Modified SNR Gap Approximation for Resource Allocation in LDPCCoded Multicarrier Systems The signaltonoise ratio (SNR) gap approximation provides a closedform expression for the SNR required for a coded modulation system to achieve a given target error performance for a given constellation size. This approximation has been widely used for resource allocation in the context of trelliscoded multicarrier systems (e.g., for digital subscriber line communication). In this contribution, we show that the SNR gap approximation does not accurately model the relation between constellation size and required SNR in lowdensity paritycheck (LDPC) coded multicarrier systems. We solve this problem by using a simple modification   the SNR gap approximation instead, which fully retains the analytical convenience of the former approximation. The performance advantage resulting from the proposed modification is illustrated for singleuser digital subscriber line transmission.

Non DataAided SNR Estimation for UAV OFDM Systems Signaltonoise ratio (SNR) estimation is essential in the unmanned aerial vehicle (UAV) orthogonal frequency division multiplexing (OFDM) system for getting accurate channel estimation. In this paper, we propose a novel nondataaided (NDA) SNR estimation method for UAV OFDM system to overcome the carrier interference caused by the frequency offset. First, an absolute value series is achieved which is based on the sampled received sequence, where each sampling point is validated by the data length apart. Second, by dividing absolute value series into the different series according to the total length of symbol, we obtain an output series by stacking each part. Third, the root mean squares of noise power and total power are estimated by utilizing the maximum and minimum platform in the characteristic curve of the output series after the wavelet denoising. Simulation results show that the proposed method performs better than other methods, especially in the low synchronization precision, and it has low computation complexity.