Transcript
Full-duplex without Strings: Enabling Fullduplex with Half-duplex Clients Karthikeyan Sundaresan, Mohammad Khojastepour, Eugene Chai, Sampath Rangarajan
NEC Labs America MobiCom 2014
Full-duplex Transmitting + receiving on same timefrequency resource
FD Base Station
Key challenge: Self-interference SI
Several advancements in full-duplex design – Antenna + RF + digital cancelation – Three, two and single antenna designs – Co-existence with MIMO
FD Client
– Focus on peer-peer FD networks
1. 2. 3. 4.
“Achieving single channel, full duplex wireless communication”, J. Choi et. al. MobiCom, 2010. “Experiment-driven characterization of full-duplex wireless systems”, M. Duarte et. al. IEEE Transactions on Wireless Communications, 2012. “MIDU: Enabling MIMO Full-duplex”, E. Aryafar et al. MobiCom 2012. “Full-duplex radios”, D. Bharadia et. al., Sigcomm 2013.
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Distributed Full-duplex Can we enable FD communication (2x multiplexing gain) with HD clients in a single cell?
FD Base Station
– Easier to embed FD functionality in BS/AP
SI
Distributed FD
UDI
– Uplink from one client and downlink to another client
Key challenge: uplink-downlink
HD Client
HD Client
interference (UDI)
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Potential Solutions for UDI
Impact of UDI depends on topology
d
UDI (d – distance between BS and DL client)
Large impact for comparable distances
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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
Approach Leverage spatial interference alignment to address UDI between HD clients
– Use multiple antennas at HD clients – Pack interference in lesser dimensions
y2
y1 x2
x1
Efficient: same channel
x1,x2
x3
x4
y3
Scalable: co-exist with MIMO x3 x1
Deployable: only as challenging
y1,y2
y3,y4 y4
x3,x4
x4
x2
DL
UL
as MU-MIMO systems
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Challenges CSI overhead for UDI – More clients (dimensions), easier IA, but more overhead
V0
(N)
Constructing a feasible IA solution
N streams
N streams
U1
....
....
V1
U2
....
....
– MIMO precoders (V), receiver filers (U) at clients and AP
....
U0
V2
(N)
(N)
Handling clients with
heterogeneous antenna capabilities
Optimizing rate for the FD streams
FDoS: System that addresses above challenges to enable FD with HD clients
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(1) Applying IA to FD Networks Results .... N/2
....
U1
N/2
....
– N even: 4 clients necessary to address UDI and enable 2N streams – N odd: 6 clients necessary (symmetric) – N odd: 5 clients necessary (asymmetric)
N/2
1
V2
....
1
....
U3
....
– Constant overhead: CSI between 4 or 6 clients – Does not scale with N
U2
N/2
....
Focus on symmetric FD networks
V1
V3
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(2) Constructing IA Solution Receiver spatial dimensions – Desired (1:1) – Interference suppression (1:1) – IA (1:many)
.....
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(2) Constructing IA Solution
cyclic p+1
...
Determine IA solution
...
Construct a feasible IAN
p+1
... ...
p+1 p
acyclic
p+1 p
...
...
cyclic
q
...
...
At most one cycle in IAN Closed-form IA solution
...
Find resulting IA solution for acyclic part
p+1
...
Select IA solution for cyclic part
p+1
acyclic p
...
p
– With 4 (6) clients for N even (odd)
K-q
...
...
for symmetric FD networks
...
2N streams achievable even with UDI
p
p
...
...
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Example: N=5, 6 clients, 10 streams
H10v01
H10v02
H13v31 H12v22
H23v31
H20v04
H21v12
H11v11 H11v12 H12v21
H20v03
2
2 V1
2
2 V2
H21v11 H22v22 H22v21
H30v05 H32v21
H33v31 H31v12
H31v11
1
1
V3
H32v22
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(3) Heterogeneous Clients Clients with different number of antennas
– Affects number of FD streams supported
(N) ? streams
(M)
(N)
....
....
– Combination of symmetric and asymmetric FD networks
(M)
? streams
....
Different IA construction required
....
M+N streams achievable with FD
....
(N)
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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
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Results (1) – UDI Suppression
10-15 dB 15-20 dB
10-20 dB of median UDI suppression out of 30 dB
Results (2) – Rate Performance
1.75-2x FD rate gain 1.5-2x gain over schemes not addressing UDI Not addressing UDI can degrade performance to worse than HD
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
Thanks!
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(3) Rate Optimization Very challenging problem – MU-MIMO precoding on DL and UL coupled through IA between DL-UL
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
Jointly pick N/2 vectors each for V1,V2 that maximize rate of N UL streams subject to IA
Fix receive filter U0 for AP from UL optimization
Given V1,V2, pick receiver filters U1,U2 orthogonal to sub-space spanned by interference
UL clients (V1,V2)
AP (U0)
DL clients (U1,U2)
Distributed realization of IA solution – Overhead reduced further by half Pick precoder V0 at AP to maximize rate of N DL streams
AP (V0) 18
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
AP solicits/delivers block ACKs similar to MU-MIMO
Distributed computation of solution (AP broadcasts only one precoder)
AP coordinates joint UL and DL transmissions during FD
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Results (3) – Heterogeneity
(4)
....
2 streams
6 streams sent in heterogeneous set-up Leverages heterogeneous antenna capabilities effectively
(4)
....
.... (2)
....
.... (2)
4 streams
(4)
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
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