1. Location Aided Routing

    Routing in multi-hop CRNs is an important problem that affects the performance of the entire network. Different from traditional routing protocols in adhoc networks, routing in CRNs has to deal with a number of challenges, including adapting to the dynamic changes of spectrum availability, the heterogeneity of resources, and synchronization between nodes on different channels.
    Moreover, any of today’s wireless devices are location-enabled, e.g. through the GPS system or network-based localization, and this is expected to become more ubiquitous in the future. In addition, location information of CRN nodes can be obtained via FCC Geolocation-Databases or estimated via measurements accurately. This motivates the work on location aided routing for CRNs.
    For example, LAUNCH uses a greedy location-based approach for CRNs. However, evaluating routing protocols for CRNs is considered a challenging problem. Therefore, we introduced CogFrame as a framework that facilitates the implementation and evaluation of CRNs routing protocols on conventional laptops.

  2. Routing Cognitive Engine

    One routing metric may not lead to the best route selection all the time due to network changes. This is particularly true for CRNs where PUs’ activities affect the channel availability in both space and time domains. This motivates the work on the Routing Cognitive Engine. The main idea of the Routing Cognitive Engine is to dynamically switch among different routing solutions and different routing metrics based on the surrounding network conditions. 

    Routing Cognitive Engine is responsible of making the decision of using one metric or more than one metric and the weighting of each metric. It will also be responsible of selecting one of the CRN routing algorithms based on the network characteristics, e.g. number of PUs, number of SUs, mobility degree, etc. Reinforcement learning is a potential candidate for the cognitive engine with its ability to perceive the surrounding environment conditions. More information about the progress toward building the Routing Cognitive Engine can be found here.


  3. Spectrum Management

    The key challenge in Cognitive Radio networks is dealing with the spectrum in an intelligent manner.

    Spectrum Sensing:
    A CR is designed to be aware of and sensitive to the changes in its surrounding, which makes spectrum sensing an important requirement for the realization of CR networks. Spectrum sensing is basically the capability of a CR to determine the presence of primary user transmissions through the observations of the CR user and identify the current spectrum availability accordingly.
    - In this subtask, The main interest is the classification of signal. We use the flexibility of fuzzy inference fusion to combine the results of two existing sensing schemes (Energy detection and Correlation based methods) to classify the received signal and decide whether it is a primary signal, a secondary signal or just noise.
    - Unfortunately, Depending on one CR to detect the presence of primary operation can be highly unreliable. Enhancement of sensing reliability requires a system of spatially distributed multiple sensors cooperating together. We present a channel assignment algorithm applied to a centered CR network. Best candidates of node which are expected to give accurate results are selected via the presented algorithm to perform the sensing task and report their local decisions to the fusion center at which a global decision is made. 

    Spectrum Decision:

    Cognitive radio networks require capabilities to decide on the best spectrum band among the available bands according to the QoS requirements of the applications. A typical spectrum decision process mainly consists of two functionalities: a)Spectrum Characterization: Through RF observation, CR users characterize the available spectrum bands by considering the received signal strength, interference, and number of users currently residing in the spectrum. b)Spectrum Selection: According to the observed spectrum availability, CR users allocate the best spectrum band to satisfy QoS requirements.

    Spectrum Sharing:

    Spectrum sharing provides the capability to maintain the QoS of CR users without causing interference to the PUs by coordinating the channel access as well as allocating communication resources adaptively. Thus, spectrum sharing is performed in the middle of a communication session and shares some functionalities with spectrum sensing.

    Spectrum Mobility:

    If the specific portion of the spectrum in use by a SU is required by a PU, the communication needs to be continued in another vacant portion of the spectrum. This notion is called spectrum mobility. Spectrum mobility is closely related to time-varying network topology and spectrum availability, resulting in link failure on the end-to-end route.