1. Channel Dimension Reduction in MU Operation
July 2010
doc.: IEEE /0803r0
July 2010
Channel Dimension Reduction in MU Operation
Date:
Authors:
Nir Shapira et al, Celeno Communications

2. July 2010
doc.: IEEE /0803r0
July 2010
Abstract
Channel dimension reduction (DR) is a means to reduce number of degrees of freedom (receive chains/modes) for users in MU operation
Increases flexibility in MU group formation
Reduces channel sounding overhead
We show the benefit for the AP to report the reduced dimension to the users
The case of non-resolvable LTFs
Reduce CSI feedback overhead
We present simulation results
Nir Shapira et al, Celeno Communications

3. What is Dimension Reduction
July 2010
doc.: IEEE /0803r0
July 2010
What is Dimension Reduction
Dimension Reduction (DR) is a means for the beamformer to reduce the dimension of the channel matrix between beamformer and users
In this context we treat only the case of User-side Dimension Reduction
Dimension reduction can be done by removing user antennas (receive-chains) from the channel matrix
Dimension reduction can be done by using only part of user’s eigen-modes
Nir Shapira et al, Celeno Communications

4. The Need For Dimension Reduction
July 2010
doc.: IEEE /0803r0
July 2010
The Need For Dimension Reduction
Dimension reduction is needed in order to serve users that have many antennas/modes (the case where the sum of users antennas is larger than AP antennas)
E.g. 4 antenna AP and 3 antenna users. W/O dimension reduction MU not used
The option of serving only part of users’ antennas/modes adds more flexibility for making Grouping decisions
Smart dimension reduction can significantly increase overall throughput
Nir Shapira et al, Celeno Communications

5. The Need For Dimension Reduction – cont’
July 2010
doc.: IEEE /0803r0
July 2010
The Need For Dimension Reduction – cont’
Dimension reduction can significantly reduce CSI feedback overhead
CSI feedback overhead is a major issue in the effectiveness of MU operation
Dimension reduction can reduce the computational complexity of precoding matrix and MU grouping decisions
Nir Shapira et al, Celeno Communications

6. Implicit Vs. Explicit Dimension Reduction
July 2010
doc.: IEEE /0803r0
July 2010
Implicit Vs. Explicit Dimension Reduction
Implicit Reduction – AP makes “internal” reduction decision
AP sounds entire channel and makes “internal” DR decision
Resolvable LTFs: user estimates full channel/interference from LTF’s. No need to report DR to user
Non-Resolvable LTFs: not supported. User cannot “deduce” dimension reduction
Explicit Reduction – AP reports dimension allocation decision to users
The dimension reduction itself can be done by the users
Supports both resolvable and non-resolvable cases
DR on CSI feedback for reduced overhead
Nir Shapira et al, Celeno Communications

7. Explicit DR – Reduction in User
July 2010
doc.: IEEE /0803r0
July 2010
Explicit DR – Reduction in User
AP controls just the dimension size per user
AP sends dimension νk to the k’th user
νk ≤nk (nk number of user antennas)
AP can still choose sk ≤ νk (sk number of user streams) per frame
User makes reduction operation on CSI feedback
User sees only channel from AP to user. DR decision is suboptimal
Simulations show performance loss vs. a “know-all” user is small
Opt 1: receive chain (antenna) selection
User can choose the antennas having maximum RSSI
User deletes rows of non-used antennas from CSI matrix
In case of non-resolvable LTFs, user need to store indices of used antennas
Nir Shapira et al, Celeno Communications

8. Explicit DR – Reduction in User, cont’
July 2010
doc.: IEEE /0803r0
July 2010
Explicit DR – Reduction in User, cont’
Opt 2: eigen-mode selection
User calculates receive matrix U from SVD (H=UDV*, H being the full channel estimate)
User selects first νk columns of U corresponding to best νk eigen-modes
We denote as the first stage receive matrix equal to first νk columns of U
User returns effective reduced dimension channel as CSI feedback
In case of non-resolvable LTFs, user must store first stage receive matrix for MU packet reception
Upon actual MU packet detection user can add second stage receive matrix
Nir Shapira et al, Celeno Communications

9. Explicit DR – Dimension Size Control
July 2010
doc.: IEEE /0803r0
July 2010
Explicit DR – Dimension Size Control
Dimension size per user can be defined in MU group definition and/or in a special DR control element
In case user belongs to several MU groups, it can have a different reduced dimension in each group
We recommend adding a “full-CSI” bit to NDP announcement in case AP wants to sound full channel
AP might occasionally sound full channel to make new dimension allocation
No need to redefine MU group
Other alternatives:
Per user dimension request in NDP announcement
AP can sound users as single-users in case it needs a particular user’s full channel
Nir Shapira et al, Celeno Communications

10. Explicit DR – Reduction by AP
July 2010
doc.: IEEE /0803r0
July 2010
Explicit DR – Reduction by AP
AP has full channel knowledge and can make “globally optimal” dimension reduction for all users
AP control of participating user antennas is most practical
Control over user receive eigen-modes requires assumption on receiver design
AP can signal the antenna selection decision in the MU group definition
Add a bit-field per user, wherein each bit signifies selection/un-selection of user antenna (receive-chain)
In the Reduction-by-AP option, the AP explicitly controls both the dimension allocation and antenna selection
Nir Shapira et al, Celeno Communications

11. July 2010
Simulation Results
Nir Shapira et al, Celeno Communications

12. Simulation assumptions
July 2010
doc.: IEEE /0803r0
July 2010
Simulation assumptions
TGn channel model D
Users are equally distanced from AP
Antenna correlation assumes a linear array with antenna spacing = 0.5 λ
BW 20 MHz
Channel aging of 20 mS, assuming
Velocity = Km/hr
Noisy channel estimation for CSI
Interference cancellation in users’ side (resolvable LTFs)
All methods aimed to maximize total AP throughput
Optimization over serviced users in group, dimension reduction, assigned streams
All MU methods use ZF block diagonalization
See annex for more details on simulation methods
Nir Shapira et al, Celeno Communications

13. July 2010
doc.: IEEE /0803r0
July 2010
Compared Methods
Different methods differ by the degrees of freedom for optimization
Simulated dimension reduction methods include:
User-directed, RSSI-based antenna selection
User-directed mode selection
Exhaustive AP-directed antenna selection at the users
For user-directed modes we have simulated 2 policies:
Exhaustive optimization on dimension per user
Equal allocation of dimension per user
As a reference we simulated both TDMA and MU w/o dimension reduction
Nir Shapira et al, Celeno Communications

14. 4→2,2,2,2 without estimation errors
July 2010
doc.: IEEE /0803r0
July 2010
4→2,2,2,2 without estimation errors
Perfect CSI at AP (no aging and noise)
4 AP antennas, 4 users, each with 2 antennas
Nir Shapira et al, Celeno Communications

15. July 2010
doc.: IEEE /0803r0
July 2010
Discussion
As expected, high-SNR gain of SDMA over TDMA is 4 streams/2 streams = 2
Antenna selection at the clients side similar to AP-directed antenna selection
Large gain from optimizing over dimensions vector
At avg. SNR of 25 dB, comparing USER_MODE_SELECT_EXHAUSTIVE vs. FULL_DIM_SDMA we see 25 % gain w/o correlation, and 45 % gain w correlation
Nir Shapira et al, Celeno Communications

16. Results: 4→2,2,2,2 with aging+noise on CSI
July 2010
doc.: IEEE /0803r0
July 2010
Results: 4→2,2,2,2 with aging+noise on CSI
4 AP antennas, 4 users, each with 2 antennas
Aging of 20 mS
SNR dependant noise on CSI, assuming single LTF, no smoothing
Nir Shapira et al, Celeno Communications

17. Results: 4→2,2,2,2 discussion July 2010
doc.: IEEE /0803r0
July 2010
Results: 4→2,2,2,2 discussion
SDMA Gain over TDMA reduced due to CSI errors
Mode selection at clients slightly better than AP-directed selection (w correlation)
At avg. SNR of 25 dB, comparing USER_MODE_SELECT_EXHAUSTIVE vs. FULL_DIM_SDMA we see 30 % gain w/o correlation, and 20 % gain w correlation
Nir Shapira et al, Celeno Communications

18. Results: 8→3,3,3,3 with aging/noise on CSI
July 2010
doc.: IEEE /0803r0
July 2010
Results: 8→3,3,3,3 with aging/noise on CSI
8 AP antennas, 4 users, each with 3 antennas
Aging of 20 mS
SNR dependant noise on CSI, assuming single LTF, no smoothing
Nir Shapira et al, Celeno Communications

19. Results: 8→3,3,3,3 discussion July 2010
doc.: IEEE /0803r0
July 2010
Results: 8→3,3,3,3 discussion
User mode selection similar to AP antenna selection
At avg. SNR of 25 dB, comparing USER_MODE_SELECT_EXHAUSTIVE vs. FULL_DIM_SDMA we see 10 % gain both w and w/o correlation
Nir Shapira et al, Celeno Communications

20. July 2010
doc.: IEEE /0803r0
July 2010
Conclusions
Dimension reduction is a good strategy for MU precoding design
Increased throughput relative to system constrained to full dim
Explicit DR can significantly reduce sounding overhead
Explicit DR is essential for non-resolvable LTF mode
User controlled DR can achieve most of the performance gain
We recommend to adopt a mechanism whereby the AP controls the number of dimensions per user
Nir Shapira et al, Celeno Communications

21. Annex July 2010 July 2010 doc.: IEEE 802.11-10/0803r0
Nir Shapira et al, Celeno Communications

22. Simulation Method Optimized parameters are: July 2010 Rate selection:
doc.: IEEE /0803r0
July 2010
Simulation Method
Rate selection:
TX matrix optimization already includes optimizing number of streams per user
TX matrix + channel → SINR vector per active user (over streams and bins)
SINR vector mapped to effective SNR, used to select modulation and coding from AWGN curves (same modulation for all streams of a given user)
Optimized parameters are:
Serviced subset of users
Reduced dimension per serviced user
Number of streams per serviced user after dimension reduction
Number of streams may be smaller than the dimension
For example, a single stream is possible regardless of the dimension
Nir Shapira et al, Celeno Communications

23. Compared Methods July 2010 TDMA FULL_DIM_SDMA
doc.: IEEE /0803r0
July 2010
Compared Methods
TDMA
Single user mode
FULL_DIM_SDMA
No dimension reduction
Run over all subsets with total num. users antennas ≤ num. AP antennas
Block diagonalization + per-user SVD + per-user num. streams optimization
USERS_ANTENNA_SELECT_EXHAUSTIVE:
Exhaustive run over all subsets and over all dimensions per-active user
For a fixed dimensions vector d: user k selects the d(k) strongest antennas
Block diagonalization + per-user SVD + per-user num. streams optimization
USERS_MODE_SELECT_EXHAUSTIVE:
Same as above, but now user k selects the d(k) strongest modes
USERS_ANTENNA_SELECT_FIXED_DIM:
Same as USERS_ANTENNA_SELECT_EXHAUSTIVE, but with d vector forced to equal dimension per-serviced user
USERS_MODE_SELECT_FIXED_DIM
AP_ANTENNA_SELECT_EXHAUSTIVE:
Exhaustive run over all subsets, all dim. vectors d and all selections of d(k) antennas for user k
Nir Shapira et al, Celeno Communications