Primary user activity is a major bottleneck for existing routing protocols in cognitive radio networks. Typical routing protocols avoid areas that are highly congested with primary users, leaving only a small fragment of available links for secondary route construction. In addition, wireless links are prone to channel impairments such as multipath fading; which renders the quality of the available links highly fluctuating. In this paper, we investigate using cooperative communication mechanisms to reveal new routing opportunities, enhance route qualities, and enable true coexistence of primary and secondary networks. As a result, we propose Undercover: a cooperative routing protocol that utilizes the available location information to assist in the routing process. Specifically, our protocol revisits a fundamental assumption taken by the state of the art routing protocols designed for cognitive radio networks. Using Undercover, secondary users can transmit in the regions of primary users activity through utilizing cooperative communication techniques to null out transmission at primary receivers via beamforming. In addition, the secondary links qualities are enhanced using cooperative diversity. To account for the excessive levels of interference typically incurred due to cooperative transmissions, we allow our protocol to be interference-aware. Thus, cooperative transmissions are penalized in accordance to the amount of negatively affected secondary flows. We evaluate the performance of our proposed protocol via NS2 simulations which show that our protocol can enhance the network goodput by a ratio reaches up to 250 compared to other popular cognitive routing protocols with minimal added overhead.
Our previous work @cite_32 considered using beamforming in the routing layer. However, we only proposed a route maintenance mechanism to alleviate the need for route re-establishment upon the detection of a PU which limits the usefulness of beamforming. In this paper, we consider cooperative beamforming a foundation for building successful cognitive radio routing protocols. We understand the true potential of employing cooperative beamforming, mitigate its introduced interference effects, and provide practical mechanisms for efficient cooperative group construction.
@cite_32
2013925279 2540490069 1970927455 2031857436
In this paper we investigate the cooperative beamforming in cognitive radio network (CRN) composed of a primary network (PN) and a secondary network (SN). In the SN, the transmitter has to communicate with the receiver with the help of the hybrid relays which use cooperative beamforming to retransmit data. The relays will select amplify-and-forward (AF) or decode-and-forward (DF) according to the signal to interference pulse noise ratio (SINR). The aim of this paper is to maximize the received SINR in the SN with different power constraints and the QoS requirement of the primary user (PU). Numerical results compare the performance of the SN between relays with beamforming and relays without beamforming which indicates that the gap between them is related to the maximum interference power at primary destination, while is irrelevant to maximum the total transmitted power of the relays. Besides there is a tradeoff between the performance of the SN and the number of the relays. In this paper, we study resource allocation for a multicarrier-based cognitive radio (CR) network. More specifically, we investigate the problem of secondary users’ energy-efficiency (EE) maximization problem under secondary total power and primary interference constraints. First, assuming cooperation among the secondary base stations (BSs), a centralized approach is considered to solve the EE optimization problem for the CR network where the primary and secondary users are using either orthogonal frequency-division multiplexing (OFDM) or filter bank based multicarrier (FBMC) modulations. We propose an alternating-based approach to solve the joint power-subcarrier allocation problem. More importantly, in the first place, subcarriers are allocated using a heuristic method for a given feasible power allocation. Then, we conservatively approximate the nonconvex power control problem and propose a joint successive convex approximation-Dinkelbach algorithm (SCADA) to efficiently obtain a solution to the nonconvex power control problem. The proposed algorithm is shown to converge to a solution that coincides with the stationary point of the original nonconvex power control subproblem. Moreover, we propose a dual decomposition-based decentralized version of the SCADA. Second, under the assumption of no cooperation among the secondary BSs, we propose a fully distributed power control algorithm from the perspective of game theory. The proposed algorithm is shown to converge to a Nash-equilibrium (NE) point. Moreover, we propose a sufficient condition that guarantees the uniqueness of the achieved NE. Extensive simulation analyses are further provided to highlight the advantages and demonstrate the efficiency of our proposed schemes. 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. In this paper, we introduce a distributed dynamic routing algorithm in multi-hop cognitive radio (CR) networks, in which secondary users (SUs) want to minimize their interference to the primary users (PUs) while keeping the delay along the route low. We employ a cognitive pilot channel (CPC) for SUs to be able to access the information about PUs, including PUs' locations and channel conditions. Medial axis with a relaxation factor is used as a reference path for the routing, along which we develop a hierarchical structure for multiple sources to reach their destinations. We introduce a temporal and spatial dynamic non-cooperative game to model the interactions among the SUs as well as their influences on the PUs, and obtain by backward induction a set of mixed (behavioral) Nash equilibrium strategies. We also employ a multi-stage fictitious play learning algorithm for distributed routing, which minimizes the overall interference from the SUs to the PUs, as well as the average packet delay along the route from the SU nodes to their destinations. Simulation results show that our proposed algorithm can avoid congestion in the CR network and minimize delay while keeping the interference level low.