Applications of Cognitive Radio Networks

The term cognitive radio (CR), originally coined in the late 1990s, envisaged a radio that is aware of its operational environment so that it can dynamically and autonomously adjust its radio-operating parameters to accordingly adapt to the different situations. Cognition is achieved through the so-called cognitive cycle, consisting of the observation of the environment, the orientation and planning that leads to making appropriate decisions in accordance with specific operation goals, and finally, the execution of these decisions (e.g., access to the appropriate channel). Decisions can be reinforced by learning procedures based on the past observations and the corresponding results of prior actuations

Interference Alignment for Cognitive Radio Communications and Networks: A Survey

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Interference alignment (IA) is an innovative wireless transmission strategy that has shown to be a promising technique for achieving optimal capacity scaling of a multiuser interference channel at asymptotically high-signal-to-noise ratio (SNR). Transmitters exploit the availability of multiple signaling dimensions in order to align their mutual interference at the receivers. Most of the research has focused on developing algorithms for determining alignment solutions as well as proving interference alignment’s theoretical ability to achieve the maximum degrees of freedom in a wireless network. Cognitive radio, on the other hand, is a technique used to improve the utilization of the radio spectrum by opportunistically sensing and accessing unused licensed frequency spectrum, without causing harmful interference to the licensed users. With the increased deployment of wireless services, the possibility of detecting unused frequency spectrum becomes diminished. Thus, the concept of introducing interference alignment in cognitive radio has become a very attractive proposition. This paper provides a survey of the implementation of IA in cognitive radio under the main research paradigms, along with a summary and analysis of results under each system model.Peer reviewe

Evolutionary Game Theory Perspective on Dynamic Spectrum Access Etiquette

In this paper, we describe the long-term evolution of societies of secondary users in dynamic spectrum access networks. Such an understanding is important to help us anticipate future trends in the organization of large-scale distributed networked deployments. Such deployments are expected to arise in support of a wide variety of applications, including vehicular networks and the Internet of Things. Two new biologically-inspired spectrum access strategies are presented here, and compared with a random access baseline strategy. The proposed strategies embody a range of plausible assumptions concerning the sensing capabilities and social characteristics of individual secondary users. Considering these strategies as the basis of a game against the field, we use replicator dynamics within an evolutionary game-theoretic analysis to derive insights into the physical conditions necessary for each of the strategies to be evolutionarily stable. Somewhat surprisingly, we find that the physical channel conditions almost always uniquely determine which one of the three (pure) strategies is selected, and that no mixed strategy ever survives. We show that social tendencies naturally become advantageous for secondary users as they find themselves situated in network environments with heterogeneous channel resources. Hardware test-bed experiments confirm the validity of the analytic conclusions. Taken together, these results predict the emergence of social behavior in the spectrum access etiquette of secondary users as cognitive radio technology continues to advance and improve. The experimental results show an increase in the throughput of up to 90%, when strategy evolution is continuously operational, compared with any static strategy. We present use cases to envision the potential application of the proposed evolutionary framework in real-world scenarios

Dynamic Spectrum Sharing in Cognitive Radio Networks Using Truthful Mechanisms and Virtual Currency

In cognitive radio networks, there are scenarios where secondary users (SUs) utilize opportunistically the spectrum originally allocated to primary users (PUs). The spectrum resources available to SUs fluctuates over time due to PUs activity, SUs mobility and competition between SUs. In order to utilize these resources efficiently spectrum sharing techniques need to be implemented. In this paper we present an approach based on game-theoretical mechanism design for dynamic spectrum sharing. Each time a channel is not been used by any PU, it is allocated to SUs by a central spectrum manager based on the valuations of the channel reported by all SUs willing to use it. When an SU detects a free channel, it estimates its capacity according to local information and sends the valuation of it to the spectrum manager. The manager calculates a conflict-free allocation by implementing a truthful mechanism. The SUs have to pay for the allocation an amount which depends on the set of valuations. The objective is not to trade with the spectrum, but to share it according to certain criteria. For this, a virtual currency is defined and therefore monetary payments are not necessary. The spectrum manager records the credit of each SU and redistributes the payments to them after each spectrum allocation. The mechanism restricts the chances of each SU to be granted the channel depending on its credit availability. This credit restriction provides an incentive to SUs to behave as benefit maximizers. If the mechanism is truthful, their best strategy is to communicate the true valuation of the channel to the manager, what makes possible to implement the desired spectrum sharing criteria. We propose and evaluate an implementation of this idea by using two simple mechanisms which are proved to be truthful, and that are tractable and approximately efficient. We show the flexibility of these approach by illustrating how these mechanisms can be modified to achieve different sharing objectives which are trade-offs between efficiency and fairness. We also investigate how the credit restriction and redistribution affects the truthfulness of these mechanisms.This work was supported by the Spanish government through Projects TIN 2008-06739-C04-02 and TIN 2010-21378-C02-02.Vidal Catalá, JR.; Pla, V.; Guijarro Coloma, LA.; Martínez Bauset, J. (2013). Dynamic Spectrum Sharing in Cognitive Radio Networks Using Truthful Mechanisms and Virtual Currency. Ad Hoc Networks. 11:1858-1873. https://doi.org/10.1016/j.adhoc.2013.04.010S185818731

Trust-based mechanisms for secure communication in cognitive radio networks

Cognitive radio (CR) technology was introduced to solve the problem of spectrum scarcity to support the growth of wireless communication. However, the inherent properties of CR technology make such networks more vulnerable to attacks. This thesis is an effort to develop a trust-based framework to ensure secure communication in CRN by authenticating trustworthy nodes to share spectrum securely and increasing system's availability and reliability by selecting the trustworthy key nodes in CRNs

Quasi-Nash Equilibria for Non-Convex Distributed Power Allocation Games in Cognitive Radios

In this paper, we consider a sensing-based spectrum sharing scenario in cognitive radio networks where the overall objective is to maximize the sum-rate of each cognitive radio user by optimizing jointly both the detection operation based on sensing and the power allocation, taking into account the influence of the sensing accuracy and the interference limitation to the primary users. The resulting optimization problem for each cognitive user is non-convex, thus leading to a non-convex game, which presents a new challenge when analyzing the equilibria of this game where each cognitive user represents a player. In order to deal with the non-convexity of the game, we use a new relaxed equilibria concept, namely, quasi-Nash equilibrium (QNE). A QNE is a solution of a variational inequality obtained under the first-order optimality conditions of the player's problems, while retaining the convex constraints in the variational inequality problem. In this work, we state the sufficient conditions for the existence of the QNE for the proposed game. Specifically, under the so-called linear independent constraint qualification, we prove that the achieved QNE coincides with the NE. Moreover, a distributed primal-dual interior point optimization algorithm that converges to a QNE of the proposed game is provided in the paper, which is shown from the simulations to yield a considerable performance improvement with respect to an alternating direction optimization algorithm and a deterministic game

Enabling Spectrum Sharing in Secondary Market Auctions

Wireless spectrum is a scare resource, but in practice much of it is under-used by current owners. To enable better use of this spectrum, we propose an auction approach to dynamically allocate the spectrum in a secondary market. Unlike previous auction approaches, we seek to take advantage of the ability to share spectrum among some bidders while respecting the needs of others for exclusive use. Thus, unlike unlicensed spectrum (e.g. Wi-Fi), which can be shared by any device, and exclusive-use licensed spectrum, where sharing is precluded, we enable efficient allocation by supporting sharing alongside quality-of-service protections. We present SATYA (Sanskrit for “truth”), a strategyproof and scalable spectrum auction algorithm whose primary contribution is in the allocation of a right to contend for spectrum to both sharers and exclusive-use bidders. Achieving strategyproofness in our setting requires appropriate handling of the externalities created by sharing. We demonstrate SATYA’s ability to handle heterogeneous agent types involving different transmit powers and spectrum needs through extensive simulations.Engineering and Applied Science

A Thompson Sampling Approach to Channel Exploration-Exploitation Problem in Multihop Cognitive Radio Networks

International audienceCognitive radio technology is a promising solution to the exponential growth in bandwidth demand sustained by increasing number of ubiquitous connected devices. The allocated spectrum is opened to the secondary users conditioned on limited interference on the primary owner of the band. A major bottleneck in cognitive radio systems is to find the best available channel quickly from a large accessible set of channels. This work formulates the channel exploration-exploitation dilemma as a multi-arm bandit problem. Existing theoretical solutions to a multi-arm bandit are adapted for cognitive radio and evaluated in an experimental test-bed. It is shown that a Thompson sampling based algorithm efficiently converges to the best channel faster than the existing algorithms and achieves higher asymptotic average throughput. We then propose a multihop extension together with an experimental proof of concept