BackgroundWe aimed to measure ocular biometric characteristics in older cataract patients from western China.MethodsOcular biometry records were retrospectively analyzed for 6933 patients with cataracts (6933 eyes) at least 50 years old who were treated at West China Hospital of Sichuan University.ResultsPartial coherence laser interferometry gave the following population averages: axial length (AL), 24.32 ± 2.42 mm; anterior chamber depth (ACD), 3.08 ± 0.47 mm; keratometric power (K), 44.23 ± 1.66 diopters; and corneal astigmatism (CA), 1.00 ± 0.92 diopters. The percentage of individuals with AL > 26.5 mm was 13.66%, while the percentage with CA > 1.0 diopters was 35.54%. Mean AL and ACD showed a trend of decrease with increasing age (P < 0.001). AL correlated positively with ACD (Spearman coefficient, 0.542) and CA (0.111), but negatively with K (− 0.411) (all P < 0.01). K also correlated negatively with ACD (− 0.078, P < 0.01).ConclusionsThese results show, for the first time, that older cataract patients from western China have similar ocular biometric characteristics as other populations. The high prevalence of severe axial myopia warrants further investigation.

No abstract

Sparse code multiple access (SCMA) is a promising code-domain non-orthogonal multiple access (NOMA) scheme for the enabling of massive machine-type communication. In SCMA, the design of good sparse codebooks and efficient multiuser decoding have attracted tremendous research attention in the past few years. This paper aims to leverage deep learning to jointly design the downlink SCMA encoder and decoder with the aid of autoencoder. We introduce a novel end-to-end learning based SCMA (E2E-SCMA) design framework, under which improved sparse codebooks and low-complexity decoder are obtained. Compared to conventional SCMA schemes, our numerical results show that the proposed E2E-SCMA leads to significant improvements in terms of error rate and computational complexity.

The synergistic amalgamation of sparse code multiple access (SCMA) and multiple-input multiple-output (MIMO) technologies can be exploited for improving spectral efficiency and providing enhanced wireless services to massive users. In this case, however, channel estimation is a burning issue with the increasing number of users and/or antennas. To tackle this problem, we propose a novel non-coherent transmission scheme for SCMA, referred to as NC-SCMA. In the proposed NC-SCMA, each user first maps its binary data to sparse codewords, and then perform differential modulation on the nonzero dimensions. Upon receiving all users' signals, we leverage the channel hardening effect to carry out differential demodulation and multi-user detection without any instantaneous channel state information. In addition, the design of the sparse codebooks in the NC-SCMA system is investigated with the aid of the pair-wise probability. Numerical results demonstrate the superiority of the proposed technique over the benchmark scheme in terms of bit error rate performance.

No abstract