Achievable transmission capacity of cognitive radio networks with cooperative relaying

Abstract

With the rapid development of cognitive radio technologies, spectrum sharing becomes a promising approach to improving the efficiency of spectrum utilization and mitigating the spectrum scarcity problem. Previous research on cognitive networks argues that secondary users can only work under a low-transmission power in an underlay spectrum sharing model, especially when the primary transmitter is far away from the primary receiver. Motivated by the idea of cooperative communications, in this paper, we propose a cooperative framework in which a primary transmitter, being aware of the existence of the secondary network, may select a secondary user that is not in transmitting or receiving mode to relay its traffic. The feasible relay location region and optimal power ratio between the primary network and the secondary network are derived in the underlay spectrum sharing model. Based on the optimal power ratio, we derive the maximum achievable transmission capacity of the secondary network under the outage constraints from both the primary and the secondary network with or without cooperative relaying. Numerical results indicate that secondary users can achieve a higher transmission capacity with cooperative relaying, and that the capacity gain of the cooperative network is significantly affected by the location of the relay and the network system parameters.

Methods of increasing two-way transmission capacity of wireless ad hoc networks

“…Specifically, they first presented the exact outage probability for amplify-and-forward and decode-and-forward protocols in a Poisson field of interferers, and then, they derived transmission capacity of such networks. For cognitive radio networks, in [11], Jing et al proposed a cooperative framework in which a primary sender, being aware of the existence of the secondary network, may select a secondary user that is not in transmitting or receiving mode to relay its traffic. The feasible relay location region and optimal power ratio between the primary network and the secondary network are derived in the underlay spectrum sharing model.…”

“…where inequality (**) holds according to the Cauchy-Schwarz inequality, and calculating process is given in Eq. (11). For the Laplace transform of the interference in Formula (8), we flip the order of integration and expectation, and λ (r) = 2πλr is the intensity function of PPP a T .…”

“…The transmission capacity provides a framework to derive closed-form bounds for the interference distribution by using stochastic geometry when the locations of nodes form a Poisson point process *Correspondence: guangshunli@sohu.com 2 School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826 Shandong, China Full list of author information is available at the end of the article (PPP). Most of the previous works on deriving transmission capacity only focused on one-way transmission capacity (reverse communication from the destination to the source is ignored), which considered the effect of various physical and medium access layer techniques such as successive interference cancellation [4], multiple antennas [5][6][7][8], guard zone-based scheduling [9], and cooperative relaying [10,11].…”

Research on Outage Performance Optimization of Cognitive Relay Network with Cooperative Sensing

“…Making the first derivative of ( ) P out k with respect to k, we can obtain (14) It is noted that a and b are positive constants, and a < 1. When…”

“…In [13], the outage performance of cognitive relay networks was researched, and it was improved under relay-assisted transmission. In [14], authors investigated the transmission capacity of cognitive radio network under the outage constraints from the primary network with or without cooperative relaying, and analyzed the impact of the location of the relay.…”