1 WHITE PAPER Hardik Gandhi, Radio IP Development Manager, Debbie Greenstreet, Strategic Marketing Director, Joe Quintal, Senior Applications Engineer, Texas Instruments Digital Radio Front-End strategies provide gamechanging benefits for small cell base stations Abstract It is no secret that small cell base stations are expected to be a major lifesaver in the wireless data deluge by helping to provide significant capacity gains as part of 3G and 4G wireless heterogeneous networks. While the industry standards and algorithms are mostly in place for these heterogeneous networks to function, power, performance and cost hurdles must be met before small cell base station solutions manifest into practical reality. Base station manufacturers tend to focus their attention on the performance and attributes of the small cell baseband System on Chips (SoCs). The baseband SoC silicon and software does contribute to a significant portion of the small cell solution performance, however the digital radio front end portion of the design can have a substantial impact as well and is often overlooked. This white paper focuses on the digital front end portion of the small cell base station and delves into design factors that play a significant role in achieving the performance and power targets demanded by small cells. Introduction Explosive growth in cellular data usage is dictating communication infrastructure evolution, pushing for increased capacity and reduction in cost and environmental impact. Heterogeneous networks coexisting macro/pico/femto cells, along with advanced receivers and transmitters to maximize spectral effi ciency can provide the required boost in capacity. At the same time, improving power effi ciency will be a key consideration for next-generation radio architectures. As wireless service providers and base station manufacturers aggressively push towards deployment of small cell base stations, they are faced with challenges to meet viable business and network performance models: approximately a 10x reduction of power consumption, size and cost compared to the traditional macro base station. A lot of focus has been given to single-chip base station SoCs, along with optimized small cell software as a key strategy for achieving these objectives, and rightfully so as the SoC architecture and software plays a signifi cant factor in small cell cost, power, size and performance. The digital radio front ends of macro base stations are typically on separate boards, sometimes even in separate enclosures. Since the output power for small cells is signifi cantly lower than for traditional macro base stations, one approach could be to scale down the macro design (perhaps also reducing functionality) to achieve acceptable performance at reduced size and cost. However, as we make the paradigm shift from a macro to a small cell base station, there are additional opportunities for further integration of the digital radio front end technology and the baseband processing executed by the SoC, as well as portions of the radio front end with the analog RF circuitry. Newer interface standards offer more optimized board layout and interconnect options, offering additional optimization. Finally, by leveraging the radio front end power amplifi er linearization techniques, even further gains can be made in small cell solution power reductions without compromising performance.
2 2 Texas Instruments From macro cells to small cells Traditional macro base station architectures can roughly be divided into three functional categories: control processing, baseband (BB) processing and radio front end. While some level of integration of these functions is happening today, the control, baseband and some of the radio front end functions of a macro base station are typically on a single board, with the bulk of the radio front end often times on a separate board, and maybe even in a separate enclosure, as depicted in Figure 1. Macro base-station radios often have dozens of integrated circuits partitioned along functional boundaries, with baseband and control processing in a leading-edge process node digital CMOS SoC, digital front end technologies in digital CMOS ASICs or ASSPs, data-converters in CMOS/BiCMOS technologies with 1 8 converters per device, and RF up and down conversion functions like I/Q modulators and mixers, buffers, attenuators, etc. in typically separate devices with optimal noise and linearity performance. Figure 1: Macro base-station radios often have dozens of integrated circuits partitioned along functional boundaries When small cell base stations became an integral topic of heterogeneous network planning several years ago, the success strategy hinged on the assumption that they would be much smaller and lower power and hence, employ a single-board solution with much fewer numbers of discrete components compared to macro cells. Since then, as operators and the 3GPP organization continue to hone the small cell requirements and algorithms, the performance requirements and complexity has grown, providing further challenges in meeting those form factor and power expectations. While macro cells typically range in output power from anywhere between 10W to 60W or more, the small cell umbrella covers a variety of applications with different output power ranges (transmit power at the antenna): Indoor femto cells: <200mW P out, often Power over Ethernet (PoE) requirements Enterprise femto cells: mW P out, often PoE+ requirements
3 Texas Instruments 3 Outdoor pico cells: 1 2W P out Micro cells: 2 5W P out and larger To meet area and cost targets in small cells, the stringent performance requirements imposed on analog/ RF components are often relaxed, with the expectation that evolution of digital impairment correction algorithms would help recover any loss in performance. For example, in order to lower size, the transceiver front ends would use surface or bulk acoustic wave (SAW or BAW) duplexers, which are much smaller and cheaper than the ceramic/cavity duplexers normally used in macro cells. But they add signifi cantly to post-pa RF power loss. Due to this the RF PAs for small cells often need to operate at 2 3dB higher P out than what the antenna actually radiates, pushing them further into non-linear operating regions, in turn requiring linearization algorithms to ensure compliance with spectral emissions and modulation accuracy limits. The fact that a vast majority of, perhaps all, small cell base stations will be deployed with enclosures that have no special provisions for air cooling makes reducing every watt of power consumption (and resulting heat dissipation) ever so important. Along with the motivation to reduce power, cost and area, small cells also need to support newer features like network listening modes for spectral sniffi ng and synchronization, and be more frequency agile to be able to support different 3G/4G frequency bands and signal bandwidths with as few variants of the design as possible. These requirements drive a rethink of traditional architectures, potentially evolving into a confi guration as depicted in Figure 2. Figure 2: Small cell base station architectures strive for single-board solutions with minimal number of discrete components Next-generation small cell architectures can achieve these goals by taking advantage of advances in many areas: Aggressive power optimization solutions PA linearization techniques like digital pre-distortion (DPD) in combination with crest factor reduction (CFR) techniques help lower overall system power consumption signifi cantly, enabling more PA choices cheaper PAs and less expensive board and enclosure designs, which help minimize BOM cost
4 4 Texas Instruments PA Linearization techniques also provide margin for system performance extending the reach and performance of a small cell solution (bigger cell size, less interference) which maximizes system effi ciency Flexible IF architectures and optimized interfaces For traditional high-performance macro base stations, a costly heterodyne design has until now remained the de facto radio topology. A direct-conversion, or homodyne architecture, where the RF signal is directly down-converted to a BB signal or vice versa without any intermediate frequency stages has many attractive features. It is also referred to as a zero IF architecture. A zero-if (or very-low-if) approach enables dramatically reducing component count and thus footprint and cost of radio transceivers. Reducing the number of parts also simplifi es the supply chain and manufacturing and improves yield. Zero-IF architectures provide a great deal of fl exibility in frequency planning and allow multi-mode and/or multiband operations with minimal changes to baseband digital and analog circuitry. Zero IF is not a new concept: radio designers have used these architectures for low-end handsets and some base-station designs. Historically, many of the benefi ts offered by a zero-if architecture have been offset by a series of problems I/Q distortion, DC offset and LO leakage being some of the most critical. Until recently, these technical barriers in receiver design have prohibited its practical implementation for high-performance base stations. But with advances in digital SoCs, novel, adaptive digital compensation techniques can be implemented cost-effectively to mitigate these problems, and allow radio designers to fully exploit all the advantages this architecture choice brings to the table. Newer interface standards, like JESD204B, help optimize board-level layout, reduce power consumption and enable faster time to market compared to traditional parallel LVDS/LVCMOS interfaces. Higher levels of integration Aggressive integration of traditionally discrete digital-/analog-processing components into area, cost and power-optimized System-on-Chips. As shown in Figure 2, all of the high throughput digital processing could be combined in one optimized leading-edge digital process node BB SoC, combining control, baseband and radio processing, including some or all elements of the digital radio front end processing. All of the data conversion and RF up/down conversion processing could be combined in another radio SoC in a process node suited for analog/rf performance, with elements of the digital front end processing included to provide required analog/rf impairment correction. Every watt of power saved with the above techniques translates into a proportional reduction in operational expenditure for the carrier (OPEX) as well as into proportional reduction in cooling (enclosure) costs, and may provide a wider choice in component selection and resulting reduction in capital expenditure (CAPEX). With
5 Texas Instruments 5 the sheer number of small cell deployments projected, even an 10W power saving per small cell base station (typical savings with CFR/DPD for a 0.5W 2 2 small cell) using advanced technologies can translate to signifi cant savings in lifetime operating expenses (~$1M/year for a single dense metro with 70k small cells deployed), as well as reducing the environmental impact of vast scale deployment. Digitally assisted RF transceiver architectures utilizing the techniques described in this paper take us a step closer to an all-digital radio and help next-generation base-station transceivers meet some of the newer challenges as they emerge. These novel technologies enable the small cell architecture to take on a singleboard solution as depicted in Figure 2, and are discussed in more detail in the rest of the paper. Digital Front End technologies overview The air interface for cellular base-station radios requires essential digital, analog and RF signal-processing components to prepare the modulated samples for transmission, or extract modulated data from received signals at the antenna, and is functionally partitioned as depicted in Figure 3. Backhaul Interface Layer 3 Transport Layer 2 Layer 1 DFE Analog Antenna Radio Interface Packets Frames Samples RF Carriers Figure 3: Functional partitioning of cellular base station processing components Beyond providing an interface (LVDS/LVCMOS or JESD204A/B SerDes) to analog-to-digital and digitalto-analog data converters, the digital front end blocks perform a variety of critical functions which can be roughly classifi ed into two categories: 1. Channelization and re-sampling functions these are mandatory signal-processing functions to be performed for any type of base station [micro or macro, Time Division Duplex (TDD) or Frequency Division Duplex (FDD), 3G or 4G] Carrier fi ltering to comply with spectral emission masks and spectral leakage requirements, including root-raised-cosine fi ltering (RRC) and/or linear channel equalization. Tuning and channel aggregation/distribution essential for multi-carrier and/or multi-standard base stations. Gain, phase, delay adjustments and power-measurement functions Figure 4 on the following page shows a fl exible digital up/down converter block (DUC/DDC) that performs these manda tory channelization and resampling functions. Programmable fi nite impulse response (FIR) fi lters,
6 6 Texas Instruments Small cells to/from L1/L2 processing Macro cells to/from CPRI/OBSAI To CFR or From RX Symbol rate Base band I/Q Data Up sampled composite Antenna streams Figure 4: Block diagram of an implementation of essential channelization and re-sampling functions farrow-based resampling fi lters, cascaded-integrator-comb (CIC) fi lters and numerically controlled oscillators (NCOs) and mixers are the essential signal processing blocks that perform these functions. 2. Power amplifier (PA) linearization and RF impairment correction functions these are optional in the true sense, but often mandated by system effi ciency and cost requirements. Every Analog/RF component suffers from some impairment (group delay, non-linear distortion, gain/phase imbalance) at its optimal operating point (best effi ciency, best dynamic range, best noise fi gure) that can be corrected by digital pre- or post-processing. Some of the key algorithms for impairment correction include: Crest Factor Reduction required to limit signal peak to average power ratios to reduce PA peak power and linearity requirements and hence system cost Digital Pre-Distortion required to improve system linearity to allow PAs to be operated more effi ciently, reducing both system cost (CAPEX) and operating expenses (OPEX) I/Q distortion and DC offset/lo-leakage correction essential to enable zero-if system architectures, which in turn help reduce system cost and improve fl exibility. Crest Factor Reduction Fourth-generation multi-carrier communication systems based on orthogonal frequency division multiplexing (OFDM) as well as third-generation code-division multiple-access (CDMA) based systems exhibit signals with high peak-to-average ratios (PARs), also known as crest factors. The non-constant envelope-modulation techniques employed in such systems have stringent Error Vector Magnitude (EVM) requirements. This requires a highly linear PA amplitude and phase response, often resulting in an increased PA back-off (driving the PAs at lower output power levels) to maintain acceptable linearity to meet spectral mask and modulation accuracy requirements. PAs are most power effi cient at close to peak drive levels. A high PA back-off results in a drastic reduction in PA effi ciency.
7 Texas Instruments 7 Crest-factor-reduction techniques strive to reduce the peak-to-average power ratios of transmit signals by deliberately introducing noise into the signals within the EVM and Adjacent Channel Power Ratio (ACPR) limits imposed by the standards. Multi-carrier (and even single-carrier LTE) signals can have a peak-to-average power ratio (PAR) as high as 12dB. The application of CFR can reduce signal peaks by as much as 4 6dB, with acceptable in-band EVM degradation, allowing the PA to operate at higher input/output power levels (resulting in more effi ciency) while maintaining linearity at the output of the PA. Even for the smallest category of small cells with mW antenna output power, the 4 6dB decrease in PA peak power through the use of CFR enables a roughly 2 4 difference in PA size and power effi ciency, and a corresponding reduction in cost. This should be a key trade-off when doing power budgeting to meet small cell power over Ethernet (PoE) limits. Figure 5 shows CFR processing for a typical multi-carrier 3G test model signal. The Complementary Cumulative Distribution Function (CCDF) plot on the right shows the signal sample distribution before and after CFR. As can be seen, the signal peak-to-average power ratio is reduced from over 11dB to under 7dB. CFR can reduce the PAR even more, but would be limited by the EVM limits set for the standard. The plot on the left shows a snippet of the time domain waveform around a signal peak. A cancellation pulse scaled to the appropriate gain and phase is applied to the signal to reduce the peak amplitude to below a set threshold. The cancellation pulse needs to have an appropriate spectral shape suited to limit most of the noise in-band so as not to violate spectral mask requirements. Cancelled Peaks CFR Threshold CFR Input (blue) CFR Output (red) Cancellation (green) CFR Output CFR Input Figure 5: Crest-factor-reduction performance example CFR is usually applied in baseband at low sample rates suitable for digital signal processing. But the PAs operate on signals up-converted to RF frequencies. This up-conversion process will generate signal peaks that may not be visible at low baseband sample rates. For optimal peak cancellation, CFR signal processing
8 8 Texas Instruments has to apply special interpolation techniques to estimate the RF signal peaks and apply fractionally adjusted cancellation pulses to the signal. Also special care needs to be taken to accommodate multiple overlapping peaks to limit over cancellation. Digital Pre-Distortion RF power amplifi ers achieve maximum power effi ciency near the saturation point of the PA (often listed as Psat or P1dB or P3dB in PA datasheets). Due to the inherent nature of LDMOS/GaN/GaAs power transistors, the PAs are most nonlinear near Psat (as shown in Figure 6 below), introducing severe distortion effects into the transmitted signals. As shown in Figure 7 on the following page, these effects manifest themselves as in-band distortion (degraded Error Vector Magnitude EVM) and increased out-of-band spectral re-growth (degraded Adjacent Channel Leakage Ratio ACLR). Minimum EVM and ACLR requirements are defi ned by regulatory bodies and OEMs need to add suffi cient margin on top of these requirements to allow for performance variations with temperature and time (component aging). Very often the PA drive levels are backed off such that the signal falls within the linear region of the PA to avoid these distortion effects. But at these backoff regions, the PA power effi ciency is extremely poor. Linearization techniques need to be employed to operate the PA close to its saturation region, where it offers maximum output power and best power effi ciency. A variety of power amplifi er linearization techniques such as RF feed-forward, RF feedback, RF/IF predistortion and post-distortion have been proposed and implemented over the years. Of these, adaptive Digital Pre-Distortion (DPD) schemes have proven to be the most effi cient and cost effective compared to traditional CFR reduces signal peaks to concentrate the signal power within a limited region allows pushing the PA output power higher DPD allows the PA to be operated closer to its saturation region to maximize efficiency - while still meeting Spectral Mask and Modulation Accuracy requirements Ideal (linear) PA response Optimal PA power efficiency close to the saturation points (P1dB/P3dB) P3dB P1dB Typical PA response Output Power PA transistor characteristics cause signal distortion in and beyond this region because the output power drops below the ideal (linear) curve Without CFR/DPD, the PA must be operated in this region to avoid distortion Input Power Figure 6: Typical RF power amplifier response
9 Texas Instruments 9 Non-Linear distortion causes out of band spectral leakage (ACPR degradation) Non-Linear and linear distortion causes in-band error (EVM degradation) Figure 7: Effects of RF PA non-linearities analog/rf linearization techniques. Effi ciencies of traditional LDMOS Class AB power amplifi ers widely in use today when operated under a back-off condition range from 3 10%. However, with crest factor reduction and adaptive digital pre-distortion techniques, the effi ciencies can be improved by 3 to 5 times. Newer PA topologies such as advanced Doherty, or Class AB with drain modulation, in combination with digital predistortion and newer GaN or GaAs power transistors, can achieve effi ciencies over 50%. For small cell power amplifi ers, DPD allows the PA to operate linearly closer to Psat, improving system effi ciency. DPD also enables more PA choices cheaper PAs and less expensive board design minimizes BOM cost, and provides margin for system performance extending the reach and performance of a small cell base station (bigger cell size, less interference). As discussed later in this whitepaper, DPD can provide from ~1W/antenna to ~20W/antenna system power savings for small cells ranging from 100mW to 2W antenna output power with exponential increase in savings beyond that. The idea behind Digital Pre-Distortion (DPD) is to cascade a non-linear system prior to the PA, which provides an inverse response to the PA such that the cascaded system has a linear response. As shown in Figure 8 on the following page, f(.) is the inverse of g(.) such that y is a linear representation of x (with the desired amplifi cation factor). RF power amplifi ers have complex non-linear models, made more complicated by the presence of memory effects. Memory effects refer to the bandwidth-dependant nonlinear behavior often exhibited by RF PAs. These encompass envelope memory effects and frequency response memory effects. Envelope memory effects are primarily a result of thermal hysteresis and electrical properties inherent to PAs. Frequency memory effects are due to the variations in the frequency spacing of the transmitted signal and are characterized by shorter time constants. These memory effects and non-linearity in general changes over time and temperature and requires a real time adaptive DPD correction. The plots in Figure 9 on page 11 show amplitude and phase responses of a typical RF PA, where with DPD the resulting outputs (in blue) are clearly much more linear compared to the original PA outputs (in red). Typically, the harder a PA is driven to maximize its effi ciency, the more severe the non-linear distortion and the
10 10 Texas Instruments DPD models a response inverse to the PA response Typical PA response Input Signal Pre-Distorted Signal PA Output Signal DPD cancels out PA non-linearity and helps eliminate the distortion effects (ACPR and EVM degradation) from the PA output signal. Figure 8: A model for PA linearization via digital pre-distortion wider the bandwidth a PA has to support, the more severe the memory effects are, making the need for DPD even more pressing. The DPD non-linearity order, memory depth and adaptation rates are often PA and signal dependent, and a commercially viable small cell DPD solution has to envision all usage scenarios and draw reasonable tradeoffs between performance and hardware and software computational requirements. DC offset and I/Q distortion compensation Direct conversion or zero-if radio architectures are a popular choice for small cell transceiver design since they use fewer components and are easier to integrate comparing with the conventional heterodyne architectures. But those benefi ts also come with several well-known impairments, namely I/Q imbalance and DC offset. In a direct conversion transceiver, a quadrature modulator (QM) implements I/Q modulation and RF upconversion, while a quadrature demodulator (QDM) realizes I/Q demodulation and RF downconversion. Because of the limitation in analog circuit precision, the quadrature carriers used in QM and QDM cannot have exactly the same amplitude and a perfect 90-degree phase difference that are essential for accurate signal conversion. Similarly, the analog reconstruction fi lters on the I and Q paths may not match exactly. These imperfections are called I/Q imbalance, which causes cross talk between I and Q channels and creates undesired images of the original signal. In addition, because of limited isolation in analog components, some of the local oscillator (LO) power leaks into the RF output, which creates LO spikes in transmitted signal and DC offset in
11 Texas Instruments 11 Figure 9: Power amplifier memory effects received signal. For traditional high IF heterodyne architectures these unwanted images can be fi ltered out by analog/rf fi lters. But for zero-if architectures these unwanted distortion images fall very close to or right on top of the signal bands of interest, degrading signal SNR/EVM and/or violate spectral emissions limits, and are very expensive (or often impossible) to eliminate via analog fi ltering. Digital pre- and post-compensation techniques have to be employed to compensate for these distortions. Further complicating matters, these distortion components are often frequency-dependent and may vary over time as components age, requiring adaptive cancellation techniques. Most existing compensation techniques treat transmit and receive I/Q imbalances separately. To compensate for I/Q imbalance in the transmitter, the receive side is either assumed to have dedicated feedback loops or digital demodulators with perfect quadrature carriers. To compensate for I/Q imbalance in the receiver, the transmit side is usually assumed to be free of I/Q imbalance. For optimal zero-if transceivers, the above limitations are often impractical, resulting in the transmitter and receiver distortion components coexisting and overlapping with each other, making extraction of the respective distortion compensation coeffi cients ever more complicated. JESD204 The push for ever more compact small cell enclosures to minimize visual impact and installation costs necessitates highly condensed transceiver board layouts. Traditional data-converter interfaces based on parallel LVDS or LVCMOS signaling protocols require considerable board area and a great deal of analysis and special layouts to minimize skew across data bits and optimize the setup/hold times relative to the clock traces. Board bring up with high-speed parallel interfaces is a big challenge and often gates system/software bring-up and delays time to market. JESD204 is a new standard that defi nes a serial communications link between data converters (ADCs and DACs) and other devices such as FPGAs, DSPs, ASICs and clocking devices. Similar to other more well-known signaling protocols like PCIe or CPRI, this SerDes-based interface highly simplifi es the digital
12 12 Texas Instruments data interface between devices. With the clock embedded in the data stream and embedded algorithms to optimize sampling of the data bits, this simplifi es the routing between devices because there are much fewer lanes on the PCB, and it simplifi es system design and board bring-up no setup/hold time margin across as many as 16 LVDS pairs with one data clock to worry about. This new standard reduces the number of I/Os and thus pin count of devices allowing for smaller packages, and offers a fl exible and scalable solution to accommodate different data traffi c needs (e.g., multiple ADCs on one JESD differential pair). JESD204A (ratifi ed in 2008) and JESD204B (ratifi ed in 2011) both provide support for multiple lanes per converter or multiple converters per lane. JESD204B supports data rates up to 12.5 Gbps compared to Gbps for JESD204A. In addition, the JESD204B Subclass 1 operating mode provides support for accurate synchronization across multiple converters. Multiple transmitters and receivers can get synchronized in order to obtain a deterministic latency across multiple devices. This requires the use of an external system reference signal (also known as SysRef) for synchronization. SysRef signals and device clocks need to be distributed with matched length to all devices in order for the internal local multi frame clocks (LMFC) to be synchronized properly. This ensures that the SysRef signal gets processed at the same instant across all devices. But the JESD204B traces don t have to be length matched. This provides a great deal of fl exibility in board layouts while still maintaining synchronization and deterministic latency across devices. The spatial benefi ts can be clearly seen in Figure 10. Dual 14bit Data converter DDR LVDS 16 diff pairs (14 data + clock + sync) Dual 14bit Data converter JESD204B 4 diff 3.1Gbit or 2 diff 6.2Gbit Figure 10: Comparison of sample layouts with traditional LVDS and newer JESD interfaces Shrinking cost of linearization Current macro cell system architectures often employ discrete DFE solutions with built-in linearization functions (see reference 1 for an example). With macro cells typically designed for 10W 60W or higher antenna output power, the 1 2W per antenna linearization cost is a small fraction of the overall system power budget and is dwarfed by the power savings linearization brings to the table. For small cell applications, with these discrete linearization solutions, the break-even PA output power at which linearization starts adding net benefi t is high. But with high levels of integration and digital technology
13 Texas Instruments 13 scaling, benefi t of linearization may be seen even for the lowest class of small cell applications. Many technological advances contribute to the shrinking cost of linearization, some of them being: High levels of integration in fi ner lithography SoCs: As advanced DFE technologies were evolving over the last decade or so, low investment cost (but high power and high production cost) ASIC/ASSP or FPGA solutions were effective given the pace of change. But as DFE technologies have become more mature, and with built in fl exibility in newer DFE architectures, integration with other high-throughput baseband and control-processing functions in deep sub-micron process nodes becomes more attractive, and allows for signifi cant reduction in power and cost for the same functionality compared to discrete solutions. As an example and shown in Figure 11, over 4 reduction in power and 7 reduction in cost (die area) can be seen as we progress from stand-alone DFE solutions in 90nm to integrated DFE solutions in 28nm. Technology Scaling nm 80 Area nm 40nm 65nm Power Figure 11: Reduction in power and area with semiconductor technology scaling More power and area effi cient interfaces between devices: Higher amounts of integration, and new high-speed serial interfaces can help with substantial reduction in board area and cost, eliminating what would have been seen as barriers to implementing advanced high-sample-rate and adaptive algorithms. As we saw earlier, with JESD204B a 4 to 8 reduction in the number of interface signals can be seen compared to traditional LVDS interfaces. Exploiting synergies enabled due to integration: Various resources can be time shared between linearization and other functions required for small cell operation like sharing the feedback path with network listening for clock synchronization or spectral monitoring, or sharing a DSP processor for L1/L2 processing as well as DPD and I/Q offset compensation adaptation. This helps reduce the number of components on the board signifi cantly, and makes optimal use of all available resources.
14 14 Texas Instruments The theoretical analysis shown in the graph in Figure 12 is based on the following assumptions: 2 increase in PA effi ciency with CFR (typical 3dB PAR reduction and a linear PA effi ciency curve) 3 increase in PA effi ciency with DPD 4 drop in Linearization power from discrete to integrated DFE. From this simplistic analysis it is apparent that with integrated DFE, benefi ts of linearization can be seen even with output power as low as 150mW (region highlighted in the fi rst gray circle), whereas with discrete DFE, the break even power at which linearization adds value is around 0.5W (region highlighted in second gray circle). As seen from the graph in Figure 12, even at 150mW output power, greater than 2W power saving per transmitter can be realized by including integrated CFR and DPD in the solution Especially important when trying to meet PoE (power over Ethernet) requirements. Total Power Consumption - PA + Linearization (W) Output Power (W) Power Consumption without CFR/DPD (W) Power Consumption with CFR, no DPD (W) Figure 12: Theoretical analysis of linearization benefits Power Consumption with discrete CFR & DPD (W) Power Consumption with integrated CFR & DPD (W) Lowering the bar To validate the benefi ts of linearization for the lowest class of small cells, a variety of small cell power amplifi ers from different vendors were evaluated in the lab with and without linearization, and the results are encapsulated in Figure 13 on the following page. A 4-carrier W-CDMA test model signal was used for this analysis. The PAs were tuned to output 200mW of transmit power, which after accounting for the 2 3dB post PA loss due to fi lters/duplexers would correspond to a mW power level at the antenna. Per antenna cost of integrated linearization (CFR/DPD datapath power, additional analog/rf power for requisite feedback and differences in sampling/interface rates and power consumed in executing the adaptation algorithms) was computed using 28-nm benchmarks and factored into the above analysis. PA biasing and/or drain voltage can be tweaked to trade-off effi ciency and linearity. For the above experiments, without linearization, the PAs were biased to optimal linearity to meet spectral mask (ACPR)
15 Texas Instruments 15 requirements. With linearization, the PAs were biased to optimal effi ciency, and the linearization algorithms provided required ACPR improvement to meet spectral mask requirements. As can be summarized from the results in Figure 13, even for this low end of small cell applications, with CFR/DPD one can select a PA with > 3 better PA effi ciency (>27% compared to <8% for a PA without DPD, or <7% for a PA without CFR/DPD). After factoring in the cost of linearization, 2.3 improvement in system efficiency is realized while transmitting the same output power and meeting ACPR requirements. For a 2 2 single-band quad carrier MIMO 3G small cell with mW output power per antenna, inclusion of linearization would provide >3.4W of system power savings, which may be critical to meet PoE budgets. For higher output power levels, benefi ts of linearization are even more dramatic as can be seen from Figure 14 on the following page. Note that the analysis in Figure 14 shows only a comparison of power consumption with or without DPD, with the assumption that the value CFR brings to the table for all classes of small cells is signifi cant enough Figure 13: Experimental analysis of CFR/DPD benefits
16 16 Texas Instruments 30.0 PA / Antenna With CFR/DPD: ~34% System Efficiency ~19W power savings/antenna Power Consumption PA+Linearization (W) PA / Antenna With CFR/DPD: ~16% System Efficiency ~1.3W power savings/antenna PA / Antenna With CFR/DPD: ~21% System Efficiency ~7.5W power savings/antenna Power Consumption with CFR only, no DPD (Average 8% PA Efficiency) (W) Power Consumption with CFR/DPD (Including PA+Linearization power) (W) Antenna Output Power (W) (Assuming 3dB post-pa loss) Figure 14: Experimental analysis of DPD benefits Exponential power savings at higher output powers that its inclusion in any small cell solution is obviously warranted. If comparing results with neither CFR nor DPD, against results with CFR and DPD, the projected power saving will be signifi cantly higher. CFR and DPD are both key to optimal linearization one without the other may give you less than half the benefi ts, especially at higher output power levels. It s also worth mentioning that PA non-linear behavior and memory effects become worse as signal bandwidth increases, leading to decreasing ACPR. Most PA datasheets provide performance results using single-carrier W-CDMA test model (5MHz) or LTE (10MHz) data. But small cell deployment scenarios call for a wide range of signal bandwidths to be supported, up to 40MHz occupied and beyond, sometimes with noncontiguous carrier placements like in LTE rel10 and 11 with inter- or intra-band carrier aggregation, which increases the edge-to-edge signal spread to be supported. The expanding signal bandwidth requirements make the need for state-of-the-art linearization performance even more pressing, which may not be seem obvious from reading PA datasheets. State-of-the-art DFE capabilities integrated in next-generation TI KeyStone II SoCs Integrated digital front-end radio technology blocks are key additions to next-generation TI SoCs based on the KeyStone II architectures, like the optimized dual-mode, dual-band small cell solution (TCI6630K2L) shown in Figure 15 on the following page. Some of the key elements are briefl y described below. DDUC Multiple digital up/down converter modules (shown as DDUCs in Figure 15) provide support for a variety of signal types (W-CDMA, LTE-5MHz, LTE-10MHz, LTE-20MHz, etc.) with single- or multiband frequency confi gurations, with fl exibility that allows re-confi guring the BTS from single-mode 3G/4G to mixed-mode 3G/4G and vice versa. Also supported is both inter- and intra-band carrier
17 Texas Instruments 17 Multicore Navigator 28 nm ARM A15 ARM A15 1MB L * + << * + C66x DSP << * C66x DSP 1MB L2 per C66x Core Multicore Shared Memory Controller 2MB L3 * + << * + C66x DSP << * C66x DSP 2MB TeraNet Bit Rate Radio AccelerationPac Front End Dig. Radio Security AccelerationPac Air and IP System Services NAND DDR3/3L 72b 1333 Power Manager Debug EMIF and IO USB3 SPI System Monitor EDMA PktDMA USIM Packet AccelerationPac 1G Ethernet Switch High Speed SerDes Lanes PCIe 1GbE I 2 C UART GPIO CPRI/OBSAI JESD204B Figure 15: Block diagram of a KeyStone II-based TI baseband SoC with integrated digital radio functionality aggregation with wide carrier separation fl exibility. As heterogenous network (Het-Net) strategies evolve over the next few years, fl exibility to switch between carrier types, signal bandwidths and frequency bands is of utmost importance. CFR State-of-the-art algorithms in the TI CFR modules include advanced features like multiple stages of peak cancellation with provisions to estimate fractional peaks and limit over cancellation, automatic estimation of the CFR cancellation pulse shapes based on signal spectral content monitoring, dynamic threshold adjustments and automatic gain control loops. As can be seen from the results in Figure 16 below, optimal CFR cancellation and resultant signal PAR needs to be traded off against system EVM budget, and a CFR algorithm that offers minimal EVM degradation with maximum PAR reduction will be key to meeting system power and linearity constraints. DPD An advanced datapath employing an optimized Volterra model to implement the PA pre-inverse is an integral part of the TI DPD module. The Volterra coeffi cients are often adapted iteratively using a Single-carrier LTE20: Peak PAR (db) EVM (%) Dual-carrier LTE20: Peak PAR (db) Carrier 0 EVM (%) Carrier 1 EVM (%) Figure 16: Example TI CFR results (PAR vs. EVM) for single- and dual-carrier LTE-20 signals
18 18 Texas Instruments variety of least-squares type algorithms (Conjugate Gradient, Kalman, etc.) which can be implemented in software on the high-performance fl oating-point DSP or ARM cores, with additional hardware acceleration options available for faster iteration times. Figure 17 shows the PA output spectrum without DPD (red) and after DPD correction has been applied (blue) for a representative small cell PA biased to optimal effi ciency. As can be seen from this fi gure, TI s high-performance DPD enables the PA output spectrum to meet spectral mask requirements while achieving highest obtainable power effi ciency for a given PA. Adjacent Channel ACLR limit: -45dBc Alternate Channel ACLR limit: -50dBc Pre-DPD PA Output Spectrum Violates Spectral Mask Post-DPD PA Output Spectrum - Meets Spectral Mask With Margin Figure 17: Example TI DPD performance results for a small cell PA TXRX Novel joint I/Q distortion and DC offset/lo-leakage correction techniques like the one showed in Figure 18 below and implemented in the TI TXRX modules (and accompanying integrated analog/ RF transceiver solution AFE750x) can be used to jointly separate the transmit and receive side I/Q Figure 18: A joint TX/RX I/Q distortion cancellation technique
19 Texas Instruments 19 imbalance by introducing phase rotations in the analog domain. Since the phase rotations have different effects on transmit and receive side I/Q imbalance, it enables using advanced digital algorithms to separate the distortion effects and be able to pre-compensate (on the transmitter) or post-compensate (on the receiver). Using real-time adaptive blind- or calibration-based least squares algorithms with frequency dependent or frequency-independent compensation, optimal signal SNR and emissionsmask compliance can be achieved. Figure 19 shows example results using such an algorithm, where the I/Q distortion and DC offset images that would have violated spectral emissions requirements (or degraded signal SNR if falling in-band underneath an adjacent carrier) are effectively eliminated down to the system noise fl oor. Figure 19: Example I/Q distortion and DC offset cancellation results In addition to the above, TI s digital radio modules include other functions like front-end and back-end Automatic Gain Control loops (AGCs) that help maximize data-converter effi ciency and reduce dynamic range required in baseband processing, transmit/receive equalizers to compensate for analog/rf fi lter droop and phase distortion effects and digital protection functions to limit signal excursions to prevent damage to the PA and associated circuitry. These are welcome additions to any BTS transceiver design, providing signifi cant performance boost and RF/analog component cost reduction at the expense of low-cost integrated digital logic. State-of-the-art TI discrete and integrated data-converter, RF, clocking devices and BB SoCs now support JESD204B subclasses 0 and 1 interfaces for optimal board designs, and enabling rapid system bring-up. Last, but not least, integrated and tested, production-ready platform software from TI supporting not just the baseband processing, but the digital radio processing, such as DDC/DUC, CFR and DPD libraries, as well as analog/rf control enables rapid integration of system components and quick ramp to production. As shown in Figure 20 on the following page, TI s baseband SoC (TCI6630K2L) with integrated digital front-end technologies, closely coupled with TI s integrated radio transceiver solution (AFE750x) and other clocking, power and RF devices from TI, with production ready software, enables a high-performance small
20 20 Texas Instruments PoE GigE PHY WIFI 2x A15 GigE Switch PCIe USIM UART GPS TCI6630K2L Network CoProcessor PTP, Sync I2C SPI DAC 4x C66x USB DDR3 DDR3 Acceleration Pacs DDUC / CFR / DPD/TXRX EMIF Flash AVS JESD204B JESD204B PMU Clocking AFE750x 2x2 TX and RX 2x TX Dig 2x RX Dig TRX Auxiliary Receiver RX Dig AFE750x 2x2 TX and RX 2x TX Dig 2x RX Dig TRX Auxiliary Receiver RX Dig 2x DAC 2x ET ADC ADC 2x DAC 2x ET ADC ADC 2x TX RF 2x RX RF RX RF 2x TX RF 2x RX RF RX RF Combiner Duplexer HPA LNA Figure 20: TI s small cell system solution cell solution with optimized power consumption meeting PoE requirements, with a low BOM cost, fast time to market, and fl exibility to support evolving Het-Net strategies. Conclusion Integrated DFE solutions bring the benefi ts of linearization to lower output power systems; the use of CFR/DPD for PA output as low as 200mW ( mW at the antenna) seems not only viable, but mandatory to meet stringent PoE requirements. >3.4W total system power savings with CFR+DPD (compared to a solution with no CFR, no DPD) for a 2 2 indoor small cell with a 25.5W overall system power budget seems very compelling. Power savings increase exponentially with higher output powers. Taking into account higher post-pa losses of 2 3dB for small cells, savings can be much more. >15W saved with DPD (compared to a solution with CFR but no DPD) for a 2 2 system at 1W output power per PA ( W output power at the antenna) >38W saved with DPD (compared to a solution with CFR but no DPD) for a 2 2 system at 2W output power per PA (1 1.25W output power at the antenna) Dynamic nature of LTE signals, coupled with multi-mode and multiband requirements and the need to extract the last ounce of system power savings make the need for best-in-class CFR and DPD algorithms in integrated base-stations ever more compelling.
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