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Pyrazines are important flavor components and healthy active components in Baijiu, which including tetramethylpyrazine (TTMP). During the brewing process, the traceability of microbial communities and the content distribution characteristics of TTMP are important for improving the quality and style characteristics of compound-flavored Baijiu (CFB). However, the traceability analysis of microorganisms in fermented grains (FG)—used in the production of CFB—lacks quantitative and systematic evaluation. In this study, the microbial communities and TTMP content of Jiuqu (JQ), Liangpei (LP), FG, and pit mud (CP) used in CFB production were characterized; further, coordinate and discriminant analyses were employed to determine differences in microbial communities. Additionally, traceability and correlation analyses were performed to reveal the origin of microbial communities in FG. The source, content, and distribution characteristics of TTMP based on the brewing process have also been discussed. The results showed that most of the bacterial and fungal communities at different levels of FG came from other sources, and the microorganisms of Cladosporium, Acetobacter, Aspergillus, Methanosarcina, and Bacillus were considered have a osculating correlations with TTMP content of FG. Taken together, this study provides insights into the origin of microbial communities in FG and the distribution characteristics of TTMP based on the CFB brewing process. The current findings are conducive for optimizing the fermentation process and improving the quality and style characteristics of CFB.

To enhance the decoding efficiency of Globally Coupled (GC) LDPC codes, we incorporated Early Termination (ET) and Forced Convergence (FC) into the local/global two-phase decoding algorithm to expedite the decoding process. The two-phase decoding scheme integrates the ET technique to halt unnecessary iterations in the local decoding phase while employing the FC technique to accelerate convergence in the global phase decoding. The application of ET technology in the local decoding of GC-LDPC codes will not cause performance loss as in traditional block codes and will cause considerable complexity gains. For a longer code length and larger convergence differences between nodes’ global codes, the FC technique operates more efficiently in global code than local code. Two variants are proposed for the ET scheme in the local decoding, namely ET-1 and ET-2. The initial variant, ET-1, predicts whether local decoding can be successful according to data characteristics and stop the local decoding iteration that is not expected to be successful in time. In the case of ET-2, the saved local iterations are transformed to global decoding equally. The results show that ET-1 saves considerable decoding time complexity and ET-2 improves the performance of the GC-LDPC code with the same decoding time complexity. The combined approach of ET-1 with FC reduces the decoding time complexity up to 42% at a low Signal Noise Rate region while maintaining its performance; ET-2-FC two-phase decoding saves approximately 25% decoding time complexity while improving the BER by about 0.18 dB and FER by about 0.23 dB.

Unmanned aerial vehicle (UAV) air-to-ground (AG) communication plays a critical role in the evolving space–air–ground integrated network of the upcoming sixth-generation cellular network (6G). The integration of massive multiple-input multiple-output (MIMO) systems has become essential for ensuring optimal performing communication technologies. This article presents a novel dual-driven learning-based network for millimeter-wave (mm-wave) massive MIMO symbol detection of UAV AG communications. Our main contribution is that the proposed approach combines a data-driven symbol-correction network with a model-driven orthogonal approximate message passing network (OAMP-Net). Through joint training, the dual-driven network reduces symbol detection errors propagated through each iteration of the model-driven OAMP-Net. The numerical results demonstrate the superiority of the dual-driven detector over the conventional minimum mean square error (MMSE), orthogonal approximate message passing (OAMP), and OAMP-Net detectors at various noise powers and channel estimation errors. The dual-driven MIMO detector exhibits a 2–3 dB lower signal-to-noise ratio (SNR) requirement compared to the MMSE and OAMP-Net detectors to achieve a bit error rate (BER) of 1×10−2 when the channel estimation error is −30 dB. Moreover, the dual-driven MIMO detector exhibits an increased tolerance to channel estimation errors by 2–3 dB to achieve a BER of 1×10−3.

The microbial composition and volatile components of fermented grains (FG) and pit mud (PM) are crucial for the quality and flavor of compound-flavor baijiu (CFB). The physicochemical indices, culturable microorganisms, microbial communities, and volatile components of FG and PM were analyzed and correlated in our research. Considering FG and PM, amplicon sequencing was used to analyze the microbial community and the volatile components were detected by headspace solid-phase microextraction-gas chromatography–mass spectrometry (HS-SPME). For FG, redundancy analysis and correlation perfume Circos were used to clarify the correlations between the dominant microbial community and volatile components. The results showed that Aspergillus, Pichia, and Rhizopus were the main fungal microflora in FG and PM, whereas Lactobacillus and Bacillus were the dominant bacteria in FG, and Methanosarcina and Clostridium sensu stricto 12 were the dominant bacteria in the PM. The microbial community and volatile compounds in the CB sampled from the bottom layers of the FG were greatly affected by those in the PM. There were 32 common volatile components in CB and PM. For FG, most of the volatile components were highly correlated with Lactobacillus, Bacillus, Aspergillus, Pichia, and Monascus, which includes alcohols, acids and esters. This study reveals correlations between microbial composition, volatile components, and the interplay of FG and PM, which are conducive to optimizing the fermentation process and improving the quality of CFB base.

Sparse-view CT reconstruction is a fundamental task in computed tomography to overcome undesired artifacts and recover the details of textual structure in degraded CT images. Recently, many deep learning-based networks have achieved desirable performances compared to iterative reconstruction algorithms. However, the performance of these methods may severely deteriorate when the degradation strength of the test image is not consistent with that of the training dataset. In addition, these methods do not pay enough attention to the characteristics of different degradation levels, so solely extending the training dataset with multiple degraded images is also not effective. Although training plentiful models in terms of each degradation level can mitigate this problem, extensive parameter storage is involved. Accordingly, in this paper, we focused on sparse-view CT reconstruction for multiple degradation levels. We propose a single degradation-aware deep learning framework to predict clear CT images by understanding the disparity of degradation in both the frequency domain and image domain. The dual-domain procedure can perform particular operations at different degradation levels in frequency component recovery and spatial details reconstruction. The peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and visual results demonstrate that our method outperformed the classical deep learning-based reconstruction methods in terms of effectiveness and scalability.

The faster-than-Nyquist (FTN) signaling technology overlaps multiple streams of data within a symbol period by compressing the transmit symbol time/frequency-domain interval, thus breaking the orthogonality of the Nyquist pulse waveform and achieving the purpose of transmitting more data in a limited bandwidth.A relatively comprehensive explanation of the technical principles of FTN was elaborated, its development status was sorted out, and the related key questions——the relationship between FTN capacity and Shannon capacity, and the intrinsic connection of related technologies were answered.Finally, The possibility of FTN application in 6G was discussed, and its value and challenges in 6G were presented.That is, FTN is a highly spectrally efficient transmission scheme that approximates Shannon's capacity, but excessive receiver complexity may be a key impediment to its use in 6G applications.

This paper analyzes the physical layer security performance of hybrid automatic repeat request (HARQ) systems with the assistance of an intelligent reflecting surface (IRS) and aims to reveal the primary factors that enhance PLS. First, closed-form expressions for the connection outage probability (COP) and secrecy outage probability (SOP) in HARQ with chase combining (HARQ-CC) are acquired using the generalized-K (KG) distribution. Then, these two critical metrics are derived while adopting HARQ with incremental redundancy (HARQ-IR), resorting to the mixture gamma (MG) distribution and the Mellin transform. Diversity and coding gain are also addressed through an asymptotic analysis of the COP and SOP. Finally, an evaluation of the numerical results demonstrates that a greater gain in the main channel and the wiretap channel can be produced by increasing the number of meta-surfaces rather than increasing the maximum transmission number, except for the higher signal-to-noise (SNR) region of HARQ-IR where the latter is preferred. This finding provides a significant guidance for the joint configuration of IRS and HARQ to achieve secure communication.

Fog radio access networks (F-RANs) can effectively alleviate fronthaul loads and reduce content transmission delay by migrating cloud services to the network edge. This paper addresses a cooperative caching scenario in F-RAN, where each mobile user can acquire the requested contents from any one of its associated fog-computing-based access points (F-APs). However, caching disparate contents in different F-APs will lead to different content delivery delays, since mobile users suffer from diverse channel fadings and interferences when they download contents from different F-APs. Considering limited caching storage in each F-AP, diverse user preferences, unpredictable user mobility and time-varying channel states, an average transmission delay minimization problem is formulated. With the aid of dueling deep-Q-network framework, a delay-aware cache update policy is proposed for mobile users in F-RAN. The proposed cache update policy will decide to replace the stored contents in F-APs with the proper contents at each time slot. Compared with first in first out, least recently used and least frequently used caching policies, simulation experiments are performed to evaluate the performance of the proposed algorithm. Simulation results illustrate that the proposed caching policy yields better average hit ratio and lower average transmission delay than other traditional caching policies.

By connecting multiple short, local low-density parity-check (LDPC) codes with a global parity check, the globally coupled (GC) LDPC code can attain high performances with low complexities. The typical design of a local code is a quasi-cyclic (QC) LDPC for which the code length is proportional to the size of circulant permutation matrix (CPM). The greatest common divisor (GCD)-based full-length row multiplier (FLRM) matrix is constrained by a lower bound of CPM size to avoid six length cycles. In this paper, we find a new lower bound for the CPM size and propose an algorithm to determine the minimum CPM size and the corresponding FLRM matrix. Based on the new lower bound, two methods are proposed to construct the GC-QC-LDPC code of grith 8 based on the GCD based FLRM matrix. With the proposed algorithm, the CPM size can be 45% less than that given by sufficient condition of girth 8. Compared with the conventional GC-LDPC construction, the codes constructed with the proposed method have improved performance and are more flexible in code length and code rate design.

High quality transmission of Virtual Reality (VR) video depends strictly on bandwidth and delay requirements. It becomes possible with the maturity and commercialization of 5G technology. However, to transmit VR video streaming over wireless communication system, we must cope with the challenge that fluctuation exists in the wireless channel conditions. For example, available channel bandwidth, packet loss rate, and interference level may vary with time due to channel fading and user's mobility. The problem is more prominent when transmitting naked-eye 3D video which generally consists of multiple viewpoints with different resolutions. In order to optimize the quality of experience (QoE) of watching naked-eye VR video over wireless networks, this paper proposes a network-assisted neural adaptive video streaming algorithm (NAVSA). Specifically, we present a modified QoE function to describe the quality of naked-eye 3D streaming quantitatively, which not only considers the quality of naked-eye 3D video itself, but also considers the phenomenon of rebuffering and video fluctuation that occurs during video transmission. Next, with the network-assisted feedback, the physical layer information, the buffer occupancy of the video client, and the size of the next video chunk are collected to train a reinforcement learning model. Based on dynamic adaptive streaming over HTTP (DASH), the model can automatically choose appropriate viewpoints and resolutions corresponding to the current condition of the wireless networks such that the network capacity can be fully explored. To verify the performance of our proposed NAVSA, we simulate 3 naked-eye 3D video application scenarios on the NS3 platform. The results show that the performance of NAVSA is about 5~8% better than some state-of-the-art algorithms in wireless networks.

With the rapid growing demand for transmission rate, transmission security is becoming increasingly important. This paper proposes a novel scheme aiming at transmission security enhancement on the basis of the spatial modulation technology. In the proposed scheme, the transmitter maps the information bits to the index of a receive antenna, then a precoding vector is randomly selected in the null space of the targeted antenna. Due to the randomness of the precoding vector, it's difficult for the eavesdropper in unknown locations to detect the index of the targeted antenna using maximum-likelihood detection, hence its receiving performance can be greatly degraded compared with the legitimate receiver. Both theoretical analysis and simulation results verified the secure enhancement performance of the proposed scheme. Moreover, unlike most of the existing schemes which require that the transmitter should equip more antenna than the receiver, our scheme works well even if the transmitter has less antennas than the receiver.

By offloading storage and computing resources to the edge of networks, mobile edge computing (MEC) is emerged as a promising architecture to reduce the transmission delay and bandwidth waste for mobile multimedia services. This paper focuses on a multi-service scenario in the MEC systems, where the MEC server can provide three multimedia services including live streaming, buffered streaming and low latency enhanced mobile broadband applications for edge users at the same time. In order to satisfy various quality of service (QoS) requirements for different multimedia applications, the 5G QoS model is applied. Notably, the packets from the multimedia applications with the same or similar requirements are mapped into the same QoS flow, and each QoS flow is processed individually. Therefore, how to effectively schedule the limited radio resource for QoS flows is an intractable problem. To address the problem above, a QoS evaluation model is designed, and a QoS maximization problem is formulated. Furthermore, a deep reinforcement learning method, deep-Q-network, is adopted to make decisions to allocate radio resource dynamically. Compared with round-robin and priority-based scheduling algorithms, the simulation results validate that the proposed algorithm outperforms other resource scheduling algorithms for multi-service scenario.

The reconstruction of sparsely sampled projection data will generate obvious streaking artifacts, resulting in image quality degradation and affecting medical diagnosis results. Wavelet transform can effectively decompose directional components of image, so the artifact features and edge details with high directionality can be better detected in the wavelet domain. Therefore, a hybrid domain method based on wavelet transform is proposed in this paper for the sparse-view CT reconstruction. The reconstruction model combines wavelet, spatial, and radon domains to restore the projection consistency and enhance image details. In addition, the global distribution of artifacts requires the network to have a large receptive field, so that a multi-level wavelet transform network (MWCNN) is applied to the hybrid domain model. Wavelet transform is used in the encoding part of the network to reduce the size of feature maps instead of pooling operation and inverse wavelet transform is deployed in the decoding part to recover image details. The proposed method can achieve PSNR of 41.049 dB and SSIM of 0.958 with 120 projections of three angular intervals, and obtain the highest values in this paper. Through the results of numerical analysis and reconstructed images, it shows that the hybrid domain method is superior to the single-domain methods. At the same time, the multi-level wavelet transform model is more suitable for CT reconstruction than the single-level wavelet transform.

The future 6G mobile communication network will support an unprecedented amount of Internet of Things (IoT) devices, which will boost the demand for low cost terminals under the principle of green communication. One of the critical issues for low cost terminals is the sampling rate of analog-to-digital converters (ADCs) at the receivers. A high sampling rate of the ADC gives rise to a high energy consumption and high hardware cost for the terminal. In the conventional multi-user OFDM systems, all users have to sample the received signal with a sampling rate that is larger than or equal to the Nyquist rate, despite only a small fraction of the bandwidth (number of subcarriers) is allocated to each user. This paper proposes a low sampling rate receiver design for multi-antenna multi-user OFDM systems. With the aid of zero-forcing precoding, the sampling rate of the receiver can be reduced to 1/K of the Nyquist rate, where K is the number of users. The simulation results show that with a significant reduction in sampling rate, performance loss is insignificant and acceptable in terms of bit error rate, mutual information and peak-to-average power ratio.

Limited-view Computed Tomography (CT) can be used to efficaciously reduce radiation dose in clinical diagnosis, it is also adopted when encountering inevitable mechanical and physical limitation in industrial inspection. Nevertheless, limited-view CT leads to severe artifacts in its imaging, which turns out to be a major issue in the low dose protocol. Thus, how to exploit the limited prior information to obtain high-quality CT images becomes a crucial issue. We notice that almost all existing methods solely focus on a single CT image while neglecting the solid fact that, the scanned objects are always highly spatially correlated. Consequently, there lies bountiful spatial information between these acquired consecutive CT images, which is still largely left to be exploited. In this paper, we propose a novel hybrid-domain structure composed of fully convolutional networks that groundbreakingly explores the three-dimensional neighborhood and works in a “coarse-to-fine” manner. We first conduct data completion in the Radon domain, and transform the obtained full-view Radon data into images through FBP. Subsequently, we employ the spatial correlation between continuous CT images to productively restore them and then refine the image texture to finally receive the ideal high-quality CT images, achieving PSNR of 40.209 and SSIM of 0.943. Besides, unlike other current limited-view CT reconstruction methods, we adopt FBP (and implement it on GPUs) instead of SART-TV to significantly accelerate the overall procedure and realize it in an end-to-end manner.

As an extension of source polarization and channel polarization, this paper considers joint source-channel polarization, which results in a joint source-channel coding (JSCC) scheme using a quasiuniform systematic polar code (SPC). In this JSCC scheme, the source with side information is encoded as a systematic polar codeword and only parity bits are transmitted through the channel. The indices of systematic bits are quasi-uniform, which enable the source and the channel to be jointly polarized to either a high entropy part or a low entropy part. The analysis reveals that the quasi-uniform SPC cannot be constructed via original polar coding. To solve this problem, additional bit-swap coding is introduced to modify original polar coding and construct this kind of SPCs. The proposed JSCC scheme can asymptotically approach the information-theoretical limit. For the noiseless channel, the proposed scheme is degraded into classic Slepain-Wolf coding or lossless source coding based on parity approach.

Accurate estimation of channel log-likelihood ratio (LLR) is crucial to the decoding of modern channel codes like turbo, low-density parity-check (LDPC), and polar codes. Under an additive white Gaussian noise (AWGN) channel, the calculation of LLR is relatively straightforward since the closed-form expression for the channel likelihood function can be perfectly known to the receiver. However, it would be much more complicated for heterogeneous networks where the global noise (i.e., noise plus interference) may be dominated by non-Gaussian interference with an unknown distribution. Although the LLR can still be calculated by approximating the distribution of global noise as Gaussian, it will cause performance loss due to the non-Gaussian nature of global noise. To address this problem, we propose to use bi-Gaussian (BG) distribution to approximate the unknown distribution of global noise, for which the two parameters of BG distribution can easily be estimated from the second and fourth moments of the overall received signals without any knowledge of interfering channel state information (CSI) or signaling format information. Simulation results indicate that the proposed BG approximation can effectively improve the word error rate (WER) performance. The gain of BG approximation over Gaussian approximation depends heavily on the interference structure. For the scenario of a single BSPK interferer with a 5 dB interference-to-noise ratio (INR), we observed a gain of about 0.6 dB. The improved LLR estimation can also accelerate the convergence of iterative decoding, thus involving a lower overall decoding complexity. In general, the overall decoding complexity can be reduced by 25 to 50%.

Hybrid precoding is widely studied in millimetre-wave (mmWave) massive MIMO systems due to low cost as well as low power consumption. In general, there are two kinds of hybrid precoding structures: one is fully connected structure (FCS), where each radio frequency (RF) chain is connected to all antennas, and the other is partially connected structure (PCS), where each RF chain is connected to a sub-array. In this paper, we investigate the optimal hybrid precoder design problem for mmWave massive MIMO systems based on PCS, since this kind of structure is more practical for antenna deployment. We first focus on the optimization of analog precoder (AP) and propose two AP design schemes for high signalto-noise ratio (SNR) condition and low SNR condition, respectively. For each of the schemes, the original optimization problem is reformulated to single-stream optimal transmitter beamforming problem with per-antenna power constraint, which has an optimal solution. Then, the optimal digital precoder is obtained by water-filling algorithm after AP is determined. Moreover, upper bounds of the achievable data rate for the proposed schemes with closed-form expression are derived.

Channel modeling is crucial to the development and evaluation of modern wireless communication systems including satellite communication system, since there might be critical safty-of-life applications. Also, the channel model is of great importance to the performance evaluation of mobile communication systems. In recent years, encouraged by the widely application of unmanned aerial vehicles, the research on channel modeling for aerial and aeronautical communications attract lots of interests. In the published articles, stationary and non-stationary channel models have been developed for air-to-ground communications based on regular shaped geometry-based stochastic model (RS-GBSM). The modeling of air-to-air or satellite-to-aircraft (S2A) communication is still quite simple or completely lacking. For obtaining more precise model of S2A channel, this paper presents an analytical non-stationary S2A channel mode based on RS-GBSM with considerations on line-of-sight path, specular reflection path, and ground scattering path. Analytical expressions of the channel impulse responses, the transfer functions, the auto-correlation functions, and the Doppler power spectrum density based on 3-path model are derived and simulated. Also, the distributions of the path antennation, the path delay, and the normalized Doppler shift based on uniform distribution of the scatterers are derived, simulated and fitted.

In this paper, we studied the secure transmission of a hybrid automatic repeat request with chase combining (HARQ-CC) system, under the existence of multiple eavesdroppers and limited latency. First, we analyzed some critical performance metrics, including connection outage probability (COP), secrecy outage probability (SOP) and effective secrecy throughput (EST). Then, to maximize the EST, three optimization problems of rate adaption were discussed: (i) optimizing the code rate with a given secrecy redundancy rate by a parameterized closed-form solution; (ii) optimizing the secrecy redundancy rate with a given code rate by a fixed-point method; (iii) optimizing both code rate and secrecy redundancy rate by an iterative optimization algorithm. We also considered COP and SOP constraints among the problems while corresponding solutions were deduced. Finally, numerical and simulated results verified our conclusions that the approximated SOP matches well with Monte–Carlo simulation for a strict reliable constraint, and that the optimized transmitting rate enhances EST efficiently with multiple eavesdroppers and retransmissions. Moreover, the influence of the number of eavesdroppers on secrecy performance was analyzed. Briefly, secrecy performance inevitably deteriorates with increasing number of eavesdroppers due to raised information leakage.

Cooperative routing is one of the most widely used technologies for improving the energy efficiency and energy balance of wireless multi-hop networks. However, the end-to-end energy cost and network lifetime are greatly restricted if the cooperative transmission model is not designed properly. The main aim of this paper is to explore a two-stage cooperative routing scheme to further improve the energy efficiency and prolong the network lifetime. A two-stage cooperative (TSC) transmission model is firstly designed in which the core helper is introduced to determine the helper set for cooperation. Then, the two-stage link cost is formulated where x, the weight of residual energy, is introduced to be adjusted for different design goals. By selecting the optimal helper set, the two-stage link cost of each link can be optimized. Finally, based on the designed TSC transmission model and the optimized two-stage link cost, a distributed two-stage cooperative routing (TSCR) scheme is further proposed to minimize the end-to-end cooperative routing cost. Simulation results evaluate the effect of x on the different performance metrics. When x equals 0, TSCR can achieve the shortest end-to-end transmission delay and highest energy efficiency, while a larger x can achieve a longer network lifetime. Furthermore, simulation results also show that the proposed TSCR scheme can effectively improve both the energy efficiency and network lifetime compared with the existing schemes.

This paper proposes a distributed joint source-channel coding (DJSCC) scheme using polar-like codes. In the proposed scheme, each distributed source encodes source message with a quasi-uniform systematic polar code (QSPC) or a punctured QSPC, and only transmits parity bits over its independent channel. These systematic codes play the role of both source compression and error protection. For the infinite code-length, we show that the proposed scheme approaches the information-theoretical limit by the technique of joint source-channel polarization with side information. For the finite code-length, the simulation results verify that the proposed scheme outperforms the distributed separate source-channel coding (DSSCC) scheme using polar codes and the DJSCC scheme using classic systematic polar codes.

This paper investigates the sum capacity of a single-cell multi-user system under the constraint that the transmitted signal is adopted from M-ary two-dimensional constellation with equal probability for both uplink, i.e., multiple access channel (MAC), and downlink, i.e., broadcast channel (BC) scenarios. Based on the successive interference cancellation (SIC) and the entropy power Gaussian approximation, it is shown that both the multi-user MAC and BC can be approximated to a bank of parallel channels with the channel gains being modified by an extra attenuate factor that equals to the negative exponential of the capacity of interfering users. With this result, the capacity of MAC and BC with arbitrary number of users and arbitrary constellations can be easily calculated which in sharp contrast with using traditional Monte Carlo simulation that the calculating amount increases exponentially with the increase of the number of users. Further, the sum capacity of multi-user under different power allocation strategies including equal power allocation, equal capacity power allocation and maximum capacity power allocation is also investigated. For the equal capacity power allocation, a recursive relation for the solution of power allocation is derived. For the maximum capacity power allocation, the necessary condition for optimal power allocation is obtained and an optimal algorithm for the power allocation optimization problem is proposed based on the necessary condition.

With the constraint of antenna space, spatial correlation and mutual coupling must be considered to accurately predict the system performance for massive MIMO systems. Increasing the antenna quantity can degrade the system performance due to mutual coupling. Antenna selection systems have better performance and lower hardware cost than full-MIMO systems. However, the conventional selection combining (SC) scheme consumes a great amount of training overhead and has high operational complexity in the presence of mutual coupling. This paper proposes a group switch-and-examine combining (GSEC) scheme for massive MIMO systems with the spatial correlation and mutual coupling existing at both the transmitter and receiver. Simulation results demonstrate that the proposed GSEC scheme provides better effective capacity performance and lower operational complexity than the conventional selection combining (SC) and full-MIMO scheme.