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NETWORK MIMO

NETWORK MIMO. By: Matthew Anderson. Class: EE497A, Wireless Communication Date: 04/26/2010. Overview. Introduction to Network MIMO Theory Behind Network MIMO Simulation and Results Conclusion. Introduction. The Future of Wireless Communication More Users Higher Data Rates

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NETWORK MIMO

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  1. NETWORK MIMO By: Matthew Anderson Class: EE497A, Wireless Communication Date: 04/26/2010

  2. Overview • Introduction to Network MIMO • Theory Behind Network MIMO • Simulation and Results • Conclusion

  3. Introduction • The Future of Wireless Communication • More Users • Higher Data Rates • More Interference!

  4. Introduction • What is Network MIMO? • Network MIMO – an assortment of techniques relying on coordination among multiple transmitters or receivers to increase: • Spectral Efficiency • Data Rates • Immunity to Interference

  5. Introduction • How does Network MIMO work? • Network MIMO – based on a realization that in any wireless system interference is a superposition of signals received at the “wrong” access point but destined for the same network • Interference contains useful information! And if processed correctly, we can use this information • Allows individual receivers to be serviced by multiple transmitters

  6. Theory Behind Network MIMO • Interference Problem

  7. Theory Behind Network MIMO • Current Solution(s) Set 1 and Set 2 are necessarily mutually exclusive Useful transmission over a set of frequencies (set 1)* Useful transmission over a set of frequencies (set 2) * Mobile Switching Center * Note, sets of frequencies (FDMA) can be replaced by sets of time (TDMA) or types of code (CDMA)

  8. Theory Behind Network MIMO • Network MIMO Solution(s) Useful transmission over a set of frequencies (set 1)* Useful transmission over a set of frequencies (set 2) * Mobile Switching Center * Note, sets of frequencies (FDMA) can be replaced by sets of time (TDMA), types of code (CDMA) but do NOT need to be as Network MIMO allows for SDMA Set 1 and Set 2 are NOT necessarily mutually exclusive

  9. Theory Behind Network MIMO • Motivating Example 1: Uplink • Two users, each with one transmit antenna, transmit signals, X1 and X2, at a single frequency, f: • X1f and X2f • Two, spatially separated, base stations pick up the transmitted signals after they have been modulated by their respective (independent) channels: • Hr1t1, Hr1t2, Hr2t1, and Hr2t2 • Hritj = Channel between receiver “i” and transmitter “j” • Therefore the received signals at the base stations are: • Yr1f=Hr1t1X1f+Hr1t2X2f+Noise • Yr2f=Hr2t1X1f+Hr2t2X2f+Noise • With accurate channel estimation techniques (often using pilots) the channel coefficients can be determined, leaving us with two equations and two unknowns (and noise): • Yr1f=Hr1t1X1f+Hr1t2X2f+Noise • Yr2f=Hr2t1X1f+Hr2t2X2f+Noise • Taken separately, i.e. without Network MIMO, each receiver sees one equation with two unknowns and must treat either X1f or X2f as noise • Resulting in lower SINRs or higher BERs for a given PT • Or necessitating higher PTs for a given SINR/BER • When the base stations cooperate, i.e. with Network MIMO, sending the information to a common node for processing, both unknowns can be solved for.

  10. Theory Behind Network MIMO • Motivating Example 2: Uplink • One user, with two spatially separated transmit antennas, transmits two signals, X1 and X2, at a single frequency, f: • X1f and X2f • A base station with two spatially separated antennas picks up the transmitted signals after they have been modulated by their respective (independent) channels: • Hr1t1, Hr1t2, Hr2t1, and Hr2t2 • Hritj = Channel between receiver “i” and transmitter “j” • Therefore the received signals at the base station is: • Yr1f=Hr1t1X1f+Hr1t2X2f+Noise • Yr2f=Hr2t1X1f+Hr2t2X2f+Noise • With accurate channel estimation techniques (often using pilots) the channel coefficients can be determined, leaving us with two equations and two unknowns (and noise): • Yr1f=Hr1t1X1f+Hr1t2X2f+Noise • Yr2f=Hr2t1X1f+Hr2t2X2f+Noise • Taken separately, i.e. without Network MIMO, each receiver sees one equation with two unknowns and must treat either X1f or X2f as noise • One of the users signals interferes with the other • When the base stations cooperate, i.e. with Network MIMO, sending the information to a common node for processing, both unknowns can be solved for. • Since both messages originate from the same user, the result is increased data rates

  11. Theory Behind Network MIMO • Key Features • Advantages • Increased data rates (faster downloads, live streaming) • Reduced BER (more reliable comm. or lower transmit power) • Increased spectral efficiency (more users per cell) • Disadvantages • Requires more complex decoders (more processing) • Requires additional channel estimation ( more overhead) • Not (yet) practical in environments with quickly varying channels

  12. Simulation • Goal • Examine the effect of Network MIMO on the BER in a cell based OFDM system for various SNRs • Overview of System • Multiuser OFDM environment • 16 Users and 16 APs, 1 User and 1 AP per cell, cell size of 36m2 • 64 Subcarriers, 10kHz spacing, 7.75MHz main carrier frequency • 4-Point QAM scheme • Rayleigh fading channel with correlation among finite amounts of contiguous subcarrier frequencies • Path Loss constant of 2.5, Shadow Fading variance of 0dB,Bc=160kHz • ~18 million bits transmitted to get average SNR values • MMSE Detector (A variation of the decorrelating detector) used at APs

  13. Simulation • Code Flow Chart Determine Channel Matrix Generate Messages to be Sent Channel Properties Message Properties Randomly Generate User Positions Calculate Necessary User Transmit Powers Modulate Messages and Transmit Decode Received Messages & Calculate BER Number of APs & Users Cell Coverage Area Implemented KEY Inputs Data Flow abc Input Flow ABC Functions

  14. Simulation

  15. Conclusion • Network MIMO is an effective way to reduce interference and BERs if extra overhead and processing are available • Network MIMO requires accurate data about each users channels and so can be impractical in highly time-selective channels • Writing simulation codes can be quite challenging

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