CN116614826B - Coverage and capacity optimization method for simultaneous transmission and reflection surface network - Google Patents
Coverage and capacity optimization method for simultaneous transmission and reflection surface network Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
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- H04W24/02—Arrangements for optimising operational condition
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- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The invention discloses a method for optimizing coverage and capacity of a simultaneous transmission and reflection surface network. Firstly, the coverage range and capacity performance of a simultaneous transmission and reflection surface network are analyzed, then a multi-target near-end strategy optimization algorithm based on an action value updating strategy is provided, and finally, a group of optimal solutions are obtained to approach the pareto front. The invention provides a coverage and capacity optimization method for a simultaneous transmission and reflection surface network, which has good application value.
Description
Technical Field
The invention relates to the field of wireless communication, in particular to a method for covering and optimizing capacity of a simultaneous transmission and reflection surface network.
Background
In order to support the ever-increasing heterogeneous quality of service requirements of future wireless networks, such as high data rates, low latency, high reliability, large-scale connections, etc., an emerging communication paradigm, i.e., a reconfigurable intelligent surface-controlled wireless communication environment, has been proposed. The reconfigurable intelligent surface can provide line-of-sight links for users located in obstructed areas by reflection to improve coverage and capacity of conventional wireless networks. However, conventional reconfigurable smart surfaces have a maximum 180 ° coverage, where a "dead zone" is still present on the back of the reconfigurable smart surface. To overcome this limitation, a new concept named simultaneous transmitting and reflecting surface has become attractive. The simultaneous transmitting and reflecting surfaces are capable of simultaneously transmitting and reflecting incident signals, which contributes to full spatial coverage, as compared to conventional reconfigurable smart surfaces. As a new communication paradigm, how the simultaneous transmission and reflection surfaces behave in terms of coverage and capacity is a very interesting issue. Note that coverage and capacity optimization is one of the typical operational tasks mentioned in the third generation partnership project. Since coverage and capacity have a variety of contradictory relationships, it is important to optimize them simultaneously. For example, high transmit power contributes to large coverage, but high inter-cell interference can reduce capacity performance. For this reason, a multi-objective machine learning algorithm may be a potential solution. In contrast to single-objective algorithms, multi-objective machine learning algorithms are able to handle inherent conflicts between objectives, achieving a set of optimal solutions by coordinating and compromising the requirements of the objectives. Therefore, inspired by the advantages of simultaneous transmitting and reflecting surface, simultaneous transmitting and reflecting surface wireless networks have been considered as one of the candidates for next generation wireless communication systems, and simultaneous optimizing of coverage and capacity is also a key issue in simultaneous transmitting and reflecting surface wireless networks.
Disclosure of Invention
In view of the deficiencies of the prior art, the present invention is directed to a method of coverage and capacity optimization for a simultaneous transmission and reflection surface network.
In order to achieve the above purpose, the present invention adopts the following technical methods:
a coverage and capacity optimization method for a simultaneous transmission and reflection surface network comprises the following specific processes:
step one: analysis of coverage and capacity performance of simultaneous transmitting and reflecting surface networks:
the communication system comprises A single antenna base stations and N s A plurality of simultaneous transmitting and reflecting surfaces; wherein a single antenna base station is placed on the boundary of its service area, each simultaneous transmitting and reflecting surface containing K elements; the service area is discretized into N grids, each of which can only use a transmission or reflection pattern of simultaneous transmission and reflection surfaces; furthermore, for each simultaneous transmitting and reflecting surface, defining delta e { Tr, re } to represent the transmitting or reflecting mode, K elements will be divided into K Tr Each transmission element K Re The reflecting elements, i.e. k=k Tr +K Re The method comprises the steps of carrying out a first treatment on the surface of the Thus, the coefficients of the simultaneous transmitting and reflecting surfaces can be defined as:
wherein θ Tr And theta Re Representing a transmission surface coefficient matrix and a reflection surface coefficient matrix, respectively, wherein θ Tr And theta Re Representing a transmission surface coefficient matrix and a reflection surface coefficient matrix respectively, e and j respectively represent the 1 st transmission element amplitude, the K-th transmission element amplitude Tr Amplitude of transmission element, amplitude of reflection element 1, K Re Amplitude of each reflection element, natural logarithmic base and imaginary unit, < ->Respectively represent the 1 st transmission element phase and the K th transmission element phase Tr A transmission element phase, a 1 st reflection element phase and a K th transmission element phase Re A plurality of reflective element phases; definition of the a-th single antenna base station to the n-th s The channels of the simultaneous transmitting and reflecting surfaces are +.>The channel from the a single antenna base station to the n grid is h a，n Nth, n s The channel of the element in delta mode in the simultaneous transmitting and reflecting surface to the nth grid is +.>H represents conjugation, then the nth mesh receives the signal from the nth base station via the nth base station s The signals of the simultaneous transmitting and reflecting surfaces are:
where x represents the transmission signal and,representing zero mean and variance as delta 2 Additive white gaussian noise of (2); defining the reference signal received power as the reference signal received power from all possible sources, the nth grid reference signal received power being expressed as +.> In addition, the signal-to-interference-and-noise ratio of the nth grid may be expressed as:
assume that the reference signal received power minimum threshold of all grids is RSRP th The weighted coverage of the time step t is:
wherein,representation->Coverage weight corresponding to grid, +.>Indicating that the reference signal received power reaches a threshold RSRP th Is a grid set of (a); according to SINR n The capacity of the N grids of the entire service area can be expressed as:
wherein w is cap，n And B represents the capacity weight and bandwidth of the nth mesh, respectively; a, a * And n s * Then is dependent onA corresponding value;
step two: multi-objective near-end policy optimization algorithm based on action value update policy:
according to the Markov decision process, an action value based update strategy is introduced, under which the Markov decision process is expressed asWherein->And->Representing a preference space and a preference function, respectively, +.>In order to be a state space,is a movement space->Is a bonus space; p is a transition probability matrix indicating the probability of transitioning the current state to the next state; defining a controller as a proxy that controls both base stations to formulate a strategy from a single antenna base station to a grid, i.e. a strategy for adjustment of phase shift and transmit power, by means of simultaneous transmission and reflection surfaces; then the clip theory based loss function will be defined as:
wherein the method comprises the steps ofAnd->Representing the current policy parameter and the updated policy parameter, respectively, < >>Andrespectively representing a current policy and a new policy; />And E is the probability ratio of the dominance function and clip theory respectively; along with the training, the optimal parameters and the corresponding strategies are output until convergence.
Step three: and on the basis of the second step, a group of optimal solutions are obtained to approach the pareto front.
The present invention also provides a computer readable storage medium having stored therein a computer program which when executed by a processor implements the above method.
The invention also provides a computer device comprising a processor and a memory for storing a computer program; the processor is configured to implement the above-described method when executing the computer program.
The invention has the beneficial effects that: the invention provides a high-efficiency multi-target machine learning algorithm which can realize simultaneous optimization of coverage and capacity in a simultaneous transmission and reflection surface network and has good application value.
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FIG. 1 is a general idea of a method according to an embodiment of the invention.
Detailed Description
The present invention will be further described with reference to the accompanying drawings, wherein the present embodiment is provided with the technical method as a premise, and a detailed implementation manner and a specific operation process are provided, and the protection scope of the present invention is not limited to the present embodiment.
The present embodiment provides a coverage and capacity optimization method for a simultaneous transmission and reflection surface network, as shown in fig. 1, in which coverage and capacity performance of the simultaneous transmission and reflection surface network are analyzed first, then a multi-objective near-end policy optimization algorithm based on an action value update policy is provided, and finally a set of optimal solutions are obtained to approach pareto fronts. The specific process is as follows:
step one: the coverage and capacity performance of the simultaneous transmitting and reflecting surface network was analyzed.
The communication system comprises A single antenna base stations and N s And simultaneously transmitting and reflecting surfaces. Wherein a single antenna base station is placed on the boundary of its service area, each simultaneous transmitting and reflecting surface contains K elements. The service area is discretized into N grids, each of which can only use a transmission or reflection pattern of simultaneous transmission and reflection surfaces. Furthermore, for each simultaneous transmitting and reflecting surface, defining delta e { Tr, re } to represent the transmitting or reflecting mode, K elements will be divided into K Tr Each transmission element K Re The reflecting elements, i.e. k=k Tr +K Re . Thus, the coefficients of the simultaneous transmitting and reflecting surfaces can be defined as:
wherein θ Tr And theta Re Representing a transmission surface coefficient matrix and a reflection surface coefficient matrix, respectively, wherein θ Tr And theta Re Representing a transmission surface coefficient matrix and a reflection surface coefficient matrix respectively, e and j respectively represent the 1 st transmission element amplitude, the K-th transmission element amplitude Tr Amplitude of transmission element, amplitude of reflection element 1, K Re Amplitude of each reflection element, natural logarithmic base and imaginary unit, < ->Respectively represent the 1 st transmission element phase and the K th transmission element phase Tr A transmission element phase, a 1 st reflection element phase and a K th transmission element phase Re A plurality of reflective element phases; definition of the a-th single antenna base station to the n-th s The channels of the simultaneous transmitting and reflecting surfaces are +.>The channel from the a single antenna base station to the n grid is h a，n Nth, n s The channel of the element in delta mode in the simultaneous transmitting and reflecting surface to the nth grid is +.>H represents conjugation, then the nth mesh receives the signal from the nth base station via the nth base station s The signals of the simultaneous transmitting and reflecting surfaces are:
where x represents the transmission signal and,representing zero mean and variance as delta 2 Additive white gaussian noise of (c). From the received signal, defining the reference signal received power as the maximum useful signal power (received power minus noise power) from all possible sources (including the base station, transmission or reflection mode of the simultaneous transmission and reflection surfaces), the reference signal received power of the nth grid can be expressed as +.>In addition, the signal-to-interference-and-noise ratio of the nth grid may be expressed as:
assume that the reference signal received power minimum threshold of all grids is RSRP th The weighted coverage of the time step t is:
wherein,representation->Coverage weight corresponding to grid, +.>Indicating that the reference signal received power reaches a threshold RSRP th Is a grid set of (a) grid sets. According to SINR n The capacity of the N grids of the entire service area can be expressed as:
wherein w is cap，n And B represents the capacity weight and bandwidth of the nth mesh, respectively; a, a * And n s * Then is dependent onCorresponding values.
Step two: multi-objective near-end policy optimization algorithm based on action value update policy:
in accordance with the Markov decision process, in a conventional multi-objective near-end policy optimization algorithm, the Markov decision process may be employedThe tuple is expressed asWherein->For the state space +.>Is a movement space->To reward space. p is a transition probability matrix indicating the probability of transitioning the current state to the next state. The controller is defined as a proxy that controls both base stations to formulate a strategy from a single antenna base station to the grid, i.e. a strategy for adjustment of phase shift and transmit power, by means of simultaneous transmission and reflection surfaces. In order to further improve the efficiency of the multi-target near-end policy optimization algorithm, an update policy based on action values is introduced in the embodiment. Under this strategy, the Markov decision process is re-represented as +.>Wherein->And->Representing the preference space and the preference function, respectively, then the clip theory based loss function will be defined as:
wherein the method comprises the steps ofAnd->Representing the current policy parameter and the updated policy parameter, respectively, < >>Andrepresenting the current policy and the new policy, respectively. />And E is the probability ratio of the dominance function and clip theory respectively. Along with the training, the optimal parameters and the corresponding strategies are output until convergence.
Step three: and on the basis of the second step, a group of optimal solutions are obtained to approach the pareto front. Various corresponding changes and modifications may be suggested to one skilled in the art in view of the foregoing teachings and all such changes and modifications are intended to be included within the scope of the appended claims.
Claims (3)
1. A method for simultaneous transmission and reflection surface network coverage and capacity optimization, characterized by the specific procedures:
step one: analysis of coverage and capacity performance of simultaneous transmitting and reflecting surface networks:
the communication system comprises A single antenna base stations and N s A plurality of simultaneous transmitting and reflecting surfaces; wherein a single antenna base station is placed on the boundary of its service area, each simultaneous transmitting and reflecting surface containing K elements; the service area is discretized into N grids, each of which can only use a transmission or reflection pattern of simultaneous transmission and reflection surfaces; furthermore, for each simultaneous transmitting and reflecting surface, defining delta e { Tr, re } to represent the transmitting or reflecting mode, K elements will be divided into K Tr Each transmission element K Re The reflecting elements, i.e. k=k Tr +K Re The method comprises the steps of carrying out a first treatment on the surface of the Thus, the coefficients of the simultaneous transmitting and reflecting surfaces can be defined as:
wherein θ Tr And theta Re Representing a transmission surface coefficient matrix and a reflection surface coefficient matrix respectively,e and j respectively represent the 1 st transmission element amplitude, the K-th transmission element amplitude Tr Amplitude of transmission element, amplitude of reflection element 1, K Re Amplitude of each reflection element, natural logarithmic base and imaginary unit,respectively represent the 1 st transmission element phase and the K th transmission element phase Tr A transmission element phase, a 1 st reflection element phase and a K th transmission element phase Re A plurality of reflective element phases; definition of the a-th single antenna base station to the n-th s The channels of the simultaneous transmitting and reflecting surfaces are +.>The channel from the a single antenna base station to the n grid is h a,n Nth, n s The channel of the element in delta mode in the simultaneous transmitting and reflecting surface to the nth grid is +.>H represents conjugation, then the nth mesh receives the signal from the nth base station via the nth base station s The signals of the simultaneous transmitting and reflecting surfaces are:
where x represents the transmission signal and,representing zero mean and variance as delta 2 Additive gauss of (a)White noise; defining the reference signal received power as the reference signal received power from all possible sources, the nth grid reference signal received power being expressed as +.> In addition, the signal-to-interference-and-noise ratio of the nth grid may be expressed as:
assume that the reference signal received power minimum threshold of all grids is RSRP th The weighted coverage of the time step t is:
wherein,representation->Coverage weight corresponding to grid, +.>Indicating that the reference signal received power reaches a threshold RSRP th Is a grid set of (a); according to SINR n The capacity of the N grids of the entire service area can be expressed as:
wherein w is cap,n And B respectively represents the nthCapacity weight and bandwidth of the grid; a, a * And n s * Then is dependent onA corresponding value;
step two: multi-objective near-end policy optimization algorithm based on action value update policy:
according to the Markov decision process, an action value based update strategy is introduced, under which the Markov decision process is expressed asWherein->And->Representing a preference space and a preference function, respectively, +.>For the state space +.>Is a movement space->Is a bonus space; p is a transition probability matrix indicating the probability of transitioning the current state to the next state; defining a controller as a proxy that controls both base stations to formulate a strategy from a single antenna base station to a grid, i.e. a strategy for adjustment of phase shift and transmit power, by means of simultaneous transmission and reflection surfaces; then the clip theory based loss function will be defined as:
wherein the method comprises the steps ofAnd->Representing the current policy parameter and the updated policy parameter, respectively, < >>And->Respectively representing a current policy and a new policy; />And E is the probability ratio of the dominance function and clip theory respectively; along with the training, outputting optimal parameters and corresponding strategies until convergence;
step three: and on the basis of the second step, a group of optimal solutions are obtained to approach the pareto front.
2. A computer readable storage medium, characterized in that the computer readable storage medium has stored therein a computer program which, when executed by a processor, implements the method of claim 1.
3. A computer device comprising a processor and a memory, the memory for storing a computer program; the processor is configured to implement the method of claim 1 when executing the computer program.
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