For conventional wireless networks, the main target of resource allocation (RA) is to efficiently utilize the available resources. Generally, there are no changes in the available spectrum, thus static spectrum allocation policies were adopted. However, these allocation policies lead to spectrum under-utilization. In this regard, cognitive radio networks (CRNs) have received great attention due to their potential to improve the spectrum utilization. In general, efficient spectrum management and resource allocation are essential and very crucial for CRNs. This is due to the fact that unlicensed users should attain the most benefit from accessing the licensed spectrum without causing adverse interference to the licensed ones. The cognitive users or called secondary users have to effectively capture the arising spectrum opportunities in time, frequency, and space to transmit their data. Mainly, two aspects characterize the resource allocation for CRNs: 1) primary (licensed) network protection and 2) secondary (unlicensed) network performance enhancement in terms of quality-of-service, throughput, fairness, energy efficiency, etc. CRNs can operate in one of three known operation modes: 1) interweave; 2) overlay; and 3) underlay. Among which the underlay cognitive radio mode is known to be highly efficient in terms of spectrum utilization. This is because the unlicensed users are allowed to share the same channels with the active licensed users under some conditions. In this paper, we provide a survey for resource allocation in underlay CRNs. In particular, we first define the RA process and its components for underlay CRNs. Second, we provide a taxonomy that categorizes the RA algorithms proposed in literature based on the approaches, criteria, common techniques, and network architecture. Then, the state-of-the-art resource allocation algorithms are reviewed according to the provided taxonomy. Additionally, comparisons among different proposals are provided. Finally, directions for future research are outlined.

Resource Allocation for Underlay Cognitive Radio Networks: A Survey

Due to harsh environment, large number of sensors, limited energy, and spectrum scarcity, intelligent sensing becomes a key issue to enable many practical applications in industrial Internet of Things (IoT). In such an industrial environment with noise and interference, an efficient cooperative spectrum sensing (CSS) scheme can achieve spectrum sharing between primary users (PUs) and secondary users (SUs), and effectively solve the spectrum scarcity and reduce energy consumption to make the IoT smarter. As a vital part of CSS, decision transmission (DT) between SUs and fusion center (FC) plays a crucial role. In traditional DT, each SU will transmit its local decision to FC with orthogonal channel in each sensing, which does not consider the packet error and packet loss due to noise during transmission, and aggravates spectrum scarcity and energy consumption. An energy-efficient reliable DT (ERDT) scheme is proposed to enhance CSS in industrial IoT, which considers both packet error and packet loss. First, the CSS mathematical model based on DT is formulated. Second, with rigorous mathematical deduction, the correct decision probability and the energy consumption are analyzed for both ERDT and DT based on logic OR-rule and AND-rule under three cases, respectively: 1) bit error only; 2) packet loss only; and 3) both bit error and packet loss. Detailed simulation results show that, compared with DT, the proposed ERDT can increase correct decision probability and reduce energy consumption for CSS under three different cases. When the existence probability of PU is 50%, the energy consumption of ERDT is only half of that of DT in CSS. Furthermore, when there are 30 SUs in CSS, the existence probability of PU is 50%, both pocket loss rate and bit error rate are 0.05, and the correct decision probability of ERDT is approaching to 1 for CSS in industrial IoT.

ERDT: Energy-Efficient Reliable Decision Transmission for Intelligent Cooperative Spectrum Sensing in Industrial IoT

The cognitive radio CR paradigm for designing next-generation wireless communications systems is becoming increasingly popular, and different aspects of it are being implemented in currently available wireless systems. In the last decade, a significant amount of research efforts has been made to solve CR challenges, and several standards related to CR and dynamic spectrum access have been developed. Also, there have been advances in software-defined radio platforms to implement the CR systems. In this article, we provide a comprehensive survey on the evolution of CR research covering aspects such as spectrum sensing, measurements and statistical modeling of spectrum usage, physical layer aspects such as waveform and modulation design, multiple access, resource allocation and power control, cognitive learning, adaptation and self-configuration, multihop transmission and routing, and robustness and security in CR networks. Also, state-of-the-art research on the economics of CR networks, CR simulation tools, testbeds and hardware prototypes, CR applications, and CR standardization efforts is summarized. Emerging trends on CR research and open research challenges related to the cost-effective and large-scale deployment of CR systems are outlined. Copyright © 2013 John Wiley & Sons, Ltd.

Evolution and future trends of research in cognitive radio: a contemporary survey

Cognitive-inspired class-statistic matching with triple-constrain for camera free 3D object retrieval

As an enabling technology for dynamic spectrum access (DSA), cognitive radio (CR) is widely regarded as one of the most promising technologies for future the fifth generation (5G) wireless communications Although there have been significant prior researches to combat interference on primary users (PUs), the problem of mitigating mutual interference between secondary users (SUs), -which is tightly coupled with SU's spectrum leasing- is still not understood well This paper proposes a DSA algorithm based on game theory, which jointly performs spectrum leasing and interference mitigation among SUs The problem is modeled as an oligopolistic competition using Stackelberg model We have carefully studied the SU's spectrum utilization behavior with respect to various criteria of the proposed game theoretic model Simulation results shows that, Compared with Cournot game model, the proposed scheme enables SUs to efficiently utilize the licensed spectrum shared with PUs in a dynamic environment while maximizing the spectrum utilization

Dynamic Spectrum Access Algorithm Based on Game Theory in Cognitive Radio Networks

This paper investigates the optimal scheduling of cognitive radio network links under an interference temperature (IT) model. A link layer model based on the IT model has been developed. A mathematical cross-layered model for the cognitive radio network link scheduling problem under the IT model is presented. The proposed cross-layered model optimizes the performance of the cognitive radio network across several network layers. The objective is to activate as many simultaneous primary/secondary links while the IT constraints are satisfied. The IT-based link layer scheduling can potentially increase the network performance. The proposed model advocates the potential benefits of adopting a location-aware and IT-based MAC protocol in modern wireless networks.

An Optimal Cross-Layer Framework for Cognitive Radio Network Under Interference Temperature Model

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PaperIO: A 3D Interface towards the Internet of Embedded Paper-Craft

In this paper, we show that the aggregate network throughput of a cognitive radio network can be significantly increased by using primary transmitter location sensing in addition to conventional spectrum sensing  To achieve this, we propose a novel multi-channel, cross-layer PHY-MAC protocol for CR ad hoc networks, which infers concurrent transmission opportunities from location sensing information We identify four main problems and provide solutions which enable our location aware MAC First, secondary nodes achieve location information without any interaction with primary nodes by combining RSSI and direction of arrival information (estimated from an antenna array) The location sensing capability can be integrated into the sensing module which also performs conventional channel sensing Second, a distributed collaborative sensing algorithm is developed based on sensing module information which broadens nodes' knowledge of locations and spectrum occupancy Third, a simple condition is proposed to control interference on primary network Fourth, a decentralized scheduling scheme is presented to solve the MAC problem for location aware cognitive radio networks, in which channels are allocated to nodes based on their locations Simulations show that the proposed approach significantly increases the performance of the CR network The influence of protocol parameters on network performance is also investigated

Location Aware CR-MAC: A multi-channel cross layered PHY-MAC protocol for cognitive radio ad hoc networks

This paper investigates the problem of optimal scheduling for cognitive radio network links in order to maximize the aggregated cognitive radio network throughput. The problem is formulated as an integer linear programming (ILP) problem, which schedules cognitive radio links to maximize the aggregated network throughput subject to primary and cognitive network protection. The proposed model is capable of scheduling multiple concurrent cognitive radio links which can coexist with a set of primary links. This approach can significantly increase the aggregated network throughput and utilize the spectrum more efficiently by utilizing user location information. The ILP problem is optimally solved for specific network scenarios and numerical results are presented.

Borhan (Brian) Jalaeian

Papers

An Optimal Cross-Layer Framework for Cognitive Radio Network Under Interference Temperature Model

PaperIO: A 3D Interface towards the Internet of Embedded Paper-Craft

Location Aware CR-MAC: A multi-channel cross layered PHY-MAC protocol for cognitive radio ad hoc networks

An optimal scheduling framework for concurrent transmissions in wireless cognitive radio networks

URSABench: A System for Comprehensive Benchmarking of Bayesian Deep Neural Network Models and Inference methods