CN106231665A - Resource allocation methods based on the switching of RRH dynamic mode in number energy integrated network - Google Patents

Abstract

The invention discloses a kind of several can resource allocation methods based on the switching of RRH dynamic mode in integrated network, comprise the following steps: S1, determine network model and host-host protocol；S2, calculate energy signal and data signal that each user receives, calculate data throughout and the energy harvesting amount of each user；S3, definition evaluation function, determine optimization aim；S4, the beam designing of data and the energy transmission determined according to channel information between RRH and the user of optimum；S5, the data transmission user in a time slot, RRH model selection, RRH power are allocated into places Optimization Solution；S6, selecting system the corresponding data transmission selection of user, the distribution of RRH power and the RRH model selection of the maximum time slot of evaluation function sum as the optimal choice of system, and calculate RRH according to the method for step S4 the optimal beam of user designed.The present invention is by the optimized distribution to power Yu RRH resource, hence it is evident that handling capacity and energy that the system that improves is overall gather in performance.

Description

Resource allocation method based on RRH dynamic mode switching in digital energy integrated network

Technical Field

The invention belongs to the technical field of digital-energy integrated communication networks, and particularly relates to a time slot and power resource allocation scheme which is based on RRH dynamic mode switching and meets overall requirements of data and energy of different user sets in a C-RAN scene.

Background

Data and Energy Integrated Communication Networks (Networks) are Communication Networks evolved from Data and Energy cooperative transmission technologies, and are different from conventional Networks in that they can not only perform Data Communication between users and base stations, but also meet Energy requirements of different users, such as wireless charging of Communication devices. The basic protocol stack comprises: a physical layer, a link layer, a network layer and an application layer. The physical layer is mainly responsible for encoding and decoding of energy and data information, and additionally comprises the design of beam forming and the introduction of full duplex technology, and meanwhile, the utilization and elimination of interference can also be embodied in the physical layer. The link layer is mainly responsible for reasonable allocation of communication resources, including optimal allocation of resources such as different time slots, frequencies, and spaces, and for control of data rate and management of energy. The network layer is mainly responsible for the routing design in the energy-counting integrated network, and various requirements of network users, such as low time delay or low forwarding energy consumption, are met by designing various routing algorithms. The application layer is more biased to the overall planning of specific networks, including the network architecture design of wireless sensor networks, cognitive radio networks, traditional cellular networks and the like.

The application of the digital energy integrated network in a multi-user scene in the future will be very popular, because the technology not only can meet the data requirements of different users, but also can transmit energy to the users in a certain wireless way, thereby ensuring the energy supply requirements. However, the influence of the distance factor on the system performance has to be studied while considering multiple users. Users farther from the transmitting base station receive inherently less energy than other users closer to the base station, thus resulting in unfairness between different users. The traditional data information network introduces a relay technology aiming at distance factors, thereby greatly increasing the throughput of the system. However, for the digital-to-energy integrated network, the energy to the destination node through two hops is much lower than one hop, so the relay is not a good choice. While another new beamforming technique may consider a user with a long distance to take a beam forming design first, the improvement of energy transmission is very little. With the development of 5G technology, the multi-point cooperative application is becoming wider, wherein a Cloud Radio Access Network (C-RAN) will be a key Network technology in the future. In the C-RAN, a system has a plurality of Remote Radio Heads (RRHs) distributed throughout a scene, and each user may select a number of RRHs for access. Compared with the traditional networking mode of the central base station, the distributed RRH can effectively increase the network coverage rate, better take care of edge users in the network, improve the overall throughput of the system and improve the overall resource utilization efficiency. Therefore, the technology can be applied to the energy-counting integrated network, and the distance unfairness of the user is better solved by establishing a distributed RRH mode.

The research of the current wireless data and energy simultaneous transmission technology mostly focuses on the division of a data signal and an energy signal in the same signal, and makes a certain balance between the two. However, the radio transmission efficiency in these systems is not necessarily satisfactory. According to research, the efficiency of wireless energy transmission in a higher frequency band is higher, and the transmission of data information is opposite, so that in order to improve the respective transmission efficiency of data and energy, data and energy signals can be considered to be separated, different signals are transmitted separately, the energy signal is transmitted in a higher frequency band, and the data signal is transmitted in a lower frequency band, so that two different transmission modes of the RRH, namely a data transmission mode and an energy transmission mode, are generated, and the RRH can dynamically adjust the switching of the two modes to improve the performance of the system. Meanwhile, the time slot and power resource allocation of the system can be further optimized, and joint optimization is achieved.

In a conventional cellular network, there is only one central base station, which results in that the signals received by edge users are much weaker than those received by other users in the cellular network. In addition, while the user is satisfied with data communication, a certain energy requirement is sometimes generated, which requires that the system can perform data energy coordination transmission on the user by controlling the selection of the communication mode. For different users, the demands for data and energy are different, for example, some users prefer to access the internet wirelessly, and some users prefer to charge their mobile phones, which results in a series of resource allocation problems.

In addition, two mechanisms of data transmission and energy transmission are introduced, which inevitably causes the problem of different requirements of users on data and energy. In a communication scenario, some users are focused on the data rate of the communication, while some users are more focused on harvesting of energy for device charging, thus requiring some coordination of the different needs among these different users.

Disclosure of Invention

The invention aims to solve the problems of unfairness caused by user distance factors in a traditional cellular network, different data and energy requirements of different users and high complexity and difficult realization of an optimal algorithm, and provides a suboptimal resource allocation method based on RRH dynamic mode switching in a digital-energy integrated network, which obviously improves the overall throughput and energy harvesting performance of a system by optimally allocating power resources.

The purpose of the invention is realized by the following technical scheme: the resource allocation method based on RRH dynamic mode switching in the digital energy integrated network comprises the following steps:

s1, determining a network model and a transmission protocol; the communication between the users and the RRHs is divided by time slots, and communication parameters (e.g., channel information) may be different for each time slot;

s2, calculating the energy signal and the data signal received by each user, and calculating the data throughput and the energy harvest of each user;

s3, defining an evaluation function, and determining an optimization target according to the evaluation function;

s4, determining the optimal beam design of data and energy transmission between the RRH and the user according to the channel information;

s5, performing sub-optimal solution on the data transmission user, RRH mode selection, and RRH power allocation in a time slot, comprising the following sub-steps:

s51, initializing data transmission user selection, RRH power distribution and RRH mode selection scheme in the time slot;

s52, solving the optimal selection of data transmission users, RRH power distribution and RRH mode selection schemes in the time slot by adopting a dynamic iterative suboptimal algorithm, and calculating the sum of evaluation functions of the system in the time slot;

s6, sequentially carrying out sub-optimization solution on the data transmission users, RRH mode selection and RRH power distribution in each time slot, selecting the selection of the data transmission users, RRH power distribution and RRH mode selection corresponding to the time slot with the maximum sum of the evaluation functions of the system as the optimal selection of the system, and calculating the optimal beam design of the RRH to the users according to the method in the step S4 to complete the global optimization of the system.

Further, step S1 includes the following sub-steps:

s11, recording the number of users in the model scene as K, wherein each user is provided with an antenna; the method comprises the following steps that N RRHs are arranged in a scene, and M antennas are installed on each RRH; each RRH has two working modes, namely an energy transmission mode and a data transmission mode, and the two modes can be correspondingly switched according to requirements; let the maximum transmission power of RRH be P_maxAll RRHs also have a limit on total transmit power due to the central processor management, defined asNoting that the noise power between channels is σ²；

S12, the communication between users and RRH is divided by time slot, for each time slot, in order to avoid collision interference, only one user is allowed to carry out data transmission, and other users carry out energy transmission, because the frequency bands of the energy signal and the data signal are different, the mutual interference can not be generated; the system selects the optimal data transmission user, selects the corresponding digital energy transmission mode of each RRH at the same time, defines a user selection vector l, in the time slot, if the user i carries out data transmissionTransmit, order_i1, if it is transmitting energy, let l_i0; defining an RRH mode selection vector r, for the t RRH, if it selects the data transmission mode, let r_tIf not, let r_t＝0。

Further, the step S2 is specifically implemented as follows: in each time slot, the channel from the t-th RRH to the i-th user is denoted as h_t,iWhereinThe unit beam of the t-th RRH is designed asPower allocation of p_t(ii) a The energy signal and the data signal received by the user are respectively expressed as:

the data signal received by the ith user is represented as:

$y_i^D=l_i\underset{t=1}{\overset{N}{\Σ}}{r}_t\sqrt{p_t}{h}_{t,i}^H{\mathrm{\ω}}_t{x}_0+z$

wherein,represents h_t,iZ is white Gaussian noise, x₀A random signal of unit power;

the energy signal received by the ith user is expressed as:

$y_i^E=(1-l_i)\underset{t=1}{\overset{N}{\Σ}}(1-r_t)\sqrt{p_t}{h}_{t,i}^H{\mathrm{\ω}}_t{x}_0+z$

in the time slot, according to shannon's theorem, the data throughput of the ith user is:

$R_i=l_i\mathrm{\τ}\log (1+\frac{\underset{t=1}{\overset{N}{\Σ}}{r}_t{p}_t{h}_{t,i}^H{\mathrm{\ω}}_t{\mathrm{\ω}}_t^H{h}_{t,i}}{{\mathrm{\σ}}^2})$

in the time slot, according to the shannon theorem, the sum of the energy harvesting amounts of the ith user is as follows:

$E_i=(1-l_i)\mathrm{\τ}\underset{t=1}{\overset{N}{\Σ}}(1-r_t)p_t{h}_{t,i}^H{\mathrm{\ω}}_t{\mathrm{\ω}}_t^H{h}_{t,i}$

is omega_tThe conjugate transpose of (c).

Further, the step S3 is specifically implemented as follows: defining a joint merit function:

η_i＝α_iR_i+β_iE_i

wherein, α_iAnd β_iA data factor and an energy factor corresponding to each user i, respectively, and α if a user i prefers data communication_iGreater than β_iIf a user i prefers energy harvesting to charge the device β_iGreater than α_i；

In order to increase the sum of the satisfaction degrees of all users of the whole system, the sum of the evaluation functions of the users used by the system needs to be maximized, and the following optimization problem is obtained:

$\underset{l,p,r,\mathrm{\ω}}{\max }\underset{i=1}{\overset{K}{\Σ}}{\mathrm{\η}}_i$

the limiting conditions to be met are:

C1:

C2:

C3:

C4:

C5:

the optimized variables are the selection of data transmission users in each time slot, the selection of two RRH modes, RRH transmission power distribution and RRH transmission beam design; wherein, the constraint C1 represents that the beam is a unit power vector; limit C2 represents that the transmit power of each RRH must not exceed the threshold; the limit C3 indicates that the total transmission power of all RRHs at the same time must not exceed the threshold; the limit C4 represents that at most one user can transmit data in each timeslot, and other users can only transmit energy; the constraint C5 represents that the RRH can only select one of the modes for signal transmission at a time.

Further, step S4 includes the following sub-steps:

s41, determining the optimal beam design of the RRH to the user in the data transmission mode: assuming that the ith user performs data transmission, and performing beam design on the tth RRH, because there is only one data transmission user, the optimal beam design is obtained by using the water injection principle of beam design as follows:

${\mathrm{\ω}}_t=\frac{h_{t,i}}{\left|\right|h_{t,i}\left|\right|};$

s42, determining the optimal beam design of the RRH under the energy transmission mode to the user: the beam design for the tth RRH is set to ω_tDefining a set of energy harvesting users, denoted by Φ, the total power received by the RRH transmitted signal at all energy users is expressed as:

$p^{rcv}=p_t\underset{i\∈\mathrm{\Φ}}{\Σ}{h}_{t,i}^H{\mathrm{\ω}}_t{\mathrm{\ω}}_t^H{h}_{t,i}=p_t{\mathrm{\ω}}_t^H{h}_t^{\′}{\mathrm{\ω}}_t$

wherein,

at this time, the optimization problem is to makeAnd (4) maximizing, solving the optimization problem by a quadratic method to obtain the optimal beam design of the RRH to the user in the energy transmission mode.

Further, the specific implementation manner of step S51 is: since the complexity of the optimization algorithm is relatively high and difficult to implement, the optimization problem of step S3 is to be solved by a sub-optimization algorithm combining iterations; firstly, randomly determining a user as a data transmission user, and performing energy transmission on other users, then randomly generating a selection scheme of an RRH mode, meeting a fifth constraint in an optimization target, and finally randomly generating a power distribution scheme according to a second constraint and a third constraint in the optimization target to serve as initialization of an iterative algorithm.

Further, step S52 includes the following sub-steps:

s521, fixing the data transmission user selection scheme and the RRH power allocation scheme generated by the last iteration, fixing the data transmission user selection scheme and the RRH power allocation scheme generated by initialization during the first iteration, and solving the optimal RRH mode selection scheme: because the power of each RRH transmission is known, an optimal RRH mode scheme is worked out according to power distribution; since the energy or data signals of the user are both direct superpositions of multiple RRH signals, and the transmission of different RRHs is independent for a single data or energy signal, a global overall RRH mode selection scheme is determined by considering the mode selection of each RRH; the specific method comprises the following steps: for the t-th RRH, calculating the benefit of selecting a data transmission mode and the benefit of selecting an energy transmission mode, and comparing the benefits and the benefits to select a better mode for transmission; the data throughput and energy harvest of the ith RRH for the ith user are obtained according to the beam design of step S41 and the throughput and energy obtained in step S2, and are expressed as:

$R_{t,i}={\mathrm{\τl}}_{i}log(1+\frac{p_t{h}_{t,i}^H{\mathrm{\ω}}_t{\mathrm{\ω}}_t^H{h}_{t,i}}{{\mathrm{\σ}}^2})$

$E_{t,i}=\mathrm{\τ}(1-l_i)p_t{h}_{t,i}^H{\mathrm{\ω}}_t{\mathrm{\ω}}_t^H{h}_{t,i}$

the gains in the two modes are expressed as:

${\mathrm{\η}}_t^D=\underset{i=1}{\overset{K}{\Σ}}{\mathrm{\α}}_i{R}_{t,i}$

${\mathrm{\η}}_t^E=\underset{i=1}{\overset{K}{\Σ}}{\mathrm{\β}}_i{E}_{t,i}$

comparing them ifSelecting a data transmission mode by the tth RRH, and otherwise selecting an energy transmission mode;

s522, fixing the data transmission user selection scheme and the RRH mode selection scheme output in the step S521, and determining an optimal RRH power allocation scheme: the data throughput and energy harvest contributions of the tth RRH to user i are:

$R_{t,i}=r_t{\mathrm{\τl}}_{i}log(1+\frac{p_t{\mathrm{\ξ}}_{t,i}}{{\mathrm{\σ}}^2})$

E_t,i＝(1-r_t)τ(1-l_i)p_tξ_t,i

wherein,

the sum of the evaluation functions of all users in the system is:

$\mathrm{\η}=\underset{t=1}{\overset{N}{\Σ}}\underset{i=1}{\overset{K}{\Σ}}({\mathrm{\α}}_i{R}_{t,i}+{\mathrm{\β}}_i{E}_{t,i})$

further, the optimization objectives are obtained as follows:

solving the optimization target to obtain the sum of the optimal evaluation functions to obtain the optimal RRH power distribution scheme;

s523, fixing the RRH power distribution scheme and RRH mode selection scheme output in the step S522, and determining an optimal data transmission user selection scheme: comparing the sum of the corresponding system evaluation functions when each user is taken as a data transmission user, then selecting the user corresponding to the maximum sum of the evaluation functions as the optimal data transmission user selection scheme, and returning to the step S521;

and S524, performing loop iteration calculation on the S521, the S522 and the S523 according to the sequence of S521 → S522 → S523 → S521 → S522 → S523 → S521, and if the difference value of the sum of the evaluation functions of two adjacent iterations is smaller than a set convergence threshold, considering that the iterations are converged and stopping the iteration to obtain the final joint optimization result of the data transmission user selection scheme, the RRH power allocation scheme and the RRH mode selection.

The invention has the beneficial effects that:

1. the invention solves the problems of unfairness caused by user distance factors in the traditional cellular network and different data and energy requirements of different users; considering the frequency band difference between the energy signal and the data signal, the scheme of RRH dynamic mode switching, optimal power distribution and user selection is adopted, an evaluation function is introduced to carry out normalization optimization on the data and energy requirements of the users so as to flexibly coordinate the requirements of different users on the energy and the data, and the overall throughput and the energy harvesting performance of the system are obviously improved through the optimal distribution of power resources; the method has the advantages that the target optimization is carried out according to different data and energy requirements of different users, so that the satisfaction degree of the users based on the data and the energy is greatly improved;

2. by introducing an evaluation function combining data throughput and energy harvest, the data demand and the energy demand of a user can be more flexibly coordinated, so that the user can be better met, and the performance of a communication system is improved.

Drawings

FIG. 1 is a flow chart of a resource allocation method of the present invention;

FIG. 2 is a diagram of a digital energy integrated network model according to the present invention.

Detailed Description

Noun means:

DEIN: the number can be integrated into a network. The difference from the traditional communication network is that the data information interaction between the network nodes can be carried out, and the energy mutual transmission between the network nodes can also be carried out.

Beam forming: under the multi-input multi-output system, a plurality of antennas are arranged on the transmitter and the receiver. The signal can be intensively transmitted towards the receiving end by adjusting the transmitting power, the transmitting phase, the polarization mode and the like of each antenna, so that the loss of power in other useless directions is reduced. The vector formed by all antenna transmit signals at one end is the beamforming vector.

C-RAN: cloud radio access network. The data layer and the control layer are separated in a distributed connection mode, a user can realize information interaction by connecting RRHs, and a plurality of RRHs realize resource sharing and dynamic scheduling through a central control pool, so that the resource utilization rate and the flexibility are improved.

RRH: a remote radio head in the C-RAN. A plurality of RRHs are distributed in a distributed mode in a cell for users to access, so that the bottleneck problem of edge users is relieved, and the network coverage rate is increased.

The water injection principle is as follows: when the power distribution of each antenna is carried out in beam forming, more power is distributed to the antenna with good channel, and less power is distributed to the antenna with poor channel, thereby maximizing the transmission rate.

Wireless transmission efficiency: the ratio of the receiving power of the receiving end to the transmitting power of the transmitting end can be divided into wireless energy transfer efficiency and wireless data transfer efficiency according to different transmission contents.

The technical scheme of the invention is further explained by combining the attached drawings.

As shown in fig. 1, a resource allocation method based on RRH dynamic mode switching in a digital energy integrated network includes the following steps:

s1, determining a network model and a transmission protocol; as shown in fig. 2, the communication between the user and the RRH is divided by time slots, and communication parameters (e.g., channel information) of each time slot may be different; the method specifically comprises the following substeps:

s11, recording the number of users in the model scene as K, wherein each user is provided with an antenna; the method comprises the following steps that N RRHs are arranged in a scene, and M antennas are installed on each RRH; each RRH has two working modes, namely an energy transmission mode and a data transmission mode, and the two modes can be correspondingly switched according to requirements; let the maximum transmission power of RRH be P_maxAll RRHs also have a limit on total transmit power due to the central processor management, defined asBetween recording channelsNoise power of sigma²；

S12, the communication between users and RRH is divided by time slot, for each time slot, in order to avoid collision interference, only one user is allowed to carry out data transmission, and other users carry out energy transmission, because the frequency bands of the energy signal and the data signal are different, the mutual interference can not be generated; the system selects the optimal data transmission user, selects the corresponding digital energy transmission mode of each RRH at the same time, defines a user selection vector l, and enables l if user i carries out data transmission in the time slot_i1, if it is transmitting energy, let l_i0; defining an RRH mode selection vector r, for the t RRH, if it selects the data transmission mode, let r_tIf not, let r_t＝0。

S2, calculating the energy signal and the data signal received by each user, and calculating the data throughput and the energy harvest of each user; the specific implementation method comprises the following steps: in each time slot, the channel from the t-th RRH to the i-th user is denoted as h_t,iWhereinThe unit beam of the t-th RRH is designed asPower allocation of p_t(ii) a The energy signal and the data signal received by the user are respectively expressed as:

the data signal received by the ith user is represented as:

$y_i^D=l_i\underset{t=1}{\overset{N}{\Σ}}{r}_t\sqrt{p_t}{h}_{t,i}^H{\mathrm{\ω}}_t{x}_0+z$

wherein,represents h_t,iZ is white Gaussian noise, x₀A random signal of unit power;

the energy signal received by the ith user is expressed as:

$y_i^E=(1-l_i)\underset{t=1}{\overset{N}{\Σ}}(1-r_t)\sqrt{p_t}{h}_{t,i}^H{\mathrm{\ω}}_t{x}_0+z$

in the time slot, according to shannon's theorem, the data throughput of the ith user is:

$R=l_i\mathrm{\τ}log(1+\frac{\underset{t=1}{\overset{N}{\Σ}}{r}_t{p}_t{h}_{t,i}^H{\mathrm{\ω}}_t{\mathrm{\ω}}_t^H{h}_{t,i}}{{\mathrm{\σ}}^2})$

in the time slot, according to the shannon theorem, the sum of the energy harvesting amounts of the ith user is as follows:

$E_i=(1-l_i)\mathrm{\τ}\underset{t=1}{\overset{N}{\Σ}}(1-r_t)p_t{h}_{t,i}^H{\mathrm{\ω}}_t{\mathrm{\ω}}_t^H{h}_{t,i}$

is omega_tThe conjugate transpose of (c).

S3, defining an evaluation function, and determining an optimization target according to the evaluation function; the specific implementation method comprises the following steps: defining a joint merit function:

η_i＝α_iR_i+β_iE_i

wherein, α_iAnd β_iA data factor and an energy factor corresponding to each user i, respectively, and α if a user i prefers data communication_iGreater than β_iIf a user i prefers energy harvesting to charge the device β_iGreater than α_i；

In order to increase the sum of the satisfaction degrees of all users of the whole system, the sum of the evaluation functions of the users used by the system needs to be maximized, and the following optimization problem is obtained:

$\underset{l,p,r,\mathrm{\ω}}{\max }\underset{i=1}{\overset{K}{\Σ}}{\mathrm{\η}}_i$

the limiting conditions to be met are:

C1:

C2:

C3:

C4:

C5:

the optimized variables are the selection of data transmission users in each time slot, the selection of two RRH modes, RRH transmission power distribution and RRH transmission beam design; wherein, the constraint C1 represents that the beam is a unit power vector; limit C2 represents that the transmit power of each RRH must not exceed the threshold; the limit C3 indicates that the total transmission power of all RRHs at the same time must not exceed the threshold; the limit C4 represents that at most one user can transmit data in each timeslot, and other users can only transmit energy; the constraint C5 represents that the RRH can only select one of the modes for signal transmission at a time.

S4, determining the optimal beam design of data and energy transmission between the RRH and the user according to the channel information; the method specifically comprises the following substeps:

s41, determining the optimal beam design of the RRH to the user in the data transmission mode: assuming that the ith user performs data transmission, and performing beam design on the tth RRH, because there is only one data transmission user, the optimal beam design is obtained by using the water injection principle of beam design as follows:

${\mathrm{\ω}}_t=\frac{h_{t,i}}{\left|\right|h_{t,i}\left|\right|};$

s42, determining the optimal beam design of the RRH under the energy transmission mode to the user: the beam design for the tth RRH is set to ω_tDefining a set of energy harvesting users, denoted by Φ, the total power received by the RRH transmitted signal at all energy users is expressed as:

$p^{rcv}=p_t\underset{i\∈\mathrm{\Φ}}{\Σ}{h}_{t,i}^H{\mathrm{\ω}}_t{\mathrm{\ω}}_t^H{h}_{t,i}=p_t{\mathrm{\ω}}_t^H{h}_t^{\′}{\mathrm{\ω}}_t$

wherein,

at this time, the optimization problem is to makeAnd (4) maximizing, solving the optimization problem by a quadratic method to obtain the optimal beam design of the RRH to the user in the energy transmission mode.

S5, performing sub-optimal solution on the data transmission user, RRH mode selection, and RRH power allocation in a time slot, comprising the following sub-steps:

s51, initializing data transmission user selection, RRH power distribution and RRH mode selection scheme in the time slot; the specific implementation mode is as follows: since the complexity of the optimization algorithm is relatively high and difficult to implement, the optimization problem of step S3 is to be solved by a sub-optimization algorithm combining iterations; firstly, randomly determining a user as a data transmission user, and performing energy transmission on other users, then randomly generating a selection scheme of an RRH mode, meeting a fifth constraint in an optimization target, and finally randomly generating a power distribution scheme according to a second constraint and a third constraint in the optimization target to serve as initialization of an iterative algorithm.

S52, solving the optimal selection of data transmission users, RRH power distribution and RRH mode selection schemes in the time slot by adopting a dynamic iterative suboptimal algorithm, and calculating the sum of evaluation functions of the system in the time slot; the method specifically comprises the following substeps:

s521, fixing the data transmission user selection scheme and the RRH power allocation scheme generated by the last iteration, fixing the data transmission user selection scheme and the RRH power allocation scheme generated by initialization during the first iteration, and solving the optimal RRH mode selection scheme: because the power of each RRH transmission is known, an optimal RRH mode scheme is worked out according to power distribution; since the energy or data signals of the user are both direct superpositions of multiple RRH signals, and the transmission of different RRHs is independent for a single data or energy signal, a global overall RRH mode selection scheme is determined by considering the mode selection of each RRH; the specific method comprises the following steps: for the t-th RRH, calculating the benefit of selecting a data transmission mode and the benefit of selecting an energy transmission mode, and comparing the benefits and the benefits to select a better mode for transmission; the data throughput and energy harvest of the ith RRH for the ith user are obtained according to the beam design of step S41 and the throughput and energy obtained in step S2, and are expressed as:

$R_{t,i}={\mathrm{\τl}}_{i}log(1+\frac{p_t{h}_{t,i}^H{\mathrm{\ω}}_t{\mathrm{\ω}}_t^H{h}_{t,i}}{{\mathrm{\σ}}^2})$

$E_{t,i}=\mathrm{\τ}(1-l_i)p_t{h}_{t,i}^H{\mathrm{\ω}}_t{\mathrm{\ω}}_t^H{h}_{t,i}$

the gains in the two modes are expressed as:

${\mathrm{\η}}_t^D=\underset{i=1}{\overset{K}{\Σ}}{\mathrm{\α}}_i{R}_{t,i}$

${\mathrm{\η}}_t^E=\underset{i=1}{\overset{K}{\Σ}}{\mathrm{\β}}_i{E}_{t,i}$

comparing them ifSelecting a data transmission mode by the tth RRH, and otherwise selecting an energy transmission mode;

s522, fixing the data transmission user selection scheme and the RRH mode selection scheme output in the step S521, and determining an optimal RRH power allocation scheme: the data throughput and energy harvest contributions of the tth RRH to user i are:

$R_{t,i}=r_t{\mathrm{\τl}}_{i}log(1+\frac{p_t{\mathrm{\ξ}}_{t,i}}{{\mathrm{\σ}}^2})$

E_t,i＝(1-r_t)τ(1-l_i)p_tξ_t,i

wherein,

the sum of the evaluation functions of all users in the system is:

$\mathrm{\η}=\underset{t=1}{\overset{N}{\Σ}}\underset{i=1}{\overset{K}{\Σ}}({\mathrm{\α}}_i{R}_{t,i}+{\mathrm{\β}}_i{E}_{t,i})$

further, the optimization objectives are obtained as follows:

solving the optimization target to obtain the sum of the optimal evaluation functions to obtain the optimal RRH power distribution scheme;

s523, fixing the RRH power distribution scheme and RRH mode selection scheme output in the step S522, and determining an optimal data transmission user selection scheme: comparing the sum of the corresponding system evaluation functions when each user is taken as a data transmission user, then selecting the user corresponding to the maximum sum of the evaluation functions as the optimal data transmission user selection scheme, and returning to the step S521;

and S524, performing loop iteration calculation on the S521, the S522 and the S523 according to the sequence of S521 → S522 → S523 → S521 → S522 → S523 → S521, and if the difference value of the sum of the evaluation functions of two adjacent iterations is smaller than a set convergence threshold, considering that the iterations are converged and stopping the iteration to obtain the final joint optimization result of the data transmission user selection scheme, the RRH power allocation scheme and the RRH mode selection.

S6, sequentially carrying out sub-optimization solution on the data transmission users, RRH mode selection and RRH power distribution in each time slot, selecting the selection of the data transmission users, RRH power distribution and RRH mode selection corresponding to the time slot with the maximum sum of the evaluation functions of the system as the optimal selection of the system, and calculating the optimal beam design of the RRH to the users according to the method in the step S4 to complete the global optimization of the system.

It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.

Claims (7)

1. The resource allocation method based on RRH dynamic mode switching in the digital energy integrated network is characterized by comprising the following steps:

s1, determining a network model and a transmission protocol; the communication between users and RRHs is divided by time slots;

s2, calculating the energy signal and the data signal received by each user, and calculating the data throughput and the energy harvest of each user;

s3, defining an evaluation function, and determining an optimization target according to the evaluation function;

s4, determining the optimal beam design of data and energy transmission between the RRH and the user according to the channel information;

s5, performing sub-optimal solution on the data transmission user, RRH mode selection, and RRH power allocation in a time slot, comprising the following sub-steps:

s51, initializing data transmission user selection, RRH power distribution and RRH mode selection scheme in the time slot;

s52, solving the optimal selection of data transmission users, RRH power distribution and RRH mode selection schemes in the time slot by adopting a dynamic iterative suboptimal algorithm, and calculating the sum of evaluation functions of the system in the time slot;

s6, sequentially carrying out sub-optimization solution on the data transmission users, RRH mode selection and RRH power distribution in each time slot, selecting the selection of the data transmission users, RRH power distribution and RRH mode selection corresponding to the time slot with the maximum sum of the evaluation functions of the system as the optimal selection of the system, and calculating the optimal beam design of the RRH to the users according to the method in the step S4 to complete the global optimization of the system.

2. The method for resource allocation based on RRH dynamic mode switching in the digital integrated network of claim 1, wherein the step S1 comprises the following sub-steps:

s11, recording the number of users in the model scene as K, wherein each user is provided with an antenna; the method comprises the following steps that N RRHs are arranged in a scene, and M antennas are installed on each RRH; each RRH has two working modes, namely an energy transmission mode and a data transmission mode, and the two modes can be correspondingly switched according to requirements; let the maximum transmission power of RRH be P_maxAll RRHs have a limit on total transmit power due to central processor management, defined asNoting that the noise power between channels is σ²；

S12, communication between users and RRH is divided by time slot, for each time slot, only one user is allowed to avoid collision interferenceData transmission is carried out, other users carry out energy transmission, and the energy signal and the data signal have different frequency bands, so that mutual interference cannot be generated; the system selects the optimal data transmission user, selects the corresponding digital energy transmission mode of each RRH at the same time, defines a user selection vector l, and enables l if user i carries out data transmission in the time slot_i1, if it is transmitting energy, let l_i0; defining an RRH mode selection vector r, for the t RRH, if it selects the data transmission mode, let r_tIf not, let r_t＝0。

3. The resource allocation method based on RRH dynamic mode switching in the digital integrated network according to claim 2, wherein the step S2 is specifically implemented as follows: in each time slot, the channel from the t-th RRH to the i-th user is denoted as h_t,iWhereinThe unit beam of the t-th RRH is designed asPower allocation of p_t(ii) a The energy signal and the data signal received by the user are respectively expressed as:

the data signal received by the ith user is represented as:

$y_i^D=l_i\underset{t=1}{\overset{N}{\Σ}}{r}_t\sqrt{p_t}{h}_{t,i}^H{\mathrm{\ω}}_t{x}_0+z$

wherein,represents h_t,iZ is white Gaussian noise, x₀A random signal of unit power;

the energy signal received by the ith user is expressed as:

$y_i^E=(1-l_i)\underset{t=1}{\overset{N}{\Σ}}(1-r_t)\sqrt{p_t}{h}_{t,i}^H{\mathrm{\ω}}_t{x}_0+z$

in the time slot, according to shannon's theorem, the data throughput of the ith user is:

$R_i=l_i\mathrm{\τ}log(1+\frac{\underset{t=1}{\overset{N}{\Σ}}{r}_t{p}_t{h}_{t,i}^H{\mathrm{\ω}}_t{\mathrm{\ω}}_t^H{h}_{t,i}}{{\mathrm{\σ}}^2})$

in the time slot, according to the shannon theorem, the sum of the energy harvesting amounts of the ith user is as follows:

$E_i=(1-l_i)\mathrm{\τ}\underset{t=1}{\overset{N}{\Σ}}(1-r_t)p_t{h}_{t,i}^H{\mathrm{\ω}}_t{\mathrm{\ω}}_t^H{h}_{t,i}$

is omega_tThe conjugate transpose of (c).

4. The resource allocation method based on RRH dynamic mode switching in the digital integrated network according to claim 3, wherein the step S3 is specifically implemented as follows: defining a joint merit function:

η_i＝α_iR_i+β_iE_i

wherein, α_iAnd β_iA data factor and an energy factor corresponding to each user i, respectively, and α if a user i prefers data communication_iGreater than β_iIf a user i prefers energy harvesting to charge the device β_iGreater than α_i；

In order to increase the sum of the satisfaction degrees of all users of the whole system, the sum of the evaluation functions of the users used by the system needs to be maximized, and the following optimization problem is obtained:

$\underset{l,p,r,\mathrm{\ω}}{max}\underset{i=1}{\overset{K}{\Σ}}{\mathrm{\η}}_i$

the limiting conditions to be met are:

$\begin{array}{cc}C1:& \left|\right|{\mathrm{\ω}}_t\left|\right|=1,\∀1\≤t\≤N\end{array}$

$\begin{array}{cc}C2:& p_t\≤P_{\max },\∀1\≤t\≤N\end{array}$

$\begin{array}{cc}C3:& \underset{t=1}{\overset{N}{\Σ}}{p}_t\≤P_{max}^{CP}\end{array}$

$\begin{array}{cc}C4:& \underset{i=1}{\overset{K}{\Σ}}{l}_i=1\end{array}$

$\begin{array}{cc}C5:& r_t=1or0,\∀1\≤t\≤N\end{array}$

the optimized variables are the selection of data transmission users in each time slot, the selection of two RRH modes, RRH transmission power distribution and RRH transmission beam design; wherein, the constraint C1 represents that the beam is a unit power vector; limit C2 represents that the transmit power of each RRH must not exceed the threshold; the limit C3 indicates that the total transmission power of all RRHs at the same time must not exceed the threshold; the limit C4 represents that at most one user can transmit data in each timeslot, and other users can only transmit energy; the constraint C5 represents that the RRH can only select one of the modes for signal transmission at a time.

5. The method for resource allocation based on RRH dynamic mode switching in the digital integrated network of claim 4, wherein the step S4 comprises the following sub-steps:

s41, determining the optimal beam design of the RRH to the user in the data transmission mode: assuming that the ith user performs data transmission, and performing beam design on the tth RRH, because there is only one data transmission user, the optimal beam design is obtained by using the water injection principle of beam design as follows:

${\mathrm{\ω}}_t=\frac{h_{t,i}}{\left|\right|h_{t,i}\left|\right|};$

s42, determining the optimal beam design of the RRH under the energy transmission mode to the user: the beam design for the tth RRH is set to ω_tDefining a set of energy harvesting users, denoted by Φ, the total power received by the RRH transmitted signal at all energy users is expressed as:

$p^{rcv}=p_t\underset{i\∈\mathrm{\Φ}}{\Σ}{h}_{t,i}^H{\mathrm{\ω}}_t{\mathrm{\ω}}_t^H{h}_{t,i}=p_t{\mathrm{\ω}}_t^H{h}_t^{\′}{\mathrm{\ω}}_t$

wherein,

at this time, the optimization problem is to makeMaximizing, solving the optimization problem through a quadratic method to obtain the optimal beam of the RRH to the user in the energy transmission modeAnd (5) designing.

6. The resource allocation method based on RRH dynamic mode switching in the digital integrated network according to claim 5, wherein the specific implementation manner of step S51 is as follows: solving the optimization problem of step S3 by a joint iterative suboptimal algorithm; firstly, randomly determining a user as a data transmission user, and performing energy transmission on other users, then randomly generating a selection scheme of an RRH mode, meeting a fifth constraint in an optimization target, and finally randomly generating a power distribution scheme according to a second constraint and a third constraint in the optimization target to serve as initialization of an iterative algorithm.

7. The method for resource allocation based on RRH dynamic mode switching in the digital integrated network of claim 6, wherein the step S52 comprises the following sub-steps:

s521, fixing the data transmission user selection scheme and the RRH power allocation scheme generated by the last iteration, fixing the data transmission user selection scheme and the RRH power allocation scheme generated by initialization during the first iteration, and solving the optimal RRH mode selection scheme: because the power of each RRH transmission is known, an optimal RRH mode scheme is worked out according to power distribution; since the energy or data signals of the user are both direct superpositions of multiple RRH signals, and the transmission of different RRHs is independent for a single data or energy signal, a global overall RRH mode selection scheme is determined by considering the mode selection of each RRH; the specific method comprises the following steps: for the t-th RRH, calculating the benefit of selecting a data transmission mode and the benefit of selecting an energy transmission mode, and comparing the benefits and the benefits to select a better mode for transmission; the data throughput and energy harvest of the ith RRH for the ith user are obtained according to the beam design of step S41 and the throughput and energy obtained in step S2, and are expressed as:

$R_{t,i}={\mathrm{\τl}}_{i}log(1+\frac{p_t{h}_{t,i}^H{\mathrm{\ω}}_t{\mathrm{\ω}}_t^H{h}_{t,i}}{{\mathrm{\σ}}^2})$

$E_{t,i}=\mathrm{\τ}(1-l_i)p_t{h}_{t,i}^H{\mathrm{\ω}}_t{\mathrm{\ω}}_t^H{h}_{t,i}$

the gains in the two modes are expressed as:

${\mathrm{\η}}_t^D=\underset{i=1}{\overset{K}{\Σ}}{\mathrm{\α}}_i{R}_{t,i}$

${\mathrm{\η}}_t^E=\underset{i=1}{\overset{K}{\Σ}}{\mathrm{\β}}_i{E}_{t,i}$

comparing them ifSelecting a data transmission mode by the tth RRH, and otherwise selecting an energy transmission mode;

s522, fixing the data transmission user selection scheme and the RRH mode selection scheme output in the step S521, and determining an optimal RRH power allocation scheme: the data throughput and energy harvest contributions of the tth RRH to user i are:

$R_{t,i}=r_t{\mathrm{\τl}}_{i}log(1+\frac{p_t{\mathrm{\ξ}}_{t,i}}{{\mathrm{\σ}}^2})$

E_t,i＝(1-r_t)τ(1-l_i)p_tξ_t,i

wherein,

the sum of the evaluation functions of all users in the system is:

$\mathrm{\η}=\underset{t=1}{\overset{N}{\Σ}}\underset{i=1}{\overset{K}{\Σ}}({\mathrm{\α}}_i{R}_{t,i}+{\mathrm{\β}}_i{E}_{t,i})$

further, the optimization objectives are obtained as follows:

$\underset{p_t}{max}\mathrm{\η}$

solving the optimization target to obtain the sum of the optimal evaluation functions to obtain the optimal RRH power distribution scheme;

s523, fixing the RRH power distribution scheme and RRH mode selection scheme output in the step S522, and determining an optimal data transmission user selection scheme: comparing the sum of the corresponding system evaluation functions when each user is taken as a data transmission user, then selecting the user corresponding to the maximum sum of the evaluation functions as the optimal data transmission user selection scheme, and returning to the step S521;

and S524, performing loop iteration calculation on the S521, the S522 and the S523, if the difference value of the sum of the evaluation functions of two adjacent iterations is less than a set convergence threshold, considering that the iterations are converged, and stopping the iteration to obtain a final joint optimization result of the data transmission user selection scheme, the RRH power distribution scheme and the RRH mode selection.