History Of Cognitive Radio Computer Science

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The sophistication possible in a software-defined radio has now reached the level where each radio can conceivably perform beneficial tasks that help the user, help the network, and help minimize spectral congestion. Radios are already demonstrating one or more of these capabilities in limited ways. A simple example is the adaptive digital European cordless telephone (DECT) wireless phone, which finds and uses a frequency within its allowed plan with the least noise and interference on that channel and time slot. Of these capabilities, conservation of spectrum is already a national priority in international regulatory planning. This book leads the reader through the technologies and regulatory considerations to support three major applications that raise an SDR's capabilities and make it a cognitive radio:

Today, we see Internet search engines reflecting the advanced state of artificial intelligence (AI). In networking, DARPA and industrial developers at Xerox, BBN Technologies, IBM, ATT, and Cisco each developed computer-networking techniques, which evolved into the standard Ethernet and Internet we all benefit from today. The Internet Engineering Task Force (IETF), and many wireless-networking researchers continue to evolve networking technologies with a specific focus on making radio networking as ubiquitous as our wired Internet. These researchers are exploring wireless networks that range from access directly via a radio access point to more advanced techniques in which intermediate radio nodes serve as repeaters to forward data packets toward their eventual destination in an ad hoc network topology. All of these threads come together as we arrive today at the cognitive radio era. Cognitive radios are nearly always applications that sit on top of an SDR, which in turn is implemented largely from digital signal processors and general-purpose processors (GPPs) built in silicon. In many cases, the spectral efficiency and other intelligent support to the user arises by sophisticated networking of many radios to achieve the end behavior, which provides added capability and other benefits to the user.

Cognitive Radio

In this section, we define cognitive radio and investigate the algorithms and types of technologies that already exist. 

Definitions of Cognitive Radio

As any newly emerging technology; the definition of "cognitive radio" can be seen in many different ways. In fact, the term Cognitive Radio means different things to different audiences. The

earlier definition by Joseph Mitola in his dissertation titled "Cognitive Radio - An Integrated Agent

Architecture for Software Defined Radio", was given as follows. The cognitive radio identifies

the point at which wireless PDAs and the related networks are sufficiently computationally

intelligent on the subject of radio resources and related computer-to-computer communications to

a- Detect user communications needs as a function of use context

b- Provide radio resources

and wireless services most appropriate to those needs. Cognitive radio increases the awareness that computational entities in radios have of their locations, users, networks, and the larger environment. Mitola included the concept of machine learning as a property of cognitive radio. Mitola's definition on cognitive radio includes a high level of awareness and autonomy, in a sense that cognition tasks, that might be performed, range in difficulty from the goal driven choice of RF band, air interface, or protocol to higher-level tasks of planning, learning, and evolving new upper layer protocols. The FCC gave the following definition on cognitive radio [795]. A cognitive radio is a radio that can change its transmitter parameters based on interaction with the environment in which it operates. At the same time, it should also note that FCC refers to a SDR as a transmitter in which the operating parameters can be altered by making a change in software that controls the operation of the device without changes in the hardware components that affect the radio frequency emissions. It went on to claim that the majority of cognitive radios will probably be SDRs, but neither having software nor being field reprogrammable are requirements of a cognitive radio. To summarize from the aforementioned two versions of definitions on cognitive radio, we can see that Mitola emphasized the level of device/network intelligence which adapts to user activity; while the FCC seems primarily concerned with a regulatory friendly view, focused on transmitter behavior at the moment. Therefore, the relationship between the cognitive radio and SDR from the views of Mitola and the FCC can be seen in Figure 9.3, where cognitive radio adapts to the spectrum environment; while SDR adapts to the network environment. They partially overlap in their functionalities.

Basic Cognitive Algorithms

It is therefore not difficult to discern that a fully functional cognitive radio should have the ability to do the following works:

Tune to any available channel in the target band.

Establish network communications and operate in all or part of the channel.

Implement channel sharing and power control protocols which adapt to spectra occupied by multiple heterogeneous networks.

Implement adaptive transmission bandwidths, data rates, and error correction schemes to obtain the best throughput


Implement adaptive antenna steering to focus transmitter power in the direction

required to optimize received signal strength.

The core of a cognitive radio is its inherent intelligence, which makes it different from any normal

wireless terminal available today, in either 2- or 3G systems. This intelligence will allow a cognitive

radio to scan all possible frequency spectra before it makes an intelligent decision on how and whento make use of a particular sector of the spectrum for communications. Therefore, it is inevitable that a cognitive radio needs great signal processing power to deal with the vast amounts of data it captures from various radio channels. Thus, the capability to process all those enormous amounts of data on a real-time or quasi-real-time basis is a must for any cognitive radio.

It is still too early to specify exactly the algorithms that a cognitive radio should use at the moment

of writing this book. However, we would like to provide some evidence as to how a primitive cognitive radio may behave. Obviously, any cognitive radio has to use the following two protocols for its very basic operation:



The DFS was originally used to describe a technique to avoid radar signals by 802.11a networks

which operate in the 5 GHz U-NII band. Now, it has been generalized to refer to an automatic

frequency selection process intended to achieve some specific objective (like avoiding harmful interference to a radio system with a higher regulatory priority). On the other hand, TPC was originally a mechanism for 802.11a networks to lower aggregate transmit power by 3 dB from the maximum regulatory limit to protect Earth Exploration Satellite Systems (EESS) operations. Now it has been generalized to a mechanism that adaptively sets transmit power based on the spectrum or regulatory environment. These two protocols will become a must for all cognitive radios.

In addition, a cognitive radio should have IPD capability, which is another key cognitive

radio behavior. The IPD is the ability to detect an incumbent user (one with regulatory priority)

based on a specific spectrum signature. The operation of IPD bears the following characteristics:

1- DFS requires an IPD protocol to identify unoccupied or lightly used frequencies.

2- IPD includes detection schemes focused on the characteristics of the specific incumbents in the band, or bands, that the cognitive radio is designed to support.

3- IPD eliminates the need for geo-location techniques (GPS, etc.) to determine the location of the radio and, using a database, identifies unused channels.

As both TPC and IPD algorithms are intuitive, as suggested by its name, we will only explain

the implementation of the DFS cognitive algorithms in depth, in the following text. The DFS algorithm was originally proposed in the ITU-R recommendation M.1461 [807] to avoid possible interference to existing radar operations in the vicinity. Many radar systems and unlicensed devices operating co-channels in proximity could produce a scenario where mutual interference is

experienced. The DFS methodology is used to compute the received interference power levels at

the radar and unlicensed device receivers. A DFS algorithm may provide a means of mitigating this

interference by causing the unlicensed devices to migrate to another channel once a radar system

has been detected on the currently active channel. This model first considers the interference caused by the radar to the unlicensed device at the output of the unlicensed device antenna. If the received interference power level at the output of the unlicensed device antenna exceeds the DFS detection threshold, the unlicensed device will cease transmissions and move to another channel.

Conceptual Classifications of Cognitive Radios

The characteristic features of a cognitive radio have a lot to do with the spectrum facts in different regions or countries. If we are only looking at the US market, we will see that a lot of spectra have been assigned for licensed use by the FCC. Actual spectrum use varies dramatically from region to region: spectrum is more congested in urban areas, and hardly used in rural areas. Some licensed services only operate in a few locations nationally (for example, Fixed Satellite Services). Even in urban areas, only a fraction of available spectra is in continuous use. We have to admit that, in terms of reclaiming fallow spectrum, a lot of low hanging fruit is available for harvest using cognitive techniques. Regulatory activity is just beginning to open up opportunities to reclaim lightly used spectra for new services. Currently, there are two conceptual forms of cognitive radios. One is called full cognitive radio, in which every possible parameter observed by the wireless node and/or the network is taken into account while making a decision on the transmission and/or reception parameter change. The other is called Spectrum Sensing Cognitive Radio, which is a special case of Full Cognitive Radio in which only the RF spectrum is observed.

Also, depending on the parts of the spectrum available for cognitive radio, we can distinguish

"Licensed Band Cognitive Radio" and "Unlicensed Band Cognitive Radio." When a cognitive radio

is capable of using bands assigned to licensed users, apart from the utilization of unlicensed bands

such as the U-NII band or the ISM band, it is called a Licensed Band Cognitive Radio. One of the

Licensed Band Cognitive Radio-like systems is the IEEE 802.15 Task group 2 specification. On the other hand, if a cognitive radio can only utilize the unlicensed parts of a RF spectrum, it is an Unlicensed Band Cognitive Radio. An example of an Unlicensed Band Cognitive Radio is IEEE

802.19. Although cognitive radio was initially thought of as an SDR extension (Full Cognitive Radio), most of the current research work is focused on Spectrum Sensing Cognitive Radio, particularly on the utilization of TV bands for communication. The essential problem of Spectrum Sensing Cognitive Radio is the design of high-quality spectrum sensing devices and algorithms for exchanging spectrum sensing data between different nodes in a cognitive radio network. It has been shown in that a simple energy detector cannot guarantee the accurate detection of signal presence. This calls for more sophisticated spectrum sensing techniques and requires that information about spectrum sensing must be regularly exchanged between nodes. In [805], the authors showed that the increasing number of cooperating sensing nodes decreases the probability of false detection.

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