arXiv subjects

Shi Jin

Publications and source records attributed to Shi Jin.

At least 487 records · Page 27Linked to original sources

arXiv2020

On the Uplink Transmission of Multi-user Extra-large Scale Massive MIMO Systems

With the inherent benefits, such as, better cell coverage and higher area throughput, extra-large scale massive multiple-input multiple-output (MIMO) has great potential to be one of the key technologies for the next generation wireless communication systems. However, in practice, when the antenna dimensions grow large, spatial non-stationarities occur and users will only see a portion of the base station antenna array, which we call visibility region (VR). To assess the impact of spatial non-stationarities, in this paper, we investigate the uplink transmission of extra-large scale massive MIMO systems by considering VRs. In particular, we first propose a subarray-based system architecture for extra-large scale massive MIMO systems. Then, tight closed-form uplink spectral efficiency (SE) approximations with linear receivers are derived. With the objective of maximizing the achievable SE, we also propose schemes for the subarray phase coefficient design. In addition, based on the obtained ergodic achievable SE approximations, two statistical channel state information (CSI)-based greedy user scheduling algorithms are developed. Our results indicate that the statistical CSI-based greedy joint user and subarray scheduling algorithm collaborating with the on-off switch-based subarray architecture is a promising practical solution for extra-large scale massive MIMO systems.

cs.IT↗

arXiv2020

Solving Sparse Linear Inverse Problems in Communication Systems: A Deep Learning Approach With Adaptive Depth

Sparse signal recovery problems from noisy linear measurements appear in many areas of wireless communications. In recent years, deep learning (DL) based approaches have attracted interests of researchers to solve the sparse linear inverse problem by unfolding iterative algorithms as neural networks. Typically, research concerning DL assume a fixed number of network layers. However, it ignores a key character in traditional iterative algorithms, where the number of iterations required for convergence changes with varying sparsity levels. By investigating on the projected gradient descent, we unveil the drawbacks of the existing DL methods with fixed depth. Then we propose an end-to-end trainable DL architecture, which involves an extra halting score at each layer. Therefore, the proposed method learns how many layers to execute to emit an output, and the network depth is dynamically adjusted for each task in the inference phase. We conduct experiments using both synthetic data and applications including random access in massive MTC and massive MIMO channel estimation, and the results demonstrate the improved efficiency for the proposed approach.

eess.SP↗

arXiv2020

Integrated Communication and Localization in mmWave Systems

As the fifth-generation (5G) mobile communication system is being commercialized, extensive studies on the evolution of 5G and sixth-generation mobile communication systems have been conducted. Future mobile communication systems are evidently evolving towards a more intelligent and software-reconfigurable functionality paradigm that can provide ubiquitous communication and also sense, control, and optimize wireless environments. Thus, integrating communication and localization by utilizing the highly directional transmission characteristics of millimeter-wave (mmWave) is a promising route. This approach not only expands the localization capabilities of a communication system but also provides new concepts and opportunities to enhance communication. In this paper, we explain the integrated communication and localization in mmWave systems, in which these processes share the same set of hardware architecture and algorithm. We also perform an overview of the key enabling technologies and the basic knowledge on localization. Then, we provide two promising directions for studies on localization with extremely large antenna array and model-based neural networks. We also discuss a comprehensive guidance for location-assisted mmWave communications in terms of channel estimation, channel state information feedback, beam tracking, synchronization, interference control, resource allocation, and user selection. Finally, this paper outlines the future trends on the mutual assistance and enhancement of communication and localization in integrated systems.

eess.SP↗

arXiv2020

A Fine-Grained Analysis of mmWave Heterogeneous Networks

A fine-grained analysis of the cache-enabled networks is crucial for system design. In this paper, we focus on the meta distribution of the signal-to-interference-plus-noise-ratio (SINR) in the mmWave heterogeneous networks where the base stations (BS) in each tier are modeled as Poisson point process (PPP). By utilizing stochastic geometry, we derive the moments of the conditional success probability, based on which the exact expression of meta distribution and its beta approximation are derived. In addition, key performance metrics, the success probability, the variance of the conditional success probability, the mean local delay and the network jitter are achieved. The distinguishing characteristics of the mmWave communications, including different path loss laws for line-of-sight and non-line-of-sight links and directional beamforming are incorporated into the analysis. The simulation results reveal the impact of the key network parameters, such as blockage parameter, bias factor, number of antenna elements and density on the performance.

cs.IT↗

arXiv2020

Wireless Communications with Reconfigurable Intelligent Surface: Path Loss Modeling and Experimental Measurement

Reconfigurable intelligent surfaces (RISs) comprised of tunable unit cells have recently drawn significant attention due to their superior capability in manipulating electromagnetic waves. In particular, RIS-assisted wireless communications have the great potential to achieve significant performance improvement and coverage enhancement in a cost-effective and energy-efficient manner, by properly programming the reflection coefficients of the unit cells of RISs. In this paper, free-space path loss models for RIS-assisted wireless communications are developed for different scenarios by studying the physics and electromagnetic nature of RISs. The proposed models, which are first validated through extensive simulation results, reveal the relationships between the free-space path loss of RIS-assisted wireless communications and the distances from the transmitter/receiver to the RIS, the size of the RIS, the near-field/far-field effects of the RIS, and the radiation patterns of antennas and unit cells. In addition, three fabricated RISs (metasurfaces) are utilized to further corroborate the theoretical findings through experimental measurements conducted in a microwave anechoic chamber. The measurement results match well with the modeling results, thus validating the proposed free-space path loss models for RIS, which may pave the way for further theoretical studies and practical applications in this field.

eess.SP↗

arXiv2020

The LISE package: solvers for static and time-dependent superfluid local density approximation equations in three dimensions

Nuclear implementation of the density functional theory (DFT) is at present the only microscopic framework applicable to the whole nuclear landscape. The extension of DFT to superfluid systems in the spirit of the Kohn-Sham approach, the superfluid local density approximation (SLDA) and its extension to time-dependent situations, time-dependent superfluid local density approximation (TDSLDA), have been extensively used to describe various static and dynamical problems in nuclear physics, neutron star crust, and cold atom systems. In this paper, we present the codes that solve the static and time-dependent SLDA equations in three-dimensional coordinate space without any symmetry restriction. These codes are fully parallelized with the message passing interface (MPI) library and take advantage of graphic processing units (GPU) for accelerating execution. The dynamic codes have checkpoint/restart capabilities and for initial conditions one can use any generalized Slater determinant type of wave function. The code can describe a large number of physical problems: nuclear fission, collisions of heavy ions, the interaction of quantized vortices with nuclei in the nuclear star crust, excitation of superfluid fermion systems by time dependent external fields, quantum shock waves, domain wall generation and propagation, the dynamics of the Anderson-Bogoliubov-Higgs mode, dynamics of fragmented condensates, vortex rings dynamics, generation and dynamics of quantized vortices, their crossing and recombinations and the incipient phases of quantum turbulence.

nucl-th↗

arXiv2020

Random Batch Algorithms for Quantum Monte Carlo simulations

Random batch algorithms are constructed for quantum Monte Carlo simulations. The main objective is to alleviate the computational cost associated with the calculations of two-body interactions, including the pairwise interactions in the potential energy, and the two-body terms in the Jastrow factor. In the framework of variational Monte Carlo methods, the random batch algorithm is constructed based on the over-damped Langevin dynamics, so that updating the position of each particle in an $N$-particle system only requires $\mathcal{O}(1)$ operations, thus for each time step the computational cost for $N$ particles is reduced from $\mathcal{O}(N^2)$ to $\mathcal{O}(N)$. For diffusion Monte Carlo methods, the random batch algorithm uses an energy decomposition to avoid the computation of the total energy in the branching step. The effectiveness of the random batch method is demonstrated using a system of liquid ${}^4$He atoms interacting with a graphite surface.

physics.comp-ph↗

arXiv2020

Joint Channel Assignment and Power Allocation for Multi-UAV Communication

Unmanned aerial vehicle (UAV) swarm has emerged as a promising novel paradigm to achieve better coverage and higher capacity for future wireless network by exploiting the more favorable line-of-sight (LoS) propagation. To reap the potential gains of UAV swarm, the remote control signal sent by ground control unit (GCU) is essential, whereas the control signal quality are susceptible in practice due to the effect of the adjacent channel interference (ACI) and the external interference (EI) from radiation sources distributed across the region. To tackle these challenges, this paper considers priority-aware resource coordination in a multi-UAV communication system, where multiple UAVs are controlled by a GCU to perform certain tasks with a pre-defined trajectory. Specifically, we maximize the minimum signal-to-interference-plus-noise ratio (SINR) among all the UAVs by jointly optimizing channel assignment and power allocation strategy under stringent resource availability constraints. According to the intensity of ACI, we consider the corresponding problem in two scenarios, i.e., Null-ACI and ACI systems. By virtue of the particular problem structure in Null-ACI case, we first recast the formulation into an equivalent yet more tractable form and obtain the global optimal solution via Hungarian algorithm. For general ACI systems, we develop an efficient iterative algorithm for its solution based on the smooth approximation and alternating optimization methods. Extensive simulation results demonstrate that the proposed algorithms can significantly enhance the minimum SINR among all the UAVs and adapt the allocation of communication resources to diverse mission priority.

eess.SP↗

arXiv2020

MIMO Transmission through Reconfigurable Intelligent Surface: System Design, Analysis, and Implementation

Reconfigurable intelligent surface (RIS) is a new paradigm that has great potential to achieve cost-effective, energy-efficient information modulation for wireless transmission, by the ability to change the reflection coefficients of the unit cells of a programmable metasurface. Nevertheless, the electromagnetic responses of the RISs are usually only phase-adjustable, which considerably limits the achievable rate of RIS-based transmitters. In this paper, we propose an RIS architecture to achieve amplitude-and-phase-varying modulation, which facilitates the design of multiple-input multiple-output (MIMO) quadrature amplitude modulation (QAM) transmission. The hardware constraints of the RIS and their impacts on the system design are discussed and analyzed. Furthermore, the proposed approach is evaluated using our prototype which implements the RIS-based MIMO-QAM transmission over the air in real time.

eess.SP↗

arXiv2020

A Survey of Prototype and Experiment for UAV Communications

Unmanned aerial vehicle (UAV) communications have attracted significant attention from both academia and industry. To facilitate the large-scale usage of UAVs for various applications in practice, we provide a comprehensive survey on the prototype and experiment for UAV communications. To this end, we first provide an overview on the general architecture of the prototype and experiment for UAV communications, and then present experimental verification for air-to-ground channel models and UAV energy consumption models. Next, we discuss measurement experiments on two promising paradigms of UAV communications, namely cellular-connected UAVs and UAV-enabled aerial communication platforms. For the former, we focus on the feasibility study and address the interference mitigation issue. For UAV-enabled aerial communication platforms, we present three scenarios, namely UAV-enabled aerial base stations, UAV-enabled aerial relays and UAV-enabled aerial data collection/dissemination. Finally, we point out some promising future directions for prototype and experimental measurements for UAV communications.

cs.IT↗

arXiv2020

Model-Driven Deep Learning for Massive MU-MIMO with Finite-Alphabet Precoding

Massive multiuser multiple-input multiple-output (MU-MIMO) has been the mainstream technology in fifth-generation wireless systems. To reduce high hardware costs and power consumption in massive MU-MIMO, low-resolution digital-to-analog converters (DAC) for each antenna and radio frequency (RF) chain in downlink transmission is used, which brings challenges for precoding design. To circumvent these obstacles, we develop a model-driven deep learning (DL) network for massive MU-MIMO with finite-alphabet precoding in this article. The architecture of the network is specially designed by unfolding an iterative algorithm. Compared with the traditional state-of-the-art techniques, the proposed DL-based precoder shows significant advantages in performance, complexity, and robustness to channel estimation error under Rayleigh fading channel.

cs.IT↗

arXiv2020

Model-Driven Deep Learning for Massive Multiuser MIMO Constant Envelope Precoding

Constant envelope (CE) precoding design is of great interest for massive multiuser multi-input multi-output systems because it can significantly reduce hardware cost and power consumption. However, existing CE precoding algorithms are hindered by excessive computational overhead. In this letter, a novel model-driven deep learning (DL)-based network that combines DL with conjugate gradient algorithm is proposed for CE precoding. Specifically, the original iterative algorithm is unfolded and parameterized by trainable variables. With the proposed architecture, the variables can be learned efficiently from training data through unsupervised learning approach. Thus, the proposed network learns to obtain the search step size and adjust the search direction. Simulation results demonstrate the superiority of the proposed network in terms of multiuser interference suppression capability and computational overhead.

eess.SP↗

arXiv2020

Arbitrary manipulations of dual harmonics and their wave behaviors based on space-time-coding digital metasurface

Space-time modulated metasurfaces have attracted significant attention due to the additional degree of freedom in manipulating the electromagnetic (EM) waves in both space and time domains. However, the existing techniques have limited wave control capabilities, leading to just a few feasible schemes like regulation of only one specific harmonic. Here, we propose to realize independent manipulations of arbitrarily dual harmonics and their wave behaviors using a space-time-coding (STC) digital metasurface. By employing different STC sequences to the reflection phase of the metasurface, independent phase-pattern configurations of two desired harmonics can be achieved simultaneously, which further leads to independent beam shaping at the two harmonic frequencies. An analytical theory is developed to offer the physical insights in the arbitrary dual-harmonic manipulations of spectra and spatial beams, which is verified by experiments with good agreements. The presented STC strategy provides a new way to design multifunctional programmable systems, which will find potential applications such as cognitive radar and multi-user wireless communications.

physics.app-ph↗

arXiv2020

3D Scene Based Beam Selection for mmWave Communications

In this paper, we present a novel framework of 3D scene based beam selection for mmWave communications that relies only on the environmental data and deep learning techniques. Different from other out-of-band side-information aided communication strategies, the proposed one fully utilizes the environmental information, e.g., the shape, the position, and even the materials of the surrounding buildings/cars/trees that are obtained from 3D scene reconstruction. Specifically, we build the neural networks with the input as point cloud of the 3D scene and the output as the beam indices. Compared with the LIDAR aided technique, the reconstructed 3D scene here is achieved from multiple images taken offline from cameras and thus significantly lowers down the cost and makes itself applicable for small mobile terminals. Simulation results show that the proposed 3D scene based beam selection can outperform the LIDAR method in terms of accuracy.

eess.SP↗

arXiv2020

PrecoderNet: Hybrid Beamforming for Millimeter Wave Systems with Deep Reinforcement Learning

In this letter, we investigate the hybrid beamforming for millimeter wave massive multiple-input multiple-output (MIMO) system based on deep reinforcement learning (DRL). Imperfect channel state information (CSI) is assumed to be available at the base station (BS). To achieve high spectral efficiency with low time consumption, we propose a novel DRL-based method called PrecoderNet to design the digital precoder and analog combiner. The DRL agent takes the digital beamformer and analog combiner of the previous learning iteration as state, and these matrices of current learning iteration as action. Simulation results demonstrate that the PrecoderNet performs well in spectral efficiency, bit error rate (BER), as well as time consumption, and is robust to the CSI imperfection.

eess.SP↗

arXiv2020

Model-Driven DNN Decoder for Turbo Codes: Design, Simulation and Experimental Results

This paper presents a novel model-driven deep learning (DL) architecture, called TurboNet, for turbo decoding that integrates DL into the traditional max-log-maximum a posteriori (MAP) algorithm. The TurboNet inherits the superiority of the max-log-MAP algorithm and DL tools and thus presents excellent error-correction capability with low training cost. To design the TurboNet, the original iterative structure is unfolded as deep neural network (DNN) decoding units, where trainable weights are introduced to the max-log-MAP algorithm and optimized through supervised learning. To efficiently train the TurboNet, a loss function is carefully designed to prevent tricky gradient vanishing issue. To further reduce the computational complexity and training cost of the TurboNet, we can prune it into TurboNet+. Compared with the existing black-box DL approaches, the TurboNet+ has considerable advantage in computational complexity and is conducive to significantly reducing the decoding overhead. Furthermore, we also present a simple training strategy to address the overfitting issue, which enable efficient training of the proposed TurboNet+. Simulation results demonstrate TurboNet+'s superiority in error-correction ability, signal-to-noise ratio generalization, and computational overhead. In addition, an experimental system is established for an over-the-air (OTA) test with the help of a 5G rapid prototyping system and demonstrates TurboNet's strong learning ability and great robustness to various scenarios.

eess.SP↗

arXiv2020

Spatio-Temporal Analysis of Cellular Networks with Cell-Center/Edge Users

Emergence of various types of services has brought about explosive growth of traffic as well as diversified traffic characteristics in the cellular networks. To have a comprehensive understanding of the influences caused by various traffic status is vital for the deployment of the next-generation wireless networks. In this paper, we develop a mathematical analytical model by utilizing queuing theory and stochastic geometry where the randomness of the traffic and the geographical locations of the interferers can be captured. We derive the b-th moments of the conditional success probability and the closed-form expressions of the meta distribution for the cell-center users (CCUs) and the cell-edge users (CEUs), respectively. Fixed-point equations are then formulated to obtain the exact value of the meta distribution by taking the random arrival traffic into consideration and the impact of the random arrival traffic on the queue status is revealed. In addition, the mean local delays for CCUs nad CEUs are derived and the corresponding regions for CCUs and CEUs where the mean local delays maintain finite are obtained. Finally, the impact of the critical network parameters on the meta distribution and the mean local delay is investigated with the numerical results.

cs.IT↗

arXiv2020

Analog Versus Hybrid Precoding for Multiuser Massive MIMO with Quantized CSI Feedback

In this letter, we study the performance of a downlink multiuser massive multiple-input multiple-output (MIMO) system with sub-connected structure over limited feedback channels. Tight rate approximations are theoretically analyzed for the system with pure analog precoding and hybrid precoding. The effect of quantized analog and digital precoding is characterized in the derived expressions. Furthermore, it is revealed that the pure analog precoding outperforms the hybrid precoding using maximal-ratio transmission (MRT) or zero forcing (ZF) under certain conditions, and we theoretically characterize the conditions in closed form with respect to signal-to-noise ratio (SNR), the number of users and the number of feedback bits. Numerical results verify the derived conclusions on both Rayleigh channels and mmWave channels.

cs.IT↗