A Novel Multiobjective Framework for Cell Switch-Off in Dense Cellular Networks alez G 1 , Halim Yanikomeroglu 2 , David Gonz´ ıa-Lozano 1 , Silvia Ruiz Boqu´ e 1 Mario Garc´ 1 Departament of Signal Theory and Communications Universitat Polit` ecnica de Catalunya, Spain 2 Departament of System and Computer Enginnering Carleton University, Canada IEEE International Conference on Communications (ICC) 2014 Mobile and Wireless Networking Symposium Sydney, Australia: 10-14 June 2014
Outline
Introduction Framework Description Closing Remarks Motivation 1 Energy Efficiency (EE) in cellular networks. Cellular industry is growing exponentially . Hyper dense small cells deployment → boost energy consumption! H. Yanikomeroglu @ IEEE ICC ’14 Multiobjective Cell Switch-Off 1 / 20 ◮
Introduction Framework Description Closing Remarks Motivation 1 Energy Efficiency (EE) in cellular networks. Cellular industry is growing exponentially . Hyper dense small cells deployment → boost energy consumption! 2 Why switch-off base stations? Energy consumption models Solution approach → Multiobjective Optimization (aggregate capacity ↔ active cells). H. Yanikomeroglu @ IEEE ICC ’14 Multiobjective Cell Switch-Off 1 / 20 ◮
Introduction Framework Description Closing Remarks CSO : Problem statement & practical insights Main intuition Switch off lightly loaded base stations to save energy. H. Yanikomeroglu @ IEEE ICC ’14 Multiobjective Cell Switch-Off 2 / 20 ◮
Introduction Framework Description Closing Remarks CSO : Problem statement & practical insights Main intuition Switch off lightly loaded base stations to save energy. The challenge The CSO problem consists in determining the largest set of cells that can be switched off without compromising the QoS . H. Yanikomeroglu @ IEEE ICC ’14 Multiobjective Cell Switch-Off 2 / 20 ◮
Introduction Framework Description Closing Remarks CSO : Problem statement & practical insights Main intuition Switch off lightly loaded base stations to save energy. The challenge The CSO problem consists in determining the largest set of cells that can be switched off without compromising the QoS . Theoretical aspects: Deployments density . Traffic behavior . Network capacity . ICIC . H. Yanikomeroglu @ IEEE ICC ’14 Multiobjective Cell Switch-Off 2 / 20 ◮
Introduction Framework Description Closing Remarks CSO : Problem statement & practical insights Main intuition Switch off lightly loaded base stations to save energy. The challenge The CSO problem consists in determining the largest set of cells that can be switched off without compromising the QoS . Theoretical aspects: Practical aspects: Deployments density . Coverage . Traffic behavior . Switch on/off transitions . Network capacity . Architecture . ICIC . Others . H. Yanikomeroglu @ IEEE ICC ’14 Multiobjective Cell Switch-Off 2 / 20 ◮
Introduction Framework Description Closing Remarks CSO : Related work Ref. Context C1 C2 C3 C4 C5 C6 [283] CSO Heuristic CE P Full buffer × × [284] CSO Analytical CE P Full buffer × × [285] Planning: how to deploy cell for minimizing energy consumption Analytical NA P NA NA × [286] CSO Heuristic CE Poisson × × × [287] Cell size adaptation Heuristic CE Full buffer × × × [288] An interesting RRM strategy for energy savings Heuristic CE Poisson × × × [289] CSO Heuristic Both Full buffer � × × [277] CSO Analytical CE Poisson × × × [290] CSO Analytical CE P Full buffer × × [291] CSO Heuristic CE P P Full buffer × [292] CSO Heuristic CE P P Full buffer × [293] CSO Heuristic CE � P Realistic × [294] CSO Heuristic SD P Realistic × × [295] Cell size adaptation Heuristic Both Realistic × × × [296] CSO Heuristic CE Full buffer × × × [297] CSO Heuristic CE Full buffer × × × [298] CSO Heuristic CE Several models × × × [299] Impact of power reduction on coverage and capacity Analytical NA � � NA Full buffer P: Partially CE: Centralized SD: Semidistributed DI: Distributed Heuristic is the preferred approach. H. Yanikomeroglu @ IEEE ICC ’14 Multiobjective Cell Switch-Off 3 / 20 ◮
Introduction Framework Description Closing Remarks CSO : Related work Ref. Context C1 C2 C3 C4 C5 C6 [283] CSO Heuristic CE P Full buffer × × [284] CSO Analytical CE P Full buffer × × [285] Planning: how to deploy cell for minimizing energy consumption Analytical NA P NA NA × [286] CSO Heuristic CE Poisson × × × [287] Cell size adaptation Heuristic CE Full buffer × × × [288] An interesting RRM strategy for energy savings Heuristic CE Poisson × × × [289] CSO Heuristic Both Full buffer � × × [277] CSO Analytical CE Poisson × × × [290] CSO Analytical CE P Full buffer × × [291] CSO Heuristic CE P P Full buffer × [292] CSO Heuristic CE P P Full buffer × [293] CSO Heuristic CE � P Realistic × [294] CSO Heuristic SD P Realistic × × [295] Cell size adaptation Heuristic Both Realistic × × × [296] CSO Heuristic CE Full buffer × × × [297] CSO Heuristic CE Full buffer × × × [298] CSO Heuristic CE Several models × × × [299] Impact of power reduction on coverage and capacity Analytical NA � � NA Full buffer P: Partially CE: Centralized SD: Semidistributed DI: Distributed Most of solutions require real-time centralized operation. H. Yanikomeroglu @ IEEE ICC ’14 Multiobjective Cell Switch-Off 3 / 20 ◮
Introduction Framework Description Closing Remarks CSO : Related work Ref. Context C1 C2 C3 C4 C5 C6 [283] CSO Heuristic CE P Full buffer × × [284] CSO Analytical CE P Full buffer × × [285] Planning: how to deploy cell for minimizing energy consumption Analytical NA P NA NA × [286] CSO Heuristic CE Poisson × × × [287] Cell size adaptation Heuristic CE Full buffer × × × [288] An interesting RRM strategy for energy savings Heuristic CE Poisson × × × [289] CSO Heuristic Both Full buffer � × × [277] CSO Analytical CE Poisson × × × [290] CSO Analytical CE P Full buffer × × [291] CSO Heuristic CE P P Full buffer × [292] CSO Heuristic CE P P Full buffer × [293] CSO Heuristic CE � P Realistic × [294] CSO Heuristic SD P Realistic × × [295] Cell size adaptation Heuristic Both Realistic × × × [296] CSO Heuristic CE Full buffer × × × [297] CSO Heuristic CE Full buffer × × × [298] CSO Heuristic CE Several models × × × [299] Impact of power reduction on coverage and capacity Analytical NA � � NA Full buffer P: Partially CE: Centralized SD: Semidistributed DI: Distributed Coverage analysis is oftem missed. H. Yanikomeroglu @ IEEE ICC ’14 Multiobjective Cell Switch-Off 3 / 20 ◮
Introduction Framework Description Closing Remarks Multiobjective Optimization : Essentials 1 Target : problems with conflicting criteria. F = { f i ( x ) : R n → R , i = 1 , 2 , · · · , m } H. Yanikomeroglu @ IEEE ICC ’14 Multiobjective Cell Switch-Off 4 / 20 ◮
Introduction Framework Description Closing Remarks Multiobjective Optimization : Essentials 1 Target : problems with conflicting criteria. F = { f i ( x ) : R n → R , i = 1 , 2 , · · · , m } 2 Structure : Design variables: x = [ x 1 , x 2 , · · · , x n ], ( x ∈ X ). Feasible set: X = X 1 × X 2 × · · · × X n , (domains). Objetive space: f : X → R m , f ( x ) = [ f 1 ( x ) , f 2 ( x ) , · · · , f n ( x ) ]. Constraints. H. Yanikomeroglu @ IEEE ICC ’14 Multiobjective Cell Switch-Off 4 / 20 ◮
Introduction Framework Description Closing Remarks Multiobjective Optimization : Essentials 1 Target : problems with conflicting criteria. F = { f i ( x ) : R n → R , i = 1 , 2 , · · · , m } 2 Structure : Design variables: x = [ x 1 , x 2 , · · · , x n ], ( x ∈ X ). Feasible set: X = X 1 × X 2 × · · · × X n , (domains). Objetive space: f : X → R m , f ( x ) = [ f 1 ( x ) , f 2 ( x ) , · · · , f n ( x ) ]. Constraints. 3 Optimality : Pareto efficiency ( x ⋆ and the set X ⋆ ). x 1 ≻ x 2 , ⇐ ⇒ f i ( x 1 ) ≤ f i ( x 2 ) ∧ ∃ j | f j ( x 1 ) < f j ( x 2 ) x ⋆ ∈ X ⋆ ⇐ ⇒ ∄ x ∈ X | x ≻ x ⋆ . H. Yanikomeroglu @ IEEE ICC ’14 Multiobjective Cell Switch-Off 4 / 20 ◮
Introduction Framework Description Closing Remarks Multiobjective Optimization : Essentials Figure: A representation of the Pareto Front. H. Yanikomeroglu @ IEEE ICC ’14 Multiobjective Cell Switch-Off 4 / 20 ◮
Introduction Framework Description Closing Remarks Multiobjective Optimization : Essentials (a) The hypervolume indicator ( υ ). (b) The nonuniformity index ( ̺ ). Figure: Quality measures in multiobjective optimization. � � � ̺ = d f + d l + � N − 1 i =1 | d i − ¯ d | υ ( X ⋆ , x ref ) = Λ x | x ≺ ˆ ˆ x ≺ x ref d f + d l + ¯ d ( N − 1) x ∈X H. Yanikomeroglu @ IEEE ICC ’14 Multiobjective Cell Switch-Off 4 / 20 ◮
Introduction Framework Description Closing Remarks System Model Downlink of an OFDMA cellular network → L cells. Target → Average ICI conditions (full reuse). Any topology : → Network geometry ( G ∈ R A × L ). Flexible analysis : → Operator-defined QoS policies. H. Yanikomeroglu @ IEEE ICC ’14 Multiobjective Cell Switch-Off 5 / 20 ◮
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