Modeling and Mitigation of Interference in Multi-Antenna Receivers

Transcription

1 Modeling and Mitigation of Interference in Multi-Antenna Receivers Aditya Chopra September 16,

2 about me Member of the Wireless Networking and Communications Group at The University of Texas at Austin since Completed projects ADSL testbed (Oil & Gas) Spur modeling/mitigation (NI) 2 x 2 wired multicarrier communications testbed using PXI hardware, x86 processor, real-time operating system and LabVIEW Detect and classify spurious signals; fixed and floating-point algorithms to mitigate spurs Currently active projects Interference modeling and mitigation (Intel) Impulsive noise mitigation in OFDM (NI) Powerline communications (TI, Freescale, SRC) Statistical models of interference; receiver algorithms to mitigate interference; MATLAB toolbox Non-parametric interference mitigiation for wireless OFDM receivers using PXI hardware, FPGAs, and LabVIEW Modeling and mitigating impulsive noise; building multichannel multicarrier communications testbed using PXI hardware, x86 processor, real-time operating system, LabVIEW 2

3 Interference in wireless communication systems is caused by communicating and non-communicating source emissions Non-communicating devices Microwave ovens Powerlines Wireless systems Nearby wireless users Coexisting protocols Computational Platform Clocks, amplifiers, co-located transceivers Introduction Modeling (CoLo) Modeling (Dist) Outage Performance Receiver Design 3

4 Interference may severely impair communication performance of wireless systems g Channel g Channel 7 J. Shi, A. Bettner, G. Chinn, K. Slattery, and X. Dong, A study of platform EMI from LCD panels - impact on wireless, root causes and mitigation methods, Proc. IEEE Int. Symp. on EM Compatibility, Aug Introduction Modeling (CoLo) Modeling (Dist) Outage Performance Receiver Design 4

5 Interference mitigation has been an active area of research over the past decade INTERFERENCE MITIGATION STRATEGY Hardware design - Receiver shielding Network planning - Resource allocation - Basestation coordination - Partial frequency re-use Receiver algorithms - Interference cancellation - Interference alignment - Statistical interference mitigation LIMITATIONS Does not mitigate interference from devices using same spectrum Requires user coordination Slow updates Require user coordination and channel state information Statistical methods require accurate interference models Introduction Modeling (CoLo) Modeling (Dist) Outage Performance Receiver Design 5

6 I employ a statistical approach to the interference modeling and mitigation problem Proposed solution 1. Develop a statistical-physical model of interference generation 2. Model statistics of interference in multi-antenna receivers 3. Analyze performance of conventional multi-antenna receivers 4. Develop multi-antenna receiver algorithms using statistical models of interference Introduction Modeling (CoLo) Modeling (Dist) Outage Performance Receiver Design 6

7 A statistical-physical model of interference generation and propagation is proposed Key Features Co-located receiver antennae ( ) Interferers are common to all antennae ( ) or exclusive to n th antenna ( n ) Interferers are stochastically distributed in space as a 2D Poisson point process with intensity λ 0 ( ), or λ n ( ) Interferer free guard-zone ( ) of radius δ Power law propagation and fast fading n System model with a 3-antenna receiver in a Poisson field of interferers δ Introduction Modeling (CoLo) Modeling (Dist) Outage Performance Receiver Design 7

8 I derive joint statistics of interference observed by multi-antenna receivers 1. Wireless networks with guard zones 2. Wireless networks without guard zones Introduction Modeling (CoLo) Modeling (Dist) Outage Performance Receiver Design 8

9 Using the system model, I express the sum interference at the n th antenna Y n = A i0 e jφ i0 Hi0,ne jθ i0,n r i0 γ 2 i 0 S 0 + A in e jφin Hin e jθin,n r in i n S n γ 2 FADING CHANNEL PATHLOSS INTERFERER EMISSION COMMON INTERFERERS EXCLUSIVE INTERFERERS Next, I derive the statistics of Y for different network models Introduction Modeling (CoLo) Modeling (Dist) Outage Performance Receiver Design 9

10 I derive interference statistics in networks with guard zones as a mix of isotropic and i.i.d. Class A noise Joint characteristic function Φ w = e A 0e w 2 2 Ω0 N e A ne w n=1 A n λ n δ 2, Ω n A n δ γ 2 2 Ωn Amplitude distribution of interference From common interferers: Isotropic Middleton Class A From exclusive interferers: Independent Middleton Class A Introduction Modeling (CoLo) Modeling (Dist) Outage Performance Receiver Design 10

11 Interference statistics in networks without guard zones are a mix of isotropic and i.i.d. alpha stable noise Joint characteristic function Φ w = e σ 0 w α e σ n ω n α N n=1 α = 4 γ, σ n λ n Amplitude distribution of interference From common interferers: Isotropic symmetric alpha stable From exclusive interferers: Independent symmetric alpha stable Introduction Modeling (CoLo) Modeling (Dist) Outage Performance Receiver Design 11

12 Simulation results indicate a close match between proposed statistical models and simulated interference Tail probability of simulated interference in networks with guard zones Tail probability of simulated interference in networks without guard zones PARAMETER VALUES γ 4 λ 0 = 10 3, λ n = 0 δ 1.2 (w/ GZ), 0 (w/out GZ) λ 0 = , λ n = Introduction Modeling (CoLo) Modeling (Dist) Outage Performance Receiver Design 12

13 My framework for multi-antenna interference across co-located antennae results in joint statistics that are 1. Spatially isotropic (common interferers) 2. Spatially independent (exclusive interferers) 3. In a continuum between isotropic and independent (mixture) for two impulsive distributions 1. Middleton Class A (networks with guard zones) 2. Symmetric alpha stable (networks without guard zones) Introduction Modeling (CoLo) Modeling (Dist) Outage Performance Receiver Design 13

14 In networks without guard zones, I incorporate antenna separation into the system model Applications Cooperative MIMO Distributed antenna systems Two-hop communication Temporal modeling of interference in mobile receivers Decentralized network (δ = 0) with 2 receive antennae ( ) in a Poisson field of interferers ( ) d Introduction Modeling (CoLo) Modeling (Dist) Outage Performance Receiver Design 14

15 Interference statistics are derived via the joint characteristic function (Φ) for three scenarios Co-located antennae (d = 0) : Φ ω 1, ω 2 = e σ ω 1 2 +ω 2 2 α 2 Infinitely distant antennae (d ) : Φ ω 1, ω 2 = e σ ω 1 α +ω 2 α Distributed antennae (0 < d < ) : Φ ω 1, ω 2 e ν d σ ω 1 2 +ω 2 2 α ν d σ ω 1 α +ω 2 α I use curve fitting to approximate v d e adα Introduction Modeling (CoLo) Modeling (Dist) Outage Performance Receiver Design 15

16 I use the proposed framework to evaluate outage performance of conventional multi-antenna receivers 1. Pre-detection diversity combiners 2. Post-detection diversity combiners Introduction Modeling (CoLo) Modeling (Dist) Outage Performance Receiver Design 16

17 SELECT BEST Multi-antenna receivers combine antenna outputs either before, or after the decoding block w 1 Pre-detection Combining Post-detection combining X w N + X y = hx + n wy Equal Gain Combiner Selection Combiner Maximum Ratio Combiner w = 1 N w n = I hn =max{h} w = h Introduction Modeling (CoLo) Modeling (Dist) Outage Performance Receiver Design 17

18 I derive theoretical outage probability expressions for pre- and post-detection diversity combiners RECEIVER ALGORITHM Equal Gain Combining Maximum Ratio Combining OUTAGE PROBABILITY P SIR < θ C 0 θ α 2 λ 0 + λ e N 1 α 2 C 0 θ α 2E h α α h 2 α + Selection Combining C 0 θ α N 2 1 n+1 N n=1 1 h 2 2α NC n n! Post Detection Combining C 0 1 m+1 m /γ! nc m m 1! sin 2π θ α π C 0 m=1 γ sin 2π γ γ N θ Nα 2 Introduction Modeling (CoLo) Modeling (Dist) Outage Performance Receiver Design 18

19 Sim Expr match simulated outage Only common interferers 5% exclusive interferers Only exclusive interferers Next, I design robust receivers using interference statistics Introduction Modeling (CoLo) Modeling (Dist) Outage Performance Receiver Design 19

20 Using my knowledge of interference statistics, I design algorithms which outperform conventional receivers 1. Improved pre-detection diversity combiners 2. Improved antenna selection in cooperative reception Introduction Modeling (CoLo) Modeling (Dist) Outage Performance Receiver Design 20

21 Antenna Weight (w) I propose two diversity combining algorithms that are robust to impulsive interference Deviation in an antenna output y n is defined as Δ n = y n median{ y } Proposed diversity combiners 1. Hard-limiting combiner w n = 1 Δn <Th n 2. Soft-limiting combiner w n = e AΔ nh n Deviation ( Hard Limiting (T=1) Soft Limiting (A=1) Introduction Modeling (CoLo) Modeling (Dist) Outage Performance Receiver Design 21

22 My proposed diversity combiners exhibit better outage performance compared to conventional combiners PARAMETER VALUES Pathloss coeff. (γ) 4 Guard- zone radius (δ ) 0 Common interferer density(λ 0 ) Excl. intfr. density(λ n ) HL combiner parameter (T) 1 SL combiner parameter (A) 2 Introduction Modeling (CoLo) Modeling (Dist) Outage Performance Receiver Design 22

23 In conclusion, the contributions of my dissertation are 1. A framework for modeling multi-antenna interference Interference statistics are mix of isotropic and independent 2. Statistical modeling of multi-antenna interference Co-located antennae in networks without guard zones Two geographically separate antennae in networks with guard zones 3. Outage performance analysis of conventional receivers in networks without guard zones 4. Design of receiver algorithms with improved performance in impulsive interference 23

24 thank you 24

25 Interference statistics are derived via the joint characteristic function (Φ) for three scenarios of antenna separation log {Φ ω 1, ω 2 } = λ 1 R a ω 1 2 r γ + a ω 2 2 r d γ dr d = 0: Φ ω 1, ω 2 = e σ ω 1 2 +ω 2 2 d : Φ ω 1, ω 2 = e σ ω 1 α +ω α 2 0 < d < : Φ ω 1, ω 2 = e ν d σ ω 1 2 +ω 2 2 α 2 α ν d σ ω 1 α +ω 2 α Introduction Modeling (CoLo) Modeling (Dist) Outage Performance Receiver Design 25

26 Intuitively, interference statistics lie in a continuum between isotropic and independent d = 0 d = 0 < d < 26

27 A framework of common/exclusive interferers unifies interference models in co-located/distributed antennae Common Interferers Exclusive Interferers Next, I use this framework to analyze communication performance of multiantenna receivers 27