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Power Efficient MIMO Techniques for 3GPP LTE and Beyond

This paper explores innovative MIMO transmission techniques designed for the 3GPP LTE framework and beyond, focusing on delivering high data rates while significantly reducing power consumption. We examine the integration of MIMO-OFDMA systems, enhancing user performance with reduced transmission power from base stations. Through theoretical analysis and simulation results, we demonstrate methods for maintaining quality of service while improving system reliability and performance. Our findings indicate that energy savings of up to 10dB can be achieved without compromising user experience.

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Power Efficient MIMO Techniques for 3GPP LTE and Beyond

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  1. 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

  7. System and Channel Model

  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

  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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