The Achievable Rate of Interweave Cognitive Radio in the Face of Sensing Errors

Patel, Aaqib; Khan, Mohammed Zafar Ali; Merchant, S. N.; Desai, U.B.; Hanzo, Lajos

doi:10.1109/access.2016.2612941

Cited by 33 publications

(16 citation statements)

References 90 publications

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“…On the other hand, if we have a simple limiting distribution for (7), which closely approximates the CDF values for moderate and large values of M, we can obtain a closed form expression for the optimum P s that satisfies (6b). Therefore, using tools from EVT, we formulate the following theorem to determine the limiting distribution of (7), when γ is the SIR in an κ − µ shadowed fading environment.…”

Section: Secondary User Power Control Policymentioning

confidence: 99%

Transmit Power Policy and Ergodic Multicast Rate Analysis of Cognitive Radio Networks in Generalized Fading

Subhash

Srinivasan

Kalyani

et al. 2020

IEEE Trans. Commun.

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This paper determines the optimum secondary user power allocation and ergodic multicast rate of point-to-multipoint communication in a cognitive radio network in the presence of outage constraints for the primary users. Using tools from extreme value theory (EVT), it is first proved that the limiting distribution of the minimum of independent and identically distributed (i.i.d.) signal-to-interference ratio (SIR) random variables (RVs) is a Weibull distribution, when the user signal and the interferer signal undergo independent and non-identically distributed (i.n.i.d.) κ − µ shadowed fading. This limiting distribution is then used for determining the optimum transmit power of a secondary network in an underlay cognitive radio network subject to outage constraints at the primary network in a generalized fading scenario. Furthermore, the asymptotic ergodic multicast rate of secondary users is analyzed for varying channel fading parameters. Index Termsextreme value theory, κ − µ shadowed fading, outage probability, cognitive radio I. INTRODUCTIONWith the advances in wireless technology, the presence of wireless devices has become ubiquitous. Furthermore, with the advent of the Internet of Things (IoT), the number of connected devices accessing the spectrum is set to increase in the coming times. With the upcoming increase in devices and hence increasing the traffic, it will be very hard to find free spectrum.Cognitive radio (CR) is one of the promising techniques mitigating spectrum scarcity in wireless communication systems [1]-[4]. In cognitive radio networks (CRNs), there are three popular modes of spectrum sharing between primary users (PU) and secondary users (SU) -underlay, overlay and interweave [5]- [8]. As a further development, the authors of [9], [10] have studied the security aspects of a CR system in the presence of eavesdroppers.Throughout this paper, we consider the underlay mode, in which the secondary network is allowed to access the spectrum allocated to the primary network provided that the interference caused by the SU transmitter does not unduly deteriorate the performance of the primary network.An important problem in CRNs is the choice of power policy at the SU-Tx (transmitter), so that the interference at the PU-Rx (receiver) remains below an admissible threshold. Several authors [11]-[15] have studied the performance of underlay CRNs under various interference constraints. In [16], different-power adaptive transmit antenna selection (TAS) schemes were analyzed for the underlay CRN. Furthermore, the authors of [17] have determined the optimal rate sharing parameters for both the SU and the PU, so that the achievable rates were maximized.Similarly, recent contributions [18], [19] have also considered the performance of an interferencelimited underlay CRN relying on continuous power adaptation at the SU. In [19], the secondary transmitter is assumed to transmit information to the specific SU, having the kth highest signalto-interference ratio (SIR). The authors of [18] have also investigated t...

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Section: Secondary User Power Control Policymentioning

confidence: 99%

Transmit Power Policy and Ergodic Multicast Rate Analysis of Cognitive Radio Networks in Generalized Fading

Subhash

Srinivasan

Kalyani

et al. 2020

IEEE Trans. Commun.

Self Cite

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“…In this interweave system, the coexistence of primary and secondary signals is not allowed and this feature is controlled via the probability of detection P d and the probability of false alarm P fa . Using the state of operations of the primary and secondary transmitters, denoted respectively S p and S s , we can define the probability of detection and the probability of false alarm as [36], where the inactivity of the PU occurs with P (S p = 0) = p that reduces the activity probability to be P (S p = 1) = 1 − p. To characterize the proper Gaussian signaling and the improper Gaussian signaling, we present the following definitions. Definition 1: [11], [13] If the pseudo-variance of a complex random variable x is zero i.e.…”

Section: Interweave Cognitive Radio System Modelmentioning

confidence: 99%

Benefits of Improper Gaussian Signaling in Interweave Cognitive Radio With Full and Partial CSI

Hedhly

Amin

Alouini

2020

IEEE Trans. Cogn. Commun. Netw.

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In this paper, we investigate the impact of using improper Gaussian signaling (IGS) scheme on the interweave cognitive radio (CR) paradigm when the cognitive user has access to full or partial channel-state-information (CSI). Throughout this work, we analyze the performance of both the primary user (PU) and secondary user (SU) in terms of instantaneous achievable rate and outage probability, for given sensing and detection capabilities. Then, we optimize the IGS scheme for full and partial CSI scenarios to maximize the achievable rate of the SU while maintaining a minimum quality-of-service (QoS) requirement for the PU. Meanwhile, we derive the corresponding feasibility conditions for the interweave cognitive radio system operation. IGS based systems are proven to provide notable gain over the conventional proper Gaussian signaling, which is considered our benchmark scheme. The IGS scheme is observed to be further beneficial at low SU detection capabilities, strong interference from the cognitive user to the licensed user, and low gain of the PU direct link. Index Terms-Interweave, cognitive radio, improper Gaussian signaling, asymmetric signaling, miss detection, interference channels. Rp (0, 0) > R min and subsequently is reduced to Rp 1 > R min , where Rp 1 is defined in 8. scenarios: Case II-a: P s,opt = p s,max , whenp s1 ≥ p s,max that is equivalent to I s ≤ I s,PGS. Case II-b: P s,opt =p s , whenp s1 < p s,max that is equivalent to I s > I s,PGS .

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“…Consider an IoT network, where the IoT devices/nodes scavenge environmental energy, and operate under the interweave spectrum sharing cognitive radio discipline [56], [57], with the aim of improving energy and spectrum efficiencies as well as achieving ultra-reliable communication. The IoT nodes (as SUs) transmit their data in a frame-based structure, in which the time is divided into slots, called frames.…”

Section: System Model a Iot Network Modelmentioning

confidence: 99%

Availability-Reliability-Stability Trade-Offs in Ultra-Reliable Energy-Harvesting Cognitive Radio IoT Networks

Amini

Baidas

2020

IEEE Access

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With the rise of the Internet-of-Things (IoT), new requirements have been brought into communication networks to make them more efficient, sustainable, and self-sufficient. Requirements, such as availability and ultra-reliability combined with the solutions of energy-harvesting and dynamic spectrum access, make the analyses of such networks more complex, while imposing different performance trade-offs. This paper analyzes the performance of ultra-reliable energy-harvesting cognitive radio Internet-of-Things (UR-EH-CR-IoT) networks, and provides analytical derivations for different IoT network metrics, such as GoodPut, reliability, collision probability, availability, and stability, so as to investigate their trade-offs. A new metric for network availability is defined based on energy availability and spectrum accessibility for UR-EH-CR-IoT networks, while incorporating transmission diversity. The effect of IoT network parameters, such as sensing time, diversity transmission, and number of packets in a data frame, is examined on the IoT network performance metrics. Lastly, the derived expressions are utilized to optimize the GoodPut, subject to various practical constraints. INDEX TERMS Availability, cognitive radio, collision probability, diversity transmission, energyharvesting, Internet-of-Things, stability, ultra-reliability. 1 This can be seen by noting that diversity transmission is only based on transmitting a fixed number of packet replicas. To the best of our knowledge, there are no existing results on the implementation complexity of diversity transmission.

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The Achievable Rate of Interweave Cognitive Radio in the Face of Sensing Errors

Cited by 33 publications

References 90 publications

Transmit Power Policy and Ergodic Multicast Rate Analysis of Cognitive Radio Networks in Generalized Fading

Transmit Power Policy and Ergodic Multicast Rate Analysis of Cognitive Radio Networks in Generalized Fading

Benefits of Improper Gaussian Signaling in Interweave Cognitive Radio With Full and Partial CSI

Availability-Reliability-Stability Trade-Offs in Ultra-Reliable Energy-Harvesting Cognitive Radio IoT Networks

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