RECENT DEVELOPMENT OF ERROR CONTROL CODES FOR FUTURE COMMUNICATION AND STORAGE SYSTEMS

The outstanding performance of error control codes has attracted the interest of the information and communications theory community and industry partners for more than 20 years since Turbo codes were invented.In recent years,there are more and more scenarios in modern communication and digital storage systems,such as wireless communications,optical communications,fl ash memories,computer hard drives,sensor networks,

Intelligent Task Offloading and Collaborative Computation in Multi-UAV-Enabled Mobile Edge Computing

This article establishes a three-tier mobile edge computing(MEC) network, which takes into account the cooperation between unmanned aerial vehicles(UAVs). In this MEC network, we aim to minimize the processing delay of tasks by jointly optimizing the deployment of UAVs and offloading decisions,while meeting the computing capacity constraint of UAVs. However, the resulting optimization problem is nonconvex, which cannot be solved by general optimization tools in an effective and efficient way. To this end, we propose a two-layer optimization algorithm to tackle the non-convexity of the problem by capitalizing on alternating optimization. In the upper level algorithm, we rely on differential evolution(DE) learning algorithm to solve the deployment of the UAVs. In the lower level algorithm, we exploit distributed deep neural network(DDNN) to generate offloading decisions. Numerical results demonstrate that the two-layer optimization algorithm can effectively obtain the near-optimal deployment of UAVs and offloading strategy with low complexity.

Cooperative Jamming for Physical Layer Security in Hybrid Satellite Terrestrial Relay Networks

To integrate the satellite communications with the LTE/5G services, the concept of Hybrid Satellite Terrestrial Relay Networks(HSTRNs) has been proposed. In this paper, we investigate the secure transmission in a HSTRN where the eavesdropper can wiretap the transmitted messages from both the satellite and the intermediate relays. To effectively protect the message from wiretapping in these two phases, we consider cooperative jamming by the relays, where the jamming signals are optimized to maximize the secrecy rate under the total power constraint of relays. In the first phase, the Maximal Ratio Transmission(MRT) scheme is used to maximize the secrecy rate, while in the second phase, by interpolating between the sub-optimal MRT scheme and the null-space projection scheme, the optimal scheme can be obtained via an efficient one-dimensional searching method. Simulation results show that when the number of cooperative relays is small, the performance of the optimal scheme significantly outperforms that of MRT and null-space projection scheme. When the number of relays increases, the performance of the null-space projection approaches that of the optimal one.

V2X-Communication Assisted Interference Minimization for Automotive Radars

With the development of automated driving vehicles, more and more vehicles will be fitted with more than one automotive radars, and the radar mutual interference will become very significant. Vehicle to everything (V2X) communication is a potential way for coordinating automotive radars and reduce the mutual interference. In this paper, we analyze the positional relation of the two radars that interfere with each other, and evaluate the mutual interference for different types of automotive radars based on Poisson point process (PPP). We also propose a centralized framework and the corresponding algorithm, which relies on V2X communication systems to allocate the spectrum resources for automotive radars to minimize the interference. The minimum spectrum resources required for zero-interference are analyzed for different cases. Simulation results validate the analysis and show that the proposed framework can achieve near-zero-interference with the minimum spectrum resources.

AI-Enhanced Constellation Design for NOMA System: A Model Driven Method

Grant-free Non-orthogonal Multiple Access(GF-NOMA)is a promising technology for massive access users and sporadic small-packet transmission for Beyond the 5th Generation mobile communication system(B5G)/the 6th Generation mobile communication system(6G).One of the key aspects in GF-NOMA system is the signature/constellation design.However,due to the channel variation and random activation of users,conventional optimization approaches seem unsuitable for such complex models.In this paper,as an initial attempt,we propose a human intelligence(HI)-guided artificial intelligence(AI)-enhanced signature/constellation design method.By separate design of modulation and power allocation inspired by prior knowledge,the proposed deep neuron network(DNN)for NOMA signature/constellation design not only has smaller size of DNN and less training data,but also has stronger interpretability.In the last section,via simulations we demonstrate that in terms of bit error rate,the proposed scheme can achieve significant performance gain over the conventional NOMA schemes.

HAS Dynamic Buffer-Driven Resource Management to Enhance QoE in Mobile Network

Hypertext transfer protocol(HTTP) adaptive streaming(HAS) plays a key role in mobile video transmission. Considering the multi-segment and multi-rate features of HAS, this paper proposes a buffer-driven resource management(BDRM) method to enhance HAS quality of experience(QoE) in mobile network. Different from the traditional methods only focusing on base station side without considering the buffer, the proposed method takes both station and client sides into account and end user's buffer plays as the drive of whole schedule process. The proposed HAS QoE influencing factors are composed of initial delay, rebuffering and quality level. The BDRM method decomposes the HAS QoE maximization problem into client and base station sides separately to solve it in multicell and multi-user video playing scene in mobile network. In client side, the decision is made based on buffer probe and rate request algorithm by each user separately. It guarantees the less rebuffering events and decides which HAS segment rate to fetch. While, in the base station side, the schedule of wireless resource is made to maximize the quality level of all access clients and decides the final rate pulled from HAS server. The drive of buffer and twice rate request schemes make BDRMtake full advantage of HAS's multi-segment and multi-rate features. As to the simulation results, compared with proportional fair(PF), Max C/I and traditional HAS schedule(THS) methods, the proposed BDRM method decreases rebuffering percent to 1.96% from 11.1% with PF and from 7.01% with THS and increases the mean MOS of all users to 3.94 from 3.42 with PF method and from 2.15 with Max C/I method. It also guarantees a high fairness with 0.98 from the view of objective and subjective assessment metrics.

Distributed Deep Learning for Cooperative Computation Offloading in Low Earth Orbit Satellite Networks

Low earth orbit(LEO) satellite network is an important development trend for future mobile communication systems, which can truly realize the“ubiquitous connection” of the whole world. In this paper, we present a cooperative computation offloading in the LEO satellite network with a three-tier computation architecture by leveraging the vertical cooperation among ground users, LEO satellites, and the cloud server, and the horizontal cooperation between LEO satellites. To improve the quality of service for ground users, we optimize the computation offloading decisions to minimize the total execution delay for ground users subject to the limited battery capacity of ground users and the computation capability of each LEO satellite. However, the formulated problem is a large-scale nonlinear integer programming problem as the number of ground users and LEO satellites increases, which is difficult to solve with general optimization algorithms. To address this challenging problem, we propose a distributed deep learning-based cooperative computation offloading(DDLCCO) algorithm, where multiple parallel deep neural networks(DNNs) are adopted to learn the computation offloading strategy dynamically. Simulation results show that the proposed algorithm can achieve near-optimal performance with low computational complexity compared with other computation offloading strategies.

Secure Transmission in Downlink Non-Orthogonal Multiple Access Based on Polar Codes

Non-orthogonal multiple access(NOMA)is deemed to have a superior spectral efficiency and polar codes have became the channel coding scheme for control channel of enhanced mobile broadband(eMBB)in the fifth generation(5G)communication systems.In this paper,NOMA combined with polar codes is used to achieve secure transmission.Both degraded wiretap channel and non-degraded wiretap channel are considered,where an eavesdropper intercepts the communication between base station(BS)and users.For the degraded wiretap channel scenario,a secure polar encoding scheme is proposed in NOMA systems with power allocation to achieve the maximum secrecy capacity.With regard to the nondegraded wiretap channel,a polar encoding scheme with multiple-input-single-output(MISO)system is proposed,where artificial noise is generated at BS to confuse the eavesdropper’s channel via transmit beamforming.The security and the secure rate are employed respectively in order to measure the secrecy performance.We prove that the proposed schemes for each scenario can achieve the secure rate and can transmit the signal securely and reliably.The simulation results show that the eavesdropper hardly decoding the secure signal when the legitimate receiver can decode the signal with very low block error rate(BLER).

Radio Link Parameters Based QoE Measurement of Voice Service in GSM Network

Recently, Quality of Experience (QoE) of voice service has been paid more attentions because it represents the performance of voice service subjectively perceived by the end users. And speech quality is commonly used to measure the QoE value. In this paper, a speech quality assessment algorithm is proposed for GSM network, aiming to predict and monitor QoE of voice service based on radio link parameters with low complexity for operators. Multiple Linear Regression (MLR) and Principal Component Analysis (PCA) are combined and used to establish the mapping model from radio link parameters to speech quality. Data set for model training and testing is obtained from real commercial network of China Mobile. The experimental results show that with sufficient training data, this algorithm can predict radio speech quality with high accuracy and could be used to monitor speech quality of mobile network in real time.

6G无线覆盖扩展技术

根据国际电信联盟关于IMT-2030愿景,第6代移动通信系统(the 6th generation mobile networks,6G)的覆盖服务需求将从单场景覆盖向多场景覆盖扩展,6G基础设施的部署也将逐步从2D覆盖向3D覆盖扩展、从局部覆盖向全球覆盖扩展、从中低频段融合使用向更高频段按需开启.上述需求使得6G在提升容量的同时,需要进一步考虑无线覆盖扩展需求.本文针对如何在6G网络结构时空尺度跨度大、全场景业务需求差异大、超密集覆盖能耗大等关键挑战下实现容量和能效约束下的覆盖能力扩展这一重大科学问题,首先提出了面向6G无线覆盖扩展的智能柔性组网架构;其次研究了面向6G无线覆盖扩展的关键技术,包括面向6G广域覆盖的多维立体空天地覆盖扩展技术、面向深度覆盖的超密集异构覆盖扩展技术和面向6G平滑度覆盖的超高速移动覆盖扩展技术;接着分析了基于语义通信的覆盖扩展技术;最后给出了6G全场景无线覆盖扩展仿真验证,通过定义6G无线覆盖扩展技术指标体系,进行了典型场景的覆盖性能仿真验证.

On optimization of cooperative MIMO for underlaid secrecy Industrial Internet of Things

In this paper,physical layer security techniques are investigated for cooperative multi-input multi-output(C-MIMO),which operates as an underlaid cognitive radio system that coexists with a primary user(PU).The underlaid secrecy paradigm is enabled by improving the secrecy rate towards the C-MIMO receiver and reducing the interference towards the PU.Such a communication model is especially suitable for implementing Industrial Internet of Things(IIoT)systems in the unlicensed spectrum,which can trade off spectral efficiency and information secrecy.To this end,we propose an eigenspace-adaptive precoding(EAP)method and formulate the secrecy rate optimization problem,which is subject to both the single device power constraint and the interference power constraint.This precoder design is enabled by decomposing the original optimization problem into eigenspace selection and power allocation sub-problems.Herein,the eigenvectors are adaptively selected by the transmitter according to the channel conditions of the underlaid users and the PUs.In addition,a simplified EAP method is proposed for large-dimensional C-MIMO transmission,exploiting the additional spatial degree of freedom for a low-complexity secrecy precoder design.Numerical results show that by transmitting signal and artificial noise in the properly selected eigenspace,C-MIMO can eliminate the secrecy outage and outperforms the fixed eigenspace precoding methods.Moreover,the proposed simplified EAP method for the large-dimensional C-MIMO can significantly improve the secrecy rate.

Secure Transmission Scheme for Parallel Relay Channels Based on Polar Coding

This paper considers the use of polar codes to enable secure transmission over parallel relay channels.By exploiting the properties of polar codes over parallel channels, a polar encoding algorithm is designed based on Channel State Information（CSI） between the legitimate transmitter（Alice） and the legitimate receiver（Bob）.Different from existing secure transmission schemes, the proposed scheme does not require CSI between Alice and the eavesdropper（Eve）. The proposed scheme is proven to be reliable and shown to be capable of transmitting information securely under Amplify-and-Forward（AF） relay protocol, thereby providing security against passive and active attackers.

A QoE-based jointly subcarrier and power allocation for multiuser multiservice networks

Quality of experience(QoE) is widely applied to reflect user's satisfaction of the network service,which exactly conforms to the user-centric concept in 5G. In this paper, we propose a QoE-based subcarrier and power allocation algorithm for the downlink transmission of a multiuser multiservice system. For the subcarrier allocation algorithm, the rate proportional fairness factor is defined to ensure the fairness between users. Based on different QoE models of three services, i.e., file down(FD), video streaming and voice over internet protocol(VOIP), a multi-objective optimization method is exploited to allocate the power resource by minimizing the total power consumption and maximizing the mean opinion score(MOS) value of users simultaneously. Simulation results indicate that the proposed algorithm has less power consumption and higher QoE performance than the traditional proportional fairness(PF) algorithm. In addition, the proposed algorithm can achieve nearly the same fairness performance as the PF algorithm. Moreover, when the number of subcarriers becomes larger, the power assumption will be less but with little influence on both the QoE and fairness performances.

Coverage performance of the multilayer UAV-terrestrial HetNet with CoMP transmission scheme

To support the ubiquitous connectivity requirement of sixth generation communication,unmanned aerial vehicles(UAVs)play a key role as a major part of the future communication networks.One major issue in UAV communications is the interference resulting from spectrum sharing and line-of-sight links.Recently,the application of the coordinated multipoint(CoMP)technology has been proposed to reduce the interference in the UAV-terrestrial heterogeneous network(HetNet).In this paper,we consider a three-dimensional(3D)multilayer UAV-terrestrial HetNet,where the aerial base stations(ABSs)are deployed at multiple different altitudes.Using stochastic geometry,we develop a tractable mathematical framework to characterize the aggregate interference and evaluate the coverage probability of this HetNet.Our numerical results show that the implementation of the CoMP scheme can effectively reduce the interference in the network,especially when the density of base stations is relatively large.Furthermore,the system parameters of the ABSs deployed at higher altitudes dominantly influence the coverage performance of the considered 3D HetNet.