text:
The Effect of Natural or Simulated Altitude Training on High-Intensity Intermittent Running Performance in Team-Sport Athletes: A Meta-Analysis
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While adaptation to hypoxia at natural or simulated altitude has long been used with endurance athletes, it has only recently gained popularity for team-sport athletes.
To analyse the effect of hypoxic interventions on high-intensity intermittent running performance in team-sport athletes.
A systematic literature search of five journal databases was performed. Percent change in performance (distance covered) in the Yo-Yo intermittent recovery test (level 1 and level 2 were used without differentiation) in hypoxic (natural or simulated altitude) and control (sea level or normoxic placebo) groups was meta-analyzed with a mixed model. The modifying effects of study characteristics (type and dose of hypoxic exposure, training duration, post-altitude duration) were estimated with fixed effects, random effects allowed for repeated measurement within studies and residual real differences between studies, and the standard-error weighting factors were derived or imputed via standard deviations of change scores. Effects and their uncertainty were assessed with magnitude-based inference, with a smallest important improvement of 4% estimated via between-athlete standard deviations of performance at baseline.
Ten studies qualified for inclusion, but two were excluded owing to small sample size and risk of publication bias. Hypoxic interventions occurred over a period of 7–28 days, and the range of total hypoxic exposure (in effective altitude-hours) was 4.5–33 km h in the intermittent-hypoxia studies and 180–710 km h in the live-high studies. There were 11 control and 15 experimental study-estimates in the final meta-analysis. Training effects were moderate and very likely beneficial in the control groups at 1 week (20 ± 14%, percent estimate, ± 90% confidence limits) and 4-week post-intervention (25 ± 23%). The intermittent and live-high hypoxic groups experienced additional likely beneficial gains at 1 week (13 ± 16%; 13 ± 15%) and 4-week post-intervention (19 ± 20%; 18 ± 19%). The difference in performance between intermittent and live-high interventions was unclear, as were the dose of hypoxia and inclusion of training in hypoxia.
Hypoxic intervention appears to be a worthwhile training strategy for improvement in high-intensity running performance in team-sport athletes, with enhanced performance over control groups persisting for at least 4 weeks post-intervention. Pending further research on the type of hypoxia, dose of hypoxia and training in hypoxia, coaches have considerable scope for customising hypoxic training methods to best suit their team's training schedule.
The authors gratefully acknowledge the support of the authors of the selected studies in providing further information when requested.
Author Contributions
MH supported and overviewed the literature search and selection of relevant articles and was the primary advisor regarding data interpretation and review of the manuscript. CL conducted the literature search, reviewed articles, selected relevant articles, interpreted analysed data and constructed the first draft of manuscript. WH was the primary advisor regarding data synthesis and inclusion of studies for meta-analysis, performed the meta-analysis and advised on interpretation of results.
Compliance with Ethical Standards
Preparation of this article was supported by the Lincoln University Research Fund. However, the funder did not contribute in any way to the development of the protocol or the interpretation of results.
Conflict of interest
Michael Hamlin, Catherine Lizamore and Will Hopkins declare that they have no conflicts of interest relevant to the content of this analysis.
Ethical approval
This article was a meta-analysis and therefore did not require separate human ethics approval.
- 1.Bishop DJ, Girard O. Determinants of team-sport performance: implications for altitude training by team-sport athletes. Br J Sports Med. 2013;47(Suppl 1):17–21.CrossRefGoogle Scholar
- 2.Spencer M, Rechichi C, Lawrence S, et al. Time-motion analysis of elite field hockey during several games in succession: a tournament scenario. J Sci Med Sport. 2005;8(4):382–91.CrossRefPubMedGoogle Scholar
- 3.Virr JL, Game A, Bell GJ, et al. Physiological demands of women's rugby union: time–motion analysis and heart rate response. J Sport Sci. 2014;32(3):239–47.CrossRefGoogle Scholar
- 4.Di Salvo V, Gregson W, Atkinson G, et al. Analysis of high intensity activity in premier league soccer. Int J Sports Med. 2009;30(3):205–12.CrossRefPubMedGoogle Scholar
- 5.Kempton T, Sullivan C, Bilsborough JC, et al. Match-to-match variation in physical activity and technical skill measures in professional Australian Football. J Sci Med Sport. 2015;18(1):109–13.CrossRefPubMedGoogle Scholar
- 6.Faude O, Koch T, Meyer T. Straight sprinting is the most frequent action in goal situations in professional football. J Sport Sci. 2012;30(7):625–31.CrossRefGoogle Scholar
- 7.Gharbi Z, Dardouri W, Haj-Sassi R, et al. Aerobic and anaerobic determinants of repeated sprint ability in team sports athletes. Biol Sport. 2015;32(3):207–12.CrossRefPubMedPubMedCentralGoogle Scholar
- 8.Bishop D, Girard O, Mendez-Villanueva A. Repeated-sprint ability—part II. Sports Med. 2011;41(9):741–56.CrossRefPubMedGoogle Scholar
- 9.Bangsbo J, Iaia FM, Krustrup P. The Yo-Yo intermittent recovery test: a useful tool for evaluation of physical performance in intermittent sports. Sports Med. 2008;38(1):37–51.CrossRefPubMedGoogle Scholar
- 10.Atkins SJ. Performance of the Yo-Yo intermittent recovery test by elite professional and semiprofessional rugby league players. J Strength Cond Res. 2006;20(1):222–5.PubMedGoogle Scholar
- 11.Castagna C, Impellizzeri FM, Rampinini E, et al. The Yo–Yo intermittent recovery test in basketball players. J Sci Med Sport. 2008;11(2):202–8.CrossRefPubMedGoogle Scholar
- 12.Krustrup P, Mohr M, Amstrup T, et al. The Yo-Yo intermittent recovery test: physiological response, reliability, and validity. Med Sci Sports Exerc. 2003;35(4):697–705.CrossRefPubMedGoogle Scholar
- 13.Mohr M, Krustrup P. Yo-Yo intermittent recovery test performances within an entire football league during a full season. J Sport Sci. 2014;32(4):315–27.CrossRefGoogle Scholar
- 14.Veale JP, Pearce AJ, Carlson JS. The Yo-Yo intermittent recovery test (level 1) to discriminate elite junior Australian football players. J Sci Med Sport. 2010;13(3):329–31.CrossRefPubMedGoogle Scholar
- 15.Billaut F, Gore CI, Aughey RI. Enhancing team-sport athlete performance: is altitude training relevant? Sports Med. 2012;42(9):751–67.CrossRefPubMedGoogle Scholar
- 16.Manimmanakorn A, Hamlin MJ, Ross JJ, et al. Effects of low-load resistance training combined with blood flow restriction or hypoxia on muscle function and performance in netball athletes. J Sci Med Sport. 2013;16(4):337–42.CrossRefPubMedGoogle Scholar
- 17.Kilding AE, Dobson BP, Ikeda E. Effects of acutely intermittent hypoxic exposure on running economy and physical performance in basketball players. J Strength Cond Res. 2016;30(7):2033–42.CrossRefPubMedGoogle Scholar
- 18.Czuba M, Waskiewicz Z, Zajac A, et al. The effects of intermittent hypoxic training on aerobic capacity and endurance performance in cyclists. J Sports Sci Med. 2011;10:175–83.PubMedPubMedCentralGoogle Scholar
- 19.Czuba M, Zając A, Maszczyk A, et al. The effects of high intensity interval training in normobaric hypoxia on aerobic capacity in basketball players. J Hum Kinet. 2013;39:103–14.CrossRefPubMedPubMedCentralGoogle Scholar
- 20.Girard O, Amann M, Aughey R, et al. Position statement–altitude training for improving team-sport players' performance: current knowledge and unresolved issues. Br J Sports Med. 2013;47(Suppl 1):1–12.CrossRefGoogle Scholar
- 21.Girard O, Brocherie F, Millet GP. Effects of altitude/hypoxia on single- and multiple-sprint performance: a comprehensive review. Sports Med. 2017. (Epub ahead of print).Google Scholar
- 22.McLean B, Gore C, Kemp J. Application of 'live low-train high' for enhancing normoxic exercise performance in team sport athletes. Sports Med. 2014;44(9):1275–87.CrossRefPubMedGoogle Scholar
- 23.Stray-Gundersen J, Levine BD. Live high, train low at natural altitude. Scand J Med Sci Sports. 2008;18(s1):21–8.CrossRefPubMedGoogle Scholar
- 24.Brocherie F, Girard O, Faiss R, Millet GP. Effects of repeated-sprint training in hypoxia on sea-level performance: a meta-analysis. Sports Med. 2017;47(8):1–10.CrossRefGoogle Scholar
- 25.Lundby C, Robach P. Does 'altitude training' increase exercise performance in elite athletes?: Should altitude training be recommended to elite athletes? Exp Physiol. 2016;101(7):783–8.CrossRefPubMedGoogle Scholar
- 26.Millet GP, Brocherie F, Faiss R, et al. Clarification on altitude training. Exp Physiol. 2017;102(1):130–1.CrossRefPubMedGoogle Scholar
- 27.Lundby C, Robach P. Reply. Exp Physiol. 2017;102(1):132–3.CrossRefPubMedGoogle Scholar
- 28.Moher D, Liberati A, Tetzlaff J, et al. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Br Med J. 2009;339(7716):332–6.Google Scholar
- 29.Burtsher M, Pachinger O, Ehrenbourg I, et al. Intermittent hypoxia increases exercise tolerance in elderly men with and without coronary artery disease. Int J Cardiol. 2004;96:247–54.CrossRefGoogle Scholar
- 30.Burtscher M, Haider T, Domej W, et al. Intermittent hypoxia increases exercise tolerance in patients at risk for or with mild COPD. Respir Physiol Neurobiol. 2009;165(1):97–103.CrossRefPubMedGoogle Scholar
- 31.Lizamore CA, Kathiravel Y, Elliott J, et al. The effect of short-term intermittent hypoxic exposure on heart rate variability in a sedentary population. Acta Physiol Hung. 2016;103(1):75–85.CrossRefGoogle Scholar
- 32.del Pilar Valle M, García-Godos F, Woolcott OO, et al. Improvement of myocardial perfusion in coronary patients after intermittent hypobaric hypoxia. J Nucl Cardiol. 2006;13(1):69–74.CrossRefGoogle Scholar
- 33.Haider T, Casucci G, Linser T, et al. Interval hypoxic training improves autonomic cardiovascular and respiratory control in patients with mild chronic obstructive pulmonary disease. J Hypertens. 2009;27:1648–54.CrossRefPubMedGoogle Scholar
- 34.Saeed O, Bhatia V, Formica P, et al. Improved exercise performance and skeletal muscle strength after simulated altitude exposure: a novel approach for patients with chronic heart failure. J Card Fail. 2012;18(5):387–91.CrossRefPubMedGoogle Scholar
- 35.Tin'kov AN, Aksenov VA. Effects of intermittent hypobaric hypoxia on blood lipid concentrations in male coronary heart disease in patients. High Alt Med Biol. 2002;3(3):277–82.Google Scholar
- 36.Brocherie F, Millet GP, Hauser A, et al. "Live high–train low and high" hypoxic training improves team-sport performance. Med Sci Sports Exerc. 2015;47(10):2140–9.CrossRefPubMedGoogle Scholar
- 37.Buchheit M, Hammond K, Bourdon PC, et al. Relative match intensities at high altitude in highly-trained young soccer players (ISA3600). J Sports Sci Med. 2015;14(1):98–102.PubMedPubMedCentralGoogle Scholar
- 38.Galvin HM, Cooke K, Sumners DP, et al. Repeated sprint training in normobaric hypoxia. Br J Sports Med. 2013;47(Suppl 1):74–9.CrossRefGoogle Scholar
- 39.Gatterer H, Philippe M, Menz V, et al. Shuttle-run sprint training in hypoxia for youth elite soccer players: a pilot study. J Sports Sci Med. 2014;13(4):731–5.PubMedPubMedCentralGoogle Scholar
- 40.Inness MWH, Billaut F, Aughey R. Team-sport athletes' improvement of performance on the Yo-Yo intermittent recovery test level 2, but not of time-trial performance, with intermittent hypoxic training. Int J Sports Physiol Perform. 2016;11(1):15–21.CrossRefGoogle Scholar
- 41.McLean BD, Tofari PJ, Gore CJ, et al. Changes in running performance after four weeks of interval hypoxic training in Australian footballers: a single-blind placebo-controlled study. J Strength Cond Res. 2015;29(11):3206–15.CrossRefPubMedGoogle Scholar
- 42.Zhang Y, Hu Y, Zhou F, et al. Effects of 'living high, training low' on the immune function of red blood cells and endurance performance in soccer players. J Exerc Sci Fit. 2005;3(2):81–6.Google Scholar
- 43.Inness MWH, Billaut F, Aughey RJ. Live-high train-low improves repeated time-trial and Yo-Yo IR2 performance in sub-elite team-sport athletes. J Sci Med Sport. 2017;20(2):190–5.CrossRefPubMedGoogle Scholar
- 44.Garvican-Lewis LA, Sharpe K, Gore CJ. Time for a new metric for hypoxic dose? J Appl Physiol (1985). 2016;121(1):352–5.CrossRefGoogle Scholar
- 45.Yang M. A review of random effects modelling in SAS (Release 8.2). London: Centre for multilevel modelling; 2003.Google Scholar
- 46.Hopkins WG, Marshall SW, Batterham AM, et al. Progressive statistics for studies in sports medicine and exercise science. Med Sci Sports Exerc. 2009;41(1):3–12.CrossRefPubMedGoogle Scholar
- 47.Smith TB, Hopkins WG. Variability and predictability of finals times of elite rowers. Med Sci Sports Exerc. 2011;43(11):2155–60.CrossRefPubMedGoogle Scholar
- 48.Millet GP, Roels B, Schmitt L, et al. Combining hypoxic methods for peak performance. Sports Med. 2010;40(1):1–25.CrossRefPubMedGoogle Scholar
- 49.Wilber RL, Stray-Gundersen J, Levine BD. Effect of hypoxic 'dose' on physiological responses and sea-level performance. Med Sci Sports Exerc. 2007;39(9):1590–9.CrossRefPubMedGoogle Scholar
- 50.Chapman RF, Abigail SLS, Lundby C, et al. Timing of return from altitude training for optimal sea level performance. J Appl Physiol (1985). 2014;116(7):837–43.CrossRefGoogle Scholar
- 51.Gore CJ, Sharpe K, Garvican-Lewis LA, et al. Altitude training and haemoglobin mass from the optimised carbon monoxide rebreathing method determined by a meta-analysis. Br J Sports Med. 2013;47(1):31–9.CrossRefGoogle Scholar
- 52.Buchheit M, Racinais S, Bilsborough J, et al. Adding heat to the live-high train-low altitude model: a practical insight from professional football. Br J Sports Med. 2013;47(Suppl 1):59–69.CrossRefGoogle Scholar
- 53.McLean BD, Buttifant D, Gore CJ, et al. Physiological and performance responses to a preseason altitude-training camp in elite team-sport athletes. Int J Sports Physiol Perform. 2013;8(4):391–9.CrossRefPubMedGoogle Scholar
- 54.Lundby C, Calbet JA, Robach P. The response of human skeletal muscle tissue to hypoxia. Cell Mol Life Sci. 2009;66(22):3615–23.CrossRefPubMedGoogle Scholar
- 55.Alfrey CP, Rice L, Udden MM, et al. Neocytolysis: physiological down-regulator of red-cell mass. Lancet. 1997;349(9062):1389–90.CrossRefPubMedGoogle Scholar
- 56.Rice L, Ruiz W, Driscoll T, et al. Neocytolysis on descent from altitude: a newly recognized mechanism for the control of red cell mass. Ann Intern Med. 2001;134(8):652–6.CrossRefPubMedGoogle Scholar
- 57.Garvican L, Martin D, Quod M, et al. Time course of the hemoglobin mass response to natural altitude training in elite endurance cyclists. Scand J Med Sci Sports. 2012;22(1):95–103.CrossRefPubMedGoogle Scholar
- 58.Brocherie F, Millet GP, et al. Repeated maximal-intensity hypoxic exercise superimposed to hypoxic residence boosts skeletal muscle transcriptional responses in elite team-sport athletes. Acta Physiol (Oxf). 2017. (Epub ahead of print).Google Scholar
- 59.Faiss R, Léger B, Vesin J-M, et al. Significant molecular and systemic adaptations after repeated sprint training in hypoxia. PloS One. 2013;8(2):e56522.CrossRefPubMedPubMedCentralGoogle Scholar
- 60.Stray-Gundersen J, Chapman RF, Levine BD. 'Living high-training low' altitude training improves sea level performance in male and female elite runners. J Appl Physiol (1985). 2001;91(3):1113–20.CrossRefGoogle Scholar
- 61.Chaouachi A, Manzi V, Wong DP, et al. Intermittent endurance and repeated sprint ability in soccer players. J Strength Cond Res. 2010;24(10):2663–9.CrossRefPubMedGoogle Scholar
- 62.Faiss R, Girard O, Millet GP. Advancing hypoxic training in team sports: from intermittent hypoxic training to repeated sprint training in hypoxia. Br J Sports Med. 2013;47(Suppl 1):45–50.CrossRefGoogle Scholar
- 63.Chuman K, Hoshikawa Y, Iida T, et al. Relationships between Yo-Yo intermittent recovery tests and development of aerobic and anaerobic fitness in U-13 and U-17 soccer players. Int J Sport Health Sci. 2011;9:91–7.CrossRefGoogle Scholar
- 64.Gatterer H, Klarod K, Heinrich D, et al. Effects of a 12-day maximal shuttle-run shock microcycle in hypoxia on soccer specific performance and oxidative stress. Appl Physiol Nutr Metab. 2015;40(8):842–5.CrossRefPubMedGoogle Scholar


text: Wireless Networks In-the-Loop: Emulating an RF front-end in GNU Radio Sebastian Koslowski, Martin Braun, Jens P. Elsner, Friedrich K. Jondral Karlsruhe Institute of Technology, Germany {martin.braun, jens.elsner, firstname.lastname@example.com j2pfIFt e When designing wireless networks simulations play an important part. This is especially true for the development of systems which need to operate alongside other systems in a heterogeneous radio frequency environment. In such an environment, radio front-end hardware design determines the performance to a great extent. To quantify the effects on various protocol layers, a simulation environment which accurately models the physical realities is needed. As part of such a simulation environment proposed in [1], this paper describes the steps necessary to model the digital and analog signal processing of a generic software radio frontend. The Universal Software Radio Peripheral (USRP) [2] and its RFX2400 daughterboard are modeled using the GNU Radio framework [3]. The considered effects include sample rate conversion, digital and analog filtering, IF mixing, as well as imperfections of the analog components such as I/Qimbalance, non-linearities, phase noise and noise figure, both in the transmit and receive path. The resulting USRP model has the same programming interface as the USRP and allows for seamless switching between simulation environment and real hardware. It is kept modular to allow emulation of other hardware by adjusting a set of parameters. The remainder of this paper is structured as follows. In Section II the USRP and GNU Radio are introduced. Section III describes the emulation of the USRP motherboard. A description of the analog RF hardware model is given in Section IV. Test results are shown in Section V, while Section VI concludes. II. GNU R ADIO AND THE USRP A. GNU Radio GNU Radio is a free software framework for developing software radio applications [3]. It provides signal processing functionality as well as interfaces to hardware. Software radio applications are designed as so-called flow graphs. Signal sources and sinks provide access to a number of hardware and software interfaces as well as to software synthesized signals. Host PC I. I NTRODUCTION HBF1 2 CIC I/4 FX2 FPGA CODEC e HBF ¯2 HBF2 2 CIC ¯D/2 Fig. 1. D A Daughterboard Abstract—The objective of this work is to emulate the behavior of the Universal Software Radio Peripheral as an example of an RF front-end hardware for software radios. The model includes digital and analog signal processing. The emulator is implemented in GNU Radio and is intended to be used as part of a wireless network simulator. -j2pfIFt D A Block diagram USRP motherboard Signal processing operations are implemented in atomic blocks which are then interconnected to fulfill a certain processing task. GNU Radio is a hybrid C++/Python system. For optimal performance, signal processing blocks are written in C++. Flow graphs are built in Python. Memory management and scheduling is handled by the GNU Radio framework. B. USRP The USRP is a radio front-end developed by Ettus Research LLC [2]. It is well integrated into the GNU Radio framework. The USRP is composed of a motherboard and up to four daughterboards – two each for transmission (Tx) and reception (Rx) of radio signals. Data transport to the host PC is implemented over a Universal Serial Bus (USB 2.0). Figure 1 shows the basic structure of the motherboard. It houses a Cypress FX2 USB Controller which handles data transfer from and to the host PC running the software radio application and also distributes control messages throughout the USRP. Digital signal processing is implemented on an Altera Cyclone FPGA; this includes data routing, sample rate conversion by a userdefined factor and digital mixing to baseband in the receive path. The FPGA may contain multiple signal processing units – in the default configuration it can handle up to two complex signals for both Rx and Tx. The motherboard also contains two AD9862 mixed-signal front-end processors (CODEC) which are connected to the FPGA. They perform analog to digital conversion (ADC) with a resolution of 14 bit at a sample rate of 128 MS/s and digital III. M ODEL OF USRP 0 HBF 1 HBF 2 -10 Magnitude (dB) to analog conversion (DAC) with 12 bit at 64 MS/s. Prior to the DAC the baseband signal is interpolated by a fixed factor of 4 and shifted to an intermediate frequency (IF). Each CODEC chip connects with up to two extension cards (one Rx, one Tx). The daughterboards contain the analog RF front-end hardware. A variety of daughterboards for different frequency ranges is available. In this work the RFX2400 transceiver board [2] is discussed and modeled as an example for any front-end. It integrates a complete transmitter and receiver and requires two extension card slots. A structural overview is shown in Figure 3. Direct conversion is used to shift the signal from and to the ISM-band at 2.4 GHz. Also included are low noise amplifiers (LNA), an analog bandpass and image rejection filters. The local oscillator signals are generated independently for the transmit and receive path. -20 -30 -40 -50 -60 -70 -80 -90 0 10 Fig. 2. 20 40 30 Frequency (MHz) 50 60 Halfband filters in transmit path MOTHERBOARD The significant signal processing components of the motherboard (cf. Figure 1) are modeled in GNU Radio blocks and implemented as described below. The simulation bandwidth (cf. [1]) is chosen to be 128 MS/s. A. Transmit path The transmit path of the USRP motherboard consists of a digital up converter (DUC) and a D/A converter for the transmission data. This functionality is spread over the FPGA and the CODEC chip (cf. Figure 1). The transport and buffering of the data from the host PC to the USRP induces a certain delay, which is modeled by a delay block. The delay can hence be set with the resolution of one sample. Interpolation from the incoming user-defined sample rate to the ADC input rate of 32 MS/s is accomplished with a four stage Cascaded Integrator Comb (CIC) filter. The CIC filter is implemented as a custom GNU Radio block with 16 bit fixedpoint arithmetic. The subsequent scaler compensating the word length growth is recreated as carried out in the FPGA. Further upsampling to the DAC rate of 128 MS/s is provided by the CODEC chip using two half band filters. These filters are specified to have a maximum passband ripple of 0.1 dB and an out-of-band signal suppression of 60 dB or more. The passband edge is at 12 MHz using 39 taps in the first stage and 15 in the second stage [4]. The resulting transfer functions are shown in Figure 2. For the modeling of these filters the FIRfilter block included in the GNU Radio framework is used due to its optimized design utilizing x86 instruction set extensions. Although all signal processing in the FPGA and CODEC chip is performed with fixed-point arithmetic, floating point operations are used (except for the CIC filters) for performance reasons in the model. To compensate for the higher range and precision of the floating point data format a rounding block was introduced. The block performs an intermediate conversion to fixed-point with a arbitrary word length. This block is also used to account for the quantization noise during AD/DA conversion. The CODEC chip also conducts digital mixing of the signal. This is accomplished in two steps: first, a fine modulation is performed prior to upsampling to allow shifting of up to 1/4 of the sampling rate. Then, a coarse mixer shifts the signal by 1/4 or 1/8 of the DAC rate in either direction using a 2 stage CORDIC algorithm. This two step structure was replaced by a single operation in the model. In a final step before forwarding the signal to the daughterboard model, the signal is rounded to the D/A conversion resolution of 14 bit and scaled to the appropriate output level. B. Receive path The receive path includes A/D conversion and a digital down conversion (DDC) of the two quadrature signals provided by the daughterboard. As the ADC rate is half of the simulation sample rate the signals need to be decimated by a factor of two. Anti aliasing filtering is done on the daughterboard as described in Section IV-A. The resolution of 12 bit is ensured by rounding. The DDC is carried out completely on the FPGA. The Cordic-based IF mixer is replaced by a floating-point numerically controlled oscillator (NCO) block with a uniformly distributed initial phase. To introduce a frequency drift an additional noise signal can be added to the NCOs input. The following reduction of the sample rate is realized in two steps. First, a CIC decimation filter performs half of the userdefined rate change factor. This filter is implemented in the same way as in the transmit path. Subsequently, a half band filter decimates further by a factor of two. The coefficients of this filter are the same as used in the FPGA. The resulting samples are then provided to the software radio application. IV. M ODEL OF THE USRP DAUGHTERBOARD In order to emulate the analog RF front-end on the daughterboard, its main functions up/down conversion, amplification and filtering have to be considered. All effects of the analog front-end are modeled in equivalent baseband. HF-Switch » Motherboard 1 Up Conversion II I » RX/TX 2 LO LO IV » Fig. 3. 3 Down Conversion RX2 III Block diagram RFX2400 daughterboard B. Amplification and non-linearities -20 -30 Analog Digital -40 -50 0 10 20 30 40 Frequency [MHz] 50 60 0 10 20 40 30 Frequency [MHz] 50 60 Phase [deg] 0 -50 -100 -150 -200 -250 Fig. 4. Tx image-rejection filter A. Analog filters The transmit path includes an image-rejection lowpass filter to clean up the signal after the DAC before up conversion (cf. Figure 3 – 1). It is composed of a network of discrete components. Including the surrounding level and bias network the filter has a passband attenuation of 12 dB and a cutoff frequency of approx 28 MHz as shown in Figure 4. The filter is discretized by using the impulse invariance method and implemented in GNU Radio as a third order IIR filter. For simulation the filter gain was set to 0 dB as level control is centralized in the model. The daughterboard is also equipped with a SAWTEC 855916 bandpass filter at the 2.4 GHz ISM band, placed in the transmit path (cf. Figure 3 – 2). For reception, the filter is bypassed by default using RF switches and a secondary port. According to its specifications [5] the filter has a typical passband ripple of approximately 1 dB and attenuates the signal by approximately 2 dB. The passband edge is within the guard zone near the maximum simulation frequency and the magnitude response in the simulation band is fairly flat. Furthermore, a phase response is not specified. Therefore, this filter was not included in the emulator. The receive path contains an anti-aliasing filter composed of a network of discrete components (cf. Figure 3 – 3). It is a fifth order lowpass IIR filter with a passband edge at approximately On the daughterboard, signal amplification is accomplished through multiple stages. Filters and RF switches reduce the signal level. The transmission signal is amplified in the up converter and the two subsequent low noise amplifiers (LNA) providing a maximum output power of 17 dBm [2]. The DAC has a built-in programmable gain amplifier (PGA) which allows gain reduction by up to 20 dB. The receive path includes one LNA followed by the down converter. The overall maximum gain results in 75 dB. The built-in automatic gain control has a range of 70 dB. It is not included in the emulator for now. The PGA in the AD converter allows further amplification of the signal by up to 20 dB. Non-linear effects occur in any active component, e.g., LNAs, mixers and baseband amplifiers. Additional frequency components are generated from which only those are of interest that fall into the simulation band. In particular, for devices operating with RF signals, these are third order intermodulation products. Assuming that signal levels are well below saturation, a third-order polynomial function is employed to model this non-linear behavior. In the baseband this gives 3 y(t) = α1 x(t) − α3 x2 (t)x∗ (t). 4 (1) 0 Magnitude [dB] -10 -20 -40 -60 Analog Digital -80 -100 0 10 20 40 30 Frequency [MHz] 50 60 0 10 20 30 40 Frequency [MHz] 50 60 0 Phase [deg] Magnitude [dB] 0 20 MHz. The attenuation in pass and stop band is 6 dB and 60 dB, respectively. For discretization the bilinear transform is used. Additional tweaking of the poles and zeros is necessary due to aliasing effects: two of the zeros are positioned to generate a notch at approximately 51 MHz, the remaining are moved to the band end. By scaling the poles by a factor of 0.92 the phase response deviation can be reduced. The resulting time discrete IIR filter response is shown in Figure 5. It approximates magnitude and phase within the passband fairly well. -100 -200 -300 -400 Fig. 5. Rx anti-aliasing filter TABLE I G AIN AND IIP3 OF RFX2400 TRANSMITTER COMPONENTS Component Gain [dB] IIP3 [dBm] Up converter LNA I LNA II RF switch N/A 12.5 12.0 -0.6 24.5 18.5 28 60 D. IQ-Imbalance α1 is set to 1 as gain is considered separately after applying the nonlinearities. α3 can be calculated from the third order intercept point (IIP3 or TOI) [6]. α3 = 4/3α1 · IIP3−2 (2) As there are a number of amplifying and mixing stages the non-linearities of each of these should be included into the model to allow for an exact recreation of all effects. This, however, means a great computing effort. Therefore, only one stage with non-linear behavior was included into each signal chain modeling the cumulative non-linear effect. The overall third order intercept point IIP3total can be obtained from the IIP3s and gain of the individual components in the Rx or Tx chain [6]: g1 g1 g2 1 + + + ... (3) IIP3−1 total = IIP31 IIP32 IIP33 Tables I and II outline the values specified in the components' data sheets [5], [7], [8], [9], [10], [11]. For the transmit and receive path this yields an IIP3total of 16 dBm and 38...13 dBm, depending on the AGC level. For the baseband amplification of the IF signals in the receive chain (cf. Figure 3 – IV) also even order intermodulation products are of interest. yIm/Re (t) = β1 xIm/Re (t) + β3 x2Im/Re (t). (4) Again, β1 is set to one, β3 is determined by measurement. C. Phase noise Phase noise is an effect that occurs in every oscillator. An ideal oscillator signal consists of only one frequency component fc . Real world oscillators have additional, undesired spectral components whose magnitude decreases proportionally to f −1 . This so-called phase noise or 1/f -noise is measured by its power at an offset ∆f from fc in a unit bandwidth relative to the carrier power (dBc/Hz). Discrete phase noise can be generated with a noise shaping filter with the frequency response [12] 1 . (5) HPN (z) = √ 1 − z −1 This can be implemented with an IIR filter whose denominator coefficients result from a truncated power series expansion of (5). The more taps the filter has the better the approximation at frequencies near the oscillator frequency fc . The resulting noise process np (k) is modulated onto the signal. y(k) = e jnp (k) ≈ (1 + jnp (k)), for np (k) ≪ 1 (6) During up and down conversion the IF/RF signal is mixed with the local oscillator signal. To achieve quadrature mixing a phase shifted version of the LO signal is required. This phase shift has to be exactly 90◦ , preserving the amplitude of the signal. Any mismatches in either phase or amplitude result in additional spectral components. This effect can be described by the following signal models [13]. The relative amplitude deviation shall be 2ε and the phase shift deviation 2φ. For the down conversion the resulting baseband signal can be obtained from r′ (t) = (1 − ε)rbp (t) cos(ωc t + φ)− j(1 + ε)rbp (t) sin(ωc t − φ). (7) The bandpass signal rbp (t) is replaced by its baseband signal Re{r(t) exp(jωt))} which results in 1 r′ (t) = (cos(φ) − jε sin(φ)) · r(t)+ 2 1 (ε cos(φ) + j sin(φ)) · r∗ (t). (8) 2 The up conversion yields similar results. Both signal models are incorporated into the signal chain. The parameters ε and φ can be time variant. For this simulation, however, they are assumed constant due to the unknown time dependency. Mean values for the up converter are 2φ = 0.3◦ and (1+2ε) = 0.1 dB [10] and for the down converter 2φ = −0.8◦ and (1 + 2ε) = 0.3 dB [9]. E. Noise Figure The noise figure describes the loss in the signal-to-noise ratio (SNR) as the signal passes through the different receiver components, each of them increasing the noise level by a noise factor F . NF = 10 log F = SNRin,dB − SNRout,dB (9) To determine the overall noise figure of a cascade of components Friis' formula can be used [6]. A component n is characterized by its gain gn and noise factor Fn . Ftotal = 1 + (F1 − 1) + F2 − 1 F3 − 1 + + ... g1 g1 g2 (10) Table II shows the gain and noise figures of the components in the receive signal chain of the RFX2400 daughterboard as shown in Figure 3. The overall noise figure NFtotal is 4.48 dB on the Rx2 port and 7.8 dB on the Tx/Rx port. Because not all of the components specify a noise factor in their datasheets those values have to be considered lower bounds for NFtotal . To incorporate this into the model, Johnson thermal noise is added to the received signal. It follows a normal distribution with a variance of σ 2 = 4T kB RB(Ftotal − 1). (11) where T is the temperature, kB Boltzmann's constant, R the resistance and B the simulation bandwidth [14]. The down converter's noise figure is dependent on the AGC level [9]. TABLE II G AIN , NF AND IIP3 OF RFX2400 RECEIVER COMPONENTS 70 60 Gain [dB] NF [dB] IIP3 [dBm] RF filter HF switch LNA III Down converter IF filter Baseband AMP -2.75 -0.6 12.5 39.5 -6 30 2.75 0.6 2.2 11.75 6 N/A ∞ 60.0 18.5 32.2 ∞ N/A 40 30 20 10 0 10 0 -10 USRP Emulator -10 Amplitude (dB) USRP Emulator 50 Amplitude (dB) Component -20 -4 -20 -30 Fig. 7. -2 0 Frequency (MHz) 2 4 Comparison of USRP and Emulator Rx Output -40 -50 -60 was not considered in the emulator and is therefore not observable in the emulator output. -70 B. Reception -80 -40 -60 Fig. 6. -20 20 0 Frequency (MHz) 40 60 Comparison of USRP and emulator Tx output The value in Table II is only valid when the AGC is fully open. For lesser amplification by the AGC the overall noise figure can rise to well over 60 dB. V. M EASUREMENTS A. Transmission For the evaluation of the emulator performance the following setup was used. A GNU Radio application synthesizes a signal containing two sinusoids with 800 kHz spacing, an often used setup for analyzing non-linear effects. For comparison, the USRP Tx/Rx port is connected to a spectrum analyzer. The interpolation is set to 32, the carrier frequency to 2.45 GHz. In this configuration the USRP driver chooses a LO frequency of 2.456 GHz. In order to achieve the desired carrier frequency the IF is set to -6 MHz. Figure 6 shows a comparison between the USRP and the emulator output for this scenario. It is centered at the LO frequency. The third order intermodulation products, the carrier feedthrough, the sideband caused by the IQ-imbalance and the oscillator phase noise can be observed. The USRP signal additionally contains fifth order intermodulation products as well as the thermal noise floor and a number of additional unidentified spectral components. These other tones appear to be modulated mixer spurs as outlined in the converter's datasheet [10]. The most significant ones are found at 2 and -4 times the IF. This mixer-specific behavior For measurement of the receive chain a single carrier tone at -30 dBm is used as input signal. To measure the receive spectrum of the USRP, a signal generator is connected to the RX2 port of the daughterboard. Its center frequency is set 1.2 MHz lower than the frequency the USRP and the emulator are tuned to. This allows observing some of the described effects without bypassing the IF mixer. For the emulator an equivalent baseband signal is synthesized with GNU Radio. The decimation is set to a factor of 8. The AGC attenuation is set to 33 dB resulting in a theoretical noise figure of 28 dB. In measurement, the USRP's noise level was found to be about 2 dB higher and the noise figure was adjusted accordingly. Figure 7 shows the spectral output of the USRP and the emulator. The shape of the noise floor reflects the combined CIC decimation and halfband filter response. Due to the rather strong input signal, phase noise is also observable. In addition to the desired peak of the input signal additional peaks are caused by IQ-imbalance and nonlinear baseband amplification. The USRP shows a number of additional spectral components. These appear to be spurious signals, probably introduced during the down conversion. Third order intermodulation products are not observable since only a single tone input is used. VI. C ONCLUSION The digital signal processing chain on the USRP motherboard was modeled alongside with the analog signal processing on the RFX2400 daughterboard. Modeling included the most important non-idealities of the analog front-end such as analog filters, phase noise, D/A and A/D conversion. Together with a channel model the USRP emulation allows to evaluate software radio signal processing algorithms in a reproducible manner. Verification showed that most effects are captured very well. The limits of the model lie in the modeling of higher order non-linearities, as not all spurious signals seen in measurements were covered by the component-wise analysis used in this work. R EFERENCES [1] J. Elsner, M. Braun, S. Nagel, K. Nagaraj, and F. Jondral, "Wireless Networks In-the-Loop: Software Radio as the Enabler," in Software Defined Radio Forum Technical Conference, 2009. [2] M. Ettus, "Ettus Research LLC," http://ettus.com/. [3] Free Software Foundation, "GNU Radio," http://gnuradio.org/. [4] AnalogDevices, AD9862 Mixed-Signal Front-End (MxFE) Processor, 2002, http://www.analog.com/. [5] SAWTEC, 855916 2441.8 MHz SAW Filter, 2005, http://www.triquint.com/. [6] T. J. Rouphael, RF and digital signal processing for software-defined radio. Newnes, 2009. [7] H. M. Corporation, HMC174MS8 GaAs MMIC T/R Switch, 2009, http://www.hittite.com/. [8] A. Technologies, MGA-82563 Amplifier, 2009, http://www.avagotech.com. [9] AnalogDevices, AD8347 Direct Conversion Quadrature Demodulator, 2005, http://www.analog.com/. [10] Analog-Devices, AD8349 Quadrature Modulator, 2004, http://www.analog.com/. [11] R. M. Devices, RF3315 Broadband High Linear Amplifier, 2010, http://www.rfmd.com/. [12] N. J. Kasdin, "Discrete simulation of colored noise and stochastic processes and 1/f α power law noise generation," Proceedings of the IEEE, vol. 83, no. 5, 1995. [13] P. Rykaczewski, Quadraturempfänger für Software Defined Radios : Kompensation von Gleichlauffehlern. Institut für Nachrichtentechnik der Universität Karlsruhe (TH), 2006. [14] U. Tietze and C. Schenk, Electronic circuits: handbook for design and application. Berlin: Springer, 2008.

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People have all different kinds of ideas as far as nutrition is concerned. This article contains metric tons of useful nutritional advice that you can implement right now and start on the road to healthy eating.
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As you have seen, it can be hard to ensure you are getting the right nutrition in your diet. The more you know about healthy eating, the more prepared you will be to make important decisions that can affect your entire life.Copyright 2015 , All rights Reserved.