Distributed Transceiver Power Joint Optimization Method and System for Simultaneous Interpretation and Communication System

Abstract

This invention discloses a distributed transmit / receive power joint optimization method and system for a simultaneous information and energy transmission cooperative system. The method comprises the following steps: Step 1, constructing distributed performance indicators to characterize information rate and collected energy; Step 2, each access point independently performs joint allocation of transmit power and receive power splitting factors; Step 3, users calculate the power splitting factor based on their own rate requirements, energy requirements, and battery capacity limitations. This invention solves the problem of high fronthaul link interaction in existing centralized power allocation schemes in simultaneous information and energy transmission cooperative systems.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology, specifically relating to a method and system for joint optimization of distributed transmit power and receive power splitting factors in a signal-energy co-transmission cooperative system. Background Technology

[0002] In a collaborative signal-to-energy transmission system, each user is served by multiple access points within a service cluster. In a power-splitting reception architecture, the received signal is split into two streams based on a power splitting factor, used for information decoding and energy collection respectively. Current research on the joint optimization of transmit and receive power splitting factors largely employs a centralized approach, with a central processing unit implementing optimal power allocation. This approach typically achieves better data rates and energy output, but requires significant information exchange between the central processing unit and access points, consuming substantial fronthaul link resources. A distributed approach can significantly reduce fronthaul link interaction, but implementing a distributed scheme in a collaborative signal-to-energy transmission system presents two major challenges: First, when access points do not exchange information, interference between them must be considered to improve system performance; second, since the same user is served by multiple access points within the service cluster, power allocation at the access point side will result in multiple different power splitting factors for the same user, necessitating a solution for allocating the receive power splitting factor.

[0003] To address the issue of high fronthaul link interaction in existing centralized power allocation schemes in information and energy co-transmission systems, this invention provides a distributed method and system for jointly optimizing transmit and receive power splitting factors by solving the two problems mentioned above in distributed power allocation methods. Summary of the Invention

[0004] To address the issue of high fronthaul link interaction in existing centralized power allocation schemes in information and energy co-transmission systems, this invention provides a distributed method and system for jointly optimizing transmit and receive power splitting factors.

[0005] Application scenario of this invention: a collaborative system comprising a large number of access points and a large number of users with built-in power splitters. Each user is served by the collaborative efforts of all access points within their respective service clusters.

[0006] To achieve the above-mentioned technical objectives, the present invention adopts the following technical solution:

[0007] A method for joint optimization of distributed transmit and receive power in a signal-powered simultaneous interpretation cooperative system, the specific steps of which are as follows:

[0008] Step 1: Construct distributed performance metrics to characterize information rate and energy collection.

[0009] Step 2: Each access point independently performs joint allocation of transmit power and receive power splitting factors;

[0010] Step 3: The user calculates the power shunt factor based on their own rate requirements, energy requirements, and battery capacity limitations.

[0011] Preferably, step 1 involves constructing distributed performance metrics to characterize information rate and collection energy. Specifically:

[0012] Distributed performance metrics are performance metrics that can be calculated without the need for information exchange between access points or between access points and the central processing unit. Assume K... n and Let ρ represent the set of serving users and the set of non-serving users at access point n, respectively. k (0≤ρ k ≤1) represents the power shunt factor for user k.

[0013] First, define the access point n and the user k (k∈K). n The signal-to-leakage interference-to-noise ratio between the two is

[0014]

[0015] Define the signal-to-leakage interference sum between access point n and user k as:

[0016]

[0017] Among them, S n,k For the useful signal power, I n,k For data sent from access point n to K n Interference signal power from other users within the area, for The power N received by user t from the signal transmitted from access point n to user k. n,k For channel noise variance, This refers to the signal processing noise introduced during information demodulation.

[0018] Then, based on equations (1) and (2), construct... and These are respectively used as distributed performance indicators to characterize the information rate of user k and the energy collected, where η is the energy conversion efficiency.

[0019] Preferably, in step 2, each access point independently performs the joint allocation of transmit power and receive power splitting factors. Specifically:

[0020] Taking access point n as an example, the optimization method is used to optimize the transmission power p. n,k (k∈K n and the received power shunt factor ρ k (k∈Kn ) to be jointly allocated.

[0021] First, establish an optimization problem model. The optimization objective can be user rate fairness. Maximize energy efficiency Maximize energy collection etc., where δ k p is the weighting factor. n,tot Let n be the total power consumed by access point n, including the power of transmitted signals and the power consumed by the circuitry. Assume... For access point n, the optimization problem model is as follows:

[0022]

[0023] in, These represent the minimum information rate requirement and the energy requirement for data collection, p n,k For the power of transmitting user k's signal, p n,max This represents the maximum transmit power of access point n.

[0024] Then, optimization methods such as fractional programming, continuous convex approximation, and block coordinate descent are used to solve the above optimization problem.

[0025] Preferably, in step 3, the user calculates the power shunt factor based on their own rate requirements, energy requirements, and battery capacity limitations. Specifically:

[0026] Taking user k as an example, based on its own rate requirements, energy requirements, and battery capacity limitations, ρ is calculated using the following formula. k :

[0027]

[0028] Where, ρ k Represents the received power shunt factor, and equation (4) represents ρ. k To satisfy the three inequalities in the formula, ρ k The minimum value of G k Let r represent the service cluster of user k. k,min E k,tot I represents the rate requirement of user k and the battery capacity, respectively. k,tot This represents the interference and noise power received by user k. Equation (4) is a single-variable optimization problem, which can be solved using the Lagrange multiplier method.

[0029] This invention also discloses a distributed transmit / receive power joint optimization system for a signal-energy simultaneous interpretation cooperative system, which includes the following modules:

[0030] Performance Metrics Construction Module: Constructs distributed performance metrics to characterize information rate and collected energy;

[0031] Joint allocation module for power shunting factors: Each access point independently performs joint allocation of transmit power and receive power shunting factors;

[0032] Power shunting factor calculation module: Users can calculate the power shunting factor based on their own rate requirements, energy requirements and battery capacity limitations.

[0033] The innovation of this invention lies in:

[0034] 1) The optimized performance indicators adopted take into account the interference between access points and do not require information exchange between access points, thereby improving the performance of the distributed power allocation method without increasing the interaction of the fronthaul link.

[0035] 2) The power shunt factor is calculated by stepwise optimization on the access point side and the user side, so as to achieve full utilization of energy without the need for information exchange between access points.

[0036] This invention first constructs a distributed performance index to characterize information rate and energy collection; second, each access point independently performs joint allocation of transmit power and receive power shunting factors; finally, the user side calculates the power shunting factor based on its own performance requirements and battery capacity limitations.

[0037] This invention employs a fully distributed technical solution, eliminating the need for information exchange between access points and between access points and the central processing unit, significantly reducing the requirements for fronthaul link capacity. Furthermore, the optimized performance indicators in this invention consider interference between access points, improving system performance. The power distribution factor is calculated through a step-by-step optimization approach on both the access point and user sides. Optimization on the access point side ensures the energy demand performance of users under this power allocation, while the user side further optimizes the system by leveraging the advantages of inter-access point collaboration, achieving full energy utilization. Attached Figure Description

[0038] Figure 1 This is a flowchart of a distributed transmit / receive power joint optimization method for a signal-energy simultaneous transmission and reception cooperative system according to a preferred embodiment of the present invention.

[0039] Figure 2 This is a block diagram of a distributed transmit / receive power joint optimization system for a signal-energy simultaneous transmission and reception cooperative system according to a preferred embodiment of the present invention. Detailed Implementation

[0040] The preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0041] like Figure 1As shown in this embodiment, a distributed transmit / receive power joint optimization method for a signal-powered simultaneous transmission and reception cooperative system is described. The specific application scenario of this embodiment is as follows: a cooperative system comprising a large number of access points and a large number of users with built-in power splitters. Each user is served by the cooperation of all access points within their respective service clusters. The specific steps of the method in this embodiment are as follows:

[0042] Step 1: Construct distributed performance metrics to characterize information rate and collection energy, as follows:

[0043] Distributed performance metrics are performance metrics that can be calculated without the need for information exchange between access points or between access points and the central processing unit. Assume K... n and K n Let ρ represent the set of serving users and the set of non-serving users at access point n, respectively. k (0≤ρ k ≤1) represents the power shunt factor for user k.

[0044] First, define the access point n and the user k (k∈K). n The signal-to-leakage interference-to-noise ratio between the two is

[0045]

[0046] Define the signal-to-leakage interference sum between access point n and user k as:

[0047]

[0048] Among them, S n,k For the useful signal power, I n,k For data sent from access point n to K n Interference signal power from other users within the area, for The power N received by user t from the signal transmitted from access point n to user k. n,k For channel noise variance, This refers to the signal processing noise introduced during information demodulation.

[0049] Then, based on equations (1) and (2), construct... and These are respectively used as distributed performance indicators to characterize the information rate of user k and the energy collected, where η is the energy conversion efficiency.

[0050] Step 2: Each access point independently performs joint allocation of transmit power and receive power splitting factors, as detailed below:

[0051] Taking access point n as an example, the optimization method is used to optimize the transmission power p. n,k (k∈K n and the received power shunt factor ρk (k∈K n ) to be jointly allocated.

[0052] First, establish an optimization problem model. The optimization objective can be user rate fairness. Maximize energy efficiency Maximize energy collection etc., where δ k p is the weighting factor. n,tot Let n be the total power consumed by access point n, including the power of transmitted signals and the power consumed by the circuitry. Assume... For access point n, the optimization problem model is as follows:

[0053]

[0054] in, These represent the minimum information rate requirement and the energy requirement for data collection, p n,k For the power of transmitting user k's signal, p n,max This represents the maximum transmit power of access point n.

[0055] Then, optimization methods such as fractional programming, continuous convex approximation, and block coordinate descent are used to solve the above optimization problem.

[0056] Step 3: The user calculates the power shunt factor based on their own rate requirements, energy requirements, and battery capacity limitations, as follows:

[0057] Taking user k as an example, based on its own rate requirements, energy requirements, and battery capacity limitations, ρ is calculated using the following formula. k :

[0058]

[0059] Among them, G k Let r represent the service cluster of user k. k,min E k,tot I represents the rate requirement of user k and the battery capacity, respectively. k,tot This represents the interference and noise power received by user k. Equation (4) is a single-variable optimization problem, which can be solved using the Lagrange multiplier method.

[0060] like Figure 2 As shown in the figure, this embodiment discloses a distributed transmit / receive power joint optimization system for a telemetry and communication system, which includes the following modules:

[0061] Performance Metrics Construction Module: Constructs distributed performance metrics to characterize information rate and collected energy;

[0062] Joint allocation module for power shunting factors: Each access point independently performs joint allocation of transmit power and receive power shunting factors;

[0063] Power shunting factor calculation module: Users can calculate the power shunting factor based on their own rate requirements, energy requirements and battery capacity limitations.

[0064] Other aspects of this embodiment can be found in the above method embodiments.

[0065] This invention is not limited to the specific embodiments described above. Those skilled in the art can make various adjustments or modifications within the scope of the claims, which do not affect the substantive content of this invention.

Claims

1. A method for joint optimization of distributed transmit and receive power in a signal-powered simultaneous interpretation cooperative system, characterized in that, Follow these steps: Step 1: Construct distributed performance metrics to characterize information rate and energy collection. Step 2: Each access point independently performs joint allocation of transmit power and receive power splitting factors; Step 3: The user calculates the power shunt factor based on their own rate requirements, energy requirements, and battery capacity limitations; Step 1 is as follows: Assume K n and Let ρ represent the set of serving users and the set of non-serving users at access point n, respectively. k Let ρ represent the power shunt factor for user k, (0 ≤ ρ k ≤1; Define the signal-to-leakage interference-to-noise ratio (SNR) between access point n and user k as follows: Where, (k∈K) n ;) Define the signal-to-leakage interference sum between access point n and user k as: Among them, S n,k For the useful signal power, I n,k For data sent from access point n to K n Interference signal power from other users within the area, for The power N received by user t from the signal transmitted from access point n to user k. n,k For channel noise variance, This refers to signal processing noise introduced during information demodulation. Based on equations (1) and (2), construct and These are respectively used as distributed performance indicators characterizing the information rate of user k and the energy collected, where η is the energy conversion efficiency; Step 2 is as follows: The transmission power p is optimized using an optimization method. n,k and the received power shunt factor ρ k Perform joint allocation, k∈K n ; Establish an optimization problem model; the optimization objective adopts user rate fairness. Maximize energy efficiency Maximize energy collection Where δ k p is the weighting factor. n,tot The total power consumed by access point n includes the transmitted signal power and the circuit power consumption; assuming... For access point n, the optimization problem model is as follows: in, These represent the minimum information rate requirement and the energy requirement for data collection, p n,k For the power of transmitting user k's signal, p n,max The maximum transmit power of access point n; The above optimization problem is solved using optimization methods.

2. The method for joint optimization of distributed transmit and receive power in a signal-powered simultaneous transmission and reception cooperative system as described in claim 1, characterized in that, Step 3 is as follows: Based on its own rate requirements, energy requirements, and battery capacity limitations, ρ is calculated using the following formula. k : Where, ρ k Indicates the received power shunt factor; G k Let r represent the service cluster of user k. k,min E k,tot I represents the rate requirement of user k and the battery capacity, respectively. k,tot Let represent the interference and noise power received by user k; Equation (4) is a single-variable optimization problem, which is solved using the Lagrange multiplier method.

3. A distributed transmit / receive power joint optimization system for a signal-energy simultaneous interpretation cooperative system, used to implement the distributed transmit / receive power joint optimization method for the signal-energy simultaneous interpretation cooperative system according to any one of claims 1-2, characterized in that, Includes the following modules: Performance Metrics Construction Module: Constructs distributed performance metrics to characterize information rate and collected energy; Joint allocation module for power shunting factors: Each access point independently performs joint allocation of transmit power and receive power shunting factors; Power shunting factor calculation module: Users can calculate the power shunting factor based on their own rate requirements, energy requirements and battery capacity limitations.

4. The distributed transmit / receive power joint optimization system of the information and energy co-transmission cooperative system as described in claim 3, characterized in that, The performance metric construction module is as follows: Assume K n and Let ρ represent the set of serving users and the set of non-serving users at access point n, respectively. k Let ρ represent the power shunt factor for user k, (0 ≤ ρ k ≤1;) Define the signal-to-leakage interference-to-noise ratio (SNR) between access point n and user k as follows: Where, (k∈K) n ;) Define the signal-to-leakage interference sum between access point n and user k as: Among them, S n,k For the useful signal power, I n,k For data sent from access point n to K n Interference signal power from other users within the area, for The power N received by user t from the signal transmitted from access point n to user k. n,k For channel noise variance, This refers to signal processing noise introduced during information demodulation. Based on equations (1) and (2), construct and These are respectively used as distributed performance indicators to characterize the information rate of user k and the energy collected, where η is the energy conversion efficiency.

5. The distributed transmit / receive power joint optimization system of the information and energy co-transmission cooperative system as described in claim 4, characterized in that, The joint allocation module for power shunt factors is as follows: The transmission power p is optimized using an optimization method. n,k and the received power shunt factor ρ k Perform joint allocation, k∈K n ; Establish an optimization problem model; the optimization objective adopts user rate fairness. Maximize energy efficiency Maximize energy collection Where δ k p is the weighting factor. n,tot The total power consumed by access point n includes the transmitted signal power and the circuit power consumption; assuming... For access point n, the optimization problem model is as follows: in, These represent the minimum information rate requirement and the energy requirement for data collection, p n,k For the power of transmitting user k's signal, p n,max The maximum transmit power of access point n; The above optimization problem is solved using optimization methods.

6. The distributed transmit / receive power joint optimization system of the information and energy co-transmission system as described in claim 5, characterized in that, The power shunt factor calculation module is as follows: Based on its own rate requirements, energy requirements, and battery capacity limitations, ρ is calculated using the following formula. k : Where, ρ k Indicates the received power shunt factor; G k Let r represent the service cluster of user k. k,min E k,tot I represents the rate requirement of user k and the battery capacity, respectively. k,tot The power of interference and noise received by user k is represented by equation (4). Equation (4) is a single-variable optimization problem and is solved using the Lagrange multiplier method.

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