Today’s wireless networks are characterized by a fixed spectrum assignment policy. The limited available spectrum and the inefficiency in the spectrum usage necessitate a new communication paradigm to exploit the existing wireless spectrum opportunistically. This new networking paradigm is referred to as Next Generation (xG) Networks as well as Dynamic Spectrum Access (DSA) and cognitive radio networks. Sharing of licensed spectrum is a promising approach to utilize spectrum resource in wireless communication. A cognitive radio is a smart radio which has the ability to sense the external environment, learn from the history, and make intelligent decisions to adjust its transmission parameters according to the current state of environment. Cognitive radio is the technology for supporting dynamic spectrum access. It is the policy that addresses spectrum scarcity problem around the world. Cognitive radio is turn out to be most promising technology for future wireless communication. Reinforcement learning is learning from interaction. Essentially reinforcement learning is trial and error based learning. Moreover, the spectrum management, spectrum sensing, spectrum sharing and channel selection in multichannel networks are also outlined.
Keywords: Cognitive Radio Networks; Dynamic spectrum access Networks; Next Generation Networks;
In today`s world the spectrums are regulated by governmental agencies and are assigned to license holders or services on a long term basis for large geographical regions. The radio spectrum which is necessary for wireless communication is a limited resource available in the world. In addition, a large portion of the assigned spectrum is used sporadically, where the signal Strength distribution over a large portion of the wireless spectrum is shown. The spectrum usage is concentrated on certain portions of the spectrum while a significant amount of the spectrum remains unutilized. To support various wireless applications and services in a non interfering basis, the fixed spectrum access(FSA) policy has traditionally been adopted by spectrum regulators, which assign each piece of spectrum with certain bandwidth to one or more dedicated users. By doing so, only the assigned (licensed) users have the right to exploit the allocated spectrum, and other users are not allowed to use it, regardless of whether the licensed users are using it. With the proliferation of wireless services in the last couple of decades, in several countries, most of the available spectrum has fully been allocated, which results in the spectrum scarcity problem. To avoid the spectrum scarcity problem dynamic spectrum access has been proposed to allow the radio spectrum to more efficiently be used. Dynamic spectrum access techniques allow the cognitive radio to operate in the best available channel.
2. Spectrum Access
The OSA model is illustrated in Fig. 1. In this model, a CR user carries out spectrum sensing to detect spectrum holes, i.e. portions of spectrum allocated (licensed) to some PUs but left unused for a certain time. Upon detecting one or multiple spectrum holes, the CR user reconfigures its transmission parameters (e.g., carrier frequency, bandwidth, and modulation scheme) to operate in the identified spectrum holes. While doing this, we have to vacate spectrum when any primary user become active.
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Fig.1 Opportunistic Spectrum Access
3. Cognitive Radio Network
Cognitive radio networks are being studied intensively. The major motivation for this is the heavily underutilized frequency spectrum. The development is being pushed forward by the rapid advances in software denied radio technology which enable a spectrum agile and highly configurable radio transmitter/receiver . In other words, once cognitive radios can find the opportunities using the “spectrum holes” for communications, cognitive radio networking to transport packets on top of cognitive radio links is a must to successfully facilitate useful applications and services. Cognitive radios offer the promise of being a disruptive technology that will enable the future wireless world.
The basic elements that are necessary for primary users and next generation networks are:
3.1 Primary network: An existing network infrastructure is generally referred to as the primary network, which has an exclusive right to a certain spectrum band. Examples include the common cellular and TV broadcast networks. The components of the primary network are as follows:
3.1.1 Primary User: The primary users or licensed users has license to operate in a certain spectrum band.
Primary base stations control these types of accesses without affecting any operations of secondary users.
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Fig.2 Primary and Secondary Bands
3.2 Secondary network: This is also known as next generation networks. Secondary network does not have license to operate in a license band. Hence, the spectrum access is allowed only in an opportunistic manner. Secondary networks can be deployed both as an infrastructure network and an ad hoc network as shown in Fig.2. The components of an secondary network are as follows:
3.2.1 Secondary Users: Secondary user (or unlicensed user) has no spectrum license. Hence, additional functionalities are required to share the licensed spectrum band.
In summary, the main functions for cognitive radios in next generation networks can be summarized as follows:
3.3 Spectrum sensing: Detecting unused spectrum and sharing the spectrum without harmful interference with other users.
3.4 Spectrum management: Capturing the best available spectrum to meet user communication requirements.
3.5 Spectrum mobility: Maintaining seamless communication requirements during the transition to better spectrum.
3.6 Spectrum sharing: Providing the fair spectrum scheduling method among coexisting xG users.
3.7. Routing in Wireless Cognitive Radio Network:
Routing constitutes a rather important but yet unexplored problem in xG networks. Routing in multi-hop cognitive radio networks (CRN) should be spectrum-aware. In this paper, adaptive reinforcement learning based spectrum-aware routing protocols are introduced. Compared to the previous spectrum aware routing protocols in multi-hop cognitive radio networks, it is simple and easier to implement, more cost-effective, and can avoid drawbacks in on-demand protocols but still keep adaptive and dynamic routing. The way multiple channels are utilized in multi-hop cognitive radio networks (multi-hop CRN) can be very different in comparison to traditional multi-channel networks. Two nodes can not communicate with each other if they have no common available channel. In order to adapt this situation, routing in multi-hop CRN should be spectrum aware, which means that we should integrate spectrum discovery with route discovery.
3.7.1 Routing Protocol:
In this paper we use AODV routing protocol following some changes in it. It is Ad hoc on-Demand Distance Vector routing protocol. It is protocol used in Mobile Ad hoc Networks and other wireless ad-hoc networks. It is a reactive routing protocol, meaning that it establishes a route to a destination only on demand. AODV is, as the name indicates, a distance vector routing protocol. AODV is capable of both unicast and multicast routing. In AODV, the network is silent until connection is not needed. At that point the network node that needs a connection broadcasts a request for connection. Other AODV nodes forward this message, and record the node that they heard it from, creating temporary routes back to the needy node. When a node receives such a message and already has a route to the desired node, it sends a message backwards through a temporary route to the requesting node. The needy node then begins using the route that has the least number of hops through other nodes. When a link fails, a routing error is passed back to a transmitting node, and the process repeats. The main advantage of this protocol is having routes established on demand and that destination sequence numbers are applied to find the latest route to the destination. In this paper we make changes in AODV protocol. AODV establishes a route to a destination only on demand but here we use some mathematical parameters to establish a connection. The terms used in this protocol to establish a connection are explained below,
220.127.116.11 Unicast: Unicast is the term used to describe communication where a piece of information is sent from one point to another point. In this case there is just one sender, and one receiver. Unicast is a transmission in which a packet is sent from a single source to a specified destination.
18.104.22.168 Multicast: Multicast is the term used to describe communication where a piece of information is sent from one or more points to a set of other points. In this case there is may be one or more senders, and the information is distributed to a set of receivers (there may be no receivers, or any other number of receivers).
22.214.171.124 Broadcast: Broadcast is the term used to describe communication where a piece of information is sent from one point to all other points. In this case there is just one sender, but the information is sent to all connected receivers.
4. Channel Selection in multi -channel Wireless Network:
Cognitive radio based dynamic spectrum access network is emerging as a technology to address spectrum scarcity. In this study, we assume that the channel is licensed to some primary (licensed) operator. We consider a sensor network with cognitive radio capability that acts as a secondary (unlicensed) network and uses the channel in underlay mode. The secondary network uses interference temperature model to ensure that the interference to the primary devices remain below a predefined threshold. We use Hidden Markov Model (HMM) to model the interference temperature dynamics of a primary channel. The HMM is trained using Baum-Welch procedure. The trained HMM is shown to be statistically stable. Secondary nodes use this trained HMM to predict the interference temperature of the channel in future time slots and computes the value of Channel Availability Metric (CAM) for the channel. CAM is used by secondary nodes to select a primary channel for transmission. Results of application of such trained HMMs in channel selection in multi-channel wireless network are presented. Consider a multi-channel cognitive wireless network with n channels. Let us assume that the training sequences for these channels is obtained by a designated sensor node, which construct hidden Markov model for each channel using these training sequences, as explained earlier. Let Hi represent the trained HMM for channel i, GSHi denote the binary sequence generated by Hi, |GSHi denote the length of sequence GSHi, GS1Hi denote the number of 1’s in the generated sequence GSHi, and βi denote the average gap between any two 1’s in the generated sequence GSHi. Then, we define channel availability