Method for optimizing reflecting surface in high-speed rail communication based on discrete phase shift

Abstract

The invention belongs to the technical field of high-speed rail communication, and discloses a method for optimizing an intelligent reflecting surface in high-speed rail communication based on discrete phase shift, which comprises the following steps of: constructing a high-speed rail millimeter wave communication system comprising a base station, the intelligent reflecting surface and a plurality of mobile relays; establishing a channel model of each channel in the high-speed rail millimeter wave communication system; deducing a received signal expression of each mobile relay by combining the channel model and the discrete phase shift of the intelligent reflecting surface, and calculating the reachable rate of each mobile relay; establishing a target function by taking minimization of the transmitting power of the base station as a target, and meanwhile, setting constraint conditions to construct an optimization problem; and jointly optimizing the transmission covariance matrix of the base station and the discrete phase shift of the intelligent reflecting surface, and solving an optimization problem through an alternate optimization algorithm to reduce the transmission power of the base station. By adopting the method provided by the invention, the power consumed by the base station is reduced on the premise of ensuring the communication quality of the mobile relay.

Description

Technical Field

[0001] This invention relates to the field of high-speed rail communication technology, and more specifically, to an optimization method for a reflective surface based on discrete phase shift in high-speed rail communication. Background Technology

[0002] With the rapid development of high-speed rail networks, the demand for communication in high-speed mobile scenarios is increasing daily. However, high-speed rail faces a complex wireless propagation environment during operation, making the provision of stable and high-speed data transmission services a key challenge. Existing high-speed rail millimeter-wave communication systems suffer from severe penetration and path losses and are highly sensitive to obstruction. Introducing intelligent reflector technology can help alleviate obstruction problems and improve network resilience: when the direct millimeter-wave link between the base station and the train is blocked, the train can achieve auxiliary communication through the intelligent reflector. However, existing systems mostly adopt continuous phase-shift intelligent reflector designs, relying on high-precision phase-shifting devices, resulting in complex hardware and high manufacturing costs, making it difficult to support large-scale deployment along high-speed rail lines. In addition, continuous phase-shifting devices are susceptible to factors such as temperature, vibration, and aging during long-term operation, leading to phase shift drift, which affects communication reliability and system stability, failing to meet the stringent requirements of high-speed rail for communication reliability. Existing designs also have shortcomings in terms of system energy consumption and performance. On the one hand, the base station's transmission power has not been fully optimized and is often maintained at a high level, resulting in low energy efficiency, which not only increases operating costs but also brings unnecessary environmental load. On the other hand, due to the limitations of the power allocation strategy, the actual effective coverage and system capacity are limited, making it difficult to meet the high bandwidth access needs of multiple users in a high-speed mobile environment.

[0003] In view of this, the present invention proposes an optimization method for high-speed rail communication based on discrete phase shift reflector to solve the above problems. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies and improve the communication performance of high-speed railways, this invention provides a high-speed railway communication optimization method based on discrete phase-shifting reflectors. The specific technical solution is as follows:

[0005] Step 1: Construct a high-speed rail millimeter-wave communication system including base stations, smart reflectors, and several mobile relays;

[0006] Step 2: Establish the channel model for each channel in the high-speed rail millimeter-wave communication system, and define the discrete phase shift of the intelligent reflector.

[0007] Step 3: Combining the channel model and the discrete phase shift of the smart reflector, derive the received signal expression for each mobile relay, and then calculate the achievable rate of each mobile relay accordingly.

[0008] Step 4: With the minimum transmit power of the base station as the objective, establish an objective function and set constraints to construct an optimization problem; jointly optimize the transmit covariance matrix of the base station and the discrete phase shift of the smart reflector, and solve the optimization problem through an alternating optimization algorithm to minimize the transmit power of the base station.

[0009] Furthermore, the base station is equipped with several antennas, the smart reflector is equipped with several reflective elements, and each mobile relay is equipped with several antennas; the smart reflector is also equipped with a smart controller for controlling and changing the phase shift of the incident signal of the reflective element.

[0010] Furthermore, the channel model is assumed to be a quasi-static fast fading channel and modeled as a Ricean channel model; the channel model includes the channel coefficients from the base station to the smart reflector and the channel coefficients from the smart reflector to each mobile relay.

[0011] Furthermore, the discrete phase shift of the intelligent reflective surface is represented by the adjustable phase shift of each reflective unit. The adjustable phase shift is a random variable, and the value of the random variable is restricted to a finite discrete set. The range of values ​​of the discrete set is determined by the quantization order and the number of quantization bits of the reflection phase shift.

[0012] Furthermore, the received signals of each of the mobile relays can be represented by the signals transmitted by the base station, the discrete phase shift of the smart reflector, the channel coefficients, and additive white Gaussian noise.

[0013] Furthermore, the achievable rate of each mobile relay is calculated based on an expression for its received signal.

[0014] Furthermore, the objective function is to minimize the transmit power of the base station, and the constraints include the minimum rate requirement for each mobile relay and the discrete phase shift constraint of the smart reflector.

[0015] Furthermore, the alternating optimization algorithm includes the following steps: first, fixing the transmit covariance matrix of the base station and optimizing the discrete phase shift of the smart reflector; then, fixing the discrete phase shift of the smart reflector and optimizing the transmit covariance matrix of the base station; repeating the above alternating optimization steps until the preset convergence condition is met.

[0016] The technical effects and advantages of the optimization method for a discrete phase-shift-based reflecting surface in high-speed rail communication proposed in this invention are as follows:

[0017] This invention, through its optimized algorithm, can significantly reduce the base station's transmission power, decrease system energy consumption, and improve energy efficiency, contributing to a green and environmentally friendly communication system. This not only saves operating costs but, more importantly, reduces adverse environmental impact, reflecting a strong emphasis on sustainable development. Secondly, it cleverly introduces a discrete phase-shift intelligent reflector, which can be implemented using simple switching circuits or low-precision phase-shifting devices, greatly simplifying the hardware structure and reducing manufacturing complexity and cost. Furthermore, the discrete phase-shifting characteristic makes the system more robust and reliable, enhancing its anti-interference capabilities and enabling it to cope with harsh working environments while ensuring communication quality. At the same time, it reduces reliance on high-precision components, thereby improving system maintainability and long-term stability, extending service life, and reducing maintenance costs. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of an optimization method for a reflective surface based on discrete phase shift in high-speed rail communication according to the present invention.

[0019] Figure 2 This is a graph showing the relationship between the transmission power of the present invention and the minimum rate requirement;

[0020] Figure 3 This is a schematic diagram of an optimized system for high-speed rail communication based on a discrete phase-shift reflecting surface according to the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Example 1

[0023] Please see Figure 1 As shown in this embodiment, an optimization method for a reflective surface based on discrete phase shift in high-speed rail communication includes:

[0024] Step 1: Construct a high-speed rail millimeter-wave communication system that includes base stations, intelligent reflectors, and several mobile relays;

[0025] Step 2: Establish the channel model for each channel in the high-speed rail millimeter-wave communication system, and define the discrete phase shift of the intelligent reflector.

[0026] Step 3: Combining the channel model and the discrete phase shift of the smart reflector, derive the received signal expression for each mobile relay, and then calculate the achievable rate of each mobile relay accordingly.

[0027] Step 4: With the minimum transmit power of the base station as the objective, establish an objective function and set constraints to construct an optimization problem; jointly optimize the transmit covariance matrix of the base station and the discrete phase shift of the smart reflector, and solve the optimization problem through an alternating optimization algorithm to minimize the transmit power of the base station.

[0028] A smart reflector is an artificial plane composed of a large number of reconfigurable reflective elements, each of which can independently adjust the phase of the incident electromagnetic wave. By intelligently controlling the phase of each reflective element, the smart reflector can actively reconstruct the wireless channel environment and improve signal propagation conditions.

[0029] The base station is equipped with several antennas, the smart reflector is equipped with several reflective elements and a smart controller, and each mobile relay is equipped with several antennas; the smart controller is used to change the phase shift of the incident signal of the reflective element.

[0030] The channel model is assumed to be a quasi-static fast fading channel and is modeled as a Ricean channel model (considering line-of-sight and non-line-of-sight components); the channel model includes the channel coefficients from the base station to the smart reflector, and the channel coefficients from the smart reflector to each of the mobile relays.

[0031] The discrete phase shift of the intelligent reflective surface is characterized by the adjustable phase shift of each reflective unit; the adjustable phase shift is a random variable, and its value is limited to a finite discrete set, the range of which is determined by the quantization order and the number of quantization bits of the reflection phase shift. Specifically, the discrete phase shift... In the formula, θ m This represents the adjustable phase shift of the m-th reflecting unit, which is a random variable whose values ​​are restricted to a discrete set. Among them K=2 b The quantization order of the reflection phase shift is represented by b, the number of quantization bits is b, the total number of reflection units is m, and diag(·) represents diagonal operation.

[0032] Continuous phase shifting requires high-precision phase-shifting devices, which are complex to manufacture and expensive. Discrete phase shifting, on the other hand, only requires a finite number of states and can be implemented using simpler switching circuits or low-precision phase-shifting devices, greatly reducing hardware complexity and manufacturing costs. Continuous phase-shifting devices are affected by factors such as temperature and aging, causing phase shift accuracy to drift. Discrete phase shifting states are relatively stable, have strong anti-interference capabilities, and offer higher system reliability.

[0033] The received signal of each mobile relay is represented by the signal transmitted by the base station, the discrete phase shift of the smart reflector, the channel coefficient, and additive white Gaussian noise; the achievable rate of each mobile relay (referring to the maximum theoretically achievable transmission rate of information under given signal-to-noise ratio and bandwidth conditions) is calculated based on the expression of its received signal. Specifically, the received signal of the k-th mobile relay can be expressed as:

[0034] y k =H k ΘGx+n k ,

[0035] Among them, y k This represents the received signal of the k-th mobile relay. This indicates that the signal sent by the base station satisfies Where Q≥0 represents the transmit covariance matrix of the signal transmitted from the base station. Let $\mathbf{k}$ represent the additive white Gaussian noise received by the k-th mobile relay (MR), where $\mathbf{k}$ is the sum of the sums ... This indicates the corresponding noise power. and Denotes the channel coefficients, N and N R These represent the number of antennas equipped on the base station and the k-th mobile relay (MR), respectively.

[0036] Specifically, the channel coefficient is:

[0037]

[0038] Where K represents the Rice factor. G LoS This represents the line-of-sight component, which is related to the link distance and remains stable within each time slot. G NLoS This represents the non-line-of-sight component, modeled as Rayleigh fading. G LoS and G NLoS They can be represented as:

[0039]

[0040] m∈{1,...,M},n∈{1,...,N},

[0041]

[0042] Where β0 = -61.3849 dB represents the path loss at a distance of 1 meter; d is the distance between the base station and the active RIS; α1 = 2 and α2 = 3.6 are the path loss exponents in line-of-sight and non-line-of-sight scenarios, respectively; θ m,n The phase is a randomly distributed phase with values ​​ranging from [0, 2π); while G1 NLOSEach element is a complex, circularly symmetric, zero-mean, unit-variance random variable used to characterize small-scale fading. Similarly, the channel coefficient H from the smart reflector to the k-th mobile relay can be obtained. k .

[0043] The received signal characterizes the actual transmission path of the millimeter-wave signal between the base station, the smart reflector, and the mobile relay: the base station transmits the original signal, which propagates through the wireless environment to the smart reflector, and after phase-shift modulation at the smart reflector, it continues to propagate to each of the mobile relays. Noise interference is also superimposed during the transmission process. The received signal not only clearly reflects the phase-shift modulation effect of the smart reflector on the signal, but also provides technical support for the subsequent optimization of the phase shift of the smart reflector.

[0044] The objective function is the transmit power of the base station, and the constraints include the minimum rate requirement for each mobile relay and discrete phase shift constraints. Specifically, the established optimization problem can be expressed as:

[0045]

[0046] Where Tr(·) represents the trace of the matrix, and R k Let be the channel capacity of the k-th mobile relay and

[0047]

[0048] The alternating optimization algorithm includes: fixing the base station's transmit covariance matrix to optimize the discrete phase shift of the smart reflector, then fixing the smart reflector's discrete phase shift to optimize the base station's transmit covariance matrix, and repeating the above steps until the preset convergence condition is met.

[0049] When Θ is fixed, the optimization problem becomes a convex problem, which can be solved directly. When Q is fixed, the definition is... Relax discrete constraints and utilize The optimization problem can then be transformed into:

[0050] Find V

[0051]

[0052] Rank(V) = 1,

[0053] V≥0,

[0054] Where, V = vv H ,

[0055] blkdiag(V,…,V) represents a block diagonal matrix with each diagonal component being V.

[0056] Given a fixed base station transmit covariance matrix Q, a penalty method is first used to handle the rank-one constraint, and the continuous phase shift Θ is obtained. Then, the continuous solution is quantized using a quantization algorithm to obtain the discrete phase shift Θ. For the obtained discrete phase shift, the optimal Q for the corresponding scenario is solved and the transmit power is calculated: if the transmit power corresponding to the discrete phase shift does not increase, Θ is updated; if the power increases, the current value is maintained.

[0057] After updating Θ, fix the current discrete phase shift Θ and optimize the base station transmit covariance matrix Q to further reduce transmit power. Since the optimization objective in this step is a convex function and the constraint condition is a convex set, the optimal Q can be obtained directly.

[0058] In summary, the core process of the iterative optimization algorithm submitted in this invention is as follows: first, fix the discrete phase shift Θ and update the emission covariance matrix Q; then, fix the emission covariance matrix Q and update the discrete phase shift Θ through a quantization algorithm; repeat the above two steps until the algorithm converges.

[0059] In this embodiment, parameter simulation is constructed around a high-speed rail millimeter-wave communication system assisted by a smart reflector. The specific simulation parameter configuration is as follows:

[0060] Train configuration: It adopts a high-speed train with 8 carriages, each carriage is 200m long; 6 mobile relays are randomly deployed on the roof of the train.

[0061] Communication link construction: A communication link is established by deploying a smart reflector between the base station and the mobile relay.

[0062] Equipment parameters: The base station is equipped with 3 antennas, each mobile relay is equipped with 2 antennas, and the intelligent reflector is equipped with 30 reflective units.

[0063] Coordinate settings: The spatial coordinates of each device are defined as follows: the base station is located at (20,30,10)m; the smart reflector is located at (0,0,2.5)m; the first mobile relay is located at (-20,10,2.5)m, the second mobile relay is located at (-20,20,2.5)m, the third mobile relay is located at (-20,30,2.5)m, the fourth mobile relay is located at (-20,40,2.5)m, the fifth mobile relay is located at (-20,50,2.5)m, and the sixth mobile relay is located at (-20,60,2.5)m.

[0064] Benchmark Scheme: To verify the effectiveness of this scheme, a random phase shift scheme is selected as the benchmark scheme and its performance is compared with that of this scheme.

[0065] Please see Figure 2 The graph shows the relationship between the transmission power of the proposed scheme and the minimum rate requirement. As can be seen from the graph, the transmission power of both the proposed scheme and the random phase-shift scheme increases with Q.s The increase in the minimum rate is due to the fact that the higher minimum rate requirement needs to be guaranteed by increasing the base station transmit power; furthermore, the performance of the proposed scheme is better than that of the random phase shift scheme, which fully confirms the necessity of phase shift optimization design.

[0066] In this embodiment, the optimized algorithm significantly reduces the base station's transmission power, decreases system energy consumption, and improves energy efficiency, contributing to a green and environmentally friendly communication system. This not only saves operating costs but, more importantly, reduces adverse environmental impact, reflecting a strong emphasis on sustainable development. Secondly, the ingenious introduction of a discrete phase-shift intelligent reflector, implemented using simple switching circuits or low-precision phase-shifting devices, greatly simplifies the hardware structure and reduces manufacturing complexity and cost. Furthermore, the discrete phase-shifting characteristic makes the system more robust and reliable, enhances anti-interference capabilities, and enables it to cope with harsh working environments, ensuring communication quality. Simultaneously, it reduces reliance on high-precision components, improving system maintainability and long-term stability, extending service life, and reducing maintenance costs.

[0067] Example 2

[0068] Please see Figure 3 As shown, for parts not described in detail in this embodiment, please refer to the description in Embodiment 1. An optimization system based on discrete phase shift reflecting surfaces in high-speed rail communication is provided, comprising:

[0069] The initial construction module was used to build a high-speed rail millimeter-wave communication system, which includes base stations, intelligent reflectors, and several mobile relays.

[0070] The model building module establishes the channel model of each channel in the high-speed rail millimeter-wave communication system and defines the discrete phase shift of the intelligent reflector.

[0071] The integrated calculation module, combining the channel model and the discrete phase shift of the smart reflector, derives the received signal expression for each mobile relay, and then calculates the achievable rate of each mobile relay accordingly.

[0072] The integrated optimization module is used to construct an optimization problem by establishing an objective function and setting constraints with the minimum transmission power of the base station as the objective; jointly optimizing the transmission covariance matrix of the base station and the discrete phase shift of the intelligent reflector, and solving the optimization problem through an alternating optimization algorithm to minimize the transmission power of the base station; the modules are connected to each other by wired and / or wireless means to realize data transmission between the modules.

[0073] Example 3

[0074] This embodiment discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the operation mode of the above-described optimization method for high-speed rail communication based on discrete phase-shift reflective surfaces.

[0075] Since the electronic device described in this embodiment is used to implement the optimization method of a discrete phase-shift-based reflector in high-speed rail communication according to the embodiments of this application, those skilled in the art can understand the specific implementation and various variations of the electronic device in this embodiment based on the optimization method of a discrete phase-shift-based reflector in high-speed rail communication described in the embodiments of this application. Therefore, how the electronic device implements the method in the embodiments of this application will not be described in detail here. Any electronic device used by those skilled in the art to implement the optimization method of a discrete phase-shift-based reflector in high-speed rail communication according to the embodiments of this application falls within the scope of protection of this application.

[0076] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0077] The above description is merely a preferred embodiment of the present invention, and the scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for users of ordinary technical skills, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for optimizing a reflector based on discrete phase shift in high-speed railway communication, characterized in that, include: Step 1: Construct a high-speed rail millimeter-wave communication system that includes base stations, intelligent reflectors, and several mobile relays; Step 2: Establish the channel model for each channel in the high-speed rail millimeter-wave communication system, and define the discrete phase shift of the intelligent reflector. Step 3: Combining the channel model and the discrete phase shift of the smart reflector, derive the received signal expression for each mobile relay, and then calculate the achievable rate of each mobile relay accordingly. Step 4: With the minimum transmit power of the base station as the objective, establish an objective function and set constraints to construct an optimization problem; jointly optimize the transmit covariance matrix of the base station and the discrete phase shift of the smart reflector, and solve the optimization problem through an alternating optimization algorithm to minimize the transmit power of the base station.

2. The optimization method for a reflective surface based on discrete phase shift in high-speed rail communication according to claim 1, characterized in that, The base station is equipped with several antennas, the smart reflector is equipped with several reflective elements, and each mobile relay is equipped with several antennas; the smart reflector is also equipped with a smart controller for controlling and changing the phase shift of the incident signal of the reflective element.

3. The optimization method for a reflective surface based on discrete phase shift in high-speed rail communication according to claim 1, characterized in that, The channel model assumes a quasi-static fast fading channel and is modeled as a Ricean channel model; the channel model includes the channel coefficients from the base station to the smart reflector and the channel coefficients from the smart reflector to each mobile relay.

4. The optimization method for a reflective surface based on discrete phase shift in high-speed rail communication according to claim 1, characterized in that, The discrete phase shift of the intelligent reflective surface is represented by the adjustable phase shift of each reflective unit. The adjustable phase shift is a random variable, and the value of the random variable is restricted to a finite discrete set. The range of values ​​of the discrete set is determined by the quantization order and the number of quantization bits of the reflection phase shift.

5. The optimization method for a reflective surface based on discrete phase shift in high-speed rail communication according to claim 1, characterized in that, The received signal of each mobile relay is determined by the signal transmitted by the base station, the discrete phase shift of the smart reflector, the channel coefficient of each communication link, and additive white Gaussian noise, and its expression includes the above-mentioned technical parameters.

6. The optimization method for a reflective surface based on discrete phase shift in high-speed rail communication according to claim 1, characterized in that, The achievable rate of each mobile relay can be calculated based on its received signal expression.

7. The optimization method for a reflective surface based on discrete phase shift in high-speed rail communication according to claim 1, characterized in that, The objective function is the transmit power of the base station, and the constraints include the minimum rate requirement for each mobile relay and the discrete phase shift constraint.

8. The optimization method for a reflective surface based on discrete phase shift in high-speed rail communication according to claim 1, characterized in that, The alternating optimization algorithm includes: fixing the transmit covariance matrix of the base station to optimize the discrete phase shift of the smart reflector, then fixing the discrete phase shift of the smart reflector to optimize the transmit covariance matrix of the base station, and repeating the above steps until the preset convergence condition is met.

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