Multimedia Telephony (MMTel) in 3GPP Rel-7

Came across MMTel multiple times in the last few months so decided to dig a bit more in detail.

The following from wikipedia:

The 3GPP/NGN IMS Multimedia Telephony Service (MMTel) is a global standard based on the IP Multimedia Subsystem (IMS), offering converged, fixed and mobile real-time multimedia communication using the media capabilities such as voice, real-time video, text, file transfer and sharing of pictures, audio and video clips. With MMTel, users have the capability to add and drop media during a session. You can start with chat, add voice (for instance Mobile VoIP), add another caller, add video, share media and transfer files, and drop any of these without losing or having to end the session.

The MMTel standard is a joint project between the 3GPP and ETSI/TISPAN standardization bodies. The MMTel standard is today the only global standard that defines an evolved telephony service that enables real-time multimedia communication with the characteristics of a telephony service over both fixed broadband, fixed narrowband and mobile access types. MMTel also provides a standardized Network-to-Network Interface (NNI). This allow operators to interconnect their networks which in turn enables users belonging to different operators to communicate with each other, using the full set of media capabilities and supplementary services defined within the MMTel service definition.

One of the main differences with the MMTel standard is that, in contrast of legacy circuit switched telephony services, IP transport is used over the mobile access. This means that the mobile access technologies that are in main focus for MMTel are access types such as High Speed Packet Access (HSPA), 3GPP Long Term Evolution (LTE) and EDGE Evolution that all are developed with efficient IP transport in mind.

MMTel allows a single SIP session to control virtually all MMTel supplementary services and MMTel media. All available media components can easily be accessed or activated within the session. Employing a single session for all media parts means that no additional sessions need to be set up to activate video, to add new users, or to start transferring a file. Even though it is possible to manage single-session user scenarios with several sessions – for instance, using a circuit-switched voice service that is complemented with a packet-switched video session, a messaging service or both – there are some concrete benefits to MMTel’s single-session approach. A single SIP session in an all-IP environment benefits conferencing; in particular, lip synchronization, which is quite complex when the voice part is carried over a circuit-switched service and the video part is carried over a packet-switched service. In fixed-mobile convergence scenarios, the single-session approach enables all media parts of the multimedia communication solution to interoperate.

An interesting presentation on MMTel is embedded below.

3GPP MMTel General Overview

View more presentations from Zahid Ghadialy

If you are still hungry for more on this topic then Ericsson's old presentation on MMTel is available on Slideshare here.

Smart Grids (again)

I blogged about smart grids just the other day but they seem to be the 'in thing' and keep popping up everywhere.

The very interesting picture above is from The Guardian article here, that promises that consumers will be able to cut down on their bills by taking advantage of smart meters.

Meanwhile European Commission is making Smart Grids a high priority. The following is from one of their communique:

The European Commission presented its Communication on smart grids. It sets policy directions to drive forward the deployment of future European electricity networks. Bringing together latest progress in Information and Communication technologies and network development will allow electricity current to flow exactly where and when it is needed at the cheapest cost. Smart grids will give in particular to consumers the ability to follow their actual electricity consumption in real time : smart meters will give consumers strong incentives to save energy and money. Estimates show that smart electricity grids should reduce CO2 emissions in the EU by 9% and the annual household energy consumption by 10%. They also help to ensure secure functioning of the electricity system and are a key enabler of both the internal energy market and integration of vast amounts of renewable.

You can read the complete press summary here. A new report entitled 'Smart Grids: from innovation to deployment' is available to download from here. The European Commission Smart Grids taskforce webpage is here.

The following is from IEEE Spectrum :

On 17 March, game designers at the Institute for the Future, in collaboration with us at IEEE Spectrum, ran a 24-hour forecasting game called Smart Grid 2025. Weenlisted the help of listeners like you and game players around the world to brainstorm solutions to the problems the smart grid will face. That way, by 2025—when all our homes have smart meters and utilities are linking up wind farms and solar plants to national grids—it'll be running as smoothly as it possibly can.

Steven Cherry's guest is Jake Dunagan, the game's project leader at the Institute for the Future in Palo Alto, Calif. He was on this show in early March in advance of the Smart Grid 2025 game to talk about how it would work, and now he's back to tell how it went.

This interview was recorded 4 April 2011. (Listen below)

Background on Smart Grids from the same IEEE article: One of the hottest topics in engineering is the smart grid—the idea of adding computer intelligence to a nation's basic electrical grid. The goal is to transport and use energy more efficiently in the grid itself—and also in your home. By adding intelligence to our electrical meters, fuse boxes, even our home appliances, each of us can use electricity more wisely and consume less of it.

But it's still early days for smart grid deployment. In fact, today, the smart grid still raises more questions than it answers—questions like, who will profit from the smart grid? How do we keep the smart grid from knowing too much about our personal lives? Is the smart grid dangerously hackable? Will the smart grid force you to do your laundry at night? Will the smart grid make us healthier? What kind of appliances are needed to accommodate the smart grid?

Feel free to add your thoughts in the comments.

User Data Convergence (UDC) in Release 9 and its evolution

The below is mish-mash from the specs (see refrences at the end)

With the increase of service entities and the resulting user data types, User Data Convergence (UDC) is required to ensure the consistency of storage and data models.

UDC:

• simplifies the overall network topology and interfaces

• overcomes the data capacity bottleneck of a single entry point

• avoids data duplication and inconsistency

• reduces CAPEX and OPEX.

UDC simplifies creation of new services and facilitates service development and deployment though a common set of user data.

UDC promotes service and network convergence to support the increasing number of new services including Internet services and UE applications. A new facility User Data Repository (UDR) is considered for UDC.

In UDC, all the user data is stored in a single UDR allowing access from core and service network entities.

To achieve high performance, reliability, security and scalability, the UDR entity may consist of a network of different components distributed geographically, and exposes capabilities via open interfaces in multiple access entry points.

In the current 3GPP system, user data are scattered in several domains (e.g. CS, PS, IMS) and different network entities (e.g. HLR, HSS, Application Servers). With the increase of user data entities and the resulting data types, it is more difficult for integrated services to access necessary user information from plural entities.

The scenario mentioned herein is kind of called “User Data Silo”, which is the major paradigm of user data deployment for the time being, as illustrated by Fig.1. below

With the user data silos, user data are independently accessed, stored and managed independently. That brings many challenges to network deployment and evolution. Different user data access interfaces impose complexity on network topology as well as on application development, especially for booming Internet services and incoming IP-based UE applications; separated user data increases management workload. Moreover, new networks and services such as IMS are expected, so that the introduction of their user data only makes things worse, not to mention network and service convergence even if those user data have a lot in common and are correlated to each other. Separation also undermines the value of user data mining.

User data convergence is required to ensure the consistency of storage and data models. User data convergence will simplify overall network topology and interfaces, overcome the data capacity bottleneck of a single entry point, avoid data duplication and inconsistency and reduce CAPEX and OPEX. Also it will simplify the creation of new services and facilitate service development and deployment though a common set of user data. Finally it will promote service and network convergence to support the increasing number of new services including Internet services and UE applications. In this regard, a new facility User Data Repository (UDR) should be considered for user data convergence.

As illustrated by Fig. 2 above, User Data Convergence, as opposed to User Data Silo, is simply to move the user data from where it belonged, to a facility here called User Data Repository (UDR) where it can be accessed, stored and managed in a common way. Despite of the diversity of user data structures for different services, user data can be decomposed and reformed by a common data model framework (e.g. tree-like data model, rational data model) provided by UDR. In that case, user data categorized by services can be regrouped and identified by user ID, leaving no data redundancy. Also, convergence in data model will unify the user data access interface and its protocol, which will promote new service application development. Thereby, the capability of user data convergence can be open to creation of data-less applications.

There are plenty of data distributed in the 3GPP system which is used to perform the services, for instance, the configuration data of a network entity, the session data of a multimedia call, the IP address of a terminal, etc. With respect to user data, it refers to all kinds of the information related to users who make use of the services provided by the 3GPP system.

In 3GPP system, user data is spread widely through the different entities (e.g. HLR, HSS, VLR, Application servers) and also the type of user data is various. It is of paramount importance to categorize the user data before going through the convergence of user data.

The UDC shall support multiple application user data simultaneously, e.g. HSS and others.

Any application can retrieve data from the UDC and store data in it. The applications shall be responsible of updating the UDC with the dynamic changes of the user profile due to traffic reasons (e.g. user status, user location…) or as a consequence of subscriber procedures.

User Subscription Data: Before a user can enjoy a service, he may need to subscribe the service first. The subscription data relates to the necessary information the mobile system ought to know to perform the service. User identities (e.g. MSISDN, IMSI, IMPU, IMPI), service data (e.g. service profile in IMS) , and transparent data (data stored by Application Servers for service execution) are the examples of the subscription data. This kind of user data has a lifetime as long as the user is permitted to use the service and may be modified during the lifetime. User may be accessed and configured via various means, e.g. customer service, web interface, UE Presence service. The subscription data is composed of different types such as authentication data, configuration data, etc. Different type of data may require different levels of security.

User content Data: Some applications may have to store content defined by the user and that here may be quite large (e.g. Photos, videos) User content data can reach very high volume (e.g. Hundreds of Mbytes and more), and the size required to store them may largely vary over time. They generally do not require the real time constraints as user profile data may require. Storage of user data content is not typically subject of UDR. Storage of user data content is not typically subject of UDR. UDC on user content data can be achieved by converging them with links or references, such as URLs, to other entity.

User Behaviour Data: Such data concerns the usage of services by a user as services are consumed. Generally there are event data records that can be generated on various events in the usage of services by a user and that can be used not only for charging or billing purposes but e.g. for user profiling regarding user behaviour and habits, and that can be valuable for marketing purposes. The amount of such data is also quite different from other categories, they present a cumulative effect as such data can be continuously generated by the network implying a need for corresponding storage. Usage data may require real time aspects about their collection (e.g. for on line charging), they are also often characterized by a high amount of back office processing (e.g. Billing, user profiling). Processing of user behaviour data such as for CRM, billing, data mining is not typically subject of UDR. Those might be processed with lower priority or by external systems whereby UDR supports mass data transfer.

User Status Data: This kind of user data contains call-related or session-related dynamic data (e.g. MSRN, P-TMSI), which are typically stored in VLR or SGSN. These dynamic data are only used by their owner transitorily and proprietarily, and hardly shared by other services in the short term.

Figure 4.1-1 below presents the reference UDC architecture. UDC is the logical representation of the layered architecture that separates the user data from the application logic, so that user data is stored in a logically unique repository allowing access from entities handling an application logic, hereby named Application Front Ends.

In the architecture, the User Data Repository (UDR) is a functional entity that acts as a single logical repository of user data and is unique from Application Front End’s perspective. Entities which do not store user data and that need to access user data stored in the UDR are collectively known as application front ends.

NOTE: Depending on the different network deployment, there may be more than one UDC in an operator’s network.

Application Front Ends (FE) connect to the UDR through the reference point named Ud to access user data.

Figure 4.1-2 shows how the UDC reference architecture is related to the overall network architecture by comparing a Non-UDC Network with an UDC Network. In the non-UDC Network, the figure shows NEs with their own database storing persistent user data and a NE accessing an external database; in both cases, when UDC architecture is applied, the persistent user data are moved to the UDR and concerned NEs are becoming Application-FEs (NE-FEs) according to the UDC architecture. This figure also shows that network interfaces between NEs are not impacted .A Network Element (NE), which in its original form represents application logic with persistent data storage, when the UDC architecture is applied, may become a NE Front End, since the related persistent data storage is moved to the UDR.

3GPP TS 23.335 gives more details and information flows for User Data Convergence

Further evolution of UDC is being studied part of 3GPP TR 23.845. The TR tries to address the assumption of multiple UDRs in a PLMN, to identify consequences and the possible impacts on existing UDC specifications. From a practical point of view, even if the aim is to have one single logical repository, a certain number of considerations may drive to have more than one UDR in a PLMN.

For very large networks with a very large amount of users, although an UDR may be implemented in a distributed architecture and multiple database servers with geographical distribution and geographical redundancy, an operator may consider to deploy several UDRs between which it will distribute the users.

More details in the technical report.

References:

3GPP TR 22.985: Service requirement for the User Data Convergence (UDC) (Release 9)

3GPP TS 23.335: User Data Convergence (UDC); Technical realization and information flows; Stage 2 (Release 10)

3GPP TS 29.335: User Data Convergence (UDC); User Data Repository Access Protocol over the Ud interface; Stage 3 (Release 10)

3GPP TS 32.182: User Data Convergence (UDC); Common Baseline Information Model (CBIM) (Release 10)

3GPP TR 32.901: Study on User Data Convergence (UDC) information model handling and provisioning: Example Use Cases (Release 11)

3GPP TR 29.935: Study on UDC Data Model.

3GPP TR 23.845: Study on User Data Convergence (UDC) evolution (Release 10)

LTE World Summit 2011 promising to be bigger than ever

The LTE World Summit next month, promises to be a bigger and better event than ever before. Long gone are the days when LTE used to be compared with WiMax and debated about but since then LTE has become an undisputed winner; the standard of choice.

Surprisingly there is a WiMAX & TDD Networks conference running in parallel at the same venue. Malaysia's 'Packet One' Network, which is a WiMax based network, recently decided that it will convert to TD-LTE network, probably by end of 2012. This is a blow for the WiMax standard.

Last year in Dec. Verizon launched LTE and Telia Sonera marked its first anniversary. Surely there would be lots of lessons and advice for new networks. Then there is going to be NTT Docomo, always willing to share its knowledge, technical know how and future research direction.

There are going to be four parallel tracks this time which will allow a lot of diverse topics to be covered in the shortest amount of time but it also means that some of us who would like to attend a lot of different presentations would lose out on some of these interesting presentations. Fortunately there are going to be lots of analysts and critics who are going to hopefully do justice to the conference and the presentations in blogs and tweets.

Along with the standard tracks that involve Business Models, Policy & Strategies, Spectrum, etc. there is also a track on Hetrogeneous Network Management (inc. Femtocells or as they are called now, Small cells).

There are also Breakfast briefing sessions from analysts. I would be presenting one such on the first day so please feel free to join me or any of the other analysts. I would ofcourse be blogging about the event hopefully in the same amount of detail as I always do. There will also be plenty of tweets sso be tuned to this event.

Cognitive radio – the way out of spectrum crunch?

Another presentation from the Cambridge Wireless Event on Avoiding Cellular Gridlock. One of the ways suggested in the discussions with regards to the 'Geo-location database' (see slide 12) is that they could also be done using Smart Grids. Though it sounds simple in theory, practically we may never see that happen and that would not be due to any technical reasons.

Cognitive radio – the way out of spectrum crunch?

View more presentations from Zahid Ghadialy

Nice presentation on GPS, Geotagging and its Future

Nice presentation on GPS, Geotagging and its Future

View more presentations from Zahid Ghadialy

Mobile Phone Antennas and Networks

We all remember the so called 'Antennagate' where the iPhone 4 loses coverage due to the way its held. As can be seen from the above picture, there are a lot of antennas already in the phones and yes they are on the increase with LTE and other technologies being added all the time.

Apple admitted the fault and claimed to have fixed the problem but its well known in technical circles that the fix is more of a software hack which doesn't really fix the problem just pretends to fix it. That is why the networks dread it and you can find awful lot of information on the web about the problems.

In a recent Cambridge Wireless event, I heard an interesting talk from Trevor Gill of Vodafone and one of the slides that caught my attention was the impact of these poorly designed phones on the network. The slide is embedded below.

It is estimated that the RF performance of iPhone4 is around 6dB worse than most other 3G phones. What this means is that you may be getting 4 bars of reception on your other phone where iPhone4 may be having only 1 or 2 bars or reception. So if the reception is poor with 1 or 2 bars, iPhone4 may have no reception at all.

To fix this problem, either the networks can increase the number of base stations to double the existing amount which is a huge cost to the networks and extra radiation or the phones can fix it themseles by having an extra antenna. In fact as the slide says, extra antenna on each phone would translate to increase in network capacity by 20-40%, cell area by 30% and cell edge throughput by 40-75%.

One final thing that I want to mention is that testing (RF, RRM, Conformance, etc.) are mandated by the networks for most phones but they overlook the testing procedure for phones like iPhone. What this means is that they do get a lot more new customers but they get new sets of problems. If these problems are not handled well, the impression they give is that the particular network is rubbish. Another thing is that the devices use a certain build/prototype for testing but the one that they release may contain other patches that can cause chaos. One such problem was Fast Dormancy problem that I have blogged about here.

Hopefully the networks will be a bit more careful and will put quality before quantity in future.

Smart Grids: Beyond their remit

I blogged about the Smart Grid developments, nearly 2 years back here. Since then we have started talking about the 50 Billion connected M2M devices. Though Smart Grids as such can be just limited to distributing the electricity efficiently and dynamically, it has been said that they can be used for doing more than what they have been created for.

One such discussion in a recently concluded Cambridge Wireless Event on "Avoiding Cellular Gridlock: Finding New Ways Forward in Radio" was to use these smart grids for collecting the information about its surrounding.

It is well known that quite a few whitespace exist in radio communication in every country. We can build a cognitive radio that can use these whitespace and accordingly harness these free spectrum to the advantage of the users. Now since these whitespace would be different in each country and would also change depending on if a certain frequency is allocated in one area but not in another, there would need to be a database that the devices could use to find which spectrum is available or not.

Smart grids can be used to collect this information and update the database as they would have a wide footprint, probably encompassing the whole country. Though this is just an idea that came up in discussion, there could be more similar uses of smart grids.

For those of you who do not know much about smart grids, I have embedded couple of presentation from different chapters of The IET.

Smart Grids: Beyond the technical challenges

View more presentations from Zahid Ghadialy

The Role of New Technologies: A Power Engineering Equipment Supply Base Perspective

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One thing worth mentioning is that, there is already a concern that Smart Grids could be an invasion of privacy and could also be exploited by highly skilled theives.

Picture Source: Washington Post

If you look at the picture above, an expert in smart grids could be able to point out the different signatures of power consumption match to a particular event related generally to a device. So for example of you have used a kettle that means you have not gone on holidays, or something like that.

This also gives opportunity for new devices that can randomize these signatures :)

April fool jokes...in case you missed

Other interesting and/or related links below:

Ubiquisys reveals details of RadioLight – a light-bulb integrated femtocell

Qualcomm to put Femtocells in Mutant Animals ;)

The world in 30 years

Quick Recap of MIMO in LTE and LTE-Advanced

I had earlier put up some MIMO presentations that were too technical heavy so this one is less heavy and more figures.

The following is from NTT Docomo Technical journal (with my edits):

MIMO: A signal transmission technology that uses multiple antennas at both the transmitter and receiver to perform spatial multiplexing and improve communication quality and spectral efficiency.

Spectral efficiency: The number of data bits that can be transmitted per unit time and unit frequency band.

In this blog we will first look at MIMO in LTE (Release 8/9) and then in LTE-Advanced (Release-10)

MIMO IN LTE

Downlink MIMO Technology

Single-User MIMO (SU-MIMO) was used for the downlink for LTE Rel. 8 to increase the peak data rate. The target data rates of over 100 Mbit/s were achieved by using a 20 MHz transmission bandwidth, 2 × 2 MIMO, and 64 Quadrature Amplitude Modulation (64QAM), and peak data rates of over 300 Mbit/s can be achieved using 4×4 SU-MIMO. The multi-antenna technology used for the downlink in LTE Rel. 8 is classified into the following three types.

1) Closed-loop SU-MIMO and Transmit Diversity: For closed-loop SU-MIMO transmission on the downlink, precoding is applied to the data carried on the Physical Downlink Shared Channel (PDSCH) in order to increase the received Signal to Interference plus Noise power Ratio (SINR). This is done by setting different transmit antenna weights for each transmission layer (stream) using channel information fed back from the UE. The ideal transmit antenna weights for precoding are generated from eigenvector(s) of the covariance matrix of the channel matrix, H, given by HHH, where H denotes the Hermitian transpose.

However, methods which directly feed back estimated channel state information or precoding weights without quantization are not practical in terms of the required control signaling overhead. Thus, LTE Rel. 8 uses codebook-based precoding, in which the best precoding weights among a set of predetermined precoding matrix candidates (a codebook) is selected to maximize the total throughput on all layers after precoding, and the index of this matrix (the Precoding Matrix Indicator (PMI)) is fed back to the base station (eNode B) (Figure 1).

LTE Rel. 8 adopts frequency-selective precoding, in which precoding weights are selected independently for each sub-band of bandwidth from 360 kHz to 1.44 MHz, as well as wideband precoding, with single precoding weights that are applied to the whole transmission band. The channel estimation used for demodulation and selection of the precoding weight matrix on the UE is done using a cell specific Reference Signal (RS) transmitted from each antenna. Accordingly, the specifications require the eNode B to notify the UE of the precoding weight information used for PDSCH transmission through the Physical Downlink Control Channel (PDCCH), and the UE to use this information for demodulation.

LTE Rel. 8 also adopts rank adaptation, which adaptively controls the number of transmission layers (the rank) according to channel conditions, such as the received SINR and fading correlation between antennas (Figure 2). Each UE feeds back a Channel Quality Indicator (CQI), a Rank Indicator (RI) specifying the optimal rank, and the PMI described earlier, and the eNode B adaptively controls the number of layers transmitted to each UE based on this information.

2) Open-loop SU-MIMO and Transmit Diversity: Precoding with closed-loop control is effective in low mobility environments, but control delay results in less accurate channel tracking ability in high mobility environments. The use of open-loop MIMO transmission for the PDSCH, without requiring feedback of channel information, is effective in such cases. Rank adaptation is used, as in the case of closed-loop MIMO, but rank-one transmission corresponds to open-loop transmit diversity. Specifically, Space-Frequency Block Code (SFBC) is used with two transmit antennas, and a combination of SFBC and Frequency Switched Transmit Diversity (FSTD) (hereinafter referred to as “SFBC+FSTD”) is used with four transmit antennas. This is because, compared to other transmit diversity schemes such as Cyclic Delay Diversity (CDD), SFBC and SFBC+FSTD achieve higher diversity gain, irrespective of fading correlation between antennas, and achieve the lowest required received SINR. On the other hand, for PDSCH transmission with rank of two or higher, fixed precoding is used regardless of channel variations. In this case, cyclic shift is performed before applying the precoding weights, which effectively switches precoding weights in the frequency domain, thereby averaging the received SINR is over layers.

3) Adaptive Beamforming: Adaptive beamforming uses antenna elements with a narrow antenna spacing of about half the carrier wavelength and it has been studied for use with base stations with the antennas mounted in a high location. In this case beamforming is performed by exploiting the UE Direction of Arrival (DoA) or the channel covariance matrix estimated from the uplink, and the resulting transmit weights are not selected from a codebook. In LTE Rel. 8, a UE-specific RS is defined for channel estimation in order to support adaptive beamforming. Unlike the cell-specific RS, the UE specific RS is weighted with the same weights as the data signals sent to each UE, and hence there is no need to notify the UE of the precoding weights applied at the eNode B for demodulation at the UE. However, its effectiveness is limited in LTE Rel. 8 because only one layer per cell is supported, and it is an optional UE feature for Frequency Division Duplex (FDD).

Uplink MIMO Technology

On the uplink in LTE Rel. 8, only one-layer transmission was adopted in order to simplify the transmitter circuit configuration and reduce power consumption on the UE. This was done because the LTE Rel. 8 target peak data rate of 50 Mbit/s or more could be achieved by using a 20 MHz transmission bandwidth and 64QAM and without using SU-MIMO. However, Multi-User MIMO (MU-MIMO) can be used to increase system capacity on the LTE Rel. 8 uplink, using multiple receiver antennas on the eNode B. Specifically, the specification requires orthogonalization of the demodulation RSs from multiple UEs by assigning different cyclic shifts of a Constant Amplitude Zero Auto-Correlation (CAZAC) sequence to the demodulation RSs, so that user signals can be reliably separated at the eNode B. Demodulation RSs are used for channel estimation for the user-signal separation process.

MIMO TECHNOLOGY IN LTE-ADVANCED

Downlink 8-Layer SU-MIMO Technology

The target peak spectral efficiency in LTE-Advanced is 30 bit/s/Hz. To achieve this, high-order SU-MIMO with more antennas is necessary. Accordingly, it was agreed to extend the number of layers of SU-MIMO transmission in the LTE-Advanced downlink to a maximum of 8 layers. The number of transmission layers is selected by rank adaptation. The most significant issue with the radio interface in supporting up to 8 layers is the RS structure used for CQI measurements and PDSCH demodulation.

1) Channel State Information (CSI)-RS: For CQI measurements with up-to-8 antennas, new CSI-RSs are specified in addition to cell-specific RS defined in LTE Rel. 8 for up-to-four antennas. However, in order to maintain backward compatibility with LTE Rel. 8 in LTE-Advanced, LTE Rel. 8 UE must be supported in the same band as in that for LTE-Advanced. Therefore, in LTE Advanced, interference to the PDSCH of LTE Rel. 8 UE caused by supporting CSI-RS must be minimized. To achieve this, the CSI-RS are multiplexed over a longer period compared to the cell-specific RS, once every several subframes (Figure 3). This is because the channel estimation accuracy for CQI measurement is low compared to that for demodulation, and the required accuracy can be obtained as long as the CSIRS is sent about once per feedback cycle. A further reason for this is that LTE-Advanced, which offers higher data-rate services, will be developed to complement LTE Rel. 8, and is expected to be adopted mainly in low-mobility environments.

2) UE-specific RS: To allow demodulation of eight-layer SU-MIMO, the UE-specific RS were extended for SU-MIMO transmission, using a hybrid of Code Division Multiplexing (CDM) and Frequency Division Multiplexing (FDM) (Figure 4). The UE-specific RS pattern for each rank (number of layers) is shown in Figure 5. The configuration of the UE-specific RS in LTE-Advanced has also been optimized differently from those of LTE Rel.8, extending it for SU-MIMO as well as adaptive beamforming, such as by applying twodimensional time-frequency orthogonal CDM to the multiplexing between transmission layers.

Downlink MU-MIMO Technology

In addition to the peak data rate, the system capacity and cell-edge user throughput must also be increased in LTE-Advanced compared to LTE Rel. 8. MU-MIMO is an important technology for satisfying these requirements. With MU-MIMO and CoMP transmission (described earlier), various sophisticated signal processing techniques are applied at the eNode B to reduce the interference between transmission layers, including adaptive beam transmission (zero-forcing, block diagonalization, etc.), adaptive transmission power control and simultaneous multi-cell transmission. When these sophisticated transmission techniques are applied, the eNode B multiplexes the UE-specific RS described above with the PDSCH, allowing the UE to demodulate the PDSCH without using information about transmission technology applied by the eNode B. This increases flexibility in applying sophisticated transmission techniques on the downlink. On the other hand, PMI/CQI/RI feedback extensions are needed to apply these sophisticated transmission techniques, and this is currently being discussed actively at the 3GPP.

Uplink SU-MIMO Technology

To reduce the difference in peak data rates achievable on the uplink and downlink for LTE Rel. 8, a high target peak spectral efficiency of 15 bit/s/Hz was specified for the LTE-Advanced uplink. To achieve this, support for SU-MIMO with up to four transmission antennas was agreed upon. In particular, the two-transmission-antenna SU-MIMO function is required to satisfy the peak spectral efficiency requirements of IMT-Advanced.

For the Physical Uplink Shared Channel (PUSCH), it was agreed to apply SU-MIMO with closed-loop control using multiple antennas on the UE, as well as codebook-based precoding and rank adaptation, as used on the downlink. The eNode B selects the precoding weight from a codebook to maximize achievable performance (e.g., received SINR or user throughput after precoding) based on the sounding RS, which is used for measuring the quality of the channel transmitted by the UE. The eNode B notifies the UE of the selected precoding weight together with the resource allocation information used by the PDCCH. The precoding for rank one contributes to antenna gain, which is effective in increasing cell edge user throughput. However, considering control-information overhead and increases in Peak-to-Average Power Ratio (PAPR), frequency-selective precoding is not very effective in increasing system throughput, so only wideband precoding has been adopted.

Also, for rank two or higher, when four transmission antennas are used, the codebook has been designed not to increase the PAPR. The demodulation RS, which is used for channel estimation, is weighted with the same precoding weight as is used for the user data signal transmission. Basically, orthogonalization is achieved by applying a different cyclic shift to each layer, but orthogonalizing the code region using block spread together with this method is adopted.

Uplink Transmit Diversity Technology

Closed-loop transmit diversity is applied to PUSCH as described above for SU-MIMO. Application of transmit diversity to the Physical Uplink Control Channel (PUCCH) is also being studied. For sending retransmission request Acknowledgment (ACK) and Negative ACK (NAK) signals as well as scheduling request signals, application of Spatial Orthogonal-Resource Transmit Diversity (SORTD) using differing resource blocks per antenna or an orthogonalizing code sequence (cyclic shift, block spread sequence) has been agreed upon (Figure 6). However, with LTE-Advanced, the cell design must be done so that LTE Rel. 8 UE get the required quality at cell-edges, so applying transmit diversity to the control channels cannot contribute to increasing the coverage area, but only to reducing the transmission power required.