1. Field of the Invention
The invention relates to a device and method to generate a signal, transmit the signal, and detect the signal in communication systems.
2. Description of the Related Art
Signal detection is an essential task in communication systems. Usually a preamble signal is transmitted at the beginning of a message for signal detection and parameter estimation. The preamble signal is also known as unique word. Design of preamble signals is equivalent to design sequences. The preamble signal is preferred to have favorable correlation properties, good family size, and high linear complexity. The same preamble signal is stored in a receiver. The receiver takes an input signal, performs correlation with the pre-stored preamble signal, and compares the correlation against a threshold. If the correlation is not below the threshold, the receiver declares that the signal is detected, and uses the received signal and the correlation for parameter estimation, including timing, phase and frequency.
Traditionally, the receiver has to compute the full correlation of the received signal with the entire preamble signal. An example is to transmit a maximal length sequence, and detect it by computing sample by sample a full correlation of the received signal with the pre-stored signal. Consequently, the complexity of signal detection is high. In traditional broadband communication terminals, the signal detector has to compute a significant amount of multiplications, which always claims a large portion of the receiver hardware, and is an expensive module in the receiver. Some signal detection demands too many multiplications to be supported by even the fastest hardware available on the market.
New signals with favorable features for detection must be designed to minimize signal detection complexity and reduce signal detector cost, while maintaining the same detection performance compared with traditional signal design and detection methods.
In 1968, Turyn very briefly introduced the tensor product of two sequences as a way of shifting the definition of sequences to a different group. The tensor product is also known as the Kronecker product of two sequences or simply a Kronecker sequence. Turyn did not give any hardware design to work with the Kronecker sequence.
In 1991, Karkkainen and Leppanen compared the performance of asynchronous CDMA systems using Kronecker sequences against the performance of communication systems using conventional sequences such as Gold, Kasami and maximal length sequences. They considered applying the Kronecker product to two component sequences, including: a Barker sequence with length 11 and M-sequence with length 127; a Gold sequence with length 31 and Gold sequence with length 63; a Barker sequence with length 11 and Gold sequence with length 127; and a Barker sequence with length 11 and a small Kasami sequence of length 63. The receiver first correlates the received signal to find the right phase of the first sequence. After detection and synchronization is achieved for the first sequence, the inner sequence (the first sequence) will be locked to the received signal, and then the second correlator will begin to search for the right phase of the outer sequence (the second sequence). The success of such an acquisition scheme for Kronecker sequences depends upon the receiver input SNR being large enough at Eb/N0≦15 dB to permit reliable detection of the in-phase peak in the auto-correlation for the first component sequence (the inner sequence). This design requires the length of the first sequence to be large, the system SNR to be high, and the length of the second sequence to be short. Authors have stated that the choice of the inner component sequence seems not to be so critical, and that the outer component sequence (the second sequence) must be considerably shorter than the inner sequence. The Barker sequence as an outer sequence is a very good choice. However, the Barker sequences are not recommended for the inner sequence. The study showed that Kronecker sequences cause 0.5 dB to 1.0 dB degradation in SNR at Eb/N0=15 dB in asynchronous CDMA systems. Such a loss is considered large in communication systems.
In 1997, Elders-Boll et al. investigated the sequence acquisition in communication systems with sequences constructed using the Turyn method. It was shown that the sequences constructed using the Turyn method gave 50% reduction in the sequence acquisition time compared with the maximal length sequences of the same length. However, the acquisition considered only the inner sequence, not the outer sequence, and gave a very large performance loss.
In 1999, Liu gave an example of a 3-level sequence. Liu independently proposed to detect the 3-level sequence employing three correlators and three signal detectors for every level of signal, respectively. The strength of Liu's method is the reduction of the hardware complexity of signal detector. Its weakness was that it required signal detection to be performed at every level, because if the signal detector at any level i=3 misses a component signal, then the entire signal is not detected.
In summary, previous methods of signal design and detection have employed correlation of the entire signal where the correlation is computed sample-by-sample for the received signal and the prestored entire signal. They follow the traditional theory and practice that correlation must be computed sample-by-sample for the whole signal. In designing Kronecker sequences, prior work considered only two component sequences. In detecting Kronecker sequences, the prior art emphasized detection only of the inner component sequence. If the inner component sequence is not detected, then the prior art definitely fails. Because the inner sequence contains only a fraction of the energy of the entire Kronecker sequence, the prior art has a high probability of missing the Kronecker sequence in the detection stage.
A signal detector for detecting a received signal includes an in-phase input port for receiving an in-phase received signal and a quadrature input port for receiving a quadrature received signal. An in-phase correlation device is electrically connected to the in-phase input port and a quadrature correlation device is electrically connected to the quadrature input port. A memory device is electrically connected to the in-phase and quadrature correlation devices. The memory device stores a key to be supplied to both the in-phase and quadrature correlation devices as an operand to the in-phase and quadrature received signals. The correlation creates an in-phase correlation signal and a quadrature correlation signal, respectively. An in-phase squaring device squares the in-phase correlation signal to generate an in-phase squared signal. A quadrature squaring device squares the quadrature correlation signal to generate a quadrature squared signal. An adder adds the in-phase squared signal and the quadrature squared signal to create an added signal. A comparator to compare the added signal against a threshold to determine whether the received signal is a communication signal.
Advantages of the invention will be readily appreciated as the same becomes better understood by reference to the following detailed description when considered in connection with the accompanying drawings, wherein:
Signal Generation
Referring to
In
In the area of telecommunications, signal design is also called sequence design. In a communication system, a signal for detection and estimation is generated through filtering a sequence with a pulse shaping function. The signal can be written as a convolution of the sequence with the pulse shaping function. The correlation function of the signal is determined by the correlation function of the sequence and the pulse shaping function. Because the pulse shaping function is deterministic, designing a signal is equivalent to designing a sequence.
A multiple level sequence of M≧2 levels can be constructed by applying the Kronecker product to M component sequences. We use the Kronecker product to define multiple level sequences. The construction of multiple level sequences is a generalization of the prior work by the inventor. Methods to detect multiple level signals are provided below.
Let A=(aij) be an m×k matrix and B=(bij) an n×1 matrix. The Kronecker product of A and B is denoted by AB and defined as follows:
A two level sequence is a Kronecker product of two sequences.
Let A={ai}i−1m and B={bi}i=1n be two sequences viewed as two vectors with dimension 1×m and 1×n, respectively. Then AB is called a multiple level sequence with 2 levels, or a 2-level sequence. A={ai}i−1m is called the seed sequence and B={bi}i−1n is the mapping sequence.
One has
{ai}i−1m{bi}i−1n=(a1b1, a2b1, . . . amb1, a1b2, a2b2, . . . , amb2, . . . , a1bn−1, a2bn−1, . . . , ambn−1, a1bn, a2bn, . . . , ambn) (2)
The following associate property of the Kronecker product operator is needed to define multiple level sequence with M>2 levels.
Let A=(aij) be an m1×n1 matrix, B=(bij) an m1×n2 matrix, and C=(cij) an m3×n3 matrix. Then, (AB)C=A(BC).
We define a multiple level sequence with two or more levels.
Let Ai={aij}j−1L
wherein A1A2 . . . AM is called multiple level sequence with M levels and length
The sequences Ai with 1≦i≦M are called component sequences. If a multiple level sequence is to be employed for signal detection, the lengths of component sequences are recommended to be non-decreasing, i.e.,
L
1
≦L
2
. . . L
M. (4)
An exception can occur when the auto-correlation sidelobe of a sequence with length Li is lower than the auto-correlation sidelobe of another sequence with length Lj>Li. When a multiple level sequence is employed for applications other than signal detection, there is no need to follow Eq. (4).
The construction of multiple level sequences is defined on M groups. One can view any partial product
with 1≦l<M as a seed sequence and the remaining
as a mapping sequence.
Turyn's tensor product sequence or Kronecker sequence was a 2-level sequence. Prior studies considered only 2-level sequences. They all are special cases of the multiple level sequences in the current invention. Multiple level sequences are ideal for signal detection. They are also good for many other applications.
Defined on M groups, the multiple level sequences are very rich. A full list of multiple level sequences can easily take hundreds of pages. Some examples are provided to illustrate the construction.
The following definition follows Golay's definition on skew-symmetric sequences.
Let {ai}i−1m be a sequence of odd length m=2n+1. The sequence {ai}i=1m is called skew symmetric, if
a
n+i=(−1)ian−i for any i, wherein 0≦i≦n−1. (5)
A fact about the skew symmetric sequences is that their aperiodic autocorrelation values vanish with odd shifts. For a skew symmetric sequence {ai}i=1m with length m=2n+1, its aperiodic autocorrelation with odd shift is 0.
Continuing with the Golay definition given above, a design example is provided.
In a broadband communications network, it is required to construct a family of sequences of length 108 and having a family size 16 for detection. Here, we use the concept of 2-level sequences to find a family of 16 sequences with small cross-correlation values.
There exists a family of skew symmetric sequences serving as the mapping sequence family with the following parameters: The maximum absolute value of the out-phase aperiodic autocorrelation function is 3, and the maximum absolute value of the cross-correlation function is 5. With these parameters, a family of 4 sequences are found as mapping sequences:
(1, 1, 1, 1, −1, −1, 1, −1, 1)
(−1, 1, 1, 1,−1,−1, 1, −1, −1)
(−1, 1, −1, 1, 1, −1, −1, −1, −1)
(−1, 1, −1, −1, 1, 1, −1, −1, −1)
The family of seed sequences can be constructed as:
(1, 1, 1, −1, 1, 1, −1, 1, −1, 1, 1, 1)
(1, −1, −1, −1, 1, 1, 1, 1, 1, −1, 1, −1)
(1, −1, −1, −1, −1, 1, −1, −1, 1, 1, −1, 1)
(−1, −1, 1, −1, 1, 1, 1, −1, −1, 1, 1, 1)
With these seed sequences and mapping sequences, 16 sequences with length 108 can be constructed as 2-levels sequences. ▪
Moving beyond Example 1, we recommend that skew-symmetric sequences are good candidates for seed sequences. One special family of skew-symmetric sequences that is the best for seed sequences are the Barker sequences.
A binary sequence {ai}i−m is called a Barker sequence, if the absolute value of its off-peak aperiodic autocorrelation function is bounded by 1, i.e.,
|A(τ)|=∥Σi−1m−τaiai+Σ*|≦1 for any ∀τε[1,m−1]. (6)
Only seven binary Barker sequences have been found, as listed in Table 1.
Turyn and Storer proved that there are no other odd length Barker sequences beyond length 13. It is widely believed that no other even length Barker sequences exist beyond length 4.
Barker sequences have the best overall aperiodic autocorrelation. This invention recommends Barker sequences as good candidates for both seed sequences and mapping sequences. Employing Barker sequences as both seed sequence and mapping sequence is the best choice.
For the purpose of signal detection, when the signal length L is not higher than 13, Barker sequences are the best. When the signal length is greater than 13, 2-level sequences can be constructed employing Barker sequences as seed sequence and mapping sequence. This will cover the length up to 169=13×13. Any combination BL
For example, a maximum length sequence M31 with length 31 as the seed sequence and the Barker sequence with length 13 will give a 2-level sequence M31 B13 of length 403=31×13. A three-level sequence employing the Barker sequence with length 3, the Barker sequence with length 11 and the Barker sequence with length 13 will provide a new sequence B3B11B13 with length 429. The signal detection performance using B3B11B13 is a little better than using the 2-level sequence, M31B13. The signal detector complexity using B3B11B13 can be only 27/44=61% of the complexity using M31B13. B7B11B13 gives a signal of length 1001. B7B11B11B13 is a signal of length 11011. B7B7B11B11B13B13 gives a sequence of length 1002001.
Signal Transmission
Let
be a multiple level sequence for signal detection and parameter estimation. Usually a payload follows the sequence.
In
where p(t) is the pulse shaping function and T the symbol time. The mixer 50 takes the baseband signal sb(t) and a carrier signal cos(ωct+φ) as input signals, multiplies them to give a signal
s(t)=sb(t)cos(ωct+φ) (8)
where ωc is the carrier frequency and φ the carrier phase. The transmitter 14 sends this signal through the channel 16 to the receiver 18.
Signal Detection
Referring to
r(t)=s(t)+n(t) (9)
through an input port 51 wherein n(t) is the additive white Gaussian noise. The input port 51 has a first branch 53 and a second branch 55. The received signal r(t) is transmitted equally along both the first 53 and second 55 branches.
A local oscillator 52 generates a cosine wave cos [ωL(t+δ)] and a sine wave sin [ωL(t+ε)], where ωL is the frequency of the local oscillator and δ is the timing offset. Let φL=ωLδ. The cosine wave signal is called an in-phase wave signal and the sine wave signal is called a quadrature wave signal. An in-phase channel mixer 54 mixes the received signal with the in-phase wave signal. The in-phase channel mixer 54 output signal is referred to as an in-phase received signal and is defined by
r
1(t)=r(t)cos(Δωt+Δφ)+n1(t) (10)
An in-phase low pass filter 56 removes the component at ωc+ωL from the in-phase received signal. The output of the in-phase low pass filter 56 is in the baseband, is referred to as the in-phase low pass received signal and can be written as
r
2(t)=½sb(t)cos(Δωt+Δφ)+n1(t) (11)
where Δω=ωc−ωL is the frequency offset between the transmitter oscillator and the local oscillator 52 in the receiver 18, Δφ=φ−φL is the phase difference, and n1(t) is Gaussian noise process.
An analog-to-digital converter (ADC) 58 converts the in-phase low pass received signal r2(t) to a digital in-phase received signal r1[k] at sampling rate Ns≧2 samples per symbol.
A quadrature channel mixer 60 mixes the received signal r(t) with the quadrature wave signal, e.g., the sine wave sin [ωL(t+δ)], from the local oscillator 52 and gives the quadrature received signal and can be written as
r
3(t)=r(t)sin(ωLt+φL) (12)
A low pass filter 62 for the quadrature channel removes the component at ωc+ωL in the quadrature received signal. The output signal of the low pass filter 62 is referred to as the quadrature low pass received signal and may be written as 1.
r
4(t)=−½sb(t)sin(Δωt+Δφ)+nQ(t) (13)
where nQ(t) is Gaussian noise process. n1(t) and nQ(t) are independent identically distributed. An ADC 64 converts the signal r4(t) to a digital signal rQ[k] at sampling rate Ns≧2 samples per symbol.
A signal detector 66 receives the signals from both channels and tries to detect a communications signal sb(t). If the communications signal sb(t) is detected, the signal detector 66 triggers a parameter estimator 67, and a demodulator 74 to demodulate the communications signal sb(t) using outputs 71, 73, 75, 77, discussed in greater detail subsequently. The parameter estimator 67 includes a phase estimator 68 to estimate phase, a timing estimator 70 to estimate timing offset, and a frequency estimator 72 to estimate frequency offset.
Traditional signal detectors need four correlators to perform four correlations for II, IQ, QI and QQ. The invention employs two correlation devices, one for the in-phase channel and the other for the quadrature channel. Comparing against traditional signal detectors, the complexity of the inventive signal detector 66 reduces signal detection complexity by at least 50%. Three signal detector architectures are provided in the following to represent the spirit of the invention.
Signal Detector 1
In some systems, the noise level changes both spatially and temporally. For such a system, a varying threshold can be employed to maintain a constant probability of false alarm, which is known as constant false alarm rate (CFAR) detection. A typical implementation of CFAR detection is to divide the output of the adder (186 in
The phase estimator 68 takes the output signals of the in-phase correlation device 178 and the quadrature correlation device 182 to estimate phase offset. The phase estimator 68 provides a phase estimate as:
where C1[k] is the output signal of the in-phase correlation device 178 and CQ[k] is the output signal of the quadrature correlation device 182. The phase estimate is employed by the demodulator 74 for demodulation and phase synchronization.
The timing estimator 70 takes the output of the adder 186 and searches for a peak in it. Once a peak is found, the timing estimator employs three values in the adder output signal, they being the peak value, the value right before the peak and the value right after the peak, to perform interpolation and timing offset estimate. The timing offset estimate {circumflex over (δ)} is employed to advance or delay the sampling clock and achieve the best time synchronization.
The frequency estimator 72 takes the in-phase channel ADC 58 output signal r1 and the quadrature channel ADC 64 output signal rQ, performs matched filtering, converts output signals of matched filters to continuous wave (CW) signals, applies discrete Fourier transform (DFT) to the CW signals, and estimates the frequency of CW signals as the frequency offset. The frequency estimator 72 output is an estimate to the frequency offset Δω=ωc−ωL. This estimate is employed by the demodulator 74 for demodulation and frequency synchronization.
Recall the signal sb(t) is L symbols long, and the sampling rate is Ns≧2 samples per symbol. The correlation devices 178, 182 need to compute 2NsL multiplications for every received signal sample. When L is large, the computational complexity can be high.
Signal Detector 2
The in-phase correlation device 278 output signal is squared by the squaring device 280. The output signal of the quadrature correlation device 282 is squared by the squaring device 284. The output signals of squaring devices 280, 284 are summed together by the adder 286. A comparator 288 compares the output signal of the adder against a threshold. An option is to take the square root of the adder output signal, and then compare the square root against a threshold. When the adder output signal is greater than the threshold, the signal detector 266 declares the signal has been detected. The signal detector 266 triggers the timing estimator 70, the phase estimator 68, the frequency estimator 72, and the demodulator 74. The correlation devices 278, 282 may be either correlators or matched filters which can give the same performance.
The phase estimator 68 takes the output signal C1[k] of the in-phase correlation device 278 and the output signal CQ[k] of the quadrature correlation device 282 to estimate phase offset. The phase estimator 68 provides a phase estimate as defined in Eq. (14). The phase estimate is employed by the demodulator 74 for demodulation and phase synchronization.
The timing estimator 70 takes the output of the adder 286 and searches for a peak in it. Once a peak is found, the timing estimator employs three values in the adder output signal; they being the peak value, the value right before the peak and the value right after the peak, to perform interpolation and timing offset estimate. The timing offset estimate {circumflex over (δ)} is employed to advance or delay the sampling clock and achieve the best time synchronization.
The frequency estimator 72 takes the output signal of the matched filter 290 and the output signal of the matched filter 296, converts these signals to CW signals, applies discrete Fourier transform (DFT) to the CW signals, and estimates the frequency of the CW signals as the frequency offset. The frequency estimator 72 output is an estimate to the frequency offset Δω=ωc−ωL. This estimate is employed by the demodulator 74 for demodulation and frequency synchronization.
In this signal detector architecture, 2(Ns+L) multiplications are needed to compute correlation for every sample of the received signal.
Signal Detector 3
When the signal to be detected is a multiple level signal A1A2 . . . AM, the correlation devices 3781, 3782, 378M, 3821, 3822, 382M and the signal detector 366 have a different architecture, as is shown in
The output signal of the in-phase correlation device 378M is squared by the squaring device 380. The output signal of the quadrature correlation device 382M is squared by the squaring device 384. The output signals of squaring devices 380, 384 are summed together by the adder 386. A comparator 388 compares the output signal of the adder against a threshold. When the output signal of the adder 388 is greater than the threshold, the signal detector 366 declares the signal has been detected. The signal detector 366 triggers the timing estimator 70, the phase estimator 68, the frequency estimator 72, and the demodulator 74. The correlation devices 3781, 3782, 378M, 3821, 3822, 382M can be either correlators or matched filters, which can give the same performance.
An option is to take the square root of the adder output signal, and then compare the square root against a threshold.
The phase estimator 68 takes the output signal c[k] of the in-phase correlation device 378M and the output signal CQ[k] of the quadrature correlation device 382M to estimate phase offset. The phase estimator 68 provides a phase estimate as defined in Eq. (14). The phase estimator 68 is employed by the demodulator 74 for demodulation and phase synchronization.
The timing estimator 70 takes the output signal of the adder 386 and searches for a peak in it. Once a peak is found, the timing estimator employs three values in the adder output signal; they being the peak value, the value right before the peak and the value right after the peak, to perform interpolation and timing offset estimate. The timing offset estimate {circumflex over (δ)} is employed to advance or delay the sampling clock and achieve the best time synchronization.
The frequency estimator 72 takes the output signal of the in-phase matched filter 390 and the output signal of the quadrature matched filter 396, converts these signals to CW signals, applies discrete Fourier transform (DFT) to the CW signals, and estimates the frequency of the CW signals as the frequency offset. The frequency estimator 72 output is an estimate to the frequency offset Δω=ωc−ωL. This estimate is employed by the demodulator 74 for demodulation and frequency synchronization.
In this signal detector 366, 2(Ns+L1+L2+ . . . +LM) multiplications are needed to compute correlation for every sample of the received signal, where Li is the length of the sequence Ai, and
All of the signal detectors 166, 266, 366 described herein work well for communication systems. Both the first signal detector 166 and the second signal detector 266 work for all kinds of signals, including the traditional signals and multiple level signals. When compared against a traditional signal detector using four correlation devices, e.g., correlators, the first signal detector 166 reduces the correlation complexity and multiplication hardware by 50%. Among the three signal detectors 166, 266, 366, the complexity of the first signal detector 166 is the highest because computing correlation sample by sample along the entire signal is great. The complexity of the second signal detector 266 is significantly lower than the complexity of the first signal detector 166.
Being optimized for multiple level signals, the complexity of the third signal detector 366 is the lowest. When the signal length L is large, either the third signal detector 366 or the second signal detector 266 can significantly reduce detection complexity and hardware. It shall be understood that the signal detectors in the present invention can be implemented in many ways while keeping optimal signal detection performance.
The invention has been described in an illustrative manner. It is to be understood that the terminology, which has been used, is intended to be in the nature of words of description rather than of limitation.
Many modifications and variations of the invention are possible in light of the above teachings. Therefore, within the scope of the appended claims, the invention may be practiced other than as specifically described.