Deep learning assisted time-varying channel estimation in multi-user mmWave hybrid MIMO systems

Hybrid analog/digital multiple input multiple output (MIMO) system is proposed to mitigate the challenges of millimeter wave (mmWave) communication. This architecture enables utilizing the large array gain with reasonable power consumption. However, new methods are required for the channel estimation problem of hybrid architecture-based systems due to the fewer number of radio frequency (RF) chains than antenna elements. Leveraging the sparse nature of the mmWave channels, compressed sensing (CS)-based channel estimation methods are proposed. Recently, machine learning (ML)-aided methods have been investigated to improve the channel estimation performance. Additionally, the Doppler effect should be considered for the high mobility scenarios, and we deal with the time-varying channel model. Therefore, in this article, we consider the scenario of time-varying channels for a multi-user mmWave hybrid MIMO system. By proposing a Deep Neural Network (DNN) and defining the inputs and outputs, we introduce a novel algorithm called Deep Learning Assisted Angle Estimation (DLA-AE) for improving the estimation of the Angles of Departure/Arrival (AoDs/AoAs) of the channel paths. In addition, we suggest Linear Phase Interpolation (LPI) to acquire the path gains for the data transmission instants. Simulation results show that utilizing the proposed DLA-AE and LPI methods enhance the time-varying channel estimation accuracy with low computational complexity.     

Beamspace channel estimation in wideband lens antenna array-based mmWave mMIMO-OFDM systems under beam squint

Sparse Bayesian learning (SBL) and particularly relevant vector machines (RVMs) have drawn much attention to improving the performance of existing machine learning models. The methodology depends on a parameterized prior that enforces models with weight sparsity, where only a few are non-zeros. Wideband mmWave massive multiple-input multiple-output (mMIMO) systems with lens antenna array (LAA), expect to play a key role in future fifth-generation (5G) wireless systems. To provide the beamforming gain required to overcome path loss, we consider a lens antenna array (LAA)-based beamspace mMIMO system. However, the spatial-wideband influence causes the beam squint effect to emerge, making the beamspace channel path components exhibit a unique frequency-dependent sparse structure, and thus nullifies the frequency domain common support assumption. In this paper, we first propose a channel estimation (CE) algorithm, namely a reduced-antenna selection progressive support-detection (RAS-PSD), for the wideband mmWave mMIMO-OFDM systems with LAA, which considers the beam squint effect. Secondly, by exploring Bayesian learning (BL), a Gaussian Process hyperparameter optimization-based CE (GP-HOCE) algorithm is proposed for the considered system, where both its own hyperparameter and the hyperparameters of its adjacent neighbors governs the sparsity of each coefficient. The simulation results show that the wideband beamspace channel coefficients can be estimated more efficiently than those of the existing state-of-the-art algorithms in terms of normalized mean square error (NMSE) of CE for wideband mmWave mMIMO-OFDM systems.     

Deep learning based physical layer security for terrestrial communications in 5G and beyond networks: A survey

The key principle of physical layer security (PLS) is to permit the secure transmission of confidential data using efficient signal-processing techniques. Also, deep learning (DL) has emerged as a viable option to address various security concerns and enhance the performance of conventional PLS techniques in wireless networks. DL is a strong data exploration technique which can be used to learn normal and abnormal behavior of 5G and beyond wireless networks in an insecure channel paradigm. Also, since DL techniques can successfully predict future new instances by learning from existing ones, they can successfully predict new attacks, which frequently involve mutations of earlier attacks. Thus, motivated by the benefits of DL and PLS, this survey provides a comprehensive review that overviews how DL-based PLS techniques can be employed for solving various security concerns in 5G and beyond networks. The survey begins with an overview of physical layer threats and security concerns in 5G and beyond networks. Then, we present a detailed analysis of various DL and deep reinforcement learning (DRL) techniques that are applicable to PLS applications. We present the specific use-cases of PLS design for each type of technique, including attack detection, physical layer authentication (PLA), and other PLS techniques. Then, we present an in-depth overview of the key areas of PLS where DL can be used to enhance the security of wireless networks, such as automatic modulation classification (AMC), secure beamforming, PLA, etc. Performance evaluation metrics for DL-based PLS design are subsequently covered. Finally, we provide insights to the readers about various challenges and future research trends in the design of DL-based PLS for terrestrial communications in 5G and beyond networks.     

Unsupervised LoS/NLoS identification in mmWave communication using two-stage machine learning framework

Identification of line-of-sight (LoS)/ non-LoS (NLoS) condition in millimeter wave (mmWave) communication is important for localization and unobstructed transmission between a base station (BS) and a user. A sudden obstruction in a link between a BS and a user can result in poorly received signal strength or termination of communication. Channel features obtained by the estimation of channel state information (CSI) of a user at the BS can be used for identifying LoS/NLoS condition. With the assumption of labeled CSI, existing machine learning (ML) methods have achieved satisfactory performance for LoS/NLoS identification. However, in a real communication environment, labeled CSI is not available. In this paper, we propose a two-stage unsupervised ML based LoS/NLoS identification framework to address the lack of labeled data. We conduct experiments for the outdoor scenario by generating data from the NYUSIM simulator. We compare the performance of our method with the supervised deep neural network (SDNN) in terms of accuracy and receiver characteristic curves. The proposed framework can achieve an accuracy of 87.4% and it outperforms SDNN. Further, we compare the performance of our method with other state-of-the-art LoS/NLoS identification schemes in terms of accuracy, recall, precision, and F1-score.     

Digital Self-interference cancellation in the era of machine learning: A comprehensive review

In-band full-duplex (IBFD) transmission has been identified as a promising solution to improve spectral efficiency as compared to half-duplex (HD) systems. One of the major challenges of FD systems is to effectively manage the self-interference (SI) produced by the transmitter antenna to the local receiver antenna. In this survey paper, we first review and discuss the preliminaries related to SI cancellation (SIC). Furthermore, this survey presents the advancements to the date of digital SIC approaches and highlights the advantages and limitations of each approach. The survey also summarizes different hardware platforms used for SIC along with their key features. Additionally, we also survey and compare different FD techniques for their SIC performances. Finally, this paper identifies a variety of new research areas in the era of machine learning and deep learning for digital SIC.     

胶束中的若丹明6G荧光增强和激光行为

使用阴离子表面活性剂十二烷基硫酸钠 (SDS)有效的增强了若丹明 6G染料水溶液的荧光 ,在若丹明 6G浓度分别为 5 47× 10 -7和 5 47× 10 -4 mol·L-1时 ,最大增强比率分别为 1 95和 9 7。在后一浓度下SDS的加入使若丹明 6G染料激光阈值降低 ,能量转化效率提高。不加SDS时的激光阈值功率密度约为 6 5MW·cm-2 ,加入 4 1× 10 -2 mol·L-1的SDS后 ,激光阈值功率密度降为 0 8MW·cm-2 。泵浦光功率密度为 6 5MW·cm-2 时 ,能量转化效率达到 2 5 %。同时还观察到SDS的加入使溶液吸收谱、荧光谱和染料激光发生了红移。对以上现象的物理机制进行了讨论。     

An effective approach for initial access in 5G-millimeter wave-based Vehicle to Everything (V2X) communication using Improved Genetic Algorithm

The process of establishing a directional communication link between the vehicle (VE) on the road and a roadside unit (RSU) is known as initial access process in 5G-millimeter wave(mmWave)-Vehicle to Everything (V2X) communications. Initial access is a tricky problem because substantial interruption or delay can be experienced where the RSU and the VE tries to discover the suitable beam alignment for establishing a direct communication link. Thus, it is very important to resolve this initial access problem in an effective way. Moreover, with the popularity of 5G-mmWave based V2X communication many researchers are trying to address this problem. Therefore, in this paper, we will be presenting a novel beam refinement technique that uses Improved Genetic Algorithm which is quite useful when the system is comprised of large number of antenna arrays. In this work we have considered that RSU and VE are equipped with multiple input multiple output (MIMO) antenna system. Proposed improved genetic algorithm includes some key improvements in terms of selection procedure, elitism, crossover and mutation operations. The effectiveness of the proposed work is investigated in terms of Capacity achieved vs number of transmit and receive antennas at RSU and VE, codebook size, outage probability and total transmitted power. Moreover, a detail analysis has been performed with previous state of the art works in terms of key performance metrics like: capacity achieved by 5G-V2X system, outage probability, and total transmitted power utilization. The proposed work has shown improved beam refinement by solving the initial access problem effectively.     

Secrecy sum rate maximization for a MIMO-NOMA uplink transmission in 6G networks

Non-orthogonal multiple access (NOMA), as a well-qualified candidate for sixth-generation (6G) mobile networks, has been attracting remarkable research interests due to high spectral efficiency and massive connectivity. The aim of this study is to maximize the secrecy sum rate (SSR) for a multiple-input multiple-output (MIMO)-NOMA uplink network under the maximum total transmit power and quality of service (QoS) constraints. Thanks to the generalized singular value decomposition method, the SSR of NOMA is compared with conventional orthogonal multiple access and other baseline algorithms in different MIMO scenarios. Due to the subtractive and non-convex nature of the SSR problem, the first-order Taylor approximation is exploited to transform the original problem into a suboptimal concave problem. Simulation results are provided and compared with some other benchmarks to evaluate the efficacy of the proposed method.     

Fluorescence sensor for Cu(II) based on R6G derivatives modified silicon nanowires

A fluorescence sensor for selective detection of Cu(II) is realized by covalently immobilizing derivatives of rhodamine6G (R6G) on the surface of silicon nanowires (SiNWs). It features the release of R6G from the SiNWs in the presence of Cu(II), which causes a significant enhancement of the fluorescence over other metal ions. The present Cu(II) sensor has good selectivity and sensitivity, and exhibits a linear response in the range of 0.0-7.0 μM Cu(II). Different from conventional Cu(II) sensor with fluorescence quenching, the present sensor based on fluorescence enhancement facilitates the practical application. Especially, the release of the R6G from SiNWs could be utilized as fluorescent labeling for Cu(II) in microenvironment.     

Deep learning aided beam vector assignment for massive MIMO maritime communication considering location information and handover impact

Location aided beamforming (LAB) has been proposed as a potential solution to fast beam vector (BV) selection for massive multi-input multi-output (M-MIMO) maritime communication systems. However, the existing LAB schemes hardly consider the impact from handover. In this paper, we propose to apply the Gaussian anomaly detection (GAD) to improve the system performance, particularly in the handover zone, by exploiting the information of sea lane and locations of the user equipment (UE). Furthermore, inspired by the rationale of deep learning (DL) aided image processing methods, we first transform the BV assignment problem to a location-based artificial gray value classification problem, and then utilize a convolutional deep belief networks (CDBN) to predict the BV indices from a predefined codebook constituted by location-related feature matrices. In addition, we exploit GAD to rectify the training data of CDBN, such that the prediction on BV assignments can be more accurate to achieve a higher capacity than known methods. Finally, an extensive comparison with selected existing schemes through simulations demonstrates the effectiveness of the proposed scheme.     

UAV caching in 6G networks: A Survey on models,techniques, and applications

The rapid development road map of 6G networks has posed a new set of challenges to both industrial and academic sectors. On the one hand, it needs more disruptive technologies and solutions for addressing the threefold issues including enhanced mobile broadband, massive machine-type communications, and ultra-reliable and low-latency communications. On the other hand, the ever-massive number of mobile users and Internet of Things devices conveys the huge volume of traffic throughout the 6G networks. In this context, caching is one of the most feasible technologies and solutions that does not require any system architecture changes nor costly investments, while significantly improve the system performance, i.e., quality of service and resource efficiency. Ground caching models deployed at macro base stations, small-cell base stations, and mobile devices have been successfully studied and currently extended to the air done by unmanned aerial vehicles (UAVs) to deal with the challenges of 6G networks. This paper provides a comprehensive survey of UAV caching models, techniques, and applications in 6G networks. In particular, we first investigate the entire picture of caching models moving from the ground to the air as well as the related surveys on UAV communications. Then, we introduce a typical UAV caching system and describe how it works in connection with all types of the transceivers, end users, and applications and services (A&Ss). After that, we present the recent advancements and analyses of the UAV caching models and common system performance metrics. Furthermore, the UAV caching with assisted techniques, UAV caching-enabled mechanisms, and UAV caching A&Ss are discussed to demonstrate the role of UAV caching system in 6G networks. Finally, we highlight the ongoing challenges and potential research directions toward UAV caching in 6G networks.     

A Novel and Adaptive Angle Diversity-Based Receiver for 6G Underground Mining VLC Systems

Visible light communication (VLC) is considered an enabling technology for future 6G wireless systems. Among the many applications in which VLC systems are used, one of them is harsh environments such as Underground Mining (UM) tunnels. However, these environments are subject to degrading environmental and intrinsic challenges for optical links. Therefore, current research should focus on solutions to mitigate these problems and improve the performance of Underground Mining Visible Light Communication (UM-VLC) systems. In this context, this article presents a novel solution that involves an improvement to the Angle Diversity Receivers (ADRs) based on the adaptive orientation of the Photo-Diodes (PDs) in terms of the Received Signal Strength Ratio (RSSR) scheme. Specifically, this methodology is implemented in a hemidodecahedral ADR and evaluated in a simulated UM-VLC scenario. The performance of the proposed design is evaluated using metrics such as received power, user data rate, and bit error rate (BER). Furthermore, our approach is compared with state-of-the-art ADRs implemented with fixed PDs and with the Time of Arrival (ToA) reception method. An improvement of at least 60% in terms of the analyzed metrics compared to state-of-the-art solutions is obtained. Therefore, the numerical results demonstrate that the hemidodecahedral ADR, with adaptive orientation PDs, enhances the received optical signal. Furthermore, the proposed scheme improves the performance of the UM-VLC system due to its optimum adaptive angular positioning, which is completed according to the strongest optical received signal power. By improving the performance of the UM-VLC system, this novel method contributes to further consideration of VLC systems as potential and enabling technologies for future 6G deployments.     

十二烷基硫酸钠(SDS)增强若丹明6G水溶液激光激发荧光谱研究

报道了若丹明6G水溶液添加不同浓度的表面活性剂十二烷基硫酸钠(SDS)时激光激发染料的变化,发现较低的掺入量导致R6G荧光减弱,适量SDS的加入使荧光增强,在5×10-5 mol·L-1的R6G水溶液中,加入6×10-2 mol·L-1 SDS,荧光增强因子达到3.1。当R6G浓度为1×10-4 mol·L-1时,加入2×10-2 mol·L-1,染料激光阈值显著降低。测量了不同浓度的R6G溶液的吸收光谱及加入不同浓度SDS后的荧光谱,分析了不同SDS加入量下R6G荧光减弱及增强的物理机制。     

Resource optimization for UAV-assisted mobile edge computing system based on deep reinforcement learning

Computational efficiency is a direction worth considering in moving edge computing (MEC) systems. However, the computational efficiency of UAV-assisted MEC systems is rarely studied. In this paper, we maximize the computational efficiency of the MEC network by optimizing offloading decisions, UAV flight paths, and allocating users’ charging and offloading time reasonably. The method of deep reinforcement learning is used to optimize the resources of UAV-assisted MEC system in complex urban environment, and the user’s computation-intensive tasks are offloaded to the UAV-mounted MEC server, so that the overloaded tasks in the whole system can be alleviated. We study and design a framework algorithm that can quickly adapt to task offload decision making and resource allocation under changing wireless channel conditions in complex urban environments. The optimal offloading decisions from state space to action space is generated through deep reinforcement learning, and then the user’s own charging time and offloading time are rationally allocated to maximize the weighted sum computation rate. Finally, combined with the radio map to optimize the UAC trajectory to improve the overall weighted sum computation rate of the system. Simulation results show that the proposed DRL+TO framework algorithm can significantly improve the weighted sum computation rate of the whole MEC system and save time. It can be seen that the MEC system resource optimization scheme proposed in this paper is feasible and has better performance than other benchmark schemes.     

A survey on UAV placement optimization for UAV-assisted communication in 5G and beyond networks

With the increase in capacity demands and the requirement of ubiquitous coverage in the fifth generation and beyond wireless communications networks, unmanned aerial vehicles (UAVs) have acquired a great attention owing to their outstanding characteristics over traditional base stations and relays. UAVs can be deployed faster and with much lower expenditure than ground base stations. In addition, UAVs can enhance the network performance thanks to their strong line-of-sight link conditions with their associated users and their dynamic nature that adapts to varying network conditions. Optimization of the UAV 3D locations in a UAV-assisted wireless communication network was considered in a large body of research as it is a critical design issue that greatly affects communication performance. Although the topic of UAV placement optimization was considered in few surveys, these surveys reviewed only a small part of the growing literature. In addition, the surveys were brief and did not discuss many important design issues such as the objectives of the optimization problem, the adopted solution techniques, the air-to-ground channel models, the transmission media for access and backhaul links, the limited energy nature of the UAV on-board batteries, co-channel interference and spectrum sharing, the interference management, etc. Motivated by the importance of the topic of UAV placement optimization as well as the need for a detailed review of its recent literature, we survey 100 of the recent research papers and provide in-depth discussion to fill the gaps found in the previous survey papers. The considered research papers are summarized and categorized to highlight the differences in the deployment scenario and system model, the optimization objectives and parameters, the proposed solution techniques, and the decision-making strategies and many other points. We also point to some of the existing challenges and potential research directions that have been considered in the surveyed literature and that requires to be considered