802.11-tgn-channel-models

May 2004 doc.: IEEE 802.11-03/940r4 Submission page 1 Vinko Erceg, Zyray Wireless; et al. IEEE P802.11 Wireless LANs TGn Channel Models Date: May 10, 2004 Authors: Vinko Erceg Zyray Wireless; e-Mail: verceg@zyraywireless.com Laurent Schumacher Namur University; e-Mail: laurent.schumacher@ieee.org Persefoni Kyritsi Stanford University; e-Mail: kyritsi@math.stanford.edu Andreas Molisch, Mitsubishi Electric Daniel S. Baum, ETH University Alexei Y. Gorokhov, Philips Research Claude Oestges, Louvain University Qinghua Li, Intel Kai Yu, KTH Nir Tal, Metalink Bas Dijkstra, Namur University Adityakiran Jagannatham, U.C. San Diego Colin Lanzl, Aware Valentine J. Rhodes, Intel Jonas Medbo, Ericsson Dave Michelson, UBC Mark Webster, Intersil 删除的内容: 3 删除的内容: Indoor MIMO WLAN 删除的内容: January 9 删除的内容: 4 May 2004 doc.: IEEE 802.11-03/940r4 Submission page 2 Vinko Erceg, Zyray Wireless; et al. Eric Jacobsen, Intel David Cheung, Intel Clifford Prettie, Intel Minnie Ho, Intel Steve Howard, Qualcomm Bjorn Bjerke, Qualcomm Lung Jengx, Intel Hemanth Sampath, Marvell Severine Catreux, Zyray Wireless Stefano Valle, ST Microelectronics Angelo Poloni, ST Microelectronics Antonio Forenza, University of Texas at Austin Robert W. Heath, University of Texas at Austin Abstract This document provides the channel models to be used for the High Throughput Task Group (TGn). 删除的内容: 3 May 2004 doc.: IEEE 802.11-03/940r4 Submission page 3 Vinko Erceg, Zyray Wireless; et al. List of Participants Vinko Erceg (Zyray Wireless) Laurent Schumacher (Namur University) Persefoni Kyritsi (Aalborg University) Daniel Baum (ETH University) Andreas Molisch (Mitsubishi Electric) Alexei Gorokhov (Philips Research) Valentine J. Rhodes (Intel) Srinath Hosur (Texas Instruments) Srikanth Gummadi (Texas Instruments) Eilts Henry (Texas Instruments) Eric Jacobsen (Intel) Sumeet Sandhu (Intel) David Cheung (Intel) Qinghua Li (Intel) Clifford Prettie (Intel) Heejung Yu (ETRI) Yeong-Chang Maa (InProComm) Richard van Nee (Airgo) Jonas Medbo (Ericsson) Eldad Perahia (Cisco Systems) Brett Douglas (Cisco Systems) Helmut Boelcskei (ETH Univ.) Yong Je Lim (Samsung) Massimiliano Siti (ST) Stefano Valle (ST) Steve Howard (Qualcomm) Bjorn Bjerke (Qualcomm) Qinfang Sun (Atheros) Won-Joon Choi (Atheros) Ardavan Tehrani (Atheros) Jeff Gilbert (Atheros) Hemanth Sampath (Marvell) H. Lou (Marvell) Ravi Narasimhan (Marvell) Pieter van Rooyen (Zyray Wireless) Pieter Roux (Zyray Wireless) Majid Malek (HP) Timothy Wakeley (HP) Dongjun Lee (Samsung) Tomer Bentzion (Metalink) Nir Tal (Metalink) Amir Leshem (Metalink, Bar IIan University) Guy Shochet (Metalink) Patric Kelly (Bandspeed) Vafa Ghazi (Cadence) Mehul Mehta (Synad Technologies) Bobby Jose (Mabuhay Networks) Charles Farlow (California Amplifier) Claude Oestges (Louvain University) Robert W. Heath (Univ. of Texas at Austin) Dave Michelson (UBC) Mark Webster (Intersil) Dov Andelman (Envara) Colin Lanzl (Aware) Kai Yu (KTH) Irina Medvedev (Qualcomm) John Ketchum (Qualcomm) Adrian Stephens (Intel) Jack Winters (Motia) 删除的内容: 3 May 2004 doc.: IEEE 802.11-03/940r4 Submission page 4 Vinko Erceg, Zyray Wireless; et al. Revision History Date Version Description of changes 1/9/04 01 Paragraph added in Sec. 4.1 describing K-factor simulation procedure. Paragraph added in Sec. 4.5.1 describing high AP antenna placements. 3/5/04 02 Correction of autocorrelation function and coherence time formula in Sec 4.7.1 (inclusion of fd). Equations (4) and (7) were updated. 5/4/04 03 Table IIb removed to provide consistency with updated UM (03/802r17); added table of mean and standard deviation of cluster rms delay spread in section 4.5.1; renumbered figures to be consistent. 删除的内容: 3 删除的内容: and restriction on AoA/AoD=45° removed 删除的内容: added geometry to section 4.1 (Figure 2), renumbered figures (including figures in appendices) and altered references to how clusters constructed to emphasize that they are NLOS; 删除的内容: . May 2004 doc.: IEEE 802.11-03/940r4 Submission page 5 Vinko Erceg, Zyray Wireless; et al. 1. Introduction Multiple antenna technologies are being considered as a viable solution for the next generation of mobile and wireless local area networks (WLAN). The use of multiple antennas offers extended range, improved reliability and higher throughputs than conventional single antenna communication systems. Multiple antenna systems can be generally separated into two main groups: smart antenna based systems and spatial multiplexing based multiple-input multiple-output (MIMO) systems. Smart antenna based systems exploit multiple transmit and/or receive antennas to provide diversity gain in a fading environment, antenna gain and interference suppression. These gains translate into improvement of the spectral efficiency, range and reliability of wireless networks. These systems may have an array of multiple antennas only at one end of the communication link (e.g., at the transmit side, such as multiple-input single-output (MISO) systems; or at the receive side, such as single-input multiple-output (SIMO) systems; or at both ends (MIMO) systems). In MIMO systems, each transmit antenna can broadcast at the same time and in the same bandwidth an independent signal sub-stream. This corresponds to the second category of multi-antennas systems, referred to as spatial multiplexing-based multiple-input multiple-output (MIMO) systems. Using this technology with n transmit and n receive antennas, for example, an n-fold increase in data rate can be achieved over a single antenna system [1]. This breakthrough technology appears promising in fulfilling the growing demand for future high data rate PAN, WLAN, WAN, and 4G systems. In this document we propose a set of channel models applicable to indoor MIMO WLAN systems. Some of the channel models are an extension of the single-input single-output (SISO) WLAN channel models proposed by Medbo et al. [2,3]. The newly developed multiple antenna models are based on the cluster model developed by Saleh and Valenzuela [4], and further elaborated upon by Spencer et al. [5], Cramer et al. [6], and Poon and Ho [7]. Indoor SISO and MIMO wireless channels were further analyzed in [8-18]. A step-wise development of the new models follows: In each of the three models (A-C) in [2] and three additional models distinct clusters were identified first. The number of clusters varies from 2 to 6, depending on the model. This finding is consistent with numerous experimentally determined results reported in the literature [4-7,9,10] and also using ray- tracing methods [8]. The power of each tap in a particular cluster was determined so that the sum of the powers of overlapping taps corresponding to different clusters corresponds to the powers of the original power delay profiles. Next, angular spread (AS), angle-of-arrival (AoA), and angle of departure (AoD) values were assigned to each tap and cluster (using statistical methods) that agree with experimentally determined values reported in the literature. Cluster AS was experimentally found to be in the 20o to 40o range [5-10], and the mean AoA was found to be random with a uniform distribution. With the knowledge of each tap power, AS, and AoA (AoD), for a given antenna configuration, the channel matrix H can be determined. The channel matrix H fully describes the propagation channel between all transmit and receive antennas. If the number of receive antennas is n and transmit antennas is m, the channel matrix H has a dimension of n x m. To arrive at channel matrix H, we use a method that employs correlation matrix and i.i.d. matrix (zero-mean unit variance independent complex Gaussian random variables). The correlation matrix for each tap is 删除的内容: 3 带的: 突出显示 带格式的: 突出显示 May 2004 doc.: IEEE 802.11-03/940r4 Submission page 6 Vinko Erceg, Zyray Wireless; et al. based on the power angular spectrum (PAS) with AS being the second moment of PAS [19,20]. To verify the newly developed model, we have calculated the channel capacity assuming the narrowband case and compared it to experimentally determined capacity results with good agreement. The model can be used for both 2 GHz and 5 GHz frequency bands, since the experimental data and published results for both bands were used in developing the model (average, rather than frequency dependent model). However, path loss model is frequency dependent. The paper is organized as follows. In Sec. 2 we describe SISO WLAN models. Section 3 formulates the MIMO channel matrix. Section 4 describes the clustering approach and the method for model parameters calculation. In Sec. 5 we summarize the model parameters. In Sec. 6 we briefly describe the Matlab program. Section 7 presents the antenna correlation and channel capacity results using the models, and with Sec. 8 we conclude. 2. SISO WLAN Models A set of WLAN channel models was developed by Medbo et al. [2,3]. In [2], five delay profile models were proposed for different environments (Models A-E): • Model A for a typical office environment, non-line-of-sight (NLOS) conditions, and 50 ns rms delay spread. • Model B for a typical large open space and office environments, NLOS conditions, and 100 ns rms delay spread. • Model C for a large open space (indoor and outdoor), NLOS conditions, and 150 ns rms delay spread. • Model D, same as model C, line-of-sight (LOS) conditions, and 140 ns rms delay spread (10 dB Ricean K-factor at the first delay). • Model E for a typical large open space (indoor and outdoor), NLOS conditions, and 250 ns rms delay spread. We use models A-C together with three additional models more representative of smaller environments, such as residential homes and small offices, for our modeling purposes. The resulting models that we propose are as follows: • Model A (optional, should not be used for system performance comparisons), flat fading model with 0 ns rms delay spread (one tap at 0 ns delay model). This model can be used for stressing system performance, occurs small percentage of time (locations). • Model B with 15 ns rms delay spread. • Model C with 30 ns rms delay spread. 删除的内容: 3 May 2004 doc.: IEEE 802.11-03/940r4 Submission page 7 Vinko Erceg, Zyray Wireless; et al. • Model D with 50 ns rms delay spread. • Model E with 100 ns rms delay spread. • Model F with 150 ns rms delay spread. Model mapping to a particular environment is presented in table IIb. The tables with channel coefficients (tap delays and corresponding powers) can be found in Appendix C. The path loss model that we propose consists of the free space loss LFS (slope of 2) up to a breakpoint distance and slope of 3.5 after the breakpoint distance [21]. For each of the models different break-point distance dBP was chosen L(d) = LFS(d) d <= dBP L(d) = LFS(dBP) + 35 log10(d / dBP) d > dBP (1) where d is the transmit-receive separation distance in m. The path loss model parameters are summarized in Table I. In the table, the standard deviations of log-normal (Gaussian in dB) shadow fading are also included. The values were found to be in the 3-14 dB range [16]. New Model dBP (m) Slope before dBP Slope after dBP Shadow fading std. dev. (dB) before dBP (LOS) Shadow fading std. dev. (dB) after dBP (NLOS) A (optional) 5 2 3.5 3 4 B 5 2 3.5 3 4 C 5 2 3.5 3 5 D 10 2 3.5 3 5 E 20 2 3.5 3 6 F 30 2 3.5 3 6 Table I: Path loss model parameters The zero-mean Gaussian probability distribution is given by ⎟⎟⎠ ⎞ ⎜⎜⎝ ⎛− = 2 2 2exp 2 1)( σσπ x xp (2) 删除的内容: 3 May 2004 doc.: IEEE 802.11-03/940r4 Submission page 8 Vinko Erceg, Zyray Wireless; et al. 3. MIMO Matrix Formulation We follow the MIMO modeling approach presented in [11,20] that utilizes receive and transmit correlation matrices. The MIMO channel matrix H for each tap, at one instance of time, in the A-F delay profile models can be separated into a fixed (constant, LOS) matrix and a Rayleigh (variable, NLOS) matrix [22] (4 transmit and 4 receive antennas example) ⎟⎟ ⎟⎟ ⎟ ⎠ ⎞ ⎜⎜ ⎜⎜ ⎜ ⎝ ⎛ ⎥⎥ ⎥⎥ ⎦ ⎤ ⎢⎢ ⎢⎢ ⎣ ⎡ ++ ⎥⎥ ⎥⎥ ⎥ ⎦ ⎤ ⎢⎢ ⎢⎢ ⎢ ⎣ ⎡ += ⎟⎟⎠ ⎞ ⎜⎜⎝ ⎛ +++= 44434241 34333231 24232221 14131211 1 1 1 1 1 1 44434241 34333231 24232221 14131211 XXXX XXXX XXXX XXXX K eeee eeee eeee eeee K KP H K H K KPH jjjj jjjj jjjj jjjj vF φφφφ φφφφ φφφφ φφφφ (3) where Xij (i-th receiving and j-th transmitting antenna) are correlated zero-mean, unit variance, complex Gaussian random variables as coefficients of the variable NLOS (Rayleigh) matrix HV, exp(jφij) are the elements of the fixed LOS matrix HF, K is the Ricean K-factor, and P is the power of each tap. We assume that each tap consists of a number of individual rays so that the complex Gaussian assumption is valid. P in (3) represents the sum of the fixed LOS power and the variable NLOS power (sum of powers of all taps). To correlate the Xij elements of the matrix X, the following method can be used [ ] [ ] [ ] [ ]( )1/ 2 1/ 2 Trx iid txX R H R= (4) where Rtx and Rrx are the receive and transmit correlation matrices, respectively, and Hiid is a matrix of independent zero mean, unit variance, complex Gaussian random variables, and [ ] [ ] [ ] [ ]rxijrxR txijtxR ρ ρ = = (5) where ρtxij are the complex correlation coefficients between i-th and j-th transmitting antennas, and ρrxij are the complex correlation coefficients between i-th and j-th receiving antennas. An alternative approach uses the Kronecker product of the transmit and receive correlation matrices (Hiid is an array in this case instead of matrix) [ ] [ ] [ ]{ } [ ]1 / 2X R R Htx rx iid= ⊗ (6) 删除的内容: 3 删除的内容: 删除的内容: 删除的内容: 删除的内容: [ ] [ ] [ ][ ]txiid2/1rx RHRX = 删除的内容: May 2004 doc.: IEEE 802.11-03/940r4 Submission page 9 Vinko Erceg, Zyray Wireless; et al. Following is an example of 4 x 4 MIMO channel transmit and receive correlation matrices 12 13 14 21 23 24 31 32 34 41 42 43 1 1 1 1 tx tx tx tx tx tx tx tx tx tx tx tx tx R ρ ρ ρ ρ ρ ρ ρ ρ ρ ρ ρ ρ ⎡ ⎤⎢ ⎥⎢ ⎥⎢ ⎥= ⎢ ⎥⎢ ⎥⎢ ⎥⎣ ⎦ (7) 12 13 14 21 23 24 31 32 34 41 42 43 1 1 1 1 rx rx rx rx rx rx rx rx rx rx rx rx rx R ρ ρ ρ ρ ρ ρ ρ ρ ρ ρ ρ ρ ⎡ ⎤⎢ ⎥⎢ ⎥⎢ ⎥= ⎢ ⎥⎢ ⎥⎢ ⎥⎣ ⎦ The complex correlation coefficient values calculation for each tap is based on the power angular spectrum (PAS) with angular spread (AS) being the second moment of PAS [19,20]. Using the PAS shape, AS, mean angle-of-arrival (AoA), and individual tap powers, correlation matrices of each tap can be determined as described in [20]. For the uniform linear array (ULA) the complex correlation coefficient at the linear antenna array is expressed as )()( DjRDR XYXX +=ρ (8) where λπ /2 dD = , and RXX and RXY are the cross-correlation functions between the real parts (equal to the cross-correlation function between the imaginary parts) and between the real part and imaginary part, respectively, with ∫ − = π π φφφ dPASDDXXR )()sincos()( (9) and ∫ − = π π φφφ dPASDDXYR )()sinsin()( (10) Expressions for correlation coefficients assuming uniform, truncated Gaussian, and truncated Laplacian PAS shapes can be found in [20]. To calculate the numerical values of correlation matrices we use a Matlab program developed and distributed by L. Schumacher [23] (see Sec. 6). 删除的内容: 3 删除的内容: ⎢⎢ ⎢⎢ ⎢⎢ ⎣ ⎡ = 41 31 21 1 tx tx tx txR ρ ρ ρ 删除的内容: ⎢⎢ ⎢⎢ ⎢⎢ ⎣ ⎡ = 41 31 21 1 rx rx rx rxR ρ ρ ρ May 2004 doc.: IEEE 802.11-03/940r4 Submission page 10 Vinko Erceg, Zyray Wireless; et al. Next we briefly describe the various steps in our cluster modeling approach. We • Start with delay profiles of models B-F. • Manually identify clusters in each of the five models. • Extend clusters so that they overlap, determine tap powers (see Appendix A). • Assume PAS shape of each cluster and corresponding taps (Laplacian). • Assign AS to each cluster and corresponding taps. • Assign mean AoA (AoD) to each cluster and corresponding taps. • Assume antenna configuration. • Calculate correlation matrices for each tap. In the next section we elaborate on the above steps. 4. Cluster Modeling Approach The cluster model was introduced first by Saleh and Valenzuela [4] and later verified, extended, and elaborated upon by many other researchers in [5-10]. The received signal amplitude βkl is a Rayleigh-distributed random variable with a mean-square value that obeys a double exponential decay law γτββ //22 )0,0( kll ee Tkl −Γ−= (11) where )0,0(2β represents the average power of the first arrival of the first cluster, Tl represents the arrival time of the lth cluster, and τkl is the arrival time of the kth arrival within the lth cluster, relative to Tl. The parameters Γ and γ determine the inter-cluster signal level rate of decay and the intra-cluster rate of decay, respectively. The rates of the cluster and ray arrivals can be determined using exponential rate laws )(1 1)|( − −Λ− − Λ= ll TTll eTTp (12) )(,1 1)|( − −− − = ll TTlkkl ep λλττ (13) where Λ is the cluster arrival rate and λ is the ray arrival rate. For our modeling purposes we are not using the equations (11) through (13) since the delay profile characteristics are already predetermined by the model B-F delay profiles. 4.1 Number of clusters The number of clusters found in different indoor environments varies between 1 and 7. In [5], the average number of clusters was found to be 3 for one building, and 7 for another building. 删除的内容: 3 删除的内容: The 删除的内容: In that model, the May 2004 doc.: IEEE 802.11-03/940r4 Submission page 11 Vinko Erceg, Zyray Wireless; et al. In [7] the number of clusters reported was found to be 2 for line-of-sight (LOS) and 5 for non-LOS (NLOS) conditions. Figure 1 shows Model D delay profile with clusters outlined by exponentia