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

Layered structure is widely used in the design of Low-Density Parity-Check (LDPC) code decoders due to its fast convergence speed. However, correct checking process is difficult to implement in layered decoder, which results in unnecessary iterations. In this paper, an early termination strategy is presented for layered LDPC decoder to avoid redundant number of iterations. This approach makes use of the comparison between current log-likelyhood ratios (LLRs) and updated LLRs of all variable nodes to determine termination criteria of iterations. Furthermore, a non-uniform quantization scheme and an extrinsic messages memory optimization scheme are developed for memory savings. Based on these proposed methods, an LDPC decoder for the Chinese digital mobile TV applications is implemented using a SMIC 130nm CMOS process. The decoder consumes only 171 Kbits memory while achieving 267Mbps for code rate 1/2, and 401Mbps for code rate 3/4.

Memory efficient layered decoder design with early termination for LDPC codes

The advancement of object detection algorithms makes them widely used in autonomous systems. However, due to high computational complexity of Convolutional Neural Networks(CNN), stringent latency requirement is hard to meet for real-time object detection. To address this problem, a low-latency accelerator architecture is proposed in this paper. A finegrained column-based pipeline architecture with padding skip technique is implemented to reduce the start-up time of pipeline. In order to cut down the computational time of CNN, double signed-multiplication correcting circuit is introduced. In addition, pooling unit with share buffer is proposed to reduce storage cost for pooling layer. To demonstrate our new architecture, we implement the YOLOv2-tiny deep neural network (you-only-look-once) with input size 1280×384 on ZC706 development board, improving the latency by 2.125× to 2.34× compared to previous FPGA accelerator for YOLOv2-tiny.

A Low-Latency FPGA Implementation for Real-Time Object Detection

Multiple-input multiple-output (MIMO) technique can significantly increase data throughput without sacrificing additional bandwidth. However, data detection at the receiver and its VLSI implementation is challenge due to high computation complexity. This paper presents the VLSI architecture and implementation for a 4×4 64-QAM soft-output K-Best MIMO detector. A novel deeply pipelined architecture which makes use of all the full-length ZF-augmented discarded paths (DPs) is designed to reduce complexity and improve BER performance. Furthermore, to save area and latency, two improvement methods—abandoning DPs of bottom levels and performing ZF-augmentation at the last stage are proposed. The presented detector improves the BER performance by 2.3dB at BER=10−3 compared to the conventional soft K-Best scheme when using the minimum mean squared error-sorted QR decomposition (MMSE-SQRD). It can achieve a peak throughput of 855 Mbps while consuming 223K gates, 301pJ/bit and 102 cycles for latency in SMIC 0.13µm CMOS process.

VLSI implementation of an 855 Mbps high performance soft-output K-Best MIMO detector

By introducing stochastic computing (SC), the hardware consumption of Deep Neural Network (DNN) can be reduced significantly. However, long SC sequence length is needed to maintain the accuracy, which results in extended computation time. To shorten the sequence length, we propose two high-accuracy SC computation units to improve the precision of SC-DNN, which are the rematching-based correlation-independent multiplier and the accumulator-based Rectified Linear Unit. Moreover, a length-adaptive method that uses variable lengths for different images is adopted to decrease the average sequence length. Software simulation shows that the SC design has just 0.01% accuracy loss with only the sequence length of 20 for MNIST dataset comparing to the binary system. The ASIC layout results demonstrate the area efficiency is improved by 16X comparing to the latest SC-DNN with similar accuracy loss.

Guanghui He

Papers

Memory efficient layered decoder design with early termination for LDPC codes

A Low-Latency FPGA Implementation for Real-Time Object Detection

VLSI implementation of an 855 Mbps high performance soft-output K-Best MIMO detector

Hardware Implementation of an Improved Stochastic Computing Based Deep Neural Network Using Short Sequence Length

High performance parallel turbo decoder with configurable interleaving network for LTE application