The estimation of the noise power is a core issue in wireless communication systems. In broadcasting, every OFDM frame starts with a preamble symbol, which facilitates the noise power estimation. However, the performance of preamble-based noise estimation schemes worsens in fast-changing environments and cannot efficiently track the noise variation well. In order to track the noise variation symbol to symbol, pilot-based schemes would be more fruitful. Accordingly, in this paper, we propose a novel pilot-based noise power estimation scheme for OFDM systems that estimate the noise power for each data symbol. It makes use of the pilot subcarriers reserved for channel estimation in the data symbol. The proposed algorithm computes the circular correlation between received signal and comb type sub-pilot subsequence twice to generate the noise power. Compared with the conventional preamble-based scheme, the proposed pilot-based noise algorithm generates near-ideal accuracy at different values of signal-to-noise ratio (SNR). Without the additional overheads of the preamble, the scheme offers the significant saving of computation over a conventional time domain noise estimator.

Noise power estimation for broadcasting OFDM systems

An energy cooperation method of wireless sensor networks based on partially observable Markov decision processes

Passive IIR deconvolution of two-dimensional digital systems subject to missing measurements

Fuzzified power allocation scheme for relay network

Signal-to-Noise Ratio (SNR) parameter represents the main metric to characterize the performance of signal reception. Determining this parameter is of major importance for a wide variety of communication techniques such as spectrum sensing in Cognitive Radio, Link Adaptation, and power allocation. In general, there are two kinds of SNR estimation techniques: Data-Aided (DA) and Blind Estimation (BE). By using Data-Aided estimation (DA), the receiver estimates the SNR based on prior information from the transmitter. On the other hand, by using Blind-Estimation (BE), the receiver does not have any prior-knowledge of transmission parameters. This technique is extremely used for scenarios where transmission parameters are unknown, a common situation on spectrum sensing for non-cooperative applications in Cognitive Radio. However, some reported BE algorithms have been developed exploiting specific properties of some modulation schemes, which also demands some prior knowledge of signal parameters. This work is focused on describing SNR estimation algorithms suitable for several digital and analog modulation schemes. We propose the Subspace-Based SNR estimator for spectrum sensing by using the Energy Detector and Link Adaptation applications. Comparative simulation results regarding estimator performance exhibit the high precision for several channel models. The applicability of this estimator for several analog and digital modulation schemes is also shown as well as proper performance for low SNR levels is obtained, in exchange for higher computational complexity.

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Subspace-Based SNR Estimator for Cognitive Radio and Link Adaptation

In block-mode transmission, single carrier frequency domain equalisation-based ultra-wide-band technique is a promising physical-layer candidate scheme. Here, the authors address the noise variance estimation problem for signal-to-noise ratio estimation in such systems. Our investigations focus on the estimation schemes both robust to the pilot sequence and the channel type. First, the authors, respectively, define the correlated noise samples and the uncorrelated ones, where the difference signals or the sum signals of two adjacent received pilot signals are included. Then, with the help of the Cramer–Rao lower bound (CRLB) theorem, the authors either use the correlated or the uncorrelated samples to estimate the noise variance based on the difference signals and the sum signals, respectively, regardless of the pilot sequences. The corresponding CRLBs are also given and analysed. Finally, under either the correlated or the uncorrelated noise samples, the authors propose to linearly combine the estimator of the difference signals with that of the sum signals where the weight coefficients are optimised. Since the proposed two combined estimators are not only robust to the pilot sequence but also automatically adapt the weights to the channel condition, they can significantly outperform the existing estimators.

Robust signal-to-noise ratio and noise variance estimation for single carrier frequency domain equalisation ultra-wideband wireless systems

In this paper, a training scheme for multi-relay clustered wireless sensor networks is proposed for the destination cluster head node to estimate the individual source-relay and relay-destination channels. Compared with an existing training scheme, the proposed one has shown improved efficiency with reduced power and computation time in the training of the source relay channels. Furthermore, the training power allocation problem is analyzed. Based on minimizing the total mean-square-error (MSE) of all channel estimates, two approximate solutions for the power allocation among all network nodes and all training links are derived. The first solution is adaptive to the instantaneous relay-destination channel estimates, thus requires feedback from the destination during the training process. The other solution depends on channel variances only and has lower complexity in implementation. Simulation results demonstrate that the proposed training scheme and power allocation solutions obtain lower total MSE and higher network throughput than existing schemes.

Dan Wang

Papers

Robust signal-to-noise ratio and noise variance estimation for single carrier frequency domain equalisation ultra-wideband wireless systems

Training Power Allocation Based on MSE-Minimization for Multi-Relay Amplify-and-Forward Cooperation in Wireless Sensor Networks