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W-PAN Structures Inside an OFDMA/ SDMA W-LAN

This article discusses the problem of multi-user spatial correlation in an OFDMA/SDMA WLAN network and presents a two-step approach for modeling and simulation. It includes examples of gang structures and channel measurements and provides code in MATLAB for modification and simulation.

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W-PAN Structures Inside an OFDMA/ SDMA W-LAN

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  1. W-PAN StructuresInsidean OFDMA/SDMA W-LAN Date: 2009-3-11 Authors: Jiunn-Tsair Chen

  2. Outline Problem Description Multi-User Spatial Correlation Two-Step Approach Example Gang Structures Channel Measurements Model Modification in MATLAB Code MATLAB Simulation Results Conclusions Jiunn-Tsair Chen

  3. Problem Description Key features in a TGac wireless network may include OFDMA with or without CSI dependent scheduling SDMA with multi-user transmit beam-forming making use of CSI information CSI dependent coding and modulation schemes To evaluate related algorithms in a TGac simulation platform, both on Link level and System level, channel models that reflect the properties of OFDMA and SDMA systems are required. OFDMA features: frequency selectivity, which is common in the PHY channel model but rare in the MAC channel model multi-user diversity, the channel modeling of which is rare mutual (spatial) correlation among multi-user STAs for practical scheduling design un-realistic channel models render simulation results on scheduling algorithms unreliable SDMA features: multiple non-AP STAs receive data streams simultaneously in the same time/frequency bands mutual (spatial) correlation among STAs for practical transmit beam-forming design un-realistic channel models render simulation results on beamforming algorithms unreliable Low complexity implementation is required, especially for the System-Level platform, to test OFDMA system scheduling algorithm and the CSI feedback efficiency for SDMA beam-forming Jiunn-Tsair Chen

  4. Multi-User Spatial Correlation • STAs in the same PAN as the AP see K-factor effects, STAs in a different PAN from the AP may or may not see key-hole effects • Key-hole effects may dominant, when resonant STAs in the same (shielding) room share signals coming from the same key-hole, like doorway, or small windows Jiunn-Tsair Chen

  5. Multi-User Spatial Correlation -- Examples • Intention to transmit signals to one STA without bothering the other STA should have different degree of difficulties in these three scenarios • With no consideration of WPAN structures in a WLAN, these scenarios may end up identical down-link multi-user MIMO channels, due to • Random AOA/AOD • Identical procedure to generate MIMO channels, assuming identical random seeds Jiunn-Tsair Chen

  6. STAs of the same PAN (or we may call it Gang to avoid confusion) have correlated receive covariance matrices Similarly, in the same scenario will have a 2x2 block structure Within a gang, mutual correlation is a function of STA mutual distances and other spatial parameters STAs are partitioned into gangs. The partition may not be done exclusively Multi-User Spatial Correlation, Matrix Form (1) Jiunn-Tsair Chen

  7. Two-Step Approach: Step-1 Viewing from far far away with low resolution, we can see only the central point of each antenna array, where the (1) degenerates into a 6x6 matrix with a gang structure. • Same idea applied to APs to generate , also with a gang structure • in the uplink = in the downlink, vise versa • Each off-diagonal term of the 6x6 matrix is a function of the corresponding mutual distance and gang structure • This can also serve as the channel model for systems with OBSS, which concerns TGaa Jiunn-Tsair Chen

  8. Zooming in to see array textures: Each off-diagonal element of the 6x6 matrix will be replaced by an all-one rectangular matrix scaled by the associated off-diagonal element. This is due to poor resolution among elements in a far-away antenna array Each diagonal element is replaced by the covariance matrix generated with 11n channel modeling plus the square root of the off-diagonal block sub-matrix times its own Hermitian matrix The eventual receive or transmit covariance matrices generated so is guaranteed positive definite. Cluster-wise random orientation for the 11n channel model is suggested for each user to generate Could easily degenerate to 11n channel model or channel models with no spatial correlation among multiuser STAs Physical phenomena to cover K-factor (LOS) effects impinging on multiuser STAs Key-hole effects to STAs of the same gang in a resonant room SDMA Beam-forming to distinguish STAs in a gang is much more difficult OFDMA/SDMA scheduling algorithm should avoid assigning the same time-slot/frequency-band to STAs in the same gang Two-Step Approach: Step-2 Jiunn-Tsair Chen

  9. Example Gang Structure Consider a transmitter with two antennas, and two receivers each also with two antennas Assume each receiver sees only a rank-one channel, and everything before an entrance point from an open space to the gang are identical are the spatial signatures of the incident waves, which is the signal sub-space all incoming signals project onto are the matching signatures of each receiver to capture the energy of its corresponding incident waves (2) Jiunn-Tsair Chen

  10. Example Gang Structure-1 • If , let us define and define as the receive weight vector, we have a head-on collision, regardless of , since Jiunn-Tsair Chen

  11. Example Gang Structure-2 • Let , and let be the receive weight vectors. If we have a transmit beam-forming matrix we will have orthogonal channels to each receiver, again regardless of since Jiunn-Tsair Chen

  12. Example Gang Structure-Matrix Form Define and rewrite (2) into matrix form, we have (3) • The two example gang structures can be seen as extreme cases. The off-diagonal sub-blocks of matrix , instead of all-zero matrices, equal to its diagonal sub-blocks. Jiunn-Tsair Chen

  13. Experiments are conducted with the Ralink product RT2880 as AP, talking to the Ralink product RT2860 in a cardbus as clients. Signals are dumped at the ADC output of each antenna. Channel is estimated in the frequency domain after timing/frequency synchronization and FFT. Channel estimates at receive antennas are cross correlated over frequency sub-carrier and over a time period long enough to cause customer complaints whenever system performance is bad, say one minute. Channels are measured and cross-correlated at several locations with different environment settings: Shielding room Basement parking lot Office space Channel Measurement Jiunn-Tsair Chen

  14. Channel Measurement 1, Shielding Room The shielding room is made of metal walls, and metal ceiling. People are running around outside the shielding room, while everything inside are static. A pair of receive antennas are located at either Rx-1 or Rx-2 with the only shielding room door fully/half/quarterly open. Tx Fully/Half/Quarterly Open Rx-2 Rx-1 Jiunn-Tsair Chen

  15. Measurement Results, Shielding Room Higher correlcation when the door is quarterly open for Rx1 location Independent of receiver location, Rx1 or Rx2 Independent of the antenna distance Tx Fully/Half/Quarterly Open Rx-2 Rx-1 Jiunn-Tsair Chen

  16. Channel Measurement 2, Basement Parking Lot The parking lot is relatively quiet with cars driving around only once in a while. A pair of receive antennas are located at Rx-1, 2, 3, 4, 5, and 6, where Rx-5 and 6 can barely see LOS. Rx-4 Rx-3 Rx-1 Rx-2 Tx Rx-5 Rx-6 Jiunn-Tsair Chen

  17. Measurement Results, Basement Parking Lot High correlation coefficient for Rx1-3, which are inversely proportional to antenna distance Weak cross-correlation at Rx4 Even weaker is seen at Rx 5, 6 Rx-4 Rx-3 Rx-1 Rx-2 Tx Rx-5 Rx-6 Jiunn-Tsair Chen

  18. The main office space is divided into compartments. Each compartment is surrounded with tall metal wall with only one side entrance. Meeting rooms are surrounded by wooden walls with doors and windows. A pair of receive antennas are located at Rx-1, 2, 3, 4, 5, and 6. Channel Measurement 3, Office Space Rx-4 Rx-5 Tx Rx-1 Rx-2 Rx-3 Rx-6 Jiunn-Tsair Chen

  19. High cross-correlation at Rx 1-3 and 4. Weak cross-correlation at Rx 5-6. Except for Rx1, almost all independent of the antenna distance. Measurement Results, Office Space Rx-4 Rx-5 Tx Rx-1 Rx-2 Rx-3 Rx-6 Jiunn-Tsair Chen

  20. Model Modification in MATLAB Code Parameter definition Number of gangs and the Gang Structure within Number of antennas in whatever number of transmitters and in whatever number of receivers in each gang Coordinate vector of each STA, transmitter or receiver Break down IEEE_802_11_Cases into three parts Generate the transmit covariance matrix for each transmitter, one by one, and then merge them into a transmit network covariance matrix according to the transmit gang structure Do the same for each receiver and then construct a network covariance matrix Generate Rician matrix, path loss and log-normal fading for each transmitter-receiver pair according to the STA coordinate vectors Following the same code, we will get MIMO channels from any transmitter to any receiver in the network cover structured mutual interference among multi-user STAs cover structured OBSS interferences naturally as a side benefit Jiunn-Tsair Chen

  21. MATLAB Simulation Scenario Channel model B with AoD_Offset=[20, 150; 210, 120], AoA_Offset=[120, 115; 275, 280; 310, 190] Each AP has two antennas with half a wavelength spacing, each STA has only one antenna. d = 5 meter. One data stream of Uncoded 64-QAM are transmitted at 40 sampling rate along each link Zero-Forcing DPC, with per antenna power constraint no higher than 12 dBm, are applied as the transmit beam-forming scheme Noise level are set by ambient noise and 8-bit ADC quantization noise, whichever is higher. The receiver NF set at 10 dB The wall defining the PAN boundary, at position A or B, introduce 20dB power decay Jiunn-Tsair Chen

  22. Conclusions W-PAN structures inside a W-LAN is considered and channel measurement experiments are conducted to look into this phenomenon On-going simulations repeatedly suggest that Conclusions drawn upon simulation results depend heavily on the wireless channel model Un-realistic channel models result in unreliable simulation outcomes Let us examine if the proposed OFDMA/SDMA channel model make sense Should more realistic effects be added in, like the dependence of channel structure on the distance to the transmitter Is the complexity low enough to facilitate larger scale simulation in a OFDMA/SDMA network, even with OBSS consideration Jiunn-Tsair Chen

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