Joint Physical and Network Layer Optimization
of Communication Systems:
Current Challenges and Perspectives
Dejan V. Djonin
NSERC PostDoctoral Fellow
Dept. of Electrical and Computer Engineering
University of British Columbia
E-mail: ddjonin@ece.ubc.ca
www.ece.ubc.ca/~ddjonin
Dept. of ECE, University of British Columbia
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My Brief Background…
(Sep 2003 - ) Postdoctoral Teaching
Fellow, University of British Columbia,
Department of Electrical and Computer
Engineering
(May 2000- Jun 2003) PhD Studies,
University of Victoria, Department of
Electrical and Computer Engineering
Ph.D. Thesis Title: "On Some Limiting
Performance Issues of Multiuser
Receivers in Fading Channels"
(1996 - 1999), Faculty of Electrical
Engineering in Belgrade, M.Sc. studies,
M.Sc. Thesis Title: "Application of Nonlinear One-dimensional Maps in
Generation of Error-Correction Block
Codes"
Dept. of ECE, University of British Columbia
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UBC and Vancouver
Dept. of ECE, University of British Columbia
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Presentation Outline
An Overview of My Previous Professional Results
Example: Cross-layer optimization for V-BLAST transmission
under delay constraints
Problem Formulation and Introduction
Real-Time Traffic Model + Flow Control
Channel Model: Finite State Markov Model
Mathematical Framework: Stochastic Control and MDP’s
Solution Techniques
Resource allocation for imperfectly known channel models
Perspectives: - Sensor Scheduling for Network Lifetime Maximization
- Opportunistic Spectrum Access
Dept. of ECE, University of British Columbia
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An Overview of My Previous Results (1)
Non-linear mappings in the design of error-correction codes: (M.Sc. Thesis)
•
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D.V. Djonin, D.Gacesa, "Performances of Error-Correction Codes Generated by Iterative Nonlinear Mappings", in Advances in Systems,
Signals, Control & Computers, vol. 3, Durban, SAR, ISBN 0-620-23136-10, pp. 114-118, 1998.
D.V.Djonin and D.Gacesa, "Performances of error-correction codes generated by non-linear iterative mappings", in Proc. of URSI
International Symposium on Signals, Systems, and Electronics, ISSSE 98, pp. 356 360, 1998.
D.V.Djonin and L.Manojlovich, "Application of deterministic chaos in generation of error correction block codes" , in Proc. of Second IEEE
International Caracas Conference on Devices, Circuits and Systems, pp. 343-347,1998.
D.V.Djonin, "Efficient Construction of Error-Correction Codes Generated by Iterative Non-linear Maps", in proc. of Telecommunications
Conf. TELFOR, Belgrade Yugoslavia, 1998.
D.V.Djonin, "On the application of the theory of deterministic chaos in the generation of error-correction codes", pp. 82-85, in Proc. of the
ETRAN conference, Zlatibor, Yugoslavia, 1997.
D.V.Djonin, D.Gacesa, "Performances of Error-Correction Codes Generated by Iterative Nonlinear Mappings", in Advances in Systems,
Signals, Control & Computers, vol. 3, ISBN 0-620- 23136-10, pp. 114-118, Durban, 1998.
Dept. of ECE, University of British Columbia
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An Overview of My Previous Results (2)
Performance analysis and optimization of CDMA systems (Ph.D. Thesis)
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D.V.Djonin and V.K.Bhargava, "Asymptotic Analysis of the Conventional Decision Feedback Receiver in Fading channels'', IEEE Trans. on
Wireless Communications, pp. 1066-1078, September 2003.
D.V.Djonin and V.K.Bhargava, "On the Optimal Sequence Allocation in Flat Fading Channels'', IEEE Trans. on Wireless Communication, vol.
24, no. 5, pp. 680-689, July 2003.
D.V.Djonin and V.K.Bhargava, "Comments on 'Symmetric Capacity and Signal Design for L-out-of-K Symbol-Synchronous CDMA Gaussian
Channels'", IEEE Trans. on Inf. Theory, pp. 2921-2923, vol. 50, November 2004.
D.V.Djonin and V.K.Bhargava, "Spectral Efficiency of the Feedback Receiver for Two Sets of Orthogonal Sequences" , IEEE Communication
Letters, pp. 497-499, Nov. 2002.
D.V.Djonin and V.K.Bhargava, "Spectral Efficiency of the Feedback Receiver for Multiple Orthogonal Sequence Sets", in Proc. ISWC 02
Conf., Victoria, Canada, pp.107-108, Sep. 2002.
D.V.Djonin and V. K.Bhargava, "Asymptotic Analysis of the Conventional Decision Feedback Receiver in Flat Fading Channels'', in Proc. of
ICC 2002, pp. 1368 1372, April-May 2002.
D.V.Djonin and V.K.Bhargava, "Asymptotic Analysis of the Optimal Spreading Sequence Allocation in Flat Fading Channels" , in Proc. of
VTC 2002, pp.582-585, September 2002.
D.V.Djonin, and V. K. Bhargava, "Low Complexity Receivers for Over-Saturated CDMA System'', in Proc. of the Globecom 2001, vol. 2, pp.
846 -850, Nov. 2001.
Dept. of ECE, University of British Columbia
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An Overview of My Previous Results (3)
Performance analysis of communications systems using the theory of stochastic majorization
1.
2.
D.V.Djonin, P. Tarasak and V.K.Bhargava, "On the Influence of the Power Delay Profile on the Performance of Diversity Combining Systems"
, Proc. of Globecom 2003 Conference, San Francisco, CA, vol. 3, pp. 1659 - 1663, December 2003.
D.V.Djonin and V.K.Bhargava, "On the Influence of the Power Delay Profile on the Performance of Diversity Combining Systems" , IEEE
Transactions on Wireless Communications, pp. 1854-1861, vol. 3, Sep. 2004.
Dept. of ECE, University of British Columbia
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An Overview of My Previous Results (4)
Results on Space-Time Code
1.
2.
K. C. B. Wavegedara, D.Djonin, and V.K.Bhargava, "Space-Time Coded Uplink Transmission with Decision Feedback Sequence Estimation",
in Proc. of Globecom 2004 Conference, Dallas, TX, vol. 6, pp. 3448 - 3453, Nov.-Dec. 2004.
K. C. B. Wavegedara, D.V.Djonin, and V.K.Bhargava, "Space-Time Coded Uplink Transmission with Decision Feedback Sequence Estimation",
in IEEE Trans on Wireless Comm., Nov 8th, 2005. (full paper).
Dept. of ECE, University of British Columbia
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An Overview of My Previous Results (5)
Rate and Power Control algorithms for time-varying wireless channels using Markov Decision
Processes
1.
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A.Karmokar, D.V.Djonin and V.K.Bhargava, "Cross-layer Rate and Power Adaptation Strategies for IR-HARQ Systems over Fading Channels
with Memory: A SMDP-based Approach", submitted to Trans on Communications, February 21st, 2006.
D.V.Djonin and V.Krishnamurthy, " V-BLAST Power and Rate Control under Delay Constraints in Markovian Fading Channels -Structured
Policy Learning", submitted to IEEE Trans. on Signal Processing, Jan. 25, 2006.
D.V.Djonin and V.Krishnamurthy, " V-BLAST Power and Rate Control under Delay Constraints in Markovian Fading Channels -Optimality of
Monotonic Policies", submitted to IEEE Trans. on Signal Processing, Jan. 05, 2006.
A.Karmokar, D.V.Djonin and V.K.Bhargava, "Delay Aware Power Adaptation for Incremental Redundancy Hybrid ARQ over Fading Channels
with Memory", to be presented at the ICC 2006 Conference, Istanbul, Turkey.
D.V.Djonin and V.Krishnamurthy, "Structural Results on the Optimal Transmission Scheduling Policies and Costs for Correlated Sources and
Channels", in CDC 2005, (invited paper).
Md.J.Hossain, D.V.Djonin and V.K.Bhargava, "Delay Limited Optimal and Suboptimal Power and Bit Loading Algorithms for OFDM Systems
over Correlated Fading", presented at the Globecom 2005, St. Louis, Dec. 2005.
Md.J.Hossain, D.Djonin and V.K.Bhargava, "Power and Rate Adaptation for OFDM System over Correlated Fading Channels", presented at
the IST 2005 Symposium, Dresden, Germany, June 2005.
D.V.Djonin and V.K.Bhargava, "An Upper Bound on the Throughput of Opportunistic Transmission in a Multiple-Access Fading Channel",
IEEE Trans. on Comm., pp. 1618-1621, vol. 52, Oct. 2004.
A.Karmokar, D.V.Djonin and V.K.Bhargava, "Optimal and Suboptimal Packet Scheduling over Time-Varying Flat Fading Channels", to be
published in IEEE Trans on Wireless Comm., (full paper), Jan., 2006.
A.Karmokar, D.Djonin and V.K.Bhargava, "Delay Constrained Rate and Power Adaptation over Correlated Fading Channels", in Proc. of
Globecom 2004 Conference, Dallas, TX, vol. 5, pp. 2941 - 2945, Nov.-Dec. 2004.
D.V.Djonin, A.Karmokar and V.K.Bhargava, "Rate and Power Adaptation over Correlated Fading Channels under Different Buffer Cost
Constraints", submitted to Trans on Vehicular Technology, March. 9th, 2004.
D.V.Djonin, A. Karmokar and V.K.Bhargava, "Optimal and Suboptimal Packet Scheduling over Time-Varying Flat Fading Channels" in Proc.
of ICC 2004, pp. 906-910, Paris, France, June 2004.
A.Karmokar, D.V.Djonin and V.K.Bhargava, "POMDP Based Coding Rate Adaptation for Hybrid ARQ Systems over Fading Channels with
Memory", submitted to Trans on Wireless Communications, August 18th, 2004.
Dept. of ECE, University of British Columbia
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Common Themes
• Performance Analysis of Communication Systems
• Performance Improvement of Communication Systems Through
On-line and Off-line Optimization
Main tool: Stochastic Control + Markov Decision Processes
Dept. of ECE, University of British Columbia
10/34
Presentation Outline
An Overview of My Previous Professional Results
Example: Cross-layer optimization for V-BLAST transmission
under delay constraints
Problem Formulation and Introduction
Real-Time Traffic Model + Flow Control
Channel Model: Finite State Markov Model
Mathematical Framework: Stochastic Control and MDP’s
Solution Techniques
Resource allocation for imperfectly known channel models
Perspectives: - Sensor Scheduling for Network Lifetime Maximization
- Opportunistic Spectrum Access
Dept. of ECE, University of British Columbia
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Problem Formulation and Introduction
Modern and future wireless networks will support different services
with a wide range of quality of service requirements such as delay, rate, BER
Consideration of Transmission Latency is of crucial interest
for some applications (real-time high quality audio, video transmission)
However, time-varying nature of a wireless channel poses a challenging
task to delivering a wide variety of services
the effect is similar to congestion in wireline networks
the need for transmission buffer
transmitted signals are delayed
Do these methods only apply to wireless channels?
The solution is through adaptation of transmission parameters based
on the current state and the statistical model of the channel and
supported traffic
Essentially a Cross-layer optimization approach
Dept. of ECE, University of British Columbia
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Power versus Delay Tradeoff:
A Simple Illustration
Dept. of ECE, University of British Columbia
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OSI Model
Data Link (Layer 2) At this layer,
data packets are encoded and
decoded into bits. It furnishes
transmission protocol knowledge
and management and handles
errors in the physical layer, flow
control and frame synchronization.
The data link layer is divided into two
sublayers: The Media Access
Control (MAC) layer and the
Logical Link Control (LLC) layer.
The MAC sublayer controls how a
computer on the network gains
access to the data and permission to
transmit it. The LLC layer controls
frame synchronization, flow control
and error checking.
Physical (Layer 1)This layer
conveys the bit stream - electrical
impulse, light or radio signal -through the network at the electrical
and mechanical level. It provides the
hardware and software means of
sending and receiving data on a
carrier.
Dept. of ECE, University of British Columbia
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V-BLAST transmission control model
Let fn denote the number of packets arriving at the buffer in time n.
Transmission adaptation parameters can include power,
error-correction or source coding rate (flow control)
At the beginning of the n-th time slot, the scheduler controls the packet
retrievals from the buffer and bit-loading across carriers.
Dept. of ECE, University of British Columbia
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Channel Model: FSMC
For example, a slowly varying flat Fading Rayleigh channel can be
represented as a Finite State Markov Chain (FSMC) as shown in figure:
p0,0
H0
p0,1
p1,0
p1,1
H1
p1,2
p2,1
pK-2,K-1
. . .
pK-1,K-1
pK-1,K-2
HK-1
Channel can also be modeled as an Auto Regressive (AR) model
Dept. of ECE, University of British Columbia
16/34
Presentation Outline
An Overview of My Previous Professional Results
Example: Cross-layer optimization for V-BLAST transmission
under delay constraints
Problem Formulation and Introduction
Real-Time Traffic Model + Flow Control
Channel Model: Finite State Markov Model
Mathematical Framework: Stochastic Control and MDP’s
Solution Techniques
Resource allocation for imperfectly known channel models
Perspectives: - Sensor Scheduling for Network Lifetime Maximization
- Opportunistic Spectrum Access
Dept. of ECE, University of British Columbia
17/34
Markov Decision Processes (MDP)
Markov Chain: Example
p(S1|S1)
p(S2|S1)
S1
S2
p(S2|S2)
p(S1|S2)
Markov Decision Processes: Example for state S1
Action U1, c(S1,U1)
p(S2|S1,U1)
p(S1|S1,U1)
S1
p(S2|S1,U2)
Action U2,c(S1,U2)
S2
p(S1|S1,U2)
Dept. of ECE, University of British Columbia
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Constrained MDPs
What happens if in addition to the immediate costs, c(s,u), there is
an another cost d(s,u) that corresponds to a constraint? I.e.
optimization problem is:
1 N


C  inf E  lim
c ( sn , u n ) 


 N  N i 1

1 N
s.t. lim
d ( sn , u n )  D

N  N
i 1
*
The answer can be found in the theory of Constrained Markov
Decision Processes (CMDP). CMDP can be expressed as equivalent
unconstrained MDP using Lagrangian Approach:
N
1


*
C ( D )  min sup E  lim   c( sn , un )   d ( sn , un )   D

 0
 N  N i 1

Note that policies do not have to be deterministic in CMDPs. In
general optimal policies for CMDPs are randomized.
Dept. of ECE, University of British Columbia
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V-BLAST transmission control model
Let fn denote the number of packets arriving at the buffer in time n.
Transmission adaptation parameters can include power,
error-correction or source coding rate (flow control)
At the beginning of the n-th time slot, the scheduler controls the packet
retrievals from the buffer and bit-loading across carriers.
Dept. of ECE, University of British Columbia
20/34
Presentation Outline
An Overview of My Previous Professional Results
Example: Cross-layer optimization for V-BLAST transmission
under delay constraints
Problem Formulation and Introduction
Real-Time Traffic Model + Flow Control
Channel Model: Finite State Markov Model
Mathematical Framework: Stochastic Control and MDP’s
Solution Techniques
Resource allocation for imperfectly known channel models
Perspectives: - Sensor Scheduling for Network Lifetime Maximization
- Opportunistic Spectrum Access
Dept. of ECE, University of British Columbia
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Sample Results (1)
As fading rate , the rate of decrease of average power .
As the number of antennas , average power 
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Sample Results (2)
Dept. of ECE, University of British Columbia
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Structural Results
Extracted from the paper:
MIMO Power and Rate Control under Delay Constraints in Markovian Fading Channels –
Optimality of Monotonic Policies, Dejan V. Djonin, Vikram Krishnamurthy, submitted to Trans. on
Signal Processing, Jan 2006, revised May 2006.
also to be presented at the ISIT Conference, Seattle 2006.
Dept. of ECE, University of British Columbia
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Resource allocation for imperfectly known channel models (1)
This a challenging problem as the policy has to be “learned” on-line as
the actions are being applied and observations on the incurred cost are
collected.
The appropriate framework for the solution of this problem is to
consider Q-learning, which is a version of stochastic approximation
algorithm.
For details on Q-algorithm and related topics have a look at:
D. Bertsekas and J.Tsitsiklis, “Neuro-Dynamic Programming”
Dept. of ECE, University of British Columbia
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Resource allocation for imperfectly known channel models (2)
Extracted from the Paper: Dejan Djonin, Vikram Krishnamurthy, “V-BLAST Power and Rate Control under
Delay Constraints in Markovian Fading Channels- Structured Policy Learning Algorithm”, submitted to Trans
on Signal Processing, Jan 2006.
Dept. of ECE, University of British Columbia
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Resource allocation for imperfectly known channel models (3)
Advantages of Learning based algorithms for Optimal Control
It can be proved that Q-learning algorithm converges to the optimal
solution with probability 1 (both structured and non-structured Qlearning)
These algorithms are suitable for unknown channel environments
whose statistics changes slowly over time
It is possible to incorporate more complicated delay costs in the
model: average delay cost, maximum delay guarantees, delay profile
shaping
Dept. of ECE, University of British Columbia
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Presentation Outline
An Overview of My Previous Professional Results
Example: Cross-layer optimization for V-BLAST transmission
under delay constraints
Problem Formulation and Introduction
Real-Time Traffic Model + Flow Control
Channel Model: Finite State Markov Model
Mathematical Framework: Stochastic Control and MDP’s
Solution Techniques
Resource allocation for imperfectly known channel models
Perspectives: - Sensor Scheduling for Network Lifetime Maximization
- Opportunistic Spectrum Access
Dept. of ECE, University of British Columbia
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Sensor Scheduling for Network Lifetime Maximization
h1
hN
h2
eN
e1
e2
Sensor 1
Sensor N
Sensor 2
Collaborators: Qing Zhao, Yunxia Chen (UC Davis), V.Krishnamurthy(UBC)
Dept. of ECE, University of British Columbia
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Sensor Scheduling for Network Lifetime Maximization
The problem is how to design an optimal sensor scheduling policy to
maximize the lifetime of a network as a whole
The sensor network is considered to be functioning while a predefined
portion of sensors have enough energy to transmit
Transmission energy is dependent on the channel conditions: Wi= f(hi)
Two approaches to model and solve the problem:
centralized scheduling, global state MDP
decentralized scheduling, multi-armed bandit formulation
Some results on this topic are given in:
1) Y. Chen, Q. Zhao, V. Krishnamurthy and D.V.Djonin, "Transmission Scheduling for Optimizing Sensor Network Lifetime: A Stochastic
Shortest Path Approach", submitted to IEEE Trans. on Signal Processing, Jan. 2006, revised May 2006.
2) Y. Chen, Q. Zhao, V. Krishnamurthy and D.V.Djonin, "Transmission Scheduling for Sensor Network Lifetime Maximization: A Shortest
Path Bandit Formulation", presented at the ICASSP 2006 Conference, Toulouse, France, May 2006.
Dept. of ECE, University of British Columbia
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Sensor Scheduling for Network Lifetime Maximization:
Open Problems
Further simplification of the computation of the optimal sensor
scheduling policy for centralized scheduling
Incorporation of the content based scheduling (the information sent by
different schedulers can be prioritized)
Adaptive Source Coding Control prior to transmission
Multiple Access transmission resolution
Dept. of ECE, University of British Columbia
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Opportunistic Spectrum Access
Channel 1
Channel 2 Channel 3 Channel 4 Channel 5 Channel 6 Channel 7
B1
B2
1
2
B3
3
B4
B5
B6
B7
4
5
6
7
Scheduler = f(1,…, 7)
i = Prob [channel i is available]
Bi  Bandwidth of the Channel i
Collaborator: Qing Zhao (UC Davis)
Dept. of ECE, University of British Columbia
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Opportunistic Spectrum Access:
Open Problems
Design of a computationally efficient Spectrum Access control policy
Exploration of the decentralized formulation of the problem: a restless
multi-armed bandit formulation
Protocol design for coordination between primary and secondary users
Dept. of ECE, University of British Columbia
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Thank You for Your Attention !
Dept. of ECE, University of British Columbia
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