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The high rate of wireless technology is proliferating rapidly and vision of pervasive wireless computing and communication offers the promise of many societal and individual benefits (Peter, 2009). The explosion of wireless application creates an ever-increasing demand for more wireless radio spectrum. Consumer devices such as cell phones, personal digital assistant (PDA) and laptops receive a lot of attention, the impact of wireless technology is much broader e.g. through sensor network for safety application and embedded wireless devices, entertainment, home automation, medical wearable and media systems. One of the ways to meet growing demands of higher data rates is to identify and utilize the unused resources of the radio spectrum (Gayathri,2000).  To improve the utilization efficiency of existing radio spectrum, cognitive radio network concept relies on the spectrum sensing techniques and utilize the unused parts of the spectrum to achieve higher spectral efficiency. Cognitive radio network is simple, there are licensed users known as primary users and unlicensed users known as secondary users, who opportunistically use the spectrum when the primary users are idle. With cognitive radios, the efficiency in spectrum utilization will be improved.

Cognitive radios offer the promise of being a disruptive technology innovation that will allow future wireless world. The rapid growth rate allow smart phones, tablets and other smart devices enable the convenience of our current lifestyle and these devices are exponentially increasing the traffic load in current wireless networks (Vunan, 2015). Cognitive radio is a radio system operating over multiple frequency selective wireless channels in which users can change their transmission or reception parameters to communicate efficiently by avoiding interference with licensed or unlicensed users. In contrast to cellular and optical systems which have centralized access management, cognitive radio systems are inherently distributed.

Upon the promise of cognitive wireless radio, there are still many technical drawback needed to overcome before the model is ready to be build in a real world case. Some challenges includes hidden primary users and spread spectrum primary users both of which lead a cognitive radio to wrong decision that a spectrum block is empty, leading to interference of signals with licensed user (Saluja, 2010). To realize cognitive radio network, channel allocation should be adapted according to the varying channel states and requirement predefined quality and other criterion (Fuqicing, 2007). The main purpose of spectrum allocation is assigning the usable channels to secondary users. Again, the aim of cognitive radio is usage of Frequency bands that are owned by their licensed users. Therefore, one of the most significant requirements of cognitive radio is that the interference caused by cognitive devices to licensed users remains at a negligible level.

Recently stable matching theory has emerged as a promising technique for allocation problem, which can overcome some limitations of the existing method. Some of the methods used in improving cognitive radio network are the native Bio-in-spired algorithms, self-organization paradigms, optimization algorithms, dynamic spectrum allocation and dynamic routing. For allocating resources in cognitive wireless network. The use of stable matching theory to find a stable multi-channel allocation for each secondary user assuming the existence of central coordinator responding on behalf of the primary users. The main contribution of this work is to design a novel and distributed spectrum allocation approach in a cognitive radio network when secondary users have non-identical spectrum uncertainty values. From the secondary users perspective, the ranking is done based on the interference and confidence measures regarding the presence or absence of primary users over the licensed spectrum.

Cognitive networking is a promising paradigm that deals with heterogeneous systems learns relationships among network parameters and male decisions in order to achieve local, end-t-end and management goals. Network routing protocol is the most significant and the core of the problem for communication. The cognitive network routing protocol includes the routing path generation, path routing selection and maintenance of the routing path. The routing path is generated according to network status information and shortest path is selected, selection is based on network operational status for appropriate path (Caro, 2004).

In routing algorithm, the shortest among the available paths can be precisely understood generating population (colony) of ants, keeping the track of forward-backward path discovery using transition function. Each ant can be considered as autonomous, and the overall control is completely distributed in this environment. In this perspective, the colony realizes a form of concurrent autonomic computation. Multiple paths are repeatedly tried out back and some information related to each followed path is released on the environment, which acts as a shared distributed memory preset in the pheromone trails and helping into update local table. In turn, the local content of this memory affects the stochastic decisions of the ants, such that, when there is a significant difference in the lengths of the path optimization mechanism.

Shortest path routing model most is use in real networks. In shortest path routing the optimizing strategy for path flows consists in using the minimum cost paths connecting all the node pairs in the network, where the paths are calculated independently for each pair. When we reference paradigm has a network – wide perspective, since the path flows are calculated considering all the incoming traffic sessions throughout the network. It clearing, in order to adopt such a global strategy, optimal routing requires the prior knowledge of the statistical characteristics of all the incoming flows, a requirement which is usually quite to satisfy.

Ant colony optimization function as cc common channel which enables cognitive radio node to transmission control packets is used. Each node can be individually detecting its spectrum opportunity which is set of frequency bands currently not occupied and available. In our mechanism, there are two kinds of ant forward ants and backward ants. The forward ant executes path search functions and the backward ant establishes the pheromone table when the source node has data to transmit, route discovery process starts. The source node send forward ant which uses Ant colony optimization mechanism to choose the next node through common control channel when backward ants are generated. The backward ants return the source node and update the local pheromone concentration. Once all the backward ants back to the source node, a simple local search scheme is applied. The scheme has potential to further reduce that path delay and accelerate convergence of Ant-colony optimization algorithm.

In realization, cognitive radio network present a primary application of matching theory due to the necessity of decentralized operation. Due to the fast changing channels and primary user interferences leading to route instability, therefore application of Ant-colony optimization stable matching algorithm world help in stabilizing channel allocation between primary and secondary user.

In this project, we focus on the channel allocation mechanism design aiming at maximizing the overall utility or social welfare, modeling the spectrum allocation problem.


The fast growing interest and emergence challenges in channel allocation in cognitive wireless network have motivated our interest in carrying out this study on spectrum allocation to both primary users and secondary users.


In the study, spectrum allocation in cognitive radio wireless network is considered. The problems associated with the existing method used are;

  1. Problem of interference
  2. Channel idleness
  • Starvation of spectrum of part of the user
  1. High rate of channel fluctuation
  2. Underutilization of spectrum



The aim of this work is to propose Ant colony optimization – stable matching based framework for allocation of channel in cognitive radio Network


The specific objectives of this work are as follows;

  1. To find out the various problems associated with cognitive radio network.
  2. To develop Ant-Colony-Optimization Framework and stable matching based framework integrated that will optimize and allocate spectrum.
  3. To implement the framework using JAVA


The method employed in order to achieve the aim and objectives of this work are;

  1. Review of related literatures on cognitive radio and Ant-Colony –Optimization.
  2. Data collection from relevant sources
  3. Design of ACO-stable matching based framework for spectrum sensing
  4. Implementation of (2) & (3) using Java programming language.




The scope of this study is to develop ant-colony optimization stable matching based framework for allocation of channel in cognitive radio network. The limitation of this work is to spectrum sensing in cognitive radio network only.


Cognitive wireless radio network is the key to success in handling to upcoming dynamic network patterns and exploiting this crossover the fullest extent. This study will.

  1. Give a general understanding of cognitive wireless radio network system.
  2. Provide ideas when choosing a path to solve a problem and also provide faster and smarter ways in doing things.
  3. Serve as a springboard for researchers who maybe interest in further research on this topic for vital information.





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