Massive multiple-input multiple-output (MIMO) is a promising technology to increase link capacity and energy efficiency. However, these benefits are based on available channel state information (CSI) at the base station (BS). Therefore, user equipment (UE) needs to keep on feeding CSI back to the BS, thereby consuming precious bandwidth resource. Large-scale antennas at the BS for massive MIMO seriously increase this overhead. In this paper, we propose a multiple-rate compressive sensing neural network framework to compress and quantize the CSI. This framework not only improves reconstruction accuracy but also decreases storage space at the UE, thus enhancing the system feasibility. Specifically, we establish two network design principles for CSI feedback, propose a new network architecture, CsiNet+, according to these principles, and develop a novel quantization framework and training strategy.Next, we further introduce two different variable-rate approaches, namely, SM-CsiNet+ and PM-CsiNet+, which decrease the parameter number at the UE by 38.0% and 46.7%, respectively. Experimental results show that CsiNet+ outperforms the state-of-the-art network by a margin but only slightly increases the parameter number. We also investigate the compression and reconstruction mechanism behind deep learning-based CSI feedback methods via parameter visualization, which provides a guideline for subsequent research.

In this paper, we propose a multi-beam non-orthogonal multiple access (NOMA) scheme for hybrid millimeter wave (mmWave) systems and study its resource allocation. A beam splitting technique is designed to generate multiple analog beams to serve multiple users for NOMA transmission. Compared to conventional mmWave orthogonal multiple access (mmWave-OMA) schemes, the proposed scheme can serve more than one user on each radio frequency (RF) chain. Besides, in contrast to the recently proposed single-beam mmWave-NOMA scheme which can only serve multiple NOMA users within the same beam, the proposed scheme can perform NOMA transmission for the users with an arbitrary angle-of-departure (AOD) distribution. This provides a higher flexibility for applying NOMA in mmWave communications and thus can efficiently exploit the potential multi-user diversity. Then, we design a suboptimal two-stage resource allocation for maximizing the system sum-rate. In the first stage, assuming that only analog beamforming is available, a user grouping and antenna allocation algorithm is proposed to maximize the conditional system sum-rate based on the coalition formation game theory. In the second stage, with the zero-forcing (ZF) digital precoder, a suboptimal solution is devised to solve a non-convex power allocation optimization problem for the maximization of the system sum-rate which takes into account the quality of service (QoS) constraint. Simulation results show that our designed resource allocation can achieve a close-to-optimal performance in each stage. In addition, we demonstrate that the proposed multi-beam mmWave-NOMA scheme offers a higher spectral efficiency than that of the single-beam mmWave-NOMA and the mmWave-OMA schemes.

Ribosomal RNA internal transcribed spacer-1 (ITS1) metabarcoding was used to investigate the distribution patterns of fungal communities and the factors influencing these patterns in subtropical Chinese seas, including the southern and northern Yellow Sea and the Bohai Sea. These seas were found to harbor high levels of fungal diversity, with 816 operational taxonomic units (OTUs) that span 130 known genera, 36 orders, 14 classes and 5 phyla. Ascomycota was the most abundant phylum, containing 72.18% and 79.61% of all OTUs and sequences, respectively, followed by Basidiomycota (19.98%, 18.64%), Zygomycota (1.10%, 0.11%), Chytridiomycota (0.25%, 0.04%) and Rozellomycota (0.12%, 0.006%). The compositions of fungal communities across these three sea regions were found to be vary, which may be attributed to sediment source, geographical distance, latitude and some environmental factors such as the temperature and salinity of bottom water, water depth, total nitrogen, and the ratio of total organic carbon to nitrogen. Among these environmental factors, the temperature of bottom water is the most important driver that governs the distribution patterns of fungal communities across the sampled seas. Our data also suggest that the cold-water mass of the Yellow Sea likely balances competitive relationships between fungal taxa rather than increasing species richness levels.

In this paper, we compare the resource allocation fairness of uplink communications between non-orthogonal multiple access (NOMA) schemes and orthogonal multiple access (OMA) schemes. Through characterizing the contribution of the individual user data rate to the system sum rate, we analyze the fundamental reasons that NOMA offers a more fair resource allocation than that of OMA in asymmetric channels. Furthermore, a fairness indicator metric based on Jain's index is proposed to measure the asymmetry of multiuser channels. More importantly, the proposed metric provides a selection criterion for choosing between NOMA and OMA for fair resource allocation. Based on this discussion, we propose a hybrid NOMA-OMA scheme to further enhance the users fairness. Simulation results confirm the accuracy of the proposed metric and demonstrate the fairness enhancement of the proposed hybrid NOMA-OMA scheme compared to the conventional OMA and NOMA schemes.

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