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As a key candidate technique for fifth-generation (5G) mobile communication systems, non-orthogonal multiple access (NOMA) has attracted considerable attention in the field of wireless communication. Successive interference cancellation (SIC) is the main NOMA detection method applied at receivers for both uplink and downlink NOMA transmissions. However, SIC is limited by the receiver complex and error propagation problems. Toward this end, we explore a high-performance, high-efficiency tool—deep learning (DL). In this paper, we propose a learning method that automatically analyzes the channel state information (CSI) of the communication system and detects the original transmit sequences. In contrast to existing SIC schemes, which must search for the optimal order of the channel gain and remove the signal with higher power allocation factor while detecting a signal with a lower power allocation factor, the proposed deep learning method can combine the channel estimation process with recovery of the desired signal suffering from channel distortion and multiuser signal superposition. Extensive performance simulations were conducted for the proposed MIMO-NOMA-DL system, and the results were compared with those of the conventional SIC method. According to our simulation results, the deep learning method can successfully address channel impairment and achieve good detection performance. In contrast to implementing well-designed detection algorithms, MIMO-NOMA-DL searches for the optimal solution via a neural network (NN). Consequently, deep learning is a powerful and effective tool for NOMA signal detection.

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. The predominant components of PM 2.5 were secondary inorganic ions (NH 4 + , NO 3 -and SO 4 2-) and carbonaceous compounds, which accounted for 45.9% and 24.1% of the total PM 2.5 mass, respectively. Distinct seasonal variation was observed in the mass concentrations and chemical components of PM 2.5 . The average mass concentrations of PM 2.5 were the highest in winter, followed by spring, and lowest in autumn. Light extinction coefficients (b ext ) were discussed over four seasons. (NH 4 ) 2 SO 4 was the largest contributor (28.8%) to b ext , followed by NH 4 NO 3 (24.4%), organic matter (19.5%), elemental carbon (7.4%), and coarse mass (7.2%), while fine soil, sea salt, NO 2 and Rayleigh made minor contributions, together accounting for 12.7% of b ext . During the polluted periods, the contributions of (NH 4 ) 2 SO 4 and NH 4 NO 3 to b ext increased dramatically. Therefore, in addition to control primary particulate emissions, the reduction of their precursors like SO 2 , NO x and NH 3 could effectively improve air quality and visibility in Beijing.

Abstract. Humic-like substances (HULIS) are a mixture of high-molecular-weight, water-soluble organic compounds that are widely distributed in atmospheric aerosol. Their sources are rarely studied quantitatively. Biomass burning is generally accepted as a major primary source of ambient humic-like substances (HULIS) with additional secondary material formed in the atmosphere. However, the present study provides direct evidence that residential coal burning is also a significant source of ambient HULIS, especially in the heating season in northern China based on source measurements, ambient sampling and analysis, and apportionment with source-oriented CMAQ modeling. Emission tests show that residential coal combustion produces 5 % to 24 % of the emitted organic carbon (OC) as HULIS carbon (HULISc). Estimation of primary emissions of HULIS in Beijing indicated that residential biofuel and coal burning contribute about 70 % and 25 % of annual primary HULIS, respectively. Vehicle exhaust, industry, and power plant contributions are negligible. The average concentration of ambient HULIS in PM2.5 was 7.5 µg m−3 in urban Beijing and HULIS exhibited obvious seasonal variations with the highest concentrations in winter. HULISc accounts for 7.2 % of PM2.5 mass, 24.5 % of OC, and 59.5 % of water-soluble organic carbon. HULIS are found to correlate well with K+, Cl−, sulfate, and secondary organic aerosol, suggesting its sources include biomass burning, coal combustion, and secondary aerosol formation. Source apportionment based on CMAQ modeling shows residential biofuel and coal burning and secondary formation are important sources of ambient HULIS, contributing 47.1 %, 15.1 %, and 38.9 %, respectively.