Monday, April 1, 2019

Energy Efficiency Maximisation in Large-Scale MIMO Systems

efficiency competency Maximisation in big MIMO placements abridgment of Energy talent Maximisation in Large-Scale MIMO Systems invention and Motivation1.1 BackgroundThe development of sm invention terminals and their application, the need for multimedia serve rapidly increases lately 1. The capacity of radiocommunication the Quality of Service necessities of expeditious applications of piano tuner conversation networks is increasing exp iodinentially 1.Bandwidth Efficiency is typically one of the important metrics to Systems 1, 1. Energy Efficiency become a metric for assessing the performances of wireless communications systems with some BE restrictions 1 1.1.2 enquiry MotivationsAn accu regulate poser of the total force out pulmonary tuberculosis is the primary of (BS) antennas and hail of active (UEs) for LS-MIMO systems 15.1.3 enquiry Aim and ObjectivesThe seek objectives which argon briefly explained and summarized as belowTo par the performance of the propose d uplink and downlink of LS-MIMO systems for ZF, MRT/MRC, and MMSE processing stratagems at BS.To implement a forward-looking improve model of the total power consumption for LS-MIMO system.To derive closed-form EE-maximal values of amount of (BS) antennas, quash of active (UEs), and the transmit power using ZF processing in single-cell system and new refined model of the total power consumption when the other two ar fixed.To evaluate analytic results for ZF processing scheme with complete CSI.To measure numerical results for ZF, MRT/MRC, and MMSE processing schemes processing schemes with consummate CSI in a single-cell scenario.To measure numerical results for ZR processing schemes with corrupted CSI, and in a multi-cell scenario.1.4 Main ContributionsThis thesis has contributions to k nary(prenominal)ledge in three research issues for LS-MIMO system, which are the new refined rope power consumption model, sinew efficiency maximisation with ZF processing scheme, and de ployment of imperfect CSI case and symmetric multi-cell scenario. Those main(prenominal) contributions of this thesis are summarized and exposit more detail as followsThe lap covering power consumption is the sum of the power consumed by different one-dimensional components and digital signal processing. The new refined model of the total power explicitly described how the total power consumption depends non-linearly on number of number of UEs, number of BS antennas, and transmit power.The closed-form EE expression on a lower floor the assumption of ZF processing scheme is employed in the uplink and downlink for optimal number of UEs, number of BS antennas, and transmit power for a single-cell scenario with perfect CSI. This pickaxe is driven by analytic convenience and numerical results likewise which are close to optimal.Analysis of imperfect CSI case and symmetric multi-cell scenarios deployment are elongate using the same method above. A New accomplishable rate derived f or symmetric multi-cell scenarios with ZF processing.1.5 Research MethodologyIn the first stop of the research, books review of past and current works on the area of MIMO, MU-MIMO, and LS-MIMO are extensively conducted to broaden the perspective on such areas of study. exceptmore, state of the art related to those addressed issues are deeply studied and intensively explored during this period. quest the literature review phase, implementation starts with formulating the EE maximisation problem. A new refined circuit power consumption model is proposed. All this then(prenominal) used to compute closed-form expression for the optimal number of UEs, number of BS antennas, and transmit power low the assumption of ZF processing scheme.The testing stage starts with good example. All the simulations were performed using Monte Carlo Simulation techniques in Matlab. Monte Carlo simulation can handle really complex and realistic. Monte Carlo Simulation were put to death for all the in vestigated schemes with perfect CSI, for ZF with imperfect CSI, and in a multi-cell scenarioIn the constitution stage, numerical results are used to authenticate the theoretical analysis and guide comparison amongst different processing schemes.1.6 Thesis StructureThis thesis comprises of 6 chapters, where each chapter is inter- dependent.Chapter 1 Introduction Chapter 2 LS-MIMO-An overview This chapter presents an overview of the LS-MIMO concept.Chapter 3 Literature Review- Energy Efficiency Maximisation in LS-MIMOChapter 4 Techniques to Maximise Energy Efficiency The simulation procedures will be explained in this chapter.Chapter 5 Result and Analysis This chapter describes description and evaluation for this investigation of LS-MIMO .Chapter 6 Conclusion Further fashion This chapter concludes the results of the implementations, and recommendation of developing revised model for LS-MIMO systems.LS-MIMO An Overview2.1 Introduction to LS-MIMO tuner communication is one of the most successful technologies is one of the most successful technologies in modern years, given that an exponential growth rate in wireless traffic (known as Coopers law) 1. This trend will certainly drive by for example, augmented reality and internet-of-things 1.Figure 2-16 2.2 Antenna configu proportionalitynsRadio-Frequency (RF) circuit is usually connected to its physical antennas through an RF cable in a passive AA. A Remote Radio building block (RRU) in with a Baseband Unit (BBU) has become a preferred configuration recently 1.2.3 Channel Measurements real run measurements guard been carried out in in an effort to let out the main characteristics of LS-MIMO ships 152.4 Channel setThree types of channel models have been used for evaluating the performance of wireless communications systems, namely the Correlation-Based Stochastic put (CBSM), the Parametric Stochastic Model (PSM) and the Geometry- Based Stochastic Model (GBSM) in 1.2.5 Processing SchemesPrecoding LS-MIM O is based on linear processing at the BS. BS has observation of the multiple access channels from the terminals 6. The BS applies linear secure combining to discriminate the signal transmitted 6. The simplest choice is maximum ratio (MR) combining by adding the signal components coherently. In 6, this result signal gain proportional to.Energy Efficiency Problem Literature Review3.1 System and Signal ModelThe uplink and downlink of a single-cell multiuser MIMO system operating is considered over a bandwidth of B Hz 15.3.2 Channel Model and Linear ProcessingThe M antennas at the BS are spaced apart such that the channel components between the BS antennas and the single-antenna UEs are uncorrelated 15. The channel describes propagation channel between antenna at the BS and the UE. We assume small ordered series fading distribution 15.3.3 UplinkIn 15, under the assumption of Gaussian, linear processing, and the perfect CSI, the achievable uplink rate of the th UE is (3.6)the pre-l og factor accounts for pilot overhead and is the split up of uplink transmission 15. In addition, (3.7)3.4 DownlinkA normalized precoding vector and the downlink signal to the kth is assigned a transmit power of . In 15, assuming Gaussian codebooks and perfect CSI the achievable downlink rate of the kth UE with linear processing is (3.13)3.5 Problem StatementThe EE of a communication system is measured in bit/Joule and the medium total power consumption (in Watt = Joule/second) 15.The total EE of the uplink and downlink is (3.20)Energy Efficiency Maximisation-Techniques4.1 Realistic Circuit Power Consumption ModelThe sum of the power consumed by different components and signal processing is the circuit consumption is 15. A power consumption model is proposed (3.22)4.2 Energy Efficiency Maximisation with ZF ProcessingThe EE maximisation problem is resolved under the assumption that ZF processing is employed. This solution is driven by analytic and the numerical results 15.For Z F processing, Problem 1 reduces to (3.30)4.3 Extension to Imperfect CSI and Multi-CellThe analysis is prolonged to single-cell scenarios with imperfect CSI. A new achievable rate is derived with ZF forcing processing. The achievable user rate in single-cell scenarios with imperfect CSI 15. (3.52)Simulation frame-up and Numerical Results5.1 Simulation SetupSimulations used to validate the system design guidelines under ZF processing and to break comparison with other processing schemes 15. Numerical results provided under both perfect and imperfect CSI, and for single-cell and multi-cell scenarios 15. For stimulating ZF, and MRT analytic results were executed and MMSE, and Monte Carlo simulations were performed to maximise EE 15.5.2 Single-Cell ScenarioThe chosen deployment model validated.5.3 Multi-Cell ScenarioA lot of studies have been carried out.Conclusions and Future Research6.1 ConclusionsThis thesis focuses on the cogency maximisation improvement of the LS-MIMO systems to cope with energy maximisation problem. The thesis has three main contributions all are elaborated in detail.6.2 Future ResearchSeveral recommendations, which may guide to the succeeding(a) research directions on LS-MIMO systems.Bibliography1 K. Zheng, L. Zhao, J. Mei, B. Shao, W. Xiang and L. Hanzo, Survey ofLarge- Scale MIMO Systems, in IEEE Communications Surveys Tutorials, vol.17, no. 3, pp. 1738-1760, third quarter 2015.2 D. Feng et al., A survey of energy-efficient wireless communications, IEEE Commun. Surveys Tuts., vol. 15, no. 1, pp. 167-168, beginning(a) Quart. 2012.3 T. Kailath and A. J. Paulraj, Increasing capacity in wireless broadcast systems using Distributed transmitting/Directional Reception (DTDR), U.S. Patent 5 345 599, Sep. 6, 1994.4 E. G. Larsson, F. Tufvesson, O. Edfors, and T. L. Marzetta, Massive MIMO for next multiplication wireless systems, IEEE Commun. Mag., vol. 52, no. 2, pp. 186-195, Feb. 2014.5 Views on Rel-12 and Onwards for LTE and UMTS, 3GPP RWS- 120006, HUAWEI and HiSilicon, 2013.6 E. Bjrnson, E. G. Larsson and T. L. Marzetta, Massive MIMO ten mythsand hotshot critical question, in IEEE Communications Magazine, vol. 54,no. 2, pp.114-123, February 2016.7 S. Tombaz, A. Vstberg, and J. Zander, Energy- and cost-efficient ultra- high-capacity wireless access, IEEE Wireless Commun. Mag., vol. 18, no. 5, pp. 18-24, Oct. 2011.8 E. Bjrnson, M. Kountouris, and M. Debbah, Massive MIMO and smallcells Improving energy efficiency by optimal soft-cell coordination, inProc. ICT, 2013, pp. 1-5.9 E. Bjrnson and E. Jorswieck, optimal resource allocation inCoordinated multi-cell systems, Found. Trends Commun. Inf. Theory,vol. 9, no. 2/3, pp. 113-381, 2013.10 Y. Wu, R. Zhou, and W. Zhang, Active antenna system Utilizing the widepotential of radio sources in the spatial domain, Huawei, Shenzhen,China, 2012.11 S. Payami and F. Tufvesson, Channel measurements and analysis for very large array systems at 2.6 GHz, in Proc. 6th EUCAP, Prague, Czec h Republic, Mar. 2012, pp. 433-437.12 Further Advancements for E-UTRA Physical Layer Aspects (Release 9),3GPP TS 36.814, Mar. 2010.13 H. Boche and M. Schubert, A general duality theory for uplink anddownlink beamforming, in Proc. IEEE VTC-Fall, 2002, pp. 87-91.14 R. Kumar and J. Gurugubelli, How green the LTE technology can be?inProc. Wireless VITAE, 2011, pp. 1-5.15 E. Bjrnson, L. Sanguinetti, J. Hoydis and M. Debbah, OptimalDesign of Energy-Efficient Multi-User MIMO Systems Is MassiveMIMO the Answer?, in IEEE Transactions on WirelessCommunications, vol. 14, no. 6, pp. 3059-3075, June 2015.

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