Research & Technology Group. Honeywell Technology Solutions Laboratory. Bangalore, India ... wireless technology for communication and the wireless pro-.
Performance of Multi User Detector based receivers for UWB Body Area Networks Puduru Viswanadha Reddy, Viswanath Ganapathy Research & Technology Group Honeywell Technology Solutions Laboratory Bangalore, India Email: {Puduru.Reddy, Viswanath.Ganapathy}@Honeywell.com Abstract—Recent developments in Ultra Wideband (UWB) technology have opened a way to create wireless networks for personal health care monitoring. This personal area wireless network with a set of wireless nodes mounted on body is popularly known as body area network (BAN). A typical BAN has a set of thirty to fifty wireless sensors with a low transmit power constraint. Also, the wireless channel seen in by a wireless node is unique when mounted on the human body. It has been shown that UWB supports reliable connectivity at low transmit power for body mounted wireless nodes. Further, the UWB also supports co-existence with other wireless networks and supports higher data rate. Given that large number of nodes are placed in close proximity the BAN operates in a multi-user interference limited environment. Therefore, we discuss the performance of multi-user based detectors for body area networks and compare the performance with conventional receiver architectures. We use the recently proposed channel model for the standard IEEE 802.15.4a. Our simulations show superior performance of MUD based receivers over RAKE based receivers for a body area channel.
I. I NTRODUCTION FCC has recently approved 3.1-10.6 GHz band for UWB communications with a power constraint. This has opened up possibilities for potential applications of UWB in high data rate, low power and small range communications. Further, research in wireless biomedical sensing using UWB communications has accelerated leading to new range of products for health monitoring. Wireless body area networks (BAN), where the multiple sensors are placed on the body to sense heart rate, glucose levels etc., have gained importance for health and wellness monitoring. Wireless BANs are also connected to the backbone network. Designing and implementing a reliable BAN requires several factors including understanding and modeling the body area channel, choosing the appropriate wireless technology for communication and the wireless protocol design. The medical regulations in maximum transmit power, frequency of operation, co-existence with other medical devices and battery life also plays important role in designing the wireless BAN. In a wireless BAN several sensors communicate among each other and a central device like the hand held computer at the same time. All these wireless nodes communicate asynchronously leading significant multi-user interference. Receivers based on RAKE do not cancel multi-user interference. A receiver based on multi-user detection (MUD) [1]
seems to be appropriate for the BAN environment where several devices communicate asynchronously. In this paper we evaluate the performance of MUD based receivers for body area network and compare the MUD based receiver with conventional RAKE based receivers. We used the IEEE 802.15.4a body area channel model for performance analysis. The paper is organised as follows: In section II we introduce the signal model and in III we discuss the linear and nonlinear MUD. The IEEE802.15.4a channel model [2], [3], [4], [5] is discussed in section IV. The simulation environment and performance analysis of the MUD based receiver is discussed in section V. We conclude the paper in section VI. II. S YSTEM M ODEL We consider a Nu user UWB system using DS-CDMA. (u) At the transmitter for user u, BPSK symbols ak ∈ {−1, 1} drawn from an uniform distribution are spread and modulated with chip pulses gc (t). Defining the chip waveform as Nf −1 i=0
(u)
(u)
ci gc (t − iTf ) , ci
∈ {−1, +1}(1)
(u)
where {ci } denotes the spreading code of length Nf and chip duration Tc . Note that Tf = Tc for DS-UWB system. The transmitted signal from the uth user is given by s(u) (t) =
∞ k=−∞
(u)
ak g (u) (t − kTs )
(2)
where Ts denotes the symbol duration. The symbol response for user u is then expressed as p(u) (t) = g (u) (t)∗h(u) (t) where ∗ denotes the convolution operator and h(u) (t) is the channel for user u as seen at the receiver antenna. Assuming perfect synchronism, the signal at the receiver antenna is given by r(t) =
Nu ∞ u=1 k=−∞
(u)
ak p(u) (t − kTs ) + n(t)
(3)
The received signal mixture in (3) consists of the following components: • Desired user signal whose energy is spread over several multiple symbols • ISI due to the channel memory
227 c 978-1-4244-2281-4/08/$25.00 2008 IEEE
g (u) (t) =
rkN +1 s
r(t)
rkN +1 s
r(t)
w1
Serial to Parallel Converter
rl
xk
a ˆk
l = kTr
Ns samples per frame
l = kTr
wf f 1
rl
Serial to Parallel Converter Ns samples per frame
r(k+1)N s
r(k+1)N s
wN s
Fig. 1.
Linear MUD receiver
Fig. 2.
MUI due to the presence of other users signal Additive white Gaussian noise at the receiver Our aim is to design a receiver that extracts the information of the desired user from this mixture. The receiver should effectively extract the energy from different multipaths in the presence of ISI, MUI and AWGN. Traditional receiver architectures, which employ RAKE based reception, entail knowledge about the channel and the spreading sequence of the user. We design a system which does not require explicit channel knowledge. •
III. MUD R ECEIVERS In this section we summarize the details of the Multiuser Detection based receivers discussed in [1]. We explain the design of linear and non linear MUD receivers. A. Linear MUD receiver The receiver structure of a linear MUD receiver is shown in fig. 1. The determination of the coefficients of the combiner is carried through training sequence method. The tap coefficients are determined based on MMSE criterion. Let the sampling rate Tr at the receiver be chosen such that Ts Tr = Ns > 1. Then the discrete equivalent of the received waveform becomes Nu ∞
auk pu (l − kNs ) + nl
(4)
u=1 k=−∞
Assume u = 1 is the user of interest at the receiver, we can rewrite (4) as rl =
∞
a1k p(l−kNs )+
k=−∞
∞
auk pu (l−kNs )+nl , (5)
u=1 k=−∞
where the second term represents the multiuser interference (MUI) part. Representing the MUI as ml , rl =
∞
a1k p(l − kNs ) + ml + nl .
(6)
k=−∞
Now, fragmenting the received sequence into frames of Ns samples, with each frame representing the sampled verof the user 1. That is, rk = sion of the received symbol T r(k+1)Ns , · · · , rkNs +1 , where rk represents the samples of the received waveform corresponding to the transmitted symbol a1k . The objective is to choose the linear filter taps
228
a ˜k
Feedback filter wf b
ak
wf f N s
•
rl =
a ˆk
xk
Non linear MUD receiver
ˆ = [w1 , · · · , wNs ] which minimizes the mean square error w E ||a1k − wrk ||2 . The solution of this problem is given by the Weiner-Hopf equation [6], w ˆ = γar Γ−1 (7) rr , T where Γrr = E rk rk represents the received vector autocorrelation matrix and γar = E a1k rk T is a vector representing the cross correlation between the known transmitted symbol and the corresponding received samples. The estimate at the receiver corresponding to the transmitted symbol a1l is computed as
ˆ l ). a ˆ1l = sign(wr
(8)
The linear receiver tries to mitigate the effect of MUI, ISI and noise jointly. It doesn’t distinguish MUI and ISI from noise so the output has still some amount of MUI and ISI left. In order to mitigate the effect of ISI we go for a feedback filter whose weights are jointly optimized with the forward filter (linear MUD) weights. B. Non Linear MUD receiver The UWB channel memory spans several symbols hence one could potentially use the estimates of the previous symbols while attempting to estimate the current symbol. This can be achieved by exploiting the familiar Decision Feedback principle [6]. The receiver architecture is as shown in figure. The receiver has two filters one feed-forward filter (FFF) and the other is a feedback filter (FBF). The input to the FFF for symbol k is the vector rk . The FFF is similar to the Weiner combiner studied in the previous section which generates a test statistic from the received samples. The FBF has at its input the sequence of decisions on previously detected symbols. FBF is intended to remove the part of the ISI from the current symbol caused by previously detected symbols. MMSE criterion is applied to optimize the coefficients of the two filters. It is essential to note that the input samples to FFF are spaced Tr seconds apart while the input samples of the FBF are spaced Ts seconds apart. We can represent the equalizer output as ˆ 1k−1 xk = wf f rk + wf b a
(9)
where, the row vector wf f represents Ns length FF filter and wf b is the Nb length vector representing the FB ˆ1k−1 = filter. The set of past decision is represented by a
2008 10th IEEE Intl. Conf. on e-Health Networking, Applications and Service (HEALTHCOM 2008)
1 T a ˆk−1 , · · · , a ˆ1k−Nb . The estimate a ˆ1k of a1k can be obtained ˆ1k = sign(xk ). The by passing xk through the detector i.e., a objective is to choose the filter coefficient set w = [wf f , wf b ] which minimizes the MSE (10) E ||a1k − xk ||2 = E ||a1k − wYk ||2 , T T ˆ1k−1 . Proceeding similar to previwhere Yk = rTk , a ous sections, we have the optimal solution for w as ˆ = γay Γ−1 w (11) yy where Γyy = E Yk YkT represents the autocorrelation matrix and γay = E a1k Yk T represents the cross correlation matrix. Assuming that there is no error in the feedback, the matrices can be written as T
Γrr E[rk a1k−1 ] (12) Γyy = E[ a1k−1 rTk ] σa2 .INb and γay =
γar 0
should be taken into consideration while comparing the system performance in BAN scenarios. • Exponential path loss around the body • Correlated lognormal amplitude distributions • A fixed two-cluster model • Fixed inter-cluster arrival time • Fixed inter-ray arrival time • Three scenarios corresponding to the front, side and back of the body • Indoor or outdoor environment • If the channel is Indoor-BAN then an appropriate model for multipaths must be used A. Implementation The discrete channel model consists of several bins, the amplitude in the bins are correlated and have a Log normal distribution. The correlated lognormal values are generated as follows: PdB (14) 2 Where X is a vector of N uncorrelated normal variables with unit mean and unit variance, C is the covariance matrix capturing the correlations among the bins, M is the vector of means for different bins and PdB the large scale path loss. The large scale path loss is computed as: YdB = X.chol(C) − M −
(13)
IV. B ODY A REA C HANNEL M ODEL A typical body area network consists of various sensors placed on the body. The sensors communicate with the central device using body and surroundings as the medium. Finite difference time domain (FDTD) [3] analysis has shown that electromagnetic propagation at UWB range is negligible through the human body. In addition, the transmitted signals arrive at the receiver in two different ways: one around the human body and another through reflections from the surroundings. So, the BAN channel has significantly different path loss, amplitude distortion, clustering and inter arrival-time characteristics compared to other scenarios. Experimental results have established that correlated lognormal distributions provide best fit to the multipath regardless of the position of the receiver. Further, the uncorrelated scattering assumption dealt in other models is not valid for BAN channels. We summarize the results given in [2], [4], [5] for the body area channel model. For an outdoor channel (Outdoor-BAN), which consists of a human wearing a BAN, measurements [5], [4] indicate that there are always two clusters of multi path components due to the initial wave diffracting around the body, and reflections from the ground. So, the number of clusters is always 2 and does not need to be defined as a stochastic process as in the other scenarios (indoor propagation). Further, the intercluster arrival time distribution is deterministic and depends on the exact position of the transmitters on the body. An indoor channel (Indoor-BAN) which consists of a human wearing a BAN and being present in an indoor environment. Measurements [5] indicate that along with the 2 deterministic clusters (as discussed above), the clusters due to stochastic nature of the multipaths (reflections from the indoor environment) should be taken into account. The following features
PdB = γ(d − d0 ) + P0,dB
(15)
where γ is in units of dB/meter, d is the distance between transmitter and receiver, d0 is the reference distance and P0 is the power at the reference distance. The channel model corresponding to the propagation on the body is given as: hbody (t) =
∞
βk exp (jφk ) δ (t − Δk)
(16)
k=0
where βk is the k th element of the vector Y, φk is uniformly distributed random variable and Δ is the bin size. The model parameters are taken form [2] for ‘front’, ‘size’ and ‘back’ positions of the receiver on the human body. The contribution from the ground reflections to the multi path channel model is similar to the above case except for a delay. The ground reflections contribute a cluster of multipaths at a delay τground . The channel model due to ground reflections is given as: hground (t) =
∞
βk exp (jφk ) δ (t − Δk − τground )
(17)
k=0
So, an Outdoor-BAN channel involves contributions from hbody (t) and hground (t). If we assume that the reflections ground are uncorrelated with components diffracting around the body, then the complete channel response is given as: hOutdoor-BAN (t) = hbody (t) + hground (t)
(18)
The model parameters for different scenarios and ground material are given in [2].
2008 10th IEEE Intl. Conf. on e-Health Networking, Applications and Service (HEALTHCOM 2008)
229
20log10 (βl,k ) = Γτl + γ(τl + Δk) + σΓ nl + σγ nk .
(20)
0
10
−1
10
−2
10
RAKE 2 users
BER
For an Indoor-BAN channel, several multipaths are expected due to reflections from the surroundings. The multipath distribution is stochastic in nature with several clusters and the distribution is modeled using a modified version of SV model. The multipath clusters are modeled using Poisson or Weibull distributed random variables. Whereas, the intra cluster distribution is dense, so multipath arrivals within a cluster are assumed to be uniform. [5] gives a detailed account of the modeling process. The channel model due to reflections from surroundings is given as: ∞ ∞
τ X l href (t) = √ +k . βl,k exp (jφl,k ) δ t − Δ Δ E l=0 k=0 (19) Where βl,k represents the amplitude in k th bin in lth cluster modeled as:
10
RAKE 3 users MUD 3 users RAKE 4 users
−4
10
MUD 4 users RAKE 5 users
−5
10
MUD 5 users
−6
10 −30
−20
−10
0
snr
10
20
30
Fig. 3. BER performance of MUD based receivers compared against RAKE receivers with multiple sensors placed on ‘front’ position of the body. Solid and dotted lines represent the performance of MUD and RAKE receivers respectively.
τl is the cluster arrival time modeled using a Poisson distribution as: 1 − τl −τβl−1 , (21) p (τl /τl−1 ) = e β
0
10
−1
10
−2
10
BER
where β represents the mean arrival rate. The average energy of the clusters decay exponentially at a rate of Γ dB/ns and the terms within the cluster decay at a rate of γ dB/ns. Further, the Lognormal fading in the clusters and within the clusters are modeled using σΓ and σγ respectively. Here nl and nk represent uncorrelated normal random variables with unit mean and unit variance. φl,k is uniformly distributed random variable and Δ is the bin size. The model parameters for the above channel models are given in [2], [5]. If we assume that the reflections from the indoor environment and ground are uncorrelated with components diffracting around the body, then the complete channel response is given as:
MUD 2 users
−3
−3
10
−4
10
−5
10
RAKE 2 users MUD 2 users RAKE 3 users MUD 3 users RAKE 4 users MUD 4 users RAKE 5 users MUD 5 users
−6
10 −30
−20
−10
0
snr
10
20
30
(22)
Fig. 4. BER performance of MUD based receivers compared against RAKE receivers with multiple sensors placed on ‘side’ position of the body. Solid and dotted lines represent the performance of MUD and RAKE receivers respectively.
In this section we compare the performance evaluation of MUD receivers with RAKE receivers. We use non linear MUD receiver (Linear MUD + DFE) and RAKE receiver with equalization (DFE) for comparison. We consider a DS-UWB system using the following parameters in the simulation: • Indoor-BAN channel as discussed in [5] • Gaussian Monopulse Tp = 0.8ns • Processing Gain Nf = 8 • Channel Model given in [2], [5] • Number of channel realizations used for averaging is set as 500 • Training sequence length is taken as 1500 • Number of multipaths are set to 50 • Full RAKE receiver with perfect channel knowledge is assumed The number of users the system can support is limited by the processing gain. So in order to support more number of
user one needs to go for higher processing gain. We give the performance comparison of the two receivers as a function of signal-to-noise ratio. The number of users are varied from 2 to 5 with each of the interfering users transmitting at 4 times the power of the desired user. Figures (3), (4) and (5) illustrate the performance of MUD receivers compared against RAKE receivers for sensor placed on ‘front’, ‘back’ and ‘side’ positions of the body respectively. ‘concrete’ is set as the ground material. The number of users are varied from 2 to 5. Clearly, the MUD based receivers significantly outperform RAKE receivers in mitigating the multiuser interference. Further, the RAKE receiver performance degrades with the increase in the number of users. This behavior is consistent with all the three cases. The MUD based receiver has better performance when sensors are placed on the ‘front’ position of the human body compared to ‘back’ and ‘side’ positions. Figure (3) clearly il-
hIndoor-BAN (t) = hbody (t) + hground (t) + href (t) V. S IMULATION R ESULTS
230
2008 10th IEEE Intl. Conf. on e-Health Networking, Applications and Service (HEALTHCOM 2008)
0
10
−1
10
−2
10
BER
RAKE 2 users −3
10
MUD 2 users RAKE 3 users
−4
10
MUD 3 users RAKE 4 users MUD 4 users
−5
10
RAKE 5 users MUD 5 users
−6
10 −30
−20
−10
0
snr
10
20
30
Fig. 5. BER performance of MUD based receivers compared against RAKE receivers with multiple sensors placed on ‘back’ position of the body. Solid and dotted lines represent the performance of MUD and RAKE receivers respectively.
lustrates this behavior. Due to line of sight communication and smaller path loss component this observation is expected. The performance of MUD receiver for ‘side’ and ‘back’ positions is almost similar which is quite expected due to similar path loss component and non line of sight communication. VI. D ISCUSSION A body area network needs to support large number of sensors in close proximity to monitor heart rate, glucose levels etc. In a WBAN the sensors communicate asynchronously among each other and a central device leading to significant multi-user interference. The reliability of a WBAN can degrade due to MUI. In order to mitigate MUI, MUD receivers seem to be an obvious choice. We have demonstrated that MUD receivers significantly outperform RAKE receivers in the presence of MUI. However, the complexity of MUD receivers is high compared to RAKE receivers. Currently we are working on receiver architectures to reduce the complexity by exploiting the UWB nature of transmission. R EFERENCES [1] P. S. C. Thejaswi, S. Manohar, G. Viswanath, R. Patro and M. Raina. Simple Multiuser Detectors for DS-UWB Systems. IEEE 17th International Symposium on Personal, Indoor and Mobile Radio Communications, 1-5, September 2006. [2] A. F. Molisch, K. Balakrishnan, D. Cassioli, C. C. Chong, S. Emami, A. Fort, J. Karedal, J. Kunisch, H. Schantz, U. Schuster, K. Siwiak. IEEE 802.15.4a channel model - final report, www.ieee802.org/15/pub/04/1504-0662-02-004a-channel-model-final-report-r1.pdf. [3] A. Fort, J. Ryekaert, C. Dessent, P. D. Doncker, P. Wambacq and L. V. Biesen. Ultra Wideband Channel Model for Communication around the Human Body, IEEE Journal on Selected Areas in Communications, 24: 927-933, April 2006. [4] A. Fort, C. Dessent, P. D. Doncker, P. Wambacq and L. V. Biesen. An Ultra Wideband Body Area Propagation Channel Model: From Statistics to implementation, IEEE Transactions on Microwave Theory and Techniques, 54: 1820- 1826, June 2006. [5] A. Fort, C. Dessent, P. Wambacq and L. V. Biesen. Body Area UWB RAKE Receiver Communication, IEEE International Conference on Communications Proceedings, 5, 4682-4687 June 2006. [6] J. G. Proakis, Digital Communication, 4th ed. New York: McGraw-Hill, 2001.
2008 10th IEEE Intl. Conf. on e-Health Networking, Applications and Service (HEALTHCOM 2008)
231
