This application is the U.S. national phase of International Application No. PCT/US2006/035128, filed 11 Sep. 2006, the entire contents of which is hereby incorporated by reference.
The technology described herein is related a method for detecting a time-frequency hopping pattern in OFDM (Orthogonal Frequency Division Multiplexing) systems.
Conventionally, a wireless communication channel in a single-carrier transmission system is modeled as having a time-varying impulse response g(τ,t), which may be frequency selective for any given time t due to multipaths. The channel's frequency selectivity can be estimated by observing the known pilot signal transmitted during the time of interest whereas the time selectivity is usually tracked by observing multiples of these periodically inserted known signals.
However, in a land mobile communication environment, the channel's selectivity is mainly caused by movement of the terminal. For as long as the velocity of the movement remains constant, the channel can be modeled by a time-invariant delay-Doppler response h(τ,ν), which represents the complex-valued channel gain of the scatterer incurring a delay τ and Doppler shift ν to the incoming signal. For various reasons, this fact has been used mostly as a design limitation on the frequency of pilot insertion in the time domain to avoid aliasing. Slightly more sophisticated usage of the Doppler information may be found in the channel tracking related filter design that requires the estimation of the channel's Doppler spread.
A time-frequency hopping pattern is a signal whose frequency contents change as a function of time, either periodically or non-periodically, in a specific manner. Time-frequency hopping signals have been used in many communication and radar applications. Recently, due to popular adaptation of Orthogonal Frequency Division Multiplexing (OFDM) as the multiple technology in future wireless communication systems, the potential for using them as synchronization signals has been under extensive investigation. Since an OFDM system essentially divides the radio resource into orthogonal time-frequency units, it is natural to design synchronization signals that conform with existing time-frequency partition.
In an OFDM system, pilot symbols are placed periodically in the time-frequency plane for channel estimation.
Each cell in the array represents one of the Nfft sub-carriers in an OFDM symbol, which has an interval of Ts sec. including Tcp sec. of cyclic prefix. Thus, the sub-carrier spacing is fs=1/(Ts−Tcp) Hz. For the original regularly spaced pattern, one pilot symbol is inserted every N OFDM symbols in the time domain, i.e. Tp=NTs and every M sub-carriers in the frequency domain, i.e. fp=Mfs. Each pattern may have a sub-carrier offset index 0≦φ≦M with respect to the first sub-carrier.
Any pilot pattern may be specified by a two-dimensional time-frequency array whose element C[n, m] is the complex value of the pilot symbol transmitted on the m'th sub-carrier in the n'th OFDM symbol. Unless otherwise stated, C[n,m] is “1” if a pilot symbol is present and “0” if not. The corresponding continuous-time signal of the pilot pattern over Q time domain periods can be expressed as a sequence of OFDM symbols by:
is the n'th OFDM symbol that further consists of a sequence cn[i] modulating the transmit filter pulsing function μ(t). Ignoring cyclic prefix, the pilot pattern's time-frequency array representation C[n,m] is related to the discrete-time sequence cn[i] by
To demodulate the data symbols in an OFDM system, the receiver needs to know the channel's time-frequency response H(t,f), which is the two-dimensional Fourier transform of the delay-Doppler response h(τ,ν). If sufficient numbers of the base pilot signals are observed over time and frequency, the output of the delay-Doppler correlator is a good approximation of the delay-Doppler response.
The channel is modeled as having a delay-Doppler response h(τ,ν), which represents the complex-valued channel gain of the scatterer incurring a delay τ and Doppler shift ν to the incoming signal. Assuming that the radio environment consists of a continuum of scatterers (or “targets”), each introduces a certain delay and Doppler shift to the signal propagating through it, the received signal corresponding to the pilot is given then by:
r(t)=∫ν
where z(t) is the Additive White Gaussian Noise (AWGN), τ0 and ν0 are the initial timing and frequency offset respectively, and
are the maximum delay and Doppler spread of the channel that are smaller than or equal to the values that can be supported by the pilot's density without aliasing.
Present detectors normally use correlator matched to hypothesized signals and then find peaks and compare them with a certain threshold to determine the presence of the pilot signals. This correlation process may be too computationally complex especially when there are a large number of potential hypotheses.
A known system that uses time-frequency hopping patterns as synchronization signals is disclosed in U.S. Pat. No. 6,961,364 B1, by Laroia et al. Different base stations use patterns with different slopes, and the detection algorithm is a maximum energy detector.
An object with the technology described herein is to provide an apparatus and a method for detecting a pilot pattern in an OFDM system with less complex computing compared to prior art.
An additional object is to provide a method for synchronizing and detecting communication devices in a communication network.
The technology described herein solves the problem of detecting time-frequency hopping patterns of a pilot signal that meets a certain criteria in an optimal sense. By applying the principle of Generalized Likelihood Ratio Test (GLRT), the technology described herein provides an optimal likelihood measure for a given hypothesis on the pilot pattern and its time-frequency offset. Based on this likelihood measure, the detection and synchronization of a pilot pattern can be achieved.
An advantage with the technology described herein is that it may be used in an initial synchronization of an OFDM system that uses different time-frequency hopping patterns to identify different devices.
A further advantage is that though the optimal likelihood metric is the integral of the energy at the output of a two-dimensional delay-Doppler correlator, it is not necessary to actually perform the two-dimensional delay-Doppler correlation.
An advantage with an example embodiment of the technology described herein, when pilot patterns are used by a multiple of devices that share a common structure, such as circularly shifted patterns, is that the computation of their likelihood metrics may further be simplified.
An objective with the technology described herein is to detect the presence of a pilot signal, and subsequently achieve a coarse initial time-frequency synchronization by estimating the unknown parameters (τ0,ν0).
Assuming that the initial synchronization has been achieved and thus (τ0,ν0) are known, the Maximum Likelihood (ML) estimation of the channel ĥML(τ,ν), for properly designed pilot signals such as those described above, defined over a range of (τ0≦τ≦τ0+τmax,ν0≦ν≦ν0+νmax) is related to the two-dimensional delay-Doppler image I(τ,ν) by:
where Es=∫|sp(t)|2dt is the energy of the pilot signal over the unspecified observation interval and
χsp(τ,ν)∫|sp(t)s*p(t−τ)e−2πνtdt
is the ambiguity function of the pilot signal sp(t).
In the initial synchronization phase, the initial time-frequency offset (τ0,ν0) is unknown and thus needs to be hypothesized. Therefore, the detection of the presence of a pilot pattern and subsequently the identification of the device transmitting it involves the search over the hypotheses space specified by the pilot signal sp(t) and the initial time-frequency offset (τ0,ν0).
First, start by conditioning on a given hypothesis of sp(t) and thereafter define a likelihood measure for the hypothesis that the channel's delay-Doppler response begins at (τ0,ν0) in the delay-Doppler plane and extends over the range of its maximum delay-Doppler spread. Since the channel response h(τ,ν) is not known, it is a nuisance variable that needs to be removed. This may be accomplished by applying the same method used in the Generalized Likelihood Ratio Test (GLRT). The first step is to estimate the nuisance variable h(τ,ν) assuming the hypothesis (τ0,ν0) is correct. From Eq (6), this is given by:
for τ0≦τ≦τ0+τmax and ν0≦ν≦ν0+νmax. The next step is to replace the true channel response with its estimate in the log-likelihood function:
Λ(τ0,ν0)=−∫|r(t)−∫ν
After rearranging and removing irrelevant terms, the log-likelihood function for the hypothesis becomes:
In the generalized Maximum Likelihood sense, the optimal likelihood measure for such hypothesis is thus given by Equation (10), where I(τ,ν) is the delay-Doppler image observed by the hypothesized signal sp(t).
The detection of the pilot pattern is then to evaluate and compare the values of this log-likelihood function among possible hypotheses of sp(t) and (τ0,ν0) or to compare them with a certain threshold.
Direct evaluation of Λ(τ0,ν0) from Eq. (10) is simply to perform a delay-Doppler correlation and then integrate the energy over the hypothesized range (which is the same as the hypothesis space). Although the formula is valid for all patterns that satisfy the Nyquist criterion (and can therefore be used for channel estimation), the complexity of such direct computation may be excessive for a large number of possible hypotheses that the many devices may assume. Fortunately, as will be seen in the following sections, there are alternative methods for computing the log-likelihood function without explicitly evaluating the delay-Doppler image I(τ,ν). Even greater reduction in complexity is possible if the potential pilot patterns share a certain common structure.
Discrete Implementation of the Detector
As given in Eq. (10), the generalized log-likelihood function Λ(τ0,ν0) is simply the integration over the hypothesized range of the energy of the channel's delay-Doppler image I(τ,ν), whose discrete approximation over the range (0≦τ≦τmax,0≦ν≦νmax) may be derived. The time-frequency shifted version of the delay-Doppler image may be expressed as:
is a time-frequency shifted version of the received signal. Since only the amplitude of the delay-Doppler image in the detection phase is of concern, the exponential term before the integral in Eq. (11) is moved to the other side of the equation and a new function for the channel's delay-Doppler image is defined:
where the subscripts τ0,ν0 indicate its dependence on the initial time-frequency offset hypothesis. The integral in Eq. (13) can be expressed by its discrete sums as:
where Iτ
is the output of a receive filter with a time offset τ0 and frequency offset ν0 sampled at the chip rate of 1/Tc Hz, as shown in
Discrete Frequency Domain Implementation
As stated in Eq. (5), the channel's maximum delay-Doppler spread (τmax,νmax) should not exceed the values that can be supported by the pilot's density. For the case where the integration in Eq. (10) is carried over the maximum values supported by the pilot's density, the integration can be approximated by the discrete sums of its sampled version over the range of (0≦k≦K=Nfft/M,0≦l<Q) and is given by:
The integration over a range greater than (τmax,νmax) may degrade the detection performance since unwanted noise is introduced. However, the detection complexity may be significantly reduced as will be described in the following. The detector can then trade off between performance and complexity according to the operating requirement and equipment capability.
When positioned properly within the cyclic prefix window, the convolution in the last part of Eq. (14) becomes circular and can thus be evaluated in the frequency domain using DFT (Discrete Fourier Transform):
is the length—Nfft DFT of the critically sampled receive signal corresponding to the n'th OFDM symbol and
is the discrete frequency domain representation of the pilot signal in the n'th OFDM symbol. Using
Note that C[n, m] is also non-zero only when a pilot symbol is transmitted on the m'th sub-carrier in the n'th OFDM symbol. Also note that if |C(n,m)| is constant for all (non-zero) pilot symbols and if C(n,m) forms lines of constant slope in the time-frequency plane, this embodiment is reduced to the prior art given in U.S. Pat. No. 6,961,364 B1 mentioned in the background section.
Further, note that {tilde over (R)}[n.m] can be calculated using zero-padding DFT for all possible hypotheses of the initial frequency offset ν0 if they are regularly spaced at properly chosen interval. For a particular hypothesis of τ0 for example, a length—LNfft sequence
ττ
with zero-padding will yield a length—LNfft sequence in which the sub-sequence with indices φ,φ+L, . . . , φ+L(Nfft−1) corresponds to Rτ
For regularly spaced pilot patterns or their hopping variants described above, the log-likelihood function for a given initial time-frequency offset hypothesis can be evaluated in the discrete frequency domain by:
which is simply the sum of received signal energy over the locations of the hypothesized pilot pattern on the discrete time-frequency plane.
Discrete Time Domain Implementation
In some cases, it may be more effective to calculate the log-likelihood metric directly in the time domain. For the pilot patterns described in connection with
where K=Nfft/M.
Substituting Rτ
which involves the circular self correlation of the received samples at lags that are multiples of K and a few DFT of shorter length.
Special Cases of Circularly Shifted Patterns
The detection of multiple hopping patterns may be greatly simplified if they have a certain common structure. For example, by assigning circularly shifted patterns to different devices. The detection can then be achieved by using a two-dimensional circular correlator matched to the base pattern's time-frequency map.
In addition to the artificially introduced circular shift, a pilot pattern may also have a local scale time-frequency offset, due to a variety of reasons, with respect to other references such as patterns from a different base station or the sampling point of a particular terminal. This initial offset is essentially the same as, and is denoted by (τ0,ν0) above except for the constraints that they are in the range of 0≦τ0<Ts and 0≦ν0<fs. Any offset outside this range will be folded into the indices η, μ and φ. The detection of a circularly shifted pilot pattern is then to determine its presence at a hypothesized time-frequency coordinate:
τp=ηTs+τ0
ν0=(μM+φ)fs+ν0 (24)
as shown in magnified scale in
To demonstrate how the two-dimensional correlator may be implemented, we consider an example that is simplified from the one shown in
Once the input array is set, the pattern search begins. First, the Costas array is expressed as a frequency-hopping sequence {0,2,1,4,5,3,x} of the symbol index, where “x” indicates a symbol with no pilot sub-carrier. This sequence is placed under the input array for visual illustration purpose. In a first step, the columns of the array are circularly rotated in row (sub-carrier) index by an amount corresponding to this hopping sequence, as illustrated in the second matrix 52 in
The correlation process illustrated in
The procedure for the sliding window embodiment of this special case is briefly described below. The off-line embodiment described above is the same except that the data is already loaded into the buffer and therefore the data acquisition steps can be omitted.
To evaluate the performance of the detector, Costas array pilot patterns 60 described in
Table 1 shows the parameters of the two specific arrays that are simulated. To make a fair comparison, both have a pilot density of approximately 1/256. The OFDM symbol FFT size for length—16 array is 1024. Since M=16 in this case, the maximum delay spread that can be supported is 1024/16=64 chips, which is set at the length of the cyclic prefix. The OFDM symbol FFT size for length—30 array is 512, i.e. the OFDM symbol interval is half of that in the first array. The cyclic prefix length, however, remains the same so that same maximum delay spread can be accommodated.
For each realization of the channel in the simulation, an initial random time-frequency offset (τ0,ν0) uniformly distributed within the intervals of [0, Ts) and [0,fs/2) is introduced. The correlator makes a single hypothesis of (τ0,ν0)=(0,0) when forming the input array. This corresponds to coarsely searching for the pilot patterns at an interval of Ts sec. in the time domain and fs Hz in the frequency domain. A successful detection is declared if the true target is within one symbol and one sub-carrier on either side of the detected location. After the cell identification and coarse synchronization are achieved, further refined search to establish the boundaries of the channel's delay-Doppler response can follow. This may involve some simple interpolation in case of good SNR (Signal to Noise Ratio), or coherent DFT otherwise.
Table 2 lists a few other parameters common to all simulations. Two Power Delay-Doppler Profiles are simulated. The “Flat” channel is simply a random realization of a zero-mean Gaussian variable and the “Full” channel has a “Case3×Bessel” profile that spread the entire maximum delay-Doppler region. They represent the two extremes of the channel's diversity order. The actual performance in practice should lie somewhere in between. Unless otherwise stated, the number of periods observed over time is 1.
The second set of simulations involves two cells that have the same sub-carrier offset φ=0. The first cell is located at (η,μ)=(0,0) with an average SNR of 0 dB whereas the second cell is located at (η,μ)=(6,7) with variable signal power with respect to the first cell. In addition to the offset at the receiver mentioned earlier, the two cells have a random relative time-frequency offset that is similarly distributed. A successful detection is declared in the two-cell simulation only when both cells are successfully detected. This happens when the location of the two largest metrics in the correlation output array correspond to those of the two targets.
P1 and P2 in
The performance in “Full” channel,
In addition to observing more pilot periods, the detection performance may be further improved by a few other measures or under certain conditions. For example, the number of hypotheses of the initial time-frequency offset (τ0,ν0) may be increased. This effectively increases the search density and therefore the chances of finding the peaks of the log-likelihood function at the cost of computational complexity. A network planner may also impose constraints on the identification index (η,μ,φ) to reduce the false alarm rate. Finally, the initial time-frequency offsets among the cells and terminals are most likely more than those assumed in the simulation. The symbol-alignment among the cells in particular, a basic assumption in an OFDM system, should reduce the interference quite significantly.
A detector performing the described method for detecting a pilot pattern may naturally be implemented in a node of a communication system, such as a base station, mobile telephone, or any other type of wireless communication device. The method is preferably implemented as software code stored in a memory unit, and executed by a processing device.
The underlying design principle has been to conform with the orthogonal time-frequency division format inherent in an OFDM system, thereby leading to receiver algorithms that involve mostly conversions between time and frequency (or delay and Doppler) domains preferably using DFT. Since demodulation of data symbols is also accomplished with DFT, a dedicated and flexible hardware DFT accelerator can handle almost all the computations in receiving data bits from a modem.
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PCT/US2006/035128 | 9/11/2006 | WO | 00 | 2/24/2009 |
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WO2008/003117 | 3/20/2008 | WO | A |
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