Interference Statistics and Capacity-Outage Analysis in Cognitive Radio Networks

Interference Statistics and Capacity-Outage Analysis in Cognitive Radio Networks

Mahsa Derakhshani (University of Toronto, Canada) and Tho Le-Ngoc (McGill University, Canada)
DOI: 10.4018/978-1-4666-6571-2.ch027
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Abstract

This chapter presents a study on the interference caused by Secondary Users (SUs) due to miss-detection errors and its effects on the capacity-outage performance of the Primary User (PU) in a cognitive radio network assuming Rayleigh and Nakagami fading channels. The effect of beacon transmitter placement on aggregate interference distribution and capacity-outage performance is studied considering two scenarios of beacon transmitter placement: a beacon transmitter located at a PU transmitter or at a PU receiver. Based on the developed statistical models for the interference distribution, closed-form expressions for the capacity-outage probability of the PU are derived to examine the effects of various system parameters on the performance of the PU in the presence of interference from SUs. Furthermore, the model is extended to investigate the cooperative sensing effect on aggregate interference statistical model and capacity-outage performance considering OR (i.e., logical OR operation) and Maximum Likelihood (ML) cooperative detection techniques.
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Introduction

The limitations of current static spectrum management policy drive the idea of a more dynamic and cognitive access policy to improve the efficiency of radio spectrum usage and accommodate the increasing demand for wireless communication applications. Known as the opportunistic spectrum access (OSA), the new paradigm allows cognitive SUs to identify and utilize instantaneous spectrum opportunities in the licensed spectrum, provided that the interference to the licensed PUs is limited (Akyildiz, Lee, Vuran, & Mohanty, 2006; Goldsmith, Jafar, Maric, & Srinivasa, 2009; Haykin, 2005; Mitola, 2000).

In cognitive radio networks with OSA, beacon signaling can be used by the PU in order to facilitate SUs in the detection of spectrum holes. Upon detecting the beacon, SUs will keep silent to avoid interference to the PU. Although the beacon is designed to improve the PU detection performance at each SU, there is a non-zero probability of beacon miss-detection due to noise and channel fading, and in such a case, SU transmission will cause interference to the PU (Buchwald, Kuffner, Ecklund, Brown, & Callaway, 2008; Hulbert, 2005; Yu-Chun, Haiguang, & Zhang, 2009).

The development of cognitive radio networks with OSA has to deal with many technical and practical issues, so that its full potential can be realized. One of the key design issues is sufficiently protecting PUs’ communications from the interference caused by SUs. To be able to support quality-of-service (QoS) requirements for PUs, in this chapter, we present a study on the aggregate interference imposed by SUs to a PU due to miss-detection errors. Moreover, we look into how this interference affects the performance of the PU and how it relates to design parameters. Subsequently, we propose a network-level interference constraint to ensure non-intrusive communications of SUs.

To guarantee a certain level of QoS for PUs, different approaches are considered in the OSA design literature. In (Qianchuan Zhao, Geirhofer, Tong, & Sadler, 2008; Qing Zhao, Tong, Swami, & Chen, 2007), the proposed OSA schemes limit the probability of collision of SUs with PUs. However, the collision probability is not a precise measure to protect PUs since the effects of propagation channel gains from SUs to PUs are not considered. Another proposed approach is to keep the aggregate interference level caused by SUs below a prescribed tolerable threshold for PUs assuming the perfect knowledge of instantaneous channel gains from SU transmitters to PU receivers (Gatsis, Marques, & Giannakis, 2010; Le & Hossain, 2008). However, knowing and tracking the instantaneous channel gains from SU transmitters to PU receivers might be difficult in practice.

Recognizing such practical limitations, in this chapter, we present a statistical model on the aggregate interference caused by SUs to a PU due to miss-detection errors. In particular, the probabilistic properties of the aggregate interference are investigated in consideration of random SU locations and their propagation characteristics. Based on the developed statistical model, we subsequently derive the PU capacity-outage probability (i.e., the probability that the PU capacity falls below a prescribed level). This will help to examine the effects of various system parameters on the performance of the PU in the presence of interference from SUs. Consequently, PU capacity-outage probabilities are introduced as a measure to maintain QoS for PUs in designing OSA schemes for SUs.

In a cognitive network with beacon, different locations are considered for the beacon transmitter to study the aggregate interference. The beacon transmitter is located at the PU receiver (Ghasemi & Sousa, 2008; Hulbert, 2005) or at the PU transmitter (Derakhshani & Le-Ngoc, 2012; Vu, Ghassemzadeh, & Tarokh, 2008). Accordingly, we analyze the effects of the beacon transmitter location on the aggregate interference caused by SUs and the performance of the PU dealing with such aggregate interference.

Key Terms in this Chapter

Cooperative Spectrum Sensing: Cooperative sensing is a solution to enhance the detection performance, in which secondary users collaborate with each other to sense the spectrum to find the spectrum holes.

Nakagami Fading: Nakagami distribution is a common and useful model for general fading environments, since its parameters (i.e., average received power and the fading parameter) can be adjusted to fit a variety of empirical measurements.

Cognitive Radio: A cognitive radio is an intelligent and reconfigurable wireless communication system that enables monitoring the radio environment, learning, and accordingly, adapting transmission parameters in order to achieve the optimal spectrum utilization.

Rayleigh Fading: Rayleigh distribution is a common and useful model to statistically describe the envelope of the received signal in a fading environment, where the number of multiple reflective paths is large, and there is no line-of-sight signal component (e.g., heavily built-up urban environments).

Capacity-Outage Probability: Probability that the primary user capacity falls below a prescribed level in the presence of interference from secondary users.

Spectrum Sensing: Spectrum sensing is the process of periodically monitoring a specific frequency band, aiming to identify presence or absence of primary users.

Miss-Detection Probability: Probability that secondary user misses the primary user presence due to noise and channel fading.

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