Cognitive Radio Networks

Cognitive radio networks (CRNs) implementing spectrum sharing between primary and secondary users are able to provide a needed solution for the increasing spectrum scarcity problem. This paper presents a cross layer resource allocation scheme for underlay dynamic spectrum access (DSA) in cognitive radio networks with a goal to improve end-to-end Quality of Experience (QoE) for video traffic, as measured through the Mean Opinion Score (MOS). The presented solution implements a deep Q-network (DQN) that allows a CR to perform resource allocation by observing the environmental variables over physical and network layers and takes actions to update its own parameters across these layers in order to maximize the MOS for its interactive video stream, all while maintaining interference to the primary users below a threshold limit. Simulation results show that perceived quality for the transmitted video using the proposed cross-layer scheme outperforms a benchmark comparable DQN scheme that adapts only the physical layer, often yielding an improvement of one grade scale in the MOS. Moreover, the proposed cross-layer DQN scheme achieves a balanced load on a network with routers with unequal service rates.

Cognitive radio is an innovative technique for spectrum management which uses Dynamic Spectrum Access (DSA). DSA has the potential to mitigate the spectrum scarcity by letting the Secondary User (SU) access the idle spectrum unused by the Primary User (PU). DSA improves the spectrum efficiency via spectrum sensing and optimal spectrum access. Spectrum sensing should be perfect so that the SU does not cause interference to the PU. Moreover, the channel selected should be the best for maximising throughput of the SU. In this paper we consider a best channel selection problem in Cognitive Radio Networks (CRNs). The problem is modelled as optimal stopping problem where the controller at each state can stop or continue sensing based on the channel gains achieved. There is a Markovian relation given between the channel occupancies of PU. The stopping rule comes out to be simple threshold rule. The controller stops sensing when the channel gain exceeds the given threshold.

The escalating demand for wireless communication services has led to a critical shortage of available radio spectrum. Cognitive radio networks emerge as a promising solution to this challenge by enabling dynamic and intelligent spectrum access. This paper presents an in-depth exploration of cognitive radio networks, focusing on spectrum management and coexistence strategies. We delve into the fundamental concepts of cognitive radio networks, highlighting their ability to opportunistically exploit underutilized frequency bands while ensuring minimal interference with licensed primary users. The paper covers essential topics such as spectrum sensing techniques, dynamic spectrum access, spectrum mobility, coexistence protocols, and spectrum sharing models. Case studies illustrate practical implementations in wireless sensor networks, 5G systems, and Internet of Things (IoT) applications. Moreover, we address challenges, including security and privacy concerns, and discuss the integration of cognitive radio networks with emerging technologies like AI, machine learning, and blockchain. This comprehensive overview underscores cognitive radio networks' potential to revolutionize spectrum utilization, offering a path toward efficient and harmonious wireless communication.

With the rapid development of wireless services and applications, the currently radio spectrum is becoming more crowded. How to accommodate more wireless services and applications within the limited radio spectrum becomes a big challenge faced by modern society. Cognitive radio (CR) is proposed as a promising technology to tackle this challenge by introducing secondary users (SUs) to opportunistically or concurrently access the spectrum allocated to primary users (PUs). Currently, there are two prevalent CR models: the spectrum sharing model and the opportunistic spectrum access model. In the spectrum sharing model, the SUs are allowed to coexist with the PUs as long as the interferences from SUs do not degrade the quality of service (QoS) of PUs to an unacceptable level. In the opportunistic spectrum access model, SUs are allowed to access the spectrum only if the PUs are detected to be inactive. These two models known as underlay and overlay schemes, respectively. This thesis studies a number of topics in CR networks under the framework of these two schemes. First, studied cognitive radio transmissions under QoS delay constraints. Initially, we focused on the concept: effective capacity for cognitive radio channels in order to identify the performance in the presence of QoS constraints. Both underlay and overlay schemes are studied taking into consideration the activity of primary users, and assuming the general case of channel fading as Gamma distribution. For this setting, we further proposed a selection criterion by which the cognitive radio network can choose the adequate mode of operation. Then, we studied the cognitive radio transmissions focusing on Rayleigh fading channel and assumed that no prior channel knowledge is available at the transmitter and the receiver. We investigated the performance of pilot-assisted transmission strategies. In particular, we analyzed the channel estimation using minimum mean-square-error (MMSE) estimation, and analyzed efficient resource allocation strategies. In both cases, power allocations and effective capacity optimization were obtained. Effective capacity and interference constraint were analyzed in both single-band and multi-band spectrum sensing settings. Finally, we studied optimal access probabilities for cognitive radio network using Markov model to achieve maximum throughput for both CR schemes.

In recent years, channel-hopping-based medium access control protocols have been proposed to improve the capacity in a decentralized multichannel cognitive radio (CR) network without using extra control channels. Each CR user has to stochastically follow a default channel-hopping sequence in order to locate a channel and conduct its frame transmission. In this paper, theoretical analysis is conducted on the probability of channel availability and the average frame delay for primary users (PUs) by considering the impact caused by imperfect sensing of CR users and imperfect synchronization between the primary and CR networks. According to the proposed analytical model with realistic considerations, an optimal channel-hopping sequence (OCS) approach is designed for the CR users based on a dynamic programming technique. It is designed by exploiting the optimal load balance between channel availability and channel utilization within the delay constraints of PUs. By adopting the OCS approach, maximum aggregate throughput of CR users can be achieved while considering PU's quality-of-service (QoS) requirements. Moreover, in addition to the paired CR networks, the logical partition problem that occurs in generalized CR networks will also be addressed. This problem can severely degrade the aggregate throughput due to the decreased probability of connectivity between CR users, especially in a CR network with heavy traffic. Therefore, both wake-up successive contention (WSC) and wake-up counter-reset successive contention (WCSC) algorithms are proposed to increase the number of negotiations by both exploring the blind spot of imperfect sensing and amending the contention mechanisms between CR users. Compared to conventional channel-hopping sequences, numerical results illustrate that the proposed approaches can effectively maximize aggregate throughput for CR users under the QoS requirements of PUs.

An adaptive and QoS-based spectrum awareness framework for CR networks

Pricing is an effective approach for spectrum access control in cognitive radio (CR) networks. In this paper, we study the pricing effect on the equilibrium behaviors of selfish secondary users' (SUs') data packets which are served by a CR base station (BS). From the SUs' point of view, a spectrum access decision on whether to join the queue of the BS or not is characterized through an individual optimal strategy that is joining the queue with a joining probability. This strategy also requires each SU to know the average queueing delay, which is a non-trivial problem. Toward this end, we provide queueing delay analysis by using the M/G/1 queue with breakdown. From the BS's point of view, we consider a duopoly market based on the two paradigms: the opportunistic dynamic spectrum access (O-DSA) and the mixed O-DSA & dedicated dynamic spectrum access (D-DSA). In the first paradigm, two co-located opportunistic-spectrum BSs utilize freely spectrum-holes to serve SUs. Then, we show the advantages of the cooperative scenario due to the unique solution that can be obtained in a distributed manner by using the dual decomposition algorithms. For the second paradigm, there are one opportunistic-spectrum BS and one dedicated-spectrum BS. We study a price competition between two BSs as a Stackelberg game. The cooperative behavior between two BSs is modeled as a bargaining game. In both paradigms, bargain revenues of the cooperation are always higher than those due to competition in both cases. Extensive numerical analysis is used to validate our derivation.

Empowered by the cognitive radio technology, and motivated by the sporadic channel utilization, both spatially and temporally, dynamic spectrum access networks (also referred to as cognitive radio networks and next generation wireless networks) have emerged as a solution to improve spectrum utilization and provide more flexibility to wireless communication. A cognitive radio network is composed of wireless users, referred to as secondary users, which are allowed to use a given licensed spectrum band as long as there are no primary, licensed, users occupying this band in their vicinity. This restricted spectrum access strategy leads to heterogeneity in channel availability among secondary users. Heterogeneity in channel availability forms a significant source of performance degradation for cognitive radio networks, and poses a great challenge on protocol design. In this dissertation, we propose spectrum allocation algorithms that take this heterogeneity property and its effect on the network performance into consideration. The spectrum allocation solutions proposed in this dissertation address three major objectives in cognitive radio mesh networks. The first objective is maximizing the network coverage, in terms of the total number of served clients, and at the same time simplifying the communication coordination function. To address this objective, we proposed a received based channel allocation strategy that alleviates the need for a common control channel, thus simplifying the coordination function, and at the same time maximizes the number of clients served with link reliability guarantees. We show the superiority of the proposed allocation strategy over other existing strategies. The second objective is improving the multicast throughput to compensate for the performance degradation caused by the heterogeneity in channel availability. We proposed a

As the proliferation of wireless handheld devices such as smart phones and tablets grow exponentially, new technologies have to be adopted to efficiently use the radio frequency spectrum. Cognitive Radio(CR) network offers a promising solution to improve spectrum utilization and efficiency by harnessing the available spectrum resources in real-time. However, participants in the CR network can comprise of malicious adversaries who adopt dishonest and non-cooperative strategies to attack the network. In our research, we focus on a critical attack on the CR network: Primary User Emulation (PUE). In a PUE attack, the malicious adversary violates spectrum sharing etiquette by impersonating the primary user at the expense of other unselfish secondary users. To thwart such an attack, we present a strategy to authenticate the primary user, which involves collaborative learning among CR users.

Cognitive Radio (CR) technique has been proposed for improving spectrum efficiency by dynamic spectrum access. In Cognitive Radio Networks (CRNs), unlicensed Secondary Users (SUs) with CR can utilize licensed spectrum without interfering licensed Primary Users (PUs). For effectively avoiding interference with licensed PUs and malicious attacks from jammers, a two-stage Learning-based Optimal Channel Selection (LOCS) algorithm for unlicensed SUs in distributed heterogeneous CRNs is proposed in this paper. The LOCS algorithm enables SUs to obtain real states of the licensed channels without knowing their information. Hence, SUs using LOCS algorithm can efficiently avoid collision and attack with PUs and jammers. Besides, the LOCS algorithm considers hardware limitation of the SUs, i.e., SUs can only sense and access parts of the license spectrum during any given time. SUs can select the optimal channels for spectrum sensing and data transmission by using the LOCS algorithm. Simulation results show the efficiency of our proposed algorithm in terms of collision and attack avoidance.

Cognitive Radio networks (CRN) enable cognitive users to use portions of the licensed spectrum of the primary users (PU). Dynamic spectrum access (DSA) is a design aspect that facilitates the transmission of cognitive radio (CR) users over idle channels. In many cases, it is not preferable that CR users to be treated equally. Provisioning quality of service (QoS) to some CR users is a challenge in DSA design. In this situation, The CR user traffic is divided into high and low priority classes. Sub-channel reservation (SCR) scheme is the most traditional scheme to provide priority to CR users in admission. In this paper, a new scheme named “low priority bounding” (LPB) scheme is used to provide QoS for CR users. The LPB scheme provides priority by restricting the number of low priority CR users by a threshold. This scheme maintains a suitable level of service to high priority users (as provided by SCR scheme) while improve the level of service for low priority ones. So the revenue gained from LPB scheme is optimized in comparison with SCR scheme. The impact of LPB scheme in the provided QoS is evaluated in terms of blocking probability, service termination rate, and service completion rate. Simulation results are compared with SCR scheme and it shows that the LPB scheme can significantly improve the revenue gained over the traditional SCR scheme.

Cognitive Radio (CR) is viewed as a revolutionary potential radio technology capable of offering dynamic spectrum access. CR Networks (CRNs) with such spectrum aware devices is a hopeful solution to the spectrum scarcity issue in wireless communication sector. In this paper, an effective routing solution with a cross layer design is proposed for the multi-hop CRNs. Most of the existing work uses Ad-hoc On-demand Distance Vector (AODV) protocol as the routing protocol for CRNs. In this work, a novel routing algorithm based on Routing Protocol for Low power and lossy networks (RPL) is being proposed for the CRNs. Routing is accomplished through the formation of colored Destination Oriented Directed Acyclic Graphs (DODAGs) of which the link frequencies are represented using colors. Hop count and adjacent link interference are counted as the routing metrics. Dynamic spectrum allocation is also done along with routing with the help of RPL. The CRN scenario is vulnerable to link failures due to the appearance of Primary Users (PUs). So route repairing using the Trickle algorithm offered by RPL is also implemented in this work. To implement this algorithm, a traffic aware scheduling is used. A MATLAB simulation is done to analyze the effectiveness of the proposed algorithm.

This chapter addresses a few challenges and issues in developing Cognitive Radio Networks (CRNs), and provides unique solutions to enhance security at physical layer and to boost CRN computing power in a distributed manner. In this age of vast connectivity, network security becomes more and more prominent. In addition to the security means added at the upper layers, security can be further enhanced at physical layer. In particular, location based wideband channel characteristic as a unique signature can be utilized for security enhancement. Such a scheme is proposed and examined in different configurations. Lack of computing power is another critical issue, as CRN is expected to have more and more features. Instead of increasing onboard computing power, off-board computing resources can be connected to boost overall computing power. With increased computing power, the CRNs would be able to undertake computationally heavy tasks such as executing machine-learning algorithms and performing radio intrusion detection.

Dynamic spectrum access and cognitive radio are emerging technologies to utilize the scarce frequency spectrum in an efficient and opportunistic manner. Cognitive radio, built on software defined radio, is an intelligent radio technology that updates its operating parameters to locate the unused spectrum segments. To assign these vacant bands to unlicensed users without causing harmful interference to licensed users, a novel approach is proposed in this article based on fuzzy logic. Two different fuzzy inference system models i.e. Mamdani and Sugeno systems are developed that compute spectrum access decision based on the secondary user parameters such as signal strength, distance between the primary and secondary user, spectrum utilization efficiency and degree of mobility. 81 fuzzy rules are used to obtain the output of proposed system stating the possibilities of allotment of white spaces to secondary users.

Cognitive Radio Networks (CRNs) is a formidable evolution in wireless technology owing to its potentiality to resolve spectrum scarcity crisis. In CRNs, cognitive radio user’s sense idle spectrum as well as opportunistically entrée sensed spectrum devoid of approved users. The best spectrum is said to be selected opportunistically, shared with other users with no severity to licensed users. Many CRN devices that are fueled by battery possessing constrained energy capacity. Prevailing battery mechanisms yet couldn’t contribute CRN devices works for a long time. Therefore, conservation of energy is mandatory as far as routing in CRN is concerned. In this work a machine learning based method called, Regression Cooperative Spectrum Sensing and Gradient Flying Fox Route Optimization (RCSS-GFFRO) for energy and bandwidth aware data routing with spectrum utilization in CRNs is proposed. Protection of primary users (PUs) as well as optimization of secondary user (SU) transmission included in proposed method. In the proposed method a cooperative spectrum sensing using machine learning (ML) model called, Median Regression function is applied. The Median Regression Cooperative Spectrum Sensing in our work with the bandwidth and energy efficiency ensures acceptable probability of interference to primary users due to secondary user’s access by measuring the relationship between bandwidth and energy efficiency simultaneously. Next, optimal route identification is performed using Gradient Flying Fox Route Optimization algorithm. By applying gradient optimization function minimizes the interference with primary users and increases secondary user performance via sensing time and transmission time parameters with higher routing performance. Simulation estimated with parameters.