Conference Paper

Adaptive rate transmission for spectrum sharing system with quantized channel state information

Electr. & Comput. Eng., Texas A&M Univ. at Qatar, Doha, Qatar
DOI: 10.1109/CISS.2011.5766156 Conference: Information Sciences and Systems (CISS), 2011 45th Annual Conference on
Source: IEEE Xplore

ABSTRACT The capacity of a secondary link in spectrum sharing systems has been recently investigated in fading environments. In particular, the secondary transmitter is allowed to adapt its power and rate to maximize its capacity subject to the constraint of maximum interference level allowed at the primary receiver. In most of the literature, it was assumed that estimates of the channel state information (CSI) of the secondary link and the interference level are made available at the secondary transmitter via an infinite-resolution feedback links between the secondary/primary receivers and the secondary transmitter. However, the assumption of having infinite resolution feedback links is not always practical as it requires an excessive amount of bandwidth. In this paper we develop a framework for optimizing the performance of the secondary link in terms of the average spectral efficiency assuming quantized CSI available at the secondary transmitter. We develop a computationally efficient algorithm for optimally quantizing the CSI and finding the optimal power and rate employed at the cognitive transmitter for each quantized CSI level so as to maximize the average spectral efficiency. Our results give the number of bits required to represent the CSI sufficient to achieve almost the maximum average spectral efficiency attained using full knowledge of the CSI for Rayleigh fading channels.

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    ABSTRACT: A spectrum-sharing scenario in a cognitive radio (CR) network where a secondary user (SU) shares a narrowband channel with $N$ primary users (PUs) is considered. We investigate the SU ergodic capacity maximization problem under an average transmit power constraint on the SU and $N$ individual peak interference power constraints at each primary-user receiver (PU-Rx) with various forms of imperfect channel-state information (CSI) available at the secondary-user transmitter (SU-Tx). For easy exposition, we first look at the case when the SU-Tx can obtain perfect knowledge of the CSI from the SU-Tx to the secondary-user receiver link, which is denoted as $g_{1}$, but can only access quantized CSI of the SU-Tx to PU-Rx links, which is denoted as $g_{0i}$, $i = \hbox{1}, \ldots, N$, through a limited-feedback link of $B = \log_{2}L$ b. For this scenario, a locally optimum quantized power allocation (codebook) is obtained with quantized $g_{0i}$, $i = \hbox{1}, \ldots, N$ information by using the Karush–Kuhn–Tucker (KKT) necessary optimality conditions to numerically solve the nonconvex SU capacity maximization problem. We derive asymptotic approximations for the SU ergodic capacity performance for the case when the number of feedback bits grows large $(B \rightarrow \infty)$ and/or there is a large number of PUs $(N \rightarrow \infty)$ that operate. For the interference-limited regime, where the average transmit power constraint is inactive, an alternative locally optimum scheme, called the quantized-rate allocation strategy, based on the quantized-ratio $g_{1}/\max_{i}g_{0i}$ information, is proposed. Subsequently, we relax the strong assumption of full-CSI knowledge of $g_{1}$ at the SU-Tx to imperfect $g_{1}$ knowledge that is also available at the SU-Tx. Depending on the way the SU-Tx obtains the $g_{1}$ information, the following two different suboptimal quantized power codebooks are derived for the SU ergodic capacity maximization problem: 1) the power codebook with noisy $g_{1}$ estimates and quantized $g_{0i}$, $i = \hbox{1}, \ldots, N$ knowledge and 2) another power codebook with both quantized $g_{1}$ and $g_{0i}$, $i = \hbox{1}, \ldots, N$ information. We emphasize the fact that, although the proposed algorithms result in locally optimum or strictly suboptimal solutions, numerical results demonstrate that they are extremely efficient. The efficacy of the proposed asymptotic approximations is also illustrated through numerical simulation results.
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