choong-wan park, won-chul choi, seokkwon kim and dong-jo park
DESCRIPTION
A Low Complexity Resource Allocation Algorithm with Increasing Capacity in Cooperative OFDMA Systems. Choong-Wan Park, Won-Chul Choi, Seokkwon Kim and Dong-Jo Park School of Electrical Engineering and Computer Science Korea Advanced Institute of Science and Technology (KAIST). IWCMC 2008. - PowerPoint PPT PresentationTRANSCRIPT
A Low Complexity Resource Allocation Algorithm with A Low Complexity Resource Allocation Algorithm with Increasing Capacity in Cooperative OFDMA SystemsIncreasing Capacity in Cooperative OFDMA Systems
Choong-Wan Park, Won-Chul Choi, Seokkwon Kim and Dong-Jo Park
School of Electrical Engineering and Computer Science
Korea Advanced Institute of Science and Technology (KAIST)
IWCMC 2008
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OutlineOutline
Introduction System Model and Problem Formulation Proposed Algorithm Simulation Results Conclusion
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IntroductionIntroduction
Dynamic resource allocation Multi-hop orthogonal frequency division multiple access
(OFDMA)
A conventional algorithm not suitable for cooperative OFDMA systems feedback channel gain information (CGI) excessive relay load balancing scheme iterative
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IntroductionIntroduction
The proposed algorithm Low complexity Practical to implement Increases the system capacity
Two types of receiver structures Selection Combining (SC) Maximum Ratio Combining (MRC)
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System Model and Problem FormulationSystem Model and Problem Formulation
In a cooperative OFDMA systems, assume that the BS can know the CGI that reports on the MS the total transmission power of each relay node
Assume The BS allocates the paths, subcarriers and power to the r
elay nodes and an MS A subcarrier is not shared by users
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System Model and Problem FormulationSystem Model and Problem Formulation
No decoding errors
MSMSMSMS
BSBSBSBS
RSRSRSRS
First Half
Second Half
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System Model and Problem FormulationSystem Model and Problem Formulation
Goal Maximizes the system capacity while minimum resources are guaranteed for
each user
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System Model and Problem FormulationSystem Model and Problem Formulation
K: the set of users. N: the set of all subcarriers. L: the set of all OFDM transceivers. ρk,n,l: the nth subcarrier usage index for user k through the lth path. pk,n,l : an allocated power to the lth path of user k in subcarrier n. hk,n,l : the nth subcarrier gain of user k through the lth path. : is the relay peak transmission power of the lrth OFDM transceiver. Rk is the total data rate of the kth user. Sth is the minimum number of subcarriers that should be allocated to each
user.
lrpeakp
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System Model and Problem FormulationSystem Model and Problem Formulation
Conventional Optimization problem
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Proposed AlgorithmProposed Algorithm
A conventional algorithm subcarrier allocation relay load balancing power distribution
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Proposed AlgorithmProposed AlgorithmA. RASC AlgorithmA. RASC Algorithm
1) Subcarrier Allocation with Partial Feedback A conventional algorithm needs
full CGI on all users, all subcarriers, and all paths.
best1 path: the best channel gain path of a user best2 path: efficient relay load balancing
Reduced uplink resources from (K · N · L) CGI to (K · N · 2) CGI
Reduced operation complexity from O(K ·N · Llog(K · N · L)) to O(K · N · 2 log(K · N · 2))
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Proposed AlgorithmProposed AlgorithmA. RASC AlgorithmA. RASC Algorithm
2) Efficient Relay Load Balancing step 1: power constraint
MSMS11
BSBS
RSRS11
RSRS22
RSRS33
MSMS22
MSMS33
power constraint :25
10
30
10
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Proposed AlgorithmProposed AlgorithmA. RASC AlgorithmA. RASC Algorithm
2) Efficient Relay Load Balancing step 2: refer to “best2 path”s for each user step 3: calculate the channel gain gap step 4: sort all the channel gain gaps
MSMS11
RSRS22
RSRS33
MSMS22
MSMS33
BSBS
RSRS11
“best2 path” for MS2
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Proposed AlgorithmProposed AlgorithmA. RASC AlgorithmA. RASC Algorithm
2) Efficient Relay Load Balancing step 5: Set the minimum valued path as the “target path”.
MSMS11
RSRS22
RSRS33
MSMS22
MSMS33
Sort:
MS2 MS1 MS3
2 3 6
BSBS
RSRS11
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Proposed AlgorithmProposed AlgorithmA. RASC AlgorithmA. RASC Algorithm
2) Efficient Relay Load Balancing step 6: exchange a path of subcarriers from “best1 path” to “bes
t2 path” step 7: repeat the step 5 and step 6 about all the relays that sati
sfy the step 1 condition.
MSMS11
BSBS RSRS22
RSRS33
MSMS22
MSMS33
RSRS11
power constraint :25
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20
10
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Proposed AlgorithmProposed AlgorithmB. RAMRC Algorithm
a different criterion RAMRC uses the MRC scheme at MSs
1) Modified Subcarrier Allocation with Partial Feedback direct path (hk,n,ld ),
”best1 path” (hk,n,lrb1 )
“best2 path” (hk,n,lrb2 )relay load balancing
2) Modified Relay Load Balancing “best1 path”: direct path “best2 path”: (target path) relay path
MSMSMSMS
BSBSBSBS
RSRSRSRS
basic subcarrier allocation
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Proposed AlgorithmProposed AlgorithmB. RAMRC Algorithm
If an MS decodes a signal by using SC the MS selects the better signal of y1 and y2.
(If y1 is better than y2)
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Proposed AlgorithmProposed AlgorithmB. RAMRC Algorithm
If an MS decodes a signal by using MRC
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Simulation ResultsSimulation Results
Because it takes too long to find the optimal solution of the problem by computer simulation two simulation scenarios
small-scale simulation proposed algorithm VS. optimal and conventional a
lgorithm large-scale scenario
proposed algorithm VS. conventional algorithm
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Simulation ResultsSimulation Results
Users (K): 5 Subcarriers (N): 32 Paths (L): 7 (RS: 6)
BS transmission power: 30W relay transmission power: 10W minimum subcarrier constraint
per user (Sth) is 4
BSBSBSBS RSRSRSRS2/3
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Simulation ResultsSimulation Results
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Simulation ResultsSimulation Results
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Simulation ResultsSimulation Results
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ConclusionConclusion
An adaptive resource allocation scheme for multihop OFDMA systems CGI on all users, all subcarriers, and all paths
waste of uplink resources a high level of complexity unsuitable for cooperative networks relay load balancing is impractical