which frequency bands are occupied by the primary system in a particular location. Given that multiple CR systems may attempt to transmit data over the same frequency band when the GD is used, the achievable rate of the CR systems becomes deteriorated due to interference. In the proposed technique, each (secondary) CR system determines whether it utilises vacant frequency bands by considering the cost of using them, which is calculated by taking into account the interference. The authors analyse the behaviour of the CR systems based on game theory. In particular, it is shown that the sum capacity of CR systems is maximised when the number of utilised bands is proportional to the relative channel gain with respect to the average channel gain at each CR system. In addition, the authors obtain the optimal cost for the vacant bands, which achieves the maximum sum capacity of CR systems. Finally, it is shown that the sum capacity of the (secondary) CR systems is significantly improved via proper pricing policy on the vacant frequency bands through extensive computer simulations.

Advanced Frame Structures for Hybrid Spectrum Access Strategy in Cognitive Radio Communication Systems

In this letter, we have exploited a novel hybrid-cum-improved spectrum access technique for significant improvement in the throughput and data-loss rate. This proposed technique comprises of hybrid spectrum access and improved frame structure strategies of the cognitive radio communication system. Also, the closed-form expressions for throughput and data-loss of these approaches are derived. Moreover, the proposed approaches are validated numerically as well as with reported literature.

Performance analysis of high-traffic cognitive radio communication system using hybrid spectrum access, prediction and monitoring techniques

In this paper, the hybrid spectrum access and prediction techniques are exploited simultaneously in the high-traffic cognitive radio communication system, in order to enhance the throughput and overcome the problem of waiting states. The hybrid spectrum access is responsible for throughput enhancement by escaping the waiting states whereas the spectrum prediction alleviates the sensing errors in the high-traffic communication environment. The closed-form expression for the throughput of cognitive user (CU) communication is derived and validated the proposed approach with the reported literature. Moreover, a new framework is proposed to conquer the sharing issues of conventional and proposed approaches. In addition to this, the performance metrics of proposed framework such as the data-loss, energy-loss of the CU and interference at the PU have been analyzed.

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

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

Spectral Precoding for Out-of-Band Power Reduction Under Condition Number Constraint in OFDM-Based Systems

Orthogonal frequency division multiplexing (OFDM) is a promising technique for dynamic spectrum access due to flexibility in spectrum shaping. However, out-of-band (OOB) power radiation of OFDM introduces significant interference to adjacent users. This is a serious problem in cognitive radio networks, which affects the secondary system to access the instantaneous spectrum hole. Existing methods either do not effectively reduce OOB power leakage or introduce significant bit-error-rate deterioration at the receiver. In this paper, a joint spectral precoding scheme is developed for OOB power reduction by using the matrix operations of orthogonal projection and singular value decomposition. We also propose an algorithm that designs the precoding matrix under receive performance constraint, which is then converted to matrix condition number constraint in practice. This method achieves the desirable spectrum envelope and receive performance by selecting zero-forcing frequencies. Simulation results show that OOB power decreases significantly by the proposed scheme under condition number constraint.