Lecture 6: Modeling of MIMO Channels Theoretical Foundations of Wireless Communications 1. Overview. CommTh/EES/KTH
|
|
- Clemence Edwards
- 6 years ago
- Views:
Transcription
1 : Theoretical Foundations of Wireless Communications 1 Wednesday, May 11, :00-12:00, Conference Room SIP 1 Textbook: D. Tse and P. Viswanath, Fundamentals of Wireless Communication 1 / 1 Overview Lecture 5: Spatial Diversity, MIMO Capacity SIMO, MISO, MIMO Degrees of freedom MIMO capacity : MIMO Channel Modeling 2 / 1
2 Overview Motivation: How does the multiplexing capability of MIMO channels depend on the physical environment? When can we gain (much from MIMO? How do we have to design the system? 3 / 1 Physical Modeling Line-of-Sight : SIMO Free space without scattering and reflections. Antenna separation r λ c, with carrier wavelength λ c and the normalized antenna separation r ; n r receive antennas. Distance between transmitter and i-th receive antenna: d i Continuous-time impulse between transmitter and i-th receive antenna: h i (τ = a δ(τ d i /c Base-band model (assuming d i /c 1/W, signal BW W : ( h i = a exp j 2πfcd ( i = a exp j 2πd i c λ c SIMO model: y = h x + w, with w CN (0, N 0I h: signal direction, spatial signature. 4 / 1
3 Physical Modeling Line-of-Sight : SIMO Paths are approx. parallel, i.e., d i d + (i 1 r λ c cos(φ Directional cosine Ω = cos(φ Spatial signature can be expressed as ( h = a exp j 2πd λ c Phased-array antenna. SIMO capacity (with MRC C = log (1 + P h 2 N 0 1 exp( j2π r Ω exp( j2π2 r Ω. exp( j2π(n r 1 r Ω ( = log 1 + Pa2 n r N 0 Only power gain, no degree-of-freedom gain. 5 / 1 Physical Modeling Line-of-Sight : MISO Similar to the SIMO case: t, λ c, d i, φ, Ω,... MISO channel model: y = h x + w, with w CN (0, N 0. Channel vector ( h = a exp j 2πd λ c 1 exp( j2π tω exp( j2π2 tω. exp( j2π(n t 1 tω Unit spatial signature in the directional cosine Ω: e(ω = 1/ n [1, exp( j2π Ω,..., exp( j2π(n 1 Ω] T e t(ω t and e r(ω r with n t, t and n r, r, respectively. 6 / 1
4 Physical Modeling Line-of-Sight : MIMO Linear transmit and receive array with n t, t and n r, r. Gain between transmit antenna k and receive antenna i h ik = a exp ( j2πd ik /λ c Distance between transmit antenna k and receive antenna i d ik = d + (i 1 r λ c cos(φ r (k 1 tλ c cos(φ t MIMO channel matrix (with Ω t = cos(φ t and Ω r = cos(φ r H = a ( n tn r exp j 2πd e r(ω r e t(ω t λ c H is a rank-1 matrix with singular value λ 1 = a n tn r Compare with SVD decomposition in Lecture 5: H = k λ i u i vi i=1 7 / 1 Physical Modeling Line-of-Sight : MIMO MIMO capacity ( C = log 1 + Pa2 n r n t N 0 Only power gain, no degree-of-freedom gain. n t = 1: power gain equals n r receive beamforming. n r = 1: power gain equals n t transmit beamforming. General n t, n r : power gain equals n r n t Transmit and receive beamforming. Conclusion: In LOS environment, MIMO provides only a power gain but no degree-of-freedom gain. 8 / 1
5 Physical Modeling Geographically Separated Antennas at the Transmitter Example/special case (D. Tse and P. Viswanath, Fundamentals of Wireless Communications. 2 distributed transmit antennas, attenuations a 1, a 2, angles of incidence φ r 1, φr 2, negligible delay spread. Spatial signature (n r receive antennas ( h k = a k nr exp j 2πd 1k e r(ω rk λ c Channel matrix H = [h 1, h 2] H has independent columns as long as (Ω r i = cos(φ r i Ω r = Ω r 2 Ω r 1 0 mod 1 r Two non-zero singular values λ 2 1, λ 2 2; i.e., two degrees of freedom. But H can still be ill-conditioned! 9 / 1 Physical Modeling Geographically Separated Antennas at the Transmitter Conditioning of H is determined by how the spatial signatures are aligned (with L r = n r r : cos(θ = f r (Ω r2 Ω r1 = e r(ω r1 e r(ω r2 = sin(πl r Ω r }{{} n r sin(πl r Ω r /n r =f r (Ω r2 Ω r1 Example (a 1 = a 2 = a λ 2 1 = a 2 n r (1 + cos(θ λ 2 2 = a 2 n r (1 cos(θ λ 1 λ 2 = 1 + cos(θ 1 cos(θ f r (Ω r is periodic with n r /L r. Maximum at Ω r = 0; f r (0 = 1. f r (Ω r = 0 at Ω r = k/l r with k = 1,..., n r 1. Resolvability 1/L r, if Ω r 1/L r, then the signals from the two antennas cannot be resolved. 10 / 1
6 Physical Modeling Geographically Separated Antennas at the Transmitter Beamforming pattern Assumption: signal arrives with angle φ 0; receive beamforming vector e r(cos(φ 0. A signal form any other direction φ will be attenuated by a factor (D. Tse and P. Viswanath, Fundamentals of Wireless Communications. e r(cos(φ 0 e r(cos(φ = f r (cos(φ cos(φ 0 Beamforming pattern ( φ, f r (cos(φ cos(φ 0 Main lobes around φ 0 and any angle φ for which cos(φ = cos(φ 0. In a similar way, separated receive antennas can be treated. 11 / 1 Physical Modeling LOS Plus One Reflected Path Direct path: φ t1, Ω r1, d (1, and a 1. Reflected path: φ t2, Ω r2, d (2, and a 2. Channel model follows from signal superposition with H = a b 1e r(ω r1e t(ω t1 + a b 2e r(ω r2e t(ω t2, a b i H has rank 2 as long as = a i ntn r exp ( j (i 2πd. λ c Ω t1 Ω t2 mod 1 t and Ω r1 Ω r2 mod 1 r. H is well conditioned if the angular separations Ω t, Ω r at the transmit/receive array are of the same order or larger than 1/L t,r. 12 / 1
7 Physical Modeling LOS Plus One Reflected Path Direct path: φ t1, Ω r1, d (1, and a 1. Reflected path: φ t2, Ω r2, d (2, and a 2. H can be rewritten as H = H H, with H = [a b 1e r(ω r1, a b 2e r(ω r2] and H = [ et (Ω t1 e t (Ω t2 Two imaginary receivers at points A and B (virtual relays. Since the points A and B are geographically widely separated, H and H have rank 2 and hence H has rank 2 as well. Furthermore, if H and H are well-conditioned, H will be well-conditioned as well. Multipath fading can be viewed as an advantage which can be exploited! ] 13 / 1 Physical Modeling LOS Plus One Reflected Path Significant angular separation is required at both the transmitter and the receiver to obtain a well-conditioned matrix H. If the reflectors are close to the receiver (downlink, we have a small angular separation not very well-conditioned matrix H. Similar, if the reflectors are close to the transmitter (uplink. Size of an antenna array at a base station will have to be many wavelengths to be able to exploit the spatial multiplexing effect. 14 / 1
8 Physical Modeling Summary 15 / 1 Fading General Concept Antenna lengths L t, L r limit the resolvability 2 of the transmit and receive antenna in the angular domain. Sample the angular domain at fixed angular spacings of 1/L t at the transmitter and 1/L r at the receiver. Represent the channel (the multiple paths in terms of these input and output coordinates. The (k, l-th channel gain follows as the aggregation of all paths whose transmit and receive directional cosines lie in a (1/L t 1/L r bin around the point (l/l t, k/l r. 2 Note, if Ω r,t 1/L r,t, the paths cannot be separated. 16 / 1
9 Fading Angular Domain Representation (ADR Orthonormal basis for the received signal space (n r basis vectors { S r = e r(0, e 1 r(,..., e r( nr 1 } L r L r Orthogonality follows directly from the properties of f r (Ω. Orthonormal basis for the transmitted signal space (n t basis vectors { S t = e t(0, e 1 t(,..., e t( nt 1 } L t L t Orthogonality follows directly from the properties of f t(ω. Orthonormal bases provide a very simple (but approximate decomposition of the total received/transmitted signal up to a resolution 1/L r, 1/L t. 17 / 1 Fading Angular Domain Representation Examples: Receive beamform patterns of the angular basis vectors in S r (a Critically spaced ( r = 1/2, each basis vector has a single pair of main lobes. (b Sparsely spaced ( r > 1/2, some of the basis vectors have more than one pair of main lobes. (c Densely spaced ( r < 1/2, some of the basis vectors have no pair of main lobes. 18 / 1
10 Fading ADR of MIMO (Assumption: critically spaced antennas Observation: The vectors in S t and S r form unitary matrices U t and U r with dimensions (n t n t and (n r n r, respectively. ( IDFT matrices! With 3 x a = U t x and y a = U r y we get y a = U r HU tx a + U r w = H a x a + w a, with w a CN (0, N 0I nr. Furthermore, with H = i ab i e r(ω ri e t(ω ti, we get h a kl = e r(k/l r H e t(l/l t = i ai b [e r(k/l r e r(ω ri ] [e t(ω ti e t(l/l t] }{{}}{{} (1 (2 The terms (1 and (2 are significant for the i th path if Ω ri k < 1 and L r L Ω ti k < 1. r L t L t ( Projections on the basis vectors in S r, S t. 3 The superscript a denotes angular domain quantities. 19 / 1 Fading Statistical Modeling in the Angular Domain Let T l and R k be the sets of physical paths which have most energy in directions of e t(l/l t and e r(k/l r. hkl a corresponds to the aggregated gains ai b of paths which lie in R k T l. Independence and time variation Gains of the physical paths ai b [m] are independent. The path gains hkl a [m] are independent across m. The angles {φ ri [m]} m and {φ ti [m]} m evolve slower than a b i [m]. The physical paths do not move from one angular bin to another. The path gains h kl [m] are independent across k and l. If there are many paths in an angular bin Central Limit Theorem h a kl[m] can be modeled as complex circular symmetric Gaussian. If there are no paths in an angular bin h a kl[m] 0. Since U t and U r are unitary matrices, the matrix H has the same i.i.d. Gaussian distribution as H a. 20 / 1
11 Fading Statistical Modeling in the Angular Domain Example for H a (a Small angular spread at the transmitter. (b Small angular spread at the receiver. (c Small angular spread at both transmitter and receiver. (d Full angular spread at both transmitter and receiver. 21 / 1 Fading Degrees of Freedom and Diversity Degrees of freedom Based on the derived statistical model, we get the following result: with probability 1, the rank of the random matrix H a is given by rank(h a = min{ number of non-zero rows, number of non-zero columns }. The number of non-zero rows and columns depends on two factors: Amount of scattering and reflection; the more scattering and reflection, the larger the number of non-zero entries in H a. Lengths L r and L t; for small L r, L t many physical paths are mapped into the same angular bin; with higher resolution, more paths can be represented. Diversity: The diversity is given by the number of non-zero entries in H a. Example: Same number of degrees of freedom but different diversity. 22 / 1
12 Fading Antenna Spacing So far: critically spaced antennas with r = 1/2. One-to-one correspondence between the angular windows and the resolvable bins. Setup 1: vary the number of antennas for a fixed array length L r,t. Sparsely spaced case ( r > 0.5 Beamforming patterns of some basis vectors have multiple main lobes. Different paths with different directions are mapped onto the same basis vector. Resolution of the antenna array, number of degrees of freedom, and diversity are reduced. Densely spaced case ( r < 0.5 There are basis vectors with no main lobes which do not contribute to the resolvability. Adds zero rows and columns to H a and creates correlation in H. Setup 2: vary the antenna separation for a fixed number of antennas. Rich scattering: number of non-zero rows in H a is already n r ; i.e., no improvement possible. Clustered scattering: scattered signal can be received in more bins; i.e., increasing number of degrees of freedom. 23 / 1
Lecture 6: Modeling of MIMO Channels Theoretical Foundations of Wireless Communications 1
Fading : Theoretical Foundations of Wireless Communications 1 Thursday, May 3, 2018 9:30-12:00, Conference Room SIP 1 Textbook: D. Tse and P. Viswanath, Fundamentals of Wireless Communication 1 / 23 Overview
More informationLecture 5: Antenna Diversity and MIMO Capacity Theoretical Foundations of Wireless Communications 1. Overview. CommTh/EES/KTH
: Antenna Diversity and Theoretical Foundations of Wireless Communications Wednesday, May 4, 206 9:00-2:00, Conference Room SIP Textbook: D. Tse and P. Viswanath, Fundamentals of Wireless Communication
More informationLecture 8: MIMO Architectures (II) Theoretical Foundations of Wireless Communications 1. Overview. Ragnar Thobaben CommTh/EES/KTH
MIMO : MIMO Theoretical Foundations of Wireless Communications 1 Wednesday, May 25, 2016 09:15-12:00, SIP 1 Textbook: D. Tse and P. Viswanath, Fundamentals of Wireless Communication 1 / 20 Overview MIMO
More informationLecture 9: Diversity-Multiplexing Tradeoff Theoretical Foundations of Wireless Communications 1. Overview. Ragnar Thobaben CommTh/EES/KTH
: Diversity-Multiplexing Tradeoff Theoretical Foundations of Wireless Communications 1 Rayleigh Wednesday, June 1, 2016 09:15-12:00, SIP 1 Textbook: D. Tse and P. Viswanath, Fundamentals of Wireless Communication
More informationLecture 9: Diversity-Multiplexing Tradeoff Theoretical Foundations of Wireless Communications 1
: Diversity-Multiplexing Tradeoff Theoretical Foundations of Wireless Communications 1 Rayleigh Friday, May 25, 2018 09:00-11:30, Kansliet 1 Textbook: D. Tse and P. Viswanath, Fundamentals of Wireless
More informationLecture 7 MIMO Communica2ons
Wireless Communications Lecture 7 MIMO Communica2ons Prof. Chun-Hung Liu Dept. of Electrical and Computer Engineering National Chiao Tung University Fall 2014 1 Outline MIMO Communications (Chapter 10
More informationMultiple Antennas. Mats Bengtsson, Björn Ottersten. Channel characterization and modeling 1 September 8, Signal KTH Research Focus
Multiple Antennas Channel Characterization and Modeling Mats Bengtsson, Björn Ottersten Channel characterization and modeling 1 September 8, 2005 Signal Processing @ KTH Research Focus Channel modeling
More information12.4 Known Channel (Water-Filling Solution)
ECEn 665: Antennas and Propagation for Wireless Communications 54 2.4 Known Channel (Water-Filling Solution) The channel scenarios we have looed at above represent special cases for which the capacity
More informationMaximum Achievable Diversity for MIMO-OFDM Systems with Arbitrary. Spatial Correlation
Maximum Achievable Diversity for MIMO-OFDM Systems with Arbitrary Spatial Correlation Ahmed K Sadek, Weifeng Su, and K J Ray Liu Department of Electrical and Computer Engineering, and Institute for Systems
More informationTransmit Directions and Optimality of Beamforming in MIMO-MAC with Partial CSI at the Transmitters 1
2005 Conference on Information Sciences and Systems, The Johns Hopkins University, March 6 8, 2005 Transmit Directions and Optimality of Beamforming in MIMO-MAC with Partial CSI at the Transmitters Alkan
More informationMultiple Antennas in Wireless Communications
Multiple Antennas in Wireless Communications Luca Sanguinetti Department of Information Engineering Pisa University luca.sanguinetti@iet.unipi.it April, 2009 Luca Sanguinetti (IET) MIMO April, 2009 1 /
More informationSingle-User MIMO systems: Introduction, capacity results, and MIMO beamforming
Single-User MIMO systems: Introduction, capacity results, and MIMO beamforming Master Universitario en Ingeniería de Telecomunicación I. Santamaría Universidad de Cantabria Contents Introduction Multiplexing,
More informationInformation Theory for Wireless Communications, Part II:
Information Theory for Wireless Communications, Part II: Lecture 5: Multiuser Gaussian MIMO Multiple-Access Channel Instructor: Dr Saif K Mohammed Scribe: Johannes Lindblom In this lecture, we give the
More informationELEC E7210: Communication Theory. Lecture 10: MIMO systems
ELEC E7210: Communication Theory Lecture 10: MIMO systems Matrix Definitions, Operations, and Properties (1) NxM matrix a rectangular array of elements a A. an 11 1....... a a 1M. NM B D C E ermitian transpose
More informationSolution to Homework 1
Solution to Homework 1 1. Exercise 2.4 in Tse and Viswanath. 1. a) With the given information we can comopute the Doppler shift of the first and second path f 1 fv c cos θ 1, f 2 fv c cos θ 2 as well as
More informationEE401: Advanced Communication Theory
EE401: Advanced Communication Theory Professor A. Manikas Chair of Communications and Array Processing Imperial College London Multi-Antenna Wireless Communications Part-B: SIMO, MISO and MIMO Prof. A.
More informationECE6604 PERSONAL & MOBILE COMMUNICATIONS. Week 3. Flat Fading Channels Envelope Distribution Autocorrelation of a Random Process
1 ECE6604 PERSONAL & MOBILE COMMUNICATIONS Week 3 Flat Fading Channels Envelope Distribution Autocorrelation of a Random Process 2 Multipath-Fading Mechanism local scatterers mobile subscriber base station
More informationCapacity of multiple-input multiple-output (MIMO) systems in wireless communications
15/11/02 Capacity of multiple-input multiple-output (MIMO) systems in wireless communications Bengt Holter Department of Telecommunications Norwegian University of Science and Technology 1 Outline 15/11/02
More informationA Signal-Space Analysis of Spatial Self-Interference Isolation for Full-Duplex Wireless
A Signal-Space Analysis of Spatial Self-Interference Isolation for Full-Duplex Wireless Evan Everett Rice University Ashutosh Sabharwal Rice University Abstract The challenge to in-band full-duplex wireless
More informationSpace-Time Coding for Multi-Antenna Systems
Space-Time Coding for Multi-Antenna Systems ECE 559VV Class Project Sreekanth Annapureddy vannapu2@uiuc.edu Dec 3rd 2007 MIMO: Diversity vs Multiplexing Multiplexing Diversity Pictures taken from lectures
More informationThe Optimality of Beamforming: A Unified View
The Optimality of Beamforming: A Unified View Sudhir Srinivasa and Syed Ali Jafar Electrical Engineering and Computer Science University of California Irvine, Irvine, CA 92697-2625 Email: sudhirs@uciedu,
More informationEstimation of Performance Loss Due to Delay in Channel Feedback in MIMO Systems
MITSUBISHI ELECTRIC RESEARCH LABORATORIES http://www.merl.com Estimation of Performance Loss Due to Delay in Channel Feedback in MIMO Systems Jianxuan Du Ye Li Daqing Gu Andreas F. Molisch Jinyun Zhang
More informationLecture 2. Fading Channel
1 Lecture 2. Fading Channel Characteristics of Fading Channels Modeling of Fading Channels Discrete-time Input/Output Model 2 Radio Propagation in Free Space Speed: c = 299,792,458 m/s Isotropic Received
More informationImplementation of a Space-Time-Channel-Filter
Implementation of a Space-Time-Channel-Filter Nadja Lohse, Clemens Michalke, Marcus Bronzel and Gerhard Fettweis Dresden University of Technology Mannesmann Mobilfunk Chair for Mobile Communications Systems
More informationEmmanouel T. Michailidis Athanasios G. Kanatas
Emmanouel T. Michailidis (emichail@unipi.gr) George Efthymoglou (gefthymo@unipi.gr) gr) Athanasios G. Kanatas (kanatas@unipi.gr) University of Piraeus Department of Digital Systems Wireless Communications
More informationMulti-User Gain Maximum Eigenmode Beamforming, and IDMA. Peng Wang and Li Ping City University of Hong Kong
Multi-User Gain Maximum Eigenmode Beamforming, and IDMA Peng Wang and Li Ping City University of Hong Kong 1 Contents Introduction Multi-user gain (MUG) Maximum eigenmode beamforming (MEB) MEB performance
More informationAdvanced Topics in Digital Communications Spezielle Methoden der digitalen Datenübertragung
Advanced Topics in Digital Communications Spezielle Methoden der digitalen Datenübertragung Dr.-Ing. Carsten Bockelmann Institute for Telecommunications and High-Frequency Techniques Department of Communications
More information926 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 53, NO. 3, MARCH Monica Nicoli, Member, IEEE, and Umberto Spagnolini, Senior Member, IEEE (1)
926 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 53, NO. 3, MARCH 2005 Reduced-Rank Channel Estimation for Time-Slotted Mobile Communication Systems Monica Nicoli, Member, IEEE, and Umberto Spagnolini,
More informationELG7177: MIMO Comunications. Lecture 8
ELG7177: MIMO Comunications Lecture 8 Dr. Sergey Loyka EECS, University of Ottawa S. Loyka Lecture 8, ELG7177: MIMO Comunications 1 / 32 Multi-User Systems Can multiple antennas offer advantages for multi-user
More informationOptimum Transmission Scheme for a MISO Wireless System with Partial Channel Knowledge and Infinite K factor
Optimum Transmission Scheme for a MISO Wireless System with Partial Channel Knowledge and Infinite K factor Mai Vu, Arogyaswami Paulraj Information Systems Laboratory, Department of Electrical Engineering
More informationCOMPLEX CONSTRAINED CRB AND ITS APPLICATION TO SEMI-BLIND MIMO AND OFDM CHANNEL ESTIMATION. Aditya K. Jagannatham and Bhaskar D.
COMPLEX CONSTRAINED CRB AND ITS APPLICATION TO SEMI-BLIND MIMO AND OFDM CHANNEL ESTIMATION Aditya K Jagannatham and Bhaskar D Rao University of California, SanDiego 9500 Gilman Drive, La Jolla, CA 92093-0407
More informationLimited Feedback-based Unitary Precoder Design in Rician MIMO Channel via Trellis Exploration Algorithm
Limited Feedback-based Unitary Precoder Design in Rician MIMO Channel via Trellis Exploration Algorithm Dalin Zhu Department of Wireless Communications NEC Laboratories China NLC) 11F Bld.A Innovation
More informationOn the MIMO Channel Capacity Predicted by Kronecker and Müller Models
1 On the MIMO Channel Capacity Predicted by Kronecker and Müller Models Müge Karaman Çolakoğlu and Mehmet Şafak Abstract This paper presents a comparison between the outage capacity of MIMO channels predicted
More informationCapacity Region of the Two-Way Multi-Antenna Relay Channel with Analog Tx-Rx Beamforming
Capacity Region of the Two-Way Multi-Antenna Relay Channel with Analog Tx-Rx Beamforming Authors: Christian Lameiro, Alfredo Nazábal, Fouad Gholam, Javier Vía and Ignacio Santamaría University of Cantabria,
More informationExploiting Partial Channel Knowledge at the Transmitter in MISO and MIMO Wireless
Exploiting Partial Channel Knowledge at the Transmitter in MISO and MIMO Wireless SPAWC 2003 Rome, Italy June 18, 2003 E. Yoon, M. Vu and Arogyaswami Paulraj Stanford University Page 1 Outline Introduction
More informationFull Rank Spatial Channel Estimation at Millimeter Wave Systems
Full Rank Spatial Channel Estimation at Millimeter Wave Systems siao-lan Chiang, Wolfgang Rave, Tobias Kadur, and Gerhard Fettweis Vodafone Chair for Mobile Communications Technische Universität Dresden
More informationMultiple-Input Multiple-Output Systems
Multiple-Input Multiple-Output Systems What is the best way to use antenna arrays? MIMO! This is a totally new approach ( paradigm ) to wireless communications, which has been discovered in 95-96. Performance
More informationMulti-Input Multi-Output Systems (MIMO) Channel Model for MIMO MIMO Decoding MIMO Gains Multi-User MIMO Systems
Multi-Input Multi-Output Systems (MIMO) Channel Model for MIMO MIMO Decoding MIMO Gains Multi-User MIMO Systems Multi-Input Multi-Output Systems (MIMO) Channel Model for MIMO MIMO Decoding MIMO Gains Multi-User
More informationL interférence dans les réseaux non filaires
L interférence dans les réseaux non filaires Du contrôle de puissance au codage et alignement Jean-Claude Belfiore Télécom ParisTech 7 mars 2013 Séminaire Comelec Parts Part 1 Part 2 Part 3 Part 4 Part
More informationBeam Discovery Using Linear Block Codes for Millimeter Wave Communication Networks
Beam Discovery Using Linear Block Codes for Millimeter Wave Communication Networks Yahia Shabara, C. Emre Koksal and Eylem Ekici Department of Electrical and Computer Engineering The Ohio State University,
More informationLimited Feedback in Wireless Communication Systems
Limited Feedback in Wireless Communication Systems - Summary of An Overview of Limited Feedback in Wireless Communication Systems Gwanmo Ku May 14, 17, and 21, 2013 Outline Transmitter Ant. 1 Channel N
More informationParallel Additive Gaussian Channels
Parallel Additive Gaussian Channels Let us assume that we have N parallel one-dimensional channels disturbed by noise sources with variances σ 2,,σ 2 N. N 0,σ 2 x x N N 0,σ 2 N y y N Energy Constraint:
More informationTrust Degree Based Beamforming for Multi-Antenna Cooperative Communication Systems
Introduction Main Results Simulation Conclusions Trust Degree Based Beamforming for Multi-Antenna Cooperative Communication Systems Mojtaba Vaezi joint work with H. Inaltekin, W. Shin, H. V. Poor, and
More informationOptimal Transmit Strategies in MIMO Ricean Channels with MMSE Receiver
Optimal Transmit Strategies in MIMO Ricean Channels with MMSE Receiver E. A. Jorswieck 1, A. Sezgin 1, H. Boche 1 and E. Costa 2 1 Fraunhofer Institute for Telecommunications, Heinrich-Hertz-Institut 2
More informationCHANNEL FEEDBACK QUANTIZATION METHODS FOR MISO AND MIMO SYSTEMS
CHANNEL FEEDBACK QUANTIZATION METHODS FOR MISO AND MIMO SYSTEMS June Chul Roh and Bhaskar D Rao Department of Electrical and Computer Engineering University of California, San Diego La Jolla, CA 9293 47,
More informationComparisons of Performance of Various Transmission Schemes of MIMO System Operating under Rician Channel Conditions
Comparisons of Performance of Various ransmission Schemes of MIMO System Operating under ician Channel Conditions Peerapong Uthansakul and Marek E. Bialkowski School of Information echnology and Electrical
More informationMulti-Input Multi-Output Systems (MIMO) Channel Model for MIMO MIMO Decoding MIMO Gains Multi-User MIMO Systems
Multi-Input Multi-Output Systems (MIMO) Channel Model for MIMO MIMO Decoding MIMO Gains Multi-User MIMO Systems Multi-Input Multi-Output Systems (MIMO) Channel Model for MIMO MIMO Decoding MIMO Gains Multi-User
More informationCS6956: Wireless and Mobile Networks Lecture Notes: 2/4/2015
CS6956: Wireless and Mobile Networks Lecture Notes: 2/4/2015 [Most of the material for this lecture has been taken from the Wireless Communications & Networks book by Stallings (2 nd edition).] Effective
More informationPOWER ALLOCATION AND OPTIMAL TX/RX STRUCTURES FOR MIMO SYSTEMS
POWER ALLOCATION AND OPTIMAL TX/RX STRUCTURES FOR MIMO SYSTEMS R. Cendrillon, O. Rousseaux and M. Moonen SCD/ESAT, Katholiee Universiteit Leuven, Belgium {raphael.cendrillon, olivier.rousseaux, marc.moonen}@esat.uleuven.ac.be
More informationOn the Optimality of Multiuser Zero-Forcing Precoding in MIMO Broadcast Channels
On the Optimality of Multiuser Zero-Forcing Precoding in MIMO Broadcast Channels Saeed Kaviani and Witold A. Krzymień University of Alberta / TRLabs, Edmonton, Alberta, Canada T6G 2V4 E-mail: {saeed,wa}@ece.ualberta.ca
More informationPerformance Analysis for Strong Interference Remove of Fast Moving Target in Linear Array Antenna
Performance Analysis for Strong Interference Remove of Fast Moving Target in Linear Array Antenna Kwan Hyeong Lee Dept. Electriacal Electronic & Communicaton, Daejin University, 1007 Ho Guk ro, Pochen,Gyeonggi,
More informationOn the Throughput of Proportional Fair Scheduling with Opportunistic Beamforming for Continuous Fading States
On the hroughput of Proportional Fair Scheduling with Opportunistic Beamforming for Continuous Fading States Andreas Senst, Peter Schulz-Rittich, Gerd Ascheid, and Heinrich Meyr Institute for Integrated
More informationELEC546 MIMO Channel Capacity
ELEC546 MIMO Channel Capacity Vincent Lau Simplified Version.0 //2004 MIMO System Model Transmitter with t antennas & receiver with r antennas. X Transmitted Symbol, received symbol Channel Matrix (Flat
More informationMassive MIMO: Signal Structure, Efficient Processing, and Open Problems II
Massive MIMO: Signal Structure, Efficient Processing, and Open Problems II Mahdi Barzegar Communications and Information Theory Group (CommIT) Technische Universität Berlin Heisenberg Communications and
More informationEvaluation of Suburban measurements by eigenvalue statistics
Evaluation of Suburban measurements by eigenvalue statistics Helmut Hofstetter, Ingo Viering, Wolfgang Utschick Forschungszentrum Telekommunikation Wien, Donau-City-Straße 1, 1220 Vienna, Austria. Siemens
More informationDouble-Directional Estimation for MIMO Channels
Master Thesis Double-Directional Estimation for MIMO Channels Vincent Chareyre July 2002 IR-SB-EX-0214 Abstract Space-time processing based on antenna arrays is considered to significantly enhance the
More informationReduced Complexity Space-Time Optimum Processing
Reduced Complexity Space-Time Optimum Processing Jens Jelitto, Marcus Bronzel, Gerhard Fettweis Dresden University of Technology, Germany Abstract New emerging space-time processing technologies promise
More informationMorning Session Capacity-based Power Control. Department of Electrical and Computer Engineering University of Maryland
Morning Session Capacity-based Power Control Şennur Ulukuş Department of Electrical and Computer Engineering University of Maryland So Far, We Learned... Power control with SIR-based QoS guarantees Suitable
More informationDownlink Precoding for Massive MIMO Systems Exploiting Virtual Channel Model Sparsity
1 Downlink Precoding for Massive MIMO Systems Exploiting Virtual Channel Model Sparsity Thomas Ketseoglou, Senior Member, IEEE, and Ender Ayanoglu, Fellow, IEEE arxiv:1706.03294v3 [cs.it] 13 Jul 2017 Abstract
More informationOn Limits of Multi-Antenna. Wireless Communications in. Spatially Selective Channels
On Limits of Multi-Antenna Wireless Communications in Spatially Selective Channels Tony Steven Pollock B.E.(Hons 1) (Canterbury) B.Sc. (Otago) July 2003 A thesis submitted for the degree of Doctor of Philosophy
More informationVECTOR QUANTIZATION TECHNIQUES FOR MULTIPLE-ANTENNA CHANNEL INFORMATION FEEDBACK
VECTOR QUANTIZATION TECHNIQUES FOR MULTIPLE-ANTENNA CHANNEL INFORMATION FEEDBACK June Chul Roh and Bhaskar D. Rao Department of Electrical and Computer Engineering University of California, San Diego La
More informationMassive MIMO Channel Modeling
Massive MIMO Channel Modeling Xin You carolineyou@sina.com Yongchun Wang w yongchun@sina.com Department of Electrical and Information Technology Lund University Advisor: Xiang Gao, Ove Edfors December,
More informationECE6604 PERSONAL & MOBILE COMMUNICATIONS. Week 4. Envelope Correlation Space-time Correlation
ECE6604 PERSONAL & MOBILE COMMUNICATIONS Week 4 Envelope Correlation Space-time Correlation 1 Autocorrelation of a Bandpass Random Process Consider again the received band-pass random process r(t) = g
More informationLecture 7: Wireless Channels and Diversity Advanced Digital Communications (EQ2410) 1
Wireless : Wireless Advanced Digital Communications (EQ2410) 1 Thursday, Feb. 11, 2016 10:00-12:00, B24 1 Textbook: U. Madhow, Fundamentals of Digital Communications, 2008 1 / 15 Wireless Lecture 1-6 Equalization
More informationSPARSE MULTIPATH MIMO CHANNELS: PERFORMANCE IMPLICATIONS BASED ON MEASUREMENT DATA
SPARSE MULTIPATH MIMO CHANNELS: PERFORMANCE IMPLICATIONS BASED ON MEASUREMENT DATA Michail Matthaiou 1, Akbar M. Sayeed 2, and Josef A. Nossek 1 1 Institute for Circuit Theory and Signal Processing, Technische
More informationEfficient Signaling Schemes for Wideband Space-time Wireless Channels Using Channel State Information
Efficient Signaling Schemes for Wideband Space-time Wireless Channels Using Channel State Information Eko N. Onggosanusi, Barry D. Van Veen, and Akbar M. Sayeed Department of Electrical and Computer Engineering
More informationTight Lower Bounds on the Ergodic Capacity of Rayleigh Fading MIMO Channels
Tight Lower Bounds on the Ergodic Capacity of Rayleigh Fading MIMO Channels Özgür Oyman ), Rohit U. Nabar ), Helmut Bölcskei 2), and Arogyaswami J. Paulraj ) ) Information Systems Laboratory, Stanford
More informationMIMO Capacity of an OFDM-based system under Ricean fading
MIMO Capacity of an OFDM-based system under Ricean fading Laxminarayana S. Pillutla and Sudharman K. Jayaweera Abslract-In this paper we present the multiple-input multiple-output (MIMO) capacity results
More informationSecure Degrees of Freedom of the MIMO Multiple Access Wiretap Channel
Secure Degrees of Freedom of the MIMO Multiple Access Wiretap Channel Pritam Mukherjee Sennur Ulukus Department of Electrical and Computer Engineering University of Maryland, College Park, MD 074 pritamm@umd.edu
More informationUpper Bounds on MIMO Channel Capacity with Channel Frobenius Norm Constraints
Upper Bounds on IO Channel Capacity with Channel Frobenius Norm Constraints Zukang Shen, Jeffrey G. Andrews, Brian L. Evans Wireless Networking Communications Group Department of Electrical Computer Engineering
More informationPower and Complex Envelope Correlation for Modeling Measured Indoor MIMO Channels: A Beamforming Evaluation
Power and Complex Envelope Correlation for Modeling Measured Indoor MIMO Channels: A Beamforming Evaluation Jon Wallace, Hüseyin Özcelik, Markus Herdin, Ernst Bonek, and Michael Jensen Department of Electrical
More informationTransmit Diversity for Arrays in Correlated Rayleigh Fading
1 Transmit Diversity for Arrays in Correlated Rayleigh Fading Cornelius van Rensburg and Benjamin Friedlander C van Rensburg is with Samsung Telecommunications America. E-mail: cdvanren@ece.ucdavis.edu
More informationOn the Required Accuracy of Transmitter Channel State Information in Multiple Antenna Broadcast Channels
On the Required Accuracy of Transmitter Channel State Information in Multiple Antenna Broadcast Channels Giuseppe Caire University of Southern California Los Angeles, CA, USA Email: caire@usc.edu Nihar
More informationRoot-MUSIC Time Delay Estimation Based on Propagator Method Bin Ba, Yun Long Wang, Na E Zheng & Han Ying Hu
International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 15) Root-MUSIC ime Delay Estimation Based on ropagator Method Bin Ba, Yun Long Wang, Na E Zheng & an Ying
More informationParametric Characterization and Estimation of Dispersive Multi-Path Components with SAGE in Radio Propagation Channel
Parametric Characterization and Estimation of Dispersive Multi-Path Components with SAGE in Radio Propagation Channel SIGNAL AND INFORMATION PROCESSING IN COMMUNICATIONS SYSTEMS DEPARTMENT OF COMMUNICATION
More informationThe Dirty MIMO Multiple-Access Channel
The Dirty MIMO Multiple-Access Channel Anatoly Khina, Caltech Joint work with: Yuval Kochman, Hebrew University Uri Erez, Tel-Aviv University ISIT 2016 Barcelona, Catalonia, Spain July 12, 2016 Motivation:
More information2318 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 54, NO. 6, JUNE Mai Vu, Student Member, IEEE, and Arogyaswami Paulraj, Fellow, IEEE
2318 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 54, NO. 6, JUNE 2006 Optimal Linear Precoders for MIMO Wireless Correlated Channels With Nonzero Mean in Space Time Coded Systems Mai Vu, Student Member,
More informationA Sparseness-Preserving Virtual MIMO Channel Model
A Sparseness-reserving Virtual MIMO Channel Model hil Schniter Dept. ECE, The Ohio State University Columbus, OH schniter.@osu.edu Akbar Sayeed Dept. ECE, University of Wisconsin Madison, WI akbar@engr.wisc.edu
More informationLow Resolution Adaptive Compressed Sensing for mmwave MIMO receivers
Low Resolution Adaptive Compressed Sensing for mmwave MIMO receivers Cristian Rusu, Nuria González-Prelcic and Robert W. Heath Motivation 2 Power consumption at mmwave in a MIMO receiver Power at 60 GHz
More informationRate-Optimum Beamforming Transmission in MIMO Rician Fading Channels
Rate-Optimum Beamforming Transmission in MIMO Rician Fading Channels Dimitrios E. Kontaxis National and Kapodistrian University of Athens Department of Informatics and telecommunications Abstract In this
More informationAnatoly Khina. Joint work with: Uri Erez, Ayal Hitron, Idan Livni TAU Yuval Kochman HUJI Gregory W. Wornell MIT
Network Modulation: Transmission Technique for MIMO Networks Anatoly Khina Joint work with: Uri Erez, Ayal Hitron, Idan Livni TAU Yuval Kochman HUJI Gregory W. Wornell MIT ACC Workshop, Feder Family Award
More informationUSING multiple antennas has been shown to increase the
IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. 55, NO. 1, JANUARY 2007 11 A Comparison of Time-Sharing, DPC, and Beamforming for MIMO Broadcast Channels With Many Users Masoud Sharif, Member, IEEE, and Babak
More informationSPARSE signal representations have gained popularity in recent
6958 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 57, NO. 10, OCTOBER 2011 Blind Compressed Sensing Sivan Gleichman and Yonina C. Eldar, Senior Member, IEEE Abstract The fundamental principle underlying
More informationAdvanced 3 G and 4 G Wireless Communication Prof. Aditya K Jagannathan Department of Electrical Engineering Indian Institute of Technology, Kanpur
Advanced 3 G and 4 G Wireless Communication Prof. Aditya K Jagannathan Department of Electrical Engineering Indian Institute of Technology, Kanpur Lecture - 19 Multi-User CDMA Uplink and Asynchronous CDMA
More informationLecture 4 Capacity of Wireless Channels
Lecture 4 Capacity of Wireless Channels I-Hsiang Wang ihwang@ntu.edu.tw 3/0, 014 What we have learned So far: looked at specific schemes and techniques Lecture : point-to-point wireless channel - Diversity:
More informationHybrid Analog-Digital Beamforming for Massive MIMO Systems
1 Hybrid Analog-Digital Beamforg for Massive MIMO Systems Shahar Stein, Student IEEE and Yonina C. Eldar, Fellow IEEE precoder then maps the small number of digital outputs to a large number of antennas,
More informationLecture 2. Capacity of the Gaussian channel
Spring, 207 5237S, Wireless Communications II 2. Lecture 2 Capacity of the Gaussian channel Review on basic concepts in inf. theory ( Cover&Thomas: Elements of Inf. Theory, Tse&Viswanath: Appendix B) AWGN
More informationUnder sum power constraint, the capacity of MIMO channels
IEEE TRANSACTIONS ON COMMUNICATIONS, VOL 6, NO 9, SEPTEMBER 22 242 Iterative Mode-Dropping for the Sum Capacity of MIMO-MAC with Per-Antenna Power Constraint Yang Zhu and Mai Vu Abstract We propose an
More informationINVERSE EIGENVALUE STATISTICS FOR RAYLEIGH AND RICIAN MIMO CHANNELS
INVERSE EIGENVALUE STATISTICS FOR RAYLEIGH AND RICIAN MIMO CHANNELS E. Jorswieck, G. Wunder, V. Jungnickel, T. Haustein Abstract Recently reclaimed importance of the empirical distribution function of
More informationA DIFFUSION MODEL FOR UWB INDOOR PROPAGATION. Majid A. Nemati and Robert A. Scholtz. University of Southern California Los Angeles, CA
A DIFFUION MODEL FOR UWB INDOOR PROPAGATION Majid A. Nemati and Robert A. choltz nematian@usc.edu scholtz@usc.edu University of outhern California Los Angeles, CA ABTRACT This paper proposes a diffusion
More informationMassive MIMO As Enabler for Communications with Drone Swarms
Massive MIMO As Enabler for Communications with Drone Swarms Prabhu Chandhar, Danyo Danev, and Erik G. Larsson Division of Communication Systems Dept. of Electrical Engineering Linköping University, Sweden
More informationA Precoding Method for Multiple Antenna System on the Riemannian Manifold
Journal of Communications Vol. 9, No. 2, February 2014 A Precoding Method for Multiple Antenna System on the Riemannian Manifold Lin Zhang1 and S. H. Leung2 1 Department of Electronic Engineering, City
More informationHomework 5 Solutions. Problem 1
Homework 5 Solutions Problem 1 (a Closed form Chernoff upper-bound for the uncoded 4-QAM average symbol error rate over Rayleigh flat fading MISO channel with = 4, assuming transmit-mrc The vector channel
More informationRandom Matrices and Wireless Communications
Random Matrices and Wireless Communications Jamie Evans Centre for Ultra-Broadband Information Networks (CUBIN) Department of Electrical and Electronic Engineering University of Melbourne 3.5 1 3 0.8 2.5
More informationRandom Access Protocols for Massive MIMO
Random Access Protocols for Massive MIMO Elisabeth de Carvalho Jesper H. Sørensen Petar Popovski Aalborg University Denmark Emil Björnson Erik G. Larsson Linköping University Sweden 2016 Tyrrhenian International
More informationTime Reversal Transmission in MIMO Radar
Time Reversal Transmission in MIMO Radar Yuanwei Jin, José M.F. Moura, and Nicholas O Donoughue Electrical and Computer Engineering Carnegie Mellon University Pittsburgh, PA 53 Abstract Time reversal explores
More informationECE 8201: Low-dimensional Signal Models for High-dimensional Data Analysis
ECE 8201: Low-dimensional Signal Models for High-dimensional Data Analysis Lecture 7: Matrix completion Yuejie Chi The Ohio State University Page 1 Reference Guaranteed Minimum-Rank Solutions of Linear
More informationChannel. Feedback. Channel
Space-time Transmit Precoding with Imperfect Feedback Eugene Visotsky Upamanyu Madhow y Abstract The use of channel feedback from receiver to transmitter is standard in wireline communications. While knowledge
More informationCHARACTERIZATION AND ANALYSIS OF DOUBLY DISPERSIVE MIMO CHANNELS. Gerald Matz
CHARACTERIZATION AND ANALYSIS OF DOUBLY DISPERSIVE MIMO CHANNELS Gerald Matz Institute of Communications and Radio-Frequency Engineering, Vienna University of Technology Gusshausstrasse 2/389, A-14 Vienna,
More informationWireless Communications
NETW701 Wireless Communications Dr. Wassim Alexan Winter 2018 Lecture 2 NETW705 Mobile Communication Networks Dr. Wassim Alexan Winter 2018 Lecture 2 Wassim Alexan 2 Reflection When a radio wave propagating
More information