He has published over 200 papers and holds over 40 patents, including basic patents on DMT, VDSL, and V-OFDM. MIMOpaper.tex; 12/12/2001; 9:48; p.10.
Performance of 60 GHz Virtual Cellular Networks using Multiple Receiving Antennas Invited paper selected from WPMC01 Maxime Flament and Arne Svensson Dept. of Signals and Systems, Communication Systems Group, Chalmers University of Technology, Gothenburg, Sweden
John M. Cioffi Dept. of Electrical Engineering, STARLab, Stanford University, Stanford, CA, USA Abstract. During the past years, research covering propagation, channel characterization and wireless systems performance have yield a substantial knowledge of the 60 GHz channel. The unlicensed 60 GHz frequency band presents many attractive properties for wireless communications. The environments in which the 60 GHz infrastructure are to be designed are typically propagation- and coverage-limited. This paper describes the important factors that must be taken into account when designing a WLAN architecture operating in this frequency band. Therefore, we motivate the reasons of using distributed transmitting antennas and multiple receiving antennas (MRA) in order to mitigate the poor Direction of Arrival (DoA) diversity and to exploit the spatial diversity at the receiver. Such a system can be considered as a MIMO system. We investigate the advantages of combining a Virtual Cellular Network (VCN) (using single frequency network and distributed antennas) and MRA for the downlink. Several ways to combine the signals with different levels of complexity are presented. In the most complex case using Singular Value Decomposition (SVD), it is possible to add coherently the contribution of each antenna in a virtual cell while retaining the path diversity inherent to the VCN infrastructure. The schemes yield several advantages: symbol diversity is improved, path diversity is still present, antenna gain using multiple beamformers is increased and the multipath can be reduced. The concept is applicable to most types of single frequency networks but it is especially well appropriate for the 60 GHz VCN/WLAN using OFDM. Simulations give a realistic performance for QPSK, 8-PSK, and 16-QAM baseband modulations with a 256-subcarrier OFDM using a rate 1/2–convolutional code for a N × N VCN system. Results show a Eb /N0 improvement of up to 7.4 dB using the singular value decomposition method with 16–QAM compared to the SISO coded reference. Keywords: Virtual Cellular Network (VCN), OFDM, 60 GHz, WLAN, modified Saleh-Valenzuela model, MIMO, Beamforming.
1. Introduction In the recent years, interest for broadband mobile communication in a small environment has dramatically increased. Research is being conducted by several centers on very high data rate short range and/or pointto-point communication systems. In collaboration with other research facilities, we aim to implement and demonstrate the use of high-speed 60 GHz WLAN for multimedia applications. The system infrastructure should be designed such that it can deliver high throughput in a micro-cellular network at points where special services are required. The key overall requirement is a low cost per bit rate network. The following paper reports relevant results related to the design of a 60 GHz wireless infrastructure in office environment. This paper emphasize a series of results and limitations about the realization of a broadband mobile communication system for a WLAN implementation. The investigated frequency band is ranging from 59 GHz to 64 GHz allocated for unlicensed devices. The 5 GHz bandwidth of this system can be seen as an unlimited bandwidth for very short range wireless communication devices. Despite the considerable implementation and channel drawbacks, such as limited power, large noise figure, limited VCO stability, shadow fading and multipath channels, we cannot ignore the huge potential of such a band on the battle for more bandwidth. Even though, specific applications requiring such bandwidth are still to come, instant access at very high bit rate is desirable to everyone. In a first attempt to implement this kind of infrastructure, we can foresee a poor coverage and/or a high outage probability. This means that transmission would be possible only at some particular points and in some favorable situations. First investigations were considering communication links up to 150 Mbit/s. However, as the capacity of the whole band is much larger than what one could imagine useful to a single user we should consider criterium that increase the reliability of the link instead of the maximum data rate by designing a robust and flexible air interface. In this paper, we show that the combination of VCN and multiple antennas can yield a dramatic diversity gain when it is used in combination with OFDM at 60 GHz. The next section describes briefly the features that are used in the 60 GHz wireless infrastructure. In section 3, we elaborate further the models used to analyze the communication system. A modified Saleh-Valenzuela channel model, OFDM systems, a MIMO system c 2001 Kluwer Academic Publishers. Printed in the Netherlands. MIMOpaper.tex; 12/12/2001; 9:48; p.1
2 and a channel estimation scheme are given in order to describe the whole 60 GHz communication system. Section 4 proposes a system setup and shows the performance improvement using multiple antennas for several different combining schemes. Practical issues are discussed in section 5.
2. Background 2.1. Virtual Cellular Networks Virtual Cellular Networks (VCN) use distributed access points (AP) and Single Frequency Networks within cells in order to form an adaptive wireless communication architecture. The idea of VCN originates from digital broadcasting systems, such as Digital Audio Broadcast (DAB), using single frequency networks. However, VCN uses an adaptive form of the SFN by constantly measuring and modifying the set of APs serving one single terminal. The SFN are interesting because they extend the coverage of one cell to a larger area defined by the combination of the operating APs. The advantages of VCN described in earlier work for indoor communication systems in the UHF band [6] were limited by the channel properties. The system considerations of the VCN architecture has been further developed for ubiquitous wireless access [2]. In this paper, the major motivation for VCN is the increased DoA diversity in the downlink, which was introduced in previous work [3]. Unlike conventional cellular networks, overlapping coverage of the AP cells is desirable in order to increase DoA diversity. This infrastructure is appropriate for communication systems in which propagation and coverage are the limiting factors. VCN is usually based on Orthogonal Frequency Division Multiplexing (OFDM) signals sent through physically distributed APs using the same channel and, which therefore, create a larger cell, called a virtual cell. Since VCN uses OFDM with a large cyclic prefix (CP), the receiver can mitigate the additional time dispersion of the channel and take full advantage of the combined channel state from different transmitters. In other words, the receiver can combine and demodulate the signals in a simple way with a minimal loss of performance. Moreover, little added complexity is needed for OFDM systems in order to allow a better margin on delays between VCN channels. Since the propagation conditions are difficult, the system should intelligently discern the APs forming a virtual cell for a particular user. Firstly, in order to preserve capacity, we shall not allow out-of-range APs to transmit so that power contribution from each VCN antenna exceeds a certain received power threshold. Secondly, a large number of APs in one virtual cell is not necessarily desirable since the resulting channel becomes increasingly time dispersive. We will see also that the rays from different APs are more useful if they are coming from different directions. Thirdly, in the case of good VCN channel quality, we would like to combine the contributions from each VCN AP and increase the antenna diversity gain. However, we will see that this gain is not always worth the computational complexity. 2.2. The 60 GHz frequency band The use of VCN is particularly relevant for 60 GHz (the 59–64 GHz unlicensed band) wireless LANs. At this frequency, the propagation range is dramatically limited whilst the 5 GHz bandwidth is large. Therefore, we can accept some spectral efficiency losses in order to increase the coverage of the cells. The 59.00–59.05 GHz portion of the band will be common to all users, for assistance in the coordination of real time use in the remainder of the band1 . The large bandwidth available in the 60 GHz band offers a great capacity for wireless broadband systems. For example, a single AP in an meeting room could offer, using simple modulation, a steady throughput of 200 Mbit/s to not less than 25 users at the same time, without sharing any resources. Unfortunately, it is difficult to offer proper coverage, so we need to use architecture such as VCN to circumvent the propagation issues. 1
Users must transmit an ID code, at least once per second, as an aid to resolving rapidly any interference problems. This will help the adaptive formation of virtual cells for each particular users with a good spectrum efficiency, a minimum of interference and little handover traffic.
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3 P
AP
5
2 T1
4
T2 T3 T4
t
T5 T6
1
6
Receiver
3
A2 A5 A1
A4
A6 A3
Figure 1. Illustration of the Saleh-Valenzuela model [10] with direction information [12]. Rays arrive in cluster from the same direction due to multiple reflection in materials. The taps in the discrete-time model are likely to contain power from one single cluster i.e. originating from one particular direction.
60 GHz microelectronic technologies for mobile devices are still in their infancy. The main applications proposed at this time are high capacity point-to-point links and automotive radar. Efforts have been made in order to develop testbeds and demonstrators for WLAN but with little integration and low signal complexity. As a result, we can expect that 60 GHz technologies will be affordably available for WLANs in less than 10 years. 2.3. Multiple receiving antennas The effect of shadowing at 60 GHz is very destructive [3] and therefore, VCN is used to increase the path diversity. In addition, there is a requirement for multiple receiving antennas (MRA) in order to limit the effect of short-distance variations of the received power. 60 GHz multiple receiving antennas should be easy to implement on a portable device. 2.4. Exploiting diversity The system described in this paper presents many types of diversity. First of all, VCN gives path diversity from the wireless network viewpoint, but also transmission macro-diversity. Using multiple receiving antennas, we exploit the spatial and antenna diversity at the receiver. Finally, we use coding across the subcarriers to benefit from the diversity of the frequency-selective channel.
3. Model descriptions 3.1. Saleh-Valenzuela channel model The indoor short range channel at 60 GHz has a strong multipath behavior (see for example [4]). The SalehValenzuela channel model [10], based on a double Poisson process, is adapted to describe statistically the time of arrival, DoA and the complex values that describe the E-vector states of the rays after multipath propagation, i.e. reflection and fading (see Figure 1). The rays have independent uniformly distributed phases φkl between [0, 2π) and arrive in clusters. The clusters and the rays within a cluster form two independent Poisson arrival processes with fixed rates of Λ and λ respectively. Since the original model does not provide any spatial information, Spencer et al. proposed in [12] an extension to the Saleh-Valenzuela model. The
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4 Table I. Saleh-Valenzuela channel parameters. The mean time between clusters depends on the size of the room; the mean time between rays depends on thickness and material of the walls; and, the decay time constants depend on the carrier frequency and the permittivity of the walls. Mean time between clusters Cluster decay time constant Mean time between rays Ray decay time constant
1/Λ Γ 1/λ γ
15 20 5 9
ns ns ns ns
results showed that the mean DoA of the lth cluster Φl is uniformly distributed over [0, 2π) and that the distribution of the ray DoA ωkl within clusters has a Laplace distribution with a relative low variance of σ 2 =.3 at 5.2 GHz. The impulse response is given in [12] by h (t, θ) =
∞ ∞
βkl ejφkl δ (t − Tl − τkl ) δ (θ − Θl − ωkl )
(1)
l=0 k=0
where the sum over l represents the clusters and the sum over k represents the arrival of the rays. Assuming Γ and γ for the decay time constant of the clusters and the rays, respectively, the mean square values of the amplitude of the rays βkl are 2 = β 2 e−Tl /Γ e−τkl /γ βkl (2) 00 The inter-arrival times (Tl − Tl−1 ) of the lth cluster and (τkl − τ(k−1)l ) of the k th ray within the lth cluster are determined by negative exponential random values p (Tl |Tl−1 ) = Λe−Λ(Tl −Tl−1 )
and p τkl |τ(k−1)l = λe−λ(τkl −τ(k−1)l )
(3)
and the DoA deviation ωkl of the k th ray from the mean DoA Θl in the lth cluster is described by p (ωkl ) = √
1 2σ 2
e−|
√
2ωkl /σ|
.
(4)
The resulting tap-delay model sampled at 1/B, where B is the system bandwidth, has independent Rayleigh distributed amplitudes. Depending on the type of antenna, the power delay profiles have different properties: as the direction of the arrival of the rays is correlated with the cluster, the rate of cluster arrival drops when the antenna gain increases. The set of parameters for an office room, presented in Table I, are derived from ray tracing considerations, which slightly differs from [8]. We employ wall-mounted 3 dBi AP antennas and omni-directional antennas at the receiver. The mean time between clusters depends on the size of the room; the mean time between rays depends on thickness and material of the walls; and, the decay time constants depend on the carrier frequency and the permittivity of the walls. It is therefore difficult to generalize the parameters. However, this influences little the theory presented in this paper. When two VCN APs are transmitting, the complex impulse responses are simply added together at the receiver with different delays. Since the clusters’ power are likely to arrive from different directions, we can suggest that the resulting channel terms will contain contributions from different directions and different APs. 3.2. OFDM system OFDM uses a FFT to obtain a signal constellation of high dimension. It increases the symbol time and uses discrete Fourier Transforms (DFT) and a CP of length ∆ in order to obtain a simple circular convolution between the frequency-selective channel and the transmitted symbols at the output of the OFDM demodulator. The following derivations assume that the CP is designed such that the Inter-Symbol Interference (ISI) and the Inter-Carrier Interference (ICI) can be ignored, i.e. the overall maximum excess delay of the resulting channel is smaller than the CP time, ∆/BW . The overall OFDM communication system is illustrated in Figure 2.
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5 source bits
x
Conv.
Block
Encoder
Intreleaving
Baseband
Add
IFFT
Modulation
CP
Multipath channel Received bits
Viterbi decoder
De− Interleaving
Baseband Demod
n ; h(t, θ)
Remove CP
FFT y
Figure 2. Overview of the OFDM communication system. Using VCN, the multipath channels are duplicated with uncorrelated noise n and channel h(t, θ). h1,1 (k)
n1 (k)
v1∗ (k)
u1 (k)
Antenna 1 y1 (k)
z1 (k)
h2,1 (k)
x(k)
z(k)
h1,2 (k)
v2∗ (k)
Antenna 2
n2 (k)
u2 (k) y2 (k)
z2 (k)
h2,2 (k)
Figure 3. A VCN model using two antennas on each side. hij are assumed to be known, with mean square errors depending on the SNR. In the case where the channel is unknown at the receiver, vi (k) are arbitrarily set to one and ui (k) are defined as the element of the MMSE equalizer vectors. In the case where the channel is known both at the receiver and at the transmitter, u(k) and v(k) are respectively the left and right first singular vectors calculated from a Singular Value Decomposition (SVD).
Given that h(k) is the frequency response of the channel for the k th carrier, the received signal y(k) in one OFDM signal using NFFT sub-carriers can be expressed in a Single–Input Single–Output (SISO) form by (5) y(k) = h(k)x(k) + n(k) ∀k = 0 . . . NFFT − 1 where x(k) represents the transmitted baseband symbols and n(k) is a band limited AWGN vector with a variance of σn2 = N0 /2NFFT ; the notation h makes the assumption that the channel is quasi static during the entire OFDM symbol time Ts = (NFFT + ∆)/B. For convenience, equation 5 can be rewritten in matrix form for one entire OFDM symbol (6) y = Hx + n where H = diag(h(0), h(1), . . . , h(NFFT − 1)). Keeping in mind that the VCN requires OFDM in order to operate properly, the following section describes the MIMO channel model for each sub-carrier k. 3.3. Multiple antennas model Due to the properties of the channel described in section 3.1, the received rays in one cluster are likely to arrive from one particular direction at one particular time. Thus, most of the terms of the discrete-time model will contain power from single VCN APs. For the other terms, fading occurs between the VCN channels. Since [3] describes the need for diversity at the receiver, it is sensible to investigate the performance of the VCN and multiple antennas at the receiver. Important work has been done on MIMO systems over the past years. In this section we identify the way we can use MIMO theory in VCN systems. Assuming nT active APs in a virtual cell, we have a Multiple–Input Single–Output (MISO) system where nT uncorrelated frequency responses hi (k) are involved corresponding to one branch of Figure 3. Equation 5 becomes y(k) =
nT
hi (k)x(k) + n(k).
(7)
i=1
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6 In the case where only one antenna is used at the receiver side, the nT signals will merge. The resulting channel impulse response will typically become more frequency-selective. So, the VCN channels are added incoherently and the main gain comes from the path diversity mitigating the probability of shadowing. At the receiver, the channel can be optimally equalized by u(k) using a MMSE equalizer. Assuming E(|X|2 ) = 1 and uncorrelated channels, we get z(k) = u(k)y(k) with u(k) =
nT
∗ i=1 hi (k) nT ∗ i,j=1 hi (k)hj (k)
+ σn2
.
(8)
As 60 GHz communication systems require receiving antenna diversity, we consider nR antennas (nR ≥ nT ) at the receiver side. A priori, the nT signals can be separated since (a) they are likely to come from different directions in different discrete taps and (b) the channels are uncorrelated. In the following, we will describe the different ways of combining the signals at the receiver. First, we consider that the channel is only known at the receiver. In the second case, the channel is known both at receiver and transmitters. 3.3.1. Channels known at the receiver only Given the frequency response matrix H(k) formed by the channel impulse responses hi,j (k), we have nR ×nT frequency responses containing the contribution of VCN antenna i to the receiving antenna j. The received symbol vector is (9) y(k) = H(k)x(k) + n(k) As in equation 8, it is possible using an equalization matrix U to combine, in an optimal way, the contributions of each of the VCN antennas (see Figure 3). The received signal vector z representing the contribution from VCN antenna i is expressed by z(k) = U(k)y(k) = U(k)H(k)x(k) + U(k)n(k)
(10)
where the MMSE equalization matrix is H
−1
H
U(k) = Rxx (k)H (k) H(k)Rxx (k)H (k) + σn2 I
.
(11)
The resulting symbols are combined using a Maximal Ratio Combiner (MRC). The MRC will simply compute the mean of the equalized contributions. Rxx (k) is the auto-covariance matrix of the transmitted symbols. If the same symbol x(k) is sent from the VCN antennas, Rxx (k) is a uniform matrix and the performance of the MRC is comparable to a nR -diversity degree of the MISO system described by equation 8. This technique will be evaluated in section 4. 3.3.2. Channels known at both the receiver and transmitter If we consider the knowledge of a channel at the transmitter, we can rotate the signals at the transmitter such that the channels will add coherently at the receiver. In order to do that, the channel matrix H is expressed using a singular value decomposition (SVD) H(k) = U(k)S(k)VH (k)
(12)
where S(k) is a rectangular matrix containing the singular values matrix diag(s1 (k), s2 (k), . . .), and, U(k) and V(k) are the matrices formed by the left and right singular vectors. UH (k) and V(k) are used at each side of the channel in order to form a multiple beamforming. The received symbol vector becomes z(k) = UH (k)H(k)V(k)x(k) + UH (k)n(k) = S(k)x(k) + UH (k)n(k)
(13)
Multiple beamforming gives good performance when the matrix H(k) is of full rank. However, the channel spatial fading correlation is relatively high so that using all the singular values is, in reality, not necessary.
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7 Table II. Combining methods, required channel knowledge and expected relative channel estimation noise when using p OFDM symbols as pilots Combining method
Required channel knowledge
U, S, V
∆ pNF F T Nr ∆ pNF F T Nr Nt ∆ pNF F T
u1 , s1 , v1
idem
Conventional VCN Nr = 1
h(k)
VCN-MRA Nr ≥ 1 – MRC
h(k)
Multiple beamforming using SVD Simplified beamforming using 1
st
SV
E[σe2 /σn2 ]
Instead, the power can be focussed on the most efficient beamforming vector corresponding to the first singular value of the SVD [7]. The channel rectification at the transmitter decreases the flexibility of the system and requires more computation. The nT × nR channels need to be known both at the transmitter and at the receiver making the system more complex. In our scheme, a SVD has to be performed for each subcarrier in order to shape the multiple beamforming. However, using a power iteration method, relatively low complexity is needed to find the first singular vectors for the simplified SVD method that we evaluate in section 4. 3.4. Channel estimation The channel estimation algorithm should identify nT × nR frequency-selective channels. All the channels are supposed to be limited to ∆ taps. Therefore, nT × nR × ∆ complex values have to be estimated in order to give an estimation of the channel with the same noise level. Since there is duality between the value of the taps and the frequency response, the VCN time-dispersive channels can be treated as independent frequency responses. Hence, we perform the estimation of the channel after the DFT and conserve the properties of the VCN channels. The estimation of these channels is a considerable issue. Computational issues might in fact limit the implementation of this type of system. Different estimation schemes are described in [5] [11]. We consider a slow fading channel and a packet-switched traffic organized in frames. The frame length is considered to be short enough in order to assume a constant frequency-selective channel. A preamble of p OFDM symbols is used at the beginning of the frame and a small number of pilot subcarriers track the frequency changes. Given that pNF F T /∆ ≥ Nt Nr , using p OFDM training symbols should be enough to give a channel estimate variance σe2 that is a number of times below the noise variance σn2 . Table II lists the performance of the different estimators. In section 4, we use this assumption to show the realistic performance of the system.
4. Simulations An indoor 60 GHz WLAN using VCN and OFDM modulation is simulated. The signal bandwidth is fixed to 200 MHz divided into 256 subcarriers with a CP length ∆=30 i.e. a CP time period of ∆/BW =150 ns. The subcarrier bandwidth is wide enough (781 kHz) and the symbol time is short enough (1.43 µs) such that the effect of speed can be ignored in one entire frame. QPSK, 8–PSK and 16–QAM baseband modulations are evaluated. The diversity of the frequency-selective channel is exploited by a rate R=1/2 convolutional code with constraint length 7 using a simple block interleaver across the subcarriers. No puncturing is considered. For simplicity, hard decoding is performed at the receiver after the combiner. Average received powers from different APs are assumed to be equal. The estimated channels are generated at the beginning of a frame. Estimation MSE is calculated from the noise and the number of training symbols, as listed in Table II. For comparison, the SISO performance are shown, and perfect and noisy estimates are considered. In order to simulate the channels, we use a large set of Saleh-Valenzuela channel realizations represented by tap-delay models and we form the resulting time dispersive channels out of nT randomly picked channels. VCN AP delays are uniformly distributed with maximum 30 ns. The nR channels from one VCN antenna
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8 0
0
10
10 MRC SVD ref: SISO Uncoded Perf. est. Coded Perfect est. Coded Noisy est.
MRC SVD ref: SISO Uncoded Perf. est. Coded Perfect est. Coded Noisy est.
−1
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BER
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BER
10
10
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−1
−2
−3
−4
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6 Eb/N0
8
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12
Figure 4. VCN using two antennas on each side. QPSK (left), 8–PSK (right) and 16–QAM (bottom) are evaluated with rate 1/2 convolutional coding (K=7). Perfect and noisy channel estimates are considered.
Table III. Eb /N0 improvement for different modulation schemes with convolutional coding and noisy estimates at BER = 10−3 . While the MRC method is more appropriate for QPSK, SVD becomes increasingly profitable for higher signal constellation. Modulation
MRC
SVD
QPSK 8–PSK 16–QAM
5.2 dB 5.4 dB 4 dB
6 dB 5.8 dB 6.4 dB
have a similar discrete-tap pattern but experience different tap fading due to the path differences. They are therefore generated using independent Rayleigh random values on each of the taps. The results in Figure 4 show the performance improvement for the different baseband modulation schemes. The required Eb /N0 at BER = 10−3 range from 2.5 dB to 7.8 dB for the MRC and SVD, and from 8 dB to 11.8 dB for the SISO coded reference. The diversity gain is noticeable at low BER, especially for 16-QAM. Table III gives the Eb /N0 improvement at BER = 10−3 compared to the coded SISO reference. While the MRC method is more appropriate for QPSK, SVD becomes increasingly profitable for higher signal constellation.
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9 5. Practical implementation Up-and-coming technologies such as Radio-over-fiber are candidates for supporting VCN backbone architectures. However, it would require a special infrastructure that would limit the flexibility and increase the cost of the network. Another solution, using passive repeaters placed in the LOS of the main AP in a room, could be considered at the cost of impaired delays at the receiver and limited signal flexibility at the transmitter. In this paper, we used MIMO theory in order to increase channel diversity. One could argue we should use the full properties of MIMO systems in order to increase the system throughput. In the case in which good VCN channels are present, we could indeed conceive of sending separate OFDM symbols from each VCN AP and still recover them at the receiver. However, this would destroy the integrity of the VCN signal and eliminate the original reason for the use of multiple transmitting antennas which is mainly the path diversity. Also, errors in the channel estimation would have more impact on the performance since the residual cross-correlation will interfere with other channels instead of adding incoherently. The ideas presented in this paper could be adapted to any kind of SFN infrastructure. Among others, DAB and DVB-T could use multiple antennas. However, due to the characteristics of the 60 GHz channel, the scheme is particularly well suited for the 60 GHz WLANs.
6. Conclusions In this paper, we demonstrated the considerable advantage of using multiple antennas in a VCN system based on OFDM modulation. This scheme was used in order to increase the symbol diversity at the receiver in addition to the VCN path diversity. Introducing realistic models for channel propagation and channel estimation, we showed how the signals can be recovered at the receiver. We expressed this system as a MIMO system. Different combining strategies are developed depending upon whether we know the channel at the receiver only or at both the receiver and transmitter. A VCN system with two APs and two receiving antennas was evaluated for Conventional VCN using maximum ratio combining (MRC) and VCN using simplified singular value decomposition (SVD), and comparing it to the case without antenna diversity (SISO). The system setup used QPSK, 8-PSK, and 16-QAM baseband modulation and rate 1/2 convolutional coding with a constraint length 7. Noisy channel estimates were considered in order to perform realistic simulations. The results showed the improvement of 5 dB for MRC with QPSK and up to 7.4 dB for SVD with 16–QAM compared to the SISO reference. However, complexity of the SVD scheme has to be taken into account in order to compare the results in a fair manner.
Acknowledgements This paper is a part of an investigation for the potential of 60 GHz WLAN systems in the framework of the PCC–4GW project [9, 1]. Additional financial help for the international collaboration with Stanford University was provided by the Kungliga Ingenj¨ orsvetenskapsakademien (IVA) via the Hans Werth´en-fonden. The authors would like to thank Daniel Per´ez Palomar, visiting researcher at the Department of Electrical Engineering, Stanford University, for his valuable help with MIMO theory.
References 1. 2. 3. 4.
4GWweb, 4GW home page. http://www.s3.kth.se/radio/4GW/. Bakker, J. and R. Prasad: 1999, ‘Handover in a Virtual Cellular Network’. In: Proc. of IEEE Vehicular Technology Conference. pp. 544–548. Flament, M.: 2000, ‘On 60 GHz Wireless Communication Systems’. Licenciate thesis, Chalmers University of Technology, Gothenburg, Sweden. H¨ ubner, J., S. Zeisberg, K. Koora, and A. Finger: 1997, ‘Channel model for 60 GHz indoor wireless LAN Design Based on Complex Wideband Measurements’. In: Proc. of IEEE Vehicular Technology Conference. pp. 1004–1008.
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Maxime Flament (S’96) received his M.Sc.E.E. (Ing´enieur civil) from the Free university of Brussels (ULB), Belgium, in 1997. He also received a second M.Sc.E.E. from Chalmers University of Technology as a result of an Erasmus programme. Since 1998, he is Ph.D. candidate within the Communication Systems group at Chalmers. In Dec. 2000, he presented his Dr. Ing. thesis on ”60 GHz wireless communication systems”. In 2001, Maxime spent 6 months as visiting researcher at Stanford University, CA, USA with John Cioffi’s group. He was recently awarded the Student Paper Award at the WPMC’2000. His research focuses mainly on OFDM, baseband adaptive modulation, coding, MIMO systems, high-frequency electronics and 60 GHz channels.
Arne Svensson (S’82-M’84-SM’90-F’01) received his M.Sc.E.E. and his Ph.D.E.E. from the University of Lund, Sweden, in 1979 and 1984, respectively. Since 1985, he has been with the Department of Signal and Systems at Chalmers University of Technology, Gothenburg, Sweden, where he was appointed Professor in Communication Systems in April 1993. Before 1985, he held various teaching and research positions at the University of Lund, Ericsson Radio Systems AB and Ericsson Radar Electronics AB. His consulting company BOCOM, is involved in studies and gives course in the areas of error control methods, modulation and demodulation techniques, spread spectrum and CDMA systems, and computer simulation methods for communication systems. His current interest include channel coding and decoding, digital modulation methods, channel estimation, data detection, multiuser detection, digital satellite systems, wireless IP based system, CDMA and spread spectrum systems, and personal communication networks. Prof. Svensson has published almost 30 journal papers/letters and more than 110 conference papers. In 1986, he was recognized with the IEEE Vehicular Technology Society Paper of the Year Award, and in 1984, he received the Young Scientists Award from the International Union of Radio Science, URSI. He is an editor of two scientific journals. He is a fellow of IEEE.
John M. Cioffi (S’77-M’78-SM’90-F’96) received the B.S.E.E. degree in 1978 from the University of Illinois and the Ph.D.E.E. degree in 1984 from Stanford University, Stanford, CA. He was with Bell Laboratories from 1978 to 1984 and IBM Research from 1984 to 1986. He has been with Stanford University as an electrical engineering Professor from 1986 to present. He founded Amati Communications Corporation in 1991. He currently is on the boards of several companies and is a Member of the US National Research Councils CSTB. His specific interests are in the area of high-performance digital transmission. Dr. Cioffi received the following awards: IEEE Kobayashi Medal in 2001, IEEE Millennium Medal in 2000, IEE JJ Tomson Medal in 2000, 1999 University of Illinois Outstanding Alumnus, 1991 IEEE Communications Magazine best paper, 1995 ANSI T1 Outstanding Achievement Award, and NSF Presidential Investigator from 1987-1992. He has also received awards from the National Academy of Engineering in 2001. He has published over 200 papers and holds over 40 patents, including basic patents on DMT, VDSL, and V-OFDM.
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