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Interference Mitigation by MIMO Cooperation and Coordination - Theory and Implementation Challenges
Vincent Lau Dept of ECE,
Hong Kong University of Science and Technology
Traditional Interference Management in Cellular Systems
n Frequency Reuse: ¬ Static spectrum sharing; TDM within
each cell (GSM) ¬ Adjacent cells use different frequency ¬ Widely used for voice communication ¬ Coverage radius reduced and low
spectral efficiency
n CDMA/OFDMA & Scheduling: ¬ Universal frequency reuse is possible ¬ Interference Averaging:
n Spread spectrum in time-domain (CDMA) or frequency domain (OFDMA)
¬ Statistical multiplexing of time-frequency
¬ More signal processing and complicated resource allocation
¬ The system is highly interference-limited
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F1
F3
F2
F2
F3
F2
F3
F1
F3
F1
F2
F1
F2
F1
F3
F1
F1
F3
F2
Frequency Reuse(reuse 3)
CDMA
OFDM
Spatial Interference Mitigation using MIMO Technology
n Multiple antennas provides spatial degrees of freedom for interference mitigation
n Beamforming has been widely used for spatial interference mitigation ¬ SINR is a function of T, R and q
¬ t: trade-off between controlling interference to others and maximizing signal power
¬ r: trade-off between suppressing interference and maximizing signal power
4
Transmi(ers Receivers
1,1H
1,2H
2,3H3,3H
1 1,p t
2 2,p t
3 3,p t
1r
2r
3r
1
1
1
,
[ ,..., ] (power vector) { ,..., } (Tx beamforming vectors) { ,..., } (Rx beamforming vectors) (channel matrix from Tx to Rx )
TL
L
L
l k
p p
k l
===
pT t tR r rH
Successive Interference Cancellation (SIC) and Dirty Paper Coding (DPC)
n SIC for uplink receiving
n DPC for downlink transmitting [M. Costa, TIT83]
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Decoder User 1
Encoder User 1
Decoder User 2
Encoder User 2
-+
-+
Decoder User L
…
…
y
1b̂ 2b̂ b̂L
DPC Encoder 1
DPC Encoder 2
DPC Encoder L
Concatenation
Pre-coder
It is convenient to simply view DPC as successive interference pre-cancellation at the Tx.
Achievements and Limitations of Existing Interference Mitigation Technologies
n Achievements ¬ Beamforming + DPC achieves the capacity of single cell MIMO
downlink ¬ Beamforming + SIC achieves the capacity of single cell MIMO
uplink ¬ Simple beamforming scheme (e.g., zero-forcing) + user selection
achieves a large portion of the capacity
n Limitations ¬ The inter-cell interference is not handled and is treated as noise ¬ The performance of the system is limited by inter-cell interference ¬ The cell edge users suffers from bad performance
n This motivates the idea of inter-cell interference (ICI) mitigation by cooperation and coordination between BSs
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Joint processing
I
I
I
I
I
I
Ø Multiple BSs collaborate to mitigate ICI or align interference for cancellation. Ø Improves cell-edge performance and overall throughput.
Multi-cell environment with frequency reuse factor 1
Optical fiber
Optical fiber Optical fiber
interference
Basic Concepts of Cooperative and Coordinative MIMO
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(1) Cooperative Zero-Forcing Beamforming (ZFBF)
2h1h
1e
2e
1 1g t The direction of the projection gives transmitter vector, while the norm square of the projection gives the equivalent channel
The mutual interfering multi-user channel is decomposed into multiple parallel independent SISO channels:
T1
R2 T2
R1
21 1 1g = h t
22 2 2g = h t
The optimal power allocation is easy, e.g., Water-filling is optimal for sum rate maximization with sum power constraint
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ZFBF on a Larger dimension Signal Space à achieve full DoF
(2) Coordinated Zero-Forcing (IA) n Consider a MIMO cellular network consisting of three BSs and three MSs,
where each node has 2 antennas and each BS delivers 1 data stream.
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(2) Coordinated Zero-Forcing (IA) n Breakthrough performance
¬ Interference alignment (IA) achieves the optimal throughput scaling law w.r.t. SNR in MIMO interference networks and MIMO-X networks [V. R. Cadambe, S. A. Jafar, and S. Shamai, TIT 08], [V. R. Cadambe and S. A. Jafar, TIT08].
¬ Famous for the saying “No matter how many people come to share the cake, everyone can get a half.”
n Limitations ¬ The feasibility issue arises without infinite dimension symbol
extension [C. M. Yetis, S. A. Jafar, and A. H. Kayran, TSP 10]. ¬ Requires perfect CSI: “Small channel estimation errors can be
tolerated, while larger errors reduce the diversity gain significantly.” [A. Sezgin, S. A. Jafar, and H. Jafarkhani, GlobalCOM 09’]
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(K/2)log SNR
n Co-MIMO with full cooperation ¬ All CSI and data symbols are collected at a central processor (CP) à heavy backhaul
consumption ¬ A virtual MIMO BC/MAC is created for the transmission of all users ¬ A user is served by all BSs – a big MIMO!
n Coordinated MIMO without Cooperation ¬ The CP only collects CSI à limited backhaul consumption ¬ The CP jointly optimize the user scheduling, power allocation and beamforming
vectors of all BSs ¬ A user is only severed by one BS, the signals from other BSs are treated as interference
n Co-MIMO with limited cooperation ¬ In practice, the capacity of backhaul is limited ¬ Co-MIMO is “more expensive” compared with Coordinated MIMO ¬ We have to quantize the data symbols and/or only exchange part of the data symbols
Different Levels of Cooperation
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Related Standard Activities in LTE Advanced n Coordinated multipoint transmission/reception (CoMP)
¬ Improve coverage, cell-edge throughput, and/or system throughput n CoMP schemes (especially DL) identified in LTE-A (3GPP TR 36.814)
¬ Joint processing/transmission (multipoint transmission to single UE) (a) Coherent transmission (joint precoding)
(b) Non-coherent transmission (independent precoding)
¬ Coordinated scheduling/beamforming (data is transmitted only from one cell site, and scheduling/beamforming is coordinated among cells)
(a) Coordinated scheduling (ICIC with fast cell selection (FCS)) (b) Coordinated beamforming (space-domain fast ICIC)
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n Backhaul constraint ¬ Backhaul latency ¬ Capacity constraints
n CSI Acquisition n Synchronization
¬ Timing Sync ¬ Frequency offsets Sync
Practical Challenges and Issues
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n Backhaul capacity requirements ¬ Co-MIMO requires the exchange of a large amount of information ¬ Depending on the backhaul technology, the backhaul capacity may or may not support
Co-MIMO operation ¬ Methods to reduce the Backhaul burden for Co-MIMO:
n Quantizing baseband signals n Cell Clustering (Q: What’s the optimal number of cooperative cells) n Optimized data sharing via a rate splitting
n Backhaul Latency requirements ¬ Backhaul Latency must be small compared to channel coherent time
Backhaul Constraint
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Typical Backhaul Latency in LTE system
n Objective: ¬ Accurate channel estimation (CE) with moderate overhead is basis of any
advanced Co-MIMO scheme. n Challenges:
¬ High # of channel components per cooperation area ¬ Multi cell environment with strong inter cell interference ¬ Strong variation of path loss over different cells ¬ Fast outdating of CSI ¬ Larger CSI delay due to cooperation ¬ Pilot Pollution between Cells
n Possible Research Activities: ¬ Exploiting channel reciprocity ¬ Overhead reduction schemes (compression techniques) ¬ Accurate CSI estimation under strong inter cell interference ¬ Codebook design under strong variation of path loss ¬ Robust CSI prediction schemes
CSI Estimation Quality
1/1/2009 18
n Feedback of channel information ¬ Allows transmitter adaptation and enables interference avoidance ¬ Consumes reverse link capacity: tradeoff performance gain vs. reverse link penalty
n Some approaches: ¬ Hierarchical feedback: provide more information on stronger (more relevant)
transmitters ¬ Feedback compression: Lossless vs. lossy ¬ Channel tracking: only provide feedback info for channel evolution ¬ Feedback combined with channel prediction ¬ Two-step scheduling: Coarse CSI (e.g., SINR) feedback for scheduling and refined CSI
feedback for power allocation and pre-coder calculation
CSI Feedback design for Co-MIMO
1/1/2009 19
n For Co-MIMO, the transmission from multiple BSs must be synchronized. ¬ OFDM with CP allows some margins in timing sync. ¬ However, large distances between the base stations result in large timing
offsets which may exceed CP length and lead to inter-symbol interference n Sync using primary clock reference
¬ Primary clock boards of cooperative BSs is synchronized to a common external reference clock
¬ Commercial Rubidium- and crystal-based reference clocks can be phase-locked to the GPS.
n Network synchronization ¬ Suitable for indoor BSs where GPS signal may be blocked ¬ Precise timing protocol (PTP) specified in IEEE 1588 standard is a good
candidate ¬ Packet delay spread must be kept small to achieve high accuracy of timing
sync: give higher priority for PTP packets
Timing Synchronization
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n CFO causes inter-carrier interference in OFDM systems n Challenges of CFO Sync for Co-MIMO:
¬ Each receiver need to estimate multiple CFOs of multiple BSs ¬ CFO compensation at the Rx side requires high complexity equalizer ¬ CFO compensation at the Tx side needs CFO feedback from the receiver
Carrier Frequency offsets (CFO) Synchronization
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Flexible Partial Cooperative MIMO n Consider a MIMO downlink cellular network with B Basestations (BSs)
and N mobile users (MSs) [H. Huang and V. Lau].
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Flexible Partial Cooperative MIMO n Performance and insight:
¬ Suppose the BSs are symmetrically distributed along a line and the pathloss grows exponentially w.r.t. distance.
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(1) Simulation Results: CDF of downlink user rates for different backhaul capacities per BS.
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No Coop
Full Coop
Tradeoff between user rates and backhaul capacity
USRP Platform Overview n The universal software radio platform (USRP) demonstrates a wireless
radio system using a general purpose PC n Key components of the USRP platform are:
¬ General purpose PC ¬ Universal software radio platform (USRP) board ¬ Transceiver board ¬ Indoor antennas ¬ GNU Radio (software development toolkit) ¬ Hydra (wireless testbed which supports most features specified in the IEEE 802.11n
standard)
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Host PC
TransceiverBoard
TransceiverBoard
USB
Tx
Rx
Tx
Rx
USRP Board
Connection Diagram of the USRP Board
E100 Embedded platform
Hardware Configuration of Co-MIMO Uplink
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h12
XCVR2450Board
XCVR2450Board
LANcable
Tx/Rx
Tx/RxUS
RP
Boa
rdHost PCUSB
MS #1
US
RP
Boa
rd
Host PCXCVR2450Board
USBTx/Rx
MS #2
US
RP
Boa
rd
XCVR2450Board
XCVR2450Board
Tx/Rx
Tx/RxUS
RP
Boa
rd
USB
BS #1
BS #2
XCVR2450Board
Tx/RxHost PC
Host PC
USBh11
h22
y1
y2
x1
x2
h21
BS Setup MS Setup
Upper-level Protocol and Physical Layer scheme
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TS from MS1
TS from MS2
Data from MS1
Data from MS2
MS1:
MS2:
BS1
BS2
Send CSI to BS1 via Socket communication
BS1
BS2
BS1 calculates ZF receive vectors r1,r2 and Send r2 to BS2
BS1
BS2
BS2 send r2^T*y2 to BS1, BS1 decode x1 using r1^T*y1+ r2^T*y2
Time diagram of Data transmission from MSs
Cooperative receiving at the BSs (only for decoding of x1)
Physical layer scheme: OFDM with 56 data subcarriers Cooperative receiving based on Zero-Forcing (ZF) detection at BSs Constant transmit power and uniform power allocation over subcarriers at MSs
(1) System Parameters for the Co-MIMO Experiment
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Specification Value MCS index 3 Modulation 16QAM Coding Rate R 1/2 Spatial stream 1 Data rate 26.0 Mb/s Distance 2m No. of packet 1000 packets Data bits in a packet 1000 bits / packet Signal transmit gain 5000 to 9000 Interference transmit gain 6000, 9000 Maximum transmit power 50 mW Carrier frequency 2.4 GHz Transmit antenna type 5 dBi omni-directional Receive antenna type 5 dBi omni-directional
(1) Co-MIMO Experiment Results
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Verify the cooperative gain under some practical constraints: Ø CSI estimation error Ø Timing/CFO sync error Ø Practical modulation and coding scheme
(1) Implementation Issues n Large CSI feedback delay
¬ Each CSI training costs about 10ms ¬ CSI Feedback costs about 10ms ¬ If exhaustive search is used to find the best BF vector in the codebook, it takes about
1ms for each subcarrier even when the codebook size is reduced to 256 ¬ According to channel correlation tested over time, the feedback delay must be kept
within 70ms to achieve 20db suppression gain.
31 0 5 10 15 20 25 30 35 40 45 500.982
0.984
0.986
0.988
0.99
0.992
0.994
0.996
0.998
1
1.002
X: 7Y: 0.9953
average time correlation over 56 subcarriers
per 10ms
Solutions: 1. Use structured codebook to reduce the searching time 2. Only feedback CSI for two subcarriers by exploiting the channel correlation over subcarriers
Conclusion: CSI feedback delay is a serious problem in practical implementation!
(2) System Parameters for the Coordinated MIMO Experiment
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Specification Value MCS index 1 Modulation QPSK
Coding Rate R 1/2 Spatial stream 1 Data rate 13.0 Mb/s Distance 2m
No. of packet 5000 packets Data bits in a packet 1000 bits / packet Signal transmit gain 6000 to 9000 Interference transmit gain 6000 to 9000 Maximum transmit power 50 mW
Carrier frequency 2.4 GHz Transmit antenna type 5 dBi omni-directional Receive antenna type 5 dBi omni-directional
(2) Coordinated MIMO Experiment Results
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6000 6500 7000 7500 8000 8500 900010-2
10-1
100
Tx2 Interference
Rx1
Pack
et E
rror R
ate
(PER
)
Rx1 PER - Tx2 Interference Curve (MCS1) (Tx1 Gain = 9000)
Coordinated MIMORandom Beamforming
Verify the coordination gain under some practical constraints: Ø CSI estimation error and feedback delay Ø Timing/CFO sync error, Ø Practical modulation and coding scheme
(3) Energy Saving Performance [Cui’04]
n Not always “the more Tx antennas, the better”! n Not always “the more cooperative BSs, the better”! n Effect of Pcct is significant when Pcct is non-negligible from the total power consumption
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Tx 1
Tx 2 Tx 1 Rx 1
Rx 1
Tx 1 to Rx 1 without any help (SISO link) Cooperative MIMO without CSIT (MISO)
Transmission energy per bit (exclude Pcct ) Total energy per bit (include Pcct )
Using Alamouti scheme with BPSK modulation in both typology
Due to Pcct effect
Due to NO Pcct
(1) Summary of Co-MIMO and Coordinated MIMO
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Coordinated MIMO: • Moderate performance gain • Light backhaul burden • Insensitive to Sync error • Easier for distributed Implementation • Optimal transmission scheme is unknown
Co-MIMO: • Largest performance gain • Heavy backhaul burden • Sensitive to Sync error • Centralized Implementation • Optimal transmission scheme is known
What’s the best tradeoff between coordination and cooperation?
(2) Summary of Major Implementation Challenges
n Advanced backhaul technologies to meet the requirement of Co-MIMO n CSI Estimation and Feedback
¬ Overhead of training ¬ Feedback delay makes CSI outdated
n Scalable Implementation ¬ How to adapt the cooperative level according to the network state and users’
requirement? n Adapting the number of cooperative cells for each user n Adapting the amount of information exchanged : From full cooperative to
coordinated MIMO n How to make users operating at different cooperative levels co-exit?
¬ How to balance between centralized and distributed control? n Co-MIMO and Coordinated MIMO in fast fading channel
¬ A thorough study on the performance gain of open loop Co-MIMO is still lacking
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n Energy per bit metric n Circuit power consumption Pcct is constant as long as
transmission rate ≠ 0 ¬ Further complicates the Co-MIMO and Coordinated MIMO
design n The choice of # of Tx antennas is a combinatorial problem
¬ Not always use all the Tx antennas ¬ Not always involve in more cooperative BSs ¬ Effect of Pcct is significant when Pcct is non-negligible from the
total power consumption
(3) Summary of the Green Aspects of Co-MIMO and Coordinated MIMO
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(4) Techniques Beyond Physical Layer: Interference-Aware Networking
n Physical Layer Technologies ¬ Coordinated MIMO
n Spatial interference mitigation via signal processing techniques n Infrastructure based MIMO/OFDM Networks, Multi-channel Mesh, Multi-hop
Cooperative Systems
¬ Cooperative MIMO n resolve interference issues in Cellular Systems via data cooperation
n • System Level Technologies ¬ Interference-Aware Networking
n Exploit knowledge of interference profile in MAC and networking protocol designs
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