Cognitive Radio Vehicular Networks Research Articles

Secure V2V Communications in Relay-Assisted Cognitive Radio Vehicular Networks With Imperfect CSI

Secure V2V Communications in Relay-Assisted Cognitive Radio Vehicular Networks With Imperfect CSI

Sensing and Sharing of Spectrum in Cooperative Cognitive Radio Vehicular Networks Using K-tuned Classifiers and Fuzzy Logic

This paper aims to efficiently utilize the spectrum based on sensing and sharing optimal resources like relay and power on cooperative cognitive radio in a vehicular network. Resources are allocated in the V2V environment by considering the effect of the Doppler shift. Spectrum sensing is carried out using K-tuned Nearest Neighbors algorithm, in which the K parameter is tuned to avoid misclassification-related problems that enhance the spectrum sensing. After sensing the status of the spectrum, a Fuzzy logic-based approach shares the spectrum with the help of optimized resources. Simulations are carried in different vehicle scenarios in terms of bit error rate and throughput.

Secure Cognitive Radio-Enabled Vehicular Communications under Spectrum-Sharing Constraints.

Vehicular communication has been envisioned to support a myriad of essential fifth-generation and beyond use-cases. However, the increasing proliferation of smart and intelligent vehicles has generated a lot of design and infrastructure challenges. Of particular interest are the problems of spectrum scarcity and communication security. Consequently, we considered a cognitive radio-enabled vehicular network framework for accessing additional radio spectrum and exploit physical layer security for secure communications. In particular, we investigated the secrecy performance of a cognitive radio vehicular network, where all the nodes in the network are moving vehicles and the channels between them are modeled as double-Rayleigh fading. Furthermore, adopting an underlay approach, the communication between secondary nodes can be performed by employing two interference constraint strategies at the primary receiver; (1) Strategy I: the secondary transmitter power is constrained by the interference threshold of the primary receiver, and (2) Strategy II: the secondary transmitter power is constrained by both the interference threshold of the primary receiver and the maximum transmit power of the secondary network. Under the considered strategies, we derive the exact secrecy outage probability (SOP) and ergodic secrecy capacity (ESC) expressions over double-Rayleigh fading. Moreover, by analyzing the asymptotic SOP behavior, we show that a full secrecy diversity of 1 can be achieved, when the average channel gain of the main link goes to infinity with a fixed average wiretap channel gain. From the ESC analysis, it is revealed that the ESC follows a scaling law of for large and , where and are the average channel gains of the main link and wiretap link. The numerical and simulation results verify our analytical findings.

A fuzzy arithmetic-based analytical reliability assessment framework (FAARAF): case study, cognitive radio vehicular networks with drivers

A fuzzy arithmetic-based analytical reliability assessment framework (FAARAF): case study, cognitive radio vehicular networks with drivers

LSTM-Based Channel Access Scheme for Vehicles in Cognitive Vehicular Networks With Multi-Agent Settings

In this paper, we study the channel access problem of vehicles in a cognitive radio vehicular network, where each vehicle opportunistically accesses the channel resources of the primary network in order to successfully receive the necessary data packets within a time deadline. Given the access priority constraint and the limited bandwidth of the primary network, a smart channel connection scheme is indispensable to ensure a decent quality of service (QoS) at the vehicles’ side. Due to the competitive nature of vehicles, the vehicle access control is formulated as a multi-agent access problem that comes with an intrinsic challenge, i.e. the partial observation of the information about the environment dynamics. On top of that, considering the temporal usage profile of the primary network, the environment dynamics are also time-dependant, and hence making the aforementioned access control a non-Markovian problem. Consequently, the estimation of the system states, which are used for the decision making process of a vehicle, is very challenging. To deal with the issues arising from such non-Markovian problem, we propose a vehicle connection algorithm based on a deep recurrent Q-learning network. With the aid of a recurrent Long Short Term Memory (LSTM) layer integrated into a deep Q-network, the time-correlated system states can be properly estimated, thereby improving the vehicle channel access policy. Besides, we introduce novel reward quantities that help improving the network performance and the capability to flexibly adapt to unexplored scenarios. A new structure of the cumulative reward function is also presented to balance the performance trade off between the cooperative and competitive objectives. Simulation results are provided to verify the advantage and the stability of our proposed algorithm over the benchmark schemes.

Joint Impact of Nodes Mobility and Imperfect Channel Estimates on the Secrecy Performance of Cognitive Radio Vehicular Networks Over Nakagami-m Fading Channels

This paper investigates the joint impact of nodes' mobility and imperfect channel estimates on the secrecy performance of an underlay cognitive radio vehicular network over Nakagami-m fading channels. Specifically, the secondary network consists of a single-antenna source vehicle, an N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> D</sub> -antenna destination vehicle, and an N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> E</sub> -antenna passive eavesdropper vehicle, whereas the primary network comprises of a single-antenna primary receiver vehicle. The time selective fading links arise due to nodes' mobility are modeled via first-order auto-regressive process, and the channel state information is estimated using linear minimum mean square error estimation method. Moreover, the transmit power of secondary source is constrained by both the interference threshold of the primary receiver and the maximum transmit power of secondary network. Under such a realistic scenario, we derive the analytical closed-form secrecy outage probability (SOP) and ergodic secrecy capacity expressions. Furthermore, we present asymptotic SOP analysis to obtain key insights into the system's secrecy diversity order. We also report several practical cases of interest to reveal valuable information about the system's secrecy behavior. Moreover, we illustrate the impacts of system/channel parameters, nodes' mobility, imperfect channel estimates, interference temperature limit, and the maximum available source power. Finally, the simulation studies corroborate our derived analytical findings.

Deep reinforcement learning based optimal channel selection for cognitive radio vehicular ad‐hoc network

Channel selection is a challenging task in cognitive radio vehicular networks. Vehicles have to sense the channels periodically. Due to this, a lot of time is wasted which could have been utilised for transmission of data. Employing road side units (RSUs) in sensing can prove to be useful for this purpose. The RSUs may select the channel and allocate it to the vehicles on demand. However, this sensing should be proactive. RSUs should know in advance the channel to be allocated when requested. For this purpose, a deep reinforcement learning algorithm namely deep reinforcement learning based optimal channel selection is proposed in this study for training the network according to the previously sensed data. Proposed protocol is simulated and results are compared with the existing methods. The packet delivery ratio is increased by 2%, throughput is increased by 1.8%, average delay is decreased by 2% and primary user collision ratio is reduced by 3.2% when compared with similar recent work by varying number of vehicles. On the other hand, when compared with similar recent work by varying channel availability, the packet delivery ratio is increased by 4.5 %, throughput by 4.3%, average delay is decreased by 3% and PU collision ratio by 5.5%.

Single‐channel slotted contention in cognitive radio vehicular networks

Cognitive radio vehicular ad-hoc networks (CR-VANETs) allow the vehicles to exploit the unused channels in their vicinity to fulfil the bandwidth demands of infotainment applications. Existing CR-VANET approaches aim at incorporating spectrum sensing and reporting to the widely used IEEE 802.11p standard developed for VANETs. However, all such approaches were developed for CR-VANETs with low vehicular traffic densities. Applying such approaches at high traffic densities results in long sensing periods, long contention periods with frequent collisions and unnecessary transmissions of sensing information, and variable delays that depend on the vehicular density. In this study, the authors present the single-channel slotted contention ( ( SC ) 2 ) protocol designed for highways with highly dense vehicular traffic. The ( SC ) 2 protocol counters the deficiencies of typical CR-VANETs in high-density networks using three components: random single-channel sensing, slotted contention, and implicit-OR aggregation. The proposed protocol can be easily incorporated with the IEEE 802.11p. Analytic and simulation results show that the proposed protocol increases the CR-VANET throughput and the overall spectrum utilisation while significantly decreasing the radio environment map time in high densities when compared to the conventional IEEE 802.11p protocols.

A Spectrum Handoff Scheme for Optimal Network Selection in NEMO based Cognitive Radio Vehicular Networks: A Cost Function MADM Method based on Grey Theory based Approach

Multiple Attribute Decision Making (MADM) is one of the intelligent network selection methodologies for Intelligent Transportation System (ITS). The fundamental objective of MADM method is to select the optimal network among the finite set of alternative. In this paper, we have proposed a cost function MADM method based on the grey theory based approach which outperforms the existing MADM method for ITS. The method is validated to perform a spectrum handoff for optimal network selection in Network Mobility (NEMO) based Cognitive Radio (CR) vehicular networks. In NEMO based CR vehicular networks, the Mobile Reuter (MR) requires spectrum handoff when a mobile network changes its point of attachment in CR vehicular networks. It becomes difficult for MR to select the optimal network from the available access networks. The application of MADM methods provides wider and optimum choice among the available networks with quality of service. Numerical results revel that the proposed MADM method is effective for spectrum handoff decision for optimal network selection with reduced complexity in NEMO based CR vehicular networks.

OLSR protocol for vehicular sensor network in cognitive radio

In our proposed system, the vehicular sensor network (VSN) is formulated based on the cognitive radio with the aid of optimal link state routing (OLSR) protocol. Here the cognitive radio related vehicular sensor networks are anticipated for the data communication. The optimal link state routing protocol is modified by utilising the hybrid group search and genetic algorithm (HGSO-GA) algorithm. The OLSR protocol is used to pick the adjacent nodes which disseminate the power package to diminish the quantity of control transparency in the network. The data packages are conveyed from source to destination with the help of multi point relay (MPR) vehicles, which is the subset of the chosen neighbour vehicles by each source to access all the two-hop neighbours act as forwarders. OLSR routing protocol to efficiently manage spectrum allocation that helps reduce the routing delay. The proposed technique is implemented in the working platform of NS2.

Towards low-delay and high-throughput cognitive radio vehicular networks

Abstract Cognitive Radio Vehicular Ad-hoc Networks (CR-VANETs) exploit cognitive radios to allow vehicles to access the unused channels in their radio environment. Thus, CR-VANETs do not only suffer the traditional CR problems, especially spectrum sensing, but also suffer new challenges due to the highly dynamic nature of VANETs. In this paper, we present a low-delay and high-throughput radio environment assessment scheme for CR-VANETs that can be easily incorporated with the IEEE 802.11p standard developed for VANETs. Simulation results show that the proposed scheme significantly reduces the time to get the radio environment map and increases the CR-VANET throughput.

Fairness-constrained optimized time-window controllers for secondary-users with primary-user reliability guarantees

In this paper, we design and test a primary–secondary user resource-management controller in cognitive radio vehicular networks, under hard and soft collision constraints. We cast the resource-management problem into a stochastic network utility maximization problem and derive the optimal steady-state controllers, which adaptively allocate the access time-windows to the secondary-users. We derive closed form expressions for the throughput-gain of the general controller with respect to the memoryless one, discussing conditions of applicability and advantages of each subclass. We prove that the hard controllers are able to make the outage-probability vanishing, without any throughput-loss with respect to the soft controllers. Finally, we generalize the obtained results to probabilistic fairness constrained problems and to controllers exploiting cognitive data-fusion techniques. We investigate about possible distributed implementations and show as controllers are able to adapt to unpredictable and abrupt changes of the network, without requiring any a priori knowledge of the vehicular traffic patterns.

Joint Routing and Scheduling in Cognitive Radio Vehicular Ad Hoc Networks

Joint Routing and Scheduling in Cognitive Radio Vehicular Ad Hoc Networks

A spectrum handoff scheme for optimal network selection in Cognitive Radio vehicular networks: A game theoretic auction theory approach

The recent strides in vehicular networks have emerged as a convergence of multi radio access networks having different user preferences, multiple application requirements and multiple device types. In future Cognitive Radio (CR) vehicular networks deployment, multiple radio access networks may coexist in the overlapping areas having different characteristics in terms of multiple attributes. Hence, it becomes a challenge for CR vehicular node to select the optimal network for the spectrum handoff decision. A game theoretic auction theory approach is interdisciplinary effective approach to select the optimal network for spectrum handoff. The competition between different CR vehicular node and access networks can be formulated as multi-bidder bidding to provide its services to CR vehicular node. The game theory is the branch of applied mathematics which make intelligent decision to select the optimal alternative from predetermined alternatives. Hence, this paper investigates a spectrum handoff scheme for optimal network selection using game theoretic auction theory approach in CR vehicular networks. The paper has also proposed a new cost function based multiple attribute decision making method which outperforms other existing methods. Numerical results revel that the proposed scheme is effective for spectrum handoff for optimal network selection among multiple available networks.

Context aware spectrum handoff scheme in cognitive radio vehicular networks

In future cognitive radio (CR) vehicular networks deployment, multiple radio access networks may coexist in overlapping areas. These networks may have with largely different characteristics in terms of multiple attributes. Hence, it becomes difficult for CR vehicular node to select preferred network for spectrum handoff decision. This can be done by a context aware spectrum handoff that takes multiple attributes into account for spectrum handoff decision for preferred network selection. This paper proposes a context aware spectrum handoff scheme with multiple attributes decision-making (MADM) methods for preferred network selection in CR vehicular networks. The MADM methods used are simple additive weighting and grey relational analysis. The paper also proposes a cost-based spectrum handoff scheme with MADM which outperforms other MADM methods. The spectrum handoff with MADM provides wider and optimum choice with quality of service. Numerical results show that the proposed scheme is effective for spectrum handoff decision for preferred network selection.

A Spectrum Handoff Scheme for Optimal Network Selection in NEMO Based Cognitive Radio Vehicular Networks

When a mobile network changes its point of attachments in Cognitive Radio (CR) vehicular networks, the Mobile Router (MR) requires spectrum handoff. Network Mobility (NEMO) in CR vehicular networks is concerned with the management of this movement. In future NEMO based CR vehicular networks deployment, multiple radio access networks may coexist in the overlapping areas having different characteristics in terms of multiple attributes. The CR vehicular node may have the capability to make call for two or more types of nonsafety services such as voice, video, and best effort simultaneously. Hence, it becomes difficult for MR to select optimal network for the spectrum handoff. This can be done by performing spectrum handoff using Multiple Attributes Decision Making (MADM) methods which is the objective of the paper. The MADM methods such as grey relational analysis and cost based methods are used. The application of MADM methods provides wider and optimum choice among the available networks with quality of service. Numerical results reveal that the proposed scheme is effective for spectrum handoff decision for optimal network selection with reduced complexity in NEMO based CR vehicular networks.

Context aware spectrum handoff scheme in cognitive radio vehicular networks

In future cognitive radio CR vehicular networks deployment, multiple radio access networks may coexist in overlapping areas. These networks may have with largely different characteristics in terms of multiple attributes. Hence, it becomes difficult for CR vehicular node to select preferred network for spectrum handoff decision. This can be done by a context aware spectrum handoff that takes multiple attributes into account for spectrum handoff decision for preferred network selection. This paper proposes a context aware spectrum handoff scheme with multiple attributes decision-making MADM methods for preferred network selection in CR vehicular networks. The MADM methods used are simple additive weighting and grey relational analysis. The paper also proposes a cost-based spectrum handoff scheme with MADM which outperforms other MADM methods. The spectrum handoff with MADM provides wider and optimum choice with quality of service. Numerical results show that the proposed scheme is effective for spectrum handoff decision for preferred network selection.

ProMAC: A proactive model predictive control based MAC protocol for cognitive radio vehicular networks

Cognitive Radio (CR) is a recent network paradigm that allows Secondary Users (SUs), such as, wireless devices/users, to intelligently access portions of the radio spectrum not allocated to it, without interfering with the transmission of licensed users (Primary Users (PUs)) who are allocated certain dedicated portions of the radio spectrum. This paradigm in radio communication has been successfully used in vehicular networks wherein communication can be established within vehicles (vehicle-to-vehicle) or vehicles to static stations (vehicle-to-infrastructure) without allocating dedicated frequencies. However, the challenge in CR design lies in building intelligence that helps in efficiently sensing and transmitting data through available radio spectrum channels. This paper proposes a Model Predictive Control (MPC) based Proactive Medium Access Control protocol (ProMAC) for the SUs in a CR network. To the best of our knowledge this is the first proactive MAC reported in the literature for CR. Employing ProMAC in a architecture where the number of SUs and PUs were constant, we achieved 20%,13.5% and 12% improvement in channel utilization, backoff rate and sensing delay respectively as compared to the recently proposed PO-MAC protocol, which is so far the best reported in the literature. In an architecture where the numbers of SUs varied with time, ProMAC achieved 21% and 13.17% improvement in channel utilization and backoff rate, respectively, as compared to PO-MAC. The proposed protocol is based on a self-learning engine that can evolve and improve its prediction accuracy even after deployment on field.

Performance Evaluation of Data Delivery Mechanism For Cognitive Radio Vehicular and Vehicular Ad-Hoc Networks

Recently, the Vehicular Networks are gaining increased attention for generating extensive wireless communication. They are also integrated with Cognitive Radio to further enhance their performance. However the guaranteed delivery of data is an important parameter for evaluating these networks. This paper provides a simple, but nevertheless extremely accurate, simple model to evaluate the performance of both Vehicular Ad-hoc and Cognitive Radio Vehicular Networks. The proposed analysis applies to both the networks on the basis of various parameters like delay, packet loss, packet drop ratio, throughput using extensive simulation. The results validate the assumption that Cognitive Vehicular Radios outperform the” VANET's when various types of packets are transmitted using V2 V communication.

Accessing Spectrum Databases Using Interference Alignment in Vehicular Cognitive Radio Networks

Cognitive radio (CR) vehicular networks are poised to opportunistically use the licensed spectrum for high-bandwidth intervehicular messaging, driver-assist functions, and passenger entertainment services. Recent rulings that mandate the use of spectrum databases have introduced additional challenges in this highly mobile environment, where the CR-enabled vehicles must update their spectrum data frequently and complete the data transfers with roadside base stations (BSs) in very short interaction times. This paper aims to answer two fundamental questions: 1) when to undertake local spectrum sensing, as opposed to accessing spectrum database information at a finite cost overhead; and 2) how to ensure correct packet receptions among the multiple BSs and CR vehicles using fewer slots than the messages that need to be transmitted. The contributions of this paper are twofold: First, we introduce a method of qualifying the correctness of spectrum sensing results using out-of-band 2G spectrum data using experimental results. Second, to the best of our knowledge, this is the first work on applying the concept of interference alignment (IA) in a practical network setting, leading to dramatic reduction in message transmission times. Our approach demonstrates significant reductions in the overhead of direct database queries and improvement in the accuracy of spectrum sensing for mobile vehicles.