Sensing Orders in Multi-User Cognitive Radio Networks

Sensing Orders in Multi-User Cognitive Radio Networks

Rakesh Misra (Stanford University, USA) and Arun Pachai Kannu (Indian Institute of Technology Madras, India)
DOI: 10.4018/978-1-4666-6571-2.ch009
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Abstract

In multi-channel Cognitive Radio Networks (CRNs), when the cognitive radio receivers cannot simultaneously sense more than one out of the many possible (groups of) channels, an important challenge is to determine a sensing order for each Cognitive User (CU) so as to optimize a given performance metric. The sensing-order problem is compounded in multi-user CRNs where the multiple users in the network could collide with each other. With the focus on multi-user CRNs, this chapter uses cognitive-throughput maximization as the performance metric and describes how the optimal sensing orders can be computed for different contention management strategies used by the network. In general, the optimal procedures involve a computationally expensive brute-force search, so the chapter also discusses several heuristic-based near-optimal procedures that can be used in practice.
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Introduction

In an age where the wireless RF spectrum is becoming increasingly congested and scarcer than ever, the concept of cognitive radios has attracted significant attention as a means for enhancing the efficiency of spectrum usage (Hossain, Niyato & Han, 2009). A cognitive radio is a smart radio that can intelligently detect available wireless channels in its vicinity and dynamically configure its transmission or reception parameters so as to make the best use of these spectrum holes. Cognitive radios are capable of communicating on licensed spectrum without causing interference to the incumbent or primary users of the licensed bands, and therefore hold great potential for improving the efficiency of the usage of licensed spectrum that is otherwise poorly utilized due to static frequency allocations.

However, the spectrum that a cognitive radio would be allowed to operate on can be expected to be scattered and heterogeneous in general. In other words, a cognitive radio would need to search over multiple portions of the licensed spectrum, possibly having different bandwidths and primary user characteristics, in order to select the best free channel for its use. Also, these radios are expected to be small devices, often hand-held with limited footprint, and therefore cognitive radios that can simultaneously sense more than one portion of the spectrum quickly, efficiently and reliably would be expensive to build in practice. As a result, each cognitive user (CU) in a cognitive radio network (CRN) would need to have a sensing-order i.e., an order in which it will sequentially sense the different channels until it finds a suitable channel for its communication.

The sensing-order problem in cognitive radio networks relates to finding the sensing orders for the cognitive users in the network that jointly optimize a chosen objective function. For example, if the objective is to minimize the expected sum of sensing times or equivalently, the expected sum of times to transmission, the sensing orders need to be jointly selected in a way that lets the CUs find disjoint and free channels as early as possible in their respective sensing orders. A more popular objective, which is also the objective of interest throughout this chapter, is the expected sum throughput of all CUs, also referred to as the cognitive throughput. When cognitive throughput is used as the objective, each CU needs to find not just a free channel but also a good quality channel i.e., if a channel it senses to be free has a very bad signal-to-noise ratio (SNR), it can skip the channel and continue sensing according to its sensing order with the good hope of finding a better quality channel in the future that will result in a higher effective throughput. Therefore, with cognitive throughput maximization, the sensing-order problem also encompasses determining optimal stopping rules for each CU i.e., a rule based on its instantaneous channel conditions that it can use whenever it finds a free channel to decide whether to stop sensing or continue sensing with the hope of finding a better channel in the future.

One might wonder, “If the different channels are identical except for their primary-free probabilities, isn’t it optimal to just sense the channels in the descending order of their primary-free probabilities?” Jiang, Lai, Fan and Poor (2009) showed that when there is a single CU in the network, this intuitive sensing order is indeed optimal when the CU does not use rate adaptation i.e., when the CU transmits at a constant rate irrespective of the channel quality. However, if the CU uses rate adaptation, the intuitive sensing order is not optimal in general. Finding the optimal sensing order involves computing the cognitive throughput as a function of the sensing order and using a dynamic programming approach to search for the optimal sensing order. The section titled Background briefly discusses these and other results for a single-user CRN that will set the background for the material that follows in this chapter.

This chapter focuses on multi-user CRNs. In multi-user CRNs, expressing the cognitive throughput itself becomes challenging because in addition to the complexities in a single-user CRN, the cognitive throughput also depends on how contentions are managed among CUs, or in other words, how multiple CUs are allowed to access a common set of channels. Based on earlier work (Misra & Kannu, 2012a; Misra & Kannu, 2012b), this chapter considers the following two broad lines of contention management strategies and studies the optimal sensing order separately for each of them.

Key Terms in this Chapter

Decentralized Sensing Order: A sensing order for a local node in a network where spectrum access decisions are made in a decentralized manner by the individual nodes.

Centralized Sensing Order: A sensing order for a central node in a network that makes all spectrum access decisions in a centralized manner.

Primary-Free Probability (of a channel): The probability that the channel is free of activity by the primary users of the channel.

Availability Combination: A binary vector indicating the ON/OFF state of each channel depending on whether or not it is in use by the primary users.

Availability Probability: (of a channel for a cognitive user): The probability that the channel is available for use by the cognitive user in a given time slot or, in other words, the probability that the channel is not in use by the primary users of the channel as well as all higher preemptive-priority cognitive users, if any.

Stopping Rule: A rule associated with the sensing order that a cognitive radio uses whenever it senses a channel to be free to determine whether to stop sensing at the current channel (and start transmitting data) or to continue sensing the next channel in its sensing order.

Cognitive Radio: A smart radio that can intelligently detect available wireless channels in its vicinity and dynamically configure its transmission or reception parameters to make the best use of these channels.

Sensing Order: A sequential order of channels used for sensing by a cognitive radio that is capable of sensing only one channel at a time to find a free channel for its communication.

Rate Adaptation: A dynamic technique used by wireless transmitters to match their modulation, coding and other signal parameters that affect their effective transmission rates to the prevailing conditions on the wireless links.

Cognitive Throughput: The expected sum throughput of all cognitive users in a network.

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