A Scattering Waveform Transmission Method Based on Transform Domain

Abstract

The present invention proposes a scattering waveform transmission method based on transform domain. The present invention utilizes the characteristic that channels in different space-times will not be in deep fading at the same time, and performs weighted combination on the two time domain components of the transmitted symbol sequence, thereby merging the transmitted symbols in different space-times, and using signals that have not experienced deep fading to compensate for signals that have experienced deep fading, thereby improving the stability of the system. The signal transformation and inverse transformation operations of the present invention can perfectly restore the original waveform in the absence of noise. Compared with the waveform of the existing large-scale MIMO single-carrier system, the waveform design method of the present invention has better diversity performance under the condition of unchanged data rate.

Description

Technical Field

[0001] The present invention relates to a waveform design method for a wireless communication system, and more specifically to a scattering waveform transmission method based on a double-component combined generalized weighted fractional Fourier transform (DCGWFrFT) in a large-scale multiple-in multiple-out (MIMO) scattering communication system. Background Art

[0002] Tropospheric scatter communication technology is a wireless communication technology that uses the scattering effect of radio waves on the inhomogeneity of the tropospheric medium. Its technical basis is the tropospheric scatter propagation theory. The tropospheric scatter transmission system designed using the tropospheric scatter propagation theory can achieve over-the-horizon transmission, and at the same time has moderate transmission capacity, transmission performance and reliability, as well as strong resistance to nuclear explosions and ionospheric disturbances. Due to its unique transmission characteristics, tropospheric scatter communication technology plays an irreplaceable role in a variety of different communication transmission technologies.

[0003] Scattering communication has a history of more than 60 years since its birth. At present, the capacity problem of scattering communication has become the focus of attention of countries around the world. For example, the American comtech company has currently achieved a communication rate of 210Mbps, and my country's scattering transmission rate has also reached 50Mbps in model equipment, and 155Mbps has been explored in technical research. With the gradual application of the fifth-generation mobile communication technology, high-throughput satellite technology, and millimeter-wave large-capacity microwave communication technology in communication, the transmission rate of the next-generation communication system will be improved overall. Scattering communication also needs to further improve the transmission rate so that it will not become a bottleneck for the communication transmission of the entire network.

[0004] Massive MIMO technology is one of the important technologies for improving system capacity in current mobile communications. Compared with traditional single antennas, the theoretical channel capacity of a massive MIMO communication system increases with the increase in the number of antennas at the transmitting and receiving ends without the need for additional transmission power or frequency band. Therefore, it can be foreseen that if massive MIMO technology is introduced into scattering communication, it can also provide a considerable communication rate for the scattering communication system, thereby realizing large-capacity scattering communication.

[0005] Although massive MIMO technology can provide multiplexing gain and diversity gain for the system, there is a trade-off between the two gains when the number of antennas is limited. When the communication data rate is maximized, the system will not be able to provide spatial diversity gain, making it difficult to ensure the communication stability of the system. In order to obtain better diversity performance without sacrificing communication throughput, it is necessary to consider providing additional diversity capabilities through waveform design. Summary of the invention

[0006] In view of the fact that the waveform of the existing large-scale MIMO system has a trade-off between multiplexing gain and diversity gain, resulting in a poor communication bit error rate when the number of data streams is large, the present invention proposes a scattering waveform transmission method based on the transform domain. This method uses the characteristic that channels in different space-times will not be in deep fading at the same time, and performs weighted combination of the two time domain components of the transmitted symbol sequence, thereby merging the transmitted symbols in different space-times, and using the signal that has not experienced deep fading to compensate the signal that has experienced deep fading, thereby improving the stability of the system.

[0007] The technical solution adopted by the present invention is:

[0008] A scattering waveform transmission method based on transform domain, including a transmitting end operation process and a receiving end operation process;

[0009] The sending end operation process includes the following steps:

[0010] Step 1: The transmitter modulates the bit stream sent in M ​​time slots into a transmission symbol sequence Where N t is the number of transmitting antennas, M is the number of time slots, is a plural set;

[0011] Step 2: Perform a two-component combined generalized weighted fractional Fourier transform on the transmitted symbol sequence x to obtain the transformed transmitted symbol sequence z. The transformation formula is:

[0012] z=F + x

[0013] The transformation matrix F + Defined as:

[0014]

[0015] Where I represents the identity matrix, D represents the normalized discrete Fourier transform matrix, and the transform coefficient is:

[0016]

[0017]

[0018] Where j is an imaginary unit, parameters θ0 and θ1 are randomly generated in the interval [0,2π], and satisfy θ0≠θ1+kπ, k is an arbitrary integer;

[0019] Step 3: Divide the transformed transmitted symbol sequence z into M segments in order, denoted as And send the i-th symbol sequence z in the i-th time slot i ;

[0020] The operation process at the receiving end includes the following steps:

[0021] Step 4: Go through the channel Then get the received signal:

[0022] y i =H i z i +w i

[0023] in represents additive white Gaussian noise, is the noise power, N r is the number of antennas at the receiving end;

[0024] Use the minimum mean square error detector to detect all received signals {y1,y2,...,y M},get:

[0025]

[0026] Wherein, the superscript H represents the conjugate transpose;

[0027] Step 5: Combine the detected symbol sequences at M moments into a whole, that is,

[0028]

[0029] The superscript T indicates transposition;

[0030] The received symbol sequence Perform the inverse two-component combined generalized weighted fractional Fourier transform to obtain the inverse transformed received symbol sequence The transformation formula is:

[0031]

[0032] The transformation matrix F - Defined as

[0033]

[0034] The transformation coefficients are:

[0035]

[0036]

[0037] Step 6: Right Demodulate and obtain an estimated bit stream.

[0038] The present invention has the following advantages:

[0039] 1. The present invention utilizes the characteristic that channels at different space-times will not be in deep fading at the same time, performs DCGWFrFT on the transmitted symbol sequence, thereby weightedly combining the transmitted symbols at different space-times, and compensates the fading signal with a signal that has not been deeply faded, thereby improving the stability of the system.

[0040] 2. The present invention simultaneously exploits the dual characteristics of time and space of the system, and has better diversity performance than the traditional large-scale MIMO system single carrier system.

[0041] 3. The present invention can improve diversity performance without sacrificing data rate, but at the cost of computational complexity and delay. Compared with the existing space-time coding method, it can achieve better communication stability while ensuring the same communication rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is a structural block diagram of a scattering communication system based on massive MIMO in an embodiment of the present invention;

[0043] Figure 2 It is a communication flow diagram based on DCGWFrFT;

[0044] Figure 3 It is a schematic diagram of the space-time compensation mechanism of signal DCGWFrFT transformation and inverse transformation;

[0045] Figure 4 This is a performance comparison chart of different waveform design methods (the number of transmitting and receiving dual-end antennas is 16);

[0046] Figure 5 This is a performance comparison chart of different waveform design methods (the number of transmitting and receiving dual-end antennas is 8). DETAILED DESCRIPTION

[0047] The present invention is described in detail below with reference to the accompanying drawings.

[0048] A scattering waveform transmission method based on transform domain, the structure of the large-scale MIMO scattering communication system considered in this method is as follows Figure 1 As shown in Figure 2, the communication process based on DCGWFrFT is as follows: Figure 2 The steps of this method are as follows:

[0049] Operation process of the sender:

[0050] Step 1: The transmitter modulates the bit stream sent in M ​​time slots into a symbol sequence Where N t is the number of transmitting antennas, and M is the number of time slots.

[0051] Step 2: Perform DCGWFrFT on the transmitted symbol sequence x to obtain the transformed transmitted symbol sequence z. The transformation formula is:

[0052] z=F + x

[0053] The transformation matrix F + Defined as

[0054]

[0055] Here I represents the identity matrix, D represents the normalized discrete Fourier transform matrix, and the transform coefficients are

[0056]

[0057]

[0058] The parameters θ0 and θ1 are randomly generated in the interval [0,2π] and satisfy θ0≠θ1+kπ, where k is an arbitrary integer.

[0059] Step 3: Divide the transformed transmitted symbol sequence z into M segments in order, denoted as And send the i-th symbol sequence z in the i-th time slot i .

[0060] Through the channel After receiving the signal

[0061] y i =H i z i +w i

[0062] in represents additive white Gaussian noise, N r is the number of antennas at the receiving end, is the noise power.

[0063] The operation process of the receiving end:

[0064] Step 4: Use the Minimum Mean-Squared Error (MMSE) detector to detect all received signals {y1,y2,...,y M},get

[0065]

[0066] Step 5: Combine the detected symbol sequences at M moments into a whole, that is,

[0067]

[0068] The received symbol sequence Perform inverse DCGWFrFT (IDCGWFrFT) to obtain the inverse transformed received symbol sequence The transformation formula is

[0069]

[0070] The transformation matrix F - Defined as

[0071]

[0072] Here the transformation coefficient is

[0073]

[0074]

[0075] Step 6: Right Demodulate and obtain an estimated bit stream.

[0076] Principle description:

[0077] Step 2 combines the transmitted signals of different space-times using DCGWFrFT, and step 5 restores the transformed signals to the original signals using IDCGWFrFT. The principles and derivation process of step 2 and step 5 are described below.

[0078] because

[0079]

[0080] is a permutation matrix, so the fractional Fourier transform F of the two-component combination is + It can be regarded as a weighted combination of different space-time symbols before and after the transmitted symbol sequence x, where the weight coefficient is and This process can be used Figure 3 It is assumed that the transmitted symbol sequence experiences deep fading at different space-times in the channel. Since the symbols at the same time will not all experience deep fading at different antennas with high probability, and the symbols at the same antenna will not all experience deep fading at different times with high probability, the mechanism of this method is to compensate the deep fading signal with a signal that has not experienced deep fading, thereby overcoming the impact of channel fading on detection performance.

[0081] It is proved below that in a noise-free scenario, the signal transformation and inverse transformation system of this method can perfectly restore the original signal.

[0082] Proof: In the absence of noise, the received signal at the receiving end is

[0083] y i =H i z i

[0084] In the absence of noise After passing through the MMSE detector, we get

[0085]

[0086] After combining

[0087]

[0088] Finally, after the inverse two-component combination of fractional Fourier transform, we get

[0089]

[0090] because

[0091]

[0092] because

[0093]

[0094] and

[0095]

[0096] so

[0097]

[0098] The proof is complete.

[0099] In summary, the present invention utilizes the characteristic that channels at different space-times will not be in deep fading at the same time, and performs weighted combination on the two time-domain components of the transmitted symbol sequence, thereby merging the transmitted symbols at different space-times, and using the signal that has not experienced deep fading to compensate the signal that has experienced deep fading, thereby improving the stability of the system. Theoretical analysis proves that the signal transformation and inverse transformation operations of the present invention can perfectly restore the original waveform in the absence of noise. The simulation results are shown in Figure 2. Figure 4 and Figure 5 As shown, the horizontal axis represents the transmission signal-to-noise ratio (expressed in dB), and the vertical axis represents the bit error rate, where Figure 4 The number of transmitting and receiving dual-end antennas is 16, and Figure 5The number of transmitting and receiving antennas is 8. In the simulation, 16QAM modulation is used to modulate the data, and different data streams are sent on each antenna, with a total of 10 time frames. Each time frame contains 5000 time slots, and each time slot sends a symbol. The channel state information of different time frames is generated independently, and the scattering channel is randomly generated according to the large-scale MIMO Rayleigh channel model. The parameters for waveform transformation are set to θ0=0, θ1=π / 2. The simulation results show that compared with the waveform of the existing large-scale MIMO single-carrier system, the waveform design method of the present invention has a bit error rate of 10 when the data rate remains unchanged. -3 The signal-to-noise ratio gain can be improved by about 2dB, that is, it has better diversity performance.

Claims

1. A scattering waveform transmission method based on transform domain, characterized in that: Including the operation process of the sending end and the operation process of the receiving end; The sending end operation process includes the following steps: Step 1: The transmitter modulates the bit stream sent in M ​​time slots into a transmission symbol sequence Where N t is the number of transmitting antennas, M is the number of time slots, is a plural set; Step 2: Perform a two-component combined generalized weighted fractional Fourier transform on the transmitted symbol sequence x to obtain the transformed transmitted symbol sequence z. The transformation formula is: z=F + x The transformation matrix F + Defined as: Where I represents the identity matrix, D represents the normalized discrete Fourier transform matrix, and the transform coefficient is: Where j is an imaginary unit, parameters θ0 and θ1 are randomly generated in the interval [0,2π], and satisfy θ0≠θ1+kπ, k is an arbitrary integer; Step 3: Divide the transformed transmitted symbol sequence z into M segments in order, denoted as And send the i-th symbol sequence z in the i-th time slot i ; The operation process at the receiving end includes the following steps: Step 4: Go through the channel Then get the received signal: y i =H i z i +w i in represents additive white Gaussian noise, is the noise power, N r is the number of antennas at the receiving end; Use the minimum mean square error detector to detect all received signals {y1,y2,...,y M },get: Wherein, the superscript H represents the conjugate transpose; Step 5: Combine the detected symbol sequences at M moments into a whole, that is, The superscript T indicates transposition; The received symbol sequence Perform the inverse two-component combined generalized weighted fractional Fourier transform to obtain the inverse transformed received symbol sequence The transformation formula is: The transformation matrix F - Defined as The transformation coefficients are: Step 6: Right Demodulate and obtain an estimated bit stream.

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