In this paper, we consider the problem of exploiting spectrum resources for a secondary user (SU) of a wireless communication network. We suggest that Upper Confidence Bound (UCB) algorithms could be useful to design decision making strategies for SUs to exploit intelligently the spectrum resources based on their past observations. The algorithms use an index that provides an optimistic estimation of the availability of the resources to the SU. The suggestion is supported by some experimental results carried out on a specific dynamic spectrum access (DSA) framework.

This article draws a general retrospective view on the first 10 years of cognitive radio (CR). More specifically, we explore in this article decision making and learning for CR from an equipment perspective. Thus, this article depicts the main decision making problems addressed by the community as general dynamic configuration adaptation (DCA) problems and discuss the suggested solution proposed in the literature to tackle them. Within this framework dynamic spectrum management is briefly introduced as a specific instantiation of DCA problems. We identified, in our analysis study, three dimensions of constrains: the environment's, the equipment's and the user's related constrains. Moreover, we define and use the notion of a priori knowledge, to show that the tackled challenges by the radio community during first 10 years of CR to solve decision making problems have often the same design space, however they differ by the a priori knowledge they assume available. Consequently, we suggest in this article, the "a priori knowledge" as a classification criteria to discriminate the main proposed techniques in the literature to solve configuration adaptation decision making problems. We finally discuss the impact of sensing errors on the decision making process as a prospective analysis.

Cognitive radio has been claimed to be a hopeful solution to the existing conflicts between spectrum demand growth and spectrum underutilization. The basic underlying idea of cognitive radio is to allow unlicensed users to access in an opportunistic and non-interfering manner some licensed bands temporarily unoccupied by licensed users. The cognitive radio concept relies on two basic premises: the current spectrum underutilization, which has been demonstrated in some spectrum measurements campaigns, and the ability of unlicensed users to effectively detect and identify the presence of different licensed technologies in order not to cause harmful interference. In this context, this paper reports the joint work on these two areas that is currently being carried out in the framework of the FP7 Network of Excellence in Wireless COMmunications (NEWCOM++). Concretely, this paper presents spectrum occupancy measurements conducted in the frequency range from 75 MHz to 7075 MHz that demonstrate the low degree to which spectrum is currently used in an urban outdoor environment and also describes the blind standard recognition sensor concept, a sensor embedded in a cognitive radio equipment to enable the identification of many commercial wireless standards without the need to connect to any network. The joint research in both areas is a key step in promoting and validating the idea of dynamic spectrum usage.

We suggest in this paper that many problems related to Cognitive Radio's (CR) decision making inside CR equipments can be formalized as Multi-Armed Bandit problems and that solving such problems by using Upper Confidence Bound (UCB) algorithms can lead to high-performance CR devices. An application of these algorithms to an academic Cognitive Radio problem is reported.

Recently, there has been a surge of interests in paradigms such as device-to-device (D2D) communications and radio frequency energy harvesting (RFEH) to improve the spectrum as well as energy efficiencies of next-generation decentralized cognitive radio networks. However, little attention has been paid to the dual but competing task of subband selection of any desired bandwidth in D2D mode (i.e., opportunistic vacant spectrum access) and RFEH mode as well as need to minimize the subband switching cost (SSC) for an efficient implementation. Taking these factors into account, a new D2D-RFEH policy is proposed. It consists of: 1)Bayesian approach based Tunable Thompson Sampling (TTS) algorithm to learn subband statistics, 2) Subband access scheme employing TTS algorithm for minimizing collisions among the secondary users, and 3) Mode selection scheme. The simulation results, complexity and SSC analysis validate the superiority of the proposed policy over the policies employing frequentist approach based learning algorithms.

In this article, we deal with software radio in an original manner, emphasizing the fact that software radio is a major evolution of radio technologies and a convergence of different pre-existing fields. Joseph Mitola deserves credit for formalizing and conceptualizing this evolution. We do not define software radio. Rather, we look at software radio from different perspectives. New equipments include more and more software radio “capacities,” that is, they can be reconfigured thanks to software. Certain former analog functionalities are now performed digitally. We think that the timeframe during which software radio is advantageous, compared to a velcro1 approach for multi-standard terminals, is probably over. However, software radio has taken concrete form in the military field. We show that software radio is used de facto in all equipment and products because of the technological progress to which it has led. Software radio's legacy indeed is that it has been and still is a catalyst for wireless innovation. We conclude by showing that software radio will be used to its full potential when it is the support technology for other more complex fields such as cognitive radio.