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Ali Al- Saihati ID# 200350130 Ghassan Linjawi

King Fahd University of Petroleum & Minerals  Electrical Engineering Department EE 575 Information Theory Bit Error Rate Performance of V-BLAST Detection Schemes over MIMO Channels . Ali Al- Saihati ID# 200350130 Ghassan Linjawi. OUTLINE. Introduction. Theory of V-BLAST. Problem Definition.

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Ali Al- Saihati ID# 200350130 Ghassan Linjawi

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  1. King Fahd University of Petroleum & Minerals Electrical Engineering DepartmentEE 575 Information TheoryBit Error Rate Performance of V-BLAST Detection Schemes over MIMO Channels Ali Al-Saihati ID# 200350130 GhassanLinjawi

  2. OUTLINE • Introduction. • Theory of V-BLAST. • Problem Definition. • Simulation Results. • Conclusion.

  3. 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.

  4. 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.

  5. 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

  6. 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).

  7. 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.

  8. V-BLAST Detection Algorithm

  9. 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.

  10. 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..

  11. 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

  12. 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.

  13. 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.

  14. Problem Definition • It is required to find the BER performance of the ZF, MMSE and (ML) schemes implemented in the V-BLAST system

  15. Results

  16. 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.

  17. References: [1] Nirmalend. B and Rabindranath B. “Capacity and V-BLAST Techniques for MIMO Wireless Channel”. Journal of Theoretical and Applied Information Technology, 2005- 2010. [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

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