QoS-Aware Offloading Based on Communication-Computation Resource Coordination for 6G Edge Intelligence

Driven by the demands of diverse artificial intelligence(AI)-enabled application,Mobile Edge Computing(MEC)is considered one of the key technologies for 6G edge intelligence.In this paper,we consider a serial task model and design a quality of service(QoS)-aware task offloading via communication-computation resource coordination for multi-user MEC systems,which can mitigate the I/O interference brought by resource reuse among virtual machines.Then we construct the system utility measuring QoS based on application latency and user devices’energy consumption.We also propose a heuristic offloading algorithm to maximize the system utility function with the constraints of task priority and I/O interference.Simulation results demonstrate the proposed algorithm’s significant advantages in terms of task completion time,terminal energy consumption and system resource utilization.

Integration of Communication and Computing in Blockchain-Enabled Multi-Access Edge Computing Systems

Blockchain and multi-access edge com-puting(MEC)are two emerging promising tech-nologies that have received extensive attention from academia and industry.As a brand-new information storage,dissemination and management mechanism,blockchain technology achieves the reliable transmis-sion of data and value.While as a new computing paradigm,multi-access edge computing enables the high-frequency interaction and real-time transmission of data.The integration of communication and com-puting in blockchain-enabled multi-access edge com-puting networks has been studied without a systemat-ical view.In the survey,we focus on the integration of communication and computing,explores the mu-tual empowerment and mutual promotion effects be-tween the blockchain and MEC,and introduces the resource integration architecture of blockchain and multi-access edge computing.Then,the paper sum-marizes the applications of the resource integration ar-chitecture,resource management,data sharing,incen-tive mechanism,and consensus mechanism,and ana-lyzes corresponding applications in real-world scenar-ios.Finally,future challenges and potentially promis-ing research directions are discussed and present in de-tail.

System Outage Probability and Diversity Analysis of a SWIPT Based Two-Way DF Relay Network Under Transceiver Hardware Impairments

This paper investigates the system outage performance of a simultaneous wireless information and power transfer(SWIPT)based two-way decodeand-forward(DF)relay network,where potential hardware impairments(HIs)in all transceivers are considered.After harvesting energy and decoding messages simultaneously via a power splitting scheme,the energy-limited relay node forwards the decoded information to both terminals.Each terminal combines the signals from the direct and relaying links via selection combining.We derive the system outage probability under independent but non-identically distributed Nakagami-m fading channels.It reveals an overall system ceiling(OSC)effect,i.e.,the system falls in outage if the target rate exceeds an OSC threshold that is determined by the levels of HIs.Furthermore,we derive the diversity gain of the considered network.The result reveals that when the transmission rate is below the OSC threshold,the achieved diversity gain equals the sum of the shape parameter of the direct link and the smaller shape parameter of the terminalto-relay links;otherwise,the diversity gain is zero.This is different from the amplify-and-forward(AF)strategy,under which the relaying links have no contribution to the diversity gain.Simulation results validate the analytical results and reveal that compared with the AF strategy,the SWIPT based two-way relaying links under the DF strategy are more robust to HIs and achieve a lower system outage probability.

Joint 3D Trajectory and Resource Optimization for A UAV Relay-Assisted Cognitive Radio Network

In this paper,we consider a new spectrum sharing scenario for a cognitive relay network,where a secondary unmanned aerial vehicle(UAV)relay receives information from the ground secondary base station(SBS)and transmits information to the ground secondary user(SU),coexisting with the primary users(PUs)at the same wireless frequency band.We investigate the optimization of the UAV relay’s three-dimensional(3D)trajectory to improve the communication throughput performance of the secondary network subject to the interference constraints of the PUs.The information throughput maximization problem is studied by jointly optimizing the UAV relay’s 3D trajectory and the transmit power of the SBS and the UAV,subject to the constraints on the velocity and elevation of the UAV relay,the maximum and average transmit power,and the information causality,as well as a set of interference temperature(IT)constraints.An efficient algorithm is proposed to solve the admittedly challenging non-convex problem by using the path discretization technique,the successive convex approximation technique and the alternating optimization method.Finally,simulation results are provided to show that our proposed design outperforms other benchmark schemes in terms of the throughput。

Energy-Efficient UAV Trajectory Design for Backscatter Communication: A Deep Reinforcement Learning Approach

Recently,backscatter communication(BC)has been introduced as a green paradigm for Internet of Things(IoT).Meanwhile,unmanned aerial vehicles(UAVs)can serve as aerial base stations(BSs)to enhance the performance of BC system thanks to their high mobility and flexibility.In this paper,we investigate the problem of energy efficiency(EE)for an energy-limited backscatter communication(BC)network,where backscatter devices(BDs)on the ground harvest energy from the wireless signal of a flying rotary-wing quadrotor.Specifically,we first reformulate the EE optimization problem as a Markov decision process(MDP)and then propose a deep reinforcement learning(DRL)algorithm to design the UAV trajectory with the constraints of the BD scheduling,the power reflection coefficients,the transmission power,and the fairness among BDs.Simulation results show the proposed DRL algorithm achieves close-to-optimal performance and significant EE gains compared to the benchmark schemes.

Passive Beamforming Design for Intelligent Reflecting Surface Assisted MIMO Systems

Intelligent reflecting surfaces(IRSs)constitute passive devices,which are capable of adjusting the phase shifts of their reflected signals,and hence they are suitable for passive beamforming.In this paper,we conceive their design with the active beamforming action of multiple-input multipleoutput(MIMO)systems used at the access points(APs)for improving the beamforming gain,where both the APs and users are equipped with multiple antennas.Firstly,we decouple the optimization problem and design the active beamforming for a given IRS configuration.Then we transform the optimization problem of the IRS-based passive beamforming design into a tractable non-convex quadratically constrained quadratic program(QCQP).For solving the transformed problem,we give an approximate solution based on the technique of widely used semidefinite relaxation(SDR).We also propose a low-complexity iterative solution.We further prove that it can converge to a locally optimal value.Finally,considering the practical scenario of discrete phase shifts at the IRS,we give the quantization design for IRS elements on basis of the two solutions.Our simulation results demonstrate the superiority of the proposed solutions over the relevant benchmarks.

Distributionally robust optimization based chance-constrained energy management for hybrid energy powered cellular networks

Energy harvesting has been recognized as a promising technique with which to effectively reduce carbon emis-sions and electricity expenses of base stations.However,renewable energy is inherently stochastic and inter-mittent,imposing formidable challenges on reliably satisfying users'time-varying wireless traffic demands.In addition,the probability distribution of the renewable energy or users’wireless traffic demand is not always fully known in practice.In this paper,we minimize the total energy cost of a hybrid-energy-powered cellular network by jointly optimizing the energy sharing among base stations,the battery charging and discharging rates,and the energy purchased from the grid under the constraint of a limited battery size at each base station.In solving the formulated non-convex chance-constrained stochastic optimization problem,a new ambiguity set is built to characterize the uncertainties in the renewable energy and wireless traffic demands according to interval sets of the mean and covariance.Using this ambiguity set,the original optimization problem is transformed into a more tractable second-order cone programming problem by exploiting the distributionally robust optimization approach.Furthermore,a low-complexity distributionally robust chance-constrained energy management algo-rithm,which requires only interval sets of the mean and covariance of stochastic parameters,is proposed.The results of extensive simulation are presented to demonstrate that the proposed algorithm outperforms existing methods in terms of the computational complexity,energy cost,and reliability.

Efficient self-testing system for quantum computations based on permutations

Verification in quantum computations is crucial since quantum systems are extremely vulnerable to the environment.However,verifying directly the output of a quantum computation is difficult since we know that efficiently simulating a large-scale quantum computation on a classical computer is usually thought to be impossible.To overcome this difficulty,we propose a self-testing system for quantum computations,which can be used to verify if a quantum computation is performed correctly by itself.Our basic idea is using some extra ancilla qubits to test the output of the computation.We design two kinds of permutation circuits into the original quantum circuit:one is applied on the ancilla qubits whose output indicates the testing information,the other is applied on all qubits(including ancilla qubits) which is aiming to uniformly permute the positions of all qubits.We show that both permutation circuits are easy to achieve.By this way,we prove that any quantum computation has an efficient self-testing system.In the end,we also discuss the relation between our self-testing system and interactive proof systems,and show that the two systems are equivalent if the verifier is allowed to have some quantum capacity.

Using Reduced Graphene Oxide to Generate Q-Switched Pulses in Er-Doped Fiber Laser

Using the reduced graphene oxide（rGO） as a saturable absorber（SA） in an Er-doped fiber（EDF） laser cavity,we obtain the Q-switching operation. The rGO SA is prepared by depositing the GO on fluorine mica（FM） using the thermal reduction method. The modulation depth of rGO/FM is measured to be 3.2%. By incorporating the rGO/FM film into the EDF laser cavity, we obtain stable Q-switched pulses. The shortest pulse duration is3.53 μs, and the maximum single pulse energy is 48.19 nJ. The long-term stability of working is well exhibited.The experimental results show that the rGO possesses potential photonics applications.

Load Balancing Algorithm of Controller Based on SDN Architecture Under Machine Learning

With the rapid development of cloud computing and other related services,higher requirements are put forward for network transmission and delay.Due to the inherent distributed characteristics of traditional networks,machine learning technology is diffcult to be applied and deployed in network control.The emergence of SDN technology provides new opportunities and challenges for the application of machine learning technology in network management.A load balancing algorithm of Internet of things controller based on data center SDN architecture is proposed.The Bayesian network is used to predict the degree of load congestion,combining reinforcement learning algorithm to make optimal action decision,self-adjusting parameter weight to adjust the controller load congestion,to achieve load balance,improve network security and stability.

Max-min rate optimization for multi-user MISO-OFDM systems assisted by RIS with a wideband model

Reconfigurable intelligent surfaces(RISs)have the capability to change the wireless environment smartly Considering the attenuation of subchannels and crowding users involved in the wideband system,we introduce RISs into the multi-user multi-input single-output(MU-MISO)system with orthogonal frequency division multiplexing(OFDM)for performance enhancement.Maximizing the minimum rate of dense users in an MU-MISO-OFDM system assisted by RIS with an approximate practical model is formulated as the joint optimization problem involving subcarrier allocation,transmit precoding(TPC)matrices at the base station,and RIS passive beamforming.A coalition-game subcarrier allocation(CSA)algorithm is proposed to solve space–frequency resource allocation on subcarriers,which reforms the interference topology among dense users.Fractional programming and convex optimization method are used to optimize the TPC matrices and the RIS passive beamforming,which improves the spectral efficiency synthetically across all subchannels in the wideband system.Simulation results indicate that the CSA algorithm provides a significant gain for dense users.Besides,the proposed joint optimization method shows the considerable advantage of the RISs in the MU-MISO-OFDM system.

Acoustic Goos-H?nchen effect

We report two models of the lateral displacement of acoustic-wave scattering on a fluid-solid interface that reveal an acoustic analog of the Goos-Hainchen effect in optics. This acoustic analog is called the acoustic Goos-Hainchen effect. Using newly proposed models, we made numerical calculations for the system ofa water-Perspex interface. Specifically, in the post-critical-angle region, we observed a lateral displacement （and transition time） of the reflected P-wave with respect to the incident P-wave. The first arrival of the acoustic signal from the interface is found to be a reflected P-wave rather than the sliding-refraction P-wave usually described in traditional acoustic-logging sliding P-wave theory. For both proposed models, the effective propagation speed of the reflected P-wave along the interface depends on not only the physical properties of the interracial media but also the incident angle. These observations are intriguing and warrant further investigation.

Research on ECG classification based on transfer learning

Concerning current deep learning-based electrocardiograph(ECG) classification methods, there exists domain discrepancy between the data distributions of the training set and the test set in the inter-patient paradigm. To reduce the negative effect of domain discrepancy on the classification accuracy of ECG signals, this paper incorporates transfer learning into the ECG classification, which aims at applying the knowledge learned from the training set to the test set. Specifically, this paper first develops a deep domain adaptation network(DAN) for ECG classification based on the convolutional neural network(CNN). Then, the network is pre-trained with training set data obtained from the famous Massachusetts Institute of Technology-Beth Israel Hospital(MIT-BIH) ECG arrhythmia database. On this basis, by minimizing the multi-kernel maximum mean discrepancy(MK-MMD) between the data distributions of the training set and the test set, the pre-trained network is adjusted to learn transferable feature representations. Finally, with the low-density separation of unlabeled target data, the feature representations are more transferable. The extensive experimental results show that the proposed domain adaptation method has reached a 7.58% improvement in overall classification accuracy on the test set, and achieves competitive performance with other state-of-the-arts.

Aerial intelligent reflecting surface for secure wireless networks:Secrecy capacity and optimal trajectory strategy

This work investigates the potential of the aerial intelligent reflecting surface(AIRS)in secure communication,where an intelligent reflecting surface(IRS)carried by an unmanned aerial vehicle(UAV)is utilized to help the communication between the ground nodes.Specifically,we formulate the joint design of the AIRS’s deployment and the phase shift to maximize the secrecy rate.To solve the non-convex objective,we develop an alternating optimization(AO)approach,where the phase shift optimization is solved by the Riemannian manifold optimization(RMO)method,while the deployment optimization is handled by the successive convex approximation(SCA)technique.Furthermore,to reduce the computational complexity of the RMO method,an element-wise block coordinate descent(EBCD)based method is employed.Simulation results verify the effect of AIRS in improving the communication security,as well as the importance of designing the deployment and phase shift properly.