embracing lte-a with keystone socs

Abstract

With the rapid acceleration of LTE deployments and the enhanced mobile user experience it

delivers, LTE is clearly emerging as the winning technology for mobile communications. LTE

features a simplifi ed network architecture, higher performance and lower cost per bit, all

things that operators worldwide crave. LTE-A further enhances the spectral effi ciency of both

macro cells and small cells. It increases coverage for macro cells while using small cells to

boost capacity. Re-farming existing 2G/3G spectrum into LTE/LTE-A operation with carrier

aggregation will improve spectral effi ciency for mobile networks while using higher order

Multiple Input Multiple Output (MIMO) antennas can increase user data throughput to achieve

the required LTE-A data rate of 1Gbps. Finally, coordinated multi-point and enhanced inter-cell

interference coordination at the eNodeB will optimize network effi ciency. With more than 10

years of base station expertise and successful fi eld deployments, Texas Instruments is well

positioned to answer the LTE-A network challenges with its KeyStone II SoC architecture,

a highly integrated and scalable SoC for macro and small cell solutions. KeyStone II SoCs

enable fl exible network deployment and unlock the potential of LTE-A while providing both

energy and cost effi ciency.

LTE overview

LTE is defi ned in 3GPP release 8 and 9 with fl exible carrier spectrum ranging from 1.4-MHz,

3-MHz, 5-MHz, 10-MHz, 15-MHz and 20-MHz bandwidth. It supports peak downlink data

rates of 300Mbps and peak uplink date rates of 75Mbps. LTE offers a simplifi ed network

architecture compared to 2G and 3G deployments with much lower latency and an effi cient

transport mechanism enabling higher performance with lower cost compared to legacy mobile

networks. Motivated by further improvements in the cellular user experience in scenarios with

bursty asymmetric traffi c, a reduction in cost per bit, an increase in spectrum and energy ef-

fi ciency, and the support of heterogeneous networks for higher capacity and better coverage,

3GPP working groups have been working to advance LTE with new features to LTE-A.

Introduction

With smartphones like the iPhone5, Sam-

sung Galaxy and HTC Droid all equipped

with LTE capabilities, the demands for high-

er data rates from the network will soon be-

come commonplace. Wireless operators are

seeking cost-effective, high-performance

solutions that meet the demands of user

traffic today, and position their network

architecture for the increased demands of

the future. The Third Generation Partnership

Project (3GPP) has been working at length

to establish standardized solutions to ad-

dress these network requirements, mainly

through the Long Term Evolution (LTE) and

LTE-Advanced (LTE-A) initiatives. Texas

Instruments has been a key player in this

standard-making process and has been on

the forefront of incorporating supporting

technology into TI’s KeyStone multicore ar-

chitecture, ensuring availability of LTE-A in

wireless base station System on Chip (SoC).

This paper discusses the major elements

of the LTE-A standard and how it achieves

higher throughput with increased efficien-

cies, as well as how LTE-A is addressed in

TI’s KeyStone multicore SoC architecture.

Embracing LTE-A with KeyStone SoCs

Ralf Bendlin, Ph.D.,Member of Technical Staff

Runhua Chen, Ph.D.,Member of Technical Staff

Anthony Ekpenyong, Ph.D.,Member of Technical Staff

Zhihong Lin,Strategic Marketing Manager

Eko Onggosanusi, Ph.D.,Manager, Senior Member of Technical Staff

Texas Instruments

W H I T E P A P E R

Embracing LTE-A with KeyStone SoCs October 2012

2 Texas Instruments

3GPP started an LTE Advanced feasibility study in release 9. The LTE-A specifi cations are defi ned in releases

10 onward. In addition to backward compatibility with existing LTE networks, LTE-A lays out new requirements

aimed at improving throughput, peak data rate, and spectral effi ciency at reduced cost. The requirements are

defi ned in the following categories:

Capability requirement

The capability requirements defi ne peak data rate, latency and capacity. LTE-A shall support peak data rates

of 1Gbps in the downlink and 500Mbps in the uplink. Control plane (C-Plane) and user plane (U-Plane)

latency shall be signifi cantly decreased from LTE. The C-Plane latency target is less than 10ms for transition

from dormant state to connected state, and less than 50ms for transition into camped state with the user

plane established. User plane latency is further reduced. Control plane capacity shall support at least 300

users per 5-MHz bandwidth.

System performance requirement

System performance requirements are defi ned in terms of peak, average and cell edge spectral effi ciency.

Peak spectral effi ciency is based on perfect channel conditions with all radio resources concentrated into

a single user equipment (UE). The target peak spectral effi ciency of LTE-A is 30bps/Hz in the downlink and

15bps/Hz in the uplink.

Average spectral effi ciency is defi ned as the aggregate throughput of all users in one cell normalized

by the overall cell bandwidth. The average spectral effi ciency is measured in bps/Hz/cell. It is use-case

dependent and LTE-A targets the average spectral effi ciency to be as high as possible due to various channel

conditions.

The cell-edge-user-spectral effi ciency is defi ned as the 5% point of the cumulative distribution function

(CDF) of the user throughput normalized to the overall cell bandwidth. Cell-edge-user throughput is mea-

sured in bps/Hz/cell/user and LTE-A targets the cell edge user throughput to be as high as possible.

Other system performance requirement improvements in LTE-A over LTE include VoIP capacity improve-

ment, up to 10km/h mobility enhancement over LTE and further enhanced Multimedia Broadcast/Multicast

Services (MBMS) for better spectral effi ciency.

Deployment requirement

LTE-A is expected to deploy heterogeneous networks with increased small cell density. It provides spectrum

fl exibility in addition to new spectrum bands and enables operation in wider spectrum with up to 100-MHz

bandwidth possibly aggregated through contiguous or non-contiguous inter- or intra-frequency band carrier

aggregation. Wider bandwidths enable higher peak throughput in LTE-A deployments. LTE-A shall also con-

tinue to support co-existence and interworking with legacy 2G/3G networks as was required for LTE.

LTE-A requirements


3Texas Instruments

To meet those requirements, LTE-A defi nes multiple techniques to achieve the capacity, performance and

deployment goals, so let’s look at those techniques in more detail. (In the following sections, we also use the

term eNodeB to denote an LTE base station entity. User equipment will also be denoted as UE.)

Signifi cant work has been done in several areas of 3GPP release 10 and onward to fulfi ll LTE-A requirements.

Carrier aggregation is aimed at improving spectrum fl exibility to provide wider bandwidths resulting in higher

data throughput and is also useful to reuse legacy spectrum for LTE operation. Higher order and multi-user

MIMO, up to 8×8 in the downlink and 4×4 in the uplink are proposed to increase spectral effi ciency. In 3GPP

release 11, coordinated multi-point (CoMP) is adopted with the goal of improving coverage, enhancing cell

edge throughput and increasing the overall system performance. In addition, enhanced inter-cell interference

coordination (eICIC) is introduced for increased network effi ciency and a better user experience.

Carrier aggregation (CA)

One of the solutions to increase peak data rate is to increase the transmission bandwidth. LTE-A leverages

the carrier aggregation (CA) concept to combine multiple discrete carriers called component carriers (CC)

into a single virtual wide-band carrier, up to fi ve component carriers and up to 100MHz of spectrum can be

aggregated in LTE-A systems.

Each component carrier in carrier aggregation systems can be adjacent or discrete inside one frequency

band with carrier separation to be a multiple of 300KHz; this is called intra-band carrier aggregation. Inter-

band carrier aggregation is also supported in LTE-A where different component carriers can reside in differ-

ent frequency bands. Figure 1 illustrates how carrier aggregation can be constructed through different carrier

frequencies using two carriers as an example.

LTE-A enabling

technologies

Figure 1: Component carrier’s scenarios in carrier aggregation

CC1CC1 CC2CC2

f Band 1Intraband Contiguous CA

CC1CC1 CC2CC2

f Band 1Intraband Non-Contiguous CA

CC1CC1

f Band 1

CC2CC2

f Band 2

Interband Non-Contiguous CA

Carrier separation

Carrier+bandseparation


4 Texas Instruments

LTE-A release 10 is restricted to two component carriers for carrier aggregation, and each CC can be of

1.4-MHz, 3-MHz, 5-MHz, 10-MHz, 15-MHz or 20-MHz bandwidth. The maximum number of aggregated car-

riers may be increased in later releases. Each component carrier is considered as a serving cell with different

coverage area. The primary component carrier constitutes the primary serving cell (PCell) and the rest of CCs

constitute the secondary serving cells (SCell). PCell handles radio resource management (RRM) for the UE,

it cannot be deactivated and is only changed by a handover. The SCells can be confi gured to the same UE

to boost data rate. Independent confi guration of downlink and uplink transmission modes is handled in the

Media Access Control (MAC) and Physical (PHY) layer.

There are many benefi ts to using carrier aggregation in LTE-A systems, e.g., combined and wider spec-

trum to increase the data throughout, to increase the cell coverage, and especially to improve the cell-edge

performance. CA can also mitigate interference in heterogeneous networks where a small cell and a macro

cell are using the same carrier frequency. If a UE experiences too much interference, CA in the macro cell

provides an additional carrier and the macro cell can schedule the UE to a different carrier to mitigate the

interference and improve the user experience.

Carrier aggregation imposes additional complexity in base station designs. CA with two component

carriers can be viewed as transmitting with two RF chains to the same UE. At the MAC layer, a UE can be

confi gured with two data streams each of which is destined for a different RF component in the PHY layer.

Determination of whether a UE should be confi gured for carrier aggregation is done by the scheduler after

assessing the amount of the data received from higher layers for a specifi c UE. The downlink scheduler has

to make sure it can allocate a Physical Downlink Control Channel (PDCCH) for scheduling the Physical Down-

link Shared Channel (PDSCH) for each component carrier and some priority must be given to a user that is

confi gured for CA. At the PHY layer, each CC independently goes through the same PHY procedure as before,

and a new Physical Uplink Control Channel (PUCCH) format 3 signaling is introduced to support downlink CA.

CA complexity can also affect the analog front end, especially with inter-band CA. Typical band separation

can be up to 1GHz, two independent RF data paths for each data converter and power amplifi er (PA) will be

needed and careful synchronization between the two data paths need to be devised to ensure the integrity

of the signal chain. For intra-band CA, if the carrier separation is narrow enough so that it can fi t into the

dynamic frequency range of one analog front end, two CC data paths can be aggregated using a single ana-

log device. In this case, single band CA could reduce the complexity of an eNodeB. Figure 2 on the following

page illustrates the base station data fl ow for downlink carrier aggregation on separate data paths.

Multi-antenna techniques and higher order MIMO

MIMO techniques are widely used in commercial wireless systems as one of the most effective means of

obtaining spectral effi ciency improvements. By creating additional degrees of freedom in the spatial domain,

MIMO signifi cantly improves the signal-to-noise ratio (SNR), radio link robustness and spectrum effi ciency

yielding substantial performance gain over single-antenna technologies. Two categories of MIMO schemes


5Texas Instruments

are supported in LTE: transmit diversity and spatial multiplexing. Transmit diversity sends multiple encoded

copies of a single data stream resulting in improved signal robustness against adverse channel fading and

cell coverage. Spatial multiplexing, on the other hand, leverages channel-dependent beamforming to transmit

multiple streams concurrently, thereby achieving higher data throughput.

Advanced MIMO techniques in release 10 include higher order MIMO (up to 8×8) and multi-user MIMO

(MU-MIMO) beamforming. Increasing the antenna array size to 8 effectively doubles the peak data rate com-

pared to releases 8/9 and is the only solution to meet the ITU-R requirement of 30bps/Hz spectral effi ciency.

Advanced MU-MIMO beamforming allows multiple spatially separated users to be scheduled in the same

spectrum, thereby achieving higher spatial reuse factor and improving the spectral effi ciency. MU-MIMO is

arguably one of the major enhancements in LTE-A that directly results in signifi cant cell-average through-

put gains (e.g., 20–30% in favorable channel condition). This, however, leads to several challenges in the

beamforming and scheduler implementation which often requires cross-layer optimization between the PHY

and MAC layers. Texas Instruments has conducted extensive research in the area of PHY/MAC scheduler

optimization to fully reap the benefi ts of LTE-A MIMO techniques.

Flexible and energy-effi cient operation of wireless networks is increasingly critical for mobile operators.

Toward this goal, LTE-A brings a paradigm shift in facilitating UE-specifi c MIMO beamforming enabled by

newly introduced demodulation reference signals (DMRS) and channel state information reference signals

(CSI-RS). In contrast to release 8 codebook-based (use of a predefi ned precoding look-up table for MIMO

precoding) MIMO transmission with cell-specifi c reference signals (CRS), LTE-A beamforming allows more

fl exibility for the base station with non-codebook-based transmission as enabled by DMRS. The UE-specifi c

and low-density nature of DMRS/CSI-RS allows the eNodeB to dynamically adjust transmission activities (e.g.,

ON and OFF) in accordance with network traffi c variations, making it possible to reduce the overall energy

consumption. In addition, non-codebook-based beamforming with DM-RS allows arbitrary beamforming at

Figure 2: Carrier aggregation with two CC eNodeB downlink data path example

CC2CC2

RRCRRC PDCPPDCP RCLRCL

MACMAC

PHY/Digital

Radio Front End

PHY/Digital

Radio Front End

DAC/IQModDAC/

IQMod

DAC/IQModDAC/

IQMod

PAPA

PAPA

sche

dule

rsc

hedu

ler

HARQHARQ

HARQHARQ

Mul

tiple

xer

CC1


6 Texas Instruments

the eNodeB, which is a prerequisite for advanced MU-MIMO operation. Figure 3 shows the DL SU-MIMO and

MU-MIMO data paths: SU-MIMO increases peak user throughput by dedicating all antenna resources to a

single UE whereas MU-MIMO improves the network spectral effi ciency.

In the uplink, LTE-A adds an enhancement in PUCCH to allow the UE to use two antennas by adopting spe-

cial orthogonal resource transmit diversity (SORTD) in all PUCCH formats. This ensures the orthogonality of

each transmit antenna achieves better SNR at the receive antenna with the combined signal. Uplink transmit

diversity can reduce transmit power at the UE to meet the eNodeB PUCCH SNR requirement and it can lower

the UL inter-cell or intra-cell interference and prolong UE battery life.

Early LTE releases supported a single transmit antenna at the UE. Accordingly, UL spectral effi ciency can

be increased by MU-MIMO whereby two users are assigned the same UL frequency allocation. To reduce

mutual interference, the eNodeB scheduler may select a pair of users whose channels are mutually orthogo-

nal or near-orthogonal. LTE-A further enhances UL spectral effi ciency by introducing support of two or four

transmit antennas at the UE making possible up to 4×4 SU-MIMO transmission from a single UE. Another

key enhancement of LTE-A is the scheduling fl exibility now available to the eNodeB. For example, the eNodeB

can schedule a single UE for 4-layer SU-MIMO transmission, or it can operate in MU-MIMO by scheduling

two UEs each with two-layer transmission as shown in Figure 4 on the following page.

To facilitate UL MIMO transmission, LTE-A also introduces a time-domain spreading technique across the

UL demodulation reference signals (UL DMRS) known as orthogonal cover codes. This feature allows pairing

of users with unequal UL allocations and is also used to ensure orthogonal DMRS across different antennas.

Coordinated multi-point (CoMP)

Cells in conventional wireless networks operate in a semi-independent manner, where downlink transmission

and uplink reception are performed in each cell independently. Consequently, severe inter-cell interference

Figure 3: Downlink SU-MIMO and MU-MIMO

UE1UE1

UE1

UE2UE2

DL SU-MIMO

DL MU-MIMO

Sche

dule

rSc

hedu

ler Mux

Channel coding

Modulation

MuxChannel coding

Modulation

Prec

odin

gPr

ecod

ing

UE1Data

CW1

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eNodeB

Sche

dule

rSc

hedu

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Channel coding

Modulation

MuxChannel coding

Modulation

Prec

odin

gPr

ecod

ingUE1 CW1

CW2

eNodeB

UE2Data

1

8

1

8


7Texas Instruments

Figure 4: Uplink SU-MIMO and MU-MIMO examples

UE1UE1

UE1

UE2UE2

UL SU-MIMO

UL MU-MIMOSc

hedu

ler

Sche

dule

r DeMuxChannel DecodingDeMod

DeMuxChannel DecodingDeMod M

IMO

Rece

iver

MIM

O Re

ceiv

erUE1Data

CW1

CW2

eNodeB

Sche

dule

rSc

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Channel DecodingDeMod

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IMO

Rece

iver

MIM

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erUE1 CW1

CW2

eNodeB

UE2Data

1

4

1

4

may become a fundamental bottleneck particularly for cell edge users. This problem will be increasingly

severe if there is a rapid proliferation of small cells without interference management.

CoMP is an LTE release 11 feature that allows multiple geographically distributed points (e.g., eNodeBs

or cells) to optimize their transmission/reception activities in a coordinated fashion, so that strong inter-cell

interference can be avoided or even transformed into signals that a UE can decode to improve coverage. By

leveraging multiple cells or remote radio heads (RRH) in either homogeneous and heterogeneous networks,

joint transmission and reception to and from a single UE enable a more fl exible network topology and optimi-

zation, mitigate multi-cell interference, enable dynamic load balancing, allow UEs to select the closest base

station point for transmission and reception, which in turn will reduce the UE power consumption resulting in

increased battery life in addition to a boost in data throughput and user experience.

More importantly, CoMP allows radio resources of different cells to be centralized and adaptively allocated

to better handle the traffi c fl uctuations in each eNodeB, thereby achieving increased resource utilization

across the entire network. For instance, a cell with low traffi c loading may hand over its users to a neighbor-

ing cell and be turned OFF to reduce inter-cell interference and improve the overall system performance. Dy-

namic radio resource management like this is one of the primary motivations for C-RAN (Cloud/Centralized/

Clean/Coordinated Radio Area Network) and is critical for future high-throughput and low-energy network

operation. It also facilitates improved network architectures, e.g., distributed antenna systems (DAS) with

geographically separated RRHs which are connected to a centralized baseband processor via optical fi ber.

Clearly, effi cient PHY/MAC scheduler designs must be considered to realize these benefi ts without incurring

prohibitive network processing complexity.

The CoMP participants are grouped into CoMP sets. Such grouping is performed in homogeneous networks

featuring a single eNodeB with multiple sectors or multiple high-transmit power RRH, and similarly in hetero-

geneous networks where macro cells and small cells are deployed either with the same or different cell IDs.


8 Texas Instruments

There are two main CoMP schemes, joint processing and coordinated scheduling/beamforming. Joint

processing (JP) includes joint transmission (JT) and joint reception (JR) which enable simultaneous data

transmission and reception from multiple points within a CoMP set to a single or multiple UEs, respectively.

Dynamic point selection/blanking (DPS/DPB), as part of joint processing, requires PDSCH data availability at

multiple points as well, but the data are only transmitted from one point at any given time to mitigate interfer-

ence and achieve the best performance. Figure 5 shows a CoMP set in a heterogeneous network where a

macro cell and one small cell perform joint transmission and reception, UE receive valid signals from both

eNodeBs, where in a non-CoMP network, one of the eNodeB transmissions would result in interference to

the UE.

When user data is only transmitted from one point, coordinated scheduling/beamforming (CS/CB) can

be used to improve performance by coordinating frequency assignments and beamforming vectors across

transmission points.

Heterogeneous networks create new challenges in uplink interference handling. New cell boundaries are

created by small cells within the macro area and higher uplink interference is seen due to the smaller path

loss between a UE and a victim cell. Interference randomization using cell IDs is insuffi cient when small cells

and the macro cell share the same cell ID because PUSCH/PUCCH capacity is limited. CoMP introduces the

virtual cell IDs (VCID) for PUSCH and PUCCH to increase the degrees of freedom for the uplink-cell-ID-based

randomization resulting in improved inter-cell orthogonality and reduced interference.

CoMP introduces new dimensions in scheduling complexity. The scheduler needs to be coordinated across

several points, e.g., user data and scheduling decisions need to be shared, and CoMP across different

geographically dispersed eNodeBs also requires additional backhaul capacity to carry the CoMP data. Link

adaptation and scheduling are more complicated especially when multiple CoMP schemes are combined.

Simulation results show that leveraging CoMP can improve cell edge spectral effi ciency by 20–25%, while

only marginal gain is seen in cell average throughput. The following information, as well as Figure 6 on the

following page, show cell average and cell edge performance comparisons for the following scenarios (the

performance gain is depending on the network topology and traffi c patterns):

Figure 5: CoMP scheme: joint transmission and reception

CoMP Set

Signal

Signal


• Homogenous macro – 57 macro eNodeBs, 4 Tx antennas, 30 users per macro

• Heterogeneous uniform: non-CoMP – same homogenous macro cells overlaid with four small cells

per macro, uniform user distribution, without CoMP

• Heterogeneous uniform: CoMP – same heterogeneous network with CoMP enabled

• Heterogeneous cluster: non-CoMP – heterogeneous network with clustered user distribution, simu-

lating hot spot situation, without CoMP

• Heterogeneous cluster: CoMP – heterogeneous network with clustered user distribution with CoMP

enabled

9Texas Instruments

Figure 6: Cell average and cell edge throughput comparison with and without CoMP

Cell-average gain 5% cell-edge gain

10

0.5

1

1.5

2

2.5

3

3.5

4

cell

aver

age

spec

ial e

ffici

ency

(bps

/Hz)

homogenous macroheterogenous uniform: non-CoMPheterogenous uniform: CoMPheterogenous cluster: non-CoMPheterogenous cluster: CoMP

10

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

5%-p

erce

ntile

cel

l-edg

e sp

ecia

l effi

cien

cy (b

ps/H

z)

homogenous macroheterogenous uniform: non-CoMPheterogenous uniform: CoMPheterogenous cluster: non-CoMPheterogenous cluster: CoMP

Figure 7: HetNet load balancing through Cell Range Expansion (CRE)

Macro eNodeBPico eNodeB

UE connects to macro eNodeB without CRE and to pico eNodeB with CREUE does not connect

to strongest cell with CRECRE

Cell Range Expansion(CRE)

Enhanced inter-cell interference coordination (eICIC)

Over the course of a day, the load in the network fl uctuates as users move around the network. Residential

areas see most of the activity during the evening hours when people are home from work whereas during

business hours peak traffi c occurs in offi ces, schools and downtown areas. On the weekend, users don’t go

to offi ce parks but to malls, sport events, and bars or restaurants. These dynamics in user behavior render

static network planning sub-optimal. One way to dynamically partition resources is by means of cell range

expansion (CRE). CRE uses cell-specifi c offsets called CRE bias to favor one layer of eNodeB over another. For

instance, as depicted in Figure 7 below, small cells could be favored during business hours when users are


10 Texas Instruments

at work or school whereas the macro layer could be favored during evening hours when users are at home.

This way, both CAPEX and OPEX can be reduced for the operator and user experience can be enhanced for

the end-consumer.

When no bias is confi gured, one in three users is connected to a macro base station. However, less than

one in fi ve users connects to the macro layer when the CRE bias exceeds 6dB or more. This is shown in

Figure 8.

Unfortunately, there is no free lunch and consequently, user offl oading by means of CRE increases the

interference seen by the offl oaded users connected to the small cells. To mitigate this inter-cell interference,

LTE-A introduces a time-domain inter-cell interference coordination feature called almost blank subframes

(ABS). This feature is often referred to as enhanced ICIC or eICIC. ABS creates protected resources, namely

subframes during which the small cell layer experiences signifi cantly reduced inter-cell interference. The

small cell layer can take advantage of this by scheduling cell edge users during this ABS whereas only cell

interior users are scheduled in normal subframes. The macro layer, on the other hand, schedules no or only

cell interior users during protected subframes and cell-edge users otherwise. eICIC requires tight synchroni-

zation and coordination among eNodeBs due to its time-domain nature. Since the CRE bias is user specifi c,

the network can use even more aggressive biases for advanced UEs which have interference canceling

receivers. These can tolerate a higher level of inter-cell interference resulting from larger CRE biases for they

are still able to detect and communicate with small cells under such extreme confi gurations.

Since CRE and ABS are adaptive and dynamic they can be thought of as an aspect of self-organizing

networks (SON). In particular, the eNodeB provides radio resource management functions which allow for

optimization of both the CRE bias as well as the ratio of protected ABS resources. Thus, resources can be

optimally tailored towards the current load in the network resulting in more energy- and spectral-effi cient

operation as well as lowered costs for the operator. eICIC can also be thought of as a complement to CoMP

as both aim towards improving the throughput at the edge of a cell. CoMP tries to harness the fact that cell

edge users receive multiple strong signals from several eNodeBs whereas eICIC tries to avoid dominant

Figure 8: Percentage of small cell user offl oad using different CRE bias


11Texas Instruments

interference from nearby neighboring cells. This gives the network operator ample control and choice to opti-

mize heterogeneous networks where small cells increase the number of cell-edge users due to the introduc-

tion of additional cells.

Downlink and uplink signaling enhancement

There is a general trend in LTE-A to move from cell-specifi c signals and channels to user-specifi c ones.

The reasons for this change are crucial to advancing LTE towards a more effi cient and thus greener mobile

communications standard while meeting the demand for improvements in throughput, spectral effi ciency, and

end-user experience that is expected from LTE-A. Cell-specifi c signals and channels are transmitted across

the entire system bandwidth in each physical resource block (PRB). In addition, they are present in each sub-

frame. Being cell specifi c, they are static and suboptimal for modern dense heterogeneous network deploy-

ments which strive for utmost energy and spectral effi ciency.

In LTE, cell-specifi c reference signals (CRS) are used, amongst other things, for estimating the channel

from the eNodeB to the UE for demodulation as well as CSI feedback. CRS is un-precoded, an important fea-

ture since all UEs in a cell use the same CRS irrespective of their confi gured transmission mode, speed, loca-

tion, throughput requirements and so forth. Consequently, CRS-based MIMO operation requires codebook-

based precoding because the UEs use unprecoded reference signals to estimate the channel. In addition,

CRS is tied to the Physical Cell ID (PCI) of the transmitting cell which imposes undesired restrictions to the

operation of certain deployment scenarios in heterogeneous networks with dense small cell eNodeBs. Other

potentially detrimental characteristics of CRS in dense small cell deployments are that they create interfer-

ence to neighboring cells and that they consume a lot more energy because they are transmitted across the

entire system bandwidth in all sub-frames.

Some of these properties have been addressed in LTE-A with the introduction of user-specifi c reference

signals (RS), namely, CSI-RS and demodulation RS (DMRS). As the name suggests, CSI-RS is used by the

UE to estimate the channel for CSI feedback to the serving cell. Signaling overhead and energy consumption

can be signifi cantly reduced, e.g., by confi guring larger reporting intervals for low mobility UEs and shorter

ones for high-speed UEs, respectively. Consequently, CSI-RS are not present in each sub-frame as they were

with legacy CRS, resulting in reduced overhead and greater effi ciency. CSI-RS is also required to support the

advances LTE-A brings forward relating to MIMO and CoMP as mentioned before.

Similarly, DMRS can be used by the UE to estimate the channel for demodulation purposes. In contrast

to CSI-RS and CRS, DMRS is pre-coded and only present in those PRBs actually used to transmit data to a

given UE. This not only further increases the effi ciency of the system’s energy consumption and signaling

overhead, but more importantly, allows for non-codebook-based precoding which is desirable, for instance, in

TDD deployments to piggyback channel reciprocity.

By making reference signals user-specifi c, they can also be decoupled from the PCI, an important feature

for several CoMP operation scenarios both in the downlink and uplink. By confi guring “virtual” cell IDs per



user, the network can choose the optimal cell in a semi-static or even dynamic fashion for greater energy

effi ciency and increased user throughput. These enhancements especially favor cell-edge users who might

receive signals of almost equal strength from more than one eNodeB. In addition, utmost care was taken in

the design of these new features to facilitate seamless inter-operability with legacy UEs, for example to reap

MU-MIMO, cell splitting, or diversity gains through interference averaging or avoidance.

Similar considerations have led to the standardization of an enhanced Physical Downlink Control Chan-

nel (ePDCCH) which has been cell specifi c in LTE, and was similarly transmitted across the entire system

bandwidth. The ePDCCH is confi gurable for transmission in a subset of PRBs allowing for coordination among

eNodeBs to reduce inter-cell interference. Borrowing from the design of the LTE-shared data channel, the

ePDCCH harnesses multi-user diversity and beamforming gains to increase robustness and system perfor-

mance. The ePDCCH extends LTE as it develops into even more energy-effi cient deployments and supports

the evolution of many features envisioned beyond LTE-A.

KeyStone II is the second generation of Texas Instruments’ multicore SoC architecture. It is optimized to

deliver high throughput and low latency as well as well as scalability and fl exibility to support LTE-A. Figure 9

shows the KeyStone II architecture.

At the heart of the KeyStone II architecture are the SoC infrastructure elements, Multicore Navigator and

TeraNet. Multicore Navigator provides architecture parallelism with a unifi ed interface for cores, accelerators

and I/O using hardware queues and packet Direct Memory Access (DMA) for communication, data transfer

and task management. TeraNet is the non-blocking SoC interconnect that provides high throughput to make

possible the high data rates required in LTE-A. The KeyStone II SoC infrastructure offers highly scalable

LTE-A system

solutions with

KeyStone II SoCs

Figure 9: KeyStone II architecture

Tera

Net

Multicore Navigator

EMIF I2C, UART JESD204B Hyperlink Ethernet Ethernet Ethernet EthernetSPI PCIe CPRI/OBSAI

Power Mgr SysMon

Debug EDMA

Packet and Security

Acceleration

Layer 1 Acceleration

Radio processing

Ethernet Switch

USB 3 SRIO

ARM CoresARM CoresARM Cores

DDR3 L

64/72b

DDR3 L

64/72b

Multicore Shared Memory Controller Shared L3

ARM shared L2

=== ++

--

+

-

++ **-- <<<< DSP CoresDSP Cores

+ *- << DSP Cores

DSP L2

Digital Radio Front End

DUC/DDC/CFR/DPD


13Texas Instruments

LTE solutions from small cells to macro cells and cloud RAN eNodeBs with lowest communication latency.

Multicore Navigator also enables hardware virtualization and facilitates load balancing in advanced network

deployments.

The KeyStone II architecture also features Layer 1 radio acceleration to enable cost- and energy-effi cient

Layer 1 processing. A Bit Rate Coprocessor (BCP) that is incorporated into the Layer 1 accelerationPac al-

ready has many LTE-A features built in to offl oad cycle-intensive bit-level operations from the DSP cores. DL

and UL shared-channel processing tasks including rate matching, scrambling, interleaving, Hybrid automatic

repeat request (HARQ) combining and Log-likelihood ratio (LLR) computations are all performed by the BCP

hardware.

A primary use case that showcases the fast and effi cient bit-level processing offered by the BCP is in itera-

tive processing that may be required for UL SU and MU-MIMO in LTE and LTE-A. For LTE UL MIMO process-

ing, DFT-spread transmission in Single Carrier Frequency Division Multiple Access (SC-FDMA) results in pro-

hibitive complexity for maximum likelihood-based receiver algorithms due to the DFT spreading. Alternatively,

iterative processing solutions, based on the turbo equalization concept, offer satisfactory performance with

reasonable complexity. The BCP offl oads iterative processing in the form of parallel interference cancellation

(PIC) and successive interference cancellation (SIC) techniques.

When applied to UL MIMO – both SU-MIMO and MU-MIMO in LTE-A – SIC cancels out the reconstructed

signal corresponding to a selected MIMO user from the original received signal. This effectively cancels

out the interference experienced by the other users due to the fi rst user. The “cleaner” residual signal after

cancellation is then further processed to successively decode the other MIMO users. Multiple stages of re-

construction and cancellation are also possible to improve signal detection. A similar concept is used for PIC,

where cancellation takes places simultaneously on all users in one stage.

The BCP also supports an advanced PUCCH receiver that shows 1–2 dB gain over a conventional MRC

receiver. The processing complexity associated with an advanced receiver is offl oaded to the BCP and can

be used to improve performance for PUCCH formats with large codebooks such as PUCCH Format 2 and the

new LTE-A PUCCH Format 3.

LTE-A introduces new implementation complexity especially in the MAC layer scheduler for many of its new

features such as carrier aggregation, CoMP and eICIC. The KeyStone II architecture with heterogeneous low-

power and high-performance DSP and ARM® cores, together with the Multicore Shared Memory Controller

(MSMC), provides low-latency access to DDR3 external memory yielding an excellent low-latency computa-

tion platform for LTE-A’s performance needs.

With powerful I/O connectivity, KeyStone II SoCs are the ideal solution for many different network topolo-

gies and scalable network deployments. The antenna interface supports CPRI 5.0 with 9.8Gbps per lane and

built-in antenna switching capabilities. It enables eight LTE 20-MHz channels per lane and allows fl exible

solutions for LTE-A carrier aggregation and higher-order MIMO with distributed remote radio heads. Gigabit

Ethernet with a built-in Ethernet switch reduces the overall system processing latency for LTE-A transport

Important Notice: The products and services of Texas Instruments Incorporated and its subsidiaries described herein are sold subject to TI’s standard terms and conditions of sale. Customers are advised to obtain the most current and complete information about TI products and services before placing orders. TI assumes no liability for applications assistance, customer’s applications or product designs, software performance, or infringement of patents. The publication of information regarding any other company’s products or services does not constitute TI’s approval, warranty or endorsement thereof.

SPRY218© 2012 Texas Instruments Incorporated


data processing. 50-Gbps HyperLink provides a low latency and transparent inter SoC connection so com-

plicated software protocols are unnecessary. HyperLink also enables scalable macro to cloud RAN solutions,

while JESD204B provides direct data converter interfaces enabling low-power and low-cost small-cell solu-

tions. With optimized DSP and ARM® processing horsepower, KeyStone SoCs enable lowest power-per-bit to

process all of the new LTE-A features for macro and small cells. Figure 10 depicts some exemplary macro

and cloud RAN solutions implemented with KeyStone II SoCs.

With the promise of better coverage, much higher throughput and lower latency, the LTE-Advanced standard

is poised to deliver a vastly improved mobile user experience. TI’s KeyStone II multicore architecture provides

highly integrated Layer 1, 2, 3 and transport SoC solutions that unleash the abundant throughput and spec-

tral effi ciency offered through various LTE-A features. In addition, Keystone II boasts the lowest processing

latency for low-cost and energy-effi cient LTE-A solutions. With a fl exible architecture, effi cient hardware ac-

celerators, a superior SoC infrastructure, high-speed I/O and programmable low-power DSP and ARM cores,

KeyStone II is the ideal architecture to drive high-performance and scalable heterogeneous networks and to

unlock the potential of LTE-A.

Figure 10: Building LTE-A enabled macro or Cloud RAN eNodeBs with the KeyStone II architecture

KeyStone II

SoC

KeyStone II

SoC

KeyStone II

SoC

KeyStone II

SoC

KeyStone II

SoC

KeyStone II

SoC

CPRICPRI

Ethernet Ethernet

HyperLink HyperLink

CPRI over Fiber

Remote Radio Head

Macro or Cloud RAN eNodeB, Layer 1, 2+

Integrated on-chip Ethernet and antenna switching

RRH

Conclusion

IMPORTANT NOTICE

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embracing lte-a with keystone socs

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