Joint Spectrum Sensing and Resource Allocation for OFDM-based Transmission with a Cognitive Relay

In this paper, we investigate the joint spectrum sensing and resource allocation problem to maximize throughput capacity of an OFDM-based cognitive radio link with a cognitive relay. By applying a cognitive relay that uses decode and forward (D&F), we achieve more reliable communications, generating less interference (by needing less transmit power) and more diversity gain. In order to account for imperfections in spectrum sensing, the proposed schemes jointly modify energy detector thresholds and allocates transmit powers to all cognitive radio (CR) subcarriers, while simultaneously assigning subcarrier pairs for secondary users (SU) and the cognitive relay. This problem is cast as a constrained optimization problem with constraints on (1) interference introduced by the SU and the cognitive relay to the PUs; (2) miss-detection and false alarm probabilities and (3) subcarrier pairing for transmission on the SU transmitter and the cognitive relay and (4) minimum Quality of Service (QoS) for each CR subcarrier. We propose one optimal and two sub-optimal schemes all of which are compared to other schemes in the literature. Simulation results show that the proposed schemes achieve significantly higher throughput than other schemes in the literature for different relay situations.Comment: EAI Endorsed Transactions on Wireless Spectrum 14(1): e4 Published 13th Apr 201

Robust Resource Allocation for OFDM-based Cognitive Radio in the Presence of Primary User Emulation Attack

Cognitive radio (CR) is a promising solution to improve the spectrum efficiency in which some unlicensed users are allowed to exploit frequency bands which are not used by licensed network. However, CR technology imposes some threats to the network. One of these threats is primary user emulation attack where some malicious users try to send fake signals similar to the primary user (PU) and prevent secondary users from accessing vacant bands. Moreover, the presence of a primary user emulation attacker (PUEA) leads to additional interference to the CR and consequently, the efficiency of conventional power loading algorithms will be degraded. In this paper, we propose a power allocation scheme in an orthogonal frequency-division multiplexing (OFDM) based CR in the presence of PUEA. Power allocation is performed with the aim of maximizing the downlink transmission capacity achieved by the cognitive user, while keeping the interference level at the PU below a predefined threshold. Simulation results confirm the efficiency of our proposed power loading scheme, compared to classical loading algorithms that do not consider the activity of malicious users in the radio environment

Robust Power and Subcarrier Allocation for OFDM-based Cognitive Radio Networks Considering Spectrum Sensing Uncertainties

‎In this paper‎, ‎we address power and subcarrier allocation for cooperative cognitive radio (CR) networks in the presence of spectrum sensing errors‎. ‎First‎, ‎we derive the mutual interference of primary and secondary networks affecting each other by taking into account spectrum sensing errors‎. ‎Then‎, ‎taking into account the interference constraint imposed by the cognitive network to the primary user and the power budget constraint of cognitive network‎, ‎we maximize the achievable data rates of secondary users‎. ‎Besides‎, ‎in a multi secondary user scenario‎, ‎we propose a suboptimal but low complexity power and subcarrier allocation algorithm to solve the formulated optimization problem‎. ‎Our numerical results indicate that the proposed power loading scheme increases the cognitive achievable data rates compared to classical power loading algorithms that do not consider spectrum sensing errors‎

Resource Allocation for Downlink Multi-Cell OFDMA Cognitive Radio Network Using Hungarian Method

This paper considers the problem of resource allocation for downlink part of an OFDM-based multi-cell cognitive radio network which consists of multiple secondary transmitters and receivers communicating simultaneously in the presence of multiple primary users. We present a new framework to maximize the total data throughput of secondary users by means of subchannel assignment, while ensuring interference leakage to PUs is below a threshold. In this framework, we first formulate the resource allocation problem as a nonlinear and non-convex optimization problem. Then we represent the problem as a maximum weighted matching in a bipartite graph and propose an iterative algorithm based on Hungarian method to solve it. The present contribution develops an efficient subchannel allocation algorithm that assigns subchannels to the secondary users without the perfect knowledge of fading channel gain between cognitive radio transmitter and primary receivers. The performance of the proposed subcarrier allocation algorithm is compared with a blind subchannel allocation as well as another scheme with the perfect knowledge of channel-state information. Simulation results reveal that a significant performance advantage can still be realized, even if the optimization at the secondary network is based on imperfect network information