In underlay cognitive radio networks (CRNs), the connectivity of secondary users (SUs) is difficult to be guaranteed due to the existence of primary users. Most prior studies only consider cognitive radio networks equipped with omni-directional antennas causing high interference at SUs. We name such CRNs with omni-directional antennas as Omn-CRNs. Compared with an omni-directional antenna, a directional antenna can concentrate the transmitting receiving capability at a certain direction, consequently resulting in the less interference. In this paper, we investigate the connectivity of SUs in CRNs with directional antennas (named as Dir-CRNs). In particular, we derive closed-form expressions of the connectivity of SUs of both Dir-CRNs and Omn-CRNs, thus enabling tractability. We show that the connectivity of SUs is mainly affected by two constraints: the spectrum availability of SUs and the topological connectivity of SUs. Extensive simulations validate the accuracy of our proposed models. Meanwhile, we also show that Dir-CRNs can have higher connectivity than Omn-CRNs mainly due to the lower interference, the higher spectrum availability, and the higher topological connectivity brought by directional antennas. Moreover, we also extend our analysis with considering transmission power efficiency. The simulation results show that Dir-CRNs require less transmission power to establish links than Omn-CRNs. We further investigate the throughput capacity of SUs, which is shown to heavily depend on the connectivity of SUs.
This paper presents a study on multicast communications in cognitive radio networks CRNsusing directional antennas. The objective is to maximize the throughput of the CRN. The spectrum is divided into multiple channels and licensed to the primary network. While the CRN is accessing the spectrum, the interference power is carefully controlled to avoid impacting the operation of the primary network. The mathematical model is presented and subsequently formulated as a mixed integer non-linear programming MINLP problem, which is non-deterministic polynomial-time hard. Therefore, a greedy algorithm is designed to approximate the optimal performance. The MINLP problem is then relaxed and an upper bound is developed. Simulation results are presented to compare the performance of the greedy algorithm and the upper bound, which demonstrates the efficacy of the greedy algorithm as well as the tightness of the upper bound. Copyright © 2012 John Wiley & Sons, Ltd. In this paper, we consider a cognitive radio system, consisting of a primary user (PU), a secondary user (SU) transmitter, and a SU receiver. The SUs are equipped with directional antennas. The SU transmitter first performs spectrum sensing (with errors) and then transmits data. We assume the SU and PU can coexist and the SU transmits at two power levels, according to the result of spectrum sensing (i.e., whether the spectrum is sensed idle or busy). We establish a lower bound on the ergodic capacity of the channel between SU transmitter and receiver, and study how spectrum sensing errors affect the bound. Furthermore, we explore the optimal SU transmit power levels and the optimal directions of SU transmit and receive antennas, such that the lower bound is maximized, subject to average transmit power and average interference power constraints. Through numerical simulations, we show that (compared with the case when the SUs use omni-directional antennas) directional antennas can significantly improve the lower bound in the presence of spectrum sensing errors, subject to the constraints. In this paper, we present an analytical modeling framework for supporting and evaluating the impact of shadowing and beamforming on the topological connectivity of cognitive radio ad-hoc networks (CRAHNs) where primary users (PUs) are equipped with omnidirectional antennas while secondary users (SUs) are equipped with directional antennas such as uniform linear array (ULA) antenna and uniform circular array (UCA) antenna. The main features and contributions in this paper are as follows. First, we derive a formula for calculating effective coverage area of a node in secondary network by taking the effect of path loss, antenna model, and beamforming scheme into consideration. Second, we mathematically analyze the expected number of neighbors and communication probability of a SU based on the effective coverage area of SU and the spatial---temporal existence of PU's operation. We also derive the expression of the upper bound of path connectivity between two arbitrary SUs in the networks. Third, we point out that UCA antenna is the most suitable antenna for CRAHNs. We find the optimal number of elements corresponding to each type of directional antenna at which the highest connectivity can be achieved. The validity of our analysis is verified by comparing with simulations. The results in this paper provide efficient guidelines for system designers to characterize and optimize the connectivity of CRAHNs with beamforming. The unpredictable activities of primary users (PUs) make the channel availabilities in cognitive radio networks (CRNs) very unstable, which causes routing in CRNs to be more difficult than in traditional wireless networks. Specifically, when a source node needs to select a route to reach the destination, the “optimal” route during the route selection phase may not be optimal in the data transmission phase. In this paper, we propose a novel routing protocol based on the traditional source routing protocols. We consider the angle dimension by assuming that directional antennas are equipped on every node, which facilitate the marking of boundaries of PUs. We use the USRP Gnuradio testbed to show the sensing result differences of different directions at the boundary area of a PU. For every optional route between a source and a destination node, we evaluate its reliability and other performance by evaluating the PU areas through which it passes and by estimating the possible transmission rate. Based on these parameters, we propose an algorithm for route selection, considering both reliability and delay. Our routing protocol only requires very limited piggyback information, and it is highly adaptable under the dynamic channel availabilities. We evaluate our approach through extensive simulations.