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Sphere Decoding Algorithm for MIMO Detection

Sphere Decoding Algorithm for MIMO Detection. Arslan Zulfiqar. Motivation. Future mobile applications include Mobile TV High Speed Internet Future wireless systems need to provide High Data Rate High Quality of Service (QoS) Key challenges Hostile propagation environment

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Sphere Decoding Algorithm for MIMO Detection

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  1. Sphere Decoding Algorithm for MIMO Detection ArslanZulfiqar

  2. Motivation • Future mobile applications include • Mobile TV • High Speed Internet • Future wireless systems need to provide • High Data Rate • High Quality of Service (QoS) • Key challenges • Hostile propagation environment • Bandwidth is a limited resource • How do we meet these challenges? • Multiple-Input multiple-output (MIMO) systems

  3. Motivation contd. • How can MIMO help? • Spatial Multiplexing • Diversity • MIMO has been proposed in modern wireless standards • IEEE 802.11n • IEEE 802.16e (WiMax) • 3GPP LTE • Tradeoff: Increased complexity of the decoder!

  4. Problem Formulation • Simple model of a communication system: Channel Estimate Channel RX vector TX vector MIMO Decoding M decoded symbols N receive antennas M complex symbols to be transmitted M transmit antennas How do we do this?

  5. Problem Formulation contd. • First, convert the problem involving complex quantities to one that involves real quantities dimensions scale by 2. • Optimal ML solution= Dimensions Model

  6. ML solution • How do we compute ? • Brute force search • Search over all • Smart search: Sphere decoding algorithm • This algorithm finds a subset of that lie in a sphere around

  7. Experiment • 64-QAM constellation • QAM alphabet set = ={-7,-5,-3,-1,1,3,5,7} • 4x6 MIMO system • SNR considered: • 15dB,18dB,20dB • Inputs to MIMO decoder: • received vector • channel matrix

  8. Experiment: Brute Force Search • ML equation: • Total number of possibilities for ~16 minutes!

  9. Experiment: Sphere Decoding Algorithm • ML equation: • Proposed by Fincke and Pohst • Pick a radius such that, d

  10. Experiment: Sphere Decoding Algorithm

  11. Experiment: Sphere Decoding Algorithm

  12. Optimizations • Parallel tree traversal • Look ahead transformation • Schnorr-Euchner modification

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