Modeling 4G & 5G Systems in SystemVue - · PDF file5G will have hundreds of array...
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Modeling 4G & 5G Systems in SystemVue Keysight EEsof EDA August 28, 2014
Modeling 4G & 5G Systems in SystemVue - · PDF file5G will have hundreds of array antenn對a elements at mmWave frequency.\爀屲Simulation: ... • Self interference cancellation
Use the animation to show today’s network and limitations. This will then progress to the various advantages that the 5G revolution will provide: Today’s Network: We are all using this today. Mobile data is a very real addition to the 2G and early 3G networks. It also works well…some of the time. Who here is always happy with the coverage, speed, and reliability of their current mobile device? We switch between WiFi and Cellular but it is often clumsy and we have to be aware of which system we are using In many cases, operations at cell edges or in a large crowd are unreliable at best and simply do not work at the worst Today’s system is reported to consume 2% of the worlds total generated electrical power Tomorrows Network: Click #1: A massive increase in capacity driven by much smaller cells and more infrastructure will help manage service in a crowd. In this case, the crowd is served by multiple very small cells that are focused on where those people are. Click #2: The addition of millimeter Wave technology, previously only used in Radar, point-to-point, and aerospace defense systems, will allow for the 100X increase in data rates for key applications (typically video). This will be implemented with highly directional antennas and will provide the speed that the high-end data users will demand. Click #3: The new network has to support more things communicating in different ways. The car has now had a nomadic base station installed which is what provides one direction of communication to the user who is in a different cell. The user in the car now gets his communications through a small cell and the car itself is linked to the network for advertising, collision avoidance, and traffic guidance. Click #4: All of these innovative new approaches will not work without a redesign of the network topology. The computing engines will have to be centralized so that software can react to moving network capacity around the area; so that the speeds required over the air can be managed by the network, and so that we make the most efficient use of the network resources including power, spectrum, computing, and data capacity. The network will be driven by massive parallel computing platforms which will centralize the way that radio access works. Interesting approaches: today Mobile Association with a particular EnB is determined almost exclusively on best SNR on the downlink. But that may not be the best overall interference QoS for everyone, or SE, or EE. New Hueristics will be developed to ensure the best use of NW resources and still maintain the QoS and hopefully the QoE.
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5G Wireless: Opportunities to Innovate
– Design
– Simulate
– Calibrate
– Emulate
– Validate
Why this will be exciting to us: 1 GHz 10 GHz 100 GHz 1 THz 10 THz 100 THz 1PHz
Most importantly, this is an exciting time for us. Innovation will span from “DC to Daylight” and to improve capacity, efficiency, data-rates and speed, denser networks, and very complex interoperability challenges. 5G means we have to design much more energy efficient systems, new capabilities to use the communications spectrum we already use, expand our wireless access communications to mmWave, and even move our fiber-optic capability to digital speeds in the terabits/second. Not only will we get to help with all of these technical areas, we will be involved in the process from research to deployment with Design, Simulation, Calibration, Emulation, and Validation tools across this entire range of technical needs. All of the new hardware and software you will be inventing will need testing. But before that, you will need to model and simulate these technologies as they are developed and as you work to make them work with each other. Today our team will describe to you some exciting new tools that will enable this early process. I speak for Keysight and the entire industry in saying I look forward to this exiting time, not because of the new technology and business opportunities, but because we thrive in working closely with you all as market leaders.
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Agenda – 4G/5G Technology Overview
– 4G standards references growing into pre-5G
• Link Adaptation Technique
• Coordinated Multi-Points
• Inter-band Carrier Aggregation and 2D Digital Pre-distortion
• Wi-Fi Offload • Small cell • Backhaul • Software
defined radio network
• GP Instrument • Minor modular
adaption
5G Maintain Leadership Rel 14,15,16
• FD-MIMO (TR 36.873)
• Active Array Antenna
• 3D Channel Model
• CoMP Enhance • Inter-eNB CA • Control plane
overhead reduction
• MTC
• Combined external event driven control
• Simulation Acceleration
• Simulation in enterprise IT infrastructure
• L1, L2 cross simulation
• HetNet • Mobile Relay • Coexistence • Self-
interference cancellation
• Multi-channel • Wide
bandwidth • Real time
FPGA
Thro
ughp
ut(%
)
EbNo(dB)
OFDM GFDM
PHYSICAL LAYER ANTENNA SIMULATION MEASURE MISC.
??? ??? ???
*ESL Perspective
Presenter
Presentation Notes
This is summarized 4G and 5G technology roadmap for Electronic System Level simulation and T&M perspective aligned with 3GPP standard update and new 5G technology forecast. PHY: Current 4G physical layer built in based on OFDM technique and has been faced with some technical limitations against 5G technical demands such as spectral efficiency and flexible spectrum access. Now, many multi-carrier based new waveform technologies (FBMC, filtered OFDM etc…) has been studied to address this requirements. Antenna: Low order MIMO technique applied in 4G standard is adapting higher order MIMO by adding more antenna elements. B4G is deploying full-dimension MIMO and 3D concept with elevation change of beamforming. 5G will have hundreds of array antenna elements at mmWave frequency. Simulation: To address closed feedback loop and adaptive modulation and coding scheme for 4G, we needed special dynamic dataflow simulation technique. As future wireless communications need more antenna elements and complex signaling implementation, current simulation technology also need to be evolved equipped with accelerated simulation technique and robust handling of event driven simulation requirements. Measurement: General purpose bench top instrument has been dominant position for cellular T&M market with some level of modular instrument usage by now. But, as new mobile product has more antenna channel, wider bandwidth and higher frequency, the instrument concept will also change toward modular based multi-channel, wide bandwidth, higher interface speed and real time capable. Misc: 5G is not only revolution of mobile communications technology but also convergence of other network and service technology, many other technologies research have been conducted together.
The need for expressive power beyond that provided by decidable dataflow techniques is becoming increasingly important in design and implementation signal processing systems. This is due to the increasing levels of application dynamics that must be supported in such systems, such as the need to support multi-standard and other forms of multi-mode signal processing operation; variable data rate processing; and complex forms of adaptive signal processing behaviors.
Mitigating varying radio channel issue is always hot topic for communications system designers. LTE, LTE-A system uses various techniques to address this issue and we will take a look two of them today. Adaptive Modulation and Coding (AMC) as the link adaptation technique to adapt transmission parameters, choice of modulation scheme and choice of FEC code rate dynamically to the channel. Hybrid automatic repeat request (hybrid ARQ or HARQ) is a combination of high-rate forward error-correcting coding and ARQ error-control.
LTE-A DL 2x2 MIMO Throughput, Extended Vehicle Channel, AMC enabled
Simulation Results with Ideal Receiver Design
Presenter
Presentation Notes
The graph shows LTE-A 2x2 MIMO throughput simulation results under the condition of extended vehicle channel and swept SNR with adaptive modulation and coding function activated. Simulating link adaptation with various channel environment is not easy task but very challenging and time consuming task for wireless system communications architect and research engineers. At first they need to design ideal reference modem for both transmitter and receiver and then put mathematical channel models between transmitter and receiver defined at 3GPP standard documentation. While this first step is not a trivial, second step is challenging more. The simulator need to support UE feed back mechanism to eNB to report channel quality and HARQ communication dynamically. Also, SNR value need to be swept during the simulation and the throughput measurement which should be conducted simultaneously. The whole simulation is not just several days job but task for multiple months and even not guaranteed without of full verification.
The next step after the simulation is real measurement for device under test with some instrument. Usually, one box tester type of instrument used in this validation phase and very useful to test UE under realistic environments. However, it is not always possible when new standards is just released. Before T&M vendor provide appropriate solution in the market, the mobile product designers need to use general vector signal generator and analyzer with integration of previously designed link level simulator.
* Adaptive modulation and coding can be used to adjust the modulation scheme and coding rate, and thus the data rate, to match the instantaneous channel conditions.
Decide MCS
Predict feedback
Schedule feedback
Adapt feedback rate
CQI Quantize
DOWNLINK
UPLINK CQI
Measure SNR
Base Station
Mobile Station
Presenter
Presentation Notes
The higher the channel quality, the higher is the used modulation order and code rate. Channel quality in downlink is measured in UE using the reference symbols. Upon this measurement, so-called CQIs, which are channel quality indicators are generated and sent to eNodeB. Each CQI value corresponds to a specific modulation scheme and a specific code rate, which are selected by eNodeB for the downlink transmission. PUCCH: The control channel transmitted by uplink users which contains information including channel quality info, acknowledgements, and scheduling requests. Through the CQI(Channel Quality Indicator) feedback. Periodic reporting: PUCCH(Physical Uplink Control Channel) Aperiodic reporting: PUSCH(Physical Uplink Shared Channel) Dynamic CQI resource allocation
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What affect CQI Reporting Level?
• Channel, noise and interference level
• Performance of receiver (e.g. noise figure of analog front end, performance of the DSP modules)
In LTE/LTE-A downlink, the quality of channel is measured in the UE and sent to the eNodeB in the form of so-called CQIs (Channel Quality Indicator). The quality of the measured signal depends not only on the channel, the noise and the interference level but also on the quality of the receiver, e.g. on the noise figure of the analog front end and performance of the digital signal processing modules. That means a receiver with better front end or more powerful signal processing algorithms delivers a higher CQI. Usually, BB and RF development organization is entirely separated and doesn’t collaborated well to address this type of multi domain issue. They usually set certain level of design margin and verify their BB and RF part separately while it is very risky in terms of whole product design quality in system level. System architect and product development leader should take care this part and it usually validated with system level simulator and we will take a look an example to the next slide.
Implement dynamic feedback mechanism for • Hybrid ARQ • CQI • Transport block size information
Define link level system parameters • FDD/TDD • Transmission Mode • Bandwidth etc…
Map CQI to MCS Throughput measurement
Presenter
Presentation Notes
To instructor: Explain LTE-A 2x2 MIMO DL AMC example in the order of Source > Receiver > Closed loop feed back mechanism > Fading channel > CQI2MCS map > Throughput measurement > Sweep simulation.
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HARQ Process
– Protocol : the number of HARQ retransmissions targeted by HARQ protocol
– MAC : HARQ is lower part of the MAC entity. If a radio block fails due to the CRC evaluation, a retransmission is issued
– PHY : L1 is used for signaling to indicate need for retransmission
How to implement this behavior in link level simulation?
Presenter
Presentation Notes
HARQ(Hybrid ARQ) is pretty complicated process and not easy to catch up in very detail within couples minutes. The objective of this slide is to get a little bit of sense about HARQ technique. HARQ process is controlled through multiple layers, protocol, Mac and Phy. Also, a different mode of HARQ process is used depending on whether it is for FDD or TDD and whether it is for Uplink and Downlink. The question we will address is how this complex process can be simplified for link level simulation without loosing performance verification goal.
This example shows LTE-A downlink channel coding signal processing part. To instructor: explain HARQ controller and channel coding procedure aligned with CQI_bit, HARQ_bit.
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Simulation Technique
– Real world systems (ex: AMC, HARQ) may involve dynamic behavior that cannot be modeled under SDF(synchronous data flow) semantics
– The number of samples consumed and produced for each execution of a DDF(dynamic data flow) block can change dynamically at runtime
the consumption rate can change dynamically at runtime
Dynamic connection : # of M x N samples M x N
N M
Presenter
Presentation Notes
In synchronous data flow technique, a schedule of a data flow graph is computed and compiled before simulation execution. While SDF is most matured dataflow modeling specially for digital signal processing, it cannot address dynamic behavior like AMC and HARQ process easily. Beyond SDF, dynamic data flow (DDF) is the most general data flow model of computation. In dynamic data flow, the number of samples consumed and produced for each execution of a DDF block can change dynamically at runtime. Such flexibility gives DDF sufficient expressive power to model various dynamic behavior, but at the expense of losing compile-time scheduling capabilities. In general, dynamic data flow requires significant amount of runtime overheads to determine execution order, detect deadlock, and allocate/re-allocate buffers. To efficiently model such dynamic but matched rate changes, SystemVue uses variable-size vectors (matrices) to encapsulate variable numbers of samples for dynamic data flow processing. In this approach, blocks process a vector (matrix) of data at a time, and vector (matrix) size can change dynamically at runtime.
Coordinated multipoint transmission and reception actually refers to a wide range of techniques that enable dynamic coordination or transmission and reception with multiple geographically separated eNBs. Its aim is to enhance the overall system performance, utilize the resources more effectively and improve the end user service quality. One of the key parameters for LTE as a whole, and in particular 4G LTE Advanced is the high data rates that are achievable. These data rates are relatively easy to maintain close to the base station, but as distances increase they become more difficult to maintain. Obviously the cell edges are the most challenging. Not only is the signal lower in strength because of the distance from the base station (eNB), but also interference levels from neighboring eNBs are likely to be higher as the UE will be closer to them. 4G LTE CoMP, Coordinated Multipoint requires close coordination between a number of geographically separated eNBs. They dynamically coordinate to provide joint scheduling and transmissions as well as proving joint processing of the received signals. In this way a UE at the edge of a cell is able to be served by two or more eNBs to improve signals reception / transmission and increase throughput particularly under cell edge conditions.
Figure. Joint Processing or Dynamic cell selection
Dynamic points selection
Coherent/Non-coherent transmission
PMI/CQI/RI feedback extensions
Presenter
Presentation Notes
In essence, 4G LTE CoMP, Coordinated Multipoint falls into two major categories: Coordinated scheduling or beamforming: This often referred to as CS/CB (coordinated scheduling / coordinated beamforming) is a form of coordination where a UE is transmitting with a single transmission or reception point - base station. However the communication is made with an exchange of control among several coordinated entities. Joint processing: Joint processing occurs where there is coordination between multiple entities - base stations - that are simultaneously transmitting or receiving to or from UEs
To instructor: Explain LTE-A DL SISO DPS example in the order of Sources with different Cell-ID > Fading channel> Receiver > CQI feedback > Throughput gain at low SNR
CSI-RS Channel State Information Reference Signal is the Channel State Information Reference Signal and is used by the User Equipment (e.g. cell phone) to estimate the channel and report channel quality information (CQI Channel Quality Indicator) to the base station. In Release 8, Cell-specific RS (C-RS) was designed for use in channel estimation for up to 4-layer spatial multiplexing, with separate C-RS Cell-specific RS sequences for each antenna port (0-3). With the addition of up to 8-layer spatial multiplexing in Release 10 came the need for 8-layer channel estimation. However, extending C-RS to 8 layers would add more signaling overhead than was desired, so the CSI channel state information Reference Signal was added. Since Release 8/9 UEs are not aware of CSI-RS and will see CSI-RS as interference (which can be placed in PDSCHPhysical Downlink Shared Channel resource elements), the placement of CSI-RS was designed to be sparse in both the time and frequency domains to minimize the effect on these UEs. And, although the sparse placement of CSI-RS means that CQI will be reported over longer time intervals than C-RS CQI, the target UE devices for higher-layer spatial multiplexing are static or low-mobility devices, so this should not be a major issue. CSI-RS is transmitted on different antenna ports (15-22) than C-RS (although likely sharing physical antennas with other antenna ports), and instead of using only time/frequency orthogonality like C-RS, CSI-RS uses code-domain orthogonality as well. Further consideration on reference signal design can be in the following areas: -Non-zero-power and zero-power CSI-RS have been introduced in Rel-10 for CSI measurement and reporting perspectives. CSI-RS may be re-used for CoMP to identify and measure the downlink channel status of multiple transmission points. Points can be allocated orthogonal resources avoiding mutual interference between the CSI-RS transmissions. New types of CSI-RS configurations may be considered to facilitate CoMP CSI measurements. Enhancements to CSI-RS for improved interference and/or timing estimation are not precluded. -The reference signals for interference measurements for DL CoMP feedback may be considered. -Enhancement of existing DMRS may be considered, e.g. •DMRS orthogonality enhancement -Consider performance requirements on CSI-RS and DM-RS to ensure flexible mapping of antenna ports to transmission points. Compared to Rel. 10, DL overhead increase due to multiple CSI-RS and/or muting patterns may be expected. UL overhead increase due to CSI measurement related to multiple points may be expected, and/or UL overhead increase due to SRS transmissions related to multiple points may be expected. Most of the CoMP schemes considered in this study rely on TM9 for PDSCH transmission for UEs beyond Rel-10. When comparing with baseline schemes, especially ones that are not based on TM9, the additional overhead of DM-RS and CSI-RS should be taken into account. Due to the presence of CSI-RS REs in an RB, the puncturing of PDSCH transmissions may lead to some performance degradation for Release 8 and 9 UEs. Scheduling restrictions may be applied to avoid performance degradations, Several MMSE receiver implementations are possible, depending on the degree of available interference information at the UE. It is generally understood that cell edge performance is improved when directional structure of the interference information is available at the receiver. Coordinated transmission in support of interference aware receivers may improve the UE interference estimation possibilities, leading to further improved cell edge performance. The signalling needed for such coordinated transmission techniques may require specification changes.
Rel. 11 has introduced MMSE-Interference Rejection Combining(MMSE-IRC) receivers as a mobile terminal interference rejection and suppression technology to mitigate the effects of these interference signals and increase user throughput even in areas that are recently experiencing high interference. Rel. 8 receivers support MIMO transmission technology, so receivers were equipped with at least two antennas since it was first introduced. The MMSE-IRC receivers in Rel. 11, are able to use the multiple receiver antennas to create points, in the arrival direction of the interference signal, where the antenna gain drops and use theme to suppress the interference signal in the figure. To address this new receiver design and validation in early R&D phase, we may need to simulate this new algorithm in system level. Let’s see the example to the next slide.
Demo procedure: Open LTE-A_DL_2x2_MIMO_Throughput example and explain schematic Open Receiver sub-network and go to LTE-A DL_ChEstimator block, review MMSE algorithm parameter Put MatlabScript part into schematic and explain how to add custom algorithm written in Matlab code Show C++ custom model building procedure for C++ based custom algorithm integration
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Demo & Discussion
– Use LTE-A_DL_DPS example
– Review Transmitter and Receiver Blocks
– Review fading channel model parameters
– Review throughput simulation result using graph
– Discuss why CoMP is important for 4G and 5G research
Carrier aggregation is used in LTE-Advanced in order to increase the bandwidth, and thereby increase the bitrate. Since it is important to keep backward compatibility with R8 and R9 UEs the aggregation is based on R8/R9 carriers. Carrier aggregation can be used for both FDD and TDD, see figure a) and b) for contiguous and non-contiguous carrier aggregation example. Each aggregated carrier is referred to as a component carrier, CC. The component carrier can have a bandwidth of 1.4, 3, 5, 10, 15 or 20 MHz and a maximum of five component carriers can be aggregated, hence the maximum aggregated bandwidth is 100 MHz. In FDD the number of aggregated carriers can be different in DL and UL. However, the number of UL component carriers is always equal to or lower than the number of DL component carriers. The individual component carriers can also be of different bandwidths. For TDD the number of CCs as well as the bandwidths of each CC will normally be the same for DL and UL. The easiest way to arrange aggregation would be to use contiguous component carriers within the same operating frequency band (as defined for LTE), so called intra-band contiguous. This might not always be possible, due to operator frequency allocation scenarios. For non-contiguous allocation it could either be intra-band, i.e. the component carriers belong to the same operating frequency band, but have a gap, or gaps, in between, or it could be inter-band, in which case the component carriers belong to different operating frequency bands.
There will be 1 TB per CC unless spatial multiplexing is used
Logical Channel
Transport Channel
Presenter
Presentation Notes
Now, let’s talk about how we could modeling this carrier aggregation for link level simulation. At first, we may need to understand the basic protocol of component carrier assignment and resource block scheduling at the MAC and PHY level. In the carrier aggregation schemes, CCs are allocated statically or dynamically when UEs attach to the network. Basically each component carrier is treated as an R8 carrier as shown on the right. When it need to be combined, the signaling information about scheduling on CCs must be provided DL as well as HARQ ACK/NACK per CC.
• ACK/NACK for each carrier on single PUCCH (format 3)
LTE-Advanced carrier aggregation
DL: 300 Mbps
UL: 50 Mbps
Presenter
Presentation Notes
This slide shows little bit more about signaling stuff for CA. "If a UE is getting data from multiple carriers, how can it report ACK/NACK ?" If you are a designer, how do you handle this ? I think anybody might think of following options. Option 1 : Let UE send 'ACK' only when it was successful to decode PDSCH from both carriers and transmit NACK if any of PDSCH is failed to be decoded. --> This is technically possible, you would clearly see this would be very inefficient way and cause a lot of unnecessary retransmission. Option 2 : Let UE send ACK or NACK for each carrier separately. --> This would not sound perfect way, but we can use the PUCCH format 1b for this case. Option 3 : Let UE send ACK or NACK for each carrier on a single PUCCH. --> This would sound the best, but you can easily guess we would need a new PUCCH format since all the existing PUCCH format (Rel 8 format) is designed to send ACK/NAC for one carrier. In real implementation (specification), Option 2 and 3 are adopted. PUCCH format 1b is used for Option 2 and a PUCCH Format 3 is used for Option 3.
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Intra-Band Carrier Aggregation – Multiple CCs are used inside of a single frequency
Band (3GPP defined bands)
– CCs can be contiguous or non-contiguous or both if more than 2 are used
– Some chipsets support this mode with a single receiver
LTE Advanced defines two types of carrier aggregation: Intra-band Carrier Aggregation and Inter-band Carrier Aggregation. Intra-band CA is defined to be the case where multiple LTE channels inside a single 3GPP defined band are aggregated. The component carriers can be contiguous or non-contiguous or both if more than three CCs are used. The advantage of this method of aggregation is that it can be implemented with a single receiver and transmitter in the UE. By requiring only a single transceiver in the UE, costs are reduced and operation is simplified. It is fairly easy for the UE designer to create a receiver that has a wide enough bandwidth to capture the component carriers in its IF. Then the baseband chipset can individually demodulate the component carriers and assemble the multiple data streams into a single packet data stream. Likewise, the transmitter of the UE can be made to have sufficient bandwidth to modulate the combined bandwidth of the component carriers. The downside is that the operator must have sufficient spectrum to have multiple component carriers in the same band. As previously mentioned, most operators do not have the spectrum in a single band to operate multiple LTE carriers. For those operators who do have the spectrum, intra-band carrier aggregation is an attractive method to increase throughput while maintaining backwards compatibility with existing LTE User Equipment that does not support carrier aggregation.
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Inter-Band Carrier Aggregation – CCs are in different frequency bands
– Allows carriers to combine their spectrum assets to gain higher throughput
– More expensive to implement since UE must support 2 receivers
– Probably the most common network implementation since it optimizes the spectrum holdings of many carriers
The other defined mode of aggregation in LTE Advanced in Inter-band CA. For Inter-band CA, the component carriers are located in different frequency bands. This allows carriers with spectrum in different bands to aggregate their spectrum to achieve the performance and throughput of 20 MHz LTE systems. The downside is that the User Equipment supporting Inter-band CA must have at least dual receivers and potentially dual transceivers if they are to support Uplink Inter-band CA. Inter-band CA is the most likely implementation for most operators since spectrum is more available in different frequency bands.
Digital predistortion (DPD) is a common method for linearizing the transmitter PA to improve power and efficiency while maintaining linearity. The technique consists of adding a digital block before the PA that adaptively applies distortion with an inverse characteristic of the PA so that the output is linear. To efficiently utilize the available spectrum and provide service for different standards or carrier aggregation of LTE-A, the transmitter must support multiple frequency bands. This presents significant challenges to DPD development. In a multi-band scenario with CA, the carriers are placed far apart from each other driving a single amplification stage. Due to bandwidth limitations, separate transmit paths are necessary for each band.
The PA output power versus input power characteristic has a saturation region (show). The DPD+PA is intended to operate along the linear output characteristic (show). However, the DPD model is usable only up to the point where linear operation results in the saturated output power. That point defines the maximum correctable input power (show).
• Filter overlap factor K : number of multicarrier symbols which overlap in the time domain
Presenter
Presentation Notes
Currently, OFDM plays a role of the leader in practical realizations of multicarrier signaling, however it suffers from various limitations, raised by the researchers and manufacturers for many years. Filter Bank Multicarrier based solutions tend to become the successor of OFDM in the context of future wireless communications systems.
• A time offset of half a QAM symbol period(T/2) is applied to either the real part or the imaginary part of the QAM symbol
• For two successive sub-channels, say m and m+1, the offset are applied to the real part of the QAM symbol in sub-channel , while it is applied to the imaginary part of the QAM symbol in sub-channel m+1.
The first block is OQAM preprocessing block which converts the QAM symbols into OQAM. There are two steps to convert QAM symbols into OQAM, firstly, a simple complex to real conversion required. We must know that the conversion will be different for even and odd sub-channels as shown in the picture. This conversion increases the sample rate by 2. Secondly, the conversion is followed by multiplication sequence 𝜃𝑚,𝑛 , where n is discrete time variable that runs at twice the rate of l. The pattern of real and imaginary samples must follow the sign of the 𝜃𝑚,𝑛 sequences. Converting the QAM symbols to OQAM format involves two important specificities: A time offset of half a QAM symbol period(T/2) is applied to either the real part or the imaginary part of the QAM symbol when the OQAM signal is generated. For two successive sub-channels, say m and m+1, the offset are applied to the real part of the QAM symbol in sub-channel , while it is applied to the imaginary part of the QAM symbol in sub-channel m+1.
𝑤𝑤𝑅𝑓𝑅: M is number of subcarriers 𝑑𝑅, 𝑛 𝑐𝑠 𝑡𝑤𝑅 𝑓𝑅𝑟𝑙 𝑒𝑟𝑙𝑆𝑅𝑑 𝑠𝑠𝑚𝑆𝑓𝑙
𝜃𝑅, 𝑛 𝑐𝑠 𝑗(𝑅 + 𝑛)
𝑔𝑅(m) is impulse response of the filters
* Filter overlap factor K : number of multicarrier symbols which overlap in the time domain.
* OFDM can be implemented by set K as 1
� .𝑀−1
𝑘=0
� 𝑑𝑅, 𝑛
∞
𝑛=−∞
𝜃𝑅, 𝑛 𝑔𝑅 𝑚 − 𝑛𝑀/2
𝑠 𝑚
Presenter
Presentation Notes
The output signal of filter bank s[m] is complex value. We can express the discrete-time baseband signal at the output of an FBMC transmitter based on OQAM modulation as the equation. In the terminology of filter banks, the first filter in the bank, the filter associated with the zero frequency carrier, is called the prototype filter, because the other filters are deduced from it through frequency shifts. Prototype filters are characterized by the overlapping factor K, which is the ratio of the filter impulse response duration Θ to the multicarrier symbol period T . The factor K is also the number of multicarrier symbols which overlap in the time domain. Generally, K is an integer number and, in the frequency domain, it is the number of frequency coefficients which are introduced between the FFT filter coefficients.
In the post-processing operation there are 2 steps. Firstly, the real part should be taken after multiplication by sequence 𝜃 . The second operation is real-to-complex conversion, where two successive real-valued symbols (with one multiplied by j) form a complex-valued symbol 𝑐 𝑘 𝑙 . This conversion decreases the sample rate by a factor 2.
The SignalCombiner model combines multiple input signals with different sample rates, different characterization (carrier) frequencies, and different bandwidths into a single signal at the specified characterization frequency and sample rate.
Presenter
Presentation Notes
to talk about the equivalent way of doing this if you wanted to do this in HW or in SignalStudio or Matlab. I think SV is truly quite innovative here and if yes, maybe we can drive that point home?
Cross Domain Simulation
Keysight EEsof EDA
August 28, 2014
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Motivation
– Design problem spans to different technology domains (Baseband signal processing, RF circuit design, Radio access networking)
– System level problem cannot be solved in any one domain alone
– RF circuit verification now needs using a realistic representation of the complex modulated RF signal
– Baseband and RF team entirely isolated and use different type of tools
– Needs unified BB/RF design and verification flow
Signal quality degraded by: • PA Compression • Intermods • Spectral Spread • LO Phase Noise • BPF Filter Effects
Phase Noise
Gain, NF & Compression
Characteristics Ripple, Group Delay & BPF
Characteristics
Signal quality degraded by: • Different multi-carrier waveform • Apply different prototype filter
Gain, phase imbalance, IQ
offset
Different waveform, modulation
Presenter
Presentation Notes
SystemVue provide multiple methods for BB and RF co-simulation. The example shows first method of representing RF part using data flow simulation. Some of the blocks using black arrow are characterize the RF path in terms of its frequency response, non-linear behavior, thermal noise, and phase noise performance by representing its behavioral in the time domain. This approach help system architect fast communications system modeling addressing both BB and RF characteristics.
The second method is combining time domain simulation engine (Data Flow) and a frequency domain simulation engine (Spectrasys). A Data Flow simulation is used to understand a communication system at the algorithmic level using time domain analysis for baseband and RF signals. The analysis for an RF signal is bandpass with the signal bandpass information bandwidth centered at the RF signal carrier frequency (more typically called the RF characterization frequency). A Data Flow analysis inherently only deals with signal forward transmission flow for which impedance is not a relevant concept. A Spectrasys simulation is used to understand the behavior of a system in the frequency domain. This analysis is broadband and includes the many harmonics and intermodulation products associated with non-linearities such as amplifiers and mixers. It inherently includes signal flows for forward transmissions, reflections due to impedance mismatches, and reverse transmissions due to non-ideal isolations. The third method is SystemVue-ADS cosimulation by transferring double-type data samples from ADS Cosim model to the corresponding cosimulation block in ADS through shared memory.
A VTB (Verification Test Bench) is a SystemVue Data Flow design that can be used to verify the performance of a circuit design in its design/simulation environment (ADS or GoldenGate). The benefits of using a VTB is that the performance of the circuit design can be verified using real world complex modulated signals conforming to advanced wireless standards such as 2G/3G/4G including 5G candidate waveform. A VTB is created by a system designer in the SystemVue environment. The end user of a VTB is a circuit designer working in the ADS or GoldenGate environment. The key part of a VTB design is the SVE_Link model. This model is a placeholder for the actual circuit design to be tested (DUT). When a VTB is simulated inside the ADS or GoldenGate environment the circuit simulator is co-simulating with the standalone SystemVue Data Flow simulation engine.
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Full Duplex Radio
– The devices transmit and receive signals simultaneously at the same frequency
– The new breakthrough in wireless communications
– Theoretically double the spectral efficiency
– Self interference cancellation need to be addressed at both baseband and RF domain
Full duplex radio technology, where the devices transmit and receive signals simultaneously at the same center-frequency, is the new breakthrough in wireless communications. Such frequency-reuse strategy can theoretically double the spectral efficiency, compared to traditional half duplex (HD) systems, namely time-division duplexing (TDD) and frequency-division duplexing (FDD).
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Simulations
– Electro magnetic simulation
– RF circuit simulation
– Baseband algorithm verification
– System level simulation and performance evaluation
Multiple-antenna (MIMO) technology is becoming mature for wireless communications and has been incorporated into wireless broadband standards like LTE and Wi-Fi. Basically, the more antennas the transmitter/receiver is equipped with, the more the possible signal paths and the better the performance in terms of data rate and link reliability. The price to pay is increased complexity of the hardware (number of RF amplifier frontends) and the complexity and energy consumption of the signal processing at both ends.
TR 25.966 Spatial channel model (SCM) for Multiple Input Multiple Output (MIMO) simulations
TR 36.814 Further advancements for E-UTRA physical layer aspects
TR 37.976 Measurement of radiated performance for Multiple Input Multiple Output (MIMO) and multi-antenna reception for High Speed Packet Access (HSPA) and LTE terminals
TR 37.977 Verification of radiated multi-antenna reception performance of User Equipment (UE)
TR 36.873 3D-channel model for LTE
ICT-317669-METIS/D1.2 Initial channel models based on measurements
Define 5G Channel Model Requirements • Spatial consistency and mobility • Diffuse versus specular scattering • Very large antenna arrays • Frequency range • Complexity vs. Accuracy • Applicability of the existing and proposed models on the 5G requirements
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Massive MIMO
– The use of a very large number of service antennas operated fully coherent and adaptive
– System Model : M transmit antenna with maximum S streams, K users each with a single antenna
– Brings huge improvements in throughput and energy efficiency when combined with simultaneous scheduling of a large number of UEs
– Originally envisioned for time division duplex(TDD), but can potentially be applied in frequency division duplex(FDD)
Massive MIMO makes a clean break with current practice through the use of a very large number of service antennas (e.g., hundreds or thousands) that are operated fully coherently and adaptively. Extra antennas help by focusing the transmission and reception of signal energy into ever-smaller regions of space. This brings huge improvements in throughput and energy efficiency, in particularly when combined with simultaneous scheduling of a large number of user terminals (e.g., tens or hundreds). Massive MIMO was originally envisioned for time division duplex (TDD) operation, but can potentially be applied also in frequency division duplex (FDD) operation.�Other benefits of massive MIMO include the extensive use of inexpensive low-power components, reduced latency, simplification of the media access control (MAC) layer, and robustness to interference and intentional jamming. The anticipated throughput depends on the propagation environment providing asymptotically orthogonal channels to the terminals, and experiments have so far not disclosed any limitations in this regard. While massive MIMO renders many traditional research problems irrelevant, it uncovers entirely new problems that urgently need attention; for example, the challenge of making many low-cost low-precision components work effectively together, the need for efficient acquisition scheme for channel state information, resource allocation for newly-joined terminals, the exploitation of extra degrees of freedom provided by an excess of service antennas, reducing internal power consumption to achieve total energy efficiency reductions, and finding new deployment scenarios.
• AS AoA/AoD • PAS • Doppler spectrum • Correlation • Rician K factor
Channel sounding Parameters estimation
Statistics & modeling
SystemVue Simulation
SAGE Maximum likelihood estimation algorithm No limitation for number of path, suitable for both LOS and NLOS scenarios Can estimate all the channel parameters including path loss and path delay of each path
Iteration needed, large computing amount
ESPRIT Subspace based algorithm
Maximum estimating number of path is limited by number of Rx, will be fail under NLOS scenario
cannot estimate path loss and path delay small computing amount
• Use the following keywords for • 5G: www.keysight.com/find/5G • SystemVue : www.keysight.com/find/eesof-systemvue • LTE & LTE-A : www.keysight.com/find/cellular or
www.keysight.com/find/eesof-systemvue-lte-advanced • MIMO Channel :
