ofdma based two-hop cooperative relay network resources allocation mohamad khattar awad, xuemin...
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OFDMA Based Two-hop Cooperative OFDMA Based Two-hop Cooperative Relay Network Resources AllocationRelay Network Resources Allocation
Mohamad Khattar Awad, Xuemin (Sherman) Shen
Student Member, IEEE Senior Member, IEEEDepartment of Electrical & Computer Engineering, University
of Waterloo, Waterloo, Ontario, N2L 3G1, Canada
ICC 2008
Speaker: Chan-Ying Lien (HuHu)
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OutlineOutline
• Introduction• System Model• Problem Formulation• Proposed Algorithm and Complexity Analysis• Performance Evaluation• Conclusions
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IntroductionIntroduction
• The channel suffers from– Frequency selective fading
• OFDMA
– Distance dependent fading (i.e., large-scale fading)
• Amplify-and-Forward scheme (AF)
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IntroductionIntroduction
• To further exploit the wireless channel capacity– A RS can cooperate with a SS in the Time Division
Duplex (TDD) scheme.
• The problem of resource allocation in this network is NP-Complete
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IntroductionIntroduction
• A low complexity resource allocation protocol
– Each SS communicates with the BS • in non-cooperative mode • in cooperative mode with only one of the available RSs
– Users are allocated a sufficient number of subcarriers to guarantee their minimum rate requirements
– Each subcarrier is exclusively allocated to one SS and RS pair
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System ModelSystem Model
• Consider a single cell scenario– One BS– Multiple fixed RSs– Multiple SSs
RSRSRSRS RSRSRSRS
RSRSRSRS
RSRSRSRSRSRSRSRS
RSRSRSRS
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System ModelSystem Model
• Full channel state information (CSI)
• SSs and RSs report their CSI to the BS– SS: minimum rate requirements
• The central resource allocation unit at the BS performs the resource allocation– The subcarriers assignments and RSs assignments to ea
ch SS and RS
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System ModelSystem Model
• TDD• Half-duplex• The SS transmits while the RS and BS receive in
the first half of the time slot• In the second half of the slot, the RS transmits to
the BS
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Problem FormulationProblem Formulation
• A SSs– A = {s1, … , sa, … , sA}
• B RSs– B = {r1, … , rb, … , rB}
• A subscriber stations share a total of Nsc subcarriers available to the cell– N = {1, … , n, … ,Nsc}
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Problem FormulationProblem Formulation
• The maximum achievable rate in (bits/sec/Hz) by s
a on subcarrier n with the cooperation of rb is given by
a: MSb: RSd: BSβb : rb’s amplifying gain
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Problem FormulationProblem Formulation
• The direct transmission maximum achievable rate in (bits/sec/Hz) over both time slots is given by
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Problem FormulationProblem Formulation
• Binary Integer Programming (BIP)– Satisfying SSs’ minimum rate requirements ca
– yab {0, 1}∈• yab = 1 means that sa is cooperating with rb
– xnab {0, 1}∈
• xnab = 1 means that the subcarrier n is allocat
ed to the pair sa− rb
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Problem FormulationProblem Formulation
• Because both SS and RS transmit on the same frequency, but in different time slots, and the resource allocation is time independent, the BS d can be represented by a virtual relay rB+1
• Adding a virtual relay station enlarges the set B to B+
• To use a uniform cost function in the optimization problem
where δ(・ ) is the Dirac delta functionβn
(B+1) = 0d = B+1
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Problem FormulationProblem Formulation
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Proposed Algorithm and Complexity AnalysisProposed Algorithm and Complexity Analysis
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Performance EvaluationPerformance Evaluation
300m150m
RSRSRSRS RSRSRSRS
RSRSRSRS
RSRSRSRSRSRSRSRS
RSRSRSRS
A(MS) = 50B(RS) = 6
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Performance EvaluationPerformance Evaluation
• The large-scale fading is distance dependant and follows the inverse-power law:
D is the distance between the transmitter and receiver in metersκ is the path loss exponent|αn|2 is the nth subcarrier channel gain at the transmitter
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Performance EvaluationPerformance Evaluation
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Performance EvaluationPerformance Evaluation
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Performance EvaluationPerformance Evaluation
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Performance EvaluationPerformance Evaluation
• 25 SSs (A = 25), 4 RSs (B = 4)
• 64 subcarriers (Nsc = 64)
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ConclusionsConclusions
• In this paper, the resource allocation for the two-hop OFDMA cooperative relay networks has been addressed.
• Numerical and complexity analysis demonstrate that the proposed algorithm achieves near optimal allocation in relatively short running time.
• In particular, the algorithm achieves about 70% to 90% of the optimum in only 2% to 12% of the optimization tool running time.
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Thank You