Cognitive Radio Cellular Networks Research Articles

Dynamic Frequency Assignment for Cognitive Radio Cellular Networks Using Hysteretic Noisy Chaotic Neural Network

Dynamic Frequency Assignment for Cognitive Radio Cellular Networks Using Hysteretic Noisy Chaotic Neural Network

Assessment of Spectrum Handoff Performance in Cognitive Radio Cellular Networks

Spectrum Handoff is an essential process to assure unified, effective, and robust service for cognitive users (CUs) in cognitive radio cellular networks (CRCNs). This letter models the key handoff performance measuring metrics: link maintenance probability, link failure probability, and service completion probability of a non-stationary CU and analyzes the impact of mobility parameters to investigate the characteristics and behavior of a CRCN. In addition, we present the impact of general service time distributions on service completion probability. Results show that the service time distributions have a significant impact on spectrum handoff performance metrics for cases of higher mobility.

Geo-Location Information Aided Spectrum Sensing in Cellular Cognitive Radio Networks.

Apart from the received signal energy, auxiliary information plays an important role in remarkably ameliorating conventional spectrum sensing. In this paper, a novel spectrum sensing scheme aided by geolocation information is proposed. In the cellular cognitive radio network (CCRN), secondary user equipments (SUEs) first acquire their wireless fingerprints via either received signal strength (RSS) or time of arrival (TOA) estimation over the reference signals received from their surrounding base-stations (BSs) and then pinpoint their geographical locations through a wireless fingerprint (WFP) matching process in the wireless fingerprint database (WFPD). Driven by the WFPD, the SUEs can easily ascertain for themselves the white licensed frequency band (LFB) for opportunistic access. In view of the fact that the locations of the primary user (PU) transmitters in the CCRN are either readily known or practically unavailable, the SUEs can either search the WFPD directly or rely on the support vector machine (SVM) algorithm to determine the availability of the LFB. Additionally, in order to alleviate the deficiency of single SUE-based sensing, a joint prediction mechanism is proposed on the basis of cooperation of multiple SUEs that are geographically nearby. Simulations verify that the proposed scheme achieves higher detection probability and demands less energy consumption than the conventional spectrum sensing algorithms.

Tradeoff Analysis between Spectral and Energy Efficiency Based on Sub-Channel Activity Index in Wireless Cognitive Radio Networks

In recent years, there has been a rapid evolution of wireless technologies that has led to the challenge of high demand for spectral resources. To overcome this challenge, good spectrum management is required that calls for more efficient use of the spectrum. In this paper, we present a general system, which makes a tradeoff between the spectral efficiency (SE) and energy efficiency (EE) in the cellular cognitive radio networks (CCRN) with their respective limits. We have analyzed the system taking into account the different types of power used in the CCRN, namely the spectrum detection power (Zs) and the relay power (Zr). Optimal policy for emission power allocation formulated in the function of sub-channel activity index (SAI) as an optimization problem in order to maximize spectrum utilization and minimize the energy consumption in the base station of the secondary system energy consumption, is subject to different constraints of the main user system. We also evaluate the collaborative activity index of the sub-channel describing the activity of the primary users in the CCRN. The theoretical analyses and simulation results sufficiently demonstrate that the SE and EE relationship in the CCRN is not contrary and thus the achievement of optimal tradeoff between SE and EE. By making a rapprochement with a cognitive cellular network where SBSs adopts an equal power allocation strategy for sub-channels, the results of our proposed scheme indicate a significant improvement. Therefore, the model proposed in this paper offers a better tradeoff between SE and EE.

On the Evaluation of Handover Exchange Schemes Between Two Cognitive Radio Base Stations with and without Buffers

This article investigates and evaluates a handover exchange scheme between two secondary users (SUs) moving in different directions across the handover region of neighboring cell in a cognitive radio network. More specifically, this investigation compares the performance of SUs in a cellular cognitive radio network with and without channel exchange scheme. The investigation shows reduced handover failure, blocking, forced and access probabilities respectively, for handover exchange scheme with buffer as compared to exchange scheme without buffer. It also shows transaction within two cognitive nodes within a network region. The system setup is evaluated through system simulation.

Predictive spectrum occupancy probability‐based spatio‐temporal dynamic channel allocation map for future cognitive wireless networks

AbstractFor a cognitive radio (CR) user to dynamically access the available primary user channels, the spectrum sensing data is required at various locations; however, the data is not generally available at the points present in between the two spectrum sensors. To obtain this data, it is also not feasible to conduct the spectrum measurement surveys at every location. This calls for an interpolation‐based spectrum occupancy data collection in the space. Moreover, the information about the future primary user activity is necessary for highly enhanced dynamic channel allocation with better quality of experience (QoE) of CR users in terms of improved channel utilization and reduced spectrum sensing energy requirement. Most of the existing works are inclined toward either spatial spectrum interpolation or temporal spectrum prediction. In this work, a novel hybridized approach combining the Kriging‐based statistical spatial interpolation and the recurrent neural networks–based temporal prediction of the spectrum has been proposed to obtain the spectrum occupancy probability information using empirical spectrum measurements data in cellular CR networks. Furthermore, the aforementioned framework has been utilized for dynamic channel allocation with the help of a newly investigated CR channel allocation scheme. The corresponding results have been validated through various CR QoE measures and compared with the existing channel allocation strategies, where it is found that the proposed channel allocation scheme significantly outperforms the other schemes with the enhanced QoE of CR. Finally, a spatio‐temporal channel allocation map has been presented for secondary user to perform its best possible CR operation with efficient dynamic spectrum access.

Robust Transceiver Design in Full-Duplex MIMO Cognitive Radios

We consider a full duplex (FD) multiple-input multiple-output (MIMO) underlay cognitive radio cellular network, in which an FD secondary base-station (BS) serves multiple half-duplex uplink (UL) and downlink secondary users (SUs) at the same time and frequency. We assume that the channel state information (CSI) available at the transmitters is imperfect, and the errors of the CSI are assumed to be norm bounded. Under the impact of channel uncertainty, we address the sum mean-squared-errors minimization problem subject to individual power constraints at the UL SUs, a total power-constraint at the secondary BS, and the interference constraints on the primary users by the secondary network. By transforming the problem into an equivalent semidefinite programming (SDP), we propose an iterative alternating algorithm to compute the transceiver matrices jointly. Moreover, to reduce the high computational complexity of the SDP method, we develop a cutting-set method, which solves the problem by alternating between an optimization step (transceiver design) and a pessimization step (worst-case channel analysis). Numerical results are presented to show the effectiveness and robustness of the proposed algorithms.

Sum-Throughput Maximization in Downlink OFDMA-based Cellular Cognitive Radio Networks

Sum-Throughput Maximization in Downlink OFDMA-based Cellular Cognitive Radio Networks

Modeling and performance analysis for mobile cognitive radio cellular networks

In this paper, teletraffic performance and channel holding time characterization in mobile cognitive radio cellular networks (CRCNs) under fixed-rate traffic with hard-delay constraints are investigated. To this end, a mathematical model to capture the effect of interruption of ongoing calls of secondary users (SUs) due to the arrival of primary users (PUs) is proposed. The proposed model relies on the use of an independent potential interruption time associated with the instant of possible interruption for each ongoing call in every visited cell. Then, a Poisson process is used to approximate the secondary users’ call interruption process due to the arrival of PUs. Based on this model and considering that unencumbered service time (UST) and cell dwell time (CDT) of SUs are independent generally distributed random variables, analytical formulae for both the probability distributions of channel holding times and inter-cell handoff attempts rate are derived. Also, a novel approximated closed-form mathematical expression for call forced termination probability of SUs is derived under the restriction that the UST is exponentially distributed. Additionally, by considering all the involved time variables exponentially distributed and employing fractional channel reservation to prioritize intra- and inter-cell handoff call attempts over new call requests, a queuing analysis to evaluate the call-level performance of CRCNs in terms of the maximum Erlang capacity is developed. The accuracy of our proposed mathematical models is extensively investigated under a variety of different evaluation scenarios for all the considered call-level performance metrics. Numerical results demonstrate that channel holding time statistics are highly sensitive to both interruption probability of ongoing secondary calls and type of probability distribution functions used to model CDT and UST. From the teletraffic perspective, numerical results reveal that the system Erlang capacity largely depends on the relative value of the mean secondary service time to the mean primary service time and the primary channels’ utilization factor. Also, the obtained results show that there exists a critical utilization factor of the primary resources from which it is no longer possible to guarantee the required quality of service of SUs and, therefore, services with hard-delay constraints cannot be even supported in CRCNs.

Distributed Energy-Spectrum Trading in Green Cognitive Radio Cellular Networks

Reducing the power consumption of base stations is crucial to enhancing the energy efficiency of cellular networks. As the number of mobile users increases exponentially, enhancing the spectrum efficiency is also critical in order to accommodate more users. In this paper, by exploiting the cooperation between secondary base stations (SBSs) and primary base stations (PBSs), we propose a new energy spectrum trading model to enhance the energy as well as spectrum efficiency of cellular networks. In our scheme, by leveraging cognitive radio, PBSs share some portion of their licensed spectrum with SBSs, and SBSs, in exchange, provide data service to the primary users under their coverage. We first prove that the power consumption minimization problem is NP-hard. Then, to decrease the computational complexity, we design an efficient distributed auction model including green energy aware bidding (GEAB) and adaptive bid selection (ABS) algorithms, to achieve a good approximation of the optimal solution in less time. Our simulation results show that the cooperation between PBS and SBSs via ABS and GEAB algorithms can significantly improve the energy and spectral efficiency of cellular networks by nearly doubling the number of offloaded users and reducing the PBS power consumption by up to 40% as compared to existing approaches. Furthermore, green energy utilization among SBSs is increased by nearly 25%.

Quality of Service Analysis for the Real-Time Secondary Users in Cognitive Radio Cellular Networks

Cognitive radio technology can improve the spectrum utilization in wireless networks by providing the capability for the secondary users to opportunistically use the frequency spectrum which is originally dedicated to the licensed users. In this paper the quality of service parameters for the real-time (RT) secondary users are analyzed in an overlay cognitive radio cellular network. RT packets are delay sensitive and can tolerate a limited amount of waiting time. Otherwise they will be useless and hence will be dropped. On the other hand, the availability of the frequency channels for SUs in cognitive radio networks (CRNs) depends on the activity of licensed users. Therefore, the support of RT services in CRNs requires elaborate considerations in various functionalities of the network. Here a practical cellular structure for the network is considered. Furthermore, a packet scheduling scheme is proposed and an analytical framework is developed for the performance analysis of RT secondary users. The effect of errors of spectrum sensing is also taken into account in the performed analysis. The analytical results are verified by simulations. The obtained results are very helpful for the support of RT traffic in cellular CRNs.

Quality of Experience Driven Multi-User Video Streaming in Cellular Cognitive Radio Networks With Single Channel Access

We investigate the problem of streaming multi-user videos over the downlink of a cognitive radio network (CRN), where each cognitive user (CU) can access one channel at a time. We first consider the case where each CU can sense one channel at a time slot at most. To make the problem tractable, we tackle the optimal spectrum sensing and access problems separately and develop matching-based optimal algorithms to the subproblems, which yield an overall suboptimal solution. We then consider the case where each CU can sense multiple channels. We show that under the assumption that all the spectrum sensors work on the same operating point, a two-step approach can derive the optimal spectrum sensing and access policies that maximize the quality of experience (QoE) of the streaming videos. The superior performance of the proposed approaches is validated with simulations and comparisons with benchmark schemes, where a performance gain from 25% to 30% is demonstrated.

A Spectrum and Energy Cooperation Strategy in Hierarchical Cognitive Radio Cellular Networks

In this letter, we proposed a novel spectrum and energy cooperation strategy in a hierarchical cognitive radio (HCR) cellular network. In our design, the primary receiver (PR) is equipped with a time-splitting energy harvesting (EH) device such that the primary system (PS) can share its spectrum to the secondary system (SS) and harvest the radiation for charging from the secondary base station (SB) in return. To achieve such a win–win strategy, the SB’s precoder and the time-splitting factor are jointly optimized by maximizing the SS’s information rate, providing that the PS’s information rate and harvested energy are guaranteed. The design problem is equivalently formulated and solved via a semidefinite relaxation (SDR) optimization approach. Simulation results show the promising performance of the proposed cooperation strategy.

DCCC-MAC: A Dynamic Common-Control-Channel-Based MAC Protocol for Cellular Cognitive Radio Networks

We propose a novel dynamic common-control-channel-based medium access control (DCCC-MAC) protocol for cellular (centralized) cognitive radio (CR) networks. Specifically, unlike the traditional dedicated-control-channel-based medium access control (MAC) protocols, the proposed MAC protocol eliminates the requirement of a dedicated channel for control information exchange. During a given transmission frame, the common control channel (CCC) is selected by a cooperating set of secondary users (SUs) by using a support-vector-machine (SVM)-based learning technique. In the DCCC-MAC protocol, the frame duration is divided into four main phases as follows: spectrum sensing, CCC selection, data transmission, and beaconing. The SUs that participate in the CCC selection process are allocated channels for data transmission during a frame interval using a scheduling process, whereas the other SUs have to contend to access the channels. We present an analytical approach to calculate the minimum required number of minislots in the transmission frame for a given number of SUs in the CCC selection process. The saturation throughput of the proposed MAC protocol is analyzed in closed form. To this end, the numerical and simulation results are presented to quantify the performance of the proposed DCCC-MAC protocol. We also compare the performance of the DCCC-MAC protocol with that of two other state-of-the-art CR MAC protocols that use CCCs.

On Green Cognitive Radio Cellular Networks: Dynamic Spectrum and Operation Management

We study a profit maximization problem related to cognitive radio cellular networks in an environmentally friendly framework. The objective of the primary network (PN) and the secondary network (SN) is to maximize their profits while respecting a certain carbon dioxide (CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) emission threshold. In this paper, the PN can switch off some of its base stations (BSs) powered by microgrids, and hence leases the spectrum in the corresponding cells, to reduce its footprint. The corresponding users are roamed to the SN infrastructure. In return, the SN receives a certain roaming cost and its users can freely exploit the spectrum. We study two scenarios in which the profits are either separately or jointly maximized. In the disjoint maximization problem, two low-complexity algorithms for PN and SN BS on/off switching are proposed to maximize the profit per CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emission utility and determine the amount of the shared bandwidth. In the joint maximization approach, the low-complexity algorithm is based on maximizing the sum of weighted profits per CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> . Selected numerical results illustrate the collaboration performance versus various system parameters. We show that the proposed algorithms achieve performances close to those obtained with the exhaustive search method, and that the roaming price and the renewable energy availability are crucial parameters that control the collaboration of both networks.

A Decomposition Approach to Quality-Driven Multiuser Video Streaming in Cellular Cognitive Radio Networks

We tackle the challenging problem of streaming multiuser videos over the downlink of a cellular cognitive radio network (CRN), where each cognitive user (CU) can sense and access multiple channels at a time. Spectrum sensing, channel assignment, and power allocation strategies are jointly optimized to maximize the quality of service (QoS) for the CUs. We show that the formulated mixed integer nonlinear programming (MINLP) problem can be decomposed into two subproblems: 1) SP1 for the optimal spectrum sensing strategy and 2) SP2 for the optimal channel assignment and power allocation, without sacrificing optimality. We show that SP1 can be optimally solved if there is no restriction on the sensing capability for each CU, and develop a column generation (CG)-based algorithm to solve SP2 iteratively in a distributed manner. We also develop a heuristic algorithm for spectrum sensing with greatly reduced requirement on CU hardware, while still achieving a highly competitive sensing performance. We analyze the proposed algorithms with respect to complexity and time efficiency, and derive a performance upper bound. The proposed algorithms are validated with simulations.

On Characterization of Feasible Interference Regions in Cognitive Radio Networks

In an underlay cognitive radio network (CRN), in order to guarantee that all primary users (PUs) achieve their target-signal-to-interference-plus-noise ratios (target-SINRs), the interference caused by all secondary users (SUs) to the primary receiving-points should be controlled. To do so, the feasible cognitive interference region (FCIR), i.e., the region for allowable values of interference at all of the primary receiving-points, which guarantee the protection of the PUs, needs to be formally characterized. In the state-of-the-art interference management schemes for underlay CRNs, it is considered that all PUs are protected if the cognitive interference for each primary receiving-point is lower than a maximum threshold, the so called interference temperature limit (ITL) for the corresponding receiving-point. This is assumed to be fixed and independent of ITL values for other primary receiving-points, which corresponds to a box-like FCIR. In this paper, we characterize the FCIR for uplink transmissions in cellular CRNs and for direct transmissions in ad-hoc CRNs. We show that the FCIR is in fact a polyhedron (i.e., the maximum feasible cognitive interference threshold for each primary receiving-point is not a constant, and it depends on that for the other primary receiving-points). Therefore, in practical interference management algorithms, it is not proper to consider a constant and independent ITL value for each of the primary receiving-points. This finding would significantly affect the design of practical interference management schemes for CRNs. To demonstrate this, based on the characterized FCIR, we propose two power control algorithms to find the maximum number of admitted SUs and the maximum aggregate throughput of the SUs in infeasible and feasible CRNs, respectively. For two distinct objectives, our proposed interference management schemes outperform the existing ones. The numerical results also demonstrate how the assumption of fixed ITL values leads to poor performance measures in CRNs.

CPC-based backward-compatible network access for LTE cognitive radio cellular networks

Network access plays an important role in LTE cognitive radio (LTE-CR) cellular networks in determining users’ experiences. An overview is first carried out on network access schemes in existing cognitive cellular networks such as IEEE 802.22 and IEEE 1900.4, based on which it can be seen that cognitive pilot channel (CPC)-based network access is a promising scheme for LTECR, which may provide fast network access and put no stringent requirements on terminals. Next, considering the implementation issues of CPC in practical systems, a CPC-based backward- compatible network access scheme should be designed for LTE-CR to facilitate the application of CR in LTE networks, which could exploit existing LTE structures and technologies to carry CPC information. To achieve this, a new system information block (SIB) should be designed to carry CPC on a current physical downlink shared channel with little standards effort. Moreover, load awareness is introduced so that LTE-CR is activated only when the system load is high. The complete process of this SIB-CPC-based backward-compatible and loadaware network access is described, and its performance is evaluated via simulations. It is shown that the blocking ratio of LTE-CR can be reduced notably compared to that of conventional LTE without CR. Moreover, by selecting an appropriate load threshold to activate LTE-CR, the average user throughput of LTE can be improved with near-zero blocking ratio by offloading users to complementary cognitive spectra.

A Novel Chaos Based Cost Function for Power Control of Cognitive Radio Networks

The game theoretic approach has been recently considered as potential solutions for power control in cognitive radio network (CRNs). It is well known that a critical issue in applying game theory is the selection of its proper cost function. In this letter, we introduce a new chaos based cost function to design power control algorithm and analyse the dynamic spectrum sharing issue in the uplink of cellular CRNs. Particularly, for secondary users as the game players in underlay scenarios, we define utility/cost functions taking into account the interference from and the interference tolerance of the primary users. We prove the existence of the Nash equilibrium in this power control game, leading to significantly lower power consumption and relatively fast convergence rate when compared to the other existing game algorithms.

On Joint Power and Admission Control in Underlay Cellular Cognitive Radio Networks

We investigate the problem of designing efficient and low-complexity centralized algorithms for joint power and admission control in a cellular cognitive radio network (CRN) which coexists with a primary radio network (PRN) in a spectrum underlay fashion. We first derive a simple one-to-one relation between the signal-to-interference-plus-noise ratio (SINR) vector and its corresponding power vector of all users of the CRN and PRN, and based on this we propose two new admission metrics. Then, in an infeasible system, where the minimum acceptable target-SINRs for all primary and secondary users are not simultaneously reachable, two centralized algorithms are proposed. These algorithms aim at removing the minimal number of secondary users (based on the proposed admission metrics), subject to the constraint that all primary users are supported with their target-SINRs. In an infeasible system, our proposed algorithms outperform other existing algorithms in terms of complexity and secondary users' outage ratio. Furthermore, for a feasible system, where all secondary users can be admitted along with all primary users, by using our derived one-to-one relation between SINR and power vector, we solve the problems of maximizing aggregate throughput and max-min quality-of-service (QoS) for secondary users, both subject to the constraint that all primary and secondary users are supported with their minimum target-SINRs.