切换机制研究
合集下载
相关主题
- 1、下载文档前请自行甄别文档内容的完整性，平台不提供额外的编辑、内容补充、找答案等附加服务。
- 2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
- 3、如文档侵犯您的权益，请联系客服反馈,我们会尽快为您处理(人工客服工作时间：9:00-18:30)。
expected total rewardυ(s)and the optimal policyδ*(s).
To solve our problem, the value iteration algorithmwas used.
16
Thanks!
2020/8/3ห้องสมุดไป่ตู้
接入选择
集中式的接入选择算法 分布式的接入选择算法
2020/8/3
集中式选择算法
Force-based load balancing Power-efficient interface
2020/8/3
分布式接入选择算法
M.Ylianttila V.A.deSousa X.Gelabert O.Yilmaz
The problem is formulated as a Markov Decision Process(MDP) . There is a link reward functionassociated with the QoS of the connection and also a signaling cost functionassociated with the signaling overhead and processing load of the vertical handoff.
11
Vertical Handoff Decision Epochs
12
State Space
Network
Units of Bandwidth
Units of Delay
M denotes the number of collocated networks. Bmdenotes set of available bandwidth form network m. Dmdenotes set of delay from network m.
is:
15
Optimality Equations
Let υ(s)denote the maximum expected reward given initial state s
Then, the optimality equations are given by:
The solutions of the optimality equations correspond to the maximum
Both functions are introduced to capture the tradeoffsbetween the network
resources utilized by a connection and the processing load incurred in the network. Objective: determine an optimal policywhich maximizes the total expected reward per connection.
2020/8/3
MDP切换决策机制
Title: “A Vertical Handoff Decision Algorithm in
Heterogeneous Wireless Networks” Group: Current research at UBC
2020/8/3
Research at UBC: Vertical Handoff Decision Algorithm
Decision Epochs
Xtdenotes the state at decision epoch t. Ytdenotes the action chosen at decision epoch t. N denotes the time that the connection terminates.
where υπ(s)is the total expected reward, given policy π is used with initial state s, and discount factor λ.
10
Model Formulation
An MDP model consists of five elements: decision epochs, states, actions, rewards and transitions probabilities.
13
Rewards
Reward Function
Link Reward Function
Signaling Cost Function
14
Actions
where M is the number of collocated networks.
Transition Probabilities
Given the current state s=[i,b1,d1, …,bm,dm] and the chosen action be a, the probability functionthat the next state s’=[j,b’1,d’1, …,b’m,d’m]
异构网络垂直切换机制简介
精选课件
2020/8/3
报告内容
异构网络系统的结合方式介绍 垂直切换触发方式介绍 垂直切换机制介绍
2020/8/3
异构网络系统的结合方式
2020/8/3
垂直切换
切换触发 接入选择 切换执行
2020/8/3
切换触发
受迫型垂直切换 非受迫型垂直切换
2020/8/3
To solve our problem, the value iteration algorithmwas used.
16
Thanks!
2020/8/3ห้องสมุดไป่ตู้
接入选择
集中式的接入选择算法 分布式的接入选择算法
2020/8/3
集中式选择算法
Force-based load balancing Power-efficient interface
2020/8/3
分布式接入选择算法
M.Ylianttila V.A.deSousa X.Gelabert O.Yilmaz
The problem is formulated as a Markov Decision Process(MDP) . There is a link reward functionassociated with the QoS of the connection and also a signaling cost functionassociated with the signaling overhead and processing load of the vertical handoff.
11
Vertical Handoff Decision Epochs
12
State Space
Network
Units of Bandwidth
Units of Delay
M denotes the number of collocated networks. Bmdenotes set of available bandwidth form network m. Dmdenotes set of delay from network m.
is:
15
Optimality Equations
Let υ(s)denote the maximum expected reward given initial state s
Then, the optimality equations are given by:
The solutions of the optimality equations correspond to the maximum
Both functions are introduced to capture the tradeoffsbetween the network
resources utilized by a connection and the processing load incurred in the network. Objective: determine an optimal policywhich maximizes the total expected reward per connection.
2020/8/3
MDP切换决策机制
Title: “A Vertical Handoff Decision Algorithm in
Heterogeneous Wireless Networks” Group: Current research at UBC
2020/8/3
Research at UBC: Vertical Handoff Decision Algorithm
Decision Epochs
Xtdenotes the state at decision epoch t. Ytdenotes the action chosen at decision epoch t. N denotes the time that the connection terminates.
where υπ(s)is the total expected reward, given policy π is used with initial state s, and discount factor λ.
10
Model Formulation
An MDP model consists of five elements: decision epochs, states, actions, rewards and transitions probabilities.
13
Rewards
Reward Function
Link Reward Function
Signaling Cost Function
14
Actions
where M is the number of collocated networks.
Transition Probabilities
Given the current state s=[i,b1,d1, …,bm,dm] and the chosen action be a, the probability functionthat the next state s’=[j,b’1,d’1, …,b’m,d’m]
异构网络垂直切换机制简介
精选课件
2020/8/3
报告内容
异构网络系统的结合方式介绍 垂直切换触发方式介绍 垂直切换机制介绍
2020/8/3
异构网络系统的结合方式
2020/8/3
垂直切换
切换触发 接入选择 切换执行
2020/8/3
切换触发
受迫型垂直切换 非受迫型垂直切换
2020/8/3