Received Signal, Interference and Noise

Size: px
Start display at page:

Download "Received Signal, Interference and Noise"

Transcription

1 Optimum Combining Maximum ratio combining (MRC) maximizes the output signal-to-noise ratio (SNR) and is the optimal combining method in a maximum likelihood sense for channels where the additive impairment is additive white Gaussian noise. When the additive channel impairment is dominated by co-channel interference, it is better to use optimum combining (OC) which is designed to maximize the output signal-tointerference-plus-noise ratio (SINR). OC uses the spatial diversity not only to combat fading of the desired signal, as is the case with MRC, but also to reduce the relative power of the interfering signals at the receiver, such that the instantaneous SINR is maximized. This is achieved by exploiting the correlation of the interference across the multiple receiver antenna elements. By combining the signals that are received by multiple antennas, OC can suppress the interference and improve the output signal-to-interference-plus-noise ratio by several decibels in interference dominant environments. 1

2 Received Signal, Interference and Noise Consider a situation of a desired signal in the presence of K co-channel interferers. The signal vectors at the L receiver antennas are equal to where r k = g k,0 s 0 + K g k,i s i +ñ k, k = 1,..., L, s 0 = ( s 0,1, s 0,2,..., s 0,N ) s i = ( s i,1, s i,2,..., s i,n ) ñ k = (ñ k,1,ñ k,2,...,ñ k,n ) are the desired signal vector, i th interfering signal vector, and noise vector, respectively, N is the dimension of the signal space, and K is the number of interferers. The L received signal vectors can be stacked in a column to yield the L N received matrix where R t = r 1 r 2. r L R t = g 0 s 0 + K, g i = g i,1 g i,2. g i,l g i s i +Ñ,, Ñ = ñ 1 ñ 2. ñ L. 2

3 Signal Correlations The L L received desired-signal-plus-interference-plus noise covariance matrix is given by Φ Rt Rt = 1 2 E s 0, s i,ñ g 0 s 0 + K where ( ) H denotes complex conjugate transpose. g i s i +Ñ g 0 s 0 + K H g i s i +Ñ, (1) Likewise, the L L received interference-plus-noise covariance matrix is given by Φ Ri Ri = 1 2 E s i,ñ K K g i s i +Ñ H g i s i +Ñ. Note that the expectations are taken over a period that is much less than the channel coherence time, i.e., several modulated symbol durations so that the channel is essentially static. If the interfering signal and noise vectors are uncorrelated and Φ Rt Rt = g 0 g H 0 E av + K Φ Ri Ri = K g i g H i Ei av +N oi, (2) g i g H i E i av +N o I, respectively, where I is the L L identity matrix and E i av is the average energy in the i th interfering signal. The matrices Φ Rt Rt and Φ Ri Ri will vary at the channel fading rate. 3

4 Optimum Combining and MMSE Solution The received signals vectors r k are multiplied by controllable weights w k and summed together, i.e., the combiner output is r = L k=1 w k r k = w T Rt, where w = (w 1,w 2,...,w L ) T is the weight vector. Several approaches can be taken to find the optimal weight vector w. One approach is to minimize the mean square error J = E [ r s 0 2] where Φ Rt Rt is defined in (1) and = E [ w T Rt s 0 2] = 2w T Φ Rt Rt w 4Re { Φ s0 Rt w } 2E av Φ s0 Rt = E [ s 0 RH t ] = 2Eav g H 0. The minimum mean square error (MMSE) solution is obtained by w J = J J,, = 4w T = 0. Φ Rt Rt 4Φ s0 Rt w 1 w L The solution is w opt = Φ 1 R t Rt Φ T s 0 Rt = 2E av Φ 1 R t Rt g0. 4

5 Optimum Combining and MMSE Solution Since Φ Rt Rt = g 0 g H 0E av +Φ Ri Ri, we can write w opt = 2E av ( Φ Ri Ri +g 0 g H 0 E av) 1 g 0 = 2E av ( Φ Ri Ri +g 0 gt 0 E av) 1 g 0. Next, we apply a variation of the matrix inversion lemma resulting in (A+uv H ) 1 = A 1 A 1 uv H A 1 1+v H A 1 u w opt = 2E av = 2E av Φ 1 E avφ 1 g R i Ri 0g 0Φ T 1 R i Ri 1+E av g0φ T E av g0φ T 1 = C Φ 1 R i Ri g 0, where C = 2E av /(1+E av g0 TΦ 1 g R i Ri 0 ) is a scalar. R i Ri g 0 R i Ri g R g0 i Ri 0 Φ 1 R i Ri g 0 5

6 Optimum Combining and Maximum SINR Solution Another criterion is to maximize the instantaneous signal-to-interference-plus-noise ratio (SINR) at the output of the combiner Solving for the optimum weight vector gives ω = wt g 0 g0 HE avw w T Φ 1. w R i Ri w opt = B Φ 1 R i Ri g 0, where B is an arbitrary constant. Hence, the maximum instantaneous output SINR is ω = E av g H 0 Φ 1 R i Ri g 0. Note that the maximum instantaneous output SINR does not depend on the choice of the scalar B. Therefore, the MMSE weight vector also maximizes the instantaneous output SINR. When no interference is present, Φ Ri Ri = N o I and so that the combiner output is w opt = g 0 N o, r = L g0,k k. k=1 N o r OC reduces to MRC when no interference is present. 6

7 Performance of Optimum Combining To evaluate the performance of OC, several definitions are required as follows: average received desired signal power per antenna Ω = average received noise plus interference power per antenna average received desired signal power per antenna γ c = = E[ g 0,k 2 ]E av average received noise power per antenna N o γ i = average received ith interferer power per antenna = E[ g i,k 2 ]Eav i average received noise power per antenna N o instantaneous desired signal power at the array output ω R = average noise plus interference power at the array output instantaneous desired signal power at the array output ω = instantaneous noise plus interference power at the array output In the above definitions, average refers to the average over the Rayleigh fading, while instantaneous refers to an average over a period that is much less than the channel coherence time, i.e., several modulated symbol durations so that the channel is essentially static. Note that Ω = γ c 1+ K k=1 γ i. 7

8 Fading of the Desired Signal Only ω R is equal to where, with a single interferer, ω R = E av g H 0 Φ 1 R i Ri g 0, Φ Ri Ri = E 1 av E[g 1g H 1 ]+N oi. Note that the above expectation in is over the Rayleigh fading. The pdf of ω R is and the cdf of ω R is p ωr (x) = e x/ γ c (x/ γ c ) L 1 (1+L ) γ c (L 2)! F ωr (x) = x/ γ c 0 which are valid for L 2. e y y L 1 (1+L ) (L 2)! e ((x/ γ c)l )t (1 t) L 2 dt (3) 0 e (yl )t (1 t) L 2 dtdy. (4) Note that ω R in (3) and (4) is normalized by γ c. Since γ c = (1+ )Ω for the case of a single interferer, it is apparent that ω R can be normalized by Ω as well, i.e., replace x/ γ c in the above pdf and cdf with x/(1+ )Ω. The normalization by Ω allows for a straight forward comparison of OC and MRC. 8

9 10 0 =0 =1 (0 db) 10 1 =2 (3 db) F ωr (x) 10 2 L=2 L= x/ω (db) Cdf of γ mr s for maximal ratio combining; γ c is the average branch symbol energy-to-noise ratio. 9

10 BER Performance The probability of bit error for coherently detected BPSK is given by P b = 0 Q ( 2x ) p ωr (x)dx Bogachev and Kieslev derived the bit error probability (for L 2) as where P b = ( 1)L 1 (1+L ) 2(L ) L + L 1 1+L L 2 k=1 ( L ) k 1 γ c 1+ γ c 1+ k γ c 1 1+ γ c 1+L (2i 1)!! i!(2+2 γ c ) i (2i 1)!! = (2i 1). γ c 1+L + γ c Simon and Alouini have derived the following expression which is valid for L 1: P b = γ c 1+ γ c γ c 1+L + γ c L 2 k=0 2k k 1 L 1 [4(1+ γ c )] k L L L 1 k 10

11 =0 =1 (0 db) =2 (3 db) 10 2 P b 10 3 L= L= L= Ω (db) Bit error probability for coherent BPSK and optimal combining for various values of and various number of receiver antenna elements, L. 11

12 Fading of the Desired and Interfering Signals The maximum instantaneous output SINR is equal to where, with a single interferer, ω = E av g H 0Φ 1 R i Ri g 0, Φ Ri Ri = E 1 avg 1 g H 1 +N o I. In this case, the matrix Φ Ri Ri varies at the fading rate. Using eigenvalue decomposition, the probability of bit error with coherent BPSK is P b = P b γ1 (x)p γ1 (x)dx = γ c γ c +1 L 2 1 2Γ(L)( ) L 1 γ c γ c +1 L 2 k=0 k=0 (2k)! k! 2k k 1 4( γ c +1) k π γ c exp γ c+1 erfc 4( γ c +1) k. γ c +1 12

13 =0 =1 (0 db) =2 (3 db) 10 2 P b 10 3 L=4 L= Ω (db) Bit error probability for coherent BPSK and optimal combining for various values of and various number of receiver antenna elements, L. 13

14 faded interferer o non faded interferer =0 =1 (0 db) =2 (3 db) 10 2 P b 10 3 L= L= Ω (db) Comparison of the bit error probability for coherent BPSK and optimal combining for a non-faded interferer and a faded interferer; the performance is almost identical. 14

EE6604 Personal & Mobile Communications. Week 13. Multi-antenna Techniques

EE6604 Personal & Mobile Communications. Week 13. Multi-antenna Techniques EE6604 Personal & Mobile Communications Week 13 Multi-antenna Techniques 1 Diversity Methods Diversity combats fading by providing the receiver with multiple uncorrelated replicas of the same information

More information

EE 5407 Part II: Spatial Based Wireless Communications

EE 5407 Part II: Spatial Based Wireless Communications EE 5407 Part II: Spatial Based Wireless Communications Instructor: Prof. Rui Zhang E-mail: rzhang@i2r.a-star.edu.sg Website: http://www.ece.nus.edu.sg/stfpage/elezhang/ Lecture II: Receive Beamforming

More information

Lecture 7 MIMO Communica2ons

Lecture 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 information

Optimal Receiver for MPSK Signaling with Imperfect Channel Estimation

Optimal Receiver for MPSK Signaling with Imperfect Channel Estimation This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the WCNC 27 proceedings. Optimal Receiver for PSK Signaling with Imperfect

More information

Title. Author(s)Tsai, Shang-Ho. Issue Date Doc URL. Type. Note. File Information. Equal Gain Beamforming in Rayleigh Fading Channels

Title. Author(s)Tsai, Shang-Ho. Issue Date Doc URL. Type. Note. File Information. Equal Gain Beamforming in Rayleigh Fading Channels Title Equal Gain Beamforming in Rayleigh Fading Channels Author(s)Tsai, Shang-Ho Proceedings : APSIPA ASC 29 : Asia-Pacific Signal Citationand Conference: 688-691 Issue Date 29-1-4 Doc URL http://hdl.handle.net/2115/39789

More information

A New SLNR-based Linear Precoding for. Downlink Multi-User Multi-Stream MIMO Systems

A New SLNR-based Linear Precoding for. Downlink Multi-User Multi-Stream MIMO Systems A New SLNR-based Linear Precoding for 1 Downlin Multi-User Multi-Stream MIMO Systems arxiv:1008.0730v1 [cs.it] 4 Aug 2010 Peng Cheng, Meixia Tao and Wenjun Zhang Abstract Signal-to-leaage-and-noise ratio

More information

Exploiting Partial Channel Knowledge at the Transmitter in MISO and MIMO Wireless

Exploiting 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 information

Outline - Part III: Co-Channel Interference

Outline - Part III: Co-Channel Interference General Outline Part 0: Background, Motivation, and Goals. Part I: Some Basics. Part II: Diversity Systems. Part III: Co-Channel Interference. Part IV: Multi-Hop Communication Systems. Outline - Part III:

More information

ELEC E7210: Communication Theory. Lecture 10: MIMO systems

ELEC 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 information

Design of MMSE Multiuser Detectors using Random Matrix Techniques

Design of MMSE Multiuser Detectors using Random Matrix Techniques Design of MMSE Multiuser Detectors using Random Matrix Techniques Linbo Li and Antonia M Tulino and Sergio Verdú Department of Electrical Engineering Princeton University Princeton, New Jersey 08544 Email:

More information

Lecture 8: MIMO Architectures (II) Theoretical Foundations of Wireless Communications 1. Overview. Ragnar Thobaben CommTh/EES/KTH

Lecture 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 information

Performance Analysis of Multiple Antenna Systems with VQ-Based Feedback

Performance Analysis of Multiple Antenna Systems with VQ-Based Feedback Performance Analysis of Multiple Antenna Systems with VQ-Based Feedback June Chul Roh and Bhaskar D. Rao Department of Electrical and Computer Engineering University of California, San Diego La Jolla,

More information

Impact of channel-state information on coded transmission over fading channels with diversity reception

Impact of channel-state information on coded transmission over fading channels with diversity reception Impact of channel-state information on coded transmission over fading channels with diversity reception Giorgio Taricco Ezio Biglieri Giuseppe Caire September 4, 1998 Abstract We study the synergy between

More information

Digital Band-pass Modulation PROF. MICHAEL TSAI 2011/11/10

Digital Band-pass Modulation PROF. MICHAEL TSAI 2011/11/10 Digital Band-pass Modulation PROF. MICHAEL TSAI 211/11/1 Band-pass Signal Representation a t g t General form: 2πf c t + φ t g t = a t cos 2πf c t + φ t Envelope Phase Envelope is always non-negative,

More information

BER Performance Analysis of Cooperative DaF Relay Networks and a New Optimal DaF Strategy

BER Performance Analysis of Cooperative DaF Relay Networks and a New Optimal DaF Strategy 144 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 1, NO. 4, APRIL 11 BER Performance Analysis of Cooperative DaF Relay Networks and a New Optimal DaF Strategy George A. Ropokis, Athanasios A. Rontogiannis,

More information

A SEMI-BLIND TECHNIQUE FOR MIMO CHANNEL MATRIX ESTIMATION. AdityaKiran Jagannatham and Bhaskar D. Rao

A SEMI-BLIND TECHNIQUE FOR MIMO CHANNEL MATRIX ESTIMATION. AdityaKiran Jagannatham and Bhaskar D. Rao A SEMI-BLIND TECHNIQUE FOR MIMO CHANNEL MATRIX ESTIMATION AdityaKiran Jagannatham and Bhaskar D. Rao Department of Electrical and Computer Engineering University of California, San Diego La Jolla, CA 9093-0407

More information

Diversity Combining Techniques

Diversity Combining Techniques Diversity Combining Techniques When the required signal is a combination of several plane waves (multipath), the total signal amplitude may experience deep fades (Rayleigh fading), over time or space.

More information

Lecture 5: Antenna Diversity and MIMO Capacity Theoretical Foundations of Wireless Communications 1. Overview. CommTh/EES/KTH

Lecture 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 information

Capacity of multiple-input multiple-output (MIMO) systems in wireless communications

Capacity 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 information

MASSIVE MIMO BSs, which are equipped with hundreds. Large-Scale-Fading Decoding in Cellular Massive MIMO Systems with Spatially Correlated Channels

MASSIVE MIMO BSs, which are equipped with hundreds. Large-Scale-Fading Decoding in Cellular Massive MIMO Systems with Spatially Correlated Channels 1 Large-Scale-Fading Decoding in Cellular Massive MIMO Systems with Spatially Correlated Channels Trinh Van Chien, Student Member, IEEE, Christopher Mollén, Emil Björnson, Senior Member, IEEE arxiv:1807.08071v

More information

ML Detection with Blind Linear Prediction for Differential Space-Time Block Code Systems

ML Detection with Blind Linear Prediction for Differential Space-Time Block Code Systems ML Detection with Blind Prediction for Differential SpaceTime Block Code Systems Seree Wanichpakdeedecha, Kazuhiko Fukawa, Hiroshi Suzuki, Satoshi Suyama Tokyo Institute of Technology 11, Ookayama, Meguroku,

More information

Performance Analysis of Wireless Single Input Multiple Output Systems (SIMO) in Correlated Weibull Fading Channels

Performance Analysis of Wireless Single Input Multiple Output Systems (SIMO) in Correlated Weibull Fading Channels Performance Analysis of Wireless Single Input Multiple Output Systems (SIMO) in Correlated Weibull Fading Channels Zafeiro G. Papadimitriou National and Kapodistrian University of Athens Department of

More information

Simultaneous SDR Optimality via a Joint Matrix Decomp.

Simultaneous SDR Optimality via a Joint Matrix Decomp. Simultaneous SDR Optimality via a Joint Matrix Decomposition Joint work with: Yuval Kochman, MIT Uri Erez, Tel Aviv Uni. May 26, 2011 Model: Source Multicasting over MIMO Channels z 1 H 1 y 1 Rx1 ŝ 1 s

More information

CHAPTER 14. Based on the info about the scattering function we know that the multipath spread is T m =1ms, and the Doppler spread is B d =0.2 Hz.

CHAPTER 14. Based on the info about the scattering function we know that the multipath spread is T m =1ms, and the Doppler spread is B d =0.2 Hz. CHAPTER 4 Problem 4. : Based on the info about the scattering function we know that the multipath spread is T m =ms, and the Doppler spread is B d =. Hz. (a) (i) T m = 3 sec (ii) B d =. Hz (iii) ( t) c

More information

DFT-Based Hybrid Beamforming Multiuser Systems: Rate Analysis and Beam Selection

DFT-Based Hybrid Beamforming Multiuser Systems: Rate Analysis and Beam Selection 1 DFT-Based Hybrid Beamforming Multiuser Systems: Rate Analysis and Beam Selection Yu Han, Shi Jin, Jun Zhang, Jiayi Zhang, and Kai-Kit Wong National Mobile Communications Research Laboratory, Southeast

More information

Mobile Communications (KECE425) Lecture Note Prof. Young-Chai Ko

Mobile Communications (KECE425) Lecture Note Prof. Young-Chai Ko Mobile Communications (KECE425) Lecture Note 20 5-19-2014 Prof Young-Chai Ko Summary Complexity issues of diversity systems ADC and Nyquist sampling theorem Transmit diversity Channel is known at the transmitter

More information

Es e j4φ +4N n. 16 KE s /N 0. σ 2ˆφ4 1 γ s. p(φ e )= exp 1 ( 2πσ φ b cos N 2 φ e 0

Es e j4φ +4N n. 16 KE s /N 0. σ 2ˆφ4 1 γ s. p(φ e )= exp 1 ( 2πσ φ b cos N 2 φ e 0 Problem 6.15 : he received signal-plus-noise vector at the output of the matched filter may be represented as (see (5-2-63) for example) : r n = E s e j(θn φ) + N n where θ n =0,π/2,π,3π/2 for QPSK, and

More information

Improved Detected Data Processing for Decision-Directed Tracking of MIMO Channels

Improved Detected Data Processing for Decision-Directed Tracking of MIMO Channels Improved Detected Data Processing for Decision-Directed Tracking of MIMO Channels Emna Eitel and Joachim Speidel Institute of Telecommunications, University of Stuttgart, Germany Abstract This paper addresses

More information

These outputs can be written in a more convenient form: with y(i) = Hc m (i) n(i) y(i) = (y(i); ; y K (i)) T ; c m (i) = (c m (i); ; c m K(i)) T and n

These outputs can be written in a more convenient form: with y(i) = Hc m (i) n(i) y(i) = (y(i); ; y K (i)) T ; c m (i) = (c m (i); ; c m K(i)) T and n Binary Codes for synchronous DS-CDMA Stefan Bruck, Ulrich Sorger Institute for Network- and Signal Theory Darmstadt University of Technology Merckstr. 25, 6428 Darmstadt, Germany Tel.: 49 65 629, Fax:

More information

LECTURE 16 AND 17. Digital signaling on frequency selective fading channels. Notes Prepared by: Abhishek Sood

LECTURE 16 AND 17. Digital signaling on frequency selective fading channels. Notes Prepared by: Abhishek Sood ECE559:WIRELESS COMMUNICATION TECHNOLOGIES LECTURE 16 AND 17 Digital signaling on frequency selective fading channels 1 OUTLINE Notes Prepared by: Abhishek Sood In section 2 we discuss the receiver design

More information

Lecture 9: Diversity-Multiplexing Tradeoff Theoretical Foundations of Wireless Communications 1. Overview. Ragnar Thobaben CommTh/EES/KTH

Lecture 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 information

Optimum Relay Position for Differential Amplify-and-Forward Cooperative Communications

Optimum Relay Position for Differential Amplify-and-Forward Cooperative Communications Optimum Relay Position for Differential Amplify-and-Forard Cooperative Communications Kazunori Hayashi #1, Kengo Shirai #, Thanongsak Himsoon 1, W Pam Siriongpairat, Ahmed K Sadek 3,KJRayLiu 4, and Hideaki

More information

Estimation of the Optimum Rotational Parameter for the Fractional Fourier Transform Using Domain Decomposition

Estimation of the Optimum Rotational Parameter for the Fractional Fourier Transform Using Domain Decomposition Estimation of the Optimum Rotational Parameter for the Fractional Fourier Transform Using Domain Decomposition Seema Sud 1 1 The Aerospace Corporation, 4851 Stonecroft Blvd. Chantilly, VA 20151 Abstract

More information

Noncooperative Optimization of Space-Time Signals in Ad hoc Networks

Noncooperative Optimization of Space-Time Signals in Ad hoc Networks Noncooperative Optimization of Space-Time Signals in Ad hoc Networks Ronald A. Iltis and Duong Hoang Dept. of Electrical and Computer Engineering University of California Santa Barbara, CA 93106 {iltis,hoang}@ece.ucsb.edu

More information

s o (t) = S(f)H(f; t)e j2πft df,

s o (t) = S(f)H(f; t)e j2πft df, Sample Problems for Midterm. The sample problems for the fourth and fifth quizzes as well as Example on Slide 8-37 and Example on Slides 8-39 4) will also be a key part of the second midterm.. For a causal)

More information

Pilot Optimization and Channel Estimation for Multiuser Massive MIMO Systems

Pilot Optimization and Channel Estimation for Multiuser Massive MIMO Systems 1 Pilot Optimization and Channel Estimation for Multiuser Massive MIMO Systems Tadilo Endeshaw Bogale and Long Bao Le Institute National de la Recherche Scientifique (INRS) Université de Québec, Montréal,

More information

LIKELIHOOD RECEIVER FOR FH-MFSK MOBILE RADIO*

LIKELIHOOD RECEIVER FOR FH-MFSK MOBILE RADIO* LIKELIHOOD RECEIVER FOR FH-MFSK MOBILE RADIO* Item Type text; Proceedings Authors Viswanathan, R.; S.C. Gupta Publisher International Foundation for Telemetering Journal International Telemetering Conference

More information

A Framework for Training-Based Estimation in Arbitrarily Correlated Rician MIMO Channels with Rician Disturbance

A Framework for Training-Based Estimation in Arbitrarily Correlated Rician MIMO Channels with Rician Disturbance A Framework for Training-Based Estimation in Arbitrarily Correlated Rician MIMO Channels with Rician Disturbance IEEE TRANSACTIONS ON SIGNAL PROCESSING Volume 58, Issue 3, Pages 1807-1820, March 2010.

More information

Exploiting Quantized Channel Norm Feedback Through Conditional Statistics in Arbitrarily Correlated MIMO Systems

Exploiting Quantized Channel Norm Feedback Through Conditional Statistics in Arbitrarily Correlated MIMO Systems Exploiting Quantized Channel Norm Feedback Through Conditional Statistics in Arbitrarily Correlated MIMO Systems IEEE TRANSACTIONS ON SIGNAL PROCESSING Volume 57, Issue 10, Pages 4027-4041, October 2009.

More information

On the Throughput of Proportional Fair Scheduling with Opportunistic Beamforming for Continuous Fading States

On 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 information

Module 7 : Antenna. Lecture 52 : Array Synthesis. Objectives. In this course you will learn the following. Array specified by only its nulls.

Module 7 : Antenna. Lecture 52 : Array Synthesis. Objectives. In this course you will learn the following. Array specified by only its nulls. Objectives In this course you will learn the following Array specified by only its nulls. Radiation pattern of a general array. Array synthesis. Criterion for choosing number of elements in synthesized

More information

Lecture 9: Diversity-Multiplexing Tradeoff Theoretical Foundations of Wireless Communications 1

Lecture 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 information

Estimation of the Capacity of Multipath Infrared Channels

Estimation of the Capacity of Multipath Infrared Channels Estimation of the Capacity of Multipath Infrared Channels Jeffrey B. Carruthers Department of Electrical and Computer Engineering Boston University jbc@bu.edu Sachin Padma Department of Electrical and

More information

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 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 information

Direct-Sequence Spread-Spectrum

Direct-Sequence Spread-Spectrum Chapter 3 Direct-Sequence Spread-Spectrum In this chapter we consider direct-sequence spread-spectrum systems. Unlike frequency-hopping, a direct-sequence signal occupies the entire bandwidth continuously.

More information

Constellation Precoded Beamforming

Constellation Precoded Beamforming Constellation Precoded Beamforming Hong Ju Park and Ender Ayanoglu Center for Pervasive Communications and Computing Department of Electrical Engineering and Computer Science University of California,

More information

Analysis of Random Radar Networks

Analysis of Random Radar Networks Analysis of Random Radar Networks Rani Daher, Ravira Adve Department of Electrical and Computer Engineering, University of Toronto 1 King s College Road, Toronto, ON M5S3G4 Email: rani.daher@utoronto.ca,

More information

The Gamma Variate with Random Shape Parameter and Some Applications

The Gamma Variate with Random Shape Parameter and Some Applications ITSUBISHI ELECTRIC RESEARCH LABORATORIES http://www.merl.com The Gamma Variate with Random Shape Parameter and Some Applications aaref, A.: Annavajjala, R. TR211-7 December 21 Abstract This letter provides

More information

Approximate ML Decision Feedback Block. Equalizer for Doubly Selective Fading Channels

Approximate ML Decision Feedback Block. Equalizer for Doubly Selective Fading Channels Approximate ML Decision Feedback Block 1 Equalizer for Doubly Selective Fading Channels Lingyang Song, Rodrigo C. de Lamare, Are Hjørungnes, and Alister G. Burr arxiv:1112.0725v1 [cs.it] 4 Dec 2011 Abstract

More information

Constrained Detection for Multiple-Input Multiple-Output Channels

Constrained Detection for Multiple-Input Multiple-Output Channels Constrained Detection for Multiple-Input Multiple-Output Channels Tao Cui, Chintha Tellambura and Yue Wu Department of Electrical and Computer Engineering University of Alberta Edmonton, AB, Canada T6G

More information

arxiv:cs/ v1 [cs.it] 11 Sep 2006

arxiv:cs/ v1 [cs.it] 11 Sep 2006 0 High Date-Rate Single-Symbol ML Decodable Distributed STBCs for Cooperative Networks arxiv:cs/0609054v1 [cs.it] 11 Sep 2006 Zhihang Yi and Il-Min Kim Department of Electrical and Computer Engineering

More information

Multi-Branch MMSE Decision Feedback Detection Algorithms. with Error Propagation Mitigation for MIMO Systems

Multi-Branch MMSE Decision Feedback Detection Algorithms. with Error Propagation Mitigation for MIMO Systems Multi-Branch MMSE Decision Feedback Detection Algorithms with Error Propagation Mitigation for MIMO Systems Rodrigo C. de Lamare Communications Research Group, University of York, UK in collaboration with

More information

Blind Channel Identification in (2 1) Alamouti Coded Systems Based on Maximizing the Eigenvalue Spread of Cumulant Matrices

Blind Channel Identification in (2 1) Alamouti Coded Systems Based on Maximizing the Eigenvalue Spread of Cumulant Matrices Blind Channel Identification in (2 1) Alamouti Coded Systems Based on Maximizing the Eigenvalue Spread of Cumulant Matrices Héctor J. Pérez-Iglesias 1, Daniel Iglesia 1, Adriana Dapena 1, and Vicente Zarzoso

More information

CHANNEL FEEDBACK QUANTIZATION METHODS FOR MISO AND MIMO SYSTEMS

CHANNEL 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 information

IEEE C80216m-09/0079r1

IEEE C80216m-09/0079r1 Project IEEE 802.16 Broadband Wireless Access Working Group Title Efficient Demodulators for the DSTTD Scheme Date 2009-01-05 Submitted M. A. Khojastepour Ron Porat Source(s) NEC

More information

ABSTRACT ON PERFORMANCE ANALYSIS OF OPTIMAL DIVERSITY COMBINING WITH IMPERFECT CHANNEL ESTIMATION. by Yong Peng

ABSTRACT ON PERFORMANCE ANALYSIS OF OPTIMAL DIVERSITY COMBINING WITH IMPERFECT CHANNEL ESTIMATION. by Yong Peng ABSTRACT ON PERFORMANCE ANALYSIS OF OPTIMAL DIVERSITY COMBINING WITH IMPERFECT CHANNEL ESTIMATION by Yong Peng The optimal diversity combining technique is investigated for multipath Rayleigh and Ricean

More information

VECTOR QUANTIZATION TECHNIQUES FOR MULTIPLE-ANTENNA CHANNEL INFORMATION FEEDBACK

VECTOR 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 information

CHAPTER 3 ROBUST ADAPTIVE BEAMFORMING

CHAPTER 3 ROBUST ADAPTIVE BEAMFORMING 50 CHAPTER 3 ROBUST ADAPTIVE BEAMFORMING 3.1 INTRODUCTION Adaptive beamforming is used for enhancing a desired signal while suppressing noise and interference at the output of an array of sensors. It is

More information

Approximately achieving the feedback interference channel capacity with point-to-point codes

Approximately achieving the feedback interference channel capacity with point-to-point codes Approximately achieving the feedback interference channel capacity with point-to-point codes Joyson Sebastian*, Can Karakus*, Suhas Diggavi* Abstract Superposition codes with rate-splitting have been used

More information

Comparison of Full-Duplex and Half-Duplex Modes with a Fixed Amplify-and-Forward Relay

Comparison of Full-Duplex and Half-Duplex Modes with a Fixed Amplify-and-Forward Relay Comparison of Full-Duplex and Half-Duplex Modes with a Fixed Amplify-and-Forward Relay Taneli Riihonen, Stefan Werner, and Risto Wichman Helsinki University of Technology, Finland IEEE WCNC, Budapest,

More information

ELEC546 Review of Information Theory

ELEC546 Review of Information Theory ELEC546 Review of Information Theory Vincent Lau 1/1/004 1 Review of Information Theory Entropy: Measure of uncertainty of a random variable X. The entropy of X, H(X), is given by: If X is a discrete random

More information

A Thesis for the Degree of Master. An Improved LLR Computation Algorithm for QRM-MLD in Coded MIMO Systems

A Thesis for the Degree of Master. An Improved LLR Computation Algorithm for QRM-MLD in Coded MIMO Systems A Thesis for the Degree of Master An Improved LLR Computation Algorithm for QRM-MLD in Coded MIMO Systems Wonjae Shin School of Engineering Information and Communications University 2007 An Improved LLR

More information

Massive MIMO for Maximum Spectral Efficiency Mérouane Debbah

Massive MIMO for Maximum Spectral Efficiency Mérouane Debbah Security Level: Massive MIMO for Maximum Spectral Efficiency Mérouane Debbah www.huawei.com Mathematical and Algorithmic Sciences Lab HUAWEI TECHNOLOGIES CO., LTD. Before 2010 Random Matrices and MIMO

More information

Multiuser Capacity in Block Fading Channel

Multiuser Capacity in Block Fading Channel Multiuser Capacity in Block Fading Channel April 2003 1 Introduction and Model We use a block-fading model, with coherence interval T where M independent users simultaneously transmit to a single receiver

More information

Downlink Multi-User MIMO for IEEE m

Downlink Multi-User MIMO for IEEE m Downlink Multi-User MIMO for IEEE 80216m Sivakishore Reddy Naga Sekhar Centre of Excellence in Wireless Technology 2013 Outline 1 Introduction 2 Closed Loop MU-MIMO 3 Results 4 Open Loop MU-MIMO 5 Results

More information

1.1.3 The narrowband Uniform Linear Array (ULA) with d = λ/2:

1.1.3 The narrowband Uniform Linear Array (ULA) with d = λ/2: Seminar 1: Signal Processing Antennas 4ED024, Sven Nordebo 1.1.3 The narrowband Uniform Linear Array (ULA) with d = λ/2: d Array response vector: a() = e e 1 jπ sin. j(π sin )(M 1) = 1 e jω. e jω(m 1)

More information

Homework 5 Solutions. Problem 1

Homework 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 information

This examination consists of 10 pages. Please check that you have a complete copy. Time: 2.5 hrs INSTRUCTIONS

This examination consists of 10 pages. Please check that you have a complete copy. Time: 2.5 hrs INSTRUCTIONS THE UNIVERSITY OF BRITISH COLUMBIA Department of Electrical and Computer Engineering EECE 564 Detection and Estimation of Signals in Noise Final Examination 08 December 2009 This examination consists of

More information

EE4304 C-term 2007: Lecture 17 Supplemental Slides

EE4304 C-term 2007: Lecture 17 Supplemental Slides EE434 C-term 27: Lecture 17 Supplemental Slides D. Richard Brown III Worcester Polytechnic Institute, Department of Electrical and Computer Engineering February 5, 27 Geometric Representation: Optimal

More information

Blind Instantaneous Noisy Mixture Separation with Best Interference-plus-noise Rejection

Blind Instantaneous Noisy Mixture Separation with Best Interference-plus-noise Rejection Blind Instantaneous Noisy Mixture Separation with Best Interference-plus-noise Rejection Zbyněk Koldovský 1,2 and Petr Tichavský 1 1 Institute of Information Theory and Automation, Pod vodárenskou věží

More information

2336 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 57, NO. 6, JUNE Minimum BER Linear MIMO Transceivers With Adaptive Number of Substreams

2336 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 57, NO. 6, JUNE Minimum BER Linear MIMO Transceivers With Adaptive Number of Substreams 2336 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 57, NO. 6, JUNE 2009 Minimum BER Linear MIMO Transceivers With Adaptive Number of Substreams Luis G. Ordóñez, Student Member, IEEE, Daniel P. Palomar,

More information

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 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 information

PERFORMANCE OF SMART ANTENNA ARRAYS WITH MAXIMAL-RATIO EIGEN-COMBINING

PERFORMANCE OF SMART ANTENNA ARRAYS WITH MAXIMAL-RATIO EIGEN-COMBINING PERFORMANCE OF SMART ANTENNA ARRAYS WITH MAXIMAL-RATIO EIGEN-COMBINING Constantin Siriteanu and Steven D. Blostein Department of Electrical and Computer Engineering Queen s University, Kingston, Ontario,

More information

EM Channel Estimation and Data Detection for MIMO-CDMA Systems over Slow-Fading Channels

EM Channel Estimation and Data Detection for MIMO-CDMA Systems over Slow-Fading Channels EM Channel Estimation and Data Detection for MIMO-CDMA Systems over Slow-Fading Channels Ayman Assra 1, Walaa Hamouda 1, and Amr Youssef 1 Department of Electrical and Computer Engineering Concordia Institute

More information

Single-User MIMO systems: Introduction, capacity results, and MIMO beamforming

Single-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 information

Weibull-Gamma composite distribution: An alternative multipath/shadowing fading model

Weibull-Gamma composite distribution: An alternative multipath/shadowing fading model Weibull-Gamma composite distribution: An alternative multipath/shadowing fading model Petros S. Bithas Institute for Space Applications and Remote Sensing, National Observatory of Athens, Metaxa & Vas.

More information

Interference suppression in the presence of quantization errors

Interference suppression in the presence of quantization errors Interference suppression in the presence of quantization errors Omar Bakr Mark Johnson Raghuraman Mudumbai Upamanyu Madhow Electrical Engineering and Computer Sciences, UC Berkeley, Berkeley, CA 947 Electrical

More information

Lecture 12. Block Diagram

Lecture 12. Block Diagram Lecture 12 Goals Be able to encode using a linear block code Be able to decode a linear block code received over a binary symmetric channel or an additive white Gaussian channel XII-1 Block Diagram Data

More information

A REDUCED COMPLEXITY TWO-DIMENSIONAL BCJR DETECTOR FOR HOLOGRAPHIC DATA STORAGE SYSTEMS WITH PIXEL MISALIGNMENT

A REDUCED COMPLEXITY TWO-DIMENSIONAL BCJR DETECTOR FOR HOLOGRAPHIC DATA STORAGE SYSTEMS WITH PIXEL MISALIGNMENT A REDUCED COMPLEXITY TWO-DIMENSIONAL BCJR DETECTOR FOR HOLOGRAPHIC DATA STORAGE SYSTEMS WITH PIXEL MISALIGNMENT 1 S. Iman Mossavat, 2 J.W.M.Bergmans 1 iman@nus.edu.sg 1 National University of Singapore,

More information

Digital Modulation 1

Digital Modulation 1 Digital Modulation 1 Lecture Notes Ingmar Land and Bernard H. Fleury Navigation and Communications () Department of Electronic Systems Aalborg University, DK Version: February 5, 27 i Contents I Basic

More information

Residual Versus Suppressed-Carrier Coherent Communications

Residual Versus Suppressed-Carrier Coherent Communications TDA Progress Report -7 November 5, 996 Residual Versus Suppressed-Carrier Coherent Communications M. K. Simon and S. Million Communications and Systems Research Section This article addresses the issue

More information

Incremental Coding over MIMO Channels

Incremental Coding over MIMO Channels Model Rateless SISO MIMO Applications Summary Incremental Coding over MIMO Channels Anatoly Khina, Tel Aviv University Joint work with: Yuval Kochman, MIT Uri Erez, Tel Aviv University Gregory W. Wornell,

More information

The Effect of Spatial Correlations on MIMO Capacity: A (not so) Large N Analytical Approach: Aris Moustakas 1, Steven Simon 1 & Anirvan Sengupta 1,2

The Effect of Spatial Correlations on MIMO Capacity: A (not so) Large N Analytical Approach: Aris Moustakas 1, Steven Simon 1 & Anirvan Sengupta 1,2 The Effect of Spatial Correlations on MIMO Capacity: A (not so) Large N Analytical Approach: Aris Moustakas 1, Steven Simon 1 & Anirvan Sengupta 1, 1, Rutgers University Outline Aim: Calculate statistics

More information

Blind MIMO communication based on Subspace Estimation

Blind MIMO communication based on Subspace Estimation Blind MIMO communication based on Subspace Estimation T. Dahl, S. Silva, N. Christophersen, D. Gesbert T. Dahl, S. Silva, and N. Christophersen are at the Department of Informatics, University of Oslo,

More information

Improved Multiple Feedback Successive Interference Cancellation Algorithm for Near-Optimal MIMO Detection

Improved Multiple Feedback Successive Interference Cancellation Algorithm for Near-Optimal MIMO Detection Improved Multiple Feedback Successive Interference Cancellation Algorithm for Near-Optimal MIMO Detection Manish Mandloi, Mohammed Azahar Hussain and Vimal Bhatia Discipline of Electrical Engineering,

More information

Ergodic and Outage Capacity of Narrowband MIMO Gaussian Channels

Ergodic and Outage Capacity of Narrowband MIMO Gaussian Channels Ergodic and Outage Capacity of Narrowband MIMO Gaussian Channels Yang Wen Liang Department of Electrical and Computer Engineering The University of British Columbia April 19th, 005 Outline of Presentation

More information

Single-Symbol ML Decodable Distributed STBCs for Partially-Coherent Cooperative Networks

Single-Symbol ML Decodable Distributed STBCs for Partially-Coherent Cooperative Networks This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the ICC 2008 proceedings Single-Symbol ML Decodable Distributed STBCs for

More information

2318 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 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 information

A Computationally Efficient Block Transmission Scheme Based on Approximated Cholesky Factors

A Computationally Efficient Block Transmission Scheme Based on Approximated Cholesky Factors A Computationally Efficient Block Transmission Scheme Based on Approximated Cholesky Factors C. Vincent Sinn Telecommunications Laboratory University of Sydney, Australia cvsinn@ee.usyd.edu.au Daniel Bielefeld

More information

Trust Degree Based Beamforming for Multi-Antenna Cooperative Communication Systems

Trust 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 information

Morning Session Capacity-based Power Control. Department of Electrical and Computer Engineering University of Maryland

Morning 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 information

A Systematic Description of Source Significance Information

A Systematic Description of Source Significance Information A Systematic Description of Source Significance Information Norbert Goertz Institute for Digital Communications School of Engineering and Electronics The University of Edinburgh Mayfield Rd., Edinburgh

More information

Cell throughput analysis of the Proportional Fair scheduler in the single cell environment

Cell throughput analysis of the Proportional Fair scheduler in the single cell environment Cell throughput analysis of the Proportional Fair scheduler in the single cell environment Jin-Ghoo Choi and Seawoong Bahk IEEE Trans on Vehicular Tech, Mar 2007 *** Presented by: Anh H. Nguyen February

More information

EE4512 Analog and Digital Communications Chapter 4. Chapter 4 Receiver Design

EE4512 Analog and Digital Communications Chapter 4. Chapter 4 Receiver Design Chapter 4 Receiver Design Chapter 4 Receiver Design Probability of Bit Error Pages 124-149 149 Probability of Bit Error The low pass filtered and sampled PAM signal results in an expression for the probability

More information

BER OF MRC FOR M-QAM WITH IMPERFECT CHANNEL ESTIMATION OVER CORRELATED NAKAGAMI-M FADING

BER OF MRC FOR M-QAM WITH IMPERFECT CHANNEL ESTIMATION OVER CORRELATED NAKAGAMI-M FADING OF MRC FOR M-QAM WITH IMPERFECT CHANNEL ESTIMATION OVER CORRELATED NAKAGAMI-M FADING Lennert Jacobs, George C. Alexandropoulos, Marc Moeneclaey, and P. Takis Mathiopoulos Ghent University, TELIN Department,

More information

Practicable MIMO Capacity in Ideal Channels

Practicable MIMO Capacity in Ideal Channels Practicable MIMO Capacity in Ideal Channels S. Amir Mirtaheri,Rodney G. Vaughan School of Engineering Science, Simon Fraser University, British Columbia, V5A 1S6 Canada Abstract The impact of communications

More information

Novel spectrum sensing schemes for Cognitive Radio Networks

Novel spectrum sensing schemes for Cognitive Radio Networks Novel spectrum sensing schemes for Cognitive Radio Networks Cantabria University Santander, May, 2015 Supélec, SCEE Rennes, France 1 The Advanced Signal Processing Group http://gtas.unican.es The Advanced

More information

On the Multivariate Nakagami-m Distribution With Exponential Correlation

On the Multivariate Nakagami-m Distribution With Exponential Correlation 1240 IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. 51, NO. 8, AUGUST 2003 On the Multivariate Nakagami-m Distribution With Exponential Correlation George K. Karagiannidis, Member, IEEE, Dimitris A. Zogas,

More information

List Decoding: Geometrical Aspects and Performance Bounds

List Decoding: Geometrical Aspects and Performance Bounds List Decoding: Geometrical Aspects and Performance Bounds Maja Lončar Department of Information Technology Lund University, Sweden Summer Academy: Progress in Mathematics for Communication Systems Bremen,

More information

Minimum Mean Squared Error Interference Alignment

Minimum Mean Squared Error Interference Alignment Minimum Mean Squared Error Interference Alignment David A. Schmidt, Changxin Shi, Randall A. Berry, Michael L. Honig and Wolfgang Utschick Associate Institute for Signal Processing Technische Universität

More information