OFDMA Cross Layer
Resource Control
Gwanmo Ku
Adaptive Signal Processing and Information Theory Research Group
Jan. 25, 2013
Outline
OFDMA Cross Layer Resource Control
Objective Functions
- System Throughput (L1), Total Transmit Power (L1)
Constraints
- Transmit Power Constraint (L1)
- Quality of Service (User Demand, Fairness), Buffer Status (L2-3)
- Stability (L2-3)
Generalized Cross Layer Control (GCLC)
Stochastic Network Optimization (SNO)
Network Utility Maximization (NUM)
2/20
OFDMA Cross Layer Resource Control
3/20
System Model (𝑴 users, 𝑲 subcarriers)
Base Station (eNB)
Mobile (UE)
𝒖𝟏
𝒖𝑴
…
Higher Layer
Buffer
PHY
Higher Layer
Buffer
PHY
OFDMA
Ian Wong & Brian Evans
GCLC
𝒓𝒎
OFDMA Resource Control
4/20
Objective Functions
System Throughput Maximization
Transmit Power Minimization
Constraints
Transmit Power Constraint
Quality of Service
User Demands : Each User Required Data Rate
Fairness : Minimum User Data Rate
Stability based on Buffer Status
OFDMA Resource Allocation
5/20
Notations
𝒎 ∈ {𝟏,… ,𝑴} User Index
𝒌 ∈ {𝟏,… ,𝑲} Subcarrier Index
𝒑𝒎,𝒌 : Power Control Coefficient
𝜸𝒎,𝒌 : SINR for user index 𝒎 and subcarrier index 𝒌
𝑷𝑻 Total Transmit Power Constraint
𝒓𝒎 Required Each User Data Rate
𝒓𝟎 Required Minimum User Data Rate
𝒃𝒎 Buffer Service Rate
𝑹𝒎 Overall Coding Rate for User 𝒎
OFDMA Resource Allocation
6/20
System Throughput Maximization
Power Control
𝒑𝒎,𝒌 = argmaxE 𝑤𝑚
𝑀
𝑚=1
log(1 + 𝑝𝑚,𝑘𝛾𝑚,𝑘)
𝐾
𝑘=1
𝐒𝐲𝐬𝐭𝐞𝐦 𝐓𝐡𝐫𝐨𝐮𝐠𝐡𝐩𝐮𝐭
𝐸 𝑝𝑚,𝑘
𝐾
𝑘=1
𝑀
𝑚=1
≤ 𝑃𝑇
s.t
max (𝛽𝑚𝑅𝑚𝑟0, 𝛽𝑚𝑅𝑚𝑟𝑚) ≤ 𝑤𝑚 log(1 + 𝑝𝑚,𝑘𝛾𝑚,𝑘)
𝐾
𝑘=1
Stability
OFDMA Resource Allocation
7/20
Work by Ian Wong and Brian Evans
System Throughput Maximization with Tx. Power Constraint
𝑝𝑚,𝑘 = argmax 𝐄 𝑤𝑚
𝑀
𝑚=1
log(1 + 𝑝𝑚,𝑘𝛾𝑚,𝑘)
𝐾
𝑘=1
𝐄 𝑝𝑚,𝑘
𝐾
𝑘=1
𝑀
𝑚=1
≤ 𝑃𝑇
𝑤𝑚
𝑀
𝑚=1
= 1
s.t
OFDMA Resource Allocation
8/20
Optimization Framework
Dual Optimization
𝐿 𝑝 ⋅ , 𝜆 = 𝐄 𝑤𝑚
𝑀
𝑚=1
log(1 + 𝑝𝑚,𝑘𝛾𝑚,𝑘)
𝐾
𝑘=1
+𝜆 𝑃𝑇 − 𝐄( 𝑝𝑚,𝑘
𝐾
𝑘=1
)
𝑀
𝑚=1
𝑔∗ = min𝜆≥0
Θ(𝜆)
Θ 𝜆 = max𝑝 ⋅ ∈𝑃𝑇
𝐿(𝑝 ⋅ , 𝜆)
OFDMA Resource Allocation
9/20
Dual
Θ 𝜆 = max𝑝 ⋅ ∈𝑃𝑇
𝐿(𝑝 ⋅ , 𝜆)
= 𝜆𝑃𝑇 + max𝑝 ⋅ ∈𝑃𝑇
𝐄 𝑤𝑚 log 1 + 𝑝𝑚,𝑘𝛾𝑚,𝑘 − 𝜆𝑝𝑚,𝑘
𝑀
𝑚=1
𝐾
𝑘=1
= 𝜆𝑃𝑇 + max𝑝𝑘 ⋅ ∈𝑃𝑘
𝐄 𝑤𝑚 log 1 + 𝑝𝑚,𝑘𝛾𝑚,𝑘 − 𝜆𝑝𝑚,𝑘
𝑀
𝑚=1
𝐾
𝑘=1
= 𝜆𝑃𝑇 + 𝐸 max𝑝𝑘 ⋅ ∈𝑃𝑘
𝑤𝑚 log 1 + 𝑝𝑚,𝑘𝛾𝑚,𝑘 − 𝜆𝑝𝑚,𝑘
𝑀
𝑚=1
𝐾
𝑘=1
= 𝜆𝑃𝑇 + 𝐾𝐸𝛾𝑘max
𝑚∈{1,…,𝑀}max
𝑝𝑚,𝑘≥0(𝑤𝑚 log 1 + 𝑝𝑚,𝑘𝛾𝑚,𝑘 − 𝜆𝑝𝑚,𝑘)
multilevel water filling
𝑚𝑎𝑥 𝑑𝑢𝑎𝑙 𝑢𝑠𝑒𝑟 𝑠𝑒𝑙𝑒𝑐𝑡𝑖𝑜𝑛
OFDMA Resource Allocation
10/20
A Simple Closed Form
𝑝 𝑚,𝑘(𝜆) =1
𝛾0,𝑚 𝜆−
1
𝛾𝑚,𝑘
+
𝑥 + = max(0, 𝑥)
𝛾0,𝑚 𝜆 =𝜆 ln 2
𝑤𝑚 Cutoff value
𝑔∗ = min𝜆≥0
[𝜆𝑃𝑇 + 𝐾 𝐄𝛾𝑘𝑔𝑘 𝛾𝑘 , 𝜆 ]
𝑔𝑘 𝛾𝑘 , 𝜆 = max𝑚∈{1,…,𝑀}
{𝑔𝑚,𝑘 𝛾𝑚,𝑘 , 𝜆 }
𝑔𝑚,𝑘 𝛾𝑚,𝑘 , 𝜆 = 𝑤𝑚 log 1 + 𝑝 𝑚,𝑘 𝜆 − 𝜆 𝑝 𝑚,𝑘 𝜆
OFDMA Resource Allocation
11/20
Optimal Solution
𝑔𝑚,𝑘 𝛾𝑚,𝑘 , 𝜆 = 𝑤𝑚 log 1 + 𝑝 𝑚,𝑘 𝜆 − 𝜆 𝑝 𝑚,𝑘 𝜆
=𝑤𝑚
ln 2ln
𝛾𝑚,𝑘
𝛾0,𝑚 𝜆−
𝑤𝑚
ln 2+
𝜆
𝛾𝑚,𝑘𝑢(𝛾𝑚,𝑘 − 𝛾0,𝑚 𝜆 )
𝑢 𝑥 = 0 𝑥 < 01 𝑥 ≥ 0
𝜆∗ = 𝑎𝑟𝑔min𝜆≥0
[𝜆𝑃 + 𝐾 𝑔𝑘𝑓𝑔𝑘𝑔𝑘 𝑑𝑔𝑘
∞
0
]
𝑝 𝑚,𝑘(𝜆∗) =
1
𝛾0,𝑚 𝜆∗−
1
𝛾𝑚,𝑘
+
𝑝𝑚,𝑘∗ = 𝑝 𝑚,𝑘 𝜆∗ 1(𝑚 = 𝑚𝑘
∗ )
𝑚𝑘∗ = argmax
𝑚∈{1,…,𝑀}𝑤𝑚 log 1 + 𝑝 𝑚,𝑘 𝜆∗ 𝛾𝑚,𝑘 − 𝜆∗𝑝 𝑚,𝑘 (𝜆∗)
Cross Layer Control
12/20
Generalized Cross Layer Control (GCLC)
Proposed by Georgiadis, Neely, and Tassiulas
Focus on Stability based on Queuing Statistics
• Stochastic Network Optimization
• Network Utility Maximization
Network Stability
• Differential Equation of Queuing Statistics
• Lyapunov Stability
Cross Layer Control
13/20
Stochastic Network Optimization
Buffer for user 𝑚
Arrival Rate 𝝀𝒎 Service Rate 𝝁𝒎
Backlog Queue 𝑸𝒎 (𝒕)
Network State Variable 𝑺(𝒕)
Control Action 𝑰 𝒕 ∈ 𝑰𝑺(𝒕) feasible control region under 𝑺(𝒕)
𝐐 𝑡𝑆 𝑡 ,𝐼(𝑡)
𝐐(𝑡 + 1)
Cross Layer Control
14/20
Stochastic Network Optimization
Stability Issue
𝑄𝑚 𝑡 + 1 ≤ max 𝑄𝑚 𝑡 − 𝑅𝑚𝑜𝑢𝑡 𝐼 𝑡 , 𝑆 𝑡 , 0 + 𝑅𝑚
𝑖𝑛(𝐼 𝑡 , 𝑆 𝑡 )
𝑶𝒖𝒕𝒈𝒐𝒊𝒏𝒈 𝑸𝒖𝒆𝒖𝒆 𝑬𝒏𝒕𝒆𝒓𝒊𝒏𝒈 𝑸𝒖𝒆𝒖𝒆
lim𝑡→ ∞
sup1
𝑡 𝐄{𝑄𝑚 𝜏 }
𝑡−1
𝜏=0
< ∞
Lyapunov Stability
If there exist 𝑩 > 𝟎 and 𝝐 > 𝟎, such that for all
times slot 𝒕 we have :
Then network is strongly stable, and
𝐄 ∆ 𝑡 Q 𝑡 ≤ 𝐵 − 𝜖 𝑄𝑚(𝑡)
𝑀
𝑚=1
lim𝑡→ ∞
sup1
𝑡 𝐄{Q 𝜏 }
𝑡−1
𝜏=0
<𝐵
𝜖
15/20
Cross Layer Control
16/20
Find 𝑰(𝒕)
Find 𝚲 by Lyapunov Drift
Drift Definition
𝐼∗ 𝑡 = argmax𝐼 𝑡 ∈𝐈𝑆(𝑡)
𝑊𝑎𝑏∗ 𝑡 𝜇𝑎𝑏
∗ (𝑡)
𝑎𝑏
𝑊𝑎𝑏∗ 𝑡 = max
𝜇𝑎𝑏
𝑄𝑎 𝑡 − 𝑄𝑏 𝑡 +
maximum queue backlog differential
∆ 𝑡 = 𝐿 𝑡 + 1 − 𝐿(𝑡)
𝐿 𝑡 =1
2 𝑄𝑚
2 (𝑡)
𝑀
𝑚=1
Lyapunov Drift
17/20
∆ 𝑡 = 𝐿 𝑡 + 1 − 𝐿(𝑡)
=1
2 [𝑄𝑚
2 𝑡 + 1 −
𝑀
𝑚=1
𝑄𝑚2 (𝑡)]
After applying 𝑄𝑚2 (𝑡 + 1)
Find Lyapunov Bound with Conditional Expectation
∆ 𝑡 𝐐 𝑡 ≤ …
lim𝑡→ ∞
sup1
𝑡 𝐄{𝑄𝑚 𝜏 }
𝑡−1
𝜏=0
< ∞
Network Utility Maximization (NUM)
18/20
Rate 𝐫 ∈ 𝚲 with Maximum Utility
𝐫∗ = argmax𝐫≤𝛌
𝑔 𝐫 |𝐫 ∈ 𝚲
𝑔(𝐫) : Utility Function
Minimize Cost
Generalized Cross Layer Control
19/20
General Form of GCLC
Cost Variable Vector 𝐱 : Maximum cost constraints 𝐐
Utility Variable Vector 𝐲 :Minimum utility constraints 𝐇
Stable Region 𝐫 ∈ 𝚲
• Arrival Rate Vector 𝛌
Minimize net cost
• Natural cost function 𝒇(𝒙) and Concave Utility function 𝒈(𝒚)
min𝐫≤𝛌
𝑓 𝐱 − 𝑔(𝐲)|𝐪 𝐱 ≤ 𝐐, 𝐡 𝐲 ≥ 𝐇, 𝐫 ∈ 𝚲
OFDMA Resource Control via GCLC
20/20
OFDMA via GCLC
Cost Variable Vector 𝐱 : power coefficients
• 𝐐 = 𝐏𝐓 : Total Power Constraint
Utility Variable Vector 𝐲 : user data rate 𝐲 = 𝐫
• 𝐇 : Quality of Service (User demands, Fairness)
Stable Region based on Queuing Statistics
Minimize net cost : Maximize System Throughput
min𝐫≤𝛌
𝑓 𝐱 − 𝑔(𝐲)|𝐪 𝐱 ≤ 𝐐, 𝐡 𝐲 ≥ 𝐇, 𝐫 ∈ 𝚲