Full-duplex without Strings: Enabling Fullduplex with Half-duplex Clients

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1 Full-duplex without Strings: Enabling Fullduplex with Half-duplex Clients Karthikeyan Sundaresan, Mohammad Khojastepour, Eugene Chai, Sampath Rangarajan NEC Labs America MobiCom 2014

2 Full-duplex Transmitting + receiving on same timefrequency resource Key challenge: Self-interference Several advancements in full-duplex design Antenna + RF + digital cancelation Three, two and single antenna designs Co-existence with MIMO Focus on peer-peer FD networks SI FD Base Station FD Client 1. Achieving single channel, full duplex wireless communication, J. Choi et. al. MobiCom, Experiment-driven characterization of full-duplex wireless systems, M. Duarte et. al. IEEE Transactions on Wireless Communications, MIDU: Enabling MIMO Full-duplex, E. Aryafar et al. MobiCom Full-duplex radios, D. Bharadia et. al., Sigcomm

3 Distributed Full-duplex Can we enable FD communication (2x multiplexing gain) with HD clients in a single cell? Easier to embed FD functionality in BS/AP SI FD Base Station Distributed FD Uplink from one client and downlink to another client UDI Key challenge: uplink-downlink interference (UDI) HD Client HD Client 3

4 Potential Solutions for UDI Impact of UDI depends on topology d UDI (d distance between BS and DL client) Large impact for comparable distances 4

5 Potential Solutions for UDI Impact of UDI depends on topology d UDI (d distance between BS and DL client) Implicit: leverage client separation Explicit: use side channels [Bai-Arxiv 12] Explicit: time-based interference alignment [Sahai-ITW 13] Scaling to MIMO? Explicitly address UDI in the same channel in a scalable manner 5

6 Approach Leverage spatial interference alignment to address UDI between HD clients Use multiple antennas at HD clients Pack interference in lesser dimensions Efficient: same channel Scalable: co-exist with MIMO Deployable: only as challenging as MU-MIMO systems y y 2 1 y 1,y 2 x,x 1 2 x 1 y 3 y 4 x 2 x 3 x 4 y 3,y 4 x 3,x 4 x4 x 2 x 3 x 1 DL UL 6

7 Challenges CSI overhead for UDI More clients (dimensions), easier IA, but more overhead V 0 U 0 (N)... Constructing a feasible IA solution MIMO precoders (V), receiver filers (U) at clients and AP Handling clients with heterogeneous antenna capabilities U 1 U 2 N streams N streams V 1 V 2 Optimizing rate for the FD streams (N) (N) FDoS: System that addresses above challenges to enable FD with HD clients 7

8 (1) Applying IA to FD Networks Results N even: 4 clients necessary to address UDI and enable 2N streams... N odd: 6 clients necessary (symmetric) N/2 N/2 N odd: 5 clients necessary (asymmetric) Focus on symmetric FD networks Constant overhead: CSI between 4 or 6 clients U 1 U N/2 N/ V 1 V 2 Does not scale with N U V 3 8

9 (2) Constructing IA Solution Receiver spatial dimensions Desired (1:1) Interference suppression (1:1) IA (1:many)... 9

10 (2) Constructing IA Solution Construct a feasible IAN Select IA solution for cyclic part Determine IA solution At most one cycle in IAN Closed-form IA solution 2N streams achievable even with UDI for symmetric FD networks With 4 (6) clients for N even (odd) Find resulting IA solution for acyclic part cyclic acyclic cyclic p+1 p+1 p+1 p+1 p+1 p+1 acyclic p p p p p p q K-q 10

11 Example: N=5, 6 clients, 10 streams H 10 v 01 H 10 v 02 H 13 v 31 H 12 v 22 H 11 v 11 H 11 v 12 H 12 v V 1 H 23 v 31 H20 v 04 H20 v H 21 v 12 H 21 v 11 V 2 H 22 v 22 H 22 v 21 H 30 v 05 H 32 v 21 H 31 v 11 H 33 v 31 H 31 v 12 H 32 v V 3 11

12 (3) Heterogeneous Clients Clients with different number of antennas Affects number of FD streams supported (N)... M+N streams achievable with FD Different IA construction required Combination of symmetric and asymmetric FD networks? streams (M)? streams (N) (M) (N) 12

13 Evaluation Testbed One AP and four clients (WARP nodes) with 2 or 4 antennas each FD: SI cancelation based on prior works Focus on UDI cancelation between UL and DL clients Cancelation over 64 sub-carrier OFDM, 10 MHz channel Experiments in indoor office environment Baselines HD system MU-MIMO (zero-forcing beamforming) FD without UDI cancelation Metric SINR measurements, rate translation from SINR 13

14 Results (1) UDI Suppression db db db of median UDI suppression out of 30 db

15 Results (2) Rate Performance x FD rate gain 1.5-2x gain over schemes not addressing UDI Not addressing UDI can degrade performance to worse than HD

16 Conclusions FD has potential to increase system capacity by 2x All the more powerful if HD clients can be used UDI is a key challenge in distributed FD networks FDoS: a system that leverages spatial IA to address UDI Theory and design of applying spatial IA for distributed FD Incorporates practical considerations (overhead, rate, heterogeneity) Demonstrates 1.5-2x gain in presence of UDI in practice Next steps FD with HD clients in multi-cell networks

17 Thanks! 17

18 (3) Rate Optimization Very challenging problem MU-MIMO precoding on DL and UL coupled through IA between DL-UL Jointly pick N/2 vectors each for V 1,V 2 that maximize rate of N UL streams subject to IA UL clients (V 1,V 2 ) Modular design De-couple IA from MU-MIMO precoder (rate) optimization Retain structure of IA solution for UDI Optimize DL and UL MU-MIMO precoders given IA solution Distributed realization of IA solution Overhead reduced further by half Fix receive filter U 0 for AP from UL optimization Given V 1,V 2, pick receiver filters U 1,U 2 orthogonal to sub-space spanned by interference Pick precoder V 0 at AP to maximize rate of N DL streams AP (U 0 ) DL clients (U 1,U 2 ) AP (V 0 ) 18

19 FDoS Operations Client and mode (FD vs. HD) selection based on multiplexing gain, scheduling policy Estimate CSI for UL, DL and UDI channels with reduced feedback Distributed computation of solution (AP broadcasts only one precoder) AP solicits/delivers block ACKs similar to MU-MIMO AP coordinates joint UL and DL transmissions during FD 19

20 Results (3) Heterogeneity (4)... 2 streams 4 streams (2) (4) (2) (4) 6 streams sent in heterogeneous set-up Leverages heterogeneous antenna capabilities effectively

21 Results (4) - Scalability (a) With rate optimization (b) Without rate optimization Evaluated FDoS design for larger (even/odd) N FD gains scale and more pronounced with rate optimization 21

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