System Level Modeling and Optimization of the LTE Downlink

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3. Physical Layer Modeling and LTE System Level Simulationboth due to the OFDMA resource allocation the spatial UE distribution, simulationswith a higher bandwidth (LTE supports a transmission bandwidth of up to20 MHz) and a high per-cell UE count are necessary, further increasing the potentialcomputational complexity of system level simulations relative to a single-linksimulation.1000ROI500eNodeB sitey pos [m]0−500eNodeB 3eNodeB 1eNodeB 2−1000UEs−1000 −500 0 500 1000x pos [m]Figure 3.1: Typical system level simulation setup consisting of an hexagonal grid of 19 sites,each containing three eNodeBs (sectors). Users are placed randomly over theRegion Of Interest (ROI), covering in this case a rectangle of roughly 2 500×2 200meters. In this example, two UEs are placed per eNodeB.Without taking into account extra complexity overhead, a simple simulation employingthe aforementioned cell layout with two UEs per cell and a 20 MHz bandwidthwould have a complexity 950 times higher than a 1.4 MHz single-user link level simulationdue to the increase of the number of RBs from 6 to 100 and the number ofeNodeBs from one to 57 (19 sites, 3 eNodeBs/site) 1 . If implemented via link levelsimulations, such a typical LTE system level simulation would require a simulationtime in the order of months, which is clearly not practical.3.1. System ModelAs in other contemporary communication systems, such as W-CDMA or IEEE802.11n, LTE employs Bit-Interleaved Coded Modulation (BICM), which has beenshown to improve performance compared to systems employing symbol-wise interleaving[70]. Conceptually, the PHY layer procedures described in Chapter 2 can be1 Although the feedback would still need to be computed for the whole bandwidth by each UE, ithas here been assumed that overall complexity is determined by the MIMO receiver complexity.As the cell PHY resources are shared by all attached UEs, the UE count is thus ignored in thecalculation.24
3. Physical Layer Modeling and LTE System Level Simulationdescribed as a BICM system comprised of the elements listed below [71, 72], whichare also shown in Figure 3.2:ˆ A transmitter, comprised of a channel coder, a bit interleaver (Π), and a modulator(M). It maps the input bit stream b to the transmit vector x.ˆ A channel, which outputs the symbol vector y and defines a transition probabilitydensity function (pdf) p θ (y|x) depending on the channel state, which is denotedas θ.ˆ A receiver, which outputs the received bit stream ˆb. It is comprised of an equalizerand demodulator ( M −1) , de-interleaver ( Π −1) , and channel decoder.encodingdecodingTransmitter Channel ReceiverFigure 3.2: BICM transmission model. The model comprises bit coding/decoding, bit interleaving/deinterleaving,and symbol mapping/demapping, as well as a channel,modeled as a transition probability density function.As shown in Chapter 2, bit coding and interleaving in provided by a turbo codercombined with rate matching. The symbol mapping M is implemented by meansof a 4, 16, or 64-QAM constellation with Gray mapping [39].The channel over which the symbol vector x is transmitted is modeled as a combinationof a channel impulse response and Additive White Gaussian Noise (AWGN).Assuming an LTE system with N TX transmit antennas and N RX receive antennas,the signal received at the r-th antenna, denoted as y r , can be expressed in the timedomain as the sum of the signal received from each of the N TX transmit antennaswith a later addition of AWGN noise:y r =N TX ∑t=1(h t,r ∗ x t ) + n r , (3.1)where the signal from the t-th transmit antenna, denoted as x t is convolved (∗)with the channel impulse response of length τ between the t-th transmit to the r-threceive antenna, denoted as h t,r .Assuming a CP at least as long as the length of the channel (τ), the CP introduced inthe OFDM signal generation (see Figure 2.6 in Section 2.2) eliminates inter-symbolinterference and allows for the channel impulse response h t,r for each OFDM symbolto be expressed as a complex-valued scalar. Stacking the h t,r values into a N RX ×N TXmatrix H, the received symbol vector y of length N RX can be expressed asy = Hx + n, (3.2)25
Page 1: DissertationSystem Level Modeling a
Page 5: I hereby certify that the work repo
Page 9: KurzfassungDie vorliegende Arbeit p
Page 13 and 14: ContentsContents1 Motivation and Sc
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Page 17 and 18: 1. Motivation and Scope of Work1. M
Page 19 and 20: 1. Motivation and Scope of Worklaye
Page 21 and 22: 1. Motivation and Scope of Workperf
Page 23 and 24: BibliographyThe combined throughput
Page 25 and 26: 2. 3GPP Long Term Evolution2. 3GPP
Page 27 and 28: 2. 3GPP Long Term Evolutionup to 30
Page 29 and 30: 2. 3GPP Long Term EvolutionNAS proc
Page 31 and 32: 2. 3GPP Long Term Evolutionchannel
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Page 37 and 38: 2. 3GPP Long Term EvolutionTable 2.
Page 39: 3. Physical Layer Modeling and LTE
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Page 67 and 68: 4. Extensions to the L2S Model4. Ex
Page 69 and 70: 4. Extensions to the L2S Model4.1.2
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5. Performance Evaluation of Fracti
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6. Summary and Outlook6. Summary an
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6. Summary and Outlook6.2. OutlookW
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A. SNR-independence of the CLSM Pre
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B. Correlation Matrices for Shadow
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C. Taylor Expansion of the ZF MSEC.
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C. Taylor Expansion of the ZF MSEAp
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D. Evaluation of Multi-User GainD.
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D. Evaluation of Multi-User GainAvg
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Abbreviations and AcronymsAbbreviat
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Abbreviations and AcronymsKPILLRL2S
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Abbreviations and AcronymsU-PlaneUT
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Abbreviations and AcronymsBibliogra
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Abbreviations and Acronymsdescripti
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Abbreviations and Acronyms[69] Tech
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Abbreviations and Acronymsfemto cel
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Abbreviations and Acronyms[134] Z.
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System Level Modeling and Optimization of the LTE Downlink

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