Four Ways 5G Can Improve the Battery Life of User Equipment (UE)

We have looked at different approaches in this blog and the 3G4G website on reducing the power consumption (see related posts below). In a blog post some months back, Huawei highlighted how 5G can improve the battery life of UE. The blog post mentioned four approaches, we have looked at three of them on various blogs. 

The following is from the blog post:

RRC_INACTIVE State

A UE can access network services only if it establishes a radio resource control (RRC) connection with the base station. In legacy RATs, a UE is either in the RRC_CONNECTED state (it has an RRC connection) or the RRC_IDLE state (it does not have an RRC connection). However, transitioning from the RRC_IDLE state to the RRC_CONNECTED state takes a long time, so it cannot meet the low latency requirement of some 5G services. But a UE cannot just stay in the RRC_CONNECTED state because this will consume much more UE power.

To solve this problem, 5G introduces the RRC_INACTIVE state, where the RRC connection is released but the UE context is retained (called RRC Release with Suspend), so an RRC connection can be quickly resumed when needed. This way, a UE in the RRC_INACTIVE state can access low-latency services whenever needed but consume the same amount of power as it does in the RRC_IDLE state.

DRX + WUS

Discontinuous reception (DRX) enables a UE in the RRC_CONNECTED state to periodically, instead of constantly, monitor the physical downlink control channel (PDCCH) to save power. To meet the requirements of different UE services, both short and long DRX cycles can be configured for a UE. However, when to wake up is determined by the predefined cycle, so the UE might wake up unnecessarily when there is no data scheduled.

Is there a way for a UE to wake up only when it needs to? Wake-up Signal (WUS) proposed in Release 16 is the answer. This signal can be sent before the next On Duration period (during which the UE monitors the PDCCH) so that the UE wakes up only when it receives this signal from the network. Because the length of a WUS is shorter than the On Duration Timer, using WUS to wake up a UE saves more power than using only DRX.

BWP Adaptation

In theory, working on a larger bandwidth consumes more UE power. 5G provides large bandwidths, but it is unnecessary for a UE to always work on large bandwidth. For example, if you play online mobile games on a UE, only 10 MHz of bandwidth is needed for 87% of the data transmission time. As such, Bandwidth Part (BWP) is proposed in 5G to enable UEs to work on narrower bandwidths without sacrificing user experience.

BWP adaptation enables the base station to dynamically switch between BWPs based on the UE’s traffic volume. When the traffic volume is large, a UE can work on a wide BWP, and when the traffic volume is small, the UE can work on a narrow one. BWP switching can be performed based on the downlink control information (DCI) and RRC reconfiguration messages. This ensures that a UE always works on a bandwidth that supports the traffic volume but does not consume too much power.

Maximum MIMO Layers Reduction

According to 3GPP specifications, the number of receive and transmit antennas used by a UE cannot be fewer than the maximum number of MIMO layers in the downlink and uplink, respectively. For example, when a maximum of four downlink MIMO layers are configured for a UE, the UE must enable at least four receive antennas to receive data. Therefore, if the maximum number of MIMO layers can be reduced, the UE does not have to activate as many antennas, reducing power consumption.

This can be achieved in 5G because the number of MIMO layers can be re-configured based on assistance information from UEs. After receiving a request to reduce the number of MIMO layers from a UE, the base station configures fewer MIMO layers for the UE through an RRC reconfiguration message. In this way, the UE can deactivate some antennas to save power.

T-Mobile added more cell sleep profiles (or this is just the first time i noticed it). L2100 is now turned off at night when the cell load is low. After 6am it's back on everywhere. When you load the cell it will also come back online.

Pretty significant reduction of power use. pic.twitter.com/oDYhyLeQLw

— Jeffrey | Antennekaart.nl (@antennekaart) September 26, 2022

Power consumption in the networks and the devices is a real challenge. While the battery capacity and charging speeds are increasing, it is also important to find ways to optimise the signalling parameters, etc. One such approach can be seen in the tweet above regarding regarding T-Mobile in The Netherlands, selectively switching off a carrier in the night and switching it back when the cell starts loading or in the morning.

We will see lot more innovations and optimisations to dynamically update the technologies, parameters, optimisations to ensure power savings wherever possible.

Related Posts: 

• 3G4G: What is a User Equipment (UE)?
• The 3G4G Blog: A Technical Introduction to 5G NR RRC Inactive State
• Connectivity Technology Blog: InterDigital’s 6G Vision of “Zero-energy” (ZE) air-interface designs
• The 3G4G Blog: Bandwidth Part (BWP) in 5G New Radio (NR)
• Telecoms Infrastructure Blog: Huawei Explains Antennas and Radomes
• The 3G4G Blog: What is RF Front-End (RFFE) and why is it so Important?
• The 3G4G Blog: Reducing 5G Device Power Consumption Using Connected-mode Discontinuous Reception (C-DRX)
• The 3G4G Blog: Energy Consumption in Mobile Networks and RAN Power Saving Schemes
• The 3G4G Blog: Smartphone Batteries Round-up: Technology, Charging & Recycling
• The 3G4G Blog: The Futuristic Concept of 'Smart & Intelligent' Batteries 

AI/ML Enhancements in 5G-Advanced for Intelligent Network Automation

Artificial Intelligence (AI) and Machine Learning (ML) has been touted to automate the network and simplify the identification and debug of issues that will arise with increasing network complexity. For this reason 3GPP has many different features that are already present in Release-17 but are expected to evolve further in Release-18. 

I have already covered some of this topics in earlier posts. Ericsson's recent whitepaper '5G Advanced: Evolution towards 6G' also has a good summary on this topic. Here is an extract from that:

Intelligent network automation

With increasing complexity in network design, for example, many different deployment and usage options, conventional approaches will not be able to provide swift solutions in many cases. It is well understood that manually reconfiguring cellular communications systems could be inefficient and costly.

Artificial intelligence (AI) and machine learning (ML) have the capability to solve complex and unstructured network problems by using a large amount of data collected from wireless networks. Thus, there has been a lot of attention lately on utilizing AI/ML-based solutions to improve network performance and hence providing avenues for inserting intelligence in network operations.

AI model design, optimization, and life-cycle management rely heavily on data. A wireless network can collect a large amount of data as part of its normal operations. This provides a good base for designing intelligent network solutions. 5G Advanced addresses how to optimize the standardized interfaces for data collection while leaving the automation functionality, for example, training and inference up to the proprietary implementation to support full flexibility in the automation of the network.

AI/ML for RAN enhancements

Three use cases have been identified in the Release 17 study item related to RAN performance enhancement by using AI/ML techniques. Selected use cases from the Release 17 technical report will be taken into the normative phase in the next releases. The selected use cases are: 1) network energy saving; 2) load balancing; and 3) mobility optimization.

The selected use cases can be supported by enhancements to current NR interfaces, targeting performance improvements using AI/ML functionality in the RAN while maintaining the 5G NR architecture. One of the goals is to ensure vendor incentives in terms of innovation and competitiveness by keeping the AI model implementation specific. As shown in Fig.2 (on the top) an intent-based management approach can be adopted for use cases involving RAN-OAM interactions. The intent will be received by the RAN. The RAN will need to understand the intent and trigger certain functionalities as a result.

Ericsson White Paper - 5G Advanced: Evolution towards 6G - https://t.co/9ZKnC8PDxX#Free5Gtraining #Ericsson #5G #B5G #5GAdvanced #6G #5GNR #5GC #Timeline #XR #RedCap #NetworkAutomation #EnergySavings #DeterministicNetworking #IoT pic.twitter.com/eeI4SKvIRC

— Free 5G Training (@5Gtraining) July 21, 2022

AI/ML for physical layer enhancements

It is generally expected that AI/ML functionality can be used to improve the radio performance and/or reduced the complexity/overhead of the radio interface. 3GPP TSG RAN has selected three use cases to study the potential air interface performance improvements through AI/ML techniques, such as beam management, channel state information feedback enhancement, and positioning accuracy enhancements for different scenarios. The AI/ML-based methods may provide benefits compared to traditional methods in the radio interface. The challenge will be to define a unified AI/ML framework for the air interface by adequate AI/ML model characterization using various levels of collaboration between gNB and UE.

AI/ML in 5G core

5G Advanced will provide further enhancements of the architecture for analytics and on ML model life-cycle management, for example, to improve correctness of the models. The advancements in the architecture for analytics and data collection serve as a good foundation for AI/ML-based use cases within the different network functions (NFs). Additional use cases will be studied where NFs make use of analytics with the target to support in their decision making, for example, network data analytics functions (NWDAF)- assisted generation of UE policy for network slicing.

If you are interested in studying this topic further, check out 3GPP TR 37.817: Study on enhancement for data collection for NR and ENDC. Download the latest version from here.

Related Posts: 

• The 3G4G Blog: What Is the Role of AI and ML in the Open RAN and 5G Future?
• The 3G4G Blog: 3GPP Release-18 Work Moves Into Focus as Release-17 Reaches Maturity
• The 3G4G Blog: NWDAF in 3GPP Release-16 and Release-17
• The 3G4G Blog: 5G-Advanced Flagship Features
• The 3G4G Blog: 3GPP's 5G-Advanced Technology Evolution from a Network Perspective Whitepaper
• Free 6G Training: Qualcomm Pushing the boundaries of AI research 

What Is the Role of AI and ML in the Open RAN and 5G Future?

Artificial Intelligence and Machine Learning have moved on from just being buzzwords to bringing much needed optimization and intelligence in devices, networks and infrastructure; whether on site, on the edge or in the cloud.

New Study in 3GPP RAN3 on Artificial Intelligence and Machine Learning in NG-RAN - https://t.co/o4o4kq6Wpp via @3GPPLive #Free5Gtraining #3GPP #5G #ArtificialIntelligence #MachineLearning #AIML #RAN #Algorithms #SON #5GC #5GS pic.twitter.com/pvPlG9HpPC

— Free 5G Training (@5Gtraining) October 15, 2021

Qualcomm has been very active in talking about AI/ML in webinars and on their site. A detailed blog post looking at 'What’s the role of artificial intelligence in the future of 5G and beyond?' is available here. It was posted in time for a Light Reading webinar where Gabriel Brown, Principal Analyst – Mobile Networks and 5G, Heavy Reading and Tingfang Ji, Senior Director, Engineering - Wireless R&D, Qualcomm discuss the topic. The video is embedded below and slide deck is available here.

Standardizing AI/ML in cellular communication systems. From #Qualcomm presentation, The essential role of AI in the 5G future - How machine learning is accelerating wireless innovations in the new decade and beyond - https://t.co/fwxtoKIZKR#Free5Gtraining #3G4G5G #5G #AI #ML pic.twitter.com/YS5w8KXQuw

— Free 5G Training (@5Gtraining) September 27, 2021

Louis Scialabba, Senior Director of Marketing at Mavenir, looking at AI and Analytics spoke at Layer 123 conference on the topic, 'AI/ML for Next Gen 5G Mobile Networks'. His talk is embedded below and a blog post by him on the topic, 'The RIC Opens a New World of Opportunities for CSPs' is available here.

Related Posts: 

• Free 6G Training: Qualcomm Pushing the boundaries of AI research
• The 3G4G Blog: 3GPP Release-18 Work Moves Into Focus as Release-17 Reaches Maturity
• The 3G4G Blog: 5G-Advanced Flagship Features
• Connectivity Technology Blog: 5G Indoor Precise Positioning
• The 3G4G Blog: ITU Standardization Bureau on Machine Learning for 5G
• The 3G4G Blog: AI your Slice to 5G Perfection
• The 3G4G Blog: Qualcomm Explains 5G Millimeter Wave (mmWave) Future & Integrated Access and Backhaul (IAB)
• The 3G4G Blog: Qualcomm Demoes Sub-band Half Duplex (SBHD)
• Connectivity Technology Blog: Super Uplink to improve 5G Coverage and Speeds
• Free 6G Training: One XR Device to Rule Them All! 

An Introduction to Minimization of Drive Testing (MDT)

Over the last few years, Ralf Kreher has done some fantastic posts on Minimization of Drive Testing (MDT) on this blog (links at the bottom of this post). To complement that, here is a basic introductory tutorial looking at what exactly is meant by MDT and how it's done. 

Video embedded below:

The slides from the presentation are available here.

Please check out our 3GPP SON Series videos here.

Related Posts:

• The 3G4G Blog: 3GPP MDT - How it works and what is new in Rel. 16
• The 3G4G Blog: 3GPP Minimization of Drive Test (MDT) Signaling at a Glance
• The 3G4G Blog: LTE Physical Layer Measurements of RSRP and RSRQ
• The 3G4G Blog: RRC Signalling in Rel-10 for MDT
• 3G4G: 3GPP SON Series
• 3G4G: Self-Organizing Networks / Self-Optimizing Networks (SON) 

3GPP MDT - How it works and what is new in Rel. 16

Today I launched my first video. It is about the 3GPP Minimization of Drive Test (MDT) and what is new for this feature in Rel. 16 / 5G networks.

This video explains the overall concept of the MDT feature defined by 3GPP. Individual signaling procedures for immediate and logged mode MDT reporting are presented as well as the latest enhancements for 5G networks defined in 3GPP Release 16.

Enjoy watching

Automating the 5G Core using Machine Learning and Data Analytics

One of the new entities introduced by 3GPP in the 5G Core SBA (see tutorial here) is Network Data Analytics Function, NWDAF.

3GPP TR 23.791: Study of Enablers for Network Automation for 5G (Release 16) describes the following 5G Network Architecture Assumptions:

1 The NWDAF (Network Data Analytics Function) as defined in TS 23.503 is used for data collection and data analytics in centralized manner. An NWDAF may be used for analytics for one or more Network Slice.
2 For instances where certain analytics can be performed by a 5GS NF independently, a NWDAF instance specific to that analytic maybe collocated with the 5GS NF. The data utilized by the 5GS NF as input to analytics in this case should also be made available to allow for the centralized NWDAF deployment option.
3 5GS Network Functions and OAM decide how to use the data analytics provided by NWDAF to improve the network performance.
4 NWDAF utilizes the existing service based interfaces to communicate with other 5GC Network Functions and OAM.
5 A 5GC NF may expose the result of the data analytics to any consumer NF utilizing a service based interface.
6 The interactions between NF(s) and the NWDAF take place in the local PLMN (the reporting NF and the NWDAF belong to the same PLMN).
7 Solutions shall neither assume NWDAF knowledge about NF application logic. The NWDAF may use subscription data but only for statistical purpose.

Picture Source: Application of Data Mining in the 5G Network Architecture by Alexandros Kaloxylos

Continuing from 3GPP TR 23.791:

The NWDAF may serve use cases belonging to one or several domains, e.g. QoS, traffic steering, dimensioning, security.
The input data of the NWDAF may come from multiple sources, and the resulting actions undertaken by the consuming NF or AF may concern several domains (e.g. Mobility management, Session Management, QoS management, Application layer, Security management, NF life cycle management).
Use case descriptions should include the following aspects:
1. General characteristics (domain: performance, QoS, resilience, security; time scale).
2. Nature of input data (e.g. logs, KPI, events).
3. Types of NF consuming the NWDAF output data, how data is conveyed and nature of consumed analytics.
4. Output data.
5. Possible examples of actions undertaken by the consuming NF or AF, resulting from these analytics.
6. Benefits, e.g. revenue, resource saving, QoE, service assurance, reputation.

Picture Source: Application of Data Mining in the 5G Network Architecture by Alexandros Kaloxylos

3GPP TS 23.501 V15.2.0 (2018-06) Section 6.2.18 says:

NWDAF represents operator managed network analytics logical function. NWDAF provides slice specific network data analytics to a NF. NWDAF provides network analytics information (i.e., load level information) to a NF on a network slice instance level and the NWDAF is not required to be aware of the current subscribers using the slice. NWDAF notifies slice specific network status analytic information to the NFs that are subscribed to it. NF may collect directly slice specific network status analytic information from NWDAF. This information is not subscriber specific.

In this Release of the specification, both PCF and NSSF are consumers of network analytics. The PCF may use that data in its policy decisions. NSSF may use the load level information provided by NWDAF for slice selection.

NOTE 1: NWDAF functionality beyond its support for Nnwdaf is out of scope of 3GPP.
NOTE 2: NWDAF functionality for non-slice-specific analytics information is not supported in this Release of the specification.

3GPP Release-16 is focusing on 5G Expansion and 5G Efficiency, SON and Big Data are part of 5G Efficiency.

3GPP Release-16 is going to focus on 5G expansion & efficiency. Listen to the @3GPPLive Webinar here: https://t.co/tKzXJ3cUFj pic.twitter.com/1xCZfpT9lt

— 3G4G (@3g4gUK) July 14, 2018

Light Reading Artificial Intelligence and Machine Learning section has a news item on this topic from Layer123's Zero Touch & Carrier Automation Congress:

The 3GPP standards group is developing a machine learning function that could allow 5G operators to monitor the status of a network slice or third-party application performance.

The network data analytics function (NWDAF) forms a part of the 3GPP's 5G standardization efforts and could become a central point for analytics in the 5G core network, said Serge Manning, a senior technology strategist at Sprint Corp.

Speaking here in Madrid, Manning said the NWDAF was still in the "early stages" of standardization but could become "an interesting place for innovation."

The 3rd Generation Partnership Project (3GPP) froze the specifications for a 5G new radio standard at the end of 2017 and is due to freeze another set of 5G specifications, covering some of the core network and non-radio features, in June this year as part of its "Release 15" update.

Manning says that Release 15 considers the network slice selection function (NSSF) and the policy control function (PCF) as potential "consumers" of the NWDAF. "Anything else is open to being a consumer," he says. "We have things like monitoring the status of the load of a network slice, or looking at the behavior of mobile devices if you wanted to make adjustments. You could also look at application performance."

In principle, the NWDAF would be able to make use of any data in the core network. The 3GPP does not plan on standardizing the algorithms that will be used but rather the types of raw information the NWDAF will examine. The format of the analytics information that it produces might also be standardized, says Manning.

Such technical developments might help operators to provide network slices more dynamically on their future 5G networks.

Generally seen as one of the most game-changing aspects of 5G, the technique of network slicing would essentially allow an operator to provide a number of virtual network services over the same physical infrastructure.

For example, an operator could provide very high-speed connectivity for mobile gaming over one slice and a low-latency service for factory automation on another -- both reliant on the same underlying hardware.

However, there is concern that without greater automation operators will have less freedom to innovate through network slicing. "If operators don't automate they will be providing capacity-based slices that are relatively large and static and undifferentiated and certainly not on a per-customer basis," says Caroline Chappell, an analyst with Analysys Mason .

In a Madrid presentation, Chappell said that more granular slicing would require "highly agile end-to-end automation" that takes advantage of progress on software-defined networking and network functions virtualization.

"Slices could be very dynamic and perhaps last for only five minutes," she says. "In the very long term, applications could create their own slices."

Despite the talk of standardization, and signs of good progress within the 3GPP, concern emerged this week in Madrid that standards bodies are not moving quickly enough to address operators' needs.

Caroline Chappell's talk is available here whereas Serge Manning's talk is embedded below:



I am helping CW organise the annual CW TEC conference on the topic The inevitable automation of Next Generation Networks

#CWTEC 2018 planning committee working hard to bring an exciting conference - explores the role of #AI in delivering Next Gen. Network performance; & bridges the gap between #AI/#ML & Telecos communities! @zahidtg @Bob_CWCEO @swunger Paul Ceely @bt_uk , John Haine @BristolCSN pic.twitter.com/4uGprC1usg

— Sylvia Lu (@SylviaLuUk) July 23, 2018

Communications networks are perhaps the most complex machines on the planet. They use vast amounts of hardware, rely on complex software, and are physically distributed over land, underwater, and in orbit. They increasingly provide essential services that underpin almost every aspect of life. Managing networks and optimising their performance is a vast challenge, and will become many times harder with the advent of 5G. The 4th Annual CW Technology Conference will explore this challenge and how Machine Learning and AI may be applied to build more reliable, secure and better performing networks.

Is the AI community aware of the challenges facing network providers? Are the network operators and providers aware of how the very latest developments in AI may provide solutions? The conference will aim to bridge the gap between AI/ML and communications network communities, making each more aware of the nature and scale of the problems and the potential solutions.

I am hoping to see some of this blog readers at the conference. Looking forward to learning more on this topic amongst others for network automation.

Related Post:

• Artificial Intelligence - Beyond SON for Autonomous Networks

Artificial Intelligence - Beyond SON for Autonomous Networks

What is the next step in evolution of SON? Artificial Intelligence obviously. The use of artificial intelligence (AI) techniques in the network supervisory system could help solve some of the problems of future network deployment and operation. ETSI has therefore set up a new 'Industry Specification Group' on 'Experiential Networked Intelligence' (ISG ENI) to develop standards for a Network Supervisory assistant system.

The ISG ENI focuses on improving the operator experience, adding closed-loop artificial intelligence mechanisms based on context-aware, metadata-driven policies to more quickly recognize and incorporate new and changed knowledge, and hence, make actionable decisions. ENI will specify a set of use cases, and the generic technology independent architecture, for a network supervisory assistant system based on the ‘observe-orient-decide-act’ control loop model. This model can assist decision-making systems, such as network control and management systems, to adjust services and resources offered based on changes in user needs, environmental conditions and business goals.

The introduction of technologies such as Software-Defined Networking (SDN), Network Functions Virtualisation (NFV) and network slicing means that networks are becoming more flexible and powerful. These technologies transfer much of the complexity in a network from hardware to software, from the network itself to its management and operation. ENI will make the deployment of SDN and NFV more intelligent and efficient and will assist the management and orchestration of the network.

We expect to complete the first phase of ENI work in 2019. It will include a description of use cases and requirements and terminology, including a definition of features, capabilities and policies, which we will publish in a series of informative best practice documents (Group Reports (GRs)).

This will of course require co-operation from many different industry bodies including GSMA, ITU-T, MEF, IETF, etc.

Will see how this goes.

Further reading:

• ETSI Presentation on ENI
• Network, All Intelligent. - ENI introduction, IETF

Evolution of SON in 3GPP

A good list of 3GPP Evolution of SON features. Whitepaper available here. You may also like the earlier post here.

See also: Self-Organizing Networks / Self-Optimizing Networks (SON) - 3G4G Homepage

Inside 3GPP Release-13 - Whitepaper by 5G Americas

The following is from the 5G Americas press release:

The summary offers insight to the future of wireless broadband and how new requirements and technological goals will be achieved. The report updates Release 13 (Rel-13) features that are now completed at 3GPP and were not available at the time of the publication of a detailed 5G Americas report, Mobile Broadband Evolution Towards 5G: 3GPP Release 12 & Release 13 and Beyond in June 2015.

The 3GPP standards have many innovations remaining for LTE to create a foundation for 5G.  Rel-12, which was finalized in December 2014, contains a vast array of features for both LTE and HSPA+ that bring greater efficiency for networks and devices, as well as enable new applications and services. Many of the Rel-12 features were extended into Rel-13.  Rel-13, functionally frozen in December 2015 and completed in March 2016, continues to build on these technical capabilities while adding many robust new features.

Jim Seymour, Principal Engineer, Mobility CTO Group, Cisco and co-leader of the 5G Americas report explained, “3GPP Release 13 is just a peek behind the curtain for the unveiling of future innovations for LTE that will parallel the technical work at 3GPP on 5G. Both LTE and 5G will work together to form our connected future.”

The numerous features in the Rel-13 standards include the following for LTE-Advanced:

• Active Antenna Systems (AAS), including beamforming, Multi-Input Multi-Output (MIMO) and Self-Organizing Network (SON) aspects
• Enhanced signaling to support inter-site Coordinated Multi-Point Transmission and Reception (CoMP)
• Carrier Aggregation (CA) enhancements to support up to 32 component carriers
• Dual Connectivity (DC) enhancements to better support multi-vendor deployments with improved traffic steering
• Improvements in Radio Access Network (RAN) sharing
• Enhancements to Machine Type Communication (MTC)
• Enhanced Proximity Services (ProSe)

Some of the standards work in Rel-13 related to spectrum efficiency include:                                                                                                                       

• Licensed Assisted Access for LTE (LAA) in which LTE can be deployed in unlicensed spectrum
• LTE Wireless Local Area Network (WLAN) Aggregation (LWA) where Wi-Fi can now be supported by a radio bearer and aggregated with an LTE radio bearer
• Narrowband IoT (NB-IoT) where lower power wider coverage LTE carriers have been designed to support IoT applications
• Downlink (DL) Multi-User Superposition Transmission (MUST) which is a new concept for transmitting more than one data layer to multiple users without time, frequency or spatial separation

“The vision for 5G is being clarified in each step of the 3GPP standards. To understand those steps, 5G Americas provides reports on the developments in this succinct, understandable format,” said Vicki Livingston, Head of Communications for the association.

The whitepaper as follows:

Related posts:

• Tweets from NGMN Industry Conference & Exhibition 2016
• 3GPP Release-13 whitepapers and presentations
• LTE-Advanced Pro (a.k.a. 4.5G)
• New whitepaper on Narrowband Internet of Things

Next Generation SON for 5G

There were quite a few interesting presentations in the recently concluded 5G World conference. One that caught my attention was this presentation by Huawei. SON is often something that is overlooked and is expected to be a part of deployment. The problem is that it is often vendor proprietary and does not work as expected when there is equipment from multiple vendors.

While the 4G SON in theory solves the issues that network face today, 5G SON will have to go much further and work with SDN/NFV and the sliced networks. Its going to be a big challenge and will take many years to get it right.

Here is the Huawei presentation from 5G World:

You may also be interested in:

• My old tutorial: An Introduction to Self-Organizing Networks
• Next Generation SON Free Whitepaper Download by Viavi

Feel free to let me know your thoughts as comments.

SON Update from 3GPP SA5

Below is a presentation from Christian Toche, 3GPP SA5 chairman in the SON Conference last month. I also blogged about his presentation last year which is available here.

Oct 2014: Update on 3GPP SA5 from 3G4G

SON Periodical table

Came across this in a presentation from the SON Conference back in Oct. 2013.

3GPP Rel-12 SON Status

Considering how popular the Release-11 SON post have been, here is Rel-12 status that was presented in the SON Conference in October 2013. Complete presentation embedded below:

3GPP Rel-12 SON Status from Zahid Ghadialy

You may also be interested in reading a comprehensive report prepared by David Chambers here.

SON in LTE Release-11

Very timely of 4G Americas to release a whitepaper on SON, considering that the SON conference just got over last week. This whitepaper contains lots of interesting details and the status from Rel-11 which is the latest complete release available. I will probably look at some features in detail later on as separate posts. The complete paper is embedded below and is available from 4G Americas website here.

Self-Optimizing Networks: The Benefits of SON in LTE from Zahid Ghadialy

Centralized SON

I was going through the presentation by SKT that I blogged about here and came across this slide above. SKT is clearly promoting the benefits of their C-SON (centralized SON) here.

The old 4G Americas whitepaper (here) explained the differences between the three approaches; Centralized (C-SON), Distributed (D-SON) and Hybrid (H-SON). An extract from that paper here:

In a centralized architecture, SON algorithms for one or more use cases reside on the Element Management System (EMS) or a separate SON server that manages the eNB's. The output of the SON algorithms namely, the values of specific parameters, are then passed to the eNB's either on a periodic basis or when needed. A centralized approach allows for more manageable implementation of the SON algorithms. It allows for use case interactions between SON algorithms to be considered before modifying SON parameters. However, active updates to the use case parameters are delayed since KPIs and UE measurement information must be forwarded to a centralized location for processing. Filtered and condensed information are passed from the eNB to the centralized SON server to preserve the scalability of the solution in terms of the volume of information transported. Less information is available at the SON server compared to that which would be available at the eNB. Higher latency due to the time taken to collect UE information restricts the applicability of a purely centralized SON architecture to those algorithms that require slower response time. Furthermore, since the centralized SON server presents a single point of failure, an outage in the centralized server or backhaul could result in stale and outdated parameters being used at the eNB due to likely less frequent updates of SON parameters at the eNB compared to that is possible in a distributed solution.

In a distributed approach, SON algorithms reside within the eNB’s, thus allowing autonomous decision making at the eNB's based on UE measurements received on the eNB's and additional information from other eNB's being received via the X2 interface. A distributed architecture allows for ease of deployment in multi-vendor networks and optimization on faster time scales. Optimization could be done for different times of the day. However, due to the inability to ensure standard and identical implementation of algorithms in a multi-vendor network, careful monitoring of KPIs is needed to minimize potential network instabilities and ensure overall optimal operation.

In practical deployments, these architecture alternatives are not mutually exclusive and could coexist for different purposes, as is realized in a hybrid SON approach. In a hybrid approach, part of a given SON optimization algorithm are executed in the NMS while another part of the same SON algorithm could be executed in the eNB. For example, the values of the initial parameters could be done in a centralized server and updates and refinement to those parameters in response to the actual UE measurements could be done on the eNB's. Each implementation has its own advantages and disadvantages. The choice of centralized, distributed or hybrid architecture needs to be decided on a use-case by use case basis depending on the information availability, processing and speed of response requirements of that use case. In the case of a hybrid or centralized solution, a practical deployment would require specific partnership between the infrastructure vendor, the operator and possibly a third party tool company. Operators can choose the most suitable approach depending upon the current infrastructure deployment.

Finally, Celcite CMO recently recently gave an interview on this topic on Thinksmallcell here. An extract below:

SON software tunes and optimises mobile network performance by setting configuration parameters in cellsites (both large and small), such as the maximum RF power levels, neighbour lists and frequency allocation. In some cases, even the antenna tilt angles are updated to adjust the coverage of individual cells.

Centralised SON (C-SON) software co-ordinates all the small and macrocells, across multiple radio technologies and multiple vendors in a geographic region - autonomously updating parameters via closed loop algorithms. Changes can be as frequent as every 15 minutes– this is partly limited by the bottlenecks of how rapidly measurement data is reported by RAN equipment and also the capacity to handle large numbers of parameter changes. Different RAN vendor equipment is driven from the same SON software. A variety of data feeds from the live network are continuously monitored and used to update system performance, allowing it to adapt automatically to changes throughout the day including outages, population movement and changes in services being used.

Distributed SON (D-SON) software is autonomous within each small cell (or macrocell) determining for itself the RF power level, neighbour lists etc. based on signals it can detect itself (RF sniffing) or by communicating directly with other small cells.

LTE has many SON features already designed in from the outset, with the X.2 interface specifically used to co-ordinate between small and macrocell layers whereas 3G lacks SON standards and requires proprietary solutions.

C-SON software is available from a relatively small number of mostly independent software vendors, while D-SON is built-in to each small cell or macro node provided by the vendor. Both C-SON and D-SON will be needed if network operators are to roll out substantial numbers of small cells quickly and efficiently, especially when more tightly integrated into the network with residential femtocells.

Celcite is one of the handful of C-SON software solution vendors. Founded some 10 years ago, it has grown organically by 35% annually to 450 employees. With major customers in both North and South America, the company is expanding from 3G UMTS SON technology and is actively running trials with LTE C-SON.

Quite a few companies are claiming to be in the SON space, but Celcite would argue that there are perhaps only half a dozen with the capabilities for credible C-SON solutions today. Few companies can point to live deployments. As with most software systems, 90% of the issues arise when something goes wrong and it's those "corner cases" which take time to learn about and deal with from real-world deployment experience.

A major concern is termed "Runaway SON" where the system goes out of control and causes tremendous negative impact on the network. It's important to understand when to trigger SON command and when not to. This ability to orchestrate and issue configuration commands is critical for a safe, secure and effective solution.

Let me know your opinions via comments below.

3GPP Standards Self Organizing Networks

The following is a presentation by 3GPP on Self-Organising Networks in the SON Conference 2012:

3GPP Standards Self Organizing Networks from Zahid Ghadialy

A basic tutorial on SON is available also on 3GPP website here.

A detailed list of 3GPP work items on SON is available to view and download from here.

Multi-Vendor SON in Het-Net

From a presentation by Prof. Shahram G Niri, NEC in the LTE World Summit, Barcelona.

The complete presentation is available here.

LTE Small Cells, SON and HetNets

From a presentation by Prof. Shahram G Niri, NEC in the LTE World Summit, Barcelona.

The complete presentation is available here.

3GPP Release-12 and beyond

3GPP Recently held a workshop on "Release 12 and Onward" to identify common requirements for future 3GPP radio access technologies. The goal of the workshop is to investigate what are the main changes that could be brought forward to evolve RAN toward Release 12 and onward. It is recommended that presentations in the workshop include views on:
- Requirements
- Potential technologies
- Technology roadmap for Releases 12, 13 and after

The discussions from the workshop should be used to define the work plan for Release 12 and onward in TSG-RAN.

The list of presentations and links, etc. are below and I have also embedded the Summary and Draft report, both of which can be downloaded from 3GPP website or slideshare. Here is a list of different topics and the presentations that covered them:

AdHoc Networks
AdHoc Networks - RWS-120035


Antennas, Beamforming, Transmitters, Receivers
3D-beamforming - RWS-120002
Vertical sectorization/3D beamforming via AAS - RWS-120005
Advanced receivers and joint Tx/Rx optimisation - RWS-120005
Network assistance for IC receivers - RWS-120005
Support of Active Antenna Systems - RWS-120006
Advanced transmitter beamforming - RWS-120010
Advanced receiver cancellation - RWS-120010
Vertical and 3D beamforming - RWS-120011
MIMO Enhancements - RWS-120014
New antenna configurations and 3D MIMO - RWS-120014
UE AAS (Active Antenna System) [Detailed] - RWS-120015 / RWS-120049
Cloud of Antennas (CoA) Concept - RWS-120016
Support of Massive MIMO Technology - RWS-120016
Full Dimension MIMO (FD-MIMO) System [Detailed] - RWS-120021 / RWS-120046
Cloud-RAN: Benefits and Drawbacks - RWS-120021 / RWS-120046
Further Enhanced Receivers - RWS-120022
Multiple antenna evolution - RWS-120025
3D beamforming - RWS-120026
Vision of 3D MIMO - RWS-120029
Massive MIMO & 3D MIMO - RWS-120034
Potential MIMO Enhancements - RWS-120035
Advanced Antenna Technology - RWS-120035
DL MIMO Enhancement - RWS-120037
Performance Requirement for 8Rx at eNB - RWS-120037
UE Receiver Enhancements - RWS-120039
DL MU-MIMO Enhancement - RWS-120039
Enhancement of MIMO, CoMP - RWS-120040
Advanced MIMO - RWS-120040
MIMO and COMP - RWS-120041
Role of Advanced Receivers - RWS-120041
Advanced Interference Handling - RWS-120041
Interference Suppression Subframes (ISS) and IRC Receiver [Detailed] - RWS-120051


Applications (Apps)
Efficiency for diverse small data applications - RWS-120011
Device Service/Application Awareness - RWS-120018
I-Net:”I”-centric mobile network design philosophy - RWS-120024
Application Aware Comm - RWS-120036 / RWS-120050


Backhaul and Relay
Relay backhaul enhancement - RWS-120011
LTE Backhaul - RWS-120013
Relay - RWS-120025
CoMP, backhaul and X2 interface - RWS-120027 / RWS-120048
Mobile Relay And Relay Backhaul Enhancement - RWS-120029


Baseband
Baseband resource pooling and virtualization - RWS-120011


Capacity and Coverage
Higher system capacity - RWS-120010
Capacity for Mobile Broadband: Requirements and Candidate technologies - RWS-120012
Increase N/W capacity by 1000 times - RWS-120020
Coverage Enhancement - RWS-120037
Capacity Enhancement - RWS-120038 / RWS-120047
Cell-edge Throughput Improvement - RWS-120038 / RWS-120047


Carrier Aggregation, Flexible Bandwidths and Multiflow
LTE multiflow / Inter-site CA - RWS-120002
LTE/HSDPA Carrier Aggregation - RWS-120002
Multiflow Enhancements - RWS-120002
Multi-Stream Aggregation - RWS-120006
Provide mechanisms for Flexible Bandwidth Exploitation - RWS-120008
Carrier aggregation enhancement - RWS-120019
Inter-eNB Carrier Aggregation - RWS-120021 / RWS-120046
Evolution of Carrier Aggregation - RWS-120036 / RWS-120050
CA of Alternative Spectra - RWS-120042


Cells, Carriers, C/U Planes
C/U plane split & Phantom cell - RWS-120010
Phantom cell by single/separate nodes - RWS-120010
Phantom cell: Other topics - RWS-120010
New Carrier Type for Primary Component Carrier - RWS-120011
Flexible/Reconfigurable Cells - RWS-120023
New carrier-type (NCT) enhancements - RWS-120026
Amorphous cells - RWS-120034
New Carrier Types - RWS-120035
Non-Orthogonal Access - RWS-120039
Dynamic Area Construction for UE - RWS-120040


Cognitive Radio
Cognitive radio - RWS-120034
Cognitive Networking - RWS-120036 / RWS-120050


Coordinated MultiPoint (CoMP)
CoMP Enhancements - RWS-120014
CoMP/ICIC enhancement - RWS-120019
CoMP Enhancements - RWS-120023
CoMP enhancements - RWS-120026
CoMP Technologies - RWS-120027 / RWS-120048
Enhanced CoMP - RWS-120029
Potential CoMP Enhancements - RWS-120035
CoMP - RWS-120037
CoMP Enhancement for Indoor Environment - RWS-120040
Overhauling DL CoMP - RWS-120042


Device, Handsets, UE's
Additional UE Enhancements - RWS-120018
Coordination : Multi-mode UE - RWS-120024


D2D / Device-to-Device
Device-to-Device - RWS-120003
LTE Device to Device - Proximity Based Services - RWS-120004
LTE device to device - RWS-120007
LTE direct communication - RWS-120007
Device-to-Device Communications - RWS-120014
D2D Discovery/Communication - RWS-120016
3GPP Proximity Services (ProSe) / D2D - RWS-120022
Device-to-Device communications - RWS-120026
Device-to-Device communication - RWS-120036 / RWS-120050


Data Rates and Throughputs
Higher data rate and user-experienced throughput - RWS-120010
Fairness of user throughput - RWS-120010


Deployments
LTE in Local Area Deployments & Enhancements - RWS-120004
Energy Efficient Local Area Deployments - RWS-120004
Scaling for Mass Deployment - RWS-120008
Flexible and cost-efficient NW deployments - RWS-120010
Considerations on dense NW deployment - RWS-120019


Energy Consumption, Efficiency and Savings
Energy efficiency - RWS-120005
Reduce energy consumption - RWS-120008
Energy Saving - RWS-120014
UE Power Saving - RWS-120036 / RWS-120050
NB Power Saving - RWS-120036 / RWS-120050
Energy Saving Enhancements with CoMP - RWS-120040
Energy Saving with Centralized eNB - RWS-120040


Herogeneous Networks (HetNets)
Optimisation of Het Nets performance - RWS-120005
Improved Support for Heterogeneous Networks - RWS-120006
Network hyper-densification: LTE HetNet2.0 - RWS-120007
Multi-layer HetNet Deployments - RWS-120016
HetNet for HSPA - RWS-120017
HetNet Enhancements - RWS-120023
HetNet Mobility - RWS-120029
Small cells & HetNet - RWS-120031
HetNet - RWS-120037
HetNet Enhancements for HeNB - RWS-120040


HSDPA / HSUPA / HSPA+ Enhancements
HSPA UL Enhancements - RWS-120003
Uplink Enhancements - RWS-120006
UMTS evolution: enhancing CS voice on DCH - RWS-120007
High Speed Packet Access - RWS-120012
HSPA RRM enhancement - RWS-120024
HSPA+ further evolution - RWS-120034


Interworking (HSPA, LTE)
Coordination : HSPA/LTE e-interworking - RWS-120024
Inter-RAT Coordination/CA - RWS-120037


Local-Area Access (Small Cells)
Local-Area Access - RWS-120003
LTE in Local Area Deployments & Enhancements - RWS-120004
LTE Local Area Enhancements - RWS-120004
LTE Local Area Enhancement Areas - RWS-120004
enhanced Local Area (eLA) - RWS-120010
Local Area Enhancements - RWS-120022
Improved Local Area Mobility - RWS-120022


LTE
LTE for Nomadic and Fixed Use - RWS-120018
E-PDCCH enhancement - RWS-120019
Efficiency : Paging Optimization - RWS-120024


LTE Hotspot and Indoor Enhancements (LTE-Hi)
Hotspot and Indoor Enhancements (LTE-Hi) - RWS-120006
Hotspot/indoor Scenario (LTE-Hi) - RWS-120025
Indoor & Hotspot Enhancements (LTE-Hi) [Detailed] - RWS-120029
Possible Study Items for Indoor Environment - RWS-120040


M2M / Machine Type Communications (MTC)
Machine Type Communications - RWS-120003
Improved Support for MTC - RWS-120006
Machine-to-Machine: The Internet of Things - RWS-120014
Machine Type Communications: a new ecosystem - RWS-120014
Wireless MTC and RAN optimizations for MTC - RWS-120016
Low-Cost MTC UE - RWS-120017
MTC + eDDA (enhanced Diverse data application) - RWS-120019
Further Enhancements to Support MTC - RWS-120023
MTC - RWS-120025
MTC enhancements - RWS-120026
M2M - RWS-120029
MTC and migration of traffic from 2G - RWS-120031
Machine Type Communications enhancements - RWS-120034
Machine Type Communications - RWS-120035
Extension triggered by growing M2M traffic - RWS-120038 / RWS-120047
LTE-based M2M - RWS-120041


MBMS / eMBMS
eMBMS Enhancements - RWS-120007
eMBMS - RWS-120013
UHD Multimedia Broadcast/Multicast Service - RWS-120036 / RWS-120050


Mesh Networks
Mesh Networks - RWS-120018


Network Density
Network density: Scenarios - RWS-120010


Network Architecture and Operation
Easier network operation, tolerance to failure - RWS-120005
System Architecture - RWS-120032
Evolution of LTE Networks - RWS-120034


Positioning
Positioning Enhancements - RWS-120006


Public Safety
Public Safety - RWS-120030
Operation of Public Safety System via LTE - RWS-120031
Public safety’s future in LTE [Detailed] - RWS-120033


Self Organising Networks (SON) and Minimisation of Drive Testing (MDT)
SON Evolution - RWS-120002
Enhanced MDT - RWS-120011
Network Self-Optimisation - RWS-120014
SON and MDT - RWS-120017
HetNet SON - RWS-120029
MDT & Energy Saving - RWS-120029
Autonomous Interference Coordination - RWS-120029
Large scale multi-layer centralized cooperative radio - RWS-120034
MDT Enhancement - RWS-120036 / RWS-120050
SON Enhancements - RWS-120036 / RWS-120050
MDT and eDDA - RWS-120041


Small Cells (HNB/HeNB)
UMTS evolution: small cells - RWS-120007
Wide & Local area enhancements - RWS-120010
Small Cells - RWS-120014
Small Cell Enhancement in Rel-12 - RWS-120021 / RWS-120046
HeNB Enhancement - RWS-120036 / RWS-120050
Efficient Usage of Macro and Small Cells - RWS-120038 / RWS-120047
Low-cost Low Power Nodes (LC-LPN) - RWS-120038 / RWS-120047
Small-Cell Improvements: System Aspects - RWS-120041


Spectrum
Enhanced spectrum efficiency - RWS-120005
Spectrum efficiency: eLA topics - RWS-120010
Scenarios for spectrum extension - RWS-120010
Spectrum and spectrum usage - RWS-120012
Wider Spectrum Utilization - RWS-120016
Spectral efficiency for LTE - RWS-120017
New Spectrum for Mobile Broadband Access - RWS-120021 / RWS-120046
Enabling Technologies for New Spectrum - RWS-120021 / RWS-120046
Radio Propagation - RWS-120021 / RWS-120046
Opportunistic Use of Unlicensed Spectrum for D2D Local Traffic - RWS-120023
Flexible Spectrum Utilization - RWS-120024
Spectrum Related: New Bands And CA Band Combinations - RWS-120029
Spectrum - RWS-120032
Hybrid access scheme - RWS-120034
Spectrum - RWS-120035
Spectrum and Transmission Efficiency - RWS-120039
Spectrum-Agile LTE - RWS-120041


TDD / TD-LTE
TD-LTE - RWS-120014
TDD-specific aspects - RWS-120014
TDD adaptive reconfiguration - RWS-120034
Efficient Usage of Dual Duplex Modes - RWS-120038 / RWS-120047
LTE TDD Small-Cell versus WiFi - RWS-120041


Testing
Testing and Certification - RWS-120022


Traffic and Signalling Overhead
Efficient support of diverse traffic characteristics - RWS-120005
Efficient support for variety of traffic types - RWS-120010
Enhancements for variety of traffic types - RWS-120010
Very high traffic (and signalling) scenarios - RWS-120017
Control Plane Overhead Reduction - RWS-120021 / RWS-120046
Further Enhancements to Support Diverse Data Applications - RWS-120023
Efficiency : Small data services in high mobility - RWS-120024


User Experience
Improve User experience - RWS-120009
User Challenges - RWS-120032


Video streaming, call
RAN Enhancements for Video Streaming QoE - RWS-120023
RAN Enhancements for Internet Video Call - RWS-120023


WiFi / WLAN
Cooperation between LTE/HSPA and WiFi - RWS-120005
Unlicensed spectrum: LTE & WLAN - RWS-120007
LTE integration with other RATs - RWS-120014
WiFi integration: For Beyond Rel-12 - RWS-120017
LTE-WLAN Interworking - RWS-120023
Coordination With WiFi - RWS-120029
Smarter opportunistic usage of Wi-Fi - RWS-120031
LTE TDD Small-Cell versus WiFi - RWS-120041


Others
Other identified techniques for LTE - RWS-120005
Efficient Transactions - RWS-120035
Link Enhancement Considerations - RWS-120035
Intra-RAT cooperation / Inter-RAT cooperation - RWS-120036 / RWS-120050

Here is the summary from the workshop:

Summary of 3GPP TSG-RAN Workshop on Release 12 and Onward

View more PowerPoint from Zahid Ghadialy

Complete list of Presentations

RWS-120002	Release 12 and beyond for C^4 (Cost, Coverage, Coordination with small cells and Capacity)	NSN
RWS-120003	Views on Rel-12	Ericsson & ST-Ericsson
RWS-120004	LTE evolving towards Local Area in Release 12 and beyond	Nokia Corporation
RWS-120005	Views on Release 12	Orange
RWS-120006	Views on Rel-12 and onwards for LTE and UMTS	Huawei Technologies, HiSilicon
RWS-120007	3GPP RAN Rel-12 & Beyond	Qualcomm
RWS-120008	New Solutions for New Mobile Broadband Scenarios	Telefonica
RWS-120009	Telecom Italia requirements on 3GPP evolution	Telecom Italia
RWS-120010	Requirements, Candidate Solutions & Technology Roadmap for LTE Rel-12 Onward	NTT DOCOMO, INC.
RWS-120011	Where to improve Rel-12 and beyond: Promising technologies	NEC
RWS-120012	Deutsche Telekom Requirements and Candidate Technologies	Deutsche Telekom
RWS-120013	Release 12 Prioritization Concepts	Dish Networks
RWS-120014	Towards LTE RAN Evolution	Alcatel-Lucent
RWS-120015	UE AAS (Active Antenna System)	Magnolia Broadband
RWS-120016	Requirements and Technical Considerations for RAN Rel.12 & Onwards	Fujitsu Limited
RWS-120017	Operator requirements on future RAN functionality	TeliaSonera
RWS-120018	AT&T View of Release 12 in the North America Marketplace	AT&T
RWS-120019	Major drivers, requirements and technology proposals for LTE Rel-12 Onward	Panasonic
RWS-120020	Efficient spectrum resource usage for next-generation N/W	SK Telecom
RWS-120021	Technologies for Rel-12 and onwards	Samsung Electronics
RWS-120022	LTE Rel-12 and Beyond	Renesas Mobile Europe
RWS-120023	LTE Rel-12 and Beyond: Requirements and Technology Components	Intel
RWS-120024	Considerations on further enhancement and evolution of UMTS/LTE network in R12 and onwards	China Unicom
RWS-120025	Views on LTE R12 and Beyond	CATT
RWS-120026	A proposal for potential technologies for Release 12 and onwards	ETRI
RWS-120027	A view on requirements on Rel-12 and onwards from an operator’s viewpoint	Softbank Mobile
RWS-120028	India market Requirements for Rel. 12 and beyond	CEWiT
RWS-120029	Views on LTE Rel-12 & Beyond	CMCC
RWS-120030	LTE addressing the needs of the Public Safety Community	IPWireless
RWS-120031	Vodafone view on 3GPP RAN Release 12 and beyond	Vodafone
RWS-120032	An Operator’s View of Release 12 and Beyond	Sprint
RWS-120033	Public Safety Requirements for Long Term Evolution REL-12	U.S. Department of Commerce
RWS-120034	Views on 3GPP Rel-12 and Beyond	ZTE
RWS-120035	Considerations for LTE Rel-12 and beyond	Motorola Mobility
RWS-120036	LG’s view on evolution of LTE in Release 12 and beyond	LG Electronics
RWS-120037	Views on REL-12 and Onwards	China Telecom
RWS-120038	KDDI’s Views on LTE Release 12 onwards	KDDI
RWS-120039	Evolving RAN Towards Rel-12 and Beyond	SHARP
RWS-120040	Views on enhancement of system capacity and energy efficiency toward Release12 and onward	Hitachi
RWS-120041	Beyond LTE-A: MediaTek’s view on R12	MediaTek
RWS-120042	Potential Technologies and Road Map for LTE Release 12 and Beyond	ITRI, HTC
RWS-120043	New concept to maximize the benefit of interference rejection at the UE receiver: interference suppression subframes (ISS)	Broadcom
RWS-120046	Technologies for Rel-12 and onwards	Samsung Electronics
RWS-120047	KDDI’s Views on LTE Release 12 onwards	KDDI
RWS-120048	A view on Rel-12 and onwards from an operator’s viewpoint	Softbank Mobile
RWS-120049	UE AAS (Active Antenna System)	Magnolia Broadband
RWS-120050	LG’s view on evolution of LTE in Release 12 and beyond	LG Electronics
RWS-120051	New concept to maximize the benefit of interference rejection at the UE receiver: interference suppression subframes (ISS)	Broadcom

More technically minded people want to explore the 3GPP website for the workshop links here: http://3gpp.org/ftp/workshop/2012-06-11_12_RAN_REL12/

Draft report that gives more insight into the presentations as follows:

Draft Report of 3GPP RAN Workshop on Release 12 and onwards

View more documents from Zahid Ghadialy