By Vincent Savaux, Yves Lou?t
This publication provides an set of rules for the detection of an orthogonal frequency department multiplexing (OFDM) sign in a cognitive radio context via a joint and iterative channel and noise estimation procedure. in response to the minimal suggest sq. criterion, it plays a correct detection of a person in a frequency band, via reaching a quasi-optimal channel and noise variance estimation if the sign is current, and through estimating the noise point within the band if the sign is absent.
Organized into 3 chapters, the 1st bankruptcy presents the historical past opposed to which the method version is gifted, in addition to a few fundamentals in regards to the channel records and the transmission of an OFDM sign over a multipath channel. In bankruptcy 2, the proposed iterative set of rules for the noise variance and the channel estimation is exact, and in bankruptcy three, an software of the set of rules for the free-band detection is proposed. In either Chapters 2 and three, the primary of the set of rules is gifted in an easy approach, and extra tricky advancements also are supplied. the various assumptions and assertions within the advancements and the functionality of the proposed technique are verified via simulations, and in comparison to tools of the medical literature.
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Extra resources for MMSE-Based Algorithm for Joint Signal Detection, Channel and Noise Variance Estimation for OFDM Systems
Example text
This measurement can then be used for the design of the transmitter and the receiver. For instance, at the transmitter side, the constellation type and its size can be updated according to the SNR level [KEL 00]. 25], [VAN 95]) require the knowledge of the SNR. We are particularly interested by the latter application in this work, since the estimator in Chapter 2 iteratively estimates 20 MMSE-based Algorithm for Joint Signal Detection the channel and the noise variance by means of the MMSE criterion.
8 depicts the bias of the noise variance estimation B(ˆ σ2 ) = σ ˆ 2 − σ 2 versus the FFT size when performed in case 1. 1, since the FFT size varies from M = 2 64 to M = 1, 024. Furthermore, B(ˆ σ 2 ) is compared to − σM in 2 order to validate the approximation B(ˆ σ 2 ) ≈ − σM . The curves are drawn for ρ = 10 dB, and the estimated bias values are obtained after averaging out 1,000 simulation runs. We observe that the bias is non-null irrespective of the FFT size. 0025 for M = 1, 024. 8 2 shows that the accuracy of the approximation B(ˆ σ 2 ) ≈ − σM increases with M .
We are particularly interested by the latter application in this work, since the estimator in Chapter 2 iteratively estimates 20 MMSE-based Algorithm for Joint Signal Detection the channel and the noise variance by means of the MMSE criterion. The SNR estimation methods are commonly based on three elementary steps: 1) The noise variance estimation σ ˆ 2 is first performed. 2) An estimation of the transmitted signal power Pˆs is achieved. 3) The SNR, noted ρ is finally obtained by ρˆ = Pˆs /ˆ σ2. Alternatively, the steps (2) and (3) are sometimes replaced by the following processing: 2) The second-order moment of the received signal is ˆ 2 = Pˆs + σ estimated by M ˆ 2.