Quantum computing is a promising technology that has the potential to revolutionize many areas of science and technology, including communication. In this review, we discuss the current state of quantum computing in communication and its potential applications in various areas such as network optimization, signal processing, and machine learning for communication. First, the basic principle of quantum computing, quantum physics systems, and quantum algorithms are analyzed. Then, based on the classification of quantum algorithms, several important basic quantum algorithms, quantum optimization algorithms, and quantum machine learning algorithms are discussed in detail. Finally, the basic ideas and feasibility of introducing quantum algorithms into communications are emphatically analyzed, which provides a reference to address computational bottlenecks in communication networks.

Spectrum prediction is considered as a key technology to assist spectrum decision. Despite the great efforts that have been put on the construction of spectrum prediction, achieving accurate spectrum prediction emphasizes the need for more advanced solutions. In this paper, we propose a new multi-channel multi-step spectrum prediction method using Transformer and stacked bidirectional LSTM (Bi-LSTM), named TSB. Specifically, we use multi-head attention and stacked Bi-LSTM to build a new Transformer based on encoder-decoder architecture. The self-attention mechanism composed of multiple layers of multi-head attention can continuously attend to all positions of the multichannel spectrum sequences. The stacked Bi-LSTM can learn these focused coding features by multi-head attention layer by layer. The advantage of this fusion mode is that it can deeply capture the long-term dependence of multichannel spectrum data. We have conducted extensive experiments on a dataset generated by a real simulation platform. The results show that the proposed algorithm performs better than the baselines.

Low earth orbit (LEO) satellites with wide coverage can carry the mobile edge computing (MEC) servers with powerful computing capabilities to form the LEO satellite edge computing system, providing computing services for the global ground users. In this paper, the computation offloading problem and resource allocation problem are formulated as a mixed integer nonlinear program (MINLP) problem. This paper proposes a computation offloading algorithm based on deep deterministic policy gradient (DDPG) to obtain the user offloading decisions and user uplink transmission power. This paper uses the convex optimization algorithm based on Lagrange multiplier method to obtain the optimal MEC server resource allocation scheme. In addition, the expression of suboptimal user local CPU cycles is derived by relaxation method. Simulation results show that the proposed algorithm can achieve excellent convergence effect, and the proposed algorithm significantly reduces the system utility values at considerable time cost compared with other algorithms.

The low Earth orbit (LEO) satellite networks have outstanding advantages such as wide coverage area and not being limited by geographic environment, which can provide a broader range of communication services and has become an essential supplement to the terrestrial network. However, the dynamic changes and uneven distribution of satellite network traffic inevitably bring challenges to multipath routing. Even worse, the harsh space environment often leads to incomplete collection of network state data for routing decision-making, which further complicates this challenge. To address this problem, this paper proposes a state-incomplete intelligent dynamic multipath routing algorithm (SIDMRA) to maximize network efficiency even with incomplete state data as input. Specifically, we model the multipath routing problem as a markov decision process (MDP) and then combine the deep deterministic policy gradient (DDPG) and the $K$ shortest paths (KSP) algorithm to solve the optimal multipath routing policy. We use the temporal correlation of the satellite network state to fit the incomplete state data and then use the message passing neuron network (MPNN) for data enhancement. Simulation results show that the proposed algorithm outperforms baseline algorithms regarding average end-to-end delay and packet loss rate and performs stably under certain missing rates of state data.

The development of communication networks is currently undergoing a period of transformation. This paper illustrates this transformation from the growth rate of communication users, network bandwidth, and service revenue. We also analyze the shift in the focus of network technology development from aspects such as information sources, mobile terminals, wireless channels, core networks, edge clouds, data perception, and artificial intelligence. Finally, we briefly outline the new paradigm for network research and development (R&D) in the intelligent era.

Orthogonal time frequency space (OTFS) technique, which modulates data symbols in the delay-Doppler (DD) domain, presents a potential solution for supporting reliable information transmission in high-mobility vehicular networks. In this paper, we study the issues of DD channel estimation for OTFS in the presence of fractional Doppler. We first propose a channel estimation algorithm with both low complexity and high accuracy based on the unitary approximate message passing (UAMP), which exploits the structured sparsity of the effective DD domain channel using hidden Markov model (HMM). The empirical state evolution (SE) analysis is then leveraged to predict the performance of our proposed algorithm. To refine the hyperparameters in the proposed algorithm, we derive the update criterion for the hyperparameters through the expectation-maximization (EM) algorithm. Finally, Our simulation results demonstrate that our proposed algorithm can achieve a significant gain over various baseline schemes.

This work employs intelligent reflecting surface (IRS) to enhance secure and covert communication performance. We formulate an optimization problem to jointly design both the reflection beamformer at IRS and transmit power at transmitter Alice in order to optimize the achievable secrecy rate at Bob subject to a covertness constraint. We first develop a Dinkelbach-based algorithm to achieve an upper bound performance and a high-quality solution. For reducing the overhead and computational complexity of the Dinkelbach-based scheme, we further conceive a low-complexity algorithm in which analytical expression for the IRS reflection beamforming is derived at each iteration. Examination result shows that the devised low-complexity algorithm is able to achieve similar secrecy rate performance as the Dinkelbach-based algorithm. Our examination also shows that introducing an IRS into the considered system can significantly improve the secure and covert communication performance relative to the scheme without IRS.

In vehicle edge computing (VEC), asynchronous federated learning (AFL) is used, where the edge receives a local model and updates the global model, effectively reducing the global aggregation latency. Due to different amounts of local data, computing capabilities and locations of the vehicles, renewing the global model with same weight is inappropriate. The above factors will affect the local calculation time and upload time of the local model, and the vehicle may also be affected by Byzantine attacks, leading to the deterioration of the vehicle data. However, based on deep reinforcement learning (DRL), we can consider these factors comprehensively to eliminate vehicles with poor performance as much as possible and exclude vehicles that have suffered Byzantine attacks before AFL. At the same time, when aggregating AFL, we can focus on those vehicles with better performance to improve the accuracy and safety of the system. In this paper, we proposed a vehicle selection scheme based on DRL in VEC. In this scheme, vehicle's mobility, channel conditions with temporal variations, computational resources with temporal variations, different data amount, transmission channel status of vehicles as well as Byzantine attacks were taken into account. Simulation results show that the proposed scheme effectively improves the safety and accuracy of the global model.

As conventional communication systems based on classic information theory have closely approached Shannon capacity, semantic communication is emerging as a key enabling technology for the further improvement of communication performance. However, it is still unsettled on how to represent semantic information and characterise the theoretical limits of semantic-oriented compression and transmission. In this paper, we consider a semantic source which is characterised by a set of correlated random variables whose joint probabilistic distribution can be described by a Bayesian network. We give the information-theoretic limit on the lossless compression of the semantic source and introduce a low complexity encoding method by exploiting the conditional independence. We further characterise the limits on lossy compression of the semantic source and the upper and lower bounds of the rate-distortion function. We also investigate the lossy compression of the semantic source with two-sided information at the encoder and decoder, and obtain the corresponding rate distortion function. We prove that the optimal code of the semantic source is the combination of the optimal codes of each conditional independent set given the side information.

With the development of the transportation industry, the effective guidance of aircraft in an emergency to prevent catastrophic accidents remains one of the top safety concerns. Undoubtedly, operational status data of the aircraft play an important role in the judgment and command of the Operational Control Center (OCC). However, how to transmit various operational status data from abnormal aircraft back to the OCC in an emergency is still an open problem. In this paper, we propose a novel Telemetry, Tracking, and Command (TT&C) architecture named Collaborative TT&C (CoTT&C) based on mega-constellation to solve such a problem. CoTT&C allows each satellite to help the abnormal aircraft by sharing TT&C resources when needed, realizing real-time and reliable aeronautical communication in an emergency. Specifically, we design a dynamic resource sharing mechanism for CoTT&C and model the mechanism as a single-leader-multi-follower Stackelberg game. Further, we give an unique Nash Equilibrium (NE) of the game as a closed form. Simulation results demonstrate that the proposed resource sharing mechanism is effective, incentive compatible, fair, and reciprocal. We hope that our findings can shed some light for future research on aeronautical communications in an emergency.

In this paper, ambient IoT is used as a typical use case of massive connections for the sixth generation (6G) mobile communications where we derive the performance requirements to facilitate the evaluation of technical solutions. A rather complete design of unsourced multiple access is proposed in which two key parts: a compressed sensing module for active user detection, and a sparse interleaver-division multiple access (SIDMA) module are simulated side by side on a same platform at balanced signal to noise ratio (SNR) operating points. With a proper combination of compressed sensing matrix, a convolutional encoder, receiver algorithms, the simulated performance results appear superior to the state-of-the-art benchmark, yet with relatively less complicated processing.

Immersive services are the typical emerging services in current IMT-2020 network. With the development of network evolution, real-time interactive applications emerge one after another. This article provides an overview on immersive services which focus on real-time interaction. The scenarios, framework, requirements, key technologies, and issues of interactive immersive service are presented.

In this paper, an integrated substrate gap waveguide (ISGW) filtering antenna is proposed at millimeter wave band, whose surface wave and spurious modes are simultaneously suppressed. A second-order filtering response is obtained through a coupling feeding scheme using one uniform impedance resonator (UIR) and two stepped-impedance resonators (SIRs). To increase the stopband width of the antenna, the spurious modes are suppressed by selecting the appropriate sizes of the ISGW unit cell. Furthermore, the ISGW is implemented to improve the radiation performance of the antenna by alleviating the propagation of surface wave. And an equivalent circuit is investigated to reveal the working principle of ISGW. To demonstrate this methodology, an ISGW filtering antenna operating at a center frequency of 25 GHz is designed, fabricated, and measured. The results show that the antenna achieves a stopband width of 1.6$f_0$ (center frequency), an out-of-band suppression level of 21 dB, and a peak realized gain of 8.5 dBi.

This paper proposes a high-throughput short reference differential chaos shift keying cooperative communication system with the aid of code index modulation, referred to as CIM-SR-DCSK-CC system. In the proposed CIM-SR-DCSK-CC system, the source transmits information bits to both the relay and destination in the first time slot, while the relay not only forwards the source information bits but also sends new information bits to the destination in the second time slot. To be specific, the relay employs an $N$-order Walsh code to carry additional ${{\log }_{2}}N$ information bits, which are superimposed onto the SR-DCSK signal carrying the decoded source information bits. Subsequently, the superimposed signal carrying both the source and relay information bits is transmitted to the destination. Moreover, the theoretical bit error rate (BER) expressions of the proposed CIM-SR-DCSK-CC system are derived over additive white Gaussian noise (AWGN) and multipath Rayleigh fading channels. Compared with the conventional DCSK-CC system and SR-DCSK-CC system, the proposed CIM-SR-DCSK-CC system can significantly improve the throughput without deteriorating any BER performance. As a consequence, the proposed system is very promising for the applications of the 6G-enabled low-power and high-rate communication.

In this paper, joint location and velocity estimation (JLVE) of vehicular terminals for 6G integrated communication and sensing (ICAS) is studied. We aim to provide a unified performance analysis framework for ICAS-based JLVE, which is challenging due to random fading, multipath interference, and complexly coupled system models, and thus the impact of channel fading and multipath interference on JLVE performance is not fully understood. To address this challenge, we exploit structured information models of the JLVE problem to render tractable performance quantification. Firstly, an individual closed-form Cramer-Rao lower bound for vehicular localization, velocity detection and channel estimation, respectively, is established for gaining insights into performance limits of ICAS-based JLVE. Secondly, the impact of system resource factors and fading environments, e.g., system bandwidth, the number of subcarriers, carrier frequency, antenna array size, transmission distance, spatial channel correlation, channel covariance, the number of interference paths and noise power, on the JLVE performance is theoretically analyzed. The associated closed-form JLVE performance analysis can not only provide theoretical foundations for ICAS receiver design but also provide a performance benchmark for various JLVE methods.

With the rapid development of the Industrial Internet of Things (IIoT), the traditional centralized cloud processing model has encountered the challenges of high communication latency and high energy consumption in handling industrial big data tasks. This paper aims to propose a low-latency and low-energy path computing scheme for the above problems. This scheme is based on the cloud-fog network architecture. The computing resources of fog network devices in the fog computing layer are used to complete task processing step by step during the data interaction from industrial field devices to the cloud center. A collaborative scheduling strategy based on the particle diversity discrete binary particle swarm optimization (PDBPSO) algorithm is proposed to deploy manufacturing tasks to the fog computing layer reasonably. The task in the form of a directed acyclic graph (DAG) is mapped to a factory fog network in the form of an undirected graph (UG) to find the appropriate computing path for the task, significantly reducing the task processing latency under energy consumption constraints. Simulation experiments show that this scheme's latency performance outperforms the strategy that tasks are wholly offloaded to the cloud and the strategy that tasks are entirely offloaded to the edge equipment.

In recent years, as giant satellite constellations grow rapidly worldwide, the co-existence between constellations has been widely concerned. In this paper, we overview the co-frequency interference (CFI) among the giant non-geostationary orbit (NGSO) constellations. Specifically, we first summarize the CFI scenario and evaluation index among different NGSO constellations. Based on statistics about NGSO constellation plans, we analyse the challenges in mitigation and analysis of CFI. Next, the CFI calculation methods and research progress are systematically sorted out from the aspects of interference risk analysis framework, numerical calculation and link construction. Then, the feasibility of interference mitigation technologies based on space, frequency domain isolation, power control, and interference alignment mitigation in the NGSO mega-constellation CFI scenario are further sorted out. Finally, we present promising directions for future research in CFI analysis and CFI avoidance.

In the 6G era, Space-Air-Ground Integrated Network (SAGIN) are anticipated to deliver global coverage, necessitating support for a diverse array of emerging applications in high-mobility, hostile environments. Under such conditions, conventional orthogonal frequency division multiplexing (OFDM) modulation, widely employed in cellular and Wi-Fi communication systems, experiences performance degradation due to significant Doppler shifts. To overcome this obstacle, a novel two-dimensional (2D) modulation approach, namely orthogonal time frequency space (OTFS), has emerged as a key enabler for future high-mobility use cases. Distinctively, OTFS modulates information within the delay-Doppler (DD) domain, as opposed to the time-frequency (TF) domain utilized by OFDM. This offers advantages such as Doppler and delay resilience, reduced signaling latency, a lower peak-to-average ratio (PAPR), and a reduced-complexity implementation. Recent studies further indicate that the direct interplay between information and the physical world in the DD domain positions OTFS as a promising waveform for achieving integrated sensing and communications (ISAC). In this article, we present an in-depth review of OTFS technology in the context of the 6G era, encompassing fundamentals, recent advancements, and future directions. Our objective is to provide a helpful resource for researchers engaged in the field of OTFS.

In urban Vehicular Ad hoc Networks (VANETs), high mobility of vehicular environment and frequently changed network topology call for a low delay end-to-end routing algorithm. In this paper, we propose a Multi-Agent Reinforcement Learning (MARL) based decentralized routing scheme, where the inherent similarity between the routing problem in VANET and the MARL problem is exploited. The proposed routing scheme models the interaction between vehicles and the environment as a multi-agent problem in which each vehicle autonomously establishes the communication channel with a neighbor device regardless of the global information. Simulation performed in the 3GPP Manhattan mobility model demonstrates that our proposed decentralized routing algorithm achieves less than 45.8 ms average latency and high stability of 0.05 % averaging failure rate with varying vehicle capacities.

In this paper, average bit error probability (ABEP) bound of optimal maximum likelihood (ML) detector is first derived for ultra massive (UM) multiple-input-multiple-output (MIMO) system with generalized amplitude phase modulation (APM), which is confirmed by simulation results. Furthermore, a minimum residual criterion (MRC) based low-complexity near-optimal ML detector is proposed for UM-MIMO system. Specifically, we first obtain an initial estimated signal by a conventional detector, i.e., matched filter (MF), or minimum mean square error (MMSE) and so on. Furthermore, MRC based error correction mechanism (ECM) is proposed to correct the erroneous symbol encountered in the initial result. Simulation results are shown that the performance of the proposed MRC-ECM based detector is capable of approaching theoretical ABEP of ML, despite only imposing a slightly higher complexity than that of the initial detector.

Within the framework of the 5G new radio (NR), we propose a new hybrid automatic repeat request (HARQ) scheme to improve the throughput performance. The difference between the proposed scheme and the conventional one lies in the first retransmission, where the erroneous coded block group is interleaved and superimposed (XORed) onto a fresh coded block group. At the receiver, an iterative message-passing decoding algorithm can be employed to recover the target erroneous code block group (CBG). Only when the superposed retransmission fails, the conventional incremental redundancy (IR) or repetition redundancy (RR) retransmission is initiated. In any case, since the first retransmission is along with but has negligible effect on the fresh CBG, it costs neither transmitted power nor bandwidth. Monte-Carlo simulation results reveal that the presented HARQ schemes can achieve throughput improvements up to 10% over block fading channels and up to 50% over fast fading channels in comparison with the original 5G CBG-level HARQ scheme but without excessively increasing the implementation complexity.

Federated edge learning (FEEL) technology for vehicular networks is considered as a promising technology to reduce the computation workload while keeping the privacy of users. In the FEEL system, vehicles upload data to the edge servers, which train the vehicles' data to update local models and then return the result to vehicles to avoid sharing the original data. However, the cache queue in the edge is limited and the channel between edge server and each vehicle is time-varying. Thus, it is challenging to select a suitable number of vehicles to ensure that the uploaded data can keep a stable cache queue in edge server while maximizing the learning accuracy. Moreover, selecting vehicles with different resource statuses to update data will affect the total amount of data involved in training, which further affects the model accuracy. In this paper, we propose a vehicle selection scheme, which maximizes the learning accuracy while ensuring the stability of the cache queue, where the statuses of all the vehicles in the coverage of edge server are taken into account. The performance of this scheme is evaluated through simulation experiments, which indicates that our proposed scheme can perform better than the known benchmark scheme.

Massive content delivery will become one of the most prominent tasks of future B5G/6G communication. However, various multimedia applications possess huge differences in terms of object oriented (i.e., machine or user) and corresponding quality evaluation metric, which will significantly impact the design of encoding or decoding within content delivery strategy. To get over this dilemma, we firstly integrate the digital twin into the edge networks to accurately and timely capture Quality-of-Decision (QoD) or Quality-of-Experience (QoE) for the guidance of content delivery. Then, in terms of machine-centric communication, a QoD-driven compression mechanism is designed for video analytics via temporally lightweight frame classification and spatially uneven quality assignment, which can achieve a balance among decision-making, delivered content, and encoding latency. Finally, in terms of user-centric communication, by fully leveraging haptic physical properties and semantic correlations of heterogeneous streams, we develop a QoE-driven video enhancement scheme to supply high data fidelity. Numerical results demonstrate the remarkable performance improvement of massive content delivery.

This paper investigates the security performance of a cooperative multicast-unicast system, where the users present the feature of high mobility. Specifically, we develop the non-orthogonal multiple access (NOMA) based orthogonal time frequency space (OTFS) transmission scheme, namely NOMA-OTFS, in order to combat Doppler effect as well as to improve the spectral efficiency. Further, we propose a power allocation method addressing the trade-off between the reliability of multicast streaming and the confidentiality of unicast streaming. Based on that, we utilize the relay selection strategy, to improve the security of unicast streaming. In the context of multicast-unicast streaming, our simulation findings validate the effectiveness of the NOMA-OTFS based cooperative transmission, which can significantly outperform the existing NOMA-OFDM in terms of both reliability and security.

Recently, whether the channel prediction can be achieved in diverse communication scenarios by directly utilizing the environment information gained lots of attention due to the environment impacting the propagation characteristics of the wireless channel. This paper presents an environment information-based channel prediction (EICP) method for connecting the environment with the channel assisted by the graph neural networks (GNN). Firstly, the effective scatterers (ESs) producing paths and the primary scatterers (PSs) generating single propagation paths are detected by building the scatterer-centered communication environment graphs (SC-CEGs), which can simultaneously preserve the structure information and highlight the pending scatterer. The GNN-based classification model is implemented to distinguish ESs and PSs from other scatterers. Secondly, large-scale parameters (LSP) and small-scale parameters (SSP) are predicted by employing the GNNs with multi-target architecture and the graphs of detected ESs and PSs. Simulation results show that the average normalized mean squared error (NMSE) of LSP and SSP predictions are 0.12 and 0.008, which outperforms the methods of linear data learning.

The number of IPv6 routes in todays backbone routers has grown rapidly, which has put tremendous pressure on route lookup and storage. Based on the analysis of IPv6 address prefix length and distribution characteristics, this paper proposes an IPv6 route lookup architecture called LPR-Trie. The core idea of the algorithm is to utilize more spaces and accelerate routing lookup. Moreover, we put forward the concept of virtual nodes, and leverage the link between virtual nodes and ordinary nodes to accelerate routing lookup. We provide the longest prefix routing entry (LPR) calculation algorithm to achieve the longest prefix match. The experimental results show that the virtual node mechanism increases the search speed up to 244%, and the virtual nodes have better stability by setting an appropriate keep-alive time according to the characteristics of actual traffic. This paper shows that our design improves the routing lookup speed and have better memory utilization.

With the increasing maritime activities, a great demand of wide-area maritime digital data services is needed. Therefore, Narrowband Internet of Things (NB-IoT) that can provide wide coverage has been expected as an application for maritime communication networks (MCNS). In this paper, we aim to enhance the spectral efficiency in NB-IoT by reducing the cyclic prefix (CP) overhead in random access signal without causing interference. The key point of the proposed scheme is the symbols transmitted for multiple times repeatedly in NB-IoT. Specifically, all CP are removed and multi-path fading effect is eliminated by using a repeated symbol to cover the disturbed symbol to construct a circular convolution structure of the channel with the same effect as adding CP. In addition, a single-tap equalization is still appropriate. To validate the effectiveness of the proposed scheme, simulation results are carried out with respect to the bit error ratio (BER).

Edge intelligence is anticipated to underlay the pathway to connected intelligence for 6G networks, but the organic confluence of edge computing and artificial intelligence still needs to be carefully treated. To this end, this article discusses the concepts of edge intelligence from the semantic cognitive perspective. Two instructive theoretical models for edge semantic cognitive intelligence (ESCI) are first established. Afterwards, the ESCI framework orchestrating deep learning with semantic communication is discussed. Two representative applications are present to shed light on the prospect of ESCI in 6G networks. Some open problems are finally listed to elicit the future research directions of ESCI.

In this paper, we investigate the spectrum sensing performance of a distributed satellite clusters (DSC) under perturbation, aiming to enhance the sensing ability of weak signals in the coexistence of strong and weak signals. Specifically, we propose a cooperative beamforming (BF) algorithm though random antenna array theory to fit the location characteristic of DSC and derive the average far-field beam pattern under perturbation. Then, a constrained optimization problem with maximizing the signal to interference plus noise ratio (SINR) is modeled to obtain the BF weight vectors, and an approximate expression of SINR is presented in the presence of the mismatch of signal steering vector. Finally, we derive the closed-form expression of the detection probability for the considered DSC over Shadowed-Rician fading channels. Simulation results are provided to validate our theoretical analysis and to characterize the impact of various parameters on the system performance.

This paper presents a ZUC-256 stream cipher algorithm hardware system in order to prevent the advanced security threats for 5G wireless network. The main innovation of the hardware system is that a six-stage pipeline scheme comprised of initialization and work stage is employed to enhance the solving speed of the critical logical paths. Moreover, the pipeline scheme adopts a novel optimized hardware structure to fast complete the Mod($2^{31}$-1) calculation. The function of the hardware system has been validated experimentally in detail. The hardware system shows great superiorities. Compared with the same type system in recent literatures, the logic delay reduces by 47% with an additional hardware resources of only 4 multiplexers, the throughput rate reaches 5.26 Gbps and yields at least 45% better performance, the throughput rate per unit area increases 14.8%. The hardware system provides a faster and safer encryption module for the 5G wireless network.

Motivated by 5th generation wireless systems (5G), a large number of emerging applications appear, which put forward higher requirements for the task's transmission determinacy, which refers to the delay and jitter. To satisfy the deterministic requirement, mobile edge computing (MEC) is envisioned as a promising technique for reducing the end-to-end delay significantly. In this paper, we consider delay-sensitive task and jitter-sensitive task, and then formulate the joint communications and computing optimization problem under the latency, the total bandwidth, the total transmission power of base station (BS) and the computing ability of the MEC server constraints to minimize the delay and jitter in a multi-user MEC system. Because of the problems are non-convex, we decouple them into some subproblems and propose the corresponding algorithms to obtain a suboptimal solution. Finally, numerical results show that the proposed algorithms have a significant performance gain over the traditional solution in terms of the delay and the jitter.

The convergence of information, communication, and data technologies (ICDT) has been identified as one of the developing trends of the sixth generation(6G) network. Service-based architecture (SBA) as one of the promising information technology, has been preliminarily introduced into the fifth generation (5G) core network (CN) and successfully commercialized, which verifies its feasibility and effectiveness. However, SBA mainly focuses on the control plane of CN at present and the SBA-CN user plane is being studied by the industry. In addition to further evolving the SBA-CN, SBA radio access network (RAN) should also be considered to enable an end-toend SBA, so as to meet more comprehensive and extreme requirements of future applications, as well as support fast rollout requirements of RAN devices.

The space-air-ground integrated network (SAGIN) is regarded as the key approach to realize global coverage in future network and it reaches broad access for various services. Being the new paradigm of service, immersive media (IM) has attracted users' attention for its virtualization, but it poses challenges to network performance, e.g. bandwidth, rate, latency. However, the SAGIN has limitations in supporting IM services, such as 4K/8K video, virtual reality, and interactive games. In this paper, a novel service customized SAGIN architecture for IM applications (SAG-IM) is proposed, which achieves content interactive and real-time communication among terminal users. State-of-the-art research is investigated in detail to facilitate the combination of SAGIN and service customized technology, which provides end-to-end differentiated services for users. Besides, the functional components of SAG-IM contain the infrastructure layer, perception layer, intelligence layer, and application layer, reaching the capabilities of intelligent management of the network. Moreover, to provide IM content with ultra-high-definition and high frame rate for the optimal user experience, the promising key technologies on intelligent routing and delivery are discussed. The performance evaluation shows the superiority of SAG-IM in supporting IM service. Finally, the prospects in practical application are highlighted.

with the development of 5G, the future wireless communication network tends to be more and more intelligent. In the face of new service demands of communication in the future such as super-heterogeneous network, multiple communication scenarios, large number of antenna elements and large bandwidth, new theories and technologies of intelligent communication have been widely studied, among which Deep Learning (DL) is a powerful technology in artificial intelligence(AI). It can be trained to continuously learn to update the optimal parameters. This paper reviews the latest research progress of DL in intelligent communication, and emphatically introduces five scenarios including Cognitive Radio (CR), Edge Computing (EC), Channel Measurement (CM), End to end Encoder/Decoder (EED) and Visible Light Communication (VLC). The prospect and challenges of further research and development in the future are also discussed.

Mobile edge computing is trending nowadays for its computation efficiency and privacy. The rapid development of e-commerce show great interest in mobile edge computing due to numerous rise of small and middle-sized enterprises(SMEs) in the internet. This paper predicts the overall sales volume of the enterprise through the classic ARIMA model, and notes that the behavior and arrival differences between the new and old customer groups will affect the accuracy of our forecasts, so we then use Pareto/NBD to explore the repeated purchases of customers at the individual level of the old customer and the SVR model to predict the arrival of new customers, thus helping the enterprise to make layered and accurate marketing of new and old customers through machine learning . In general, machine learning relies on powerful computation and storage resources, while mobile edge computing typically provides limited computation resources locally. Therefore, it is essential to combine machine learning with mobile edge computing to further promote the proliferation of data analysis among SMEs.

The theory and experiment of quantum information have been studied extensively in recent years, and the feasibility of quantum communication has been proved. Although the fundamental technology is not yet mature, research on quantum internet should be conducted. To implement quantum internet, an architecture that describes how quantum nodes are linked to form networks and how protocol functions are vertically composed need to be developed urgently. In this paper, we present a novel design of a cluster-based structure to describe how quantum nodes are interconnected, and how the structure can improve the performance of qubit transmission and reduce the network complexity. The idea of the quantum local area network (QLAN) is proposed as an essential component of the quantum internet. Besides, each quantum repeater links to neighboring repeaters to form a core network, and multiple QLANs are connected through the core network. The core network can be grouped into different hierarchical quantum repeater networks according to needed service requirements. For the sake of interoperability and fast prototyping, we adopt the idea of OSI layering model of the current Internet in the design of quantum internet. Finally, we elaborate on the composition of quantum nodes and the realization of end-to-end communication.

The Unmanned Aerial Vehicle (UAV) technologies are envisioned to play an important role in the era of Air-Space-Ground integrated networks. In this paper, we investigate the connectivity of a Flying Ad hoc Network (FANET) in the presence of a ground-based terminal. In particular, the connected probability of the UAV-to-UAV (U2U) link as well as that of the UAV-to-Ground (U2G) link in a three dimensional (3D) space are analyzed. Furthermore, to mitigate the aggregate interference from UAV individuals, a priority based power control scheme is implemented for enhancing the connectivity of both U2U and U2G links. Numerical results illustrate the effectiveness of the proposed analysis.

Fuzzing is an effective technique to find security bugs in programs by quickly exploring the input space of programs. To further discover vulnerabilities hidden in deep execution paths, the hybrid fuzzing combines fuzzing and concolic execution for going through complex branch conditions. In general, we observe that the execution path which comes across more and complex basic blocks may have a higher chance of containing a security bug. Based on this observation, we propose a hybrid fuzzing method assisted by static analysis for binary programs. The basic idea of our method is to prioritize seed inputs according to the complexity of their associated execution paths. For this purpose, we utilize static analysis to evaluate the complexity of each basic block and employ the hardware trace mechanism to dynamically extract the execution path for calculating the seed inputs' weights. The key advantage of our method is that our system can test binary programs efficiently by using the hardware trace and hybrid fuzzing. To evaluate the effectiveness of our method, we design and implement a prototype system, namely SHFuzz. The evaluation results show SHFuzz discovers more unique crashes on several real-world applications and the LAVA-M dataset when compared to the previous solutions.

In recent years, autonomous driving technology has made good progress, but the non-cooperative intelligence of vehicle for autonomous driving still has many technical bottlenecks when facing urban road autonomous driving challenges. V2I (Vehicle-to-Infrastructure) communication is a potential solution to enable cooperative intelligence of vehicles and roads. In this paper, the RGB-PVRCNN, an environment perception framework, is proposed to improve the environmental awareness of autonomous vehicles at intersections by leveraging V2I communication technology. This framework integrates vision feature based on PVRCNN. The normal distributions transform(NDT) point cloud registration algorithm is deployed both on onboard and roadside to obtain the position of the autonomous vehicles and to build the local map objects detected by roadside multi-sensor system are sent back to autonomous vehicles to enhance the perception ability of autonomous vehicles for benefiting path planning and traffic efficiency at the intersection. The field-testing results show that our method can effectively extend the environmental perception ability and range of autonomous vehicles at the intersection and outperform the PointPillar algorithm and the VoxelRCNN algorithm in detection accuracy.

Terahertz (THz) communications are envisioned as a key technology for the sixth-generation wireless communication system (6G). However, it is not practical to perform large-scale channel measurements with high degrees of freedom at THz frequency band. This makes empirical or stochastic modeling approaches relying on measurements no longer stand. In order to break through the bottleneck of scarce full-dimensional channel sounding measurements, this paper presents a novel paradigm for THz channel modeling towards 6G. With the core of high-performance ray tracing (RT), the presented paradigm requires merely quite limited channel sounding to calibrate the geometry and material electromagnetic (EM) properties of the three-dimensional (3D) environment model in the target scenarios. Then, through extensive RT simulations, the parameters extracted from RT simulations can be fed into either ray-based novel stochastic channel models or cluster-based standard channel model families. Verified by RT simulations, these models can generate realistic channels that are valuable for the design and evaluation of THz systems. Representing two ends of 6G THz use cases from microscopy to macroscopy, case studies are made for close-proximity communications, wireless connections on a desktop, and smart rail mobility, respectively. Last but not least, new concerns on channel modeling resulting from distinguishing features of THz wave are discussed regarding propagation, antenna array, and device aspects, respectively.