Resource allocation for OFDM-based multiuser cooperative underlay cognitive systems

Abstract

This paper investigates the resource allocation problem for a multiuser underlay cognitive system where the secondary system is allowed to transmit and cooperate with the primary system. In this scenario, the secondary users are subject to two main constraints in the presence of the primary user: their total power budget and the allowable interference threshold at the primary receiver. Power and subcarrier allocation problems are detailed in order to maximize the system sum rate. In this work, we highlight the benefits of the proposed multiuser adaptive algorithm which encompasses three phases. The first step includes the adaptive selection of the decoding strategy at the secondary receiver which is either treating interference as noise or performing successive interference cancelation or superposition coding. The second step describes the subcarrier allocation among the different users. Finally, the third step details the optimal distribution of the available power budget on the users. The problem is first treated assuming perfect channel state information (CSI). The simulation results show that our proposed scheme achieves higher secondary and sum rates compared to existing approaches with perfect CSI. The robustness of the proposed algorithm when the secondary user has only partial information about channel gains is also derived.

Resource Allocation with Successive Coding for OFDM-Based Cognitive System Subject to Statistical CSI

“…Similar to our previous paper [7], we consider a cognitive radio system model composed of one primary cell and one secondary cell. The primary system occupies a licensed bandwidth which is divided into adjacent and parallel subcarriers.…”

“…This rate will be referred as , 5is not verified, the ability to apply superposition coding (SC) at the SU is tested subject to the validation of the following set of inequalities, derived in our previous paper [7]:…”

“…and ℎ is the interference threshold. The solution of problem (16a), (16b), (16c), (16d), (16e), and (16f) is detailed and analyzed in our previous paper [7].…”

“…Even though resource allocation for cognitive radio has indeed been extensively studied in the literature, combining it with advanced receiver decoding has not yet been studied widely. In our previous work [7], we have fully detailed the combination of CR and superposition coding (SC) at the secondary user and provided the multicarrier power allocation for multiuser CR systems. We have also provided a numerical solution for the nonconvex optimization problem and proposed new results on the robustness of the proposed 2 Mathematical Problems in Engineering algorithms with imperfect CSI.…”

“…We have also provided a numerical solution for the nonconvex optimization problem and proposed new results on the robustness of the proposed 2 Mathematical Problems in Engineering algorithms with imperfect CSI. The original system model presented in [7] is further studied in the present paper, with an extension to the more realistic case where the secondary transmitter and receiver only have statistical CSI on the primary channels.…”