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Abstract-A blind (non-data-aided) SNR estimator using the statistical moments of the received signal is proposed. The proposed envelope-based non-data-aided estimator works for any time-domain Gaussian-distributed signal (e.g., OFDM signals). A closed-form expression for the estimated SNR as a function of the moments of the received signal and the Nakagami-m parameter, is derived. Interestingly, the obtained expression shows that the proposed estimator operation and performance is independent of the constellation of the received signal. Moreover, the existence of the closed-form expression results in lower implementation complexity. Furthermore, to enable theoretical performance analysis, a general mathematical expression is derived for the even moments of the received signal in terms of SNR and the Nakagami-m parameter. The performance of the proposed estimator is evaluated based on the mean-squared-error, under different conditions of the channel. An extension of our SNR estimation method into multiple antennas configurations is provided. Our results reveal that the proposed estimator works better in low SNR conditions, which is attractive to applications such as cognitive radio spectrum sharing scenarios.

and arslan@usf.edu).Abstract-We propose a new one-bit feedback scheme with scheduling decision based on the maximum expected weighted rate. We show the concavity of the 2-user case and provide the optimal solution which achieves the maximum weighted rate of the users. For the general asymmetric M -user case, we provide a heuristic method to achieve the maximum expected weighted rate. We show that the sum rate of our proposed scheme is very close to the sum rate of the full channel state information case, which is the upper bound performance.