Joint Power and Channel Minimization in Topology Control: A Cognitive Network Approach J ORGE M ORI...
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Transcript of Joint Power and Channel Minimization in Topology Control: A Cognitive Network Approach J ORGE M ORI...
Joint Power and Channel Joint Power and Channel Minimization in Topology Minimization in Topology Control: A Cognitive Network Control: A Cognitive Network ApproachApproach
JORGE MORIALEXANDER YAKOBOVICHMICHAEL SAHAILEV FAYNSHTEYN
Problem DefinitionProblem Definition
An ad-hoc wireless network topology faces two problems:
Power consumption◦Mobile devices have limited power supply
Overcrowded spectrum◦Too many devices try to use the same frequency simultaneously resulting in inteference
Previous WorkPrevious WorkInterference avoidance has led to
three viewpoints:Radio
◦Minimize channel interference at link-level
Topology◦Channel assignments made in an
already existing topology Network
◦A combination of channel assignment with routing
Previous WorkPrevious WorkTwo assumptions:
◦Power control◦Channel control
Power approaches:◦Bukhart, assigning weights to connections
that are equal to the number of radios the connection interferes with. Used with MMLIP, MAICPC and IMST algorithms.
◦Use of a radio interference function, in which the interference contribution of a radio is the maximum interference of all connections incident upon it. Used in MMMIP and LILT algorithms.
Previous Work Previous Work (Cont.) (Cont.)
Channel Control:• Connectivity of the network is fixed
and that two radios can only communicate if they share a common channel, of which there are fewer available than needed.
Researches’ ApproachResearches’ ApproachTheir work assume that radios regulate
both power and channel selection.
A two-phased, two cognitive element approach to:◦Power assignment◦Channel assignment
A game-theoretic model is used to analyze the behaviors of these elements.
MethodologyMethodologyA two-phased game model is used:
The first phase is a pure power control game where POWERCONTROL elements attempt to minimize their transmit power level and maintain network connectivity.
The output of the first phase is a power-efficient topology, which is fed into the second phase, where CHANNELCONTROL elements selfishly play the channel selection game.
Methodology Methodology (Cont.)(Cont.)
The POWERCONTROL elements utilize δ-Improvement Algorithm (DIA):
Methodology Methodology (Cont.)(Cont.)
LOCAL-RS - a localized version of the Random Sequential coloring algorithm:
Optimized Approach – Power Optimized Approach – Power ControlControlUse Minimum Spanning Tree (MST) algorithm to solve
Power Control problem: G = (V, E,W) denotes the input undirected stochastic
graph:◦ V - vertex set◦ E - edge set◦ matrix W - probability distribution of the edge weight in the
stochastic graph
Each node of the graph is a learning automaton
Resulting network is described by a triple < A, α, W >, where:◦ A = { A1, A2,..., Am } - set of the learning automata◦ α = { α 1, α 2,..., α m} - set of actions in which α i = { α i1, α
i2,..., α ij,..., α ir } defines the set of actions that can be taken by learning automata A i for each α ∈α i
◦ Weight w i j is the cost associated with edge e (i, j)
MST AlgorithmMST Algorithm
Optimized Approach – Optimized Approach – Channel ControlChannel ControlResulting network is described by the
pair < A, α >, where:◦A = {A1, A2, …, Am} denotes the set of
learning automata◦α = {α1, α2, …, αm} denotes the set of actions◦αi = {αi1, αi2, …, αir} defines the set of actions
that can be taken by learning automaton Ai, for each αi ∈ α
The set of colors with which each vertex vi can be colored from the set of actions can be taken by learning automaton Ai
Channel Control AlgorithmChannel Control Algorithm Step 1. Color selection phase
◦ For all learning automata do in parallel Each automaton Ai. Pick the colors that have not being selected yet Vertex Vi is colored with the color corresponding to the selected action The selected color is added to the list of colors (color-set) with which the graph may be
legally colored at this stage.
Step 2. Updating the dynamic threshold and action probabilities◦ If the cardinality of the color-set (in a legal coloring) created is less than or equal
to dynamic threshold Tk, then Threshold Tk is set to the cardinality of the color-set selected in this stage. All learning automata reward their actions and update action probably vectors using a LR-P
reinforcement scheme
◦ Otherwise Each learning automaton updates its probability vector by penalizing its chosen action.
Step 3. Stopping Condition◦ The process of selecting legal colorings of the graph and updating the action
probabilities are repeated until the product of the probability of choosing the colors of a legal coloring called PLC is greater than a certain threshold or the number of colorings exceeds a pre-specified threshold. The coloring which is chosen last before stopping the algorithm is the coloring with the smallest color-set among all proper colorings.
QUESTIONS?QUESTIONS?Thank you.