1. TED AND KARYN HUME CENTER FOR
NATIONAL SECURITY AND TECHNOLOGY
Resource Allocation and End-to-End Quality of Service for Cellular
Communications Systems in Congested and Contested
Environments
Mo Ghorbanzadeh
mgh@vt.edu
10/13/2015
http://www.hume.ictas.vt.edu
UNCLASSIFIED//FOR OFFICIAL USE ONLY

2. Wireless Evolution and Data Volume
• Wireless technology fast evolution allows High data rates.
• Smartphones offer multi-play services & traffic-intensive applications.
– Can push traffic beyond any estimates.
– Spectrum is scarce, expensive, & congested.
– Overprovisioning: NOT a solution (uneconomical and data hungry apps).
– Dynamic spectrum access (DSA): part of a solution.
• Spectrum efficiency & adaptability by using spectrum holes (Spectrum is a congested ).
• Shared-spectrum operation.

3. QoS Challenges and Potential Solutions
• Mobile broadband generates various traffic types with distinct quality
of service (QoS) requirements.
• Real-time and delay-tolerant applications lead to the traffic elasticity behaviors.
– QoS satisfaction elevates quality of experience (QoE), reduces subscriber churn.
– Over/Under-allocation hurts spectrum efficiency/QoS (Traffic elasticity is important).
– Cellular network traffic has a dynamic nature due to users’ temporal focus.
• Subscription-based priorities is important for efficient RRA.
– Prepaid/postpaid, roaming, national security.
• Radio Resource Allocation (RRA) should include subscriber, traffic, &
application usage differentiation.

4. Research Problem
• Designing RRA mechanisms for modern cellular communications systems to deal
with distinctive traffic types.
– Optimize for meeting bit rate requirement, elasticity behavior, jitter.
• Conveying distinct services, networks RRA entity should be able to perform resource
allocations with preferential subscriber treatment.
– Traffic for public safety, national security/emergency preparedness.
• Modern cellular networks traffic and ecosystem are highly dynamic.
– Dynamic QoS needs of the traffic produced via users’ usage behavior be considered.
– Stability of the RRA procedure under a changing system (load, application usage, etc.).
• Spectrum scarcity & its high demand requires efficient spectrum utilization.
– Considering the effect of the radio environment map (REM) on the RRA behavior.
• Designing RRA mechanism with reasonable transmission/computation overhead.
• Incorporating spectrum-additive measures into RRA methods and its caveats.
– Incumbent detection/protection. Granularity of the allocation.

5. State of the Art
E. Bjornson
Resource Allocation, QoS Interference optimization
Throughput,Channel,QoSoptimization
I. Garcia
F. Kelly
I. Hou
H. Shajaiah
G. Tychogiorgos
S. Shenker
M. Ghorbanzadeh
F. Qian
• QoS satisfaction through optimizing utility functions.
– Proportional fairness, Max-min fairness.
• Used convexity for delay tolerant application models.
• Various utility function for traffic modeling.
– Real-time traffic was modeled as sigmoidal.
• Mapping real-time. To nearest convex functions.
• Carrier aggregation, adhoc networks, etc.
• Minimize interference while maximizing throughput.
• Search algorithm solutions.
V. Kim

6. Contributions
• Proved that relaxing the real-time traffic hard decision utility into a sigmoidal form leads to
a convex resource allocation utility maximization for a mixture of sigmoidal & logarithmic
utilities (hybrid traffic).
• We migrated the problem of hybrid traffic utility-based QoS satisfaction through
from an NP-hard problem to a one with polynomial complexity.
• Proved we can decompose & solve the resource allocation for the network and UE
independently.
– Centralized and distributed (decomposed) architectures are mathematically equivalent.
• We looked at smartphone traffic & its dynamics, and mathematically proved the stability of
the resource allocation under ecosystem changes.
• Derived lower bounds for the transmission overhead of our resource allocation.
• We looked at LTE structure and rendered resource allocation more realistic by accounting
for resource blocks and channel conditions.
• Culminated in a large-scale simulation which included terrain model and network planning.

7. Research Organization
• Organization of the research work:
– Developed a QoS-minded resource allocation in cellular networks.
– Extended the model to resource block allocation.
– Incorporated spectrum sharing.
– Added channel effect, performed small/large scale simulation.

8. QoS Utility Ecosystem
0 100 200 300 400 500 600 700 800 900 1000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Throughput
QoSSatisfaction
Logarithmic Utility Function for Delay-Tolerant traffic
0 2 4 6 8 10 12 14 16 18 20
0
0.2
0.4
0.6
0.8
1
Throughput
QoSSatisfaction
Hard Realtime Utility Function for Realtime Traffic
10 20 30 40 50 60 70 80 90 100
0
0.2
0.4
0.6
0.8
1
1.2
QoSSatisfaction
Throughput
Hybrid Hard Realtime and Logarithmic Utility Function
Logarithmic
Hard Decision
0 100 200 300 400 500 600 700 800 900 100
0
0.2
0.4
0.6
0.8
1
QoSSatisfaction
Hybrid Sigmoidal and Logarithmic Utility Function
Sigmoidal
Logarithmic
• Binary integer programming.
• NP – complete complexity.
• Convex (Lagrange multiplier).
• Polynomial complexity.
• Convex (Lagrange multiplier)
• Polynomial complexity.
Ellipsoidal Method –NP Hard
Algorithm is a superset of ecosystem.

9. Convexity Proof
Already proved.
• The distributed UE and application allocations are convex.
• The centralized optimization is convex.
• We can use Lagrange multipliers to solve the problem.
• The problem has migrated from a NP complete to polynomial time.
• Lemma: Natural logarithm of aggregate utility is convex.
.)(log)(:,
1
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))(log(max))(log(max)(maxlog 
Convex objective and thus convex optimization1.
1Boyde, Convex Optimization, Cambridge University Press, 2008.
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Convex objective and thus convex optimization1.
   
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))(log(max))((maxlog  Addition of convex is convex objective, thus convex optimization1.

10. Centralized vs Distributed
],...,,...,,...,,,...,[ 1221111 21 MMNMNN rrrrrr
],...,[ 1 Mrr
],...,[ 1 iiNi rr
],...,[ 1 kkNk rr
],...,[ 1 llNl rr

11. Mathematical Equivalence
• Theorem: Application rates assigned by the distributed & centralized architectures are equal.
 Lemma: Aggregate & application slope curvature functions Si = ∂Vi/ ∂ri and Sij = ∂Vij/ ∂rij
are invertible, decreasing, and have decreasing inverse function.
rij
1 ≤ rij
2 → Sij(rij)1≤ Sij(rij)2
Centralized:
Network:
Device:

12. Rate Allocation Simulations – No Channel (1)
Application Rates
Application Bids
UE Rates
UE Bids
Application Utility

13. Application-Aware vs. Proportional Fairness
1 2 3 4 5 6
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
UE
Comparison of Application QoS SatisfactionApplication-Aware - Realtime App
Proportional Fairness - Realtime App
Application-Aware - Delay tolerant App
Proportional Fairness - Delay tolerant App
1Self-organized resource allocation in LTE systems with weighted proportional fairness, Hou, I., Chen, C., IEEE ICC 2012.
• Utility function for real-time
U(ri) = wilog(ri).
• Levenberg – Marquardt

14. Radio Resource Block Allocation
• RRA gives continuous rates, but technologies like Long Term Evolution
(LTE) assign radio resource blocks (RRBs).
– For cellular systems, continuous rates are theoretical concepts only.
– Allocate discrete resources, for RRBs, while keeping the RRA frameworks.
• Lagrangian relaxation gives 2 optimizations to get continuous rates.
– Neighbor discrete rates

15. Radio Resource Block Allocation Simulation
O(nM) →O(2M)

16. Channel Effect
• Resource allocation needs interactions between UEs and BSs.
– Radio waves undergo propagation effects, aka channel effect.
– Good channel: higher modulation and coding scheme (MCS).
– Assigning bit rate is not sufficient.
– Previously all PRBs mapped with no channel.
– Channel Quality Indicator (CQI) mapped to SNR.
• Algorithm is a superset of the ecosystem.
– Convergence may get into difficulty for low SNR situations (high steep).
CQI Modulation
Spectral
Efficiency
1
QPSK
0.1523
2 0.2344
3 0.377
4 0.6016
5 0.877
6 1.1758
7
16 QAM
1.4766
8 1.9141
9 2.4063
10
64 QAM
2.7305
11 3.3223
12 3.9023
13 4.5234
14 5.1152
15 5.5547
0
0.5
1
-10 -5 0 5 10 15 20
ϵ
SNRmin

17. Small Scale Simulation (2)
1 2 3 4 5 6 7 8 9
0
2000
4000
6000
8000
10000
12000
UE
ResourcesAllocatedtoAppswithinUEs
Application Rates

18. Cell Placement
• Fitness: Maximize coverage.
• 10 km2, 500 UEs, DL SNR .
6.2 8.6
4.4
5.6
2.8
4
6.4
5.4
5.22.84.8
5
4.2
5
6.2
2.8 3.6
6
4.6 6.4
eNodeB UEs1 2 3 4 5 6 7 8 9 10
11 12 13 14 15 16 17 18 19 20
eNodeB 4
5
10
15
20
0
5
10
15
20
25
30
35
40
45
-20
-10
0
10
20
30
40
• Bitrate: random 0.25, 1, and 5 Mbps

19. eNodeB 4 (1)
• 10 MHz LTE (67143000 REs)
• 28 UEs, 1 App.

20. eNodeB 4
• 100,000 REs.
• 28 UEs.
• Throughputs
not met.

21. Outside Effects
13.5 GHz FNPRM Model City PM, FCC filling http://apps.fcc.gov/ecfs/document/view?id=7521827440.
2 Amendment of the Commission’s Rules with Regard to Commercial Operations in the 3550-3650 MHz, FCC Docket 12-354, July 2014, http://apps.fcc.gov/ecfs/document/view?id=7521380032
3 Amendment of the Commission’s Rules with Regard to Commercial Operations in the 3550-3650 MHz, FCC Docket 12-354, August 2014, http://apps.fcc.gov/ecfs/document/view?id=7521768447
4 Radar in-Band Interference Effects on Macrocell LTE Uplink Deployments in the U.S. 3.5 GHz Band, IEEE ICNC 2014.
5 'Radar Inband and Out-of-Band Interference into LTE Macro and Small Cell Uplinks in the 3.5 GHz Band, IEEE WCNC 2015.
6 Radar Interference into LTE Base Stations in the 3.5 GHz Band, Elsevier Journal on Physical Communications, submitted.

22. Conclusions
Proved convexity of proportionally fair resource allocation utility maximization for a
mix of sigmoidal & logarithmic utilities (hybrid traffic).
• We migrated the problem of hybrid traffic utility-based QoS satisfaction through the relaxation
provided by the sigmoidal from an NP-hard to polynomial complexity.
Proved we can decompose & solve the resource allocation for the network
and UE independently.
– Centralized and distributed architectures are mathematically equivalent.
We looked at smartphone traffic & its dynamics, and mathematically proved
the stability of the resource allocation under ecosystem changes.
Derived lower bounds for the transmission overhead of our resource allocation.
We looked at LTE structure and rendered resource allocation more realistic by
accounting for resource blocks and channel conditions.
Culminated in a large-scale simulation which included terrain model and network planning.

23. Potential Future Trajectories
• Choosing serving eNodeB based on the traffic conditions.
• Combination of control theory and resource allocation.
• Parallel implementation of the resource allocation.
• Looking at situations where due to channel the slope goes up and the
convergence becomes problematic.
• Mapping the optimizations to an LTE architecture.
• Defining protocols and potentially standards.
• Software radio and LTE-in-Box applications.
• Intra-cell and inter-cell interference considerations.

24. Papers
Papers
• [c] A Utility Proportional Fairness Radio Resource Block Allocation in Cellular Networks, IEEE ICNC 2014.
• [c] A Utility Proportional Fairness Resource Allocation in Spectrally Radar-Coexistent Cellular Networks, IEEE Milcom 2014.
• [c] Implementing an Optimal Rate Allocation Tuned to the User Quality of Experience, IEEE CNC Workshop 2015.
• [c] A Utility Proportional Fairness Radio Resource Block Allocation in Cellular Networks, IEEE ICNC 2015.
• [c] Radar Inband and Out-of-Band Interference into LTE Macro and Small Cell Uplinks in the 3.5 GHz Band, IEEE WCNC 2015.
• [c] Radar in-Band Interference Effects on Macrocell LTE Uplink Deployments in the U.S. 3.5 GHz Band, IEEE CNC 2015.
• [c] A Hidden Markov Model Detection of Malicious Android Applications at Runtime, IEEE WOCC 2014.
• [c] A Neural Network Approach to Category Validation of Android Applications, IEEE ICNC 2013.
• [c] Fine-Grained End-to-End Network Model via Vector Quantization and Hidden Markov Processes, IEEE ICC 2013.
• [J] Radar Interference Effect on LTE Base Stations in the 3.5 GHz Band, Submitted to Elsevier Journal on Physical Communications.
• [J] Optimal Radio Resource Allocation for Hybrid Traffic in Cognitive Cellular Networks: Architecture and Traffic Analysis, Submitted IEEE TCCN.
• [J] Outdoor Cell Planning and Large-Scale Channel-Cognizant Optimal Resource Allocation, under preparation.
• [L] Channel-Aware Resource Allocation in Cellular Networks, under preparation.
FCC Filings
• Amendment of the Commission’s Rules with Regard to Commercial Operations in the 3550-3650 MHz, FCC Docket 12-354, July 2014.
http://apps.fcc.gov/ecfs/document/view?id=7521380032
• Amendment of the Commission’s Rules with Regard to Commercial Operations in the 3550-3650 MHz, FCC Docket 12-354, August 2014,
http://apps.fcc.gov/ecfs/document/view?id=7521768447
• 3.5 GHz FNPRM Model City PM, http://apps.fcc.gov/ecfs/document/view?id=7521827440
Provisional Patents
• Resource Block Allocation in Cellular Networks: A Utility Proportional Approach, US Patent 14-093.
• Utility Proportional Bandwidth Allocation in Radar-Coexistent Cellular Networks, US Patent 14-094.
Book
• Communications Systems in Congested Environments: Resource Allocation and End-to-End Quality of Service Solutions, Springer, Accepted for
publication 2016.

25. Application-Aware vs. Proportional Fairness
1 2 3 4 5 6
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9UEUtilitySatisfaction
UE
UE Utility Satisfaction Comparison
Application-Aware
Proportional Fairness Different Weights
1 2 3 4 5 6
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
UE
Comparison of Application QoS SatisfactionApplication-Aware - Realtime App
Proportional Fairness - Realtime App
Application-Aware - Delay tolerant App
Proportional Fairness - Delay tolerant App
1Self-organized resource allocation in LTE systems with weighted proportional fairness, Hou, I., Chen, C., IEEE ICC 2012.
• Utility function for real-time
U(ri) = wilog(ri).
• Levenberg – Marquardt