An Overview of Signal Processing Techniques for Millimeter Wave MIMO Systems
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Performance Comparisons of MIMO Techniques with Application to WCDMA Systems
EURASIP Journal on Applied Signal Processing 2004:5, 649–661 c 2004 Hindawi Publishing Corporation Performance Comparisons of MIMO Techniques with Application to WCDMA Systems Chuxiang Li Department of Electrical Engineering, Columbia University, New York, NY 10027, USA Email: [email protected] Xiaodong Wang Department of Electrical Engineering, Columbia University, New York, NY 10027, USA Email: [email protected] Received 11 December 2002; Revised 1 August 2003 Multiple-input multiple-output (MIMO) communication techniques have received great attention and gained significant devel- opment in recent years. In this paper, we analyze and compare the performances of different MIMO techniques. In particular, we compare the performance of three MIMO methods, namely, BLAST, STBC, and linear precoding/decoding. We provide both an analytical performance analysis in terms of the average receiver SNR and simulation results in terms of the BER. Moreover, the applications of MIMO techniques in WCDMA systems are also considered in this study. Specifically, a subspace tracking algo- rithm and a quantized feedback scheme are introduced into the system to simplify implementation of the beamforming scheme. It is seen that the BLAST scheme can achieve the best performance in the high data rate transmission scenario; the beamforming scheme has better performance than the STBC strategies in the diversity transmission scenario; and the beamforming scheme can be effectively realized in WCDMA systems employing the subspace tracking and the quantized feedback approach. Keywords and phrases: BLAST, space-time block coding, linear precoding/decoding, subspace tracking, WCDMA. 1. INTRODUCTION ing power and/or rate over multiple transmit antennas, with partially or perfectly known channel state information [7]. -
Miniaturized Frequency Reconfigurable Pentagonal MIMO Slot Antenna for Interweave CR Applications
Miniaturized frequency reconfigurable pentagonal MIMO slot antenna for interweave CR applications Item Type Article Authors Hussain, Rifaqat; Raza, Ali; Khan, Muhammad U.; Shamim, Atif; Sharawi, Mohammad S. Citation Hussain R, Raza A, Khan MU, Shammim A, Sharawi MS (2019) Miniaturized frequency reconfigurable pentagonal MIMO slot antenna for interweave CR applications. International Journal of RF and Microwave Computer-Aided Engineering: e21811. Available: http://dx.doi.org/10.1002/mmce.21811. DOI 10.1002/mmce.21811 Publisher Wiley Journal International Journal of RF and Microwave Computer-Aided Engineering Download date 01/10/2021 07:53:51 Link to Item http://hdl.handle.net/10754/653090 Pentagonal Slot MIMO Reconfigurable Antenna 1 Miniaturized Frequency Reconfigurable Pentagonal MIMO Slot Antenna for Interweave CR Applications Rifaqat Hussain 1, Ali Raza 2, Muhammad U. Khan 3, Atif Shammim 4 and Mohammad S. Sharawi 5 1 Electrical Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia, Email: [email protected]. 2 Department of Electrical Engineering, University of Engineering and Technology Lahore, Faisalabad Campus, Pakistan 3 Research Institute for Microwave and Millimeter-Wave Studies, National University of Science and Technology, Islamabad, 44000, Pakistan 4 Computer, Electrical and Mathematical Science and Engineering Division, King Abdullah University (KAUST) of Science and Technology, Thuwal 23955-6900, Saudi Arabia 5 Electrical Engineering Department, Polytechnique Montr´eal, Montreal, QC H3T 1J4, Canada, Email: [email protected] ABSTRACT: In this paper, a miniaturized 4-element frequency reconfigurable multiple- input-multiple-output (MIMO) antenna system is presented. The proposed design is low profile with planar configuration. The design consists of pentagonal slot-based frequency reconfigurable antenna elements. -
Full-Azimuth Beam Steering MIMO Antenna Arranged in a Daisy Chain Array Structure
micromachines Article Full-Azimuth Beam Steering MIMO Antenna Arranged in a Daisy Chain Array Structure Kazuhiro Honda 1,*, Taiki Fukushima 2 and Koichi Ogawa 1 1 Graduate School of Engineering, Toyama University, 3190 Gofuku, Toyama 930-8555, Japan; [email protected] 2 Panasonic System Networks R&D Lab. Co., Ltd., Technology Center, 2-5 Akedori, Izumi-ku, Sendai, Miyagi 981-3206, Japan; [email protected] * Correspondence: [email protected]; Tel.: +81-76-445-6759 Received: 28 August 2020; Accepted: 18 September 2020; Published: 19 September 2020 Abstract: This paper presents a multiple-input, multiple-output (MIMO) antenna system with the ability to perform full-azimuth beam steering, and with the aim of realizing greater than 20 Gbps vehicular communications. The MIMO antenna described in this paper comprises 64 elements arranged in a daisy chain array structure, where 32 subarrays are formed by pairing elements in each subarray; the antenna yields 32 independent subchannels for MIMO transmission, and covers all communication targets regardless of their position relative to the array. Analytical results reveal that the proposed antenna system can provide a channel capacity of more than 200 bits/s/Hz at a signal-to-noise power ratio (SNR) of 30 dB over the whole azimuth, which is equivalent to 20 Gbps for a bandwidth of 100 MHz. This remarkably high channel capacity is shown to be due to two significant factors; the improved directivity created by the optimum in-phase excitation and the low correlation between the subarrays due to the orthogonal alignment of the array with respect to the incident waves. -
Sources and Compensation of Skew in Single-Ended and Differential Interconnects
Published on Signal Integrity Journal, www.signalintegrityjournal.com April 2017 Sources and Compensation of Skew in Single-Ended and Differential Interconnects Eben Kunz, Oracle Corp. Jae Young Choi, Oracle Corp. Vijay Kunda, Oracle Corp. Laura Kocubinski, Oracle Corp. Ying Li, Oracle Corp. Jason Miller, Oracle Corp. Gustavo Blando, Oracle Corp. Istvan Novak, Oracle Corp. Disclaimer: This presentation does not constitute as an endorsement for any specific product, service, company, or solution. 1 Published on Signal Integrity Journal, www.signalintegrityjournal.com April 2017 Abstract In high-speed signaling with embedded clock a few ps in-pair skew may cause serious signal degradations. Point-to-point topologies, long PCB traces put the emphases on local speed variations due to bends, glass- weave, deterministic and random asymmetries. First we analyze the contributors to the delay in single-ended traces. Length in differential pairs varies due to bends and turns. At each turn the outer trace has a little extra length. We show that using the center-line trace length can give a delay estimation error up to several ps. We will show how different turns, right angle, double 45-degree or arced turn, impact delay. Second, we look at practical ways of compensating skew. A few options are looked at and their performances compared. We consider a few statistical contributors to skew and establish a limit below which compensation makes no sense. The simulated data is illustrated by the measured performance of a few simple structures. Author(s) Biography Eben Kunz graduated from MIT in 2012 with a BS and Master's in EE. -
802.11Ac MU-MIMO Bridging the MIMO Gap in Wi-Fi
Title Qualcomm Atheros, Inc. 802.11ac MU-MIMO: Bridging the MIMO Gap in Wi-Fi January, 2015 Qualcomm Atheros, Inc. Not to be used, copied, reproduced, or modified in whole or in part, nor its contents revealed in any manner to others without the express written permission of Qualcomm Atheros Inc. Qualcomm, Snapdragon, and VIVE are trademarks of Qualcomm Incorporated, registered in the United States and other countries. All Qualcomm Incorporated trademarks are used with permission. Other product and brand names may be trademarks or registered trademarks of their respective owners. This technical data may be subject to U.S. and international export, re-export, or transfer (“export”) laws. Diversion contrary to U.S. and international law is strictly prohibited. Qualcomm Atheros, Inc. 1700 Technology Drive San Jose, CA 95110 U.S.A. ©2014-15 Qualcomm Atheros, Inc. All Rights Reserved. MAY CONTAIN U.S. AND INTERNATIONAL EXPORT CONTROLLED INFORMATION Page 2 Table of Contents 1 Executive Summary ................................................................................................................................ 4 2 Why MU-MIMO? ..................................................................................................................................... 5 3 11ac Advanced Features: Transmit Beamforming (TxBF) and MU-MIMO ............................................. 6 3.1 Standardized Closed Loop TxBF .................................................................................................... 6 3.2 MU-MIMO or MU-TxBF .................................................................................................................. -
Directional Beamforming for Millimeter-Wave MIMO Systems
Directional Beamforming for Millimeter-Wave MIMO Systems Vasanthan Raghavan, Sundar Subramanian, Juergen Cezanne and Ashwin Sampath Qualcomm Corporate R&D, Bridgewater, NJ 08807 E-mail: {vraghava, sundars, jcezanne, asampath}@qti.qualcomm.com Abstract—The focus of this paper is on beamforming in a that allows the deployment of a large number of antennas in millimeter-wave (mmW) multi-input multi-output (MIMO) set- a fixed array aperture. up that has gained increasing traction in meeting the high data- Despite the possibility of multi-input multi-output (MIMO) rate requirements of next-generation wireless systems. For a given MIMO channel matrix, the optimality of beamforming communications, mmW signaling differs significantly from with the dominant right-singular vector (RSV) at the transmit traditional MIMO architectures at cellular frequencies. The end and with the matched filter to the RSV at the receive most optimistic antenna configurations1 at cellular frequencies end has been well-understood. When the channel matrix can are on the order of 4 × 8 with a precoder rank (number of be accurately captured by a physical (geometric) scattering layers) of 1 to 4; see, e.g., [9]. Higher rank signaling requires model across multiple clusters/paths as is the case in mmW 2 MIMO systems, we provide a physical interpretation for this multiple radio-frequency (RF) chains which are easier to real- optimal structure: beam steering across the different paths with ize at lower frequencies than at the mmW regime. Thus, there appropriate power allocation and phase compensation. While has been a growing interest in understanding the capabilities of such an explicit physical interpretation has not been provided low-complexity approaches such as beamforming (that require hitherto, practical implementation of such a structure in a mmW only a single RF chain) in mmW systems [10]–[15]. -
A Study of MIMO Precoding Algorithm for WCDMA Uplink
A study of MIMO precoding algorithm in WCDMA uplink Master's Thesis in Signals and Systems Sheng Huang & Mengbai Tao Department of Signals & Systems Communication Engineering Chalmers University of Technology Gothenburg, Sweden 2014 Master's Thesis EX044/2014 Abstract With rapidly increasing demand in high-speed data transmission, Multiple-Input Multiple-Output (MIMO) techniques are widely used in current communication net- works, like Wideband Code Division Multiple Access (WCDMA). MIMO in downlink of WCDMA has been studied for a long time, while in WCDMA uplink, the specification of how to choose precoder is still not defined yet. Precoding is an essential element in MIMO, aiming at mitigating interference between different data streams that are se- quences of digitally coded signals. Currently, a MIMO system suffer from two problems regarding precoding. Since channel information is needed for the precoding algorithm, one problem then is that it is difficult to model the channel characteristics in a frequency selective channel. The other problem is that only limited feedback is applicable in real system, since the cost of feedback overhead is far larger than the benefits from precod- ing if full channel information needs to be sent to the transmitter. The objective of this thesis is to investigate the maximum potential gain in the required Signal-to-Noise Ratio (SNR) range for a given Block Error Rate (BLER) by using precoding and selecting a suitable precoding algorithm in the case of limited feedback in the WCDMA uplink. The maximum potential gain of precoding is considerable (1:2 dB at 0:5 BLER com- pared to a fixed precoder) in the case of limited feedback according to our simulations. -
MIMO Technology in Wifi Systems
MIMO in WiFi Systems Rohit U. Nabar Smart Antenna Workshop Aug. 1, 2014 WiFi • Local area wireless technology that allows communication with the internet using 2.4 GHz or 5 GHz radio waves per IEEE 802.11 • Proliferation in the number of devices that use WiFi today: smartphones, tablets, digital cameras, video-game consoles, TVs, etc • Devices connect to the internet via wireless network access point (AP) Advantages • Allows convenient setup of local area networks without cabling – rapid network connection and expansion • Deployed in unlicensed spectrum – no regulatory approval required for individual deployment • Significant competition between vendors has driven costs lower • WiFi governed by a set of global standards (IEEE 802.11) – hardware compatible across geographical regions WiFi IC Shipment Growth 1.25x 15x Cumulative WiFi Devices in Use Data by Local Access The IEEE 802.11 Standards Family Standard Year Ratified Frequency Modulation Channel Max. Data Band Bandwidth Rate 802.11b 1999 2.4 GHz DSSS 22MHz 11 Mbps 802.11a 1999 5 GHz OFDM 20 MHz 54 Mbps 802.11g 2003 2.4 GHz OFDM 20 MHz 54 Mbps 802.11n 2009 2.4/5 GHz MIMO- 20,40 MHz 600 Mbps OFDM 802.11ac 2013 5 GHz MIMO- 20, 40, 80, 6.93 Gbps OFDM 160 MHz 802.11a/ac PHY Comparison 802.11a 802.11ac Modulation OFDM MIMO-OFDM Subcarrier spacing 312.5 KHz 312.5 KHz Symbol Duration 4 us (800 ns guard interval) 3.6 us (400 ns guard interval) FFT size 64 64(20 MHz)/512 (160 MHz) FEC BCC BCC or LDPC Coding rates 1/2, 2/3, 3/4 1/2, 2/3, 3/4, 5/6 QAM BPSK, QPSK, 16-,64-QAM BPSK, QPSK, 16-,64-,256- QAM Factors Driving the Data Rate Increase 6.93 Gbps QAM 802.11ac (1.3x) FEC rate (1.1x) MIMO (8x) 802.11a Bandwidth (8x) 54 Mbps 802.11 Medium Access Control (MAC) Contention MEDIUM BUSY DIFS PACKET Window • Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) • A wireless node that wants to transmit performs the following sequence 1. -
MIMO Fading Channel Capacity, Massive MIMO, Beamforming, Diversity Vs
Lecture 15 - EE 359: Wireless Communications - Winter 2020 MIMO Fading Channel Capacity, Massive MIMO, Beamforming, Diversity vs. Multiplexing, RX Design Lecture Outline • MIMO Fading Channel Capacity • Massive MIMO • Beamforming • MIMO Diversity/Multiplexing Tradeoffs • MIMO RX Design 1. MIMO Fading Channel Capacity • In fading, capacity with both transmitter and receiver knowledge is the average of the capacity for the static channel, with power allocated either by an instantaneous or average power constraint. Under the instantaneous constraint power is optimally allocated over the spatial dimension only. Under the average constraint it is allocated over both space and time. • In fading, if the channel is unknown at transmitter, uniform power allocation is optimal, but this leads to an outage probability since the transmitter doesn’t know what rate R to transmit at: ρ H Pout = p H : B log2 det IMr + HH <R . Mt 2. Massive MIMO: • When the number of TX (or RX) antennas is large, the channel becomes “static” due to the law of large numbers. Specifically 1 H lim HH = IMr . Mt→∞ Mt • The MIMO channel capacity then becomes ρ H lim B log2 det IMr + HH = B log2 det [IMr + ρIMr ] = MrB log2(1 + ρ). Mt→∞ Mt • Defining M = min(Mt, Mr), this implies that as Mt grows large, the MIMO channel capacity in the absence of TX CSI approaches C = MB log2(1 + ρ) for ρ the SNR, and hence grows linearly in M. This is also true for finite Mt and Mr but at large SNR. 3. MIMO Systems: Beamforming • Beamforming sends the same symbol over each transmit antenna with a different scale factor. -
MIMO RX Design, ISI Mitigation Techniques, Multicarrier Modulation and OFDM
Lecture 16 - EE 359: Wireless Communications - Winter 2020 MIMO RX Design, ISI Mitigation Techniques, Multicarrier Modulation and OFDM Lecture Outline • MIMO RX Design • ISI Mitigation Techniques • Multicarrier Modulation • Overlapping Subcarriers and OFDM 1. MIMO Receiver Design: Maximum Likelihood Receiver • Optimal MIMO receiver is maximum-likelihood (ML) receiver solves the following problem. 2 xˆ(y) = argminx∈X Mt ky − Hxk . • Must search over all possible x ∈ X Mt , i.e. over all possible transmitted vectors which has exponential complexity in Mt and in the constellation size. • Alternate approaches using linear processing of input vector Ax. 2. Linear MIMO Receivers: • Multiplies y with a MIMO equalization matrix A ∈ CMt×Mr to get x˜(y) ∈ CMt . • Zero-forcing receiver forces all interference from other symbols to zero: x˜(y) = H†y, where H† is H−1 if the matrix is square and invertible, otherwise we use the pseudo inverse. • When Mt ≤ Mr, and there are at least Mt linearly independent columns in H (typical for many propagation environments), the pseudo inverse (Moore-Penrose pseudoin- verse) is H† =(HHH)−1HH. † • Complexity of obtaining H is roughly cubic in Mt for square matrices. However ob- taining xˆ(y) from x˜(y) is linear in Mt. ZF can result in significant noise enhancement. • L-MMSE receiver: trades off cancellation of interference from other symbols for noise enhancemen with a regularization term that depends on SNR. The solution is x˜(y)=(HHH + λI)−1HHy = HH(HHH + λI)−1y, which reduces to zero forcing in the absence of noise (infinite SNR). • Compared to the ML detector, both the ZF and MMSE linear detectors are simpler to implement, but their BER performances are worse. -
MIMO Self-Encoded Spread Spectrum with Iterative Detection Over Rayleigh Fading Channels Shichuan Ma
University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln CSE Journal Articles Computer Science and Engineering, Department of 2010 MIMO Self-Encoded Spread Spectrum with Iterative Detection over Rayleigh Fading Channels Shichuan Ma Lim Nguyen Won Mee Jang Yaoqing Yang Follow this and additional works at: https://digitalcommons.unl.edu/csearticles This Article is brought to you for free and open access by the Computer Science and Engineering, Department of at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in CSE Journal Articles by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. Hindawi Publishing Corporation Journal of Electrical and Computer Engineering Volume 2010, Article ID 492079, 9 pages doi:10.1155/2010/492079 Research Article MIMO Self-Encoded Spread Spectrum with Iterative Detection over Rayleigh Fading Channels Shichuan Ma, Lim Nguyen, Won Mee Jang, and Yaoqing (Lamar) Yang Department of Computer and Electronics Engineering, University of Nebraska-Lincoln, Omaha, NE 68182, USA Correspondence should be addressed to Shichuan Ma, [email protected] Received 15 February 2010; Revised 15 July 2010; Accepted 16 August 2010 Academic Editor: Christian Schlegel Copyright © 2010 Shichuan Ma et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Self-encoded spread spectrum (SESS) is a novel communication technique that derives its spreading code from the randomness of the source stream rather than using conventional pseudorandom noise (PN) code. In this paper, we propose to incorporate SESS in multiple-input multiple-output (MIMO) systems as a means to combat against fading effects in wireless channels. -
Lip Synchronization in Video Conferencing
C H A P T E R 7 Lip Synchronization in Video Conferencing Chapter 3, “Fundamentals of Video Compression,” went into detail about how audio and video streams are encoded and decoded in a video conferencing system. However, the last processing step in the end-to-end chain involves ensuring that the decoded audio and video streams play with perfect synchronization. This chapter focuses on audio and video; however, video conferencing systems can synchronize any type of media to any other type of media, including sequences of still images or 3D animation. Two issues complicate the process of achieving synchronization: ■ Real-time Transport Protocol (RTP)-based video conferencing systems separate audio and video into different RTP streams on the network. ■ Video conferencing systems also typically have separate processing pipelines for audio and video within the sender and receiver endpoints. This chapter covers the process of realigning those streams at the receiver. Understanding Lip Sync Skew Lip sync is the general term for audio/video synchronization, and literally refers to the fact that visual lip movements of a speaker must match the sound of the spoken words. If the video and audio displayed at the receiving endpoint are not in sync, the misalignment between audio and video is referred to as skew. Without a mechanism to ensure lip sync, audio often plays ahead of video, because the latencies involved in processing and sending video frames are greater than the latencies for audio. Human Perceptions User-perceived objection to unsynchronized media streams varies with the amount of skew— for instance, a misalignment of audio and video of less than 20 milliseconds (ms) is considered imperceptible.