Massive multiple-input multiple-output (MIMO) is a key technology to meet the user demands in performance and quality of services (QoS) for next generation communication systems. Due to a large number of antennas and radio frequency (RF) chains, complexity of the symbol detectors increased rapidly in a massive MIMO uplink receiver. Thus, the research to find the perfect massive MIMO detection algorithm with optimal performance and low complexity has gained a lot of attention during the past decade. A plethora of massive MIMO detection algorithms has been proposed in the literature. The aim of this paper is to provide insights on such algorithms to a generalist of wireless communications. We garner the massive MIMO detection algorithms and classify them so that a reader can find a distinction between different algorithms from a wider range of solutions. We present optimal and near-optimal detection principles specifically designed for the massive MIMO system such as detectors based on a local search, belief propagation and box detection. In addition, we cover detectors based on approximate inversion, which has gained popularity among the VLSI signal processing community due to their deterministic dataflow and low complexity. We also briefly explore several nonlinear small-scale MIMO (2-4 antenna receivers) detectors and their applicability in the massive MIMO context. In addition, we present recent advances of detection algorithms which are mostly based on machine learning or sparsity based algorithms. In each section, we also mention the related implementations of the detectors. A discussion of the pros and cons of each detector is provided.

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Massive MIMO Detection Techniques: A Survey

Understand the fundamentals of wireless and MIMO communication with this accessible and comprehensive text. Viewing the subject through an information theory lens, but also drawing on other perspectives, it provides a sound treatment of the key concepts underpinning contemporary wireless communication and MIMO, all the way to massive MIMO. Authoritative and insightful, it includes over 330 worked examples and 450 homework problems, with solutions and MATLAB code and data available online. Altogether, this is an excellent resource for instructors and graduate students, as well as an outstanding reference for researchers and practicing engineers.

Foundations of MIMO Communication

Detecting objects in aerial images is challenging for at least two reasons: (1) target objects like pedestrians are very small in pixels, making them hardly distinguished from surrounding background; and (2) targets are in general sparsely and non-uniformly distributed, making the detection very inefficient. In this paper, we address both issues inspired by observing that these targets are often clustered. In particular, we propose a Clustered Detection (ClusDet) network that unifies object clustering and detection in an end-to-end framework. The key components in ClusDet include a cluster proposal sub-network (CPNet), a scale estimation sub-network (ScaleNet), and a dedicated detection network (DetecNet). Given an input image, CPNet produces object cluster regions and ScaleNet estimates object scales for these regions. Then, each scale-normalized cluster region is fed into DetecNet for object detection. ClusDet has several advantages over previous solutions: (1) it greatly reduces the number of chips for final object detection and hence achieves high running time efficiency, (2) the cluster-based scale estimation is more accurate than previously used single-object based ones, hence effectively improves the detection for small objects, and (3) the final DetecNet is dedicated for clustered regions and implicitly models the prior context information so as to boost detection accuracy. The proposed method is tested on three popular aerial image datasets including VisDrone, UAVDT and DOTA. In all experiments, ClusDet achieves promising performance in comparison with state-of-the-art detectors.

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Clustered Object Detection in Aerial Images

Object detection in high-resolution aerial images is a challenging task because of 1) the large variation in object size, and 2) non-uniform distribution of objects A common solution is to divide the large aerial image into small (uniform) crops and then apply object detection on each small crop In this paper, we investigate the image cropping strategy to address these challenges Specifically, we propose a Density-Map guided object detection Network (DMNet), which is inspired from the observation that the object density map of an image presents how objects distribute in terms of the pixel intensity of the map As pixel intensity varies, it is able to tell whether a region has objects or not, which in turn provides guidance for cropping images statistically DMNet has three key components: a density map generation module, an image cropping module and an object detector DMNet generates a density map and learns scale information based on density intensities to form cropping regions Extensive experiments show that DMNet achieves state-of-the-art performance on two popular aerial image datasets, ie VisionDrone [30] and UAVDT [4]

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Density Map Guided Object Detection in Aerial Images

In Earth observation (EO), large-scale land-surface dynamics are traditionally analyzed by investigating aggregated classes. The increase in data with a very high spatial resolution enables investigations on a fine-grained feature level which can help us to better understand the dynamics of land surfaces by taking object dynamics into account. To extract fine-grained features and objects, the most popular deep-learning model for image analysis is commonly used: the convolutional neural network (CNN). In this review, we provide a comprehensive overview of the impact of deep learning on EO applications by reviewing 429 studies on image segmentation and object detection with CNNs. We extensively examine the spatial distribution of study sites, employed sensors, used datasets and CNN architectures, and give a thorough overview of applications in EO which used CNNs. Our main finding is that CNNs are in an advanced transition phase from computer vision to EO. Upon this, we argue that in the near future, investigations which analyze object dynamics with CNNs will have a significant impact on EO research. With a focus on EO applications in this Part II, we complete the methodological review provided in Part I.

Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review-Part II: Applications

A new approach based on joint steepest descent algorithm and Jacobi iteration is proposed to iteratively realize linear detections for uplink massive multiple-input multiple-output (MIMO) systems. Steepest descent algorithm is employed to obtain an efficient searching direction for the following Jacobi iteration to speed up convergence. Moreover, promising initial estimation and hybrid iteration are utilized to further accelerate the convergence rate and reduce the complexity. Simulation results show that the proposed method outperforms Neumann Series, Richardson method, and conjugate gradient based methods, while achieving the near-optimal performance of linear detectors with a small number of iterations. Meanwhile, the FPGA implementation results demonstrate that our proposed method can achieve high throughput owing to its high parallelism.

A Near-Optimal Detection Scheme Based on Joint Steepest Descent and Jacobi Method for Uplink Massive MIMO Systems

Object detection in aerial images is widely applied in many applications. In recent years, faster region convolutional neural network shows a great improvement on object detecting in natural images. Considering the size and distribution characteristic of object in remote sensing images, the region proposal network (RPN) should be changed before being adopted. In this letter, a scale adaptive proposal network (SAPNet) is proposed to improve the accuracy of multiobject detection in remote sensing images. The SAPNet consists of multilayer RPNs which are designed to generate multiscale object proposals, and a final detection subnetwork in which fusion feature layer has been applied for better multiobject detection. Comparative experimental results show that the proposed SAPNet significantly improves the accuracy of multiobject detection.

Scale Adaptive Proposal Network for Object Detection in Remote Sensing Images

By exchanging soft information between the multiple-input multiple-output (MIMO) detector and the channel decoder, an iterative receiver can significantly improve the performance compared to the noniterative receiver. In this brief, a soft-input soft-output fixed-complexity-sphere-decoding algorithm and its very large scale integration architecture are proposed for the iterative MIMO receiver. The deeply pipelined architecture employs the optimized hybrid enumeration to search for the best child node estimate efficiently. By adding the counter hypotheses in parallel with other candidates, the proposed iterative MIMO detector improves the detection performance significantly with low detection latency. An iterative detector for an 4 × 4 64-quadrature amplitude modulation (QAM) MIMO system based on our proposed architecture is designed and implemented using the 90-nm CMOS technology. The detector can achieve a maximum throughput of 2.2 Gbit/s with an area efficiency of 3.96 Mbit/s/kGE, which is more efficient than other iterative MIMO detectors.

VLSI Implementation of a High-Throughput Iterative Fixed-Complexity Sphere Decoder

This paper proposes a flexible dual-mode soft-output multiple-input multiple-output (MIMO) detector to support open-loop and closed-loop in Chinese enhanced ultra high throughput (EUHT) wireless local area network (LAN) standard. The proposed detector uses minimum mean square error (MMSE) sorted QR decomposition (MMSE-SQRD) to produce channel preprocessing result, which is realized by a modified systolic array architecture with concurrent sorting. Moreover, the adopted square-root MMSE algorithm for closed-loop reuses MMSE-SQRD preprocessing to largely save hardware overhead. In addition, an optimized K-Best detection algorithm is proposed for open-loop, which increases throughput by odd-even parallel sorting and produces high quality soft-output with discarded paths (DPs). A flexible VLSI architecture is designed for the proposed dual-mode detector, which supports $1\times 1\sim 4\times 4$ antennas and BPSK $\sim$ 64-QAM modulation configuration. Implemented in SMIC 65 nm CMOS technology, the detector is capable of running at 550 MHz, which has a maximum throughput of 2.64 Gb/s for K-Best detection and 3.3 Gb/s for linear MMSE detection. The proposed detector is competitive to recent published works and meets the data-rate requirement of the EUHT standard.

Design and Implementation of Flexible Dual-Mode Soft-Output MIMO Detector With Channel Preprocessing

Automatic ship detection in optical remote-sensing (ORS) images has wide applications in civil and military fields. Research on ship detection in ORS images started late compared to synthetic apert...

Guanghui He

Papers

A Near-Optimal Detection Scheme Based on Joint Steepest Descent and Jacobi Method for Uplink Massive MIMO Systems

Scale Adaptive Proposal Network for Object Detection in Remote Sensing Images

VLSI Implementation of a High-Throughput Iterative Fixed-Complexity Sphere Decoder

Design and Implementation of Flexible Dual-Mode Soft-Output MIMO Detector With Channel Preprocessing

Ship detection based on fused features and rebuilt YOLOv3 networks in optical remote-sensing images