Method for optimizing energy efficiency of MC-DSSS space-air-ground integrated network

Abstract

The application relates to a kind of MC-DSSS space-air-ground integrated network energy efficiency collaborative optimization methods, belong to wireless communication field.The application implementation method is: the physical layer transmission model based on constant envelope MC-DSSS is constructed, the high PAPR of multicarrier spread spectrum signal is suppressed using CEMIC, and the operating efficiency of high power amplifier is improved.The application ensures the constant envelope characteristics of the transmitted signal while ensuring the signal orthogonality by accurate power allocation and intermodulation optimization, reduces the energy efficiency loss of the system.The application introduces the network layer switching strategy based on improved binary particle swarm optimization IBPSO-HO, establishes the hysteresis trigger mechanism and multi-attribute decision model, reduces unnecessary satellite switching and signaling overhead.The application realizes the joint optimization of physical layer waveform parameters and network layer switching decision through double-layer collaborative optimization, maintains the stability and efficiency of the system under dynamic channel conditions, ensures the communication quality, and realizes the energy efficiency collaborative optimization of MC-DSSS space-air-ground integrated network.

Description

Technical Field

[0001] This invention relates to a method for coordinated optimization of energy efficiency in an integrated space-air-ground (MC-DSSS) network, belonging to the field of wireless communication technology, specifically to a method for coordinated optimization of multi-carrier direct sequence spread spectrum (MC-DSSS) signal transmission and two-layer resources for satellite-terrestrial converged communication scenarios. Background Technology

[0002] In scenarios with limited or no ground infrastructure, such as emergency communications, marine communications, and coverage in remote areas, integrated air-space-ground networks are gradually becoming an important technological means to achieve rapid networking and wide-area coverage. Providing access to ground users through low-Earth orbit (LEO) satellites can ensure basic communication needs in complex environments. However, these scenarios typically place higher demands on terminal power consumption and link stability, making system energy efficiency issues more prominent. At the physical layer, direct sequence spread spectrum (DSSS) technology is often used to improve anti-interference capabilities and link reliability, combined with multi-carrier methods to achieve parallel transmission of multiple services. However, during the superposition of multi-carrier spread spectrum signals, a high peak-to-average power ratio (PAPR) is easily generated, causing the high-power amplifier at the transmitting end to operate in power back-off mode, reducing energy utilization efficiency. In energy-constrained application scenarios, this problem will further exacerbate the system's endurance pressure. Existing technologies mostly optimize from the perspective of power allocation or modulation methods, lacking a constraint mechanism on signal envelope characteristics, making it difficult to simultaneously consider power efficiency and transmission performance. At the network layer, since the coverage area of ​​LEO satellites changes over time, user terminals need to dynamically switch access between multiple visible satellites. In complex environments, traditional handover strategies based on a single decision metric are prone to frequent or untimely handovers, leading to link interruptions or additional signaling overhead, impacting service continuity and system energy efficiency. Furthermore, in these scenarios, there is a close relationship between transmit power control and satellite access selection: transmit power levels directly affect link quality and the range of accessible satellites, while access decisions, in turn, affect resource allocation and energy consumption. Existing methods typically optimize the physical and network layers separately, lacking a unified modeling and collaborative adjustment mechanism, making it difficult to achieve optimal overall performance under dynamic environments and energy-constrained conditions. Therefore, how to construct a cross-layer collaborative energy efficiency optimization method that considers transmit signal envelope characteristics, power allocation strategies, and satellite access mechanisms under energy-constrained and dynamic topology conditions has become an urgent technical problem to be solved. Summary of the Invention

[0003] To address the problems in existing integrated air-space-ground networks, such as high peak-to-average power ratio (PAPR) of multi-carrier spread spectrum signals leading to decreased efficiency of high-power amplifiers, frequent handovers caused by the high-speed motion of low-Earth orbit (LEO) satellites, and the lack of a joint optimization mechanism between the physical and network layers, this invention proposes a collaborative energy efficiency optimization method for integrated air-space-ground networks using MC-DSSS. It constructs a physical layer transmission model based on constant envelope MC-DSSS and utilizes CEMIC to suppress the high PAPR of multi-carrier spread spectrum signals, thereby improving the operating efficiency of high-power amplifiers. Through precise power allocation and intermodulation term optimization, this invention ensures signal orthogonality while maintaining the constant envelope characteristics of the transmitted signal, reducing system energy loss. For the handover problem caused by dynamic topology changes of LEO satellites, this invention introduces a network layer handover strategy based on improved binary particle swarm optimization (IBPSO-HO), establishing a hysteresis triggering mechanism and a multi-attribute decision model to reduce unnecessary handovers and signaling overhead, improving link stability. By constructing an alternating optimization mechanism between the physical and network layers, this invention achieves collaborative updates of waveform parameters and handover decisions, enabling the system to balance communication quality and energy efficiency in dynamic channel environments, thus achieving an overall improvement in the energy efficiency of the integrated air-space-ground network.

[0004] The objective of this invention is achieved through the following technical solution.

[0005] This invention discloses a method for coordinated energy efficiency optimization of an MC-DSSS integrated air-space-ground network, comprising the following steps:

[0006] Step 1: Construct an MC-DSSS integrated air-space-ground network system model. The ground user terminal performs multi-carrier multiplexing of multiple DSSS subcarrier signals, and introduces intermodulation terms on the basis of the useful signal through the CEMIC generalized multi-carrier constant envelope multiplexing method to generate a constant envelope transmission signal, so as to reduce the peak-to-average power ratio caused by multi-carrier superposition.

[0007] The MC-DSSS integrated air-space-ground network system model includes ground transmitter type (2) and (3), satellite receiver type (1) and satellite-ground channel type (4).

[0008] The satellite receiver is a Walker-Delta LEO satellite constellation, consisting of multiple satellites distributed across multiple orbital planes. A candidate satellite set is constructed. :

[0009]

[0010] in, for A collection of satellites; Indicates the first One satellite; for Ground users and the first The elevation angle of the satellite; The preset minimum elevation angle threshold is used to filter candidate satellites that meet the visibility requirements.

[0011] The ground user terminal adopts the MC-DSSS modulation scheme, and its baseband signal is composed of multiple independent DSSS subcarrier signals. Each DSSS signal is represented as:

[0012]

[0013] in, The chip sequence is obtained after BPSK modulation and spread spectrum. The duration is The rectangular chip waveform; This refers to the chip cycle.

[0014] To reduce the peak-to-average power ratio caused by multi-carrier superposition, the user terminal introduces intermodulation terms on the useful signal using CEMIC generalized multi-carrier constant envelope multiplexing technology. Construct a constant envelope transmitted signal :

[0015]

[0016] in, This is a direct superposition of signals; This refers to the number of subcarriers. and The first The transmit power and phase of the signal; For the Modulated digital intermediate frequency carrier signal.

[0017] The satellite-to-ground channel model considers both large-scale fading and small-scale Ricean fading. Channel gain of satellites Represented as:

[0018]

[0019] in, It is a large-scale fading coefficient that includes free space loss, atmospheric absorption, rain attenuation, and shadow attenuation. The Ricean fading channel coefficient; This is the Doppler shift caused by the relative motion of the satellite.

[0020] Step 2: Based on the model in Step 1, the power characteristics of the constant envelope signal are transformed into vector norm form, and the channel gain is introduced into the signal-to-noise ratio calculation. Taking into account the impact of physical layer power allocation and network layer satellite handover decisions on energy efficiency, and with the goal of maximizing the overall system energy efficiency, a two-layer collaborative energy efficiency optimization problem of physical layer and network layer is constructed. By setting power constraints, constant envelope constraints, and orthogonality constraints of intermodulation terms, the constant envelope characteristics of the transmitted signal are ensured while guaranteeing the orthogonality of the signal.

[0021] Based on the system model described in step one, the constant envelope signal The power characteristics are transformed into vector norm form, that is, the total power of the transmitted signal is expressed as... and channel gain Signal-to-noise ratio (SNR) calculation is introduced. Considering the combined impact of physical layer power allocation and network layer switching decisions on energy efficiency, a joint optimization problem is constructed, with the objective of maximizing the overall system energy efficiency:

[0022]

[0023] in, The physical layer useful signal coefficient vector is a complex vector composed of the power and phase design values ​​of each subcarrier signal, satisfying... This directly determines the power allocation of each subcarrier; Let be the vector of intermodulation coefficients, satisfying ; Let be the satellite connection state vector, where Indicates the connection of the first One satellite, otherwise 0; For system bandwidth; For the first The signal-to-noise ratio of a single satellite link; For high power amplifier efficiency; This refers to the circuit's power consumption. The average additional power consumption caused by switching; This is the maximum transmission power; Let be the envelope fluctuation cost function. This indicates that the constant envelope constraint is satisfied; This represents the total number of signal combination states. The signal-to-noise ratio threshold; Satellite coverage indicator; For switching delay; This is the maximum delay tolerance.

[0024] Signal-to-noise ratio in the above formula The specific expression is:

[0025]

[0026] in, For the first The channel power gain of each satellite is determined by equation (4) in step one; The power of the useful signal transmission; This represents noise power.

[0027] The constraint means the following: C1 limits the total transmit power of the useful signal to no more than the maximum transmit power. C2 represents a constant envelope constraint, which requires the transmitted signal to be constant. The time domain amplitude is constant. ,Right now By introducing a cost function This is transformed into equivalent mathematical constraints to ensure the efficient operation of high-power amplifiers; C3 requires that intermodulation terms be orthogonal to the useful signal in the signal space to avoid interfering with the demodulation of the useful signal; C4 ensures that the signal-to-noise ratio of the serving satellite is not lower than the minimum threshold requirement; C5 to C7 define the binary attributes of the satellite connection status, visibility constraints, and single connection constraints; C8 requires that the total delay of the handover process be less than the maximum delay tolerance.

[0028] Step 3: To address the high dynamic characteristics of LEO satellites, a network layer switching strategy based on the improved binary particle swarm optimization (IBPSO-HO) is introduced. This strategy combines a hysteresis triggering mechanism with multi-attribute decision-making to select satellites, reducing unnecessary satellite switching and signaling overhead. Through two-layer collaborative optimization, the two-layer collaborative energy efficiency optimization problem in Step 2 is solved alternately until the total system energy efficiency reaches the convergence threshold, thus achieving joint optimization of physical layer waveform parameters and network layer switching decisions.

[0029] Step 3.1: For the two-layer collaborative energy efficiency optimization problem in Step 2, an alternating optimization framework is used to decompose and solve it: fixed network layer connection decisions. The original problem is transformed into a sub-problem of physical layer energy efficiency optimization; the physical layer parameters are fixed. and The original problem is transformed into a sub-problem of network layer switching decision;

[0030] Step 3.2: Solve the physical layer optimization subproblem: Under constant envelope and orthogonality constraints, optimize the signal coefficient vector. and intermodulation term coefficient vector Maximizing the instantaneous energy efficiency of the physical layer; physical layer sub-optimization problem The expression is as follows:

[0031]

[0032] in, Channel gain for the currently serving satellite; Noise power; It is a constant envelope signal vector; This is the signal state matrix; It is the basis matrix of the orthogonal complementary space; A weighted matrix for measuring envelope imbalance;

[0033] The Dinkelbach method is used to transform the above fractional programming problem into a parameter subtraction form, and the ADMM alternating direction multiplier method is used to decompose the constraint problem into three subproblems: power allocation, intermodulation term coefficient optimization, and constant envelope projection, which are then solved iteratively.

[0034] Step 3.3: Solve the network layer optimization sub-problem: The objective is to select the optimal satellite to maximize the transmission rate while satisfying the handover constraints; the network layer sub-optimization problem is expressed as:

[0035]

[0036] The IBPSO-HO algorithm is used to solve the problem when the current satellite SNR is below a threshold. Furthermore, there are candidate satellites that meet the hysteresis margin requirement. Time-triggered switching; the BPSO algorithm uses a fitness function Search for the optimal target satellite with its particle velocity. and location The updated formulas are as follows:

[0037]

[0038]

[0039] in, Indicates the number of iterations of the algorithm; This represents the particle index, used to distinguish different candidate solutions in the particle swarm; This represents the particle dimension index, corresponding to the first element in the satellite selection vector. One component; Inertial weights; Here is the acceleration constant; for Random numbers from a given distribution; The optimal position for the individual; The globally optimal position;

[0040] Step 3.4, Alternating Iteration Process: The physical layer feeds back the optimized transmit power allocation to the network layer, which then updates the SNR and optimizes satellite selection accordingly. The new connection status is then fed back to the physical layer; the two layers operate alternately until the system's overall energy efficiency is achieved. Once the convergence threshold is reached, the optimal transmission power allocation and the best target satellite are determined, thus realizing the coordinated optimization of energy efficiency in the MC-DSSS integrated air-space-ground network.

[0041] Beneficial effects:

[0042] 1. The present invention discloses an energy efficiency collaborative optimization method for an integrated air-space-ground network of MC-DSSS. By constructing a physical layer transmission model based on constant envelope MC-DSSS, and using CEMIC technology to introduce intermodulation terms into the multi-carrier spread spectrum signal to achieve constant envelope transmission, compared with the traditional multi-carrier direct superposition method, it effectively suppresses the high PAPR problem, significantly improves the working efficiency of HPA, and thus improves the energy efficiency of the physical layer.

[0043] 2. The present invention discloses an energy efficiency collaborative optimization method for an integrated air-space-ground network based on MC-DSSS. Based on the physical layer transmission model of constant envelope MC-DSSS, the constant envelope signal power characteristics are transformed into vector norm form, and a signal-to-noise ratio model is constructed by combining channel gain. Taking into account the impact of physical layer power allocation and network layer switching decisions on energy efficiency, the method aims to maximize the overall system energy efficiency and constructs a two-layer collaborative energy efficiency optimization problem between the physical and network layers. By setting power constraints, constant envelope constraints, and orthogonality constraints for intermodulation terms, the method ensures both signal orthogonality and the constant envelope characteristics of the transmitted signal.

[0044] 3. The present invention discloses an energy efficiency collaborative optimization method for an integrated air-space-ground network of MC-DSSS. In view of the high dynamic characteristics of LEO satellites, it introduces a network layer intelligent handover strategy based on IBPSO-HO improved binary particle swarm optimization technology. It combines a hysteresis triggering mechanism and multi-attribute decision-making to select satellites, reduce unnecessary ping-pong handovers, and reduce handover overhead while ensuring communication quality.

[0045] 4. This invention discloses a collaborative optimization method for energy efficiency in an integrated MC-DSSS air-space-ground network. Through two-layer collaborative optimization, it comprehensively utilizes the physical layer waveform gain and the network layer switching gain, significantly improving the overall system energy efficiency compared to single-layer optimization methods. This invention can maintain the stability and efficiency of the system under dynamically changing channel conditions, achieving collaborative optimization of energy efficiency in an integrated MC-DSSS air-space-ground network while ensuring communication quality.

[0046] 5. The present invention discloses an energy efficiency collaborative optimization method for MC-DSSS integrated air-space-ground network. It establishes a non-convex fractional optimization problem with the goal of maximizing instantaneous energy efficiency, and uses the Dinkelbach method and the ADMM alternating direction multiplier method to perform equivalent transformation and efficient solution of the problem, thereby improving the energy efficiency collaborative optimization efficiency of MC-DSSS integrated air-space-ground network. Attached Figure Description

[0047] Figure 1 is a diagram of the architecture of the MC-DSSS integrated air-space-ground communication system described in this invention.

[0048] Figure 2 is a comparison of the energy efficiency and convergence performance of the physical layer algorithm described in this invention with other algorithms.

[0049] Figure 3 is a comparison of the signal envelope amplitude before and after the physical layer algorithm optimization described in this invention.

[0050] Figure 4 is a comparison of the data rate of the network layer algorithm described in this invention with other algorithms as the number of satellites changes.

[0051] Figure 5 is a comparison of the energy efficiency changes of the dual-layer collaborative energy efficiency optimization algorithm of the present invention and other algorithms under different circuit power consumption.

[0052] Figure 6 is a flowchart of an MC-DSSS integrated air-space-ground network energy efficiency collaborative optimization method according to the present invention. Detailed Implementation

[0053] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0054] like Figure 1 As shown, this embodiment discloses an energy efficiency collaborative optimization method for an MC-DSSS integrated space-air-ground network comprising terrestrial users and a LEO satellite constellation. This method improves the overall system energy efficiency through cross-layer collaboration between the physical and network layers, reducing the PAPR of multi-carrier systems and minimizing satellite handover overhead. Figure 6 As shown in this embodiment, an energy efficiency collaborative optimization method for an MC-DSSS integrated air-space-ground network is disclosed. This method mainly includes three steps: physical layer energy efficiency optimization, network layer switching optimization, and dual-layer collaborative alternating iteration. The specific implementation method is as follows:

[0055] Physical layer constant envelope signal optimization: At the physical layer, a constant envelope MC-DSSS transmit signal is constructed using CEMIC technology, and power allocation is optimized using the Dinkelbach-ADMM algorithm. The ground user terminal generates the MC-DSSS signal and introduces intermodulation terms to form a constant envelope signal. For the constructed physical layer energy efficiency optimization problem, the Dinkelbach method is used to handle the fractional objective function, and the ADMM algorithm is used to handle the constant envelope and orthogonality constraints. Simulation results show that, as Figure 2 As shown, the method of the present invention has a faster convergence speed and higher steady-state energy efficiency; as Figure 3 As shown, the optimized signal envelope is approximately constant, and PAPR is significantly reduced, thereby effectively improving the efficiency of the high-power amplifier.

[0056] Intelligent handover decision-making at the network layer: To address the frequent handover issues caused by the high-speed movement of LEO satellites, the IBPSO-HO algorithm is employed for intelligent handover decision-making at the network layer. A dual-trigger mechanism avoids ping-pong handovers, and BPSO is used to search for the optimal target satellite. Simulation results are as follows: Figure 4 As shown, compared with traditional handover algorithms, the IBPSO-HO algorithm can make full use of spatial diversity gain as the number of visible satellites increases, thereby increasing the average data rate by about 30%.

[0057] Two-layer collaborative energy efficiency optimization: This invention achieves collaborative optimization of the physical layer and the network layer through an alternating optimization framework. For example... Figure 5 As shown, under different circuit power consumption conditions, the overall system energy efficiency of the two-layer collaborative scheme is significantly higher than that of the scheme that only performs single-layer optimization, with an energy efficiency improvement of about 40% at typical operating points. This result indicates that the synergistic effect of reducing transmit power at the physical layer and reducing switching overhead at the network layer can achieve optimal system-level energy efficiency.

[0058] In summary, this embodiment significantly improves the overall energy efficiency of the integrated air-space-ground network by synergistically optimizing the physical layer constant envelope waveform design and the network layer intelligent handover strategy, while ensuring communication quality and handover reliability.

[0059] The above detailed description further illustrates the purpose, technical solution, and beneficial effects of the invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for coordinated optimization of energy efficiency in an integrated space-air-ground network using MC-DSSS, characterized in that: Includes the following steps: Step 1: Construct an MC-DSSS integrated air-space-ground network system model. The ground user terminal performs multi-carrier multiplexing of multiple DSSS subcarrier signals and introduces intermodulation terms on the basis of the useful signal through the CEMIC generalized multi-carrier constant envelope multiplexing method to generate a constant envelope transmission signal, so as to reduce the peak-to-average power ratio caused by multi-carrier superposition. Step 2: Based on the model in Step 1, the power characteristics of the constant envelope signal are transformed into vector norm form, and the channel gain is introduced into the signal-to-noise ratio calculation. Taking into account the impact of physical layer power allocation and network layer satellite handover decisions on energy efficiency, and with the goal of maximizing the overall system energy efficiency, a two-layer collaborative energy efficiency optimization problem of physical layer and network layer is constructed. By setting power constraints, constant envelope constraints, and orthogonality constraints of intermodulation terms, the constant envelope characteristics of the transmitted signal are ensured while guaranteeing the orthogonality of the signal. Step 3: To address the high dynamic characteristics of LEO satellites, a network layer switching strategy based on the improved binary particle swarm optimization (IBPSO-HO) is introduced. This strategy combines a hysteresis triggering mechanism with multi-attribute decision-making to select satellites, reducing unnecessary satellite switching and signaling overhead. Through two-layer collaborative optimization, the two-layer collaborative energy efficiency optimization problem in Step 2 is solved alternately until the total system energy efficiency reaches the convergence threshold, thus achieving joint optimization of physical layer waveform parameters and network layer switching decisions.

2. The method as described in claim 1, characterized in that: The MC-DSSS integrated air-space-ground network system model described in step one includes ground transmitter type (2) and (3), satellite receiver type (1) and satellite-ground channel type (4); The satellite receiver is a Walker-Delta structure LEO satellite constellation, consisting of multiple satellites distributed across multiple orbital planes; a candidate satellite set is constructed. : in, for A collection of satellites; Indicates the first One satellite; for Ground users and the first The elevation angle of the satellite; The preset minimum elevation angle threshold is used to filter candidate satellites that meet the visibility requirements; The ground user terminal adopts the MC-DSSS modulation scheme, and its baseband signal is composed of multiple independent DSSS subcarrier signals. Each DSSS signal is represented as: in, The chip sequence is obtained after BPSK modulation and spread spectrum. The duration is The rectangular chip waveform; The chip cycle; To reduce the peak-to-average power ratio caused by multi-carrier superposition, the user terminal introduces intermodulation terms on the useful signal using the CEMIC generalized multi-carrier constant envelope multiplexing method. Construct a constant envelope transmitted signal : in, This is a direct superposition of signals; This refers to the number of subcarriers. and The first The transmit power and phase of the signal; For the Modulated digital intermediate frequency carrier signal; The satellite-to-ground channel model considers both large-scale fading and small-scale Ricean fading. Channel gain of satellites Represented as: in, It is a large-scale fading coefficient that includes free space loss, atmospheric absorption, rain attenuation, and shadow attenuation. The Ricean fading channel coefficient; This is the Doppler shift caused by the relative motion of the satellite.

3. The method as described in claim 1, characterized in that: The two-layer collaborative energy efficiency optimization problem described in step two is as follows: Based on the MC-DSSS integrated air-space-ground network system model constructed in step one, the constant envelope signal The power characteristics are transformed into vector norm form, that is, the total power of the transmitted signal is expressed as... and channel gain We introduce signal-to-noise ratio calculation; considering the combined impact of physical layer power allocation and network layer switching decisions on energy efficiency, we construct a joint optimization problem with the goal of maximizing the overall system energy efficiency. in, The physical layer useful signal coefficient vector is a complex vector composed of the power and phase design values ​​of each subcarrier signal, satisfying... This directly determines the power allocation of each subcarrier; Let be the vector of intermodulation coefficients, satisfying ; Let be the satellite connection state vector, where Indicates the connection of the first One satellite, otherwise 0; For system bandwidth; For the first The signal-to-noise ratio of a single satellite link; For high power amplifier efficiency; This refers to the circuit's power consumption. The average additional power consumption caused by switching; This is the maximum transmission power; Let be the envelope fluctuation cost function. This indicates that the constant envelope constraint is satisfied; This represents the total number of signal combination states. The signal-to-noise ratio threshold; Satellite coverage indicator; For switching delay; Maximum delay tolerance; Signal-to-noise ratio in the above formula The specific expression is: in, For the first The channel power gain of each satellite is determined by equation (4) in step one; The power of the useful signal transmission; Noise power; The constraint conditions are as follows: C1 limits the total transmit power of the useful signal to not exceed the maximum transmit power. C2 represents a constant envelope constraint, which requires the transmitted signal to be constant. The time domain amplitude is constant. ,Right now By introducing a cost function This is transformed into equivalent mathematical constraints to ensure the efficient operation of high-power amplifiers; C3 requires that intermodulation terms be orthogonal to the useful signal in the signal space to avoid interfering with the demodulation of the useful signal; C4 ensures that the signal-to-noise ratio of the serving satellite is not lower than the minimum threshold requirement; C5 to C7 define the binary attributes of the satellite connection status, visibility constraints, and single connection constraints; C8 requires that the total delay of the handover process be less than the maximum delay tolerance.

4. The method as described in claim 1, characterized in that: The implementation method for step three is as follows: Step 3.1: For the two-layer collaborative energy efficiency optimization problem in Step 2, an alternating optimization framework is used to decompose and solve it, i.e., fixed network layer connection decisions. The original problem is transformed into a sub-problem of physical layer energy efficiency optimization; the physical layer parameters are fixed. and The original problem is transformed into a sub-problem of network layer switching decision; Step 3.2: Solve the physical layer optimization subproblem by optimizing the signal coefficient vector under constant envelope and orthogonality constraints. and intermodulation term coefficient vector Maximizing the instantaneous energy efficiency of the physical layer; physical layer sub-optimization problem The expression is as follows: in, Channel gain for the currently serving satellite; Noise power; It is a constant envelope signal vector; This is the signal state matrix; It is the basis matrix of the orthogonal complementary space; A weighted matrix for measuring envelope imbalance; The Dinkelbach method is used to transform the above fractional programming problem into a parametric subtraction form, and the ADMM alternating direction multiplier method is used to decompose the constraint problem into three subproblems: power allocation, mutual regulation coefficient optimization, and constant envelope projection, which are then solved iteratively. Step 3.3: Solve the network layer optimization sub-problem: The objective is to select the optimal satellite to maximize the transmission rate while satisfying the handover constraints; the network layer sub-optimization problem is expressed as: The IBPSO-HO algorithm is used to solve the problem when the current satellite SNR is below a threshold. Furthermore, there are candidate satellites that meet the hysteresis margin requirement. Time-triggered switching; the BPSO algorithm uses a fitness function Search for the optimal target satellite with its particle velocity. and location The updated formulas are as follows: ， in, Indicates the number of iterations of the algorithm; This represents the particle index, used to distinguish different candidate solutions in the particle swarm; This represents the particle dimension index, corresponding to the first element in the satellite selection vector. One component; Inertial weights; Here is the acceleration constant; for Random numbers from a given distribution; The optimal position for the individual; The globally optimal position; Step 3.4, Alternating Iteration Process: The physical layer feeds back the optimized transmit power allocation to the network layer, which then updates the SNR and optimizes satellite selection accordingly. The new connection status is then fed back to the physical layer; the two layers operate alternately until the system's overall energy efficiency is achieved. Once the convergence threshold is reached, the optimal transmission power allocation and the best target satellite are determined, thus achieving coordinated energy efficiency optimization of the MC-DSSS integrated air-space-ground network.

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