Cognitive radio

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A cognitive radio is an intelligent radio that can be programmed and configured dynamically. Its transceiver is designed to use the best wireless channels in its vicinity. Such a radio automatically detects available channels in wireless spectrum, then accordingly changes its transmission or reception parameters to allow more concurrent wireless communications in a given spectrum band at one location. This process is a form of dynamic spectrum management.

Description[edit]

In response to the operator's commands, the cognitive engine is capable of configuring radio-system parameters. These parameters include "waveform, protocol, operating frequency, and networking". This functions as an autonomous unit in the communications environment, exchanging information about the environment with the networks it accesses and other cognitive radios (CRs). A CR "monitors its own performance continuously", in addition to "reading the radio's outputs"; it then uses this information to "determine the RF environment, channel conditions, link performance, etc.", and adjusts the "radio's settings to deliver the required quality of service subject to an appropriate combination of user requirements, operational limitations, and regulatory constraints".

Some "smart radio" proposals combine wireless mesh network—dynamically changing the path messages take between two given nodes using cooperative diversity; cognitive radio—dynamically changing the frequency band used by messages between two consecutive nodes on the path; and software-defined radio—dynamically changing the protocol used by message between two consecutive nodes.

J. H. Snider, Lawrence Lessig, David Weinberger, and others say that low power "smart" radio is inherently superior to standard broadcast radio.[1][2][3][4]

History[edit]

The concept of cognitive radio was first proposed by Joseph Mitola III in a seminar at KTH (the Royal Institute of Technology in Stockholm) in 1998 and published in an article by Mitola and Gerald Q. Maguire, Jr. in 1999. It was a novel approach in wireless communications, which Mitola later described as:

The point in which wireless personal digital assistants (PDAs) and the related networks are sufficiently computationally intelligent about radio resources and related computer-to-computer communications to detect user communications needs as a function of use context, and to provide radio resources and wireless services most appropriate to those needs.[5]

Cognitive radio is considered as a goal towards which a software-defined radio platform should evolve: a fully reconfigurable wireless transceiver which automatically adapts its communication parameters to network and user demands.

Traditional regulatory structures have been built for an analog model and are not optimized for cognitive radio.[6] Regulatory bodies in the world (including the Federal Communications Commission in the United States and Ofcom in the United Kingdom) as well as different independent measurement campaigns[7] found that most radio frequency spectrum was inefficiently utilized. Cellular network bands are overloaded in most parts of the world, but other frequency bands (such as military, amateur radio and paging frequencies) are insufficiently utilized. Independent studies performed in some countries confirmed that observation, and concluded that spectrum utilization depends on time and place. Moreover, fixed spectrum allocation prevents rarely used frequencies (those assigned to specific services) from being used, even when any unlicensed users would not cause noticeable interference to the assigned service. Regulatory bodies in the world have been considering whether to allow unlicensed users in licensed bands if they would not cause any interference to licensed users. These initiatives have focused cognitive-radio research on dynamic spectrum access.

Terminology[edit]

Depending on transmission and reception parameters, there are two main types of cognitive radio:

  • Full Cognitive Radio (Mitola radio), in which every possible parameter observable by a wireless node (or network) is considered.[8]
  • Spectrum-Sensing Cognitive Radio, in which only the radio-frequency spectrum is considered.[9]

Other types are dependent on parts of the spectrum available for cognitive radio:

  • Licensed-Band Cognitive Radio, capable of using bands assigned to licensed users (except for unlicensed bands, such as the U-NII band or the ISM band. The IEEE 802.22 working group is developing a standard for wireless regional area network (WRAN), which will operate on unused television channels.[10][11]
  • Unlicensed-Band Cognitive Radio, which can only utilize unlicensed parts of the radio frequency (RF) spectrum.[citation needed] One such system is described in the IEEE 802.15 Task Group 2 specifications,[12] which focus on the coexistence of IEEE 802.11 and Bluetooth.[citation needed]
  • Spectrum mobility: Process by which a cognitive-radio user changes its frequency of operation. Cognitive-radio networks aim to use the spectrum in a dynamic manner by allowing radio terminals to operate in the best available frequency band, maintaining seamless communication requirements during transitions to better spectrum.
  • Spectrum sharing[9]: Spectrum sharing cognitive radio networks allow cognitive radio users to share the spectrum bands of the licensed-band users. However, the cognitive radio users have to restrict their transmit power so that the interference caused to the licensed-band users is kept below a certain threshold.
  • Sensing-based Spectrum sharing:[13] In sensing-based spectrum sharing cognitive radio networks, cognitive radio users first listen to the spectrum allocated to the licensed users to detect the state of the licensed users. Based on the detection results, cognitive radio users decide their transmission strategies. If the licensed users are not using the bands, cognitive radio users will transmit over those bands. If the licensed users are using the bands, cognitive radio users share the spectrum bands with the licensed users by restricting their transmit power.

Technology[edit]

Although cognitive radio was initially thought of as a software-defined radio extension (full cognitive radio), most research work focuses on spectrum-sensing cognitive radio (particularly in the TV bands). The chief problem in spectrum-sensing cognitive radio is designing high-quality spectrum-sensing devices and algorithms for exchanging spectrum-sensing data between nodes. It has been shown that a simple energy detector cannot guarantee the accurate detection of signal presence,[14] calling for more sophisticated spectrum sensing techniques and requiring information about spectrum sensing to be regularly exchanged between nodes. Increasing the number of cooperating sensing nodes decreases the probability of false detection.[15]

Filling free RF bands adaptively, using OFDMA, is a possible approach. Timo A. Weiss and Friedrich K. Jondral of the University of Karlsruhe proposed a spectrum pooling system, in which free bands (sensed by nodes) were immediately filled by OFDMA subbands. Applications of spectrum-sensing cognitive radio include emergency-network and WLAN higher throughput and transmission-distance extensions. The evolution of cognitive radio toward cognitive networks is underway; the concept of cognitive networks is to intelligently organize a network of cognitive radios.

Functions[edit]

The main functions of cognitive radios are:[16][17]

  • Power Control: Power control is used for both opportunistic spectrum access and spectrum sharing CR systems for finding the cut-off level in SNR supporting the channel allocation and imposing interference power constraints for the primary user's protection respectively. A classic work in studying power allocation schemes to maximize the capacity of cognitive radio networks is.[18] A joint power control and spectrum sensing is proposed for capacity maximization in.[19]
  • Spectrum sensing: Detecting unused spectrum and sharing it, without harmful interference to other users; an important requirement of the cognitive-radio network to sense empty spectrum. Detecting primary users is the most efficient way to detect empty spectrum. Spectrum-sensing techniques may be grouped into three categories:
    • Transmitter detection: Cognitive radios must have the capability to determine if a signal from a primary transmitter is locally present in a certain spectrum. There are several proposed approaches to transmitter detection:
      • Matched filter detection
      • Energy detection: Energy detection is a spectrum sensing method that detects the presence/absence of a signal just by measuring the received signal power.[20] This signal detection approach is quite easy and convenient for practical implementation. To implement energy detector, however, perfect noise variance information is required. And surprisingly when there is noise uncertainty, there is an SNR wall below which the energy detector can not reliably detect any transmitted signal.[21] In,[22] a new energy based spectrum sensing algorithm with noise variance uncertainty is proposed. This algorithm does not suffer from SNR wall and outperforms the existing signal detectors (see for example [23] and its USRP implementation [24] ). And most importantly, the relationship between the energy detector of [22] and that of [20] is quantified analytically. Also when the noise variance is known perfectly these two energy detectors achieve the same probability of detection and false alarm rates.
      • Cyclostationary-feature detection: This type of spectrum sensing algorithms are motivated because most of man made communication signals such as BPSK, QPSK, AM, OFDM exhibit cyclistationary behavior.[25] However, noise signals (typically white noise) do not experience this behavior. These detectors are robust against noise variance uncertainty.[26] The aim of such detectors is to exploit the cyclostationary nature of man made communication signals buried in noise. Cyclostationary detetors can be either single cycle or multicycle cyclostatonary.
        • Single Cycle detectors: These detectors exploit the existence of the transmitted signal just by considering one cyclic frequency location.
        • Multicycle detectors: In this detector, many cyclic frequency locations are examined to detect the presence or the absence of transmitted signals. As this detectors considers many possibilities, it usually give better performance compared to that of the single cycle detecor. But this is at the expense of additional complexity. One low complexity multicycle cyclostationary approach to detect OFDM signals can be found in.[26]
      • Moment based detector: Like in the Cyclostationary detectors, different man made signals such as BPSK, QPSK, M-ary QAM signals also have different n>2th moment values than that of white noise.[27] The moment based detector exploits this behavior to check the presence or absence of the transmitted signal. To remove the effects of noise variance uncertainty, simple ratio test will work as in.[27]
  • Wideband spectrum sensing: refers to spectrum sensing over large spectral bandwidth, typically hundreds of MHz or even several GHz. Since current ADC technology cannot afford the high sampling rate with high resolution, it requires revolutional techniques, e.g., compressive sensing and sub-Nyquist sampling.[28]
    • Cooperative detection: Refers to spectrum-sensing methods where information from multiple cognitive-radio users is incorporated for primary-user detection[29]
    • Interference-based detection
  • Null-space based CR: With the aid of multiple antennas, CR detects the null-space of the primary-user and then transmit within this null-space, such that its subsequent transmission causes less interference to the primary-user [30]
  • Spectrum management: Capturing the best available spectrum to meet user communication requirements, while not creating undue interference to other (primary) users. Cognitive radios should decide on the best spectrum band (of all bands available) to meet quality of service requirements; therefore, spectrum-management functions are required for cognitive radios. Spectrum-management functions are classified as:
    • Spectrum analysis
    • Spectrum decision

The practical implementation of spectrum-management functions is a complex and multifaceted issue, since it must address a variety of technical and legal requirements. An example of the former is choosing an appropriate sensing threshold to detect other users, while the latter is exemplified by the need to meet the rules and regulations set out for radio spectrum access in international (ITU radio regulations) and national (telecommunications law) legislation.

Cognitive radio (CR) versus intelligent antenna (IA)[edit]

An intelligent antenna (or smart antenna) is an antenna technology that uses spatial beam-formation and spatial coding to cancel interference; however, it is emerging to be extended for an intelligent multiple- or cooperative-antenna array so as to be applied to the recent complex communication environments. On the other hand, cognitive radio allows user terminals to sense whether a portion of the spectrum is being used to share spectrum with neighbor users. The following table compares the two:

Point Cognitive radio (CR) Intelligent antenna (IA)
Principal goal Open spectrum sharing Ambient spatial reuse
Interference processing Avoidance by spectrum sensing Cancellation by spatial precoding/post-coding
Key cost Spectrum sensing and multi-band RF Multiple- or cooperative-antenna arrays
Challenging algorithm Spectrum management tech Intelligent spatial beamforming/coding tech
Applied techniques Cognitive software radio Generalized dirty paper coding and Wyner-Ziv coding
Basement approach Orthogonal modulation Cellular based smaller cell
Competitive technology Ultra-wideband for greater band utilization Multi-sectoring (3, 6, 9, so on) for higher spatial reuse
Summary Cognitive spectrum-sharing technology Intelligent spectrum reuse technology

Note that both techniques can be combined as illustrated in many now a day transmission scenarios [31]

Applications[edit]

CR can sense its environment and, without the intervention of the user, can adapt to the user's communications needs while conforming to FCC rules in the United States. In theory, the amount of spectrum is infinite; practically, for propagation and other reasons it is finite because of the desirability of certain spectrum portions. Assigned spectrum is far from being fully utilized, and efficient spectrum use is a growing concern; CR offers a solution to this problem. A CR can intelligently detect whether any portion of the spectrum is in use, and can temporarily use it without interfering with the transmissions of other users. According to Bruce Fette, "Some of the radio's other cognitive abilities include determining its location, sensing spectrum use by neighboring devices, changing frequency, adjusting output power or even altering transmission parameters and characteristics. All of these capabilities, and others yet to be realized, will provide wireless spectrum users with the ability to adapt to real-time spectrum conditions, offering regulators, licenses and the general public flexible, efficient and comprehensive use of the spectrum".

Simulation of CR Networks[edit]

At present, modeling and simulation is the only paradigm which allows the simulation of complex behavior in the environments cognitive radio networks. Network simulators like OPNET, NetSim and NS2 can be used to simulate a Cognitive radio network. Areas of research using Network simulators include a) Spectrum Sensing & incumbent detection b) Spectrum Allocation and c) Measurement and modeling of Spectrum usage.

Future plans[edit]

The success of the unlicensed band in accommodating a range of wireless devices and services has led the FCC to consider opening further bands for unlicensed use. In contrast, the licensed bands are underutilized due to static frequency allocation. Realizing that CR technology has the potential to exploit the inefficiently utilized licensed bands without causing interference to incumbent users, the FCC released a Notice of Proposed Rule Making which would allow unlicensed radios to operate in the TV-broadcast bands. The IEEE 802.22 working group, formed in November 2004, is tasked with defining the air-interface standard for wireless regional area networks (based on CR sensing) for the operation of unlicensed devices in the spectrum allocated to TV service.[32]

See also[edit]

References[edit]

  1. ^ Lawrence Lessig. "Wireless Spectrum: Defining the 'Commons'". 2003. (mirror). quote: "Technologists are increasingly discussing a related kind of gain called 'cooperation gain.' ... think about a party. If I need to tell you that it's time to leave, I could choose to shout that message across the room. Shouting, however, is rude. So instead, imagine I choose to whisper my message to the person standing next to me, and he whispered it to the next person, and she to the next person, and so on. This series of whispers could get my message across the room without forcing me to shout."
  2. ^ Aaron Swartz. "Open Spectrum: A Global Pervasive Network". quote: "if nodes repeat each other's traffic. If I want to talk to someone across the room, I don't have to shout. I can just whisper it to someone near me, who can pass it on, and so on. ... as we add more transmitters, the total capacity goes up slightly, but we still have to face the fact that each transmitter's capacity goes down (just slower). Even better, we all end up using less energy (since we don't have to transmit as far), saving battery life."
  3. ^ David Weinberger. "Why Open Spectrum Matters: The End of the Broadcast Nation". quote: "Every time a broadcaster receives a license, the amount of available spectrum goes down. ... New technology, however, increases bandwidth with the number of users."
  4. ^ Dana Blankenhorn. "The war against open spectrum". 2007. quote: "If we lose ... open spectrum, we're also going to lose the open Internet".
  5. ^ http://web.it.kth.se/~maguire/jmitola/Mitola_Dissertation8_Integrated.pdf
  6. ^ Patrick S. Ryan, Wireless Communications and Computing at a Crossroads: New Paradigms and Their Impact on Theories Governing the Public's Right to Spectrum Access. Journal on Telecommunications & High Technology Law, Vol. 3, No. 2, p. 239, 2005
  7. ^ V. Valenta et al., "Survey on spectrum utilization in Europe: measurements, analyses and observations," In proceedings of IEEE CrownCom 2010
  8. ^ J. Mitola III and G. Q. Maguire, Jr., "Cognitive radio: making software radios more personal," IEEE Personal Communications Magazine, vol. 6, nr. 4, pp. 13–18, Aug. 1999
  9. ^ a b S. Haykin, "Cognitive Radio: Brain-empowered Wireless Communications", IEEE Journal on Selected Areas of Communications, vol. 23, nr. 2, pp. 201–220, Feb. 2005
  10. ^ IEEE 802.22
  11. ^ Carl, Stevenson; G. Chouinard, Zhongding Lei, Wendong Hu, S. Shellhammer & W. Caldwell (January 2009). "IEEE 802.22: The First Cognitive Radio Wireless Regional Area Networks (WRANs) Standard = IEEE Communications Magazine". IEEE Communications Magazine (US: IEEE) 47 (1): 130–138. doi:10.1109/MCOM.2009.4752688. 
  12. ^ IEEE 802.15.2
  13. ^ X. Kang et. al ``Sensing-Based Spectrum Sharing in Cognitive Radio Networks, IEEE Transactions on Vehicular Technology, vol. 58, no. 8, pp. 4649-4654, Oct 2009.
  14. ^ http://www.eecs.berkeley.edu/wireless/posters/WFW05_cognitive.pdf
  15. ^ http://ieeexplore.ieee.org/iel5/4234/30631/01413630.pdf?tp=&arnumber=1413630&isnumber=30631
  16. ^ Ian F. Akyildiz, W.-Y. Lee, M. C. Vuran, and S. Mohanty, "NeXt Generation/Dynamic Spectrum Access/Cognitive Radio Wireless Networks: A Survey," Computer Networks (Elsevier) Journal, September 2006. [1]
  17. ^ Cognitive Functionality in Next Generation Wireless Networks
  18. ^ X. Kang et. al ``Optimal power allocation for fading channels in cognitive radio networks: Ergodic capacity and outage capacity, IEEE Trans. on Wireless Commun., vol. 8, no. 2, pp. 940-950, Feb 2009.
  19. ^ F. Foukalas et. al Joint optimal power allocation and sensing threshold selection for SU's capacity maximisation in SS CRNS
  20. ^ a b H. Urkowitz Energy detection of unknown deterministic signals, IEEE Proceedings, Apr. 1967.
  21. ^ R. Tandra and A. Sahai, SNR walls for signal detection, IEEE J. Sel. Topics Signal Process., vol. 2, no. 1, pp. 4 – 17, Feb. 2008.
  22. ^ a b T. E. Bogale, L. Vandendorpe, and B. L. Long, Sensing throughput tradeoff for cognitive radio networks with noise variance uncertainty, Proc. IEEE Conference on Cognitive Radio Oriented Wireless Networks (CROWNCOM), Oulu, Finland, 2 – 4 Jun. 2014.
  23. ^ T. E. Bogale and L. Vandendorpe, Max-Min SNR signal energy based spectrum sensing algorithms for cognitive radio networks with noise variance uncertainty, IEEE Trans. Wireless. Commun., vol. 30, no. 1, pp. 280 – 290, Jan. 2014.
  24. ^ T. Bogale and L. Vandendorpe, USRP implementation of Max-Min SNR signal energy based spectrum sensing algorithms for cognitive radio networks, in Proc. IEEE International Conference on Communications (ICC), Sydney, Australia, 10 – 14 Jun. 2014.
  25. ^ W. A. Gardner, Exploitation of spectral redundancy in cyclostationary signals, IEEE Sig. Proc. Mag., vol. 8, no. 2, pp. 14 – 36, 1991.
  26. ^ a b T. E. Bogale and L. Vandendorpe, Multi-cycle cyclostationary based spectrum sensing algorithm for OFDM signals with noise uncertainty in cognitive radio networks, in Proc. IEEE Military Communications Conference (MILCOM), Orlando, FL, USA, 29 Oct. – 01 Nov. 2012, pp. 1 – 6.
  27. ^ a b T. E. Bogale and L. Vandendorpe, Moment based spectrum sensing algorithm for cognitive radio networks with noise variance uncertainty, in Proc. IEEE Conference on Information Sciences and Systems (CISS), Baltimore, MD, USA, Mar. 2013, pp. 1 – 5.
  28. ^ H. Sun, A. Nallanathan, C.-X. Wang, and Y.-F. Chen, “Wideband spectrum sensing for cognitive radio networks: a survey,” IEEE Wireless Communications, vol. 20, no. 2, pp. 74–81, April 2013.
  29. ^ Z. Li, F.R. Yu, and M. Huang, “A Distributed Consensus-Based Cooperative Spectrum Sensing in Cognitive Radios,” IEEE Trans. Vehicular Technology, vol. 59, no. 1, pp. 383-393, Jan. 2010.
  30. ^ F. Gao et. al ``Design of Learning Based MIMO Cognitive Radio Systems, IEEE Trans. Veh. Technol., vol. 59, no. 4, pp. 1707-1720, May 2010.
  31. ^ B. Kouassi, I. Ghauri, L. Deneire, Reciprocity-based cognitive transmissions using a MU massive MIMO approach. IEEE International Conference on Communications (ICC), 2013 [2]
  32. ^ Carlos Cordeiro, Kiran Challapali, and Dagnachew Birru. Sai Shankar N. IEEE 802.22: An Introduction to the First Wireless Standard based on Cognitive Radios JOURNAL OF COMMUNICATIONS, VOL. 1, NO. 1, APRIL 2006

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