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Slicing capacity-centered mode selection and resource optimization for network-assisted full-duplex cell-free distributed massive MIMO systems

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Science china(Information Sciences) 2024年第1期67卷 203-220页

作者： Jie WANG Jiamin LI Pengcheng ZHU Dongming WANG Hongbiao ZHANG Yue HAO Bin SHENG National mobile Communications research Laboratory Southeast University 2. Purple Mountain Laboratories china mobile research institute

Network-assisted full-duplex(NAFD) cell-free distributed massive multiple-input multipleoutput(MIMO) systems enable uplink(UL) and downlink(DL) communications within the same timefrequency resources,which potentially reduce latency by avoiding the overhead of switching UL/DL modes.However,how to choose UL/DL modes remains an important factor affecting system performance.With the dramatic increase in the number of users and access points(APs),massive access brings significant overhead in the mode selection.Additionally,the different quality of service(QoS) among users also makes the effective utilization of resources difficult.As one of the most promising technologies in sixth-generation(6G),network slicing enables the adaptive configuration of limited UL/DL resources through the resource isolation assisted NAFD technique.Therefore,we propose a slicing capacity-centered scheme.Under this scheme,APs are motivated by slicing requirements and associated slices to form different subsystems.Collaborative mode selection and resource allocation are performed within each subsystem to reduce overhead and improve resource utilization.To implement this scheme efficiently,a double-layer deep reinforcement learning(DRL)mechanism is used to realize the joint optimization of mode selection and resource allocation.Simulation results show that the slicing capacity-centered scheme can effectively improve resource utilization and reduce overhead.

关键词： network-assisted full-duplex network slicing mode selection resource optimization deep reinforcement learning

Reputation-based joint optimization of user satisfaction and resource utilization in a computing force network

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Frontiers of Information Technology & Electronic Engineering 2024年第5期25卷 685-700页

作者： Yuexia FU Jing WANG Lu LU Qinqin TANG Sheng ZHANG china mobile research institute Beijing 100053China Purple Mountain Laboratories Nanjing 211111China china mobile Communications Corporation Beijing 100033China

Under the development of computing and network convergence,considering the computing and network resources of multiple providers as a whole in a computing force network(CFN)has gradually become a new trend.However,since each computing and network resource provider(CNRP)considers only its own interest and competes with other CNRPs,introducing multiple CNRPs will result in a lack of trust and difficulty in unified scheduling.In addition,concurrent users have different requirements,so there is an urgent need to study how to optimally match users and CNRPs on a many-to-many basis,to improve user satisfaction and ensure the utilization of limited resources.In this paper,we adopt a reputation model based on the beta distribution function to measure the credibility of CNRPs and propose a performance-based reputation update model.Then,we formalize the problem into a constrained multi-objective optimization problem and find feasible solutions using a modified fast and elitist non-dominated sorting genetic algorithm(NSGA-II).We conduct extensive simulations to evaluate the proposed algorithm.Simulation results demonstrate that the proposed model and the problem formulation are valid,and the NSGA-II is effective and can find the Pareto set of CFN,which increases user satisfaction and resource utilization.Moreover,a set of solutions provided by the Pareto set give us more choices of the many-to-many matching of users and CNRPs according to the actual situation.

关键词： Computing force network Resource scheduling Performance-based reputation User satisfaction

Challenges and Opportunities of Sub-6 GHz Integrated Sensing and Communications for 5G-Advanced and Beyond

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Chinese Journal of Electronics 2024年第2期33卷 323-325页

作者： Yuhong HUANG china mobile research institute china mobile Communications Group Co. Ltd

The integrated sensing and communications(ISAC) technology has been perceived as a key feature of5G-Advanced and beyond mobile communication networks. Compared with sensing in millimeter-wave bands, sensing at sub-6 GHz band is an exceptional incentive to promote the industrialization process of ISAC due to its incomparable advantages in industrial development, especially for intelligent transportation and smart drone networks. This paper elaborates on the top challenges of sub-6 GHz ISAC technologies, as well as the potential solutions to improve the sensing capability.

关键词： 5G-Advanced and beyond Integrated sensing and communications Sub-6 GHz Intelligent transportation Smart drone networks

Cooperative User-Scheduling and Resource Allocation Optimization for Intelligent Reflecting Surface Enhanced LEO Satellite Communication

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china Communications 2024年第2期21卷 227-244页

作者： Meng Meng Bo Hu Shanzhi Chen Jianyin Zhang State Key Lab of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China State Key Lab of Wireless mobile Communication China Academy of Telecommunication TechnologyBeijing 100191China research institute of china mobile Beijing 100053China

Lower Earth Orbit(LEO) satellite becomes an important part of complementing terrestrial communication due to its lower orbital altitude and smaller propagation delay than Geostationary satellite. However, the LEO satellite communication system cannot meet the requirements of users when the satellite-terrestrial link is blocked by obstacles. To solve this problem, we introduce Intelligent reflect surface(IRS) for improving the achievable rate of terrestrial users in LEO satellite communication. We investigated joint IRS scheduling, user scheduling, power and bandwidth allocation(JIRPB) optimization algorithm for improving LEO satellite system throughput.The optimization problem of joint user scheduling and resource allocation is formulated as a non-convex optimization problem. To cope with this problem, the nonconvex optimization problem is divided into resource allocation optimization sub-problem and scheduling optimization sub-problem firstly. Second, we optimize the resource allocation sub-problem via alternating direction multiplier method(ADMM) and scheduling sub-problem via Lagrangian dual method repeatedly.Third, we prove that the proposed resource allocation algorithm based ADMM approaches sublinear convergence theoretically. Finally, we demonstrate that the proposed JIRPB optimization algorithm improves the LEO satellite communication system throughput.

关键词： convex optimization intelligent reflecting surface LEO satellite communication OFDM

Combining graph neural network with deep reinforcement learning for resource allocation in computing force networks

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Frontiers of Information Technology & Electronic Engineering 2024年第5期25卷 701-712页

作者： Xueying HAN Mingxi XIE Ke YU Xiaohong HUANG Zongpeng DU Huijuan YAO School of Computer Science Beijing University of Posts and TelecommunicationsBeijing 100876China School of Artificial Intelligence Beijing University of Posts and TelecommunicationsBeijing 100876China Department of Infrastructure Network Technology research China Mobile Research InstituteBeijing 100032China

Fueled by the explosive growth of ultra-low-latency and real-time applications with specific computing and network performance requirements,the computing force network(CFN)has become a hot research subject.The primary CFN challenge is to leverage network resources and computing resources.Although recent advances in deep reinforcement learning(DRL)have brought significant improvement in network optimization,these methods still suffer from topology changes and fail to generalize for those topologies not seen in training.This paper proposes a graph neural network(GNN)based DRL framework to accommodate network trafic and computing resources jointly and efficiently.By taking advantage of the generalization capability in GNN,the proposed method can operate over variable topologies and obtain higher performance than the other DRL methods.

关键词： Computing force network Routing optimization Deep learning Graph neural network Resource allocation

OFDMA-Based Unsourced Random Access in LEO Satellite Internet of Things

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china Communications 2024年第1期21卷 13-23页

作者： Jiaqi Fang Gangle Sun Wenjin Wang Li You Rui Ding National mobile Communications research Laboratory Southeast UniversityNanjing 210096China Purple Mountain Laboratories Nanjing 211100China institute of Telecommunication Satellite CASTBeijing 100094China

This paper investigates the low earth orbit(LEO)satellite-enabled coded compressed sensing(CCS)unsourced random access(URA)in orthogonal frequency division multiple access(OFDMA)framework,where a massive uniform planar array(UPA)is equipped on the satellite.In LEO satellite communications,unavoidable timing and frequency offsets cause phase shifts in the transmitted signals,substantially diminishing the decoding performance of current terrestrial CCS URA receiver.To cope with this issue,we expand the inner codebook with predefined timing and frequency offsets and formulate the inner decoding as a tractable compressed sensing(CS)problem.Additionally,we leverage the inherent sparsity of the UPA-equipped LEO satellite angular domain channels,thereby enabling the outer decoder to support more active devices.Furthermore,the outputs of the outer decoder are used to reduce the search space of the inner decoder,which cuts down the computational complexity and accelerates the convergence of the inner decoding.Simulation results verify the effectiveness of the proposed scheme.

关键词： LEO OFDMA satellite unsourced random access UPA

Design Framework of Unsourced Multiple Access for 6G Massive IoT

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china Communications 2024年第1期21卷 1-12页

作者： Chunlin Yan Siying Lyu Sen Wang Yuhong Huang Xiaodong Xu china mobile Communication research institute Beijing 100053China ZGC institute of Ubiquitous-X Innovation and Applications Beijing 100080China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China

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.

关键词： channel coding compressed sensing massive Internet-of-Things(IoT) sparse interleaverdivision multiple access(SIDMA) the sixth generation(6G)mobile communications unsourced multiple access

Lensless opto-electronic neural network with quantum dot nonlinear activation

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Photonics research 2024年第4期12卷 682-690页

作者： WANXIN SHI XI JIANG ZHENG HUANG XUE LI YUYANG HAN SIGANG YANG HAIZHENG ZHONG HONGWEI CHEN Beijing National research Center for Information Science and Technology(BNRist) Department of Electronic EngineeringTsinghua UniversityBeijing 100084China china mobile research institute Beijing 100053China MIIT Key Laboratory for Low-dimensional Quantum Structure and Devices School of Materials Sciences&EngineeringBeijing Institute of TechnologyBeijing 100081China

With the swift advancement of neural networks and their expanding applications in many fields, optical neural networks have gradually become a feasible alternative to electrical neural networks due to their parallelism, high speed, low latency, and power consumption. Nonetheless, optical nonlinearity is hard to realize in free-space optics, which restricts the potential of the architecture.

关键词： network neural networks

Impact of non-ideal UE hardware on cell-free massive MIMO network with centralized operation

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Science china(Information Sciences) 2024年第4期67卷 255-271页

作者： Ning LI Pingzhi FAN institute of mobile Communications Southwest Jiaotong University

This paper investigates the impact of non-ideal user equipment(UE) hardware on a cell-free(CF) massive MIMO(mMIMO) network with centralized operation under spatially correlated channels. The minimum mean-squared error(MMSE) estimator can be derived with the help of the generic non-ideal UE hardware model. It is demonstrated that even if the effective signal-to-noise ratio approaches infinity, pilot contamination and imperfect hardware can cause a non-zero estimation error floor. After that, a lower bound is determined for the ergodic uplink capacity of the centralized CF mMIMO network under non-ideal UE hardware. Moreover, the optimal receive combining vector is obtained to maximize the uplink spectral efficiency(SE). The maximum ratio(MR) and regularized zero-forcing(RZF) combining schemes are offered as alternatives in light of the computational complexity of the MMSE receiver. Comparing the RZF to the MMSE scheme under different levels of hardware impairments, our findings indicate that the RZF receiver suffers a negligible loss in total SE. For MR combining, a novel closed-form uplink achievable SE expression is obtained based on the MMSE estimator and the use-and-then-forget bounding technique. This expression gives vital insights into the achievable uplink performance with UE hardware impairments. Besides, for various hardware impairment factors, the impact of pilot sequence length on average sum SE is disclosed for different receive combining schemes. To increase the overall SE of the max-min fairness scheme, a heuristic fractional power control scheme with UE hardware impairments is developed, which can essentially avoid sacrificing the SE of other UEs while maximizing the SE of the unluckiest UE in the whole network. Finally,our theoretical performance analysis and power control algorithm are validated by simulation results, and fundamental design guidelines are provided for selecting hardware satisfying the practical UE requirements.

关键词： cell-free massive MIMO centralized operation spatially correlated channels hardware impairments channel estimation uplink spectral efficiency optimal receive combining pilot length design fractional power control