Hybrid NOMA Based MIMO Offloading for Mobile Edge Computing in 6G Networks

Non-orthogonal multiple access (NOMA), multiple-input multiple-output (MIMO) and mobile edge computing (MEC) are prominent technologies to meet high data rate demand in the sixth generation (6G) communication networks. In this paper, we aim to minimize the transmission delay in the MIMO-MEC in order to improve the spectral efficiency, energy efficiency, and data rate of MEC offloading. Dinkelbach transform and generalized singular value decomposition (GSVD) method are used to solve the delay minimization problem. Analytical results are provided to evaluate the performance of the proposed Hybrid-NOMA-MIMO-MEC system. Simulation results reveal that the H-NOMA-MIMO-MEC system can achieve better delay performance and lower energy consumption compared to OMA.

Network coding with diversity and outdated channel state information

Physical-layer network coding （PNC） has the potential to significantly improve the throughput of wireless networks where the channels can be modeled as additive white Gaussian noise （AWGN） channel. As extending to mul- tiple channels, this technique requires both amplitude and phase compensation at each transmitter and will lead to inef- ficient systems yielding no diversity even with perfect channel state information （CSI）. In order to avoid these limita- tions, we apply network coding with diversity （NCD） to achieve a form of selection diversity and extend NCD to coop- erative multiple access channels in this paper. However, in practical wireless communication systems, the CSI could become outdated due to the difference between the CSI used in the relay selection and data transmission phases. Hence, the selected relay may not be the best one during data transmission phase due to the dynamic change in the wireless channels. Therefore, we first explore the relation between the present and past CSIs. Exploiting this relationship, the NCD scheme with outdated CSI is investigated based on the past CSI. To evaluate the performance of this scheme, an information-theoretic metric, namely the outage capacity, is studied under this condition.

Spectrum and energy efficient multi-antenna spectrum sensing for green UAV communication

Unmanned Aerial Vehicle(UAV)communication is a promising technology that provides swift and flexible ondemand wireless connectivity for devices without infrastructure support.With recent developments in UAVs,spectrum and energy efficient green UAV communication has become crucial.To deal with this issue,Spectrum Sharing Policy(SSP)is introduced to support green UAV communication.Spectrum sensing in SSP must be carefully formulated to control interference to the primary users and ground communications.In this paper,we propose spectrum sensing for opportunistic spectrum access in green UAV communication to improve the spectrum utilization efficiency.Different from most existing works,we focus on the problem of spectrum sensing of randomly arriving primary signals in the presence of non-Gaussian noise/interference.We propose a novel and improved p-norm-based spectrum sensing scheme to improve the spectrum utilization efficiency in green UAV communication.Firstly,we construct the p-norm decision statistic based on the assumption that the random arrivals of signals follow a Poisson process.Then,we analyze and derive the approximate analytical expressions of the false-alarm and detection probabilities by utilizing the central limit theorem.Simulation results illustrate the validity and superiority of the proposed scheme when the primary signals are corrupted by additive non-Gaussian noise and arrive randomly during spectrum sensing in the green UAV communication.

Joint resource allocation for hybrid NOMA-assisted MEC in 6G networks

Multi-access Edge Computing(MEC)is an essential technology for expanding computing power of mobile devices,which can combine the Non-Orthogonal Multiple Access(NOMA)in the power domain to multiplex signals to improve spectral efficiency.We study the integration of the MEC with the NOMA to improve the computation service for the Beyond Fifth-Generation(B5G)and the Sixth-Generation(6G)wireless networks.This paper aims to minimize the energy consumption of a hybrid NOMA-assisted MEC system.In a hybrid NOMA system,a user can offload its task during a time slot shared with another user by the NOMA,and then upload the remaining data during an exclusive time duration served by Orthogonal Multiple Access(OMA).The original energy minimization problem is non-convex.To efficiently solve it,we first assume that the user grouping is given,and focuses on the one group case.Then,a multilevel programming method is proposed to solve the non-convex problem by decomposing it into three subproblems,i.e.,power allocation,time slot scheduling,and offloading task assignment,which are solved optimally by carefully studying their convexity and monotonicity.The derived solution is optimal to the original problem by substituting the closed expressions obtained from those decomposed subproblems.Furthermore,we investigate the multi-user case,in which a close-to-optimal algorithm with lowcomplexity is proposed to form users into different groups with unique time slots.The simulation results verify the superior performance of the proposed scheme compared with some benchmarks,such as OMA and pure NOMA.

EDGE ARTIFICIAL INTELLIGENCE IN 6G SYSTEMS:THEORY,KEY TECHNIQUES,AND APPLICATIONS

As the permeation of artificial intelligence(AI)in wireless applications,some data-driven and computing-intensive services are emerging,such as mobile high-definition AR/VR,and real-time fingertip interactions.Moreover,the application scenarios have been extended to penetrate the vertical industry,which have multi-dimension and diverse quality of service(QoS)requirements.Recently,the research and the development of 6G have been triggered,and much higher QoS requirements of data rate,latency,and connectivity will be raised.

A Security Enhancement Model Based on Switching Edge Strategy in Interdependent Heterogeneous Cyber-Physical Systems

With the advent of cross-domain interconnection,large-scale sensor network systems such as smart grids,smart homes,and intelligent transportation have emerged.These complex network systems often have a CPS(Cyber-Physical System)architecture and are usually composed of multiple interdependent systems.Minimal faults between interdependent networks may cause serious cascading failures between the entire system.Therefore,in this paper,we will explore the robustness detection schemes for interdependent networks.Firstly,by calculating the largest giant connected component in the entire system,the security of interdependent network systems under different attack models is analyzed.Secondly,a comparative analysis of the cascade failure mechanism between interdependent networks under the edge enhancement strategy is carried out.Finally,the simulation results verify the impact of system reliability under different handover edge strategies and show how to choose a better handover strategy to enhance its robustness.The further research work in this paper can also help design how to reduce the interdependence between systems,thereby further optimizing the interdependent network system’s structure to provide practical support for reducing the cascading failures.In the later work,we hope to explore our proposed strategies in the network model of real-world or close to real networks.

NEW ADVANCES IN NON-ORTHOGONAL MULTIPLE ACCESS

Multiple access is one of the core technologies in wireless communications,which enables wireless base stations to support a large number of terminal users and serve them simultaneously under stringent spectrum constraints.Orthogonal multiple access(OMA),as one multiple access method,has been prevailing since the first generation(1G)cellular system.However,the number of active users allowed access to the OMA system is strictly limited by the number of available orthogonal resources,since as required by OMA the communication resources allocated to different users are orthogonal in at least one radio resource dimension,e.g.,frequency,time,code,etc.Nowadays,with the rapid growth of mobile network and Internet of Things(IoT)this problem becomes more and more critical.

Achievable secrecy rate of bit-interleaved coded modulation schemes

We study the impact of various modulation mapping strategies and signal constellation shapes on the se- crecy rates achievable with bit-interleaved coded modulation （BICM） schemes. Transmission over an ergodic Rayleigh fading channel is assumed throughout this work. Various constellations and mapping techniques are considered in this work to maximize the capacity difference between the main channel and the eavesdropper channel, rather than to opti- mize the capacity of both channels. We show that in terms of achievable secrecy rate, Gray and Quasi-Gray mappings only perform Well at low SNR but outperformed by other mapping techniques when SNR increases. The proper design of signal mapping can significantly enhance the achievable secrecy rate in BICM schemes. It is indicated that the key parameter to the secrecy rate of a BICM system is the distance spectrum of Euclidean distances for mappings.

ARTIFICIAL INTELLIGENCE(AI)-DRIVEN SPECTRUM MANAGEMENT

Recent advances in communication and networking technologies are leading to a plethora of novel wireless services that range from unmanned aerial vehicle(UAV)communication to smart cognitive networks and massive Internet of Things(IoT)systems.Enabling these emerging applications over the fifth generation(5G)of wireless cellular systems requires meeting numerous challenges pertaining to spectrum sharing and management.In fact,most 5G applications will be highly reliant on intelligent spectrum management techniques,which should adapt to dynamic network environments while also guaranteeing high reliability and high quality-of-experience(QoE).

Guest editorial

With the rapid deployment of 5G communication networks,the sixthgeneration(6G)mobile network has attracted extensive attention from academia,industry,and government agencies,devoting to identifying critical drivers,performance requirements,and technological innovations.The progress of 6G networks in enhanced mobile broadband,massive machine-type communications and ultra-reliable and lowlatency communications will promote the fast development of new services,e.g.,Augmented Reality(AR),Virtual Reality(VR),holographic communications,Vehicle-to-Everything(V2X),self-driving cars,massive connections.Different services would have different features and requirements.In order to adapt to the ever-changing services,6G should not only be intelligent and open to support full broadband services,but also enhance its capabilities compared with 5G.This will challenge 6G from various aspects,such as rate,delay,coverage,access,energy consumption,interaction,reliability,and degree of intelligence.

An online anomaly detection method for stream data using isolation principle and statistic histogram

Online anomaly detection for stream data has been explored recently,where the detector is supposed to be able to perform an accurate and timely judgment for the upcoming observation.However,due to the inherent complex characteristics of stream data,such as quick generation,tremendous volume and dynamic evolution distribution,how to develop an effective online anomaly detection method is a challenge.The main objective of this paper is to propose an adaptive online anomaly detection method for stream data.This is achieved by combining isolation principle with online ensemble learning,which is then optimized by statistic histogram.Three main algorithms are developed,i.e.,online detector building algorithm,anomaly detecting algorithm and adaptive detector updating algorithm.To evaluate our proposed method,four massive datasets from the UCI machine learning repository recorded from real events were adopted.Extensive simulations based on these datasets show that our method is effective and robust against different scenarios.