Emergency communication optimization method for low earth orbit satellite heterogeneous network

Abstract

The invention discloses an emergency communication optimization method for a low earth orbit satellite heterogeneous network. The method comprises the following steps: calculating propagation loss of a communication link in the low earth orbit satellite heterogeneous network and gain in a linear array direction, and calculating a received signal of a ground terminal and an intensity ratio of the signal to interference plus noise; the energy efficiency of the low-orbit satellite and the ground network is maximized by optimizing the transmitting power of a ground base station, then the optimization problem of the transmitting power is solved through a continuous convex approximation algorithm, and regularization factors are introduced into the optimization problem of the transmitting power and limiting conditions of the optimization problem. According to the method, the antenna array radiation gains of the satellite base station and the ground base station and the signal transmission path length are calculated respectively, and the energy efficiency of the whole network is maximized by adjusting the transmitting power, the beam forming strategy and the resource allocation based on a channel recoil response function expression and path loss characteristics through an optimization algorithm.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless communication networks, and specifically relates to an emergency communication optimization method for a low-earth orbit satellite heterogeneous network. Background Art

[0002] In recent years, low-earth orbit satellite communication systems have developed rapidly due to their more superior communication functions, and the requirements for system performance, integration, and stability are also getting higher and higher. At the same time, low-earth orbit satellite communication systems are gradually regarded as an important supplement to ground communication network systems due to advantages such as short transmission delay and low launch cost.

[0003] With the application of technologies such as the "Starlink" project and the "Hongyan" system, more and more countries and companies attach importance to the research on low-earth orbit satellite-ground hybrid networks, and optimizing their comprehensive performance has development significance and reference value.

[0004] There are obvious differences between low-earth orbit satellite communication systems and ground communication systems in terms of system architecture, channel characteristics, etc., resulting in different loss characteristics. The hybrid network still has key problems such as uneven distribution of computing resources and load imbalance. Summary of the Invention

[0005] The purpose of the present invention is to provide an emergency communication optimization method for a low-earth orbit satellite heterogeneous network in view of the deficiencies of the existing technology.

[0006] To achieve the above purpose, the present invention provides an emergency communication optimization method for a low-earth orbit satellite heterogeneous network, including:

[0007] Step 1, calculate the propagation loss of the communication link in the low-earth orbit satellite heterogeneous network and the gain in the direction of the linear array. The low-earth orbit satellite heterogeneous network includes a satellite base station and multiple ground networks within the coverage range of the satellite base station. The ground network includes B ground base stations and K ground terminals. The communication link includes the communication link between the satellite base station and the ground base station and the communication link between the ground base station and the ground terminal. Both B and K are natural numbers greater than 0;

[0008] Step 2, on the premise that one satellite base station is connected to all ground base stations and one ground base station serves multiple ground terminals at the same time, calculate the received signal of the ground terminal and the signal-to-interference-plus-noise ratio;

[0009] Step 3, optimize the transmit power of the ground base station to maximize the energy efficiency of the low-earth orbit satellite and the ground network. Specifically, express the optimization problem of the transmit power of the ground base station and its constraints as:

[0010]

[0011] t = t0 + Δt, Δt = 0, 1, 2, …

[0012] wherein, To optimize the transmit power by minimizing w(t) and φ(t), w(t) is the precoding vector of the information symbol from the satellite base station to the ground terminal, φ(t) is the phase shift vector, and γ k (t) is the signal-to-interference-plus-noise ratio of the k-th ground terminal, and Γ k (t) is the minimum signal-to-interference-plus-noise ratio of the ground terminal, and a b，k is the ratio of the signal power between the b-th base station and the k-th ground terminal to the total power, t0 is the initial time, and Δt is the time interval for updating the channel state information;

[0013] Step 4. Solve the optimization problem of the transmit power by the successive convex approximation algorithm, specifically as follows:

[0014] Express the function expression to which the successive convex approximation algorithm is applied by expanding the right side term as:

[0015]

[0016] wherein, A and B are two general complex matrices, is the real part, is the imaginary part, H is the conjugate transpose, and j is the imaginary number;

[0017] By introducing the slack variable s kl (t), equivalently express the function to which the successive convex approximation algorithm is applied as:

[0018]

[0019] wherein, g k (t) is the channel model function, w k (t) is the transmit beamforming vector of the k-th ground terminal at time t, and w l (t) is the transmit beamforming vector of the l-th ground terminal at time t;

[0020] By introducing g k (t) (n) , a k (t) (n) and b k (t) (n) to identify the concave lower bound of |g k (t)w k (t)| 2 in Formula 1, specifically as follows:

[0021] g k (t) (n) = hsk (t) diag(φ(t) (n) ) H ts (t)

[0022] a k (t) (n) =g k (t) (n) w k (t) (n)

[0023] b k (t) (n) =a k (t) (n) (g k (t) (n) + w k (t) (n) )

[0024] where g k (t) (n) is the channel model function of the nth iteration, a k (t) (n) , b k (t) (n) are introduced variables for convenient calculation, h sk (t) is the channel matrix function between the satellite base station and the ground base station, H ts (t) is the channel matrix function between the ground base station and the ground terminal, diag(.) is the diagonal matrix function, w k (t) (n) is the value of w k (t) at the nth iteration, φ(t) (n) is the value of φ(t) at the nth iteration;

[0025] Transform |g k (t) w k (t)| 2 in Formula 1 as follows:

[0026]

[0027] where h k (y) is the expanded right - hand term;

[0028] Equivalently represent the above - mentioned Formula 2 as:

[0029]

[0030] Further transform the above formula into:

[0031]

[0032] Among them, μ kl (t) and are the expanded right - hand terms after transformation;

[0033] Similarly, transform the formula three into:

[0034]

[0035] And further transform the above formula into:

[0036]

[0037] Among them, v kl (t) is, are the expanded right - hand terms after transformation;

[0038] Step 5: Introduce a regularization factor for the optimization problem of the transmission power and its constraint conditions, specifically expressed as:

[0039]

[0040] t = t0+Δt, Δt = 0, 1, 2, …

[0041] Among them, ξ represents the regularization factor.

[0042] Furthermore, the calculation method of the propagation loss of the communication link is as follows:

[0043] PL = PL b +PL g +PL s +PL e

[0044] Among them, PL is the total path loss, PL b is the basic path loss, PL g is the atmospheric loss, PL s is the loss caused by ionospheric or tropospheric scintillation, PL e is the penetration loss of the signal entering the building.

[0045] Furthermore, the calculation method of the gain in the direction of the linear array is as follows:

[0046]

[0047]

[0048] Among them, ω n，m is the position vector, υ n，m is the weight factor, is the calculated gain in the direction of the linear array, is the radiation pattern of the antenna element, from the horizontal direction gain and the vertical gain A E，V is obtained by weighting with is the horizontal angle of the antenna array, θ is the elevation angle of the antenna array, and θ 3dB are the 3dB bandwidths in the horizontal and vertical directions respectively, A m is the back gain of the horizontal antenna array, SLA V is the back gain of the vertical antenna array, M and N are the numbers of antennas in the horizontal and vertical directions respectively.

[0049] Furthermore, the received signal of the ground terminal is:

[0050]

[0051] wherein, y k (t) is the received signal of the k-th ground terminal at time t, s l (t) is the transmit beamforming, ω k is the circularly complex symmetric additive white Gaussian noise at the k-th ground terminal.

[0052] Furthermore, the signal received by the k-th ground terminal and the interference plus noise γ k (t) is:

[0053]

[0054] Advantageous effects: The present invention constructs a wireless transmission model based on energy harvesting and consumption for the communication scenario of a low-earth orbit satellite heterogeneous network. By separately calculating the radiation gains of the antenna arrays of the satellite base station and the ground base station, as well as the signal transmission path lengths, based on the expression of the channel complex impulse response function and the path loss characteristics, the optimization algorithm adjusts the transmit power, beamforming strategy and resource allocation to maximize the energy efficiency of the entire network; by normalizing the channel coefficients and noise power, the reliability of the algorithm under different network scales is ensured; the optimal transmit power and beamforming strategy can be determined to minimize the energy consumption while maintaining a high communication quality; it has good stability and convergence. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 is a schematic diagram of the low-earth orbit satellite heterogeneous network according to an embodiment of the present invention;

[0056] Figure 2 is a comparison diagram of the constraint behaviors of the optimization method according to an embodiment of the present invention under different numbers of ground terminals;

[0057] Figure 3 is a comparison diagram of the convergence results of the low-earth orbit satellite heterogeneous network under different signal-to-noise ratio conditions;

[0058] Figure 4 It is a comparison chart of the convergence results of a low-earth orbit satellite heterogeneous network under different optimization algorithms. Specific implementation manners

[0059] The present invention will be further clarified below in conjunction with the accompanying drawings and specific embodiments. These embodiments are implemented on the premise of the technical solution of the present invention, and it should be understood that these embodiments are only used to illustrate the present invention and not to limit the scope of the present invention.

[0060] The embodiment of the present invention provides an emergency communication optimization method for a low-earth orbit satellite heterogeneous network, including:

[0061] Step 1, calculate the propagation loss of the communication link in the low-earth orbit satellite heterogeneous network and the gain in the direction of the linear array. Refer to Figure 1 , the above-mentioned low-earth orbit satellite heterogeneous network includes a satellite base station and a plurality of ground networks within the coverage range of the satellite base station. The ground network includes B ground base stations and K ground terminals. The communication links include the communication links between the satellite base station and the ground base stations and the communication links between the ground base stations and the ground terminals. Both B and K are natural numbers greater than 0. The set of ground terminals is denoted as K = {1, 2,..., k}, the set of ground base stations is denoted as B = {1, 2,..., b}, and the set of satellite base stations is denoted as s = {1}. It is assumed that each base station occupies a separate sub-channel, and the ACIR isolation degree between different base stations is large enough, and there will be no adjacent channel interference between them. Due to the constraints of hardware conditions, the transmission power of the ground base station and the maximum number of connectable terminals are limited.

[0062] Specifically. The calculation method of the propagation loss of the above-mentioned communication link is as follows:

[0063] PL = PL b + PL g + PL s + PL e

[0064] wherein, PL is the total path loss, PL b is the basic path loss, which is affected by the distance d between the ground terminal and the ground base station and the center frequency of the signal transmitted by the base station. PL g is the atmospheric loss, PL s is the loss caused by ionospheric or tropospheric scintillation, PL g and PL s are affected by the latitude and weather conditions, and PL e is the penetration loss of the signal entering the building, which is related to the distribution position of the ground terminals.

[0065] The calculation method of the gain in the direction of the above-mentioned linear array is as follows:

[0066]

[0067] where ω n，m is the position vector, v n，m is the weight factor, is the calculated gain in the direction of the linear array, is the radiation pattern of the antenna element, which is weighted by the horizontal direction gain and the vertical direction gain A E，V (θ), is the horizontal angle of the antenna array, θ is the elevation angle of the antenna array, and θ 3dB are the 3dB bandwidths in the horizontal and vertical directions respectively, A m is the back gain of the horizontal antenna array, SLA V is the back gain of the vertical antenna array, and M and N are the number of antennas in the horizontal and vertical directions respectively.

[0068] Step 2. On the premise that one satellite base station is connected to all ground base stations and one ground base station serves multiple ground terminals simultaneously, calculate the received signal of the ground terminal and the signal-to-interference-plus-noise strength ratio. Specifically, the received signal of the ground terminal is:

[0069]

[0070] where y k (t) is the received signal of the k-th ground terminal at time t, g k (t) is the channel model function, s l (t) is the transmit beamforming, and ω k is the circularly complex symmetric additive white Gaussian noise (AWGN) at the k-th ground terminal.

[0071] The signal-to-interference-plus-noise strength ratio (SINR) γ k (t) of the k-th ground terminal is:

[0072]

[0073] Step 3. By optimizing the transmit power of the ground base station, maximize the energy efficiency of the low-earth orbit satellite and the ground network. Specifically, express the optimization problem of the transmit power of the ground base station and its constraints as:

[0074]

[0075] t = t0 + Δt, Δt = 0, 1, 2, …

[0076] where, To minimize \(w(t)\) and \(\varphi(t)\) so as to optimize the transmit power, where \(w(t)\) is the precoding vector of the information symbol from the satellite base station to the ground terminal, \(\varphi(t)\) is the phase shift vector, and \(\gamma\) k (t) is the signal-to-noise ratio of the received signal of the \(k\)th ground terminal, and \(\Gamma\) k (t) is the minimum value of the signal-to-interference-plus-noise ratio of the ground terminal, which can be regarded as the lowest acceptable quality of service (QoS), and \(a\) b，k is the ratio of the signal power between the \(b\)th base station and the \(k\)th ground terminal to the total power, \(t_0\) is the initial time, and \(\Delta t\) is the time interval for updating the channel state information;

[0077] Step 4. Solve the above optimization problem of the transmit power through the successive convex approximation algorithm as follows:

[0078] Express the function expression to which the successive convex approximation algorithm is applied by expanding the right side term as:

[0079]

[0080] where \(A\) and \(B\) are two general complex matrices, is the real part, is the imaginary part, \(H\) is the conjugate transpose, and \(j\) is the imaginary number.

[0081] By introducing the slack variable \(s\) kl (t), Equivalently represent the function to which the successive convex approximation algorithm is applied as:

[0082]

[0083] \(w\) k (t) is the transmit beamforming vector of the \(k\)th ground terminal at time \(t\), and \(w\) l (t) is the transmit beamforming vector of the \(l\)th ground terminal at time \(t\);

[0084] By introducing \(g\) k (t) (n) , \(a\) k (t) (n) and \(b\) k (t) (n) to identify the concave lower bound of \(|g\) k (t)w k (t)| 2 in Formula 1 as follows:

[0085] \(g\) k (t) (n) = \(h\) sk (t)diag(\(\varphi(t)\) (n) \(H\) ts (t)

[0086] a k (t) (n) = g k (t) (n) w k (t) (n)

[0087] b k (t) (n) = a k (t) (n) (g k (t) (n) + w k (t) (n) )

[0088] where g k (t) (n) is the channel model function for the nth iteration, a k (t) (n) , b k (t) (n) are introduced variables for convenient calculation, h sk (t) is the channel matrix function between the satellite base station and the ground base station, H ts (t) is the channel matrix function between the ground base station and the ground terminal, diag(.) is the diagonal matrix function, w k (t) (n) is the value of w k (t) at the nth iteration, φ(t) (n) is the value of φ(t) at the nth iteration.

[0089] Transform |g k (t)w k (t)| 2 in Formula 1 as follows:

[0090]

[0091] where h k (t) is the expanded right - hand side term;

[0092] Equivalently represent Formula 2 as:

[0093]

[0094] Further transform the above formula into:

[0095]

[0096] where μ kl (t) and are the transformed expanded right - hand side terms.

[0097] Similarly, transform the above formula (3) into:

[0098]

[0099] And further transform the above formula into:

[0100]

[0101] where, v kl (t) is, is the expanded right side term after transformation.

[0102] Step 5: Introduce a regularization factor for the transmit power optimization problem and its constraints, specifically expressed as:

[0103]

[0104] t = t0 + Δt, Δt = 0, 1, 2, …

[0105] where, ξ represents the regularization factor.

[0106] See Figure 2 , Figure 2 which shows the constraint behavior of the present invention with different numbers of ground terminals. The simulation results indicate that when the number of ground terminals K takes values of 50, 100, and 150 respectively, the constraint violation parameter ξ can converge quickly. For all K values, the number of iterations is between 70 and 80, which indicates that the algorithm can ensure reaching the predefined accuracy. By normalizing the channel coefficients and noise power, the stability and reliability of the algorithm under different network scales are ensured. In addition, as the number of ground terminals increases, the number of iterations shows an increasing trend, which may be due to the increase in the complexity of the optimization problem caused by the expansion of the network scale. To adapt to this change, our algorithm adopts a dynamic resource allocation strategy, which optimizes the transmit power and beamforming strategy according to the real-time network state and user requirements.

[0107] See Figure 3 , Figure 3 which shows the comparison of the convergence results of the LEO satellite heterogeneous network under different signal-to-noise ratio conditions. The simulation results indicate that for the LEO satellite heterogeneous network from 0 to 4 seconds, the average transmit power decreases successively. The core of this algorithm lies in using a method based on semidefinite programming to dynamically adjust the optimization strategy. During the optimization process, the algorithm takes into account the influence of the change in signal-to-noise ratio on the system performance. By simulating under different signal-to-noise ratio conditions, the algorithm can determine the optimal transmit power and beamforming strategy to minimize the energy consumption while maintaining a high communication quality.

[0108] See Figure 4 , Figure 4It illustrates the comparison of the convergence results when the low-earth orbit satellite heterogeneous network is optimized by applying the method, random optimization algorithm and greedy algorithm of the present invention. The simulation results indicate that as the number of ground terminals increases, the three algorithms exhibit different convergence characteristics during the optimization process. The method of the present invention shows better stability and convergence. This benefits from the alternating optimization of the method of the present invention, which can coordinate the optimization of different modules in each step, thus maintaining the stability of the optimization process as a whole. The random optimization and greedy algorithms may quickly find a local optimal solution in a small-scale network, but as the number of ground terminals increases, their global convergence deteriorates. Therefore, they may fall into a suboptimal solution in a large-scale network, resulting in a slowdown in the convergence speed.

[0109] The above description is only the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art of this technology, the other parts not specifically described belong to the prior art or common general knowledge. Without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. An emergency communication optimization method for a low-earth orbit satellite heterogeneous network, characterized in that Including: Step 1: Calculate the propagation loss of the communication link in the low-earth orbit satellite heterogeneous network and the gain in the direction of the linear array. The low-earth orbit satellite heterogeneous network includes a satellite base station and multiple ground networks within the coverage range of the satellite base station. The ground network includes B ground base stations and K ground terminals. The communication link includes the communication link between the satellite base station and the ground base station and the communication link between the ground base station and the ground terminal. Both B and K are natural numbers greater than 0. Step 2: On the premise that one satellite base station is connected to all ground base stations and one ground base station serves multiple ground terminals simultaneously, calculate the received signal of the ground terminal and the signal-to-interference-plus-noise ratio. Step 3: Optimize the transmit power of the ground base station to maximize the energy efficiency of the low-earth orbit satellite and the ground network. Specifically, express the optimization problem of the transmit power of the ground base station and its constraints as: t = t0 + Δt, Δt = 0, 1, 2,... Among them, To minimize w(t) and φ(t) to optimize the transmit power, where w(t) is the precoding vector of the information symbol from the satellite base station to the ground terminal, φ(t) is the phase shift vector, and γ k (t) is the signal-to-interference-plus-noise ratio of the k-th ground terminal, and Γ k (t) is the minimum signal-to-interference-plus-noise ratio of the ground terminal, a b，k is the ratio of the signal power between the b-th base station and the k-th ground terminal to the total power, t0 is the initial time, and Δt is the time interval for updating the channel state information; Step 4: Solve the optimization problem of the transmit power through the successive convex approximation algorithm, specifically as follows: Express the function expression to which the successive convex approximation algorithm is applied by expanding the right side term as: where A and B are two general complex matrices, is the real part, is the imaginary part, H is the conjugate transpose, and j is the imaginary unit; By introducing a slack variable s kl (t), The function to which the successive convex approximation algorithm is applied is equivalently represented as: where g k (t) is the channel model function, and w k (t) is the transmit beamforming vector of the k-th ground terminal at time t, and w l (t) is the transmit beamforming vector of the l-th ground terminal at time t; By introducing g k (t) (n) 、a k (t) (n) and b k (t) (n) to identify the concave lower bound of |g k (t)w k (t)| 2 in Equation (1) as follows: g k (t) (n) = h sk (t) diag(φ(t) (n) ) H ts (t) a k (t) (n) = g k (t) (n) w k (t) (n) b k (t) (n) = a k (t) (n) (g k (t) (n) + w k (t) (n) ) Among them, g k (t) (n) is the channel model function for the nth iteration, a k (t) (n) , b k (t) (n) are introduced variables for convenient calculation, h sk (t) is the channel matrix function between the satellite base station and the ground base station, H ts (t) is the channel matrix function between the ground base station and the ground terminal, diag(.) is the diagonal matrix function, w k (t) (n) is the value of w k (t) at the nth iteration, and φ(t) (n) is the value of φ(t) at the nth iteration; Transform |g k (t)w k (t)| 2 in Formula 1 as follows: where h k (t) is the expanded right side term; Equivalently represent the formula two as: Further transform the above formula into: where μ kl (t) and are the expanded right-side terms after transformation; Similarly transform the formula three into: And further transform the above formula into: Among them, v kl (t) is, is the expanded right side term after transformation; Step 5: Introduce a regularization factor for the optimization problem of the transmit power and its constraints, specifically expressed as: t = t0 + Δt, Δt = 0, 1, 2,... where ξ represents the regularization factor.

2. The emergency communication optimization method for a low-earth orbit satellite heterogeneous network according to claim 1, wherein The calculation method of the propagation loss of the communication link is as follows: PL = PL b + PL g + PL s + PL e Among them, PL is the total path loss, PL b is the basic path loss, PL g is the atmospheric loss, PL s is the loss caused by ionospheric or tropospheric scintillation, PL e is the penetration loss of the signal entering the building.

3. An emergency communication optimization method for a low-earth orbit satellite heterogeneous network according to claim 1, characterized in that The calculation method of the gain in the direction of the linear array is as follows: where ω n，m is the position vector, υ n，m is the weight factor, is the gain in the direction of the calculated linear array, is the radiation pattern of the antenna element, which is obtained by weighting the horizontal direction gain and the vertical direction gain A E，V (θ), is the horizontal angle of the antenna array, θ is the elevation angle of the antenna array, and θ 3dB are the 3dB bandwidths in the horizontal and vertical directions respectively, A m is the backward gain of the horizontal antenna array, SLA V is the backward gain of the vertical antenna array, M and N are the numbers of antennas in the horizontal and vertical directions respectively.

4. An emergency communication optimization method for a low-earth orbit satellite heterogeneous network according to claim 3, characterized in that The received signal of the ground terminal is: where, y k (t) is the received signal of the k-th ground terminal at time t, s l (t) is the transmit beamforming, ω k is the circularly complex symmetric additive white Gaussian noise at the k-th ground terminal.

5. The emergency communication optimization method for a low-earth orbit satellite heterogeneous network according to claim 4, characterized in that The signal-to-interference-plus-noise ratio γ k (t) received by the k-th ground terminal is as follows:

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