Abstract-SC-FDMA (Single-Carrier Frequency Division Multiple Access) is employed in tbe 3GPP LTE (Long-Term Evolution) standard as tbe uplink transmission scbeme. Tbe SC-FDMA signal bas a low PAPR. This makes it well-suited to power efficient transmission at tbe mobile terminal. Altbough it is a common assumption to use frequency-domain linear equalization in SC-FDMA, a decision feedback equalizer (DFE) composed of a frequency-domain feedforward filter and a time-domain feedback filter can provide enbanced performance. Even when error propagation is taken into account, results show that a DFE still offers a significant performance gain over tbe conventional LE for uncoded SC-FDMA. Tbis paper demonstrates tbat SC-FDMA with DFE is capable of increasing tbe throughput in a power limited cbannel by up to 41 % compared to LE. Alternatively, for a given peak transmit power, the use of a DFE can achieve a 14% coverage extension in NLOS and 19% in LOS.
Abstract-Practical frequency domain equalization (FDE) systems generally require knowledge of the channel and the noise variance to equalize the received signal in a frequency-selective fading channel. Accurate channel estimate and noise variance estimate are thus desirable to improve receiver performance. In this paper we investigate the performance of the denoise channel estimator and the approximate linear minimum mean square error (A-LMMSE) channel estimator with channel power delay profile (PDP) mismatch and noise variance estimation errors for a single carrier frequency division multiple access (SC-FDMA) system. A windowed DFT-based noise variance estimator is also presented in this paper to improve A-LMMSE channel estimation as well as to facilitate the MMSE-FDE design. Results show that the proposed noise variance estimator remains unbiased up to a SNR of 50dB.
Discrete manufacturing enterprises are developing towards the direction of intelligence. This paper designs an intelligent equipment management platform aiming at the equipment management and production efficiency problems of discrete enterprises. Intelligent method and advanced IE technology are used to provide a safe and efficient intelligent equipment management platform for discrete manufacturing enterprises through signal processing technology, multi-sensor sorting technology, big data analysis, artificial intelligence technology and visual design (based on the principle of information processing economy). According to the improvement, monitoring, early warning and analysis can understand the status better of workshop equipment in real time, provide decision-making basis for the enterprise and workshop managers, and ultimately improve the overall efficiency and management level of the enterprise, improve the operation efficiency and emergency command ability of the enterprise, and strengthen the core competitiveness of the enterprise.
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