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BROADBAND BEAMFORMING

BROADBAND BEAMFORMING . Presented by: Kalpana Seshadrinathan. INTRODUCTION. Equalization Distortion caused by the channel results in Inter-Symbol Interference (ISI) which has to be compensated for to reduce the probability of error Such a compensator is called an equalizer

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BROADBAND BEAMFORMING

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  1. BROADBAND BEAMFORMING Presented by: Kalpana Seshadrinathan

  2. INTRODUCTION • Equalization • Distortion caused by the channel results in Inter-Symbol Interference (ISI) which has to be compensated for to reduce the probability of error • Such a compensator is called an equalizer • Problem in Broadband Communications • High Data Rates are transmitted and multipath distortion is more severe • Low complexity equalizers required without sacrificing too much in ISI mitigation

  3. TYPES OF EQUALIZERS • Maximum Likelihood Sequence Estimation (MLSE) • Optimal in terms of probability of error • Disadvantage: Exponential increase in computation requirements with increase in channel memory • Linear equalizers • Sub-optimal approach • Used when computational complexity of MLSE is prohibitive • Commonly used optimality criteria for choosing filter coefficients include mean-squared error and peak distortion

  4. BROADBAND BEAMFORMERS • MLSE is not implementable in the broadband case due to computational complexity • A broadband beamformer reduces the ISI to narrowband levels in the receiver • The space-time receiver is made up of an antenna array followed by an FIR filter bank • Optimal MAP equalization is then performed on the beamformer output

  5. Koca and Levy, 2002

  6. POWER COMPLEMENTARITY • Noise at the filter output is colored and cannot be applied to a trellis-based equalizer • Filter coefficients are hence chosen to satisfy the power complementarity property to ensure white noise at receiver output [Koca and Levy, 2002] • Power Complementarity property of an N-channel filterbank is defined by [Vaidyanathan, 1993]

  7. EFFECT OF OVERSAMPLING Oversampling in the receive filter colors the noise

  8. BEAMFORMER DESIGN • The channel is modeled by an L-ray complex baseband impulse response with Additive White Gaussian Noise • Filter coefficients are designed using Lagrangian optimization • Objective function is the mean-squared error at the beamformer output • The power complementarity constraint is imposed with suitable modifications to account for oversampling

  9. RESULTS

  10. CONCLUSIONS AND FUTURE WORK • The impulse response of the channel is seen to be shortened effectively by the beamformer • ISI is reduced to about 3 baud intervals and MAP equalization can be performed on the beamformer output • Future work may incorporate co-channel interference suppression in the beamformer design

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