**Analog‐to‐Digital Conversion for Cognitive Radio: Subsampling, Interleaving, and Compressive Sensing**

José Ramón García Oya, Fernando Muñoz Chavero and Rubén Martín Clemente

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.69241

#### Abstract

This chapter explores different analog-to-digital conversion techniques that are suitable to be implemented in cognitive radio receivers. This chapter details the fundamentals, advantages, and drawbacks of three promising techniques: subsampling, interleaving, and compressive sensing. Due to their major maturity, subsampling- and interleavingbased systems are described in further detail, whereas compressive sensing-based systems are described as a complement of the previous techniques for underutilized spectrum applications. The feasibility of these techniques as part of software-defined radio, multistandard, and spectrum sensing receivers is demonstrated by proposing different architectures with reduced complexity at circuit level, depending on the application requirements. Additionally, the chapter proposes different solutions to integrate the advantages of these techniques in a unique analog-to-digital conversion process.

Keywords: analog-to-digital conversion, cognitive radio, compressive sensing, interleaving, multistandard receivers, software-defined radio, spectrum sensing, subsampling

#### 1. Introduction

Analog-to-digital conversion (ADC) stage is one of the main bottlenecks of the high-speed telecommunications systems. This chapter presents a survey of different feasible analog-todigital conversion techniques that are suitable to overcome these difficulties and to get the software-defined radio (SDR) paradigm [1], where most functionalities, instead of being performed in the analog domain (i.e., filters and mixers), are performed in the digital domain. In SDR, the analog-to-digital conversion is implemented immediately after the antenna, and the radio frequency (RF) signal is directly converted to digital without any previous mixing

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stage. Since it is not possible to approach this idea with traditional analog-to-digital conversion from current commercial devices, this chapter describes some techniques that may be employed instead. Although the proposed systems have more restrictive specifications, these solutions reduce the final complexity, as will be detailed in this chapter. This work explores three different promising techniques: subsampling, interleaving, and compressive sensing (CS).

The proposed techniques are an appealing solution to approach the cognitive radio (CR) objectives [2, 3], which are conditioned by the physical implementation of the SDR receiver. Due to the necessity of several wireless standards coexisting in the same device, a high flexibility and programmability will be an important requirement for the proposed architectures, with the objective of being employed in multistandard receivers.

With these objectives in mind, this chapter describes in detail three different approaches for implementing the analog-to-digital conversion stage. The choice of one or other of these approaches will depend on the environment, the properties of the received signals, and the parameters that have to be optimized. For receivers where the main requirements are a highresolution and a high-analog bandwidth that covers a maximum number of communication standards, we propose a system based on subsampling techniques. For applications where the main requirement is to maximize the data acquisition rate, the proposed system is based on interleaving techniques, that is, the interconnection of several analog-to-digital converters in parallel. Finally, compressive sensing techniques will be preferred for scenarios where the spectrum can be considered sparse, that is, for a wideband spectrum with a low spectral occupancy, where it will be possible to recover the received signal and implement an estimation of the radio channel by using a reduced number of samples from the ADC. This emerging technology will be presented at architectural level, so that it will be studied from the point of view of its integration with the two main techniques detailed in this chapter, that is, subsampling and interleaving techniques, with the objective of exploiting their advantages for sparse spectrum sensing applications.
