1 / 20

FEMTOCELL PERFORMANCE STUDIES FOR LTE

FEMTOCELL PERFORMANCE STUDIES FOR LTE. Supervisor: Prof. Jyri Hämäläinen Instructor: M.Sc Zhong Zheng A part of NETS2020 project Ying Yang 18.05.2011. Outline. Introduction and motivation Femtocell challenges and problem statement Research approach & Assessment methodology

jenny
Télécharger la présentation

FEMTOCELL PERFORMANCE STUDIES FOR LTE

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. FEMTOCELL PERFORMANCE STUDIES FOR LTE Supervisor: Prof. JyriHämäläinen Instructor: M.ScZhongZheng A part of NETS2020 project Ying Yang 18.05.2011

  2. Outline • Introduction and motivation • Femtocell challenges and problem statement • Research approach & Assessment methodology • LTE Fractional Power Control (FPC) • Stochastic Frequency Domain Scheduler (SFDS) • Simulation results • Conclusion

  3. Introduction & Motivation • 3GPP LTE project aiming to achieve a high quality of services in packet switching optimized system with higher data rates and cheaper infrastructure. • (3G UMTS ↑5.8↓14 <-> LTE↑50↓100 Mbps) • 3G network drawback – Indoor coverage shortage  • Femtocell can solve this bottleneck and fulfill the requirement of LTE development • Small BS with limited power and coverage area • Suitable for indoor communication environment • Decrease macrocell traffic load dramatically (50% call, 70%SMS takes place indoor) and provide higher data rate by direct connecting to Internet through broadband or DSL

  4. Femtocell Challenges & Problem Statement • Interference to existing macro cells - Inter-macro-femtocell interference suppression • Interference between femtocells • Integration to LTE core network • Marketing and product promotion • Service stability • Price attraction

  5. Research Approach & Assessment methodology • Adjust FUE PC parameters and find an optimal set to compensate interference from neighboring MUEs while keeping minimum degradation of MUE performance • Apply a simple distributed frequency-domain scheduling strategy (SFDS) in FBS to roughly separate macro and femto spectrum in a probabilistic manner • User Throughput – The total throughput per PRB for FUE and/or MUE in bps. All the user throughputs are considered in link level. • X%-tile User Throughput – The throughput at the X%-tile point of the Cumulative Distribution Function (CDF) of the user throughput in bps, which is the key indicator for coverage and capacity performance • User SINR and Transmission Power – The experienced SINR and distributions of FUE/MUE total transmission power in dBm • Spectral Efficiency – The data transmission rate for FUE/MUE normalized by the transmission bandwidth in bps/Hz

  6. LTE Fractional Power Control (FPC) • In LTE UL, UE transmit power is determined by open-loop FPC[1] according to formula (1): (1) • maximum allowed transmit power for UE • : a user-specific parameter to control SINR target ranged from -126 to 24 dBm with step size of 1 dB • M: number of allocated PRB to UE • a cell-specific path loss compensation factor with value 0 or from rage 0.4 to 1.0 with step size of 0.1 • L: propagation path loss estimated by UE

  7. LTE FPC Cont. • UL SINR per PRB of user seen at MBS or FBS are given by: (2) (3) Where , , denotes serving BS of user • Simulation begins by exhaustive search for and values for FUE over the parameter spaces

  8. Stochastic Frequency-Domain Scheduler (SFDS) In DL, and PRBs are allocated for each MUE and FUE, respectively (4) (5) • is the available PRBs in indoor band after jth allocation • is a bias parameter

  9. SFDS Cont. • MBS can identify indoor MUEs that potentially create/receive critical interferences towards/from FBSs in UL/DLbecause MBS keep monitoring the measurement report from served MUEs[2], thus identifying close-by FBS since they have unique IDs. • SFDS rely on the MBS broadcasting signaling message and FBS made scheduling decision locally • Fast Signaling () • Slow Signaling () • In LTE UL, single carrier FDMA constraints the allocated PRBs must continues in frequency domain • MBS monitors MUEs location according to measurement report when MUE enters CSG femtocell, and signaling the indoor PRB load to nearby FBSs

  10. Simulation Scenario

  11. Simulation Parameters

  12. Channel Models • MUE-MBS • FUE-MBS • FUE-MBS, same building • MUE-FBS + • FUE-BS, different buildings + Where R is separation distance in meters, and are penetration losses due to inner wall between apartments and outer wall of the building, and are the number of penetrated walls and floors. The term accounts for the penetration loss within the apartment

  13. Optimal FUE PC parameters • decrease ( increase) -> average SINR for MUE deviates from FBS-free case due to increased ICI • When all 10 dots are clearly • Optimal PC parameter sets () max. FUE SINR while keeping minor MUE performance degradation 50%-tile SINR of FUE and MUE with

  14. UL Scheduling Gain with SFDS ‘O’:= 4; ‘△’: =24; ‘▽’: =48 • SFDS provides better FUE throughput than random PRB allocation (dash-dotted curves), especially when FBS traffic load is small (4) • When , adjusting bias parameter can further improve the scheduling performance since it reaches higher probability in to avoid interference from indoor MUEs • Slow signaling () results a few bps than fast signaling (), more suitable for scarce MBS signaling capacity CDFs of FUE PRB throughput when and

  15. UL Scheduling Gain with SFDS ‘O’:= 4; ‘△’: =24; ‘▽’: =48 • Similar simulation with 30 dB reduction compare with previous settings • Same result • High-rate users suffer significantly in terms of percentage CDFs of FUE PRB throughput when and

  16. FUE total transmission power comparison with 3 different settings: • Smaller provides less mean power level, thus impacting the UE power consumption • Selected PC sets ( and ) provide approximately 10 dB increase in PC dynamic range

  17. DL Scheduling Gain Solid curves: Fast signaling Dashed curves: Slow signaling • When , non-bias, random PRB allocation when FBS has maximum freedom to allocate PRBs to FUE. FBS has smallest collision probability of PRB allocation with other FBS in DL -> highest FUE spectral efficiency • ↑MUE performance↑ untilwhen FBS avoid using , and FUE spectral efficiency decreases due to co-interference • ↑ FUE and MUE spectral efficiency ↓because FUE try to utilize the whole bandwidth, thus causing larger probabilities for PRB assignment between cells. • Slow signaling provides few bps reduction especially with medium spectrum utilization (16 &24) 50%-tile UE spectral efficiency by SFDS with fast and slow signaling

  18. Conclusion • An optimal PC configurations for FBS is found by simulation which maximizes the FUE link quality while keeping minimum degradation to nearby MUEs • SFDS can roughly separates the spectrum usage for FUEs and MUEs in a probabilistic manner by performing biased resource selection in different part of the allocated spectrum, simulation results show significant performance gain when femtocell has low or moderate load

  19. THANK YOU! Questions?

  20. References • [1] C. U. Castellanos, D. L. Villa, C. Rosa, K. I. Pedersen, F. D.Calabrese, P. H. Michaelsen, and J. Michel, “Performance of uplink fractional power control in UTRAN LTE,” in Proceedings of the IEEE Vehicular Technology Conference VTC′08, pp. 2517-2521, May 2008. • [2] 3GPP, TR 25.367, “Mobility procedures for Home Nod B(HNB), Overall description, Stage 2”, V9.4.0, June 2010.

More Related