Parallel Interference Cancellation in beyond 3G multi-user and multi-antenna OFDM systems
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The document discusses parallel interference cancellation (PIC) techniques for uplink transmission in beyond 3G systems, detailing various investigation methods and detecting algorithms. It highlights improvements in estimate refinement and presents simulation results showcasing the performance of different quantization methods. The conclusions indicate the efficacy of PIC while acknowledging its convergence limitations and the need for further research.
Parallel Interference Cancellation in beyond 3G multi-user and multi-antenna OFDM systems
- 1. David Research Group for RF Communications 2003-5-26 1 Parallel interference cancellation in beyond 3G multi-user and multi-antenna OFDM systems David Sabater Dinter University of Kaiserslautern [email protected] Supervisor: A. Sklavos
- 2. David Research Group for RF Communications 2003-5-26 2 summary • service area based system in the uplink • transmission model • subcarrierwise investigation • optimum and suboptimum linear detection • parallel interference cancellation • PIC with improved estimate refinement • simulation results • conclusions
- 3. David Research Group for RF Communications 2003-5-26 3 uplink transmission in a service area MT AP CU AP AP MT MT ( )1 ˆd ( )2 ˆd ( ) ˆ K d ( )1 d ( )2 d ( )K d
- 4. David Research Group for RF Communications 2003-5-26 4 ( ) ( ) ( ) ( ) ( ) ( )B B 1,1 ,1 1,2 ,2 1, , B (1) (2) ( )B (1) (2) ( ) (1) (2) ( ) K K K K K K KK •= + H H H H H H d d d n n n e e e % %L % %L M O M % %L M % % M % % % M % transmission model • Additive noise vector at AP : • Received signal vector at AP : Bk Bk B B B F( ) ( ,1) ( , ) T ( )k k k N n n=n% % %K B B B F( ) ( ,1) ( , ) T ( )k k k N e e=e% % %K B F FK N KN× F 1KN ×B F 1K N × B F 1K N × • data symbol vector sent by MT k: F( , )(k) ( ,1) T ( )k Nk d d=d K
- 5. David Research Group for RF Communications 2003-5-26 5 subcarrierwise investigation ( ) ( ) ( )F 1 2 N ÷ ÷ = ÷ ÷ ÷ ÷ H 0 0 0 H 0 H 0 0 H % L % L% M M O M %L ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )B B B 1,1 2,1 ,1 1,2 2,2 ,2 1, 1, , K K K K K K ÷ ÷ = ÷ ÷ ÷ H H H H H H H H H H % % %L % % %L% M M O M % % %L B F F block diagonal matrixK N xKN •conversion of totalsystem to smaller parallel systems •significant effort reduction in •linear ZF, MMSE •non linear MLVE FN B F F matrixsparseK N xKN
- 6. David Research Group for RF Communications 2003-5-26 6 ( ) ( ) ( ) ( ) ( ) ( ) F F F FF F 2 all data vectors equiprobable and Gauss noise maximum likehood vector estimator (MLVE) ˆ arg min n n K n nn n ∈ → = − d d d e H d g %% D optimum non-linear and suboptimum linear detector ( ) ( ) ( ) ( ) ( ){ }F F F Fˆ arg max |n n n n K P ∈ = d d d e% D F F F ( ) ( ) ( )ˆ . n n n =d D e% % ( )F F F F 1( ) ( )*T ( ) ( )*Tn n n n− =D H H H% % % % • Optimum multiuser detector, • suboptimum linear detection • Example: ZF criterion F F F ( )( ) ( ) 2 min nn n −e H d%%
- 7. David Research Group for RF Communications 2003-5-26 7 parallel interference cancellation ( )( ) ( ) 1 *T *T diag diag − = = F H H R H H % % % % % % *T H% e% r% F% R% $ ( )pd $ ( )ˆ 1p −d -bank of MF ( )ˆ pu iterative MUD estimaterefinement andFECdecoding • Forward matrix: • Feedback matrix:
- 8. David Research Group for RF Communications 2003-5-26 8 no estimate refinement F , , (non zero) eigenvalues ofKNλ λ1 FR% %K • no estimate refinement, • PIC convergent if • convergence value ˆˆ ˆ=d d ( ) 1ρ ≤FR% % $d FECdemod ˆd ˆu $ˆd estimate refinement and FEC decoding spectral radius ( ) { }F FR max , , KNρ λ λ1=% % K ZF ˆ ˆ( )=∞d d
- 9. David Research Group for RF Communications 2003-5-26 9 spectral radius example 2,...,8K = { }P Rρ ≤ divergenceconvergence • • exp. Channel snapshot B 8K = R
- 10. David Research Group for RF Communications 2003-5-26 10 estimate refinement by hard quantization $d FECdemod ˆd ˆu $ˆd estimate refinement and FEC decoding • exploit knowledge of discrete • quantization of to the modulation constellation F( , )k n d ∈D $d { }F 2ˆˆ ˆarg min ( ) KN p ∈ = − d d d d D D
- 11. David Research Group for RF Communications 2003-5-26 11 estimate refinement by soft quantization $d FECdemod ˆu $ˆd estimate refinement and FEC decoding $$ ( ) ( ) 2 min E k k m md d − $$ ( )k md must satisfy estim. 2 dσ 2 d ˆ2 σ ( )tanh 2• ( )sign • mod ( ) ( ) { }ˆk k m mL d d
- 12. David Research Group for RF Communications 2003-5-26 12 -10 -5 0 5 10 15 20 10 -3 10 -2 10 -1 10 0 simulation results F( ) 1n ρ > • • • subc. • exp. channel • no quant. 4K = B 4K = ( )10 b 010log / /dBE N bP AWGN ZF PICMF
- 13. David Research Group for RF Communications 2003-5-26 13 -10 -5 0 5 10 15 20 10 -3 10 -2 10 -1 10 0 simulation results F( ) 1n ρ > ( )10 b 010log / /dBE N bP • • • subc. • exp. channel • hard quant. 4K = B 4K = AWGN ZF PICMF
- 14. David Research Group for RF Communications 2003-5-26 14 -10 -5 0 5 10 15 20 10 -3 10 -2 10 -1 10 0 simulation results F( ) 1n ρ > ( )10 b 010log / /dBE N bP • • • subc. • exp. channel • soft quant. 4K = B 4K = AWGN ZF PICMF
- 15. David Research Group for RF Communications 2003-5-26 15 -10 -5 0 5 10 15 20 10 -3 10 -2 10 -1 10 0 simulation results bP ( )10 b 010log / /dBE N • • • subc. • exp. channel • no quant. 2K = B 4K = F( ) 1n ρ ; AWGN PIC (even iterations) PIC (odd iterations) ZF
- 16. David Research Group for RF Communications 2003-5-26 16 PIC with improved estimate refinement *T H% e% r% F% $ ( )1d bank of MF iterative MUD first iterationsecond iterationthird iteration *T H% e% r% F% R% $ ( )2d -bank of MF iterative MUD demod ( )ˆ 2u $ ( )ˆ 1d *T H% e% r% F% R% $ ( )3d -bank of MF iterative MUD demod ( )ˆ 3u $ ( )ˆ 2d hard Q or soft Q • principle: input MAI-free at quantization process • starting estimate refinement at the third iteration, errors introduced by quantization method can be reduced $d
- 17. David Research Group for RF Communications 2003-5-26 17 -10 -5 0 5 10 15 20 10 -3 10 -2 10 -1 10 0 simulation results ( )10 b 010log / /dBE N bP • • • subc. • exp. channel • hard quant. • hard mod. quant. 2K = B 4K = F( ) 1n ρ > AWGN MF ZF PIC PIC mod
- 18. David Research Group for RF Communications 2003-5-26 18 -10 -5 0 5 10 15 20 10 -3 10 -2 10 -1 10 0 simulation results ( )10 b 010log / /dBE N bP • • • subc. • exp. Channel • soft quant. • soft mod. quant. 2K = B 4K = F( ) 1n ρ > AWGN MF ZF PICPIC mod
- 19. David Research Group for RF Communications 2003-5-26 19 -10 -5 0 5 10 15 20 10 -3 10 -2 10 -1 10 0 simulation results ( )10 b 010log / /dBE N bP • • • • exp. Channel • hard quant. • hard mod. quant. 3K = B 6K = F 32N = AWGN MF ZF PIC PIC mod
- 20. David Research Group for RF Communications 2003-5-26 20 -10 -5 0 5 10 15 20 10 -3 10 -2 10 -1 10 0 simulation results ( )10 b 010log / /dBE N bP • • • • exp. Channel • soft quant. • soft mod. quant. 3K = B 6K = F 32N = AWGN MF ZF PIC mod PIC
- 21. David Research Group for RF Communications 2003-5-26 21 conclusions • PIC is a flexible JD scheme • PIC is not always convergent • performance improvement with modified estimate refinement • more investigation towards PIC necessary