Multi-reflection equipment signal modulation and low-complexity detection method

Abstract

The invention belongs to the technical field of wireless communication, and particularly relates to a multi-reflection device signal modulation and beam forming collaborative optimization method. According to the method, the modulation mode of a main system signal and the modulation order of multiple reflection devices are given, and the minimum Euclidean distance of a noiseless signal of a receiving end is maximized by optimizing signal constellation points of the reflection devices and beam forming, so that the symbol error rate performance of communication between the main system and the Internet of Things is improved. The scheme provided by the invention can be suitable for signal modulation of any-order main systems and Internet of Things systems, is simple to implement, and has very high application value.

Description

Technical Field

[0001] The invention belongs to the technical field of wireless communications, and in particular relates to a multi-reflection device signal modulation and low-complexity detection method. Background Art

[0002] Passive IoT is one of the key technologies of 6G, in which thousands of IoT devices are interconnected to provide humans with an immersive and ultimate experience. The International Standardization Organization 3GPP has begun discussions on passive IoT, covering standards for its usage scenarios, key supporting technologies, and service requirements.

[0003] In recent years, symbiotic wireless communication technology has become one of the potential technologies supporting passive IoT with its high spectral efficiency and high energy efficiency. In symbiotic communication, the reflection device plays the role of IoT transmitter, uses the main system signal as the modulated carrier, and uses reflection communication to send IoT monitoring information to the receiver. The receiver uses joint demodulation to recover the main system signal and IoT device signal. In addition, the reflection device can provide a reflection multipath to improve the transmission performance of the main system.

[0004] Existing symbiotic communication modulation work mainly considers single-reflection device scenarios. However, for future applications such as smart homes and smart cities, the simultaneous access of multiple-reflection devices is a very important scenario. Therefore, it is urgent to study the collaborative modulation of signal constellation points of symbiotic communication multi-reflection devices. In particular, passive reflection devices are restricted by hardware conditions such as power consumption and energy, and the reflection coefficient must meet the peak amplitude constraint. Under this constraint, the traditional symmetrical regular constellation point structure is not optimal for reflection devices. Therefore, a new method is needed to find the optimal constellation point modulation design. In addition, the multi-reflection device signals are coupled with the main system signals, which makes signal detection very complicated. Therefore, it is urgent to design a low-complexity signal detection method. Summary of the invention

[0005] The main content of the present invention is to propose a multi-reflection device signal modulation and low-complexity detection method for passive Internet of Things. Symbiotic wireless communication is one of the potential technologies supporting passive Internet of Things. By integrating the Internet of Things device for environmental monitoring into the reflection device, the reflection device can use the principle of reflection communication to transmit Internet of Things monitoring information to the receiver. At the same time, the reflection device can provide a multipath link for the main system transmission to improve its performance. Due to the application of reflection modulation, the transmission performance of the main system and the Internet of Things communication system are coupled with each other. Designing a good signal constellation point of the reflection device can simultaneously improve the transmission performance of the main system and the Internet of Things communication.

[0006] The technical solution adopted by the present invention is:

[0007] like Figure 1As shown, the present invention considers a symbiotic wireless communication system, including a single-antenna primary system transmitter, a configuration N r antenna receiver, and K multi-antenna reflectors, each of which is configured with N k Reflection unit, remember represents the channel response from the main system transmitter to the receiver, represents the channel response from the main system transmitter to the kth reflection device, represents the channel response from the kth reflection device to the receiver. In the nth symbol period, the symbols of the main system and the kth reflection device are represented as and in and denote the signal constellation point sets of the main system and the kth reflection device respectively. The received signal at the receiver can be expressed as

[0008]

[0009] Where P t Indicates the power of the main system transmitter; represents the additive Gaussian white noise at the receiver, each element has a mean of 0 and a variance of σ 2 The complex Gaussian distribution of represents the common reflection coefficient matrix of the kth reflection device, c k (n)Φ k The time-varying reflection coefficient matrix of the kth reflection device is constructed.

[0010] The receiver recovers the main system signal and the signals of all reflected devices through joint demodulation. Assuming that the main system signal and the reflected device signal are equally likely to be sent, the maximum likelihood detection method at the receiver is:

[0011]

[0012] From the above maximum likelihood detector, it can be found that the main system signal and the IoT device signal are coupled at the receiving signal in the form of multiplication. When the direct link does not exist, that is, h d = 0, the maximum likelihood detection will have ambiguity problems. Specifically, assuming is a set of optimal estimates, then conditions, is also a set of optimal estimates. Therefore, in this case, the detection ambiguity problem will arise. A typical example of the detection ambiguity problem is This means that the traditional symmetrical constellation points are not the optimal design for reflective devices. Therefore, it is urgent to study the collaborative optimization of the signal constellation points of reflective devices for the scenario of symbiotic communication with multiple reflective devices to solve the detection ambiguity problem and improve the detection bit error rate performance. At the same time, the multiple antennas of the reflective device can perform joint beamforming to improve the equivalent channel strength, thereby improving the transmission performance.

[0013] In addition, from formula (2), it can be found that although the maximum likelihood detector can ensure the optimal demodulation performance, its complexity is very high due to the need to exhaustively search every possible constellation point combination. Proportional to M s and M k denote the modulation order of the main signal and the kth reflection device respectively. This phenomenon is more obvious when there are many reflection devices. Therefore, it is very important to design a low-complexity signal detection method.

[0014] The method of the present invention specifically comprises:

[0015] With the goal of maximizing the minimum Euclidean distance of the received signal, the optimization problem is established as:

[0016]

[0017] Among them, the constraint condition |c k [m]|≤1 represents the signal peak amplitude constraint of each reflection device, M k represents the modulation order of the kth reflection device, Constraint Φ k [n,n] = 1 represents the constant modulus constraint of the common reflection coefficient, represents the diagonal matrix of the common reflection coefficients of the kth reflection device; the constraint D min represents the minimum Euclidean distance of the noise-free signal at the receiving end, represents the channel from the kth reflection device to the receiver, represents the channel from the main system transmitter to the kth reflection device, represents the channel from the main system transmitter to the receiver, s i and j represents the i-th element and j-th element in the modulation constellation point set of the primary system transmitter, c k [p k ] and c k [q k ] represents the pth point in the kth reflection device modulation constellation point set k elements and the qth k elements;

[0018] Solve the optimization problem by converting it into two sub-problems related to the signal constellation points and beamforming variables, namely:

[0019]

[0020]

[0021] First, we solve the sub-problem about the signal constellation point. Specifically, we stack the constellation point sets of all reflection devices into a column vector, which is recorded as So that Represents the mth unit of an M×M identity matrix k The Euclidean distance between any two constellation points of the received signal is expressed as:

[0022]

[0023] in,

[0024] The optimization variable Convert to the format of vector c and introduce a non-negative auxiliary variable t to obtain:

[0025]

[0026] t≥0,

[0027] Scaling the quadratic function, we can further obtain:

[0028]

[0029] t≥0,

[0030] where c (l) represents the set of modulation constellation points of the reflection device signal obtained by optimization in the lth iteration process, so as to obtain the optimal design of the modulation signal constellation points of the reflection device by solving the optimization tool CVX;

[0031] After obtaining the optimal design of the constellation points of the modulation signal of the reflection device, the sub-problem of the beamforming variable is solved, which is:

[0032] Define variables:

[0033]

[0034] The Euclidean distance between two constellation points of the received signal is re-expressed as:

[0035]

[0036] where a = h d (si -s j ), F = Hdiag(g), φ k The vector form of the reflection coefficient matrix representing the kth reflection device, Represents the sum of the antenna numbers of all reflective devices;

[0037] Convert the matrix form of the optimization variable Φ into a vector form v, And introduce a semi-positive definite matrix Satisfy the rank-one constraint rank(V) = 1 and the matrix semi-positive definite condition Introducing a non-negative auxiliary variable t≥0, the subproblem of the beamforming variable is transformed into:

[0038]

[0039] rank(V)=1,

[0040]

[0041] in, The transformed problem is a semi-definite programming problem. After relaxing the rank-one constraint, it is solved using the standard optimization tool CVX, and then a rank-one solution is recovered using the Gaussian random method.

[0042] Finally, the original optimization problem is solved by iteratively solving two sub-problems related to signal constellation points and beamforming variables, and the optimal design of signal constellation points and beamforming variables is obtained.

[0043] The receiver detects the signal as follows:

[0044] Given a primary signal s, the reconstructed direct link signal is P t represents the transmit power of the primary system transmitter, and then the reconstructed direct link signal is subtracted from the received signal to obtain:

[0045]

[0046] Using the beamforming matrix Receive signal from Get an estimate of the signal from all reflected devices:

[0047]

[0048] The linear receive beamforming matrix is ​​expressed as:

[0049]

[0050] in h k =H k Φ k g k , σ 2 is the variance of the additive white Gaussian noise at the receiver;

[0051] make is the signal estimation set of all reflection devices when the main signal s is given, and the estimated reflection device signal set is The following demodulation method is used to recover the signal of each reflecting device:

[0052]

[0053] in, Representation vector The kth element of ;

[0054] Using the method of obtaining the estimated signal of the reflection device by giving the main signal, we can obtain M s The estimated values ​​of the main signal and the reflected device signal are s Substitute the group estimate into the maximum likelihood detector and select a group of estimates that minimize the mean square error as the final estimate:

[0055]

[0056] Finally, the signal estimation value of the reflection device is recorded as

[0057] The beneficial effect of the present invention is that, given the modulation mode of the main system signal and the modulation order of the multi-reflection device, the minimum Euclidean distance of the noise-free signal at the receiving end is maximized by optimizing the signal constellation points and beamforming of the reflection device, thereby improving the symbol error rate performance of the main system and the Internet of Things communication. The scheme of the present invention can be applied to the signal modulation of the main system and the Internet of Things system of any order, the scheme is simple to implement, and has strong application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A schematic diagram of the system composition of the present invention is shown;

[0059] Figure 2 The design diagram of the signal constellation points of the reflection device in the present invention is shown;

[0060] Figure 3 is a symbol error rate performance diagram of the main system signal in the present invention;

[0061] Figure 4 is a symbol error rate performance diagram of the reflection device signal in the present invention;

[0062] Figure 5Graph showing the low complexity signal detection performance in the present invention. DETAILED DESCRIPTION

[0063] The present invention is described in detail below in conjunction with the accompanying drawings:

[0064] Take the multi-antenna multi-reflection device symbiotic wireless communication system as an example. Figure 1 As shown, the system includes a single-antenna main system transmitter, a configuration N r A receiver with 1 antenna and K reflection devices, where the kth reflection device is configured with N k Reflection unit, remember and represents the transmitted symbol of the primary system and the kth reflection device in the nth symbol period, where and They represent the transmission signal constellation point sets of the main system and the kth reflection device respectively. The received signal of the receiver can be expressed as

[0065]

[0066] in represents the channel from the main system transmitter to the receiver, represents the channel from the main system transmitter to the kth reflection device, represents the channel from the kth reflection device to the receiver, represents the common reflection coefficient diagonal matrix of the kth reflection device, P t represents the transmit power of the primary system transmitter, and z(n) represents the additive complex Gaussian noise at the receiver.

[0067] Using the maximum likelihood detection scheme, the detection formula at the receiver is as follows

[0068]

[0069] The existing reflection device modulation adopts the traditional phase modulation scheme, but there will be detection ambiguity problems when the direct link is blocked. Therefore, the modulation method of the reflection device needs to be re-optimized. The present invention proposes a collaborative optimization method for multi-reflection device signal constellation points and beamforming. For simplicity, the time index (n) of the transmitted signal is omitted below.

[0070] Since ambiguity may cause partial overlap of constellation points of received signals, the present invention considers maximizing the minimum Euclidean distance of received signals as the goal to solve the detection ambiguity problem. The specific problem modeling is as follows:

[0071]

[0072] The first constraint represents the signal peak amplitude constraint for each reflection device, and M k represents the modulation order of the kth reflection device; the second constraint represents the constant modulus constraint of the common reflection coefficient; the third constraint D min Indicates the minimum Euclidean distance of the noise-free signal at the receiving end.

[0073] By solving problem P1, we can get the signal constellation point design and beamforming design of each reflector. Preliminary analysis shows that this problem is a non-convex problem, and the optimization variables constellation point and beamforming are coupled with each other. The solution is to use the idea of ​​alternating optimization to transform the problem into two sub-problems related to signal constellation point and beamforming variable, expressed as

[0074]

[0075] and

[0076]

[0077] (1) Algorithm for solving problem P2

[0078] Stack the constellation points of all reflection devices into a column vector, denoted as in and represents the modulation signal constellation point set of the kth reflection device. Therefore, the pth in the modulation constellation point set of the kth reflection device k The elements can be represented as in Represents the mth unit of an M×M identity matrix k List.

[0079] Therefore, the Euclidean distance between any two constellation points of the received signal can be expressed as:

[0080]

[0081] in a=h d (s i -s j ),

[0082] The optimization variable Converting to the format of vector c and introducing a non-negative auxiliary variable t, problem P2 can be transformed into:

[0083]

[0084] Problem P2-A is a non-convex quadratic constrained quadratic programming problem. For non-convex quadratic constraints, the continuous convex approximation method can obtain a linear constraint on the optimization variable by means of a first-order Taylor expansion. By scaling the quadratic function in formula (6), problem P2-A can be converted into the following form:

[0085]

[0086] where c (l) represents the set of modulation constellation points of the reflection device signal obtained by optimization in the lth iteration. So far, problem P2-B is a standard convex problem about the optimization variables c and t, which can be solved efficiently using existing optimization tools, such as the CVX optimization toolkit built into Matlab.

[0087] (2) Algorithm for solving problem P3

[0088] After solving subproblem P2-B, the optimal design of the modulation signal constellation point of the reflection device can be obtained. Next, when the optimization result of the modulation constellation point of the reflection device signal is given, the reflection coefficient matrix design can be obtained by solving subproblem P3. In order to facilitate optimization, we first define the variable

[0089]

[0090] Therefore, the Euclidean distance between two constellation points of the received signal can be re-expressed as:

[0091]

[0092] where a = h d (s i -s j ), F = Hdiag(g), φ k The vector form of the reflection coefficient matrix representing the kth reflection device, Represents the sum of the antenna numbers of all reflective devices.

[0093] Convert the matrix form of the optimization variable Φ into the vector form v. Let And introduce a semi-positive definite matrix Satisfy the rank-one constraint rank(V) = 1 and the matrix semi-positive definite condition Then, by introducing a non-negative auxiliary variable t≥0, problem P3 can be transformed into the following form

[0094]

[0095] in Problem P3-A is a semi-definite programming problem. After relaxing the rank-one constraint, it can be solved using the standard optimization tool CVX. Then, a rank-one solution is recovered using the Gaussian random method in the literature "Sidiropoulos ND, Davidson TN, Luo Z Q. Transmit beamforming for physical-layer multicasting [J]. IEEE Transactions on Signal Processing, 2006, 54 (6): 2239-2251". By iteratively solving the two sub-problems P2 and P3, the original problem P1 can be solved.

[0096] After solving problem P1, a low-complexity receiver design is proposed to recover the main signal and the signals of all reflectors. From the received signal model, it can be observed that the modulated carriers of all reflector signals are the main signal. Based on this phenomenon, a linear receiver design is proposed, which includes two steps.

[0097] Step 1: Given the primary signal s, the direct link signal can be reconstructed as Then, the reconstructed direct link signal is subtracted from the received signal to obtain

[0098]

[0099] After eliminating the direct link signal, the remaining reflected link signal can be viewed as a classic MIMO (Multiple-Input-Multiple-Output) channel model. Therefore, classic MIMO signal detection methods such as linear receive beamforming can be used to recover the signal of the reflecting device. First, use the beamforming matrix Receive signal from An estimate of the signal from all reflected devices is obtained as follows:

[0100]

[0101] The linear receive beamforming matrix is ​​expressed as

[0102]

[0103] in h k =H k Φ k g k .

[0104] remember is the signal estimation set of all reflection devices when the main signal s is given. Then, the estimated reflection device signal set is used The following demodulation methods can be used to recover the signal of each reflecting device:

[0105]

[0106] in Representation vector Since the signals of all reflection devices are recovered independently, the search complexity of formula (14) is

[0107] Step 2: Through step 1, when a main signal constellation point is given, a set of signal estimates about all reflection devices can be obtained Therefore, there are a total of M s Then, the M s The group estimate is substituted into the maximum likelihood detector, and a group of estimates that can minimize the mean square error is selected as the final estimate. The above process is given by the following formula:

[0108]

[0109] Finally, the estimated signal from the reflecting device is given by Since the search complexity of the proposed low-complexity receiver is This is much lower than the search complexity of using the maximum likelihood detector directly. Therefore, the proposed low-complexity receiver design scheme can be extended to scenarios with multiple reflection devices and is more advantageous when the number of reflection devices is large.

[0110] Below, the present invention presents simulation results to verify the effectiveness of the proposed multi-reflection device signal constellation point and beamforming collaborative optimization scheme. Specifically, the present invention considers three benchmark schemes:

[0111] Benchmark 1: Collaborative beamforming design for multi-reflection devices and phase shift keying design for constellation points

[0112] Benchmark 2: Multi-reflection device uses random beamforming design and constellation points use phase shift keying design

[0113] Benchmark 3: Multi-reflection equipment uses random beamforming design and constellation points use collaborative optimization design

[0114] For the channel model, the present invention considers the Rice channel model, where the channel contains large-scale and small-scale fading. The large-scale fading is modeled as PL(d,ξ)=10 -3 d -ξ, where d represents the distance between two nodes and ξ represents the path loss exponent. Small-scale fading includes direct path components and indirect path components. The present invention considers two reflection device scenarios, where the position of the main system transmitter is x T =(0,0), the positions of the two reflection devices are x R1 =(100,5) and x R2 =(95,-5), the receiver's position is x R =(105,5). Without loss of generality, the present invention considers that the path loss index from the primary system transmitter to the receiver is ξ 1 =3.8, the path loss index from the main system transmitter to the reflection device is ξ 2 =2.2, the path loss index from the reflection device to the receiver is ξ 3 =2.1. The noise power is set to σ 2 =-100dBm. The number of reflective units of each reflective device is 32, and the signal modulation order is 4. The main system transmitter signal adopts binary phase shift keying modulation. The number of channel realizations is 10 3 Second-rate.

[0115] Figure 2 The optimal constellation point design of the two reflective devices obtained by the proposed collaborative optimization scheme when the direct link does not exist and the receiver antenna is a single antenna is shown. For comparison, the present invention also shows the constellation point design scheme of the reflective device using traditional phase shift keying. Here, a special case is considered that the reflection link strength through the two reflective devices is consistent. In this case, it can be seen that if the traditional phase shift keying constellation point design scheme is adopted, the constellation points of the noise-free signal at the receiving end will partially overlap, resulting in demodulation ambiguity problems in the process of jointly demodulating the signal. However, by adopting the proposed constellation point optimization scheme, the above ambiguity problem can be solved. It can be found that the constellation points of the noise-free signal at the receiving end will be separated, thereby solving the above ambiguity problem. After collaborative optimization, the constellation point of the reflective device signal is no longer a regular constellation point structure with a traditional zero mean symmetry, but a constellation point structure with a non-zero mean.

[0116] Figure 3 and Figure 4The symbol error rate of the main system signal and the reflected device signal is shown when the direct link does not exist. From the perspective of symbol error rate performance, the proposed reflected device signal constellation point and collaborative beamforming optimization scheme is superior to the three benchmark schemes. It can be found that the use of benchmark schemes 1 and 2 will lead to demodulation ambiguity problems, so the symbol error rate of the main system signal and the reflected device signal is 0.5. Although benchmark scheme 3 can solve the ambiguity problem through collaborative signal constellation point design, due to its use of random beamforming strategy, the equivalent channel strength of the reflected link is very weak. Therefore, the performance of benchmark scheme 3 will be worse than the performance of the proposed constellation point and beamforming collaborative optimization scheme, which verifies the effectiveness of the proposed collaborative optimization scheme.

[0117] Figure 5 The curve of the symbol error rate performance of the reflected device signal under the low-complexity receiver scheme as a function of the transmission power is shown. Three receiver beamforming schemes are considered here, namely MRC, ZF and MMS receivers. And considering the case where the reflected link is weak, the path loss exponent of the link from the main system transmitter to the reflected device is set to 2.9. Obviously, the performance of the maximum likelihood detector is better than that of the other three linear receiver schemes. For the other three low-complexity receivers, it can be found that the performance of the MMSE receiver is better than that of the ZF and MRC receivers. This is because the MRC receiver only maximizes the signal-to-noise ratio of one reflected link and regards the signals from other reflected links as interference. The ZF receiver eliminates the signal interference from other reflected devices by matrix zero forcing. Although the ZF method performs well in the high signal-to-noise ratio range, it may amplify the noise power in the low signal-to-noise ratio range, resulting in poor symbol error rate performance. The MMSE receiver can strike a balance between eliminating the interference of the reflected signal and amplifying the noise power.

Claims

1. A multi-reflection device signal modulation and low-complexity detection method for a symbiotic wireless communication system, the system comprising a main system transmitter configured with a single antenna, a transmitter configured with N r A receiver with 1 antenna and K multi-antenna reflectors, each with N k A reflection unit; characterized in that, The method is: With the goal of maximizing the minimum Euclidean distance of the received signal, the optimization problem is established as: Among them, the constraint condition |c k [m]|≤1 represents the signal peak amplitude constraint of each reflection device, M k represents the modulation order of the kth reflection device, Constraint Φ k [n,n] = 1 represents the constant modulus constraint of the common reflection coefficient, represents the diagonal matrix of the common reflection coefficients of the kth reflection device; the constraint D min represents the minimum Euclidean distance of the noise-free signal at the receiving end, represents the channel from the kth reflection device to the receiver, represents the channel from the main system transmitter to the kth reflection device, represents the channel from the main system transmitter to the receiver, s i and j represents the i-th element and j-th element in the modulation constellation point set of the primary system transmitter, c k [p k ] and c k [q k ] represents the pth point in the kth reflection device modulation constellation point set k elements and the qth k elements; Solve the optimization problem by converting it into two sub-problems related to the signal constellation points and beamforming variables, namely: First, we solve the sub-problem about the signal constellation point. Specifically, we stack the constellation point sets of all reflection devices into a column vector, which is recorded as So that Represents the mth unit of an M×M identity matrix k The Euclidean distance between any two constellation points of the received signal is expressed as: in, a=h d (s i -s j ), The optimization variable Convert to the format of vector c and introduce a non-negative auxiliary variable t to obtain: Scaling the quadratic function, we can further obtain: where c (l) represents the set of modulation constellation points of the reflection device signal obtained by optimization in the lth iteration process, so as to obtain the optimal design of the modulation signal constellation points of the reflection device by solving the optimization tool CVX; After obtaining the optimal design of the constellation points of the modulation signal of the reflection device, the sub-problem of the beamforming variable is solved, which is: Define variables: The Euclidean distance between two constellation points of the received signal is re-expressed as: where a = h d (s i -s j ), F = Hdiag(g), φ k The vector form of the reflection coefficient matrix representing the kth reflection device, Represents the sum of the antenna numbers of all reflective devices; Convert the matrix form of the optimization variable Φ into a vector form v, And introduce a semi-positive definite matrix Satisfy the rank-one constraint rank(V) = 1 and the matrix semi-positive definite condition Introducing a non-negative auxiliary variable t≥0, the subproblem of the beamforming variable is transformed into: rank(V)=1, in, The transformed problem is a semi-definite programming problem. After relaxing the rank-one constraint, it is solved using the standard optimization tool CVX, and then a rank-one solution is recovered using the Gaussian random method. Finally, the original optimization problem is solved by iteratively solving two sub-problems related to signal constellation points and beamforming variables, and the optimal design of signal constellation points and beamforming variables is obtained. The receiver detects the signal as follows: Given a primary signal s, the reconstructed direct link signal is P t represents the transmit power of the primary system transmitter, and then the reconstructed direct link signal is subtracted from the received signal to obtain: Using the beamforming matrix Receive signal from Get an estimate of the signal from all reflected devices: The linear receive beamforming matrix is ​​expressed as: in h k =H k Φ k g k , σ 2 is the variance of the additive white Gaussian noise at the receiver; make is the signal estimation set of all reflection devices when the main signal s is given, and the estimated reflection device signal set is The following demodulation method is used to recover the signal of each reflecting device: in, Representation vector The kth element of ; Using the method of obtaining the estimated signal of the reflection device by giving the main signal, we can obtain M s The estimated values ​​of the main signal and the reflected device signal are s Substitute the group estimate into the maximum likelihood detector and select a group of estimates that minimize the mean square error as the final estimate: Finally, the signal estimation value of the reflection device is recorded as

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