13 research outputs found
Compressive Sensing-Based Grant-Free Massive Access for 6G Massive Communication
The advent of the sixth-generation (6G) of wireless communications has given
rise to the necessity to connect vast quantities of heterogeneous wireless
devices, which requires advanced system capabilities far beyond existing
network architectures. In particular, such massive communication has been
recognized as a prime driver that can empower the 6G vision of future
ubiquitous connectivity, supporting Internet of Human-Machine-Things for which
massive access is critical. This paper surveys the most recent advances toward
massive access in both academic and industry communities, focusing primarily on
the promising compressive sensing-based grant-free massive access paradigm. We
first specify the limitations of existing random access schemes and reveal that
the practical implementation of massive communication relies on a dramatically
different random access paradigm from the current ones mainly designed for
human-centric communications. Then, a compressive sensing-based grant-free
massive access roadmap is presented, where the evolutions from single-antenna
to large-scale antenna array-based base stations, from single-station to
cooperative massive multiple-input multiple-output systems, and from unsourced
to sourced random access scenarios are detailed. Finally, we discuss the key
challenges and open issues to shed light on the potential future research
directions of grant-free massive access.Comment: Accepted by IEEE IoT Journa
Compressive Sensing Based Grant-Free Random Access for Massive MTC
Massive machine-type communications (mMTC)
are expected to be one of the most primary scenarios in the
next-generation wireless communications and provide massive
connectivity for Internet of Things (IoT). To meet the demanding
technical requirements for mMTC, random access scheme with
efficient joint activity and data detection (JADD) is vital. In this
paper, we propose a compressive sensing (CS)-based grant-free
random access scheme for mMTC, where JADD is formulated
as a multiple measurement vectors (MMV) CS problem. By
leveraging the prior knowledge of the discrete constellation
symbols, we develop an orthogonal approximate message passing
(OAMP)-MMV algorithm for JADD, where the structured
sparsity is fully exploited for enhanced performance. Moreover,
expectation maximization (EM) algorithm is employed to learn
the unknown sparsity ratio of the a priori distribution and the
noise variance. Simulation results show that the proposed scheme
achieves superior performance over other state-of-the-art CS
schemes
Compressive Sensing Based Grant-Free Random Access for Massive MTC
Massive machine-type communications (mMTC) are expected to be one of the most primary scenarios in the next-generation wireless communications and provide massive connectivity for Internet of Things (IoT). To meet the demanding technical requirements for mMTC, random access scheme with efficient joint activity and data detection (JADD) is vital. In this paper, we propose a compressive sensing (CS)-based grant-free random access scheme for mMTC, where JADD is formulated as a multiple measurement vectors (MMV) CS problem. By leveraging the prior knowledge of the discrete constellation symbols, we develop an orthogonal approximate message passing (OAMP)-MMV algorithm for JADD, where the structured sparsity is fully exploited for enhanced performance. Moreover, expectation maximization (EM) algorithm is employed to learn the unknown sparsity ratio of the a priori distribution and the noise variance. Simulation results show that the proposed scheme achieves superior performance over other state-of-the-art CS schemes
Field Programmable Gate Array Based Torque Predictive Control for Permanent Magnet Servo Motors
With the increasing demand for legged robots, the importance of the joint drive is increasing. The dynamic performance of the inner-most torque/current control loop conditions the capabilities of the whole joint system. In this paper, a direct torque control based on a prediction model is proposed. The motor torque is estimated by considering calculation and measurement delay; error estimation and torque tracking error are observed and compensated. The control algorithm was implemented on a Field Programmable Gate Array (FPGA) board to apply the capabilities of concurrency calculation of the FPGA. The effectiveness of the proposed control algorithm was experimentally verified. Compared with the commonly used Field Oriented Control (FOC) current controller, the presented controller can not only improve the dynamic performance of the motor but also reduce the average switching times of the inverter
Compressive Sensing Based Joint Activity and Data Detection for Grant-Free Massive IoT Access
International audienceMassive machine-type communications (mMTC) are poised to provide ubiquitous connectivity for billions of Internetof-Things (IoT) devices. However, the required low-latency massive access necessitates a paradigm shift in the design of random access schemes, which invokes a need of efficient joint activity and data detection (JADD) algorithms. By exploiting the feature of sporadic traffic in massive access, a beacon-aided slotted grant-free massive access solution is proposed. Specifically, we spread the uplink access signals in multiple subcarriers with pre-equalization processing and formulate the JADD as a multiple measurement vectors (MMV) compressive sensing problem. Moreover, to leverage the structured sparsity of uplink massive access signals among multiple time slots, we develop two computationally efficient detection algorithms, which are termed as orthogonal approximate message passing (OAMP)-MMV algorithm with simplified structure learning (SSL) and accurate structure learning (ASL). To achieve accurate detection, the expectation maximization algorithm is exploited for learning the sparsity ratio and the noise variance. To further improve the detection performance, channel coding is applied and successive interference cancellation (SIC)-based OAMP-MMV-SSL and OAMP-MMV-ASL algorithms are developed, where the likelihood ratio obtained in the soft-decision can be exploited for refining the activity identification. Finally, the state evolution of the proposed OAMP-MMV-SSL and OAMP-MMV-ASL algorithms is derived to predict the performance theoretically. Simulation results verify that the proposed solutions outperform various state-of-the-art baseline schemes, enabling low-latency random access and high-reliable massive IoT connectivity with overloading
Massive Access in Extra Large-Scale MIMO with Mixed-ADC over Near Field Channels
Massive connectivity for extra-large multi-input multi-output (XL-MIMO)
systems is a challenging issue due to the prohibitive cost and the near-field
non-stationary channels. In this paper, we propose an uplink grant-free massive
access scheme for XL-MIMO systems, in which a mixed-analog-to-digital
converters (ADC) architecture is adopted to strike the right balance between
access performance and energy cost. By exploiting the spatial-domain structured
sparsity and the piecewise angular-domain cluster sparsity of massive access
channels, a compressive sensing (CS)-based two-stage orthogonal approximate
message passing algorithm is proposed to efficiently solve the joint activity
detection and channel estimation problem. Particularly, high-precision
quantized measurements are leveraged to perform accurate hyper-parameter
estimation, thereby facilitating the activity detection. Moreover, we adopt
subarray-wise estimation strategy to overcome the severe angular-domain energy
dispersion problem which is caused by the spatial non-stationarity of
near-field XL-MIMO channels. Simulation results verify the superiority of our
proposed algorithm over state-of-the-art CS algorithms for massive access based
on XL-MIMO with mixed-ADC architectures
Compressive sensing-based grant-free massive access for 6G massive communication
The advent of the sixth-generation (6G) of wireless communications has given rise to the necessity to connect vast quantities of heterogeneous wireless devices, which requires advanced system capabilities far beyond existing network architectures. In particular, such massive communication has been recognized as a prime driver that can empower the 6G vision of future ubiquitous connectivity, supporting Internet of Human-Machine-Things for which massive access is critical. This paper surveys the most recent advances toward massive access in both academic and industry communities, focusing primarily on the promising compressive sensing-based grant-free massive access paradigm. We first specify the limitations of existing random access schemes and reveal that the practical implementation of massive communication relies on a dramatically different random access paradigm from the current ones mainly designed for human-centric communications. Then, a compressive sensingbased grant-free massive access roadmap is presented, where the evolutions from single-antenna to large-scale antenna arraybasedbase stations, from single-station to cooperative massive multiple-input multiple-output systems, and from unsourced to sourced random access scenarios are detailed. Finally, we discuss the key challenges and open issues to shed light on the potential future research directions of grant-free massive access
Joint activity detection and channel estimation for massive IoT access based on Millimeter-Wave/Terahertz Multi-Panel Massive MIMO
The multi-panel array, as a state-of-the-art antennain-package technology, is very suitable for millimeter-wave (mmWave)/terahertz (THz) systems, due to its low-cost deployment and scalable configuration. But in the context of nonuniform array structures it leads to intractable signal processing. Based on such an array structure at the base station, this paper investigates a joint active user detection (AUD) and channel estimation (CE) scheme based on compressive sensing (CS) for application to the massive Internet of Things (IoT). Specifically, by exploiting the structured sparsity of mmWave/THz massive IoT access channels, we firstly formulate the multi-panel massive multiple-input multiple-output (mMIMO)-based joint AUD and CE problem as a multiple measurement vector (MMV)-CS problem. Then, we harness the expectation maximization (EM) algorithm to learn the prior parameters (i.e., the noise variance and the sparsity ratio) and an orthogonal approximate message passing (OAMP)-EM-MMV algorithm is developed to solve this problem. Our simulation results verify the improved AUD and CE performance of the proposed scheme compared to conventional CS-based algorithms