Ali Al-Saihati
ID# 200350130
Ghassan Linjawi
OUTLINE
 Introduction.
 Theory of V-BLAST.
 Problem Definition.
 Simulation Results.
 Conclusion.
Introduction
 MIMO system has proved to achieve high capacity compared to
SISO MISO and SIMO systems.
 For this reason, many algorithms have been proposed to reduce
the interference in the received signals caused by other
transmitters in the system.
 Also, they aim achieve closer values to the Shannon capacity
limit.
 D-BLAST (Diagonal Bell Labs Layered Space Time) and V-
BLAST (Vertical Bell Labs Layered Space Time) are such schemes
used for detection and suppression the interference in MIMO
systems.
 D-Blast, which was proposed by Gerard J. Foschini, applies
a diagonal space time coding on the data.
 By applying this algorithm, it could achieve 90% of
Shannon capacity rates as well as high spectral efficiency.
 However, due to complexity of implementing the
algorithm, V-Blast algorithm was proposed. It was
established in 1996 at Bell Labs.
 It demultiplexes the transmitted signal and then maps bit
to symbol independently for each substream.
Theory of V-BLAST.
 A single user scheme which has multiple transmitters.
 It divides the data stream into substreams and transmits them through
multiple transmitters at the same time and frequency.
 The data at the receiver are received at the same time and frequency.
 By implementing V-BLAST algorithm, the diversity gain is increased
and the bit error rate (BER) performance is improved.

The MIMO system is assumed to undergoes flat fading channel. The
system model of the output signal is given by:
y= Hx+ η
Detection Process
 The detection process consists of three operations: interference suppression
(nulling), interference cancellation (subtraction) and optimal ordering.
 The interference nulling process is carried out by projecting the received signal
into the null subspace spanned by the interfering signals.
 This process is done by using Gramm-Schmidt orthogonalization procedure
that converts a set of linearly independent vectors into orthogonal set of
vectors.
 Then, the symbol is detected.
 The interference cancellation process is done by subtracting the detected
symbol from the received signal.
 The optimal ordering ensures that the detected symbol has the highest signal
to noise ratio (SNR).
 V-BLAST algorithm integrates both, linear and nonlinear
algorithms presented in interference nulling and
interference cancellation respectively.
 There are two disadvantages in V-BLAST algorithms:
1) Error propagates during symbol detection.
2) The number of receive antennas must be greater than or
equal to the number of transmit antennas to satisfy the
interference nulling process.
V-BLAST Detection Algorithm
Modified V-BLAST Algorithm
 Since the amount of interference cancelled in each
step becomes smaller, a new algorithm was proposed.
 The algorithm stops iterating when the interference
becomes very small. Hence, it reduces complexity of
the system.
 When the value of C becomes 1, the algorithm
becomes the same as the original V-BLAST detection.
 When C becomes the algorithm becomes MMSE and
ZF detection.
ZF, ML and MMSE Models
 The weight vector for the ZF and MMSE are given by:
GZF =H+ = ( HHH )-1HH
GMMSE =H+ = ( HHH + ρ I )-1HH




ZF and MMSE are simple to implement linear algorithms.
They do not achieve high data rate at high SNR.
The ZF detection cancels the interference only
So, it enhances the noise in each iteration. At high SNR, the
MMSE detection will function like ZF detection..
 The maximum likelihood (ML) detection is given by:
G = min |y – H x|
 The ML is optimum in minimizing the error and has an
excellent performance.
 The order of complexity is |A|M where M is the number of
transmitter and A is the number of modulation
constellation.
 For example, if M = 10 and A = 2 then we need to compute
1024 times during the process
Proposed V-BLAST Algorithms
 Different proposed recursive algorithms have been proposed for
V-BLAST algorithm.
 Some of these are matrix recursive, vector recursive, greedy
ordering, scalar recursive and adaptive scalar recursion for fast
fading.
 The matrix recursive algorithm tries to find an inverse matrix
using the Sherman- Morrison formula with a given initial matrix
recursively.
 This method decreases the complexity order from quadratic to
cubic but the computation of the inverse matrix is complex.
 In vector recursive algorithm, a weight vector is found recursively
to substitute the computation of inverse matrix.
 The greedy ordering method selects the most reliable signals for
detection.
 The scalar recursion algorithm focuses on nulling the output
vector.
 The adaptive scalar recursion for fast fading changes and updates
the weight vectors and optimum ordering based on the changes
incurred during transmission.
 Using this algorithm, the complexity order reduces to a square.
Problem Definition
 It is required to find the BER performance of the ZF,
MMSE and (ML) schemes implemented in the VBLAST system
Results
Conclusion
 ML detection has better BER performance than the
MMSE and ZF detections by 15dB.
 The performance of MMSE detection is better than ZF
detection by 2- 3 dB.
 Using the adaptive scalar recursion for fast fading, the
complexity order reduces to square and the
computation becomes less compared to other
techniques.
References:
[1] Nirmalend. B and Rabindranath B. “Capacity and V-BLAST Techniques for MIMO
Wireless Channel”. Journal of Theoretical and Applied Information Technology, 20052010.
[2] P. W. Wolniansky. G. J. Foschini. G. D. Golden and R. A. Valenzuela “V-BLAST: An
Architecture for Realizing Very High Data Rates Over the Rich-Scattering Wireless
Channel”. Bell Laboratories.
[3] S. Loyka and F. Gagnon. “Performance Analysis of the V-BLASTAlgorithm: An Analytical
Approach”. 2002 International Zurich Seminar on Wireless Broadband.
[4] Taekyu Kim and Sin-Chong Park. “Reduced Complexity Detection for V-BLAST D
Systems from Iteration Canceling”. 2008.
[5] Toshiaki. K. “ Low-Complexity Systolic V-BLAST Architecture” IEEE Transactions on
Wireless Communications, 2009