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Centre for Communications Research Power Efficient MIMO Techniques for 3GPP LTE and Beyond K. C. Beh, C. Han, M. Nicolaou, S. Armour, A. Doufexi.

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Presentation on theme: "Centre for Communications Research Power Efficient MIMO Techniques for 3GPP LTE and Beyond K. C. Beh, C. Han, M. Nicolaou, S. Armour, A. Doufexi."— Presentation transcript:

1 Centre for Communications Research Power Efficient MIMO Techniques for 3GPP LTE and Beyond K. C. Beh, C. Han, M. Nicolaou, S. Armour, A. Doufexi

2 Green Radio 4 billion mobile phone users worldwide Telecommunication industry responsible for 183 million tons of CO2 MVCE framework (Core 5): Deliver high data rate services with a 100- fold reduction in power consumption

3 Green Radio and LTE LTE next major step in mobile radio communications Aim to reduce delays, improve spectrum flexibility, reduce cost of operators and end users MIMO transmission techniques improve system reliability and performance LTE support of a MIMO scheduling and precoding method with improved interface between PHY and DLC

4 Green Radio and LTE Examine performance of proposed MIMO-OFDMA scheme Consider the capabilities of MIMO-OFDMA precoding in reducing Tx. Power from Base Station (BS) Theoretical analysis and simulation results Maintain QoS levels with reduced Tx. Power

5 System and Channel Model Spatial Channel Model Extension (SCME) Urban Macro Low spatially correlated channel for all users 2x2 MIMO architecture (analysis is readily extendible to higher MIMO orders) Perfect CQI estimation and feedback Ideal Link Adaptation based on 6 Modulation and Coding Schemes (MCS)

6 System and Channel Model Transmission Bandwidth10 MHz Time Slot/Sub-frame duration0.5ms/1ms Sub-carrier spacing15kHz Sampling frequency15.36MHz (4x3.84MHz) FFT size1024 Number of occupied sub-carriers 600 Number of OFDM symbols per time slot (Short/Long CP) 7/6 CP length (μs/samples) Short(4.69/72)x6 (5.21/80)x1 Long(16.67/256)

7 System and Channel Model ModeModulationCod. RateData bits per time slot (1x1), (2x2) Bit Rate (Mbps) 1QPSK1/24000/76008/15.2 2QPSK3/46000/1140012/22.8 316 QAM1/28000/1520016/30.4 416 QAM3/412000/2280024/45.6 564 QAM1/212000/2280024/45.6 664 QAM3/418000/3420036/68.4

8 Random and Layered Random Beamforming Random Unitary Matrix applied to frequency sub-carriers on Physical Resource Block (PRB) basis Linear MMSE Receiver with interference suppression capability MIMO channels can be decomposed into separate spatial layers ESINR feedback for resource allocation Random Beamforming: All spatial layers to a single user Layered Random Beamforming: Spatial layers assigned to different users Higher Diversity

9 Unitary Codebook Based Beamforming Pre-defined set of antenna beams Pre-coders based on Fourier basis for uniform sector coverage Variable codebook size G, consisting of the unitary matrix set Large Codebook: Higher Spatial Granularity, Increased Feedback Small Codebook: Low Spatial Granularity, Lower Feedback Single-User MIMO (SU-MIMO) and Multi-User MIMO (MU-MIMO) capability

10 Feedback Considerations Full Feedback: CQI for all precoding matrices Partial Feedback: CQI on preferred beams Suboptimal performance for MU- MIMO with partial feedback Codebook size G=2 assumed

11 Theoretical Analysis Precoding schemes achieve varying degrees of Multiuser Diversity (MUD) (K=5) A target spectral efficiency achieved at different SNR levels for different schemes

12 Theoretical Analysis Target Spectral Efficiency 3bps/Hz Single User SISO Benchmark Higher benefits for increasing numbers of users K=10, MU-MIMO, Gain= 5dB

13 Simulation Results Analysis based on ideal Adaptive Modulation and Coding (AMC) Throughput = R(1-PER), Results consistent with theoretical analysis

14 Simulation Results Simulation performance predicts even higher gains Actual performance PER dependent. MU-MIMO and LRB eliminate deep fades that cause severe link degradations MU-MIMO gain @ K=10: 7dB SFBC suffers from inherent inability to exploit MUD

15 Power Efficiency and Fairness Power Efficiency associated with a cost metric and a corresponding Power Fairness Index (PFI) Low cost metric implies high power efficiency Cost MetricVariance SISO2.15930.6923 Random Beamforming0.91710.0573 Layered Random Beamforming0.86990.0214 SU-MIMO0.91720.0575 Full Feedb. MU-MIMO0.85360.0285 Partial Feedb. MU-MIMO0.86020.0312

16 Power Efficiency and Fairness PFI indication of how fairly power is allocated to different users with respect to their achieved rates Uplink improvements Schemes utilising the additional spatial layer, achieve an overall higher power allocation fairness, with PFI values consistently closer to unity.

17 Conclusions and Future Work Multiuser Diversity schemes exploiting temporal, spectral and spatial domain achieve notable performance gains. Performance gains can be translated to a power saving at the BS Theoretical Analysis consistent with simulation results Improved consistency in cost metric Improved power allocation fairness Power savings of up to 10dB can be achieved with no QoS compromise

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