Distributed adaptive optimization of LTE-TDD configuration based on UE traffic type M. Malmirchegini, R. Yenamandra, K. R. Chaudhuri and J. E. Vargas {mehrzad,ysrao,kausikr,josev}@qti.qualcomm.com

5th International Workshop on Self Organizing Networks

VTC Spring - Glasgow - 2015

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Agenda

Summary

LTE TDD Frame Structure & Traffic Adaptation (R12)

Optimum TDD UL/DL Configuration Index

Distributed Optimization of TDD UL/DL Configuration

Simulation Results

Conclusion

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Summary

In LTE-TDD, the amount of UL and DL subframes can be adjusted by

allocating more UL or DL subframes to fit different network traffic profiles.

Choosing different UL/DL configurations per cell may result in new types of

inter-cell interference such as eNodeB-to-eNodeB interference as well as UE-

to-UE interference.

In 3GPP Rel 11/12 dynamic adjustment of TDD UL/DL configurations for

heterogeneous networks (HetNets) has been introduced.

We propose an adaptive optimization of LTE TDD configuration index in mean

square error (MSE) sense based on the available UL and DL traffic demand of

all RRC connected UEs in the network.

o The proposed decentralized algorithm uses a consensus-based approach

for determination of optimum TDD configuration index across a cluster of

cells, were each cell only requires communicating to its neighboring cells

over X2 interface.

Our simulation results show that the LTE network, which is running the

proposed algorithm, provides better utilization of the available frequency-time

resources and achieves a higher UE Throughput compared to a fixed TDD

UL/DL configuration.

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TDD Frame Structure, Subframe, and Slot

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UL/DL Configurations

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Traffic Adaptation in 3GPP Release 12

Legacy TDD system typically forces all cells to use same TDD configuration though

it has potential to use different configurations by changing the SIB1 configuration

eIMTA allows a cells or cluster of cells to dynamically adapt DL/UL subframe

resources based on the actual traffic needs

For TDD eIMTA, these UL-DL configurations can be configured dynamically

Explicit L1 signaling by UE group common PDCCH • The reconfiguration DCI carries information to explicitly indicate one of the existing 7 UL-DL

configurations; The DCI size is aligned to DCI format 1C

• The periodicity of the reconfiguration DCI includes 10, 20, 40 and 80ms

The adaptation, however, may still create overwhelming interference between

neighboring cells when transmission directions are different

0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9

frame n-1 frame n

10ms periodicity

Configuration A Configuration B

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The UL/DL Configuration Index needs to be optimized to efficiently

utilize the available time-frequency resources.

Different cell might have different traffic demands:

Cell near to residential area with heavy downlink traffic

Cell near to business district with additional uplink traffic

Traffic patterns at various cells may change over time

Let 𝐶𝐷𝐿,𝑛 and 𝐶𝑈𝐿,𝑛 denote the available DL and UL traffic ratio, we

have:

Optimum UL/DL Configuration Index

The UL/DL Configuration Index should be dynamically optimized based on the

UEs traffic demands across the cluster in distributed manner over the time

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Consider a cluster of 𝑀 cells trying to collaboratively

determine an optimal TDD UL/DL configuration based on

the available cluster traffic.

The optimum TDD UL/DL configuration index in mean

square error (MSE) sense can be characterized as

where Let 𝑆𝑖 denote the number of RRC connected UEs

in the 𝑖th cell.

Optimum UL/DL Configuration Index

Cell-based weight

UE-based weight DL volume ratio of the j-th UE

served by i-th cell

DL ratio of the nth

configuration index

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We have

where

The optimum TDD UL/DL configuration index requires UE and

cell weights as well as the traffic patterns of all RRC

connected UEs within the cluster.

It is impractical as individual cells may not have all of the

necessary information.

The optimization problem needs to be solved in a distributed

manner.

Optimum UL/DL Configuration Index

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Average Consensus Algorithm

Consensus problems: A group of nodes try to reach an

agreement over a certain value, for instance average of

their initial assessments

Let 𝑥𝑖(Δ) denote the status of 𝑖-th node at time Δ. The

network will be in consensus state at time Δ if

𝑥𝑖 Δ =1

𝑀 𝑥𝑗 0 1 ≤ 𝑖 ≤𝑀𝑗=1 𝑀

Let 𝐴 = [𝑎𝑖,𝑗] represent the adjacency matrix with 𝑎𝑖,𝑖=0

and 𝑎𝑖,𝑗= 𝑎𝑗,𝑖= 1 if there exits an edge between node 𝑖

and node 𝑗

Define X t = [𝑥1 𝑡 ,⋯ , 𝑥𝑀 𝑡 ]𝑇 and D = [𝑑1, ⋯ , 𝑑𝑀]𝑇,

where 𝑑𝑖 = 𝑎𝑖,𝑗𝑀𝑗=1

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The discrete-time consensus protocol will be

where ϵ ∈ [0,1

𝑚𝑎𝑥𝑖 𝑑𝑖] and 𝐼𝑀×𝑀 represents identity matrix.

If the underlying graph is connected, i.e. there exists a

path between any two nodes in the network, we then

have:

Average Consensus Algorithm

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Distributed Optimization of TDD UL/DL Configuration

Consider a cluster of 𝑀 cells. Each cell

within cluster calculates the values of

𝛼𝑖(0) and 𝛽𝑖(0).

Each cell may then exchange these

values with its neighboring cells via X2

interface or backhaul links.

Each cell will then updates its own

values as follows:

After sufficiently large Δ, we have

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Distributed Optimization of TDD UL/DL Configuration

Upon reaching consensus, each cell can determine an optimum TDD UL/DL configuration index as follows:

We then have the following for the optimum configuration index

𝑛𝑜𝑝𝑡 = 𝑄𝛽𝑖(∆)

𝛼𝑖(∆)

where 𝑄 . function quantizes the argument to the

nearest 𝐶𝐷𝐿,𝑛 and yields the corresponding index.

Each cell will then communicate the updated TDD UL/DL configuration index to its RRC connected UEs.

Each cell in cluster will then notify all its RRC connected UEs to estimate DL and UL traffic demand for the next time interval.

This process will be repeated with time periodicity of Δ.

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Convergence Rate Analysis

The time interval Δ needs to be selected based on the network connectivity through the backhaul links.

Let 𝐸 𝑡 = 𝑋 𝑡 − 𝑥 (0)1 denote the error vector w.r.t. the consensus values. Using the Rayleigh-Ritz theorem, we have:

where 𝑃 = 𝐼𝑀×𝑀 − 𝜖 𝐷 − 𝐴 and 𝜆2 represents the second largest eigenvalue.

For a deployment with the high backhaul connectivity, the value of 𝜆2 will be small and as a result cells in the cluster can reach consensus on the optimum TDD UL/DL configuration index within the smaller time interval of Δ.

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Assume that the backhaul network through the X2 interface is deployed such that each eNodeB is only connected to its immediate.

The hexagonal deployment only requires 42 backhaul links as compared to fully connected topology with 171 backhaul links.

As the cluster size increases, the cells in cluster will require larger time interval Δ to reach consensus on the optimum UL/DL configuration index.

Convergence Rate Analysis

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Simulation Assumptions

We consider two different scenarios of DL-heavy and UL-heavy traffic.

DL-heavy scenario:

90% of the UEs are experiencing heavier DL traffic as compared to the UL traffic

10% of UEs have the same amount of DL and UL traffic

UL-heavy scenario:

90% of the UEs are experiencing heavier UL traffic as compared to the DL traffic

10% of UEs have the same amount of DL and UL traffic

We assume that all UE-based and cell-based weights are set to one

For each scenario, we assume that the overall traffic volume is the same for all UEs

The simulations have been performed using

Qualcomm LTE system-level simulator. The

simulator models the Physical and MAC

layer of LTE stack. Above table summarizes

main simulation assumptions.

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Simulation Results – DL Heavy

For DL heavy scenario, all cells within the network run the proposed algorithm and reach consensus on the optimum TDD UL/DL configuration index of 5 (allocating 8 subframes to DL and 1 subframe to UL per frame).

For non-optimized case, available time-frequency resources are not fully utilized (~84%).

For optimized case, the PRB utilization is almost 100%.

The overall UE throughput (sum of DL and UL throughput per UE) has been improved by 35% for the optimized case.

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Simulation Results – UL Heavy

For UL heavy scenario, all cells within the network run the proposed algorithm and reach consensus on the optimum TDD UL/DL configuration index of 0 (allocating 6 subframes to UL and 2 subframes to DL per frame).

For non-optimized case, available time-frequency resources are not fully utilized (~74%).

For optimized case, the PRB utilization is almost 100%.

The overall UE throughput (sum of DL and UL throughput per UE) has been improved by 25% for the optimized case.

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Conclusion

In this paper, we considered a TDD LTE network, where each eNodeB in the network has backhaul links, i.e. X2 interfaces, only with its immediate neighboring eNodeBs.

We investigated an adaptive optimization of LTE TDD configuration index in mean square error (MSE) sense based on the available DL and UL traffic of all RRC connected UEs in the network.

We proposed a distributed algorithm, where all eNodeBs in the network will reach consensus on the optimum TDD UL/DL through local communications with their neighboring eNodeBs over X2 interfaces.

Our simulation results showed that the LTE network, which is running the proposed algorithm, provides the better utilization of the available frequency-time resources and achieves 35% and 25% higher UE throughput as compared to the fixed and symmetric UL/DL configuration for DL-heavy and UL-heavy scenarios respectively.

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