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LTE - IMT advanced - Candidate Technologies

3GPP TSG RAN IMT Advanced Workshop REV-080045 Shenzhen, China, April 7-8, 2008. LTE - IMT advanced - Candidate Technologies. Content. New technologies for PHY Multi-antenna Processing & Scheduler SON Realization and Evolution.

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LTE - IMT advanced - Candidate Technologies

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  1. 3GPP TSG RAN IMT Advanced Workshop REV-080045 Shenzhen, China, April 7-8, 2008 LTE - IMT advanced -Candidate Technologies

  2. Content • New technologies for PHY Multi-antenna Processing & Scheduler • SON Realization and Evolution

  3. LTE - IMT advanced -New Technologies for PHY Multi-antenna Processing

  4. Targets • LTE/WiMAX performance results • DL Cell edge rate should be improved • UL Cell average/edge rate should be improved ALU preferred Requirements ( towards IMT Advanced) • DL peak spectral efficiency ---> 10 b/s/Hz/sector • UL peak spectral efficiency ----> 5 b/s/Hz/sector • DL average spectral efficiency - 3-4 b/s/Hz/sector • UL average spectral efficiency - 1.5-2 b/s/Hz/sector • DL cell edge spectral efficiency ---> 0.12 b/s/Hz/sector • UL cell edge spectral efficiency ---> 0.06 b/s/Hz/sector • Sector: 120°

  5. Outlook on candidate technologies • Novel MU-MIMO algorithms (PHY, MAC) • Adaptive switching between single-/multi-user/multi-site modes • Combination of spatial multiplexing and beamforming • Network MIMO concepts and algorithms (PHY, MAC) for FDD/TDD • Coherent/non-coherent solutions • Centralized (e.g.RRH) and distributed (collaboration among Node Bs) solutions • Dynamic ICIC concepts • Dynamic exchanges of resource blocks ultiziation among Node Bs • Beam Coordination between cells in collaboration • Schedulers for exploitation of the advanced MIMO and multi-site features • Cross-layer optimal resource allocation with advanced MU-MIMO/IFCO features • Multi-site scheduler with exploitation of the multi-site features • Interworking and optimization between UL/DL scheduler

  6. MIMO recommendations for LTE advanced FDD

  7. In General • Overall MIMO recommendations for LTE advanced (FDD): • Place greatest emphasis on MU-MIMO, since it has the most attractive performance-complexity tradeoff • SU-MIMO should be pursued to deliver high peak user rates for IMT-Adv requirements • Increase of DL cell edge rates by • Multi-site Collaborative MIMO (constructive data instead of interference) • Complemented by a combination • Spatial Interference Coordination (beam coordination) • Fractional frequency/time reuse Interference Coordination • Further gains in spectral efficiency are desired on uplink, • network MIMO with coherently coordinated bases.

  8. MIMO Configurations MIMO Multiple bases(Network MIMO) Single base Co-locatedantennas Distributed antennas Noncoherent(Magnitude only) Coherent(Magnitude/phase) SU-MIMO, MU-MIMO MacroscopicMIMO CollaborativeMIMO CoherentNetworkMIMO

  9. Single-site MIMO evolutions (FDD) • DL MU-MIMO based on one or both of the following approaches depending on antenna configuration, cell size and mobility: • Fixed-beams (e.g., grid-of-beams approach): Suitable for high mobility, can operate without dedicated pilots (but would benefit from them), works best with closely spaced antennas. >= 4*x xpol. Tx-antennas or 1*4 Tx antennas • User-specific beams (e.g., ZF): Suitable for low mobility, requires dedicated pilots, but potentially better interference suppression. 2*2 Tx antennas • With closely spaced antennas the same given beam could be applied over the whole bandwidth, reducing uplink feedback requirements. • Techniques (e.g., hierarchical feedback) to reduce CSI feedback requirements. • MU-MIMO with user-specific beams should be revisited with the target of reduced feedback bandwidth. • UL MU-MIMO • Performance improvement with more than 1 transmit antennas at UE (2-4) • ensure that signaling supports co-channel transmission by multiple users.

  10. data stream 1/2 MIMO channel Base- station MS 1 MS 2 data stream 3 Evolved MIMO for IMT-Advanced Extended Precoding • Combinations of Beamforming and Diversity Transmission • Beamforming for Multi-User Transmission (SDMA), based on closely spaced antenna elements (0.5 lambda) • Diversity for link enhancement and/or spatial multiplexing, based on cross-polarized antenna elements • Requires appropriately optimized codebooks for the antenna weights • For up to 8 antenna elements in a 4x2 X-pol. configuration ( compact housing)

  11. Grid of fixed beams system level results based on 3GPP LTE parameters • 7x3 cells with wrap around, av. 10 users per cell • 10 MHz BW • Control and pilot overhead considered • Score based proportional fair scheduling • NGNM case 1 parameter set: • 500m ISD, 3km/h, 20 dB Penetr. loss

  12. 1 Users estimate channel and feedback quantized state. 2 Base selects users to serve and calculates beam weights to maximize sum rate while addressing fairness. 3 Data is transmitted. MET block diagram 1 A 3 B 2 User datastreams User selection Beam-forming 2 C 3 D Channel state feedback 1 Multiuser MIMO and scheduling for limited feedback • In Multiuser (MU) MIMO, multiple streams can be allocated among different users. • MU eigenmode transmission (MET) uses channel knowledge at the Tx to form non-interfering user-specific beams. • Design codebooks whose codewords are indexed using uplink feedback bits. • Aggregate B feedback bits per signaling interval for hierarchical feedback. RelativeSum throughput gain Mobile speed (kmph) 3 50 Unitary beamforming(baseline) 1.0 1.0 MET withhierarchical feedback 1.4 1.28 K = 20 users per sector, 1 rx ant per user,B = 4, M = 4 tx ants (10l spacing) Note: Baseline values normalized to 1 for different velocities

  13. Multiple-site MIMO (FDD) • For UL, application of Network MIMO coherently coordinates a reasonable number of base stations in Rx • Standardization issues: pilot structure, signaling and X2 Interface • Issues like backhaul bandwidth and architecture, channel estimation overhead should be investigated • Coherent network MIMO for DL only in TDD mode. • Feedback requirements in FDD are likely to be too high. • Possible FDD Solution for DL as a adaptive combination of: • Multiple Site Non-coherent Collaborative MIMO to leverage Cell edge rate • Single Site MU-MIMO to leverage the cell average rate • Single site SU-MIMO (+ Tx-Div) to leverage the user rate

  14. Collaborative/Network MIMO overview Coordinate transmission and reception of signals among multiple bases. Reduces intercell interference and improves cell-edge performance and overall throughput. Collaborative MIMO: share user data and long-term noncoherent channel information. Coherent network MIMO: share user data and short-term coherent channel information.

  15. Multi-Mode Adaptive MIMO for DL/UL MAC layer Use adaptive MIMO to accommodate demand of higher data rate and wider coverage in next generation broadband wireless access • SU MIMO for peak user data rate improvement • MU MIMO for average data rate enhancement • Collaborative/Network MIMO for cell edge user data rate boost Cross-layer design A uniform MIMO platform SU-MIMO adaptive selection MU-MIMO Collaborative/ Network MIMO

  16. MIMO channel Key technologies in Multi-mode Adaptive MIMO Data + Sync Protocol for DL (Extension of eMBMS protocol); Data + Channel Estimates for UL Serving eNB/ per User Multi-dimension adaptation • Adaptation strategy • Multi-variable channel measurement • Low-rate feedback mechanism Cellular system SU-MIMO SU-MIMO enhancement • Closed-loop MIMO • Iterative MIMO receiver Multicast Anchor Collaborative/Network MIMO MU-MIMO Collaborative/Network MIMO/Beam Coordination • Implementation of multi-BS collaboration with channel information MU-MIMO optimization • MU precoding algorithm • Trade-off design of scheduler between complexity and performance eNBs have to be synchronized !!!

  17. Collaborative MIMO DL: First Simulation Results • Cell deployment • 19-cell wrap around • A serving sector surrounded by 6 adjacent • 10 users per sector • BS: 4 Tx ant. per sector; MS: 2 Rx antennas • MIMO channel: i.i.d (next step SCM) • Random scheduling (next step proportinal fair like) • Candidate Co-MIMO user decision • Each candidate Co-MIMO usercan be served by N (N=2,3) sectors • A user is candidate for Co-MIMO mode if • PRxs – PRx1 < Co-MIMO threshold • The i-th neighboring sector satisfying • PRxs – PRxi < Co-MIMO threshold • is possible to cooperatively serve the user. • Code book based feedback • Co-MIMO includes SU-MIMO for users not in collaboration • The multiple resource usage for the collaboration case is taken into account • Without control & Pilot overhead

  18. Coherent Network MIMO for UL • What is it: Interference reduction via coherent receiver coordination between multiple bases. • How does it work: Coordinating base stations compute beamforming weights that maximize SINR (MMSE) for each user. • Potential performance gains of Network MIMO for S-sector coordination Baseline: single-sector (1Tx 4Rx) • What’s needed to make it happen: • Short-term coherent channel knowledge and user data shared among coordinating bases. • Backhaul traffic increases by factor S (if all users are In collaboration) • 10% channel knowledge, 90% user data . • Time and phase-synchronized transmission among coordinating BSs. 3-sector 3-sector 9-sector 9-sector(no FFR) (w/ FFR) (no FFR) (w/ FFR) Equal user rates Unequal user rates avg. throughput 1.2x 1.9x avg.throughput 1.15x 1.15x 1.25x 1.25x “cell-edge” 1.6x 1.9x 2.7x 3.4x

  19. Efficient Channel Quality Feedback for IMT-Advanced • UL feedback channel is a bottleneck for the system performance in an FDD system. A more efficient feedback scheme provides • lower resource usage in the uplink and/or • higher downlink performance through finer granularity of the channel state information knowledge at the basestation • Compression / sourcecoding of channel state information feedback • based, e.g., on Wavelets (or other transformations) • allows variable frequency resolution over the bandwidth • e.g., UE adaptively provides high resolution of CQI on good subcarriers / resource blocks, & low resolution on bad resource blocks • Hierarchical Feedback approach • successive refinement of the quantization with imperfect channel state information at the Tx.

  20. Conclusion • Mix of these technologies allows to meet the IMT adv performance requirements • The introduction/improvement of MU-MIMO in DL and UL has a high potential to boost the cell average rate • Co-MIMO for DL can be applied to FDD system to improve cell edge performance and average cell capacity • About 70% improvement for cell edge rate rate compared with SU-MIMO • 25% improvement in average sector capacity compared with SU-MIMO • Network MIMO can be applied for the UL (FDD) for the DL/UL(TDD) • 25% improvement for cell average rate compared to MU-MIMO (further improvements from single site MU-MIMO) • Factor 3.4 gain for cell edge rate

  21. SON for IMT-Advanced Networks: Self Organizing and Optimizing Networks

  22. Target: Simplified Network Operation • Self-Organizing-Network (SON) technologies • 100% Plug and Play • Fully decentr. OMC less Network Management (prio for pico/femto layer) • Self-protection against malicious resource usage (multi-vendor problem) • Multi-RAT operation (intra 3GPP and inter 3GPP) • Self-configuration / optimization for heterogeneous networks (3GPP / non 3GPP) • Generic protocols and measurements • Generic parameters for • Handover decisions • Load balancing • QoS optimization • Multi-operator networks • RAN sharing • Equipment sharing

  23. Evolution: Phased Approach for Self-x (SON) introduction • First step (LTE-R8): • focus mainly on configuration use cases needed for first deployments • NEM centric automated configuration and tool based optimisation • Self-x support functions decentralized in eNB (for configuration and optimisation use cases) • tight control and surveillance in OMC • Second step (beyond LTE R8): • decentralised “NEM less” architecture (Pico & Femto Layer) • Complete Self-x functions put to eNB • NM/OSS: performance and alarm management, • NM/OSS: control/tuning of Self-x use cases requiring • deeper system performance analysis and simulation, • further standardisation required

  24. tools for RAN planning, configuration and optimisation deployment new site, add new cell, capacity upgrade failure cases performance optimisation conventional parameterconfiguration self-optimisation self-configuration Evolution: Phased Approach for Self-x (SON) introduction • Release 8: • RAN configuration use cases: • Add/Remove cell incl. power saving cell (Auto download of initial radio parameters from OMC) • Neighbourhood relation configuration and optimisation for LTE • Release 9 and +: • RAN optimization use cases • Cell outage compensation • LTE handover parameter optimisation • Interference optimisation for LTE • Load balancing for LTE • QoS optimization use cases • Scheduler operation optimisation for LTE • MIMO Mode Selection Optimisation for LTE

  25. Use Case “LTE Handover Parameter Optimisation” • Self-optimisation of initially configured HO parameters • Optimisation goals • Minimisation of HO failure rates for intra-LTE • Avoidance of ping-pong effects • Enhancement to Multi-RAT HO • Optimisation approach: Self-optimisation of HO parameters leading to UE handover request • HO thresholds, hysteresis, Cell Individual Offset (CIO),time to Trigger (TTT) after analysis of handover • Challenge: user throughput at HO (cell edge) • Considering QoS at cell edge during handover as constraints

  26. Use Case “Interference Coordination in UL and DL” • Dynamic or semi-static interference coordination of radio resources (example: frequency case) • Possible optimisation goals • Cell edge bitrate, improved fairness, load balancing, increased number of real time users, network capacity • Power restriction scheme and power attenuation • Indication of upper limit of Tx power per PRB relative to the rated output power • Exchange of upper limits of the Tx power per PRB and resource restrictions between neighbour eNBs • over X2 interface in intervals of 200 ms to 1 s

  27. Use Case “Scheduler optimisation” • Optimisation goals • self-optimisation of user -, cell-, cell edge throughput & delay according to operator preferences with weightings and fairness parameter • self-optimisation of network service availability per QoS label • Optimisation approach for QoS and scheduler configuration parameters • indication of estimated impact on performance and resulting QoS based on target derived from Off-line System Simulations • adaption of scheduler operation to actual traffic mix • PFMR (Proportional Fair with Minimum Rates) scheduling for tuning of cell edge bit rate and cost versus fairness proportional to experienced radio conditions

  28. Use Case “MIMO Mode Selection Optimisation for LTE” Optimisation goal of MIMO modes switching • Optimum service provisioning among attached UEs • Cell edge data rate and total cell throughput • Optimisation of network due to insufficient radio condition (SINR) at cell edge, and service availability per QoS label • Optimisation approach • Evaluation of mapping of link characteristic (rank, SINR) to MIMO modes • Configuration of MIMO thresholds and MIMO-mode switching criterions (diversity, beamforming, spatial multiplexing for SU MIMO and MU MIMO) supported by targets derived from Off-line System Simulation

  29. Self-X Architecture Evolution (priority Pico & Femto Layer) (1) 1ststep self-config Fully decentralised in eNBs & Multivendor NM Evolution Today OMC/NEM centric automated configuration

  30. NM OSS Itf-N X2-Itf Network Management  performance monitoring  KPIs  alarms  high level network performance tuning LTE RAN self-x eNB self-x self-x RAN self-optimization eNB Self-X Architecture Evolution (prio for Pico & Femto Layer) (2) • Vision of fully decentralised self-optimisation • Network management in NM OSS • network planning • alarm and performance monitoring • high level performance tuning • open Itf-N • “NEM less” network management • Fully autonomous, distributed RAN optimisation • Self-x functions in UE and eNB • measurements, UE location info • alarms, status reports, KPIs • distributed self-x algorithms • Self-x information exchange via X2 • Multi-vendor interoperability supported via X2 (to support Pico & Femto deployments) eNB

  31. Conclusion Significantly improved radio network management by SON: • emphasis on performance tuning and supervision • 100% plug and play • continuous, automated radio network optimisation w.r.t. operator preferences • innovative techniques for performance optimisation (scheduler, MIMO modes) • considerable effort reduction for operators

  32. www.alcatel-lucent.com www.alcatel-lucent.com

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