International Journal of Computer Applications in Engineering Sciences [VOL I, SPECIAL ISSUE ON CNS , JULY 2011]
[ISSN: 2231-4946]
Dynamic Spectrum Access in Cognitive Radio: A Brief Review Anita Garhwal, Partha Pratim Bhattacharya Department of Electronics and Communication Engineering Faculty of Engineering and Technology Mody Institute of Technology & Science(Deemed university) Lakshmangarh , Dist. Sikar, Rajasthan, Pin – 332311, India 1
[email protected],
Abstract - Today’s wireless networks are characterized by fixed spectrum assignment policy. The limited available spectrum and the inefficiency in the spectrum usage necessitate a new communication paradigm, referred to as Dynamic Spectrum Access (DSA) and cognitive radio networks. The dynamic spectrum access technology can allow unlicensed secondary systems to share the spectrum with the licensed primary systems. In this paper, different methods of DSA are discussed which include Gametheoretic approach, Group Intelligence technique, Markovian Queuing Model and fuzzy logic based method.
Keywords - wireless communication system, cognitive radio, fuzzy logic, spectrum management, dynamic spectrum access I. INTRODUCTION TO COGNITIVE RADIO Cognitive radio (CR) is a kind of wireless communication system in which either a network or a wireless node changes its transmission or reception parameters to communicate efficiently avoiding interference with licensed or unlicensed users. It was thought of as an ideal goal towards which a softwaredefined radio platform should evolve to a fully reconfigurable wireless black-box that automatically changes its communication variables in response to network and user demand. It is considered to be an intelligent wireless communication system that is aware of its surrounding environment (i.e., outside world), and uses the methodology of understanding-by-building to learn from the environment and adapt its internal states to statistical variations in the incoming RF stimuli by making corresponding changes in certain operating parameters (e.g., transmit-power, carrier frequency, and modulation strategy) in real-time, with two primary objectives in mind: Highly reliable communications, whenever and wherever needed; Efficient utilization of the radio spectrum.
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[email protected]
The idea of cognitive radio was first presented officially in an article by Joseph Mitola III and Gerald Q. Maguire, Jr in 1999 [1]. It was a novel approach in wireless communication that 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 [2]. A cognitive radio may also be defined as a radio that is aware of its environment and the internal state and with knowledge of these elements and any stored pre-defined objectives can make implement decisions about its behavior. More specifically, the cognitive radio technology will enable the users to determine which portions of the spectrum is available and detect the presence of licensed users when a user operates in a licensed band (spectrum sensing), (2) select the best available channel (spectrum management), (3) coordinate access to this channel with other users (spectrum sharing), and (4) vacate the channel when a licensed user is detected (spectrum mobility). Regulatory bodies in various countries (including the Federal Communications Commission in the United States, and Ofcom in the United Kingdom) found that most of the radio frequency spectrum was inefficiently utilized [2]. The idea of spectrum sharing is shown in Fig 1.
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frequency of operation. Cognitive radio networks target to use the spectrum in a dynamic manner by allowing the radio terminals to operate in the best available frequency band, maintaining seamless communication requirements during the transition to better spectrum. iv) Spectrum Sharing: It refers to providing the fair spectrum scheduling method, one of the major challenges in open spectrum usage is the spectrum sharing.
Fig 1. Spectrum Access by Cognitive Radio
The main functions of Cognitive Radios are [3-5]: i) Spectrum Sensing: It refers to detect the unused spectrum and sharing it without harmful interference with other users. It is an important requirement of the Cognitive Radio network to sense spectrum holes, detecting primary users is the most efficient way to detect spectrum holes. Spectrum sensing techniques can be classified into three categories: o 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 approaches proposed: matched filter detection energy detection o Cooperative detection: It refers to spectrum sensing methods where information from multiple Cognitive radio users is incorporated for primary user detection. o Interference based detection. ii) Spectrum Management: It is the task of capturing the best available spectrum to meet user communication requirements [6]. Cognitive radios should decide on the best spectrum band to meet the Quality of Service requirements over all available spectrum bands, therefore spectrum management functions are required for Cognitive radios, these management functions can be classified as: spectrum analysis spectrum decision iii) Spectrum Mobility: It is defined as the process when a cognitive radio user exchanges its 250 | P a g e
II. DYNAMIC SPECTRUM ACCESS Dynamic Spectrum Management (DSM), also referred to as Dynamic Spectrum Access (DSA), is a set of techniques based upon theoretical concepts in Network information theory and Game theory that is being researched and developed to improve performance of a communication network as a whole. The concept of DSM also draws principles from the fields of cross layer optimization, artificial intelligence, machine learning etc. It has been recently made possible by availability Software-Radio due to development of fast enough processors both at servers and at terminals. These are techniques for Cooperative Optimization. This can also be compared or related to optimization of one link in the network on the account of losing performance on many links negatively affected by this single optimization empowering a new realm of devices and services through optimized connectivity. DSA is a paradigm for the efficient utilization of the radio spectrum by improving the spectral efficiency and avoiding the mutual interference. With cognitive radio which employs DSA all available frequency bands including low frequency TV bands and other vacant frequency bands can be put to efficient use and local area network overloading can be avoided as cognitive radio adapts to unusual situations using flexible spectrum access. This increases data transfer speed. CRs opportunistically utilize these holes for communication without causing interference to primary users as shown in Fig 2 [7].
Fig 2. Opportunistic spectrum usage in cognitive radio
DSA is either a Centralized dynamic spectrum allocation in the case where a central controller exists that decides which spectral resources should use at what time, without providing harmful interference to the licensed users (PU) or a Distributed decentralized
Dynamic Spectrum Access in Cognitive Radio: A Brief Review
approach spontaneous access where cognitive radio (CR) autonomously decide to access the spectrum in a scenario where no central control is present [8]. The vision is to assign appropriate resources to end users only as long as they are needed for a geographically bounded region, that is, a personal, local, regional, or global cell. The spectrum access is then organized by the network, that is, by the users. First examples for self regulation in mobile radio communications are to be found in the ISM (2400– 2483.5MHz) and in the WLAN (5150–5350MHz and 5470–5725 MHz) bands. III. METHODS OF DYNAMIC SPECTRUM ACCESS Dynamic spectrum access (DSA) methods for cognitive radio can be categorized as exclusive-use, shared-use, and commons models [9]. (a) Exclusive –Use This method maintains the basic structure of the current spectrum regulation policy where spectrum bands are licensed to serve for exclusive use. The main objective is introducing flexibility in spectrum allocation and usages. Two approaches has been proposed in this model: (1) Long Term Exclusive-use a model manages spectrum using space, frequency and type of service dimensions and guarantees exclusive ownership with those constraints for prolonged periods of time. (2) Dynamic Exclusive-Use model that manages spectrum in finer scales of time, space and frequency and use dimensions. (b) Shared use of primary licensed spectrum In this model, the spectrum owned by a licensee (also referred to as the primary user) is shared by a non-license holder commonly referred to as a secondary user. This model is attractive as it increases spectrum access. Two possible models are (1) Spectrum Underlay it represents a very conservative approach to shared-use wherein the secondary users transmissions are expected to be of such low power that there is no perceptible change in the interference environment of the primary users. (2) Spectrum Overlay model, actively explored in the ongoing DARPA xG [10] program and first advocated by Mitola [11], targets for aggressive, opportunistic exploitation of whitespaces or spectrum “gaps” in spatiotemporal domain. (c) Commons This is an operating model, wherein nobody can claim exclusive use of a shared resource, it has three types: (1) Uncontrolled Commons, also referred as open spectrum access hence when a spectrum band is
managed no entity has exclusive license to the spectrum band. (2) Cooperative and Managed Commons, represents an effort to avoid the tragedy of commons by imposing a limited form of order or structure to spectrum access. (3) Private Commons is a policy mechanism for creating a managed commons where the ultimate ownership of the licensed spectrum is still centralized with the license holder. i)
Schul Lee, Hwanseok Choi, Chong –Kwon Kim [12] has proposed a Game-theoretic approach for spectrum sharing. With the Bertrand model he analyzed the impact of several secondary users’ preference to spectrum product provided by primary user. The strategy of the players (primary users of each channel) is to set the unit price of the quantity (i.e. demand from secondary users) of the spectrum. The profit function for each channel of primary user is given as follows: +
(1) Mathematically to derive Nash equilibrium they solve partial differentiation of profit function respect to for all players of the game as follows: (2) Nash equilibrium of the game is defined as set of all players’ strategies with the property that no player can increase his payoff without changing other player’s strategies. ii) A spectrum sensing technique using Group Intelligence is proposed [13] where multiple users, each with incomplete information, can learn from the group’s wisdom to reach a supposedly correct conclusion. He proposed adaptive threshold based on group intelligence accuracy of the spectral occupancy has improved by training the Secondary User (SU).This training is done with the help of the global decision derived from other SUs in the network. This approach is suitable in two ways. (1)To indicate the presence or absence of the PU separate training signal are not required. (2) Training signal derived from spatially diverse SUs should be robust and less sensitive to local channel fading .Together the SUs are expected to reach a unanimous decision, which should be correct in all situations. For a group to be wise there are certain criteria’s: Diversity of opinion, independence, Decentralization, Aggregation. iii) A Markovian Queuing Model for Dynamic Spectrum Allocation in Centralized 251 | P a g e
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Architecture is proposed [14]. The master/controller of this ad-hoc network coordinates for spectrum allocation with the surrounding CR in the network. This CR adhoc network is assumed to coexist with the network of licensed users where the controller of licensed users is updated with CR coordinating engine. The mathematical model of network of queues is shown in Fig 3.
A fuzzy logic based spectrum access method is also proposed [7]. Fuzzy logic is used because it is a multi-valued logic and many input parameters can be considered to take the decision. The model of Fuzzy logic system is shown in Fig 5.
Fig 5.Fuzzy Logic System Fig 3.Queing model for DSA in Cognitive Radio
Here Markov process is used to analyze the Queuing model. Blocking Probability is the probability that an SU request will be denied. This happens when there are no more vacant channels with the Head SU and the intended users request is discarded. The blocking probability PB for the bandwidth request made by CR that finds all the channels with Head as occupied is given by the well known Erlang-B formula as given below:
The simulation results are shown in Fig 6, 7and 8. It may be seen from the results that the chance of taking decision increases if the signal strength of the channel offered by primary user is high and the distance between primary and secondary users is low. Similarly, the chance is getting increased when enough free spectrum or channels is available and when node velocity is high.
(3) The blocking probability from equation (3) is plotted in Fig 4. Variation in PB is shown with respect to change in number of available channels in the system as 2, 5, 7, 10, 13, and 15. Blocking probability increases with increase in SU traffic in the network.
Fig 6.Opportunistic spectrum access decision possibility (Velocity=50 Km /hr and ratio of required spectrum to available spectrum=0.5)
Fig 4.Blocking probability (PB) against SU utilization in the system (ρ1 ). The variation in PB is depicted with different numbers of channels (S), available with the system.
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Dynamic Spectrum Access in Cognitive Radio: A Brief Review
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[8] Fig 7. Opportunistic spectrum access decision possibility (Distance between primary and secondary user=50 meters and signal strength=60 dBm)
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Fig 8.Opportunistic spectrum access decision possibility (Distance between primary and secondary user=50 meters and user velocity=50 Km/hr)
IV. CONCLUSION Cognitive radio is the emerging spectrum sharing technology and can be the best option for future generation wireless networks because of present spectrum crisis and uneven use of spectrum. In this paper, we have discussed different dynamic spectrum access techniques. It is seen that Markov queuing model proposed a centralized architecture whereas Gametheoretic spectrum sharing model is proposed to obtain Nash equilibrium. Using Group Intelligence model the accuracy of spectral occupancy is improved. A fuzzy logic based spectrum management technique is also discussed here which will help to take wise decision regarding spectrum sharing in cognitive networks. REFERENCES [1]
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0Mitola, J., III; Maguire, G.Q.Jr, “Cognitive radio: making software radios more personal”, Personal Communications, IEEE Volume 6, Issue 4, Aug 1999, pp 13 – 18. Gregory Staple and Kevin Werbach, “The End of Spectrum Scarcity”, IEEE Spectrum Online, March 2004. Ian F. Akyildiz, Won-Yeol Lee, Mehmet C. Vuran, Shantidev
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