A Carrier Interferometry based Channel Estimation Technique for One-Cell Reuse MIMO-OFDM/TDMA Cellular Systems Kazunari Yokomakura and Seiichi Sampei
Hiroshi Harada
Norihiko Morinaga
Graduate School of Engineering, National Institute of Information Department of Information Technology, Osaka University and Communications Technology Hiroshima International University Email:
[email protected] Email:
[email protected] Email:
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Abstract— This paper propose a channel estimation technique for multiple-input multiple-output (MIMO) - orthogonal frequency division multiplexing (OFDM) / time division multiple access (TDMA) (MIMO-OFDM/TDMA) cellular systems based on the dynamic parameter controlled - OF/TDMA (DPCOF/TDMA) systems. In the proposed scheme, impulse responses of all the transmit antennas are multiplexed in the time domain using a carrier interferometry (CI) signal, which is equivalent to an impulse signal, and each impulse response is discriminated using a time window with the length of the maximum delay time of delayed paths, in order to estimate a channel state information (CSI) using only 64 subcarriers. Computer simulation confirms that the proposed channel estimation scheme is effective even in the case of 64 subcarriers under MIMO and multi-cell conditions.
I. I NTRODUCTION With extensive research on broadband wireless access techniques, various types of beyond third generation (B3G) systems are under investigation. One of the most important requirements for the B3G systems is to support data rate of more than 100 Mbit/s with a bandwidth of about 100 MHz using a single-input single-output (SISO) scheme and around 1 Gbit/s by further employing multiple-input multiple-output (MIMO) techniques [1]. As one of the possible scheme for the B3G systems, we have proposed a dynamic parameter controlled - orthogonal frequency and time division multiple access (DPC-OF/TDMA) system [2] based on one-cell reuse OFDM/TDMA systems [3]. In this system, all the subcarriers are segmented every 64 subcarriers, each of which will be called a subchannel in the following. Terminals can use arbitrary number of subchannels depending on functionality and constraint conditions of each terminal, e.g., a small terminal such as a wristwatch-type terminal can use only one subchannel (64 subcarriers) whereas a large terminal such as a PC might use all the subchannels if available. Therefore, it is required for its channel estimation scheme to have an accuracy (resolution in frequency domain) depending on the number of subchannels to be used in both SISO and MIMO cases and in both single-cell and multi-cell environments. When a MIMO scheme is introduced in a cellular system, we have to measure the received power from all the connectable BS, as well as to identify all the channel state information (CSI) for all the combination of the transmit and receive antenna elements. For this purpose, we have to measure an impulse response between any combination of transmit and receive antenna elements for all the connectable BS antennas. One of the most typical channel estimation technique is to employ a code division multiplexing (CDM) based scheme
0-7803-9392-9/06/$20.00 (c) 2006 IEEE
in which a unique spreading code is assigned to identify both BS and antenna element. When a terminal employs only one subchannel in which 64 subcarriers are included, we can apply spreading code length of 64, which is not a sufficient number for identification of both BSs and antenna elements. For example, when the number of detectable surrounding cells is 10 and the number of transmit antenna elements in each BS is 8, the total number of the required codes is 80 (=10 cells * 8 antenna elements), which is much larger than the number of spreading code to be prepared using a spreading factor of 64. This means that channel estimation for one subchannel (64 subcarrier) transmission is a severer case than the cases for plural subchannel usage. Therefore, we have proposed a channel estimation technique using a carrier interferometry (CI) [4]-[7] combined with CDM in the frequency domain to estimate CSI with smaller number of spreading factor [8]. In the CI technique, when the same value of the amplitude and phase are assigned to all the subcarriers in the OFDM signal, its time domain signal corresponds to an impulse having a sinc waveform. Moreover, when a linear phase shift is applied to the signal, timing of the impulse can be delayed as the amount of the linear phase shift is increased. Based on this concept, we have assigned an antenna element specific time shift to each antenna element in each BS. In this scheme, the amount of time shift is designed to be sufficiently large so that the impulse responses for all the antenna elements do not overlap to each other. Moreover, the CI signal is spread in the frequency domain using a BSspecific spreading code to identify the source BS for each delay profile, which is also effective in suppressing peak level of the CI-based pilot signal. In [8], we have proposed the basic concept of the channel estimation technique, and shown some performances in SISO single-cell cases. Therefore, in this paper, we will discuss optimization of the proposed channel estimation technique in MIMO cases under multi-cell (cellular) conditions. Especially, we will focus on the impact of channel estimation error on inter-stream interference for MIMO transmission and impact of co-channel interference on the accuracy of channel estimation. II. DPC-OF/TDMA S YSTEMS Fig. 1 shows a downlink frame structure for the DPCOF/TDMA systems. In this system, a TDMA frame is segmented into 10 time slots in the time domain, each of which includes one frame message slot (FCMS) for a broadcast channel, 8 message data slots (MDSs) for user data transmission and a frame guard without any signal. Moreover, all the subcarriers (1024 subcarriers) in each time slot are segmented
CI symbol duration
TDMA frame (2.5 msec) 1 slot (0.25 msec)
8 slots (2 msec)
frame guard (0.25 msec)
1024 subcarriers =16 subchannels
IFFT
Fig. 1.
FCMS
MDS
MDS
MDS
FCMS
MDS
MDS
MDS
Frequency
64 subcarriers = 1 subchannel
FCMS
MDS
MDS
MDS
FCMS
MDS
MDS
MDS
0
Time
IFFT
Nt
Frequency Nk -2π t Nsub
Linear phase offset
Time
A downlink frame structure for the DPC-OF/TDMA system Fig. 2.
into 16 subchannels every 64 subcarriers. As shown in this figure, whole the radio resource is segmented in both time and frequency domains, thereby this system can support widerange of user rate ranging from the minimum rate achieved by using one time slot in one subchannel to 128 (= 16 subchannels * 8 time slots) times higher rate achieved by dominating whole the radio resource. Therefore, this system can support various types of terminals using the same radio interface. However, in this case, because the minimum control unit assigned to terminals consists of 64 subcarriers and terminals which can use only one subchannel (64 subcarriers) exist in the same system, it is necessary for channel estimation scheme to operate with 64 subcarriers, that is, 1) impulse response estimation 2) cell search process to identify all the connectable BSs must be operated with 64 subcarriers in both SISO and MIMO cases and in single-cell and multi-cell conditions. Moreover, when the terminal employ plural subchannels, 3) flexible channel estimation scheme regardless of the number of subchannels assigned to the terminals is also required. Especially, when a MIMO technique is introduced, we must identify impulse responses for all the combination of the transmit and receive antenna elements. In [8], we have proposed a channel estimation technique based on a CI combined with CDM in the frequency domain and this scheme is evaluated in SISO cases under single-cell conditions. However, this scheme should also be optimized in MIMO cases under multi-cell conditions in which interstream interference from other transmit antenna elements and co-channel interference from adjacent cells exist. Therefore, we will optimize the proposed channel estimation scheme in MIMO cases under multi-cell conditions considering the impact of inter-stream interference and co-channel interference. III. P ROPOSED C HANNEL E STIMATION S CHEME A. Carrier Interferometry Fig. 2 shows a concept of a carrier interferometry (CI) technique. In CI technique, the same amplitudes and phases are assigned to all the subcarriers of an OFDM signal. In this figure, when the number of subcarriers is Nsub and a signal vector which represents an amplitude and phase of each −j2π
Nt
−2π
Nt (Nsub −1)
Nsub Nsub ... e ]T subcarrier is S = [1 e (0 ≤ Nt ≤ Nsub −1), a time-domain CI signal s is represented as s = F H S = [0 . . . 0 Nsub 0 . . . 0]T (1)
ˆt N
A concept of a CI technique
64 subcarriers 1024 subcarriers (64) (64) 1 PN code Linear Phase Offset
Fig. 3.
GI Add.
pilot signal
(64) 1024-point IFFT
A pilot signal generator
where F is a Nsub -point fast Fourier transform (FFT) matrix represented as the following equation : 1 F = √
1 Nsub
1
1 .. . 1
···
−j2π N1·1
e
sub
.. . −j2π
e
(Nsub −1) Nsub
··· .. . ···
1
−j2π
e
(Nsub −1) Nsub
.. .
(2)
(Nsub −1)2 −j2π Nsub
e
Equation (1) means that a CI signal is equivalent to an impulse, thereby we can estimate an impulse response in the receiver. Moreover, according to a time-shift formula of Fourier transform, when a linear phase offset is assigned to each subcarrier, the timing of impulse in time domain can be delayed by l sample period with the amount of a slope of the linear phase offset in frequency domain. When a MIMO technique is introduced, by controlling the timing shift of the impulse, we can multiplex impulse responses from all the transmit antenna elements in each receive antenna element on the time domain without overlapping to each other. In the receiver, each impulse response can be discriminated using a time window with its length of the maximum expected delay time of delayed paths. Therefore, we can identify the CSI for all the combinations of transmit and receive antenna elements. However, because the CI signal has very high peak-toaverage power ratio (PAPR) by concentrating the energy of each subcarrier into a certain sampling point, the CI signal is spread in the frequency domain to reduce the PAPR. Moreover, we employ the spreading code as a BS-specific code for cell search process [9]. Therefore, in the proposed scheme, by employing a CI technique combined with CDM techniques, we can estimate the CSI for all the combination of transmit antenna elements and operate a cell search process with smaller number of spreading factor. B. Pilot Signal Fig. 3 shows a pilot signal generator. In this figure, 64 subcarriers with the same amplitudes and phases are spread with
Transmitter
where νm (k) is the additive white Gaussian noise (AWGN) in mth receive antenna. Next, the obtained signal is converted to the time domain signal again. In this case, because the obtained signal is equivalent to time-domain-multiplexed impulse responses, the ˆ m (n) of nth sampling time is multiplexed impulse responses h obtained as
Tx antenna 1 t
1
Tx antenna 2
Spread
t
)
linear phase offset
Ng maximum delay time of delayed paths
Receiver
t
received pilot signal GI del.
time window
FFT
[0, Ng]
DeSpread
FFT
ˆ m (n) = h
channel state from antenna 1
time window
[Ng, 2Ng]
FFT
channel state from antenna 2
t
) )
impulse responce impulse responce from antenna 2 from antenna 1
Fig. 4.
t
A block diagram for identification of impulse response
a BS-specific pseudo noise (PN) code for cell search process and given antenna specific linear phase offset to this signal to delay the timing of impulse without overlapping to the impulse responses from other transmit antenna elements. After this process, a pilot signal is generated by assigning this signal to all the subchannels for the purpose of that all the terminals can select any subchannel in one subchannel assignment case and flexibly estimate the CSI in plural subchannels assignment case. C. Channel Estimation Scheme 1) Basic Channel Estimation: Fig. 4 shows a block diagram for identification of the CSI combination of the transmit and receive antenna elements where two transmit and one receive antenna case is shown in this figure for simplification of the figure. The most important concept of this scheme is that the obtained impulse responses are multiplexed on the time domain without overlapping and each impulse response is extracted by using a time window with length of the longest expected delayed path of the delayed paths. In the transmitter, when the number of subcarriers is Nsub , the longest delay time of multipaths is Ng sample period, and the number of transmit and receive antenna elements are NT x and NRx , the transmitted signal of kth subcarrier of lth transmit antenna element is given by −j2π
Sl (k) = p(k)e
(l−1)Ng k Nsub
(3)
where p(k) is a complex PN code multiplied to kth subcarrier. In the receiver, after the received signal is converted to the frequency domain signal, the received signal is despread in frequency domain. When the complex channel gain of kth subcarrier between lth transmit antenna element and mth (m = 1, . . . , NRx ) receive antenna is Hm,l (k), the output signal Rm (k) of mth receive antenna element is represented as the following equation: N Tx (l−1)N k p∗ (k) −j2π N g sub Rm (k) = Hm,l (k)e + νm (k) |p(k)|2 l=1
=
N Tx l=1
(despreading process) + νm (k)
(l−1)N k −j2π N g sub
Hm,l (k)e
hl,m (i)δ(n − i − (l − 1)Ng ) + ηm (n) (5)
l=1 i=0
IFFT
t
N I−1 Tx
where I (INg ≤ Nsub ) is the number of multipaths, hm,l (i) is a channel gain of ith path and ηm (n) is AWGN component expressed in the time domain, where E[|νm (k)|2 ] = E[|ηm (n)|2 ] = σ 2 . In this case, if impulse responses are not overlapped (I ≤ Ng ), this means that each impulse response can be discriminated in the time domain. Therefore, each impulse response for all combination of transmit and receive antenna elements can be estimated by extracting it using a time window [(l − 1)Ng , lNg ]. As the result, the estimated impulse ˆ m,l (n) is obtained as response h ˆ m,l (n) = h
I−1
hm,l (i)δ(n − i − (l − 1)Ng ) + ηm,l (n)
(6)
i=0
where ηm,l (n) is AWGN 0 ηm (n) ηm,l (n) = 0
signal represented as (0 ≤ n < (l − 1)Ng ) ((l − 1)Ng ≤ n ≤ lNg ) (lNg < n ≤ Nsub − 1)
(7)
Finally, the estimated impulse response is converted to the frequency transfer function and we can estimate the CSI from each transmit antenna element by compensating for the linear phase offset. 2) Channel Estimation in MIMO cases under Multi-cell Conditions: In MIMO cases under multi-cell conditions, the transmission performances are deteriorated compared to SISO cases under single-cell conditions due to not only inter-stream interference caused by the channel estimation errors but also co-channel interference from adjacent cells. In the proposed scheme, because the pilot signal is shared by all the terminals, we can improve accuracy of the estimated CSI by averaging the estimated CSIs over multiple time slots and the averaged CSI is used for channel compensation in decoding and adaptive modulation and coding control. Fig. 5 shows a concept of the channel estimation improvement. In this figure, when data in pth slot is decoded, channel transfer function measured in consecutive Nslot slots prior to the pth slot are averaged. When the estimated CSI of kth ˆ k) subcarrier in pth slot is Hest (p, k), the averaged CSI H(p, is obtained as ˆ k) = H(p,
1
N slot
Nslot
q=1
Hest ((p − (Nslot − q)), k)
(8)
As a result, we can improve accuracy of the CSI by (4)
10 log10 Nslot
(dB)
(9)
Pilot TPC command RMCS Signal
Pilot Signal
slot #(p-(Nslot-1))
slot #(p-1)
10 µsec
slot #p
Data Subframe (OFDM) 190 µsec (19 OFDM Symbols)
40 µsec
GI Useful Symbol
Hest ((p-(Nslot-1)), k) f
Estimated CSI per slot
t
Hest (p-1, k) Hest (p, k) f f t
Time 2 µsec
Fig. 6.
t
1 OFDM Symbol
8 µsec
A slot format
Averaged f
10
Estimated CSI H(p, k)
0
Fig. 5.
Slot Error Rate
t
A concept of the channel estimation improvement TABLE I S IMULATION PARAMETERS
FEC Target slot error rate Path loss model Path model Maximum Doppler frequency r.m.s. delay spread Shadowing Cell model Cell radius Number of Tx antennas Number of Rx antennas Signal detection scheme BS total Tx power
IV. C OMPUTER S IMULATION A. Simulation Parameters Table I shows simulation parameters. In this simulation, we employ 4-by-4 MIMO scheme and detect data sequence by a maximum likelihood detection (MLD) [11]. Cell radius is 100 m and each cell is sectored into 3, which is wrapped by 6 cells to equivalently simulate co-channel interference for infinitely and continuously covered service area. We assume that terminals are located randomly in the 7-cell area and each terminal searches a BS with the highest received signal power in the downlink. Fig. 6 shows a slot format. A TDMA slot consists of five preamble symbols and 19 data symbols. In the preamble part, the proposed pilot signal is assigned to the first symbol and other 4 symbol is used to notify a transmit power control (TPC) command and requested modulation and coding schemes (RMCS) which is a modulation and coding information for uplink. When adaptive modulation and coding scheme is applied, a terminal estimates the received signal to interference plus noise power ratio (SINR) in each slot and decides a modulation
-1
10
-2
Estimation
0 2 4 6 8 10 Average Received SINR [dB] Fig. 7.
[Mbit/s]
Modulation (Coding rate)
100 ksymbol/s 8 µsec 2 µsec 64 No transmission, BPSK (r = 1/2), BPSK (r = 2/3), BPSK (r = 3/4) QPSK (r = 1/2), QPSK (r = 3/4) QPSK (r = 5/6), QPSK (r = 7/8) Convolutional coding (constraint length K = 7) 0.01 ITU-R M.1225 indoor to outdoor pedestrian test environment [10] Exponentially decaying 8-spike Rayleigh fading model (rich scattering) 20 Hz 200 nsec Log-normal distribution (standard deviation σ = 8 dB) 7-cell wrapping model by 3-sectored cell 100 m 4 4 MLD 18 dBm
Throughput
OFDM symbol rate Useful symbol duration Guard interval duration Number of subcarriers
Perfect
10
Nslot = 1 Nslot = 2 Nslot = 4
Fig. 8.
SER versus received SINR per receive antenna
20 18 16 14 12 10 8 6 4 2 0 -2
Perfect Estimation Nslot = 1 Nslot = 2 Nslot = 4
0 2 4 6 8 10 Average Received SINR [dB]
Throughput versus received SINR per receive antenna
scheme and coding rate according to the estimated SINR. The decided modulation scheme and coding rate is fed back to the BS as a MCS and the BS operates the modulation and coding based on the notified MCS. Because the purpose of this simulation is to evaluate accuracy of the estimated CSI, we assume TPC is not operated, and CSI estimation and RMCS notification are operated perfectly. B. Simulation Results 1) Optimization of Nslot in MIMO cases under CCI conditions: In this section, we optimize a value of Nslot in MIMO cases under CCI conditions. Under CCI conditions, accuracy of the estimated CSI degrades because of the interference plus noise power. Fig. 7 shows the slot error rate (SER) versus the average received SINR per receive antenna in the case of QPSK with its coding rate of 1/2. Fig. 8 shows performances of throughput in data subframe versus the received SINR per receive antenna. In this simulation, the pilot signal power is equal to the data symbol power. The average received SINR is defined as the following
TABLE II Nslot FOR EACH MODULATION PARAMETER Nslot 8 8 8 4 4 2 2
[Mbit/s] Throughput
40 35
20
25
Fig. 9.
Estimation Perfect
10 -2
10
30
-3
0
5
10 15 20 25 30 35 Throughput [Mbit/s]
Fig. 10.
C.D.F. of throughput
Perfect
15 10
Estimation
in the case of smaller spreading factor in MIMO multi-cell conditions.
5 0
0
10 -1 C.D.F.
Modulation parameter BPSK (r = 1/2) BPSK (r = 2/3) BPSK (r = 3/4) QPSK (r = 1/2) QPSK (r = 3/4) QPSK (r = 5/6) QPSK (r = 7/8)
10
-5 0 5 10 15 Average Received SINR [dB]
Throughput versus average received SINR per receive antenna
equation by extending the definition in [11]: SINR = r · M ·
N T x Eb NRx N0 + I0
(10)
where r is a coding rate, M is the number of bits per symbol, NT x and NRx are the number of transmit and receive antenna elements and I0 is energy of interference signal per receive antenna per bit. In these figure, “Perfect” is a performance of the known CSI and “Estimation” is a performance of the proposed scheme. When Nslot is one, the SER performance severely degrade due to impact of channel estimation error. However, when Nslot is four, these degradation can be reduced by about 1 dB because accuracy of the estimated CSI is improved by averaging the CSIs over plural slots. Actually, as shown in Fig. 8, degradation of the throughput is reduced by 1 dB and it is confirmed that the proposed scheme can estimate CSI with sufficient accuracy when the estimated CSIs are averaged over 4 slots. In this case, we decide that the value of Nslot is four in QPSK (r = 1/2) usage case. With the same manner, we have optimized Nslot for all the modulation and coding schemes by using the same way. The results are shown in Table II. 2) Performance under multi-cell condition: First, Fig. 9 shows performances of throughput versus the received SINR per receive antenna element when we employ the proposed CSI estimation scheme. Because larger number of the Nslot is employed as lower modulation level is employed (SINR is getting lower), obtained throughput performance is almost the same in any average received SINR cases. C.D.F. of the throughput is shown in Fig. 10. As shown in Fig. 10, 99% of terminals can achieve more than 10 Mbit/s and we can confirm that the almost all the terminals can perform communication. With these results, it is confirmed that the proposed channel estimation technique can estimate CSI with high accuracy even
V. C ONCLUSION We have proposed a CI-based channel estimation technique for one-cell reuse MIMO-OFDM/TDMA cellular systems. In the proposed scheme, we estimate the CSI for all the combination of transmit and receive antenna elements in the time domain CI signals combined with CDM techniques. The results confirm that the proposed scheme can estimate CSI with high accuracy even with smaller spreading factor for one-cell reuse MIMO-OFDM/TDMA cellular systems. R EFERENCES [1] “Framework and overall objectives of the future development of IMT 2000 and systems beyond IMT 2000,” Rec. ITU-R, M.1645, Jun., 2003. [2] H. Harada, A. Chang-Jun, S. Takahashi, Y. Kamio and S. Sampei, “Dynamic Parameter Controlled - Orthogonal Frequency and Time Division Multiple Access,” Proc. IEEE, PIMRC2004, Vol. 4, pp. 5-8, Barcelona, Spain, Sep. 2004. [3] T. Nakanishi, S. Sampei, H. Harada and N. Morinaga, “An OFDM Based Adaptive Modulation Scheme Employing Variable Coding Rate in Dynamic Parameter Controlled - OF/TDMA Systems,” Proc. WPMC2004, Vol. 3, pp. 34-38, Sep. 2004. [4] B. Natarajan, C. R. Nassar and S. Shatti, “Throughput Enhancement in TDMA through Carrier Interferometry Pulse Shaping,” Proc. IEEE, VTC2000-Fall, Vol. 4, pp. 1799-1803, Boston, USA, Sep. 2000. [5] B. Natarajan, C. R. Nassar and S. Shatti, “Innovative Pulse Shaping for High-Performance Wireless TDMA,” IEEE Communication Letters, Vol. 5, pp. 372-374, Sep. 2001. [6] Z. Wu, C. R. Nassar and S. Shatti, “Ultra Wideband DS-CDMA Via Innovations In Chip Shaping,” Proc. IEEE, VTC2001-Fall, Vol. 4, pp. 2470-2474, Atlantic City, USA, Oct. 2001. [7] P. R. Borbosa, Z. Wu and C. R. Nassar, “High-Performance MIMOOFDM via Carrier Interferometry,” Proc. IEEE, GLOBECOM2003, Vol. 2, pp. 853-857, San Francisco, USA, Dec. 2003. [8] K. Yokomakura, S. Sampei, H. Harada and N. Morinaga, “A Channel Estimation Scheme for Dynamic Parameter Controlled – OF/TDMA Systems,” Proc. IEEE, PIMRC2005, C06-4, Berlin, Germany, Sep. 2005. [9] H. Hatamoto, S. Sampei and H. Harada, “A Study on a Cell Search Method for Dynamic Parameter Controlled OF/TDMA Systems Downlink,” Proc. APWCS2005, Vol. 1, pp. 227-231, Hokkaido, Japan, Aug. 2005. [10] “Guidance for Evaluation of Radio Transmission for IMT-2000,” Rec. ITU-R M. 1225, 1997. [11] R. Van Nee, A. Van Zelst and G. A. Awater, “Maximum Likelihood Decoding in a Space Division Multiplexing System,” Proc. IEEE VTC2000Spring, pp. 6-10, Tokyo, Japan, May, 2000.
