Recent Advances in Cognitive Radios

Harit Mehta, harit.mehta@go.wustl.edu (A paper written under the guidance of Prof. Raj Jain) DownloadPDF

Abstract

Recent advances in the field of wireless have lead to an increase in flexibility of spectrum usage. With the fixed spectrum assignment policy much of the spectrum remains unused most of the time and is wasted. This led to the technological development of cognitive radios which optimize the spectrum usage. This led to the process of monitoring the spectrum called spectrum sensing. Spectrum sensing forms the base of cognitive radios and is one of the most important techniques that enable the cognitive radios to optimize the spectrum usage. The paper covers a detailed survey of the background of Cognitive radios: characteristics, functions and architecture. We also discuss different spectrum sensing techniques along with some of the recent advances in spectrum sensing methods. The paper further looks at the recent applications in use in the field of cognitive radios.

Keywords- Cognitive radios, spectrum sensing, primary users, secondary users, primary signal, secondary signal, energy detector, cyclostationary feature detector, matched filter, cooperative spectrum sensing.

Table of contents

1. Introduction to Cognitive Radios

With the rapid growth of wireless communication, the last decade has seen an extensive amount of growth in demand for wireless radio spectrum. Promoting competitions, innovations, investment and regulations in radio spectrum is handled by The Federal Communications Commission (FCC). The use of cognitive radio (CR) technology has led the FCC to consider more flexibility in the usage of available spectrum. In the current spectrum framework, the spectrum bands are allocated to licensed holders, also known as primary users (PUs), for large demographical regions, on a long term basis. However there is partial utilization of the allocated spectrum. This inefficient utilization of spectrum necessitates development of dynamic spectrum access techniques (DSA). The DSA allows users with no spectrum license, called secondary users (SUs), to temporally use the unused licensed spectrum.

The priority users have priority in using the spectrum; SUs need to constantly perform real time monitoring of the licensed spectrum which can be used. In doing so the SU should not violate the interference temperature. The SUs should be aware of the PUs reappearance. The technique used for sensing the PUs presence is called spectrum sensing. There are various sensing techniques such as energy detection, cyclostationary feature detection, matched filter, central cooperative sensing and distributive cooperative sensing. In spectrum sensing the SU constantly senses/checks the transmission channel for the presence of the primary signals in the channel. After sensing the spectrum the CRs allocate the spectrum to the SUs and the SUs need to reconfigure themselves in order to use the newly allocated spectrum. The block diagram of CR cycle is shown in figure 1.

Figure 1: Cognitive Radio Cycle

Figure 1: Cognitive Radio Cycle

In the past few years there have been significant developments in CRs. Here in section 2 we discuss some background topics such as CR characteristics, CR functions and CR architectures. In section 3 we discuss spectrum sensing and various spectrum sensing techniques such as energy detector, cyclostationary feature detector, matched filter detector, centralized cooperative sensing and distributed cooperative sensing. In section 4 we discuss two recent applications of CRs and in section 5 we conclude this paper.

2. Background

In this section we discuss some of the CR characteristics. We then discuss some CR functions which are necessary for a SU to efficiently manage the spectrum and at last we discuss the CR architecture in brief.

2.1 Cognitive Radio Characteristics

Some CR network characteristics are as follows:

2.2 Cognitive Radio Functions

2.3 Cognitive Radio Architecture

A cognitive radio network consists of primary networks as well as secondary networks. A primary network comprises of one or more PUs and one or more primary base stations. The PUs are licensed to use the spectrum and are coordinated by the primary base stations. PUs communicate among each other through the base station only. Generally the PUs as well as the primary base stations do not have CR properties.

On the other hand, a secondary network comprises of one or more SUs and may or may not contain a secondary base station. For SUs, the spectrum access is managed and handled by the secondary base station which acts as a hub/access point for the SU network. The SUs under the range of the same base station communicate with each other through the base station. If more than one secondary base station shares a single spectrum band then their spectrum usage and coordination is done by a central spectrum broker. A set of SUs can also connect to each other and communicate among themselves without the presence of the secondary base station. This kind of network is called an ad-hoc network. Internet of things (IoT) as well as vehicular ad-hoc network are some of the examples.

As the SUs should not cause interference with the PUs transmissions, all the SUs along with the secondary base stations are equipped with the CR properties. So whenever SUs detect the presence of a PU in a spectrum band they should immediately stop using that band and should move to some other available band to avoid interference with the PU transmission.

As shown in the figure 2, spectrum band consists of licensed as well as unlicensed bands. PUs are authorized to use the licensed bands while the SUs can only use the licensed bands when the licensed bands are idle and are not being used by the PU. If a PU starts using the licensed band on which a SU is transmitting, the SU should immediately detect PU's presence and should stop transmitting on that band and should move to some other available band. The information regarding the available bands as well as the occupied bands is provided to the SUs by the secondary base station. The secondary base station is supposed to handle the band allocation and maintain coordination among all the SUs within that network. Whenever a SU detects the presence of a PU, it sends this information to the secondary base station and the secondary base station then informs all other SUs regarding the presence of PU on that band and asks all the SUs to give up that particular band. If SUs are using an unlicensed band then they can form an ad-hoc network and can coordinate among themselves without the secondary base station.

Figure 2: Cognitive Radio Architecture

Figure 2: Cognitive Radio Architecture

Ref - [Beibei11]

3. Spectrum Sensing

Spectrum sensing refers to the task of estimating the radio channel parameters such as transmission channel characteristics, interference level, noise level, spectrum availability, power availability, etc. Spectrum sensing is mainly done in the frequency and time domain. However it can also be done in code and phase domains as well.

The unlicensed users or SUs need to continuously monitor the spectrum for the presence of the licensed users or PUs. If the PU is absent for a particular time, the SU can use that spectrum for transmission till the PU reappears. Once the PU reappears, the SU should yield that spectrum for the PU and should shift to some other unused spectrum. This implies that the SUs should continuously monitor the entire spectrum for an opportunity to use a channel that is not being used by the PU. This technique of continuously monitoring the spectrum is called spectrum sensing. Optimizing the spectrum usage being the main aim of CRs, makes spectrum sensing the most basic and important process for CRs. The unused spectrums may be available in two cases either a temporal unused spectrum or a spatial unused spectrum. A temporal unused spectrum appears when a PU does not transmit during a certain amount of time period and the SUs can use the spectrum for that time. A spatial unused spectrum appears when the PU transmits within an area and the SUs can then use that spectrum outside that area. The spectrum sensing performance however is affected by noise uncertainty, shadowing and multipath fading. The major spectrum sensing techniques that have been developed in the past decade are discussed in this section. The sensing techniques can be classified into two major types: Local spectrum sensing in which SU makes an independent decision regarding the presence of the PU and the cooperative spectrum sensing in which a group of SUs decide on the presence of the PU.

Before diving into the spectrum sensing techniques we introduce the hypothesis test, based on which the performances of the techniques are tested. The hypothesis model is as follows:

H0: y(t) = n(t),
H1: y(t) = h*x(t) + n(t)

Where y(t) is the received signal, x(t) is the primary user signal, n(t) is additive white Gaussian noise and h is the channel gain of the primary user. The hypothesis H0 is a null hypothesis which means that there is no primary signal present whereas H1 indicates the presence of the primary signal. The summary of all the spectrum sensing techniques discussed in the following sections is given in table 1 at the end of the section.

3.1 Local Spectrum Sensing



Table 1: Summary of spectrum sensing techniques

Sensing Method Decision Parameter Advantages Disadvantages Suggested Improvements
Energy Detector Comparing energy of received signal to a threshold Simple to implement, requires no prior information about the primary signal Cannot function in low SNR environment, has a high false alarm rate Enhanced Energy detector, noise adaptive energy detector, adaptive threshold energy detector
Cyclostationary Feature Detector Comparing the non zero values obtained by CSD for the cyclostationary properties of the primary signal Robust to noise, Can differentiate among different types of primary transmissions Will fail if the primary signal does not have cyclostationary property, has a high computational complexity, high cost, requires some prior knowledge of the primary signal Reducing the complexity- by dividing the input into sub series, by under sampling the signal using tunneling
Matched Filter Detector Correlating the received signal with the already know primary signal Requires less sensing time, optimum if the primary signal is known High complexity as requires separate receiver for every primary user, requires a priori knowledge about the primary signal Coherent detection using fewer details regarding the primary signal

3.2 Cooperative Spectrum Sensing

There are few limitations of the local spectrum sensing technique discussed in section [3.1]. Some of the factors that may make local spectrum sensing ineffective are uncertainty of noise, shadowing, multi-path effect and hidden primary user problem. To overcome these problems cooperative spectrum sensing (CSS) has been suggested. In CSS, multiple SUs send their local observations to a central controller and the controller decides the available channel based on a decision function and informs all the secondary stations regarding the decision of availability of channels. This approach is called centralized cooperative sensing. In the other method, called the distributive cooperative sensing, the secondary users exchange the information among themselves without the support of the central controller. In distributed approach, one of the SUs can act as a relay and help other users to improve sensing performance. When a SU detects presence of the primary signal it can use amplify and relay to help other users. However there are challenges with the CSS approach.

One of the challenges is the power utilization. If the SUs are low power devices then it becomes difficult for them to sense the channel for presence of the primary signal as it is a complex process and requires high computation as described in the previous section. Even after sensing the channel it takes a fair amount of power to transmit the sensing result to the central controller or to any other SU. Moreover it has also been observed that cooperating of all SUs is not optimal because of the different location and channels for each SU. To address this low power issue user selection method has been proposed. To obtain optimum detection rate, only a selected group of SUs, having higher SNR, cooperate to reach to a decision. Another technique called data fusion has also been proposed. In this method many different fusion rules are used to combine the decisions of the SUs at the central controller and a decision is then made based on the result obtained by the fusion of all the individual decisions. This method has two main sub types. In the first one, called the soft combination method, the SUs send their sensed or processed data to the central controller. The controller then uses different techniques such as energy detection or likelihood ratio test to make a decision and the decision is then broadcasted to all the SUs. The problem with the soft combination is the high feedback overhead. To overcome this problem hard combination has been proposed. In this method, the SUs make their own binary decision and then send this binary decision to the central controller. At the central controller a fusion scheme such as OR-scheme, AND-scheme or majority scheme to make the decision. Under the OR scheme, if any of the SU detects the PU's presence the central controller decides that the PU is present. Under the AND scheme, the PU presence is declared only if all the SUs detect it and under the majority scheme, if more than half of the SUs detect the presence of the PU, the controller declares the presence of the PU. Many other techniques have been proposed to reduce the overhead in centralized cooperative sensing such as Sequential centralized cooperative sensing, Compressive sensing and Efficient Information Sharing.

4. Applications

The ability of the CRs to monitor the Radio Frequency (RF) in the environment and their ability to adapt to the changes in the environment by changing their configurations run time make them suitable for many useful applications. Two of such applications are briefly discussed below.

4.1 Authentication Applications

A CR can learn the identity of its user(s). Authentication applications can prevent unauthorized users from using the CR. Since a radio is usually used for voice communications, there is a microphone in the system. The captured signal is encoded with a VoCoder and transmitted. The source radio can authenticate the user and add the known identity to the data stream. At the destination end, decoded voice can be analyzed for the purposes of authentication. Recently cell phones have been equipped with digital cameras. This sensor coupled with facial recognition software may be used to authenticate a user. Other biometric sensors may be used for authentication and access control.

4.2 Wireless Medical Networks

CRs can also prove helpful in establishing Medical Body Area Networks (MBAN). MBANs are generally used for implementing ubiquitous patient monitoring in hospitals. Ubiquitous monitoring can help to instantly notify the doctors regarding the vital information of patients such as blood pressure, sugar level, blood oxygen and electrocardiogram (ECG), etc. MBANs using the CR technology can help provide such information through wireless networks and thus eliminate the use of wires and tubes for monitoring the patients. MBANs help in gathering the vital information about a patient and collectively send it to the doctors which enables the doctors to act instantly and thus a patient's condition can be recognized at an early stage and enables the doctors to take appropriate action. Moreover the replacing of wires and tubes for monitoring the patient's condition with the wireless networks and sensors reduces the risk of infections and increases the patient's mobility.

5. Conclusion

Spectrum utilization has been a major topic of research in the past decade because of the increasing demand of spectrum usage due to huge amount of increase in the wireless networks. CRs have emerged as a promising technology for the optimum utilization of the available spectrum. CRs enable a SU (unlicensed user) to use a licensed spectrum whenever it is idle and a PU (licensed user) is not using the spectrum. For doing so the CRs need to adapt to operating parameters of the environment while shifting form one band to the other by tuning the frequency to the unused bands. For efficiently using the spectrum the SUs need to continuously monitor the spectrum to sense the presence or absence of PU. This technique of monitoring the spectrum, called spectrum sensing, thus forms the base of a CR system.

In this paper, we discuss the CR characteristics, CR functions and CR architectures. We also discuss two main spectrum sensing techniques namely, local spectrum sensing - each SU makes an independent decision; and cooperative spectrum sensing - a group of SUs make a collective decision. Main types of local spectrum sensing techniques such as energy detector, cyclostationary feature detector and matched filter detector are discussed in detail along with their advantages, disadvantages and a few methods to improve each of their performances. The centralized and distributed cooperative sensing techniques have also been discussed briefly. Two of the few interesting applications of the CRs - Authentication Application and Wireless Medical networks - are also discussed briefly.

6. References

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7. Acronyms


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