ReviewMulti-objective optimization for spectrum sharing in cognitive radio networks: A review
Introduction
Cognitive radio (CR) plays a significant role in utilizing the existing radio spectrum more efficiently and adaptively, which has been receiving a growing attention from the researchers in the past decade [1], [2], [3], [4]. The designing, implementation and successful operation of CRs into the legacy wireless networks, give rise to an enormous number optimization problems with multiple performance objectives which need to be addressed simultaneously. Multi-objective optimization is vital in order to address these objectives related to CRNs. Cognitive radios can be programmed and reconfigured dynamically. It has the capability to sense and analyze its surrounding radio environment in order to find out the white spaces or spectrum holes for the transmission of cognitive users. On the basis of interaction with the environment and gathering knowledge of the availability of spectrum holes, a CR can reconfigure its transmission parameters in an automatic and dynamic manner [5]. Fig. 1 shows the idea of spectrum holes. There are two categories of wireless radio users, licensed or primary users and unlicensed or secondary users. The cognitive users are referred to as unlicensed or secondary users. The primary users have paid to use radio spectrum for their services, and on the other hand the secondary or cognitive users have no license for their services.
Multi-objective optimization (MOO) can be used to address optimization problems related to CRNs in many ways. A multi-objective optimization approach based on multi-objective evolutionary algorithm is proposed in [6], where the author considered five most common communication objectives such as, to minimize bit-error rate, maximize throughput, minimize power consumption, minimize spectral interference and maximize spectral efficiency. Naeem et al. [7] provided a comprehensive survey on Resource Allocation (RA) in cooperative CRNs, where the authors also presented the taxonomy of different objectives and protocols used for resource allocation in cooperative CRNs. Ahmad et al. [8] proposed Radio Resource Allocation (RRA) in Cognitive Radio Sensor Networks (CRSNs), which is essentially an MOO problem. Currently, Wireless Sensor Networks (WSNs) are using the unlicensed Industrial, Scientific and Medicine (ISM) band for their transmissions. However, because of the advancements in the wireless technologies and their increased demands, the unlicensed ISM band becomes crowded. This problem is to be addressed by incorporating the intelligent cognitive radio devices instead of using the legacy wireless devices in existing wireless networks.
An overview of existing surveys intended for the CRNs is furnished in Table 1. It is apparent from the table that, majority of the authors have focused on different applications, regarding cognitive radio based wireless networks. Many authors proposed different types of algorithms/solution approaches, in order to address the optimization problems concerned with CRNs. Some of the authors discussed about multiple objectives related to CRNs, and focused on different aggregation methods to combine multiple objectives into a single objective function, in order to solve MOO problems using single objective optimizer. Whereas no work is presented which explores the actual complexities involved in multi-objective optimization problems related to CRNs, especially with multiple conflicting objectives. The wireless networks equipped with CR devices are called CRNs or secondary networks. These CRNs are capable of sensing the environment intelligently, in order to locate and identify the spectrum holes or white spaces, and accordingly adjusting their transmitting and receiving parameters adaptively for their transmissions. Optimization plays a significant role in all areas, such as from designing to successful implementation of cognitive radio networks. The optimization problems concerned with CRNs can be broadly categorized as single objective optimization (SOO) problems as well as multi-objective optimization (MOO) problems. In case of SOO, the main goal of the optimizer is to find the lone optimum solution in order to minimize or maximize only one objective function under various constraints. Whereas in case of MOO, the main goal of the optimizer is to find out multiple tradeoff optimal solutions in order to optimize multiple objectives simultaneously. Most of the real-world optimization problems related to CRNs call for multiple objectives. Since all the objectives are important, they need to be optimized simultaneously. This gives rise to the importance of MOO in the field of CRNs.
The rest of the paper is organized as follow. Section 2 provides the basic concepts of cognitive radios. A detailed discussion on preference-based MOO as well as real MOO is provided in Section 3, where a review on MOO in CRNs is also provided. Different types of optimization and programming techniques, used to formulate and program the optimization problems regarding to CRNs, are discussed in Section 4. Section 5 provides a detailed discussion on algorithms and solution approaches used to address optimization problems related to CRNs. In Section 6, different types of applications regarding CRs are elaborated. Finally, Section 7 concludes the paper and provides future directions.
Section snippets
Basics of cognitive radio
The concept of CR was first conceived by Joseph Mitola in order to make the software defined radio (SDR) more personal [39]. The conceptual view of CR is depicted in Fig. 2. CR is an intelligent radio communication system in which the “Intelligence” is integrated with SDR. An SDR is a programmable and reconfigurable signal processing chip, which is usually developed using digital signal processor or a general purpose microprocessor.
There are three major models of DSA, namely (1) common-use,
Multi-objective optimization
Optimization can be defined as a process to achieve the best outcome of a physical system, or some other situation under some predefined criterion. Every optimization problem calls for one or more objectives to optimize. Generally, most of the real world optimization problems involve more than one objective and therefore called as MOO problems. In an MOO problem, different solutions provide tradeoffs between different objectives. For the optimization problem having only one objective function,
Optimization types
Different types of optimization and programming techniques used to formulate and program the optimization problems in CRNs, are presented in this section. Fig. 8 depicts the taxonomy of optimization types proposed by different authors in their literature related to CRNs. These types include linear programming (LP), mixed integer linear programming (MILP), non-linear programming (NLP), mixed integer non-linear programming (MINP), convex optimization, non-convex (NC) optimization, combinatorial
Solution approaches
Optimization problems can be formulated in many different ways, and can be solved using different types of algorithms and solution approaches. A classification of solution approaches, proposed by different authors in the previous literature in order to address the optimization problems coming to cognitive radios, is depicted in Fig. 9. This taxonomy of solution approaches can be categorized as, Evolutionary, Heuristic, Analytical/Iterative, Game Theoretic and Stochastic.
Applications
In this section, we will discuss about the several application of CRs in the real world scenarios. Fig. 10 shows different types of applications related to CRs. These applications can be characterized as spectrum sensing, spectrum sharing, CR adaptation, resource allocation, learning and reasoning, and many more discussed under miscellaneous category.
Conclusion and future directions
Cognitive radio plays a significant role in order to use the radio spectrum more efficiently and adaptively as well as to address the challenges concerned with spectrum scarcity. The design, deployment, successful implementation and operation of cognitive radios into the current wireless networks, give rise to a large number of optimization problems. The optimization problems related to cognitive radio networks, exposed off with multiple and diverse performance objectives in nature, which need
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