CN110166980B - Power optimization method for distributed antenna system cache constraint in high-speed rail scene - Google Patents

Power optimization method for distributed antenna system cache constraint in high-speed rail scene Download PDF

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CN110166980B
CN110166980B CN201910401197.2A CN201910401197A CN110166980B CN 110166980 B CN110166980 B CN 110166980B CN 201910401197 A CN201910401197 A CN 201910401197A CN 110166980 B CN110166980 B CN 110166980B
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徐友云
余蜜
王小明
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Nanjing Ai Er Win Technology Co ltd
Nanjing University of Posts and Telecommunications
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • H04W4/42—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for mass transport vehicles, e.g. buses, trains or aircraft
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/02—Power saving arrangements
    • H04W52/0209—Power saving arrangements in terminal devices
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/18—TPC being performed according to specific parameters
    • H04W52/24—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters
    • H04W52/241—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters taking into account channel quality metrics, e.g. SIR, SNR, CIR, Eb/lo
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/18—TPC being performed according to specific parameters
    • H04W52/28—TPC being performed according to specific parameters using user profile, e.g. mobile speed, priority or network state, e.g. standby, idle or non transmission
    • H04W52/282—TPC being performed according to specific parameters using user profile, e.g. mobile speed, priority or network state, e.g. standby, idle or non transmission taking into account the speed of the mobile
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/30—TPC using constraints in the total amount of available transmission power
    • H04W52/36—TPC using constraints in the total amount of available transmission power with a discrete range or set of values, e.g. step size, ramping or offsets
    • H04W52/367—Power values between minimum and maximum limits, e.g. dynamic range
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    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • 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
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Abstract

The invention provides a power optimization method for distributed antenna system cache constraint in a high-speed rail scene, which comprises the following steps of: a power allocation policy; setting a Lagrange multiplier range; determining an achievable channel capacity value; calculating the amount of cache data; calculating average transmit power
Figure DEST_PATH_IMAGE002
And average channel capacity
Figure DEST_PATH_IMAGE004
(ii) a And (6) optimizing and judging. The invention can dynamically distribute power according to the relation between the real-time changing wireless transmission rate between the train and the base station and the constant data arrival rate at the access point on the train under the distributed antenna system, namely, the system transmitting power is minimized under the condition of meeting the requirements of a buffer area and time delay, thereby reducing the power consumption of the whole system.

Description

Power optimization method for distributed antenna system cache constraint in high-speed rail scene
Technical Field
The invention relates to a wireless communication signal optimal transmission method, in particular to a power optimal allocation method under the condition that a high-speed railway communication downlink meets cache constraints under the condition of considering a distributed multi-antenna technology, and belongs to the technical field of wireless communication.
Background
With the advocation of low power, energy conservation and emission reduction, the construction of a green communication network has become a research hotspot, and the same is true for a high-speed railway mobile communication system. Due to the penetration loss of train carriages and the communication requirement of users on the train, the mobile relay technology and the multi-antenna technology are adopted to improve the communication quality and the wireless transmission rate between a base station and the train in a high-speed railway scene. The wireless channel state between the base stations along the railway and the train also changes rapidly, and most railway communication is line-of-sight transmission, so the transmission rate is determined by the distance between the train and the base stations, and the position and the receiving rate of the train at the next moment can be predicted. When the channel state is poor, the base station cannot meet the communication requirement of the user on the train in time, namely the data arrival rate of the base station is greater than that of a wireless transmission plastic plant, so that part of information cannot be transmitted to the user in time and can be cached. In multi-antenna high-speed railway communication, how to reduce power consumption while meeting the requirement of a cache region is a problem to be solved.
A search of the prior art documents shows that Tao Li et al published a text entitled "QoS-differentiated available Rate regions for High Speed Wireless Communications System based on QoS differentiation" in 2015IEEE Wireless Communications and Network Conference (WCNC) Mar.2015 (Conference on Wireless Communications and network, 3 months 2015). This paper studies the performance limitations in high-speed wireless communication systems and proposes an adaptive algorithm that can achieve the maximum boundary of the achievable rate region. However, this document does not consider the multi-antenna technology, nor the matching problem between the data arrival process of the base station and the wireless transmission process. Jiaxun Lu et al published a "heated Traffic Patterns in High-speed Railway Communication Systems: Power allocation and Access selection" in IEEE Transactions on Vehicular Technology, Volume 67, Issue 12, Oct.2018, pp.12273-12287 (journal of the institute of Electrical and electronics Engineers, 2018, 10.67, 12.67, 12273-12287). The article mainly proposes that different antenna access modes are selected for different flow models in a high-speed rail scene, and the power consumption of the system is minimized through reasonable dynamic power distribution. However, the document does not consider the matching between the wireless processing procedure of the base station side and the data arrival procedure, which may cause the loss of data when the channel condition is poor. It is found through search that Chuang Zhang et al published a sentence entitled "Optimal Power Allocation With Delay Constraint between Train and Base station for Signal Transmission From Moving Train to Base station" in IEEE Transactions on Vehicular Technology Jan.2015, pp.5775-5788 (Association of Electrical and electronics Engineers, Ann. Soc. technical Commission for Electrical and electronics Engineers, 2015, 1 month, 5775 page 5788). This paper minimizes transmit power by matching the data arrival rate at the access point to the wireless transmission rate. However, the document only considers the transmission and reception of a single antenna, and does not consider the multi-antenna scenario. Compared with a single antenna, in order to achieve the same wireless transmission rate, the power consumption of the multi-antenna technology is smaller, especially when the rate requirement is larger.
The invention provides a power distribution method based on service quality (QoS) requirements between a base station and a train in a high-speed railway scene, which is found by searching the prior invention, by Li billo and the like of Qinghua university. But the invention only considers the single antenna scenario. X-roche et al, high-throughput shareholdings, has invented a method for uplink power control in a MIMO-based communication system, which provides uplink power control signals to user equipment via a power allocation scheme transmitted by a base station, mainly in a MIMO-enabled wireless communication system, by determining whether or not uplink transmission is interfered by neighboring cells. However, the invention does not consider high-speed mobility, and is only suitable for the traditional uplink low-speed mobile communication scene.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a power optimization method for distributed antenna system buffer constraint in a high-speed rail scene, which is based on the buffer area requirement of a base station end integrated unit (CU), matches the data arrival process and the service processing process of a base station, obtains power optimized allocation meeting the buffer area requirement, transmission power limitation and no packet loss or wireless delay phenomenon in the process, and then makes the base station end meet the limitation conditions by controlling and updating lagrangian multipliers, that is, the base station end obtains available transmission power and corresponding system capacity.
The invention provides a power optimization method for distributed antenna system cache constraint in a high-speed rail scene, which comprises the following steps of:
firstly, obtaining a downlink power optimization allocation strategy from a base station to a train along the way, namely analyzing the state change condition of a wireless channel between the base station and the train in the running process of the train, and judging the dynamic relation between the wireless transmission rate at the base station and the data arrival rate of the base station to determine the buffering time in the whole process;
assuming that the transmission power of the base station side integrated unit is P (t), the dynamic allocation of the transmission power is determined according to the following formula, namely a value range,
Figure BDA0002059839160000041
wherein, lambda, eta and kappa are Lagrange multipliers solving three constraint conditions, t1For the moment of first stop of buffering, t2To begin buffering again, T represents half of the period of use of the train passing through a cell, N0Representing the noise power;
second, setting Lagrange multiplier range, firstly, giving the value ranges of the Lagrange multipliers lambda, eta and kappa (namely, the value ranges of the Lagrange multipliers lambda, eta and kappa)λmax、λmin、ηmax、ηmin、κmax、κmin) Obtaining corresponding initial values of lambda, eta and kappa according to a bisection method, judging whether the initial values meet optimized constraint conditions, continuously updating the values until the constraint conditions are met, and finally obtaining the optimal lambda, eta and kappa values;
thirdly, determining the value of the reachable channel capacity of the mobile relay from the base station to the train, namely calculating the instantaneous channel capacity C (t) of the system according to the following formula,
Figure BDA0002059839160000042
wherein, p (t) represents the transmission power of the base station;
fourthly, calculating the buffer data quantity, namely judging the wireless transmission rate C (t) and the data arrival rate u of a base station end (RAU) so as to determine the time when the data needs to be buffered in the whole process, and calculating the data buffer data quantity Q according to the following formulac，
Figure BDA0002059839160000043
Where T represents half of the time taken for the train to pass through a cell, and T1For the moment of first stop of buffering, t2Represents the time of re-buffering, u represents the data arrival rate and is a constant, C (T) represents the instantaneous channel capacity, dt represents the time T is integrated, T represents the time taken by the train to travel from the position right opposite to the base station, and the value range of T is [0, T]；
The fifth step, calculate the average transmitting power PaAnd average channel capacity Ca-calculating the average transmission power P consumed by the base station during the passage of the train through the cellaAnd average channel capacity Ca. The total power consumed by the base station and the total service rate in the whole process are calculated by dividing into five time periods. Firstly, determining that the train passes a small train according to the power distribution strategy of the step oneIn the process of district, five time periods of wireless transmission rate change of the base station respectively calculate the total transmission power and the total reachable channel capacity value correspondingly consumed in the whole process, and further calculate the average transmission power P consumed by the base station in the process of running trains through the districtaAnd average channel capacity Ca；
Sixthly, judging whether data are lost or not by optimization judgment (power optimization), if so, updating the value of kappa by the dichotomy in the step two, and returning to the step three; if no data is lost, continuously judging whether the base station can transmit all data to the train in the whole process (judging the average channel capacity C of the system)aWhether the data arrival rate is not less than the data arrival rate u) or not, namely, whether infinite delay is caused or not exists, if the infinite delay exists, the value of eta is updated through the step two, and the step three is returned; if no data wireless delay exists, judging whether the power consumed by the base station is in a specified range (namely judging whether the actually consumed transmitting power meets the condition) when no data is lost or no data infinite delay exists, if not, updating the value of lambda through the step two, returning to the step three, otherwise, ending the loop, and determining the values of lambda, eta and kappa.
In order to avoid the metal penetration loss of a carriage and reduce the failure rate of user handover, a base station and a user on a train do not directly communicate, but transmit signals to the user through a mobile relay on the train. The high-speed movement of the train causes the rapid change of the channel environment, and the distance from the base station to the train and the wireless transmission rate thereof can be predicted at the next moment, so that the power can be dynamically distributed according to the relationship between the data arrival rate u and the wireless transmission rate C (t) at the base station, the data loss and infinite delay caused by the bad channel condition are avoided, and the power consumption is reduced. According to the method, a power optimization distribution strategy from a base station to a train downlink on the way under a distributed antenna system model is obtained, then the corresponding wireless transmission rate, namely the system capacity and the data buffer amount, is calculated according to the obtained power optimization distribution strategy, and the optimized power distribution is obtained by continuously updating the Lagrange multiplier. Compared with the prior art, the invention mainly considers the problem of cache in a distributed antenna system, namely, the data loss can be reduced under the constraint of the cache, and the power consumption is reduced at the same time.
As a further aspect of the present invention, in the first step, C1(t)、C2(t), A (t), B (t) satisfy the following conditions:
Figure BDA0002059839160000061
Figure BDA0002059839160000062
A(t)＝(h11(t)h22(t)-h21(t)h12(t))2，
Figure BDA0002059839160000063
wherein h is11(t)、h12(t)、h21(t)、h22(t) respectively represent large-scale fading gains between the base station side RAU and the mobile relay MR on the train.
Further, in the first step, a radio channel gain h between the base station and the train is calculated according to the following formulaij(t)，
Figure BDA0002059839160000064
Wherein d isvRepresents the vertical distance between the base station and the train (MR), dhTable distance between two adjacent base stations, drThe distance between two MRs on the train is represented, v represents the running speed of the train, T represents the time taken by the train to run from the central positions of two remote radio units at the base station end, and the value range of T in the invention is [0, T]And T is half of the time for a train to pass through a cell covered by one base station.
Preferably, the λ, η, and κ values obtained in step two are substituted into step one, and then the corresponding system channel capacity, i.e. the wireless transmission rate at the base station, is calculated through a power optimization allocation strategy.
The specific idea is as follows: in the second step, the approximate ranges of lambda, eta and kappa are estimated according to the power distribution strategy and the constraint conditions, and corresponding initial values can be obtained according to the dichotomy.
And substituting the initial value into a power distribution strategy, firstly judging whether kappa meets a condition, namely whether data overflow exists in a buffer area, then judging whether eta meets the condition, namely judging whether the average channel capacity value is not less than the data arrival rate, and finally judging whether lambda meets the condition, namely judging whether the transmission power consumed in the whole process exceeds the maximum transmission power.
In the third step, firstly, the lambda, eta and kappa values obtained in the second step are substituted into the formula in the first step,
Figure BDA0002059839160000071
Figure BDA0002059839160000072
A(t)＝(h11(t)h22(t)-h21(t)h12(t))2，
Figure BDA0002059839160000073
and is
Figure BDA0002059839160000074
And then calculating the corresponding system channel capacity, namely the wireless transmission rate of the base station end through a power optimization allocation policy.
And in the fifth step, according to a power distribution strategy, the transmission power distributed by the base station at each moment in the running process of the train can be predicted, the power distribution in the whole process is divided into five sections, the first section, the third section and the fifth section are equivalent to a water injection algorithm, the second section and the fourth section are equivalent to a channel inversion method, the transmission power consumed in each section of time and the reachable channel capacity value are calculated through an integral method, and the total transmission power correspondingly consumed in the whole process and the total reachable channel capacity value can be obtained.
And dividing the calculated total transmission power and the reachable channel capacity value in the whole process by the time spent in the whole process to obtain the average transmission power and the average channel capacity of the base station in the process that the train passes through the cell.
In the sixth step, whether data is lost is judged according to the following formula,
Qc＞Q
wherein Q iscFor caching data amount, Q is the length of the buffer area, i.e. the maximum data amount that can be cached, and is a constant if QcIf Q is greater than Q, data loss is indicated, and if Q is greater than QcIf the value is less than or equal to Q, no data loss is indicated;
whether the average channel capacity of the system is not less than the data arrival rate is judged according to the following formula,
Ca＜u
wherein, CaFor the average channel capacity of the system, u is the data arrival rate, if CaIf u, the average channel capacity of the system is less than the data arrival rate, causing infinite delay when the train leaves the cell, if CaIf the average channel capacity is more than or equal to u, the average channel capacity of the system is not less than the data arrival rate, and wireless delay is not caused;
whether the actually consumed transmission power satisfies the condition is judged according to the following formula,
Pa＞Pmax
wherein, PaFor the actual consumed transmit power, PmaxIs the maximum value of the average transmitted power, if Pa＞PmaxIf P is less than the predetermined range, the base station determines that the transmission power actually consumed does not satisfy the condition and that the power actually consumed by the base station is not within the predetermined rangea≤PmaxThen, it means that the actually consumed transmission power satisfies the condition, and the actually consumed power of the base station is within the specified range.
In the first step, PC(t) satisfies the following condition:
Figure BDA0002059839160000081
compared with the prior art, the invention adopting the technical scheme has the following technical effects: the invention can dynamically distribute power according to the relation between the real-time changing wireless transmission rate between the train and the base station and the constant data arrival rate at the access point on the train under the distributed antenna system, namely, the system transmitting power is minimized under the condition of meeting the requirements of a buffer area and time delay, thereby reducing the power consumption of the whole system.
Drawings
Fig. 1 is a model diagram of a downlink high-speed rail multi-antenna communication system with a cache according to the present invention.
Fig. 2 is an explanatory diagram of the relationship between the base station data arrival rate and its instantaneous wireless transmission rate at a constant transmission power in the present invention.
Fig. 3 is a diagram of simulation results of the strategy for minimizing power allocation in the present invention.
Fig. 4 is a comparison graph of simulation results of the scheme of the present invention and other technical schemes.
FIG. 5 is a flow chart of an optimization process of the inventive arrangements.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the attached drawings: the present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the protection authority of the present invention is not limited to the following embodiments.
The present embodiment proposes a cache-based downlink power optimization method for high-speed rail communication in a distributed antenna system, which is implemented by the following steps as shown in fig. 5:
first step, power optimized allocation strategy
The state change condition of a wireless channel between a base station and a train in the running process of the train is analyzed, and the dynamic relation between the wireless transmission rate at the base station and the data arrival rate is judged to determine the buffering time in the whole process. As shown in fig. 2, the state change of the wireless channel between the base station and the train during the running of the train is analyzed, and as the train moves at a high speed, the state of the wireless channel gradually becomes better and worse, so that according to the dynamic relationship between the wireless transmission rate at the base station and the data arrival rate of the base station, when the train just enters the cell, the wireless transmission rate at the base station cannot meet the received rate requirement, a part of data is cached, and as the channel condition between the base station and the mobile relay becomes better and worse, the wireless transmission rate at the base station cannot meet the received rate requirement at the last period of time. In this way, the buffering time in the whole process can be determined according to the dynamic relation between the wireless transmission rate and the data arrival rate of the base station.
Assuming that the transmission power of the base station is P (t), the dynamic allocation of the transmission power, i.e. the value range thereof, is determined according to the following formula,
Figure BDA0002059839160000101
wherein λ, η, and κ are lagrange multipliers solving the three constraints, respectively. Lambda is used for constraining the average transmitting power of the base station end of the system, and the average transmitting power is required not to exceed the maximum average transmitting power; eta is used for restricting the average channel capacity, and the average channel capacity of the system is required to be not less than the data arrival rate u, so that the situation of data infinite waiting can not occur in the process that the train passes through the cell; kappa is used for restricting the data buffer amount at the access point, and the total data amount of the buffer cannot exceed the length of the buffer area, otherwise, the data loss is caused. t is t1The time for ending the buffering for the first time is the time when the system channel capacity is smaller than the data arrival rate from the beginning of the train running, and the time when the system channel capacity is gradually increased to be larger than the data arrival rate along with the running of the train is at [0, t [ ]1]In the time period, the data received by the base station cannot be immediately transmitted to the mobile relay on the train by the base station, and a part of the data needs to be buffered. Since this embodiment employs multi-antenna transmission and reception, t2Indicating the moment when the buffering is started again, i.e. at t1，T]A portion of the data needs to be buffered during the time period. T represents that the train passes through a cellHalf of the time used, u denotes the data arrival rate.
Second, setting the value range of Lagrange multiplier
Setting the maximum and minimum values of the Lagrangian multipliers λ, η, and κ (i.e., λ)max、λmin、ηmax、ηmin、κmax、κminThe value of (d) where the initial value of the lagrange multiplier is obtained by a binary search.
And obtaining corresponding initial values of lambda, eta and kappa according to a bisection method, judging whether the initial values meet the optimized constraint condition, if not, continuously updating the values until the constraint condition is met, and finally obtaining the optimal lambda, eta and kappa values.
Specifically, the approximate ranges of λ, η, and κ (i.e., λ) are first estimated based on the power allocation strategy and constraintsmax、λmin、ηmax、ηmin、κmax、κminThe value of (d) and then find the corresponding initial value according to the dichotomy.
And then substituting the initial value into the power optimization allocation strategy in the first step, judging whether kappa meets the optimized constraint condition, namely whether data overflow exists in a buffer area, continuously updating the kappa value to judge again if the kappa does not meet the condition, judging whether eta meets the optimized constraint condition, namely judging whether the average channel capacity value is not less than the data arrival rate, continuously updating the eta value to judge again if the eta does not meet the condition, finally judging whether lambda meets the optimized constraint condition, namely judging whether the transmission power consumed in the whole process exceeds the maximum transmission power, continuously updating the lambda value to judge again if the lambda does not meet the condition, and obtaining the optimal lambda, eta and kappa values if the lambda meets the condition.
Thirdly, determining the accessible channel capacity value from the access point to the base station
Substituting the values of lambda, eta and kappa obtained in the step two into the formula in the step one,
Figure BDA0002059839160000111
and then calculating the corresponding system channel capacity, namely the wireless transmission rate of the base station end through a power optimization allocation policy.
The instantaneous channel capacity c (t) of the system is calculated according to,
Figure BDA0002059839160000121
wherein N is0Denotes noise power, a (t) ═ h11(t)h22(t)-h21(t)h12(t))2，
Figure BDA0002059839160000122
Figure BDA0002059839160000123
Representing the large scale fading gain between the remote radio unit and the mobile repeater on the train.
Figure BDA0002059839160000124
Fourthly, calculating the amount of cache data
Judging the instantaneous channel capacity C (t) and the data arrival rate u, and determining the first time end buffering time t according to the power distribution policy of the first step1The buffering time t is started again2And calculates the amount Q of the buffered datac。
Calculating the data amount Q of the data buffer according to the following formulac，
Figure BDA0002059839160000125
Where T represents half of the time used by the train to pass through a cell, and T1For the first end of the buffering time, t2Moment of starting buffering againU represents data arrival rate and is a constant, c (T) represents instantaneous channel capacity, dt represents integration of time T, T represents time taken for the train to travel from the central positions of two remote radio units at the base station end, and the value range is [0, T ] in the embodiment]。
The fifth step, calculate the average transmitting power PaAnd average channel capacity Ca
Calculating the average transmitting power P and the average channel capacity C consumed by the base station end in the process of the train passing through the cella. The total power consumed by the base station and the total service rate in the whole process are calculated by dividing into five time periods. Determining five time periods of the train passing through a cell according to the power distribution strategy in the first step, wherein in the first stage, the service rate of a base station is less than the data arrival rate of the base station, and data caching is started; in the second stage, the service rate of the base station is equal to the data arrival rate; in the third stage, the service rate of the base station is greater than the data arrival rate; the fourth stage is the same as the second stage, and the service rate of the base station is equal to the data arrival rate; the fifth stage is the same as the first stage, the channel condition is not ideal, the service rate of the base station is less than the data arrival rate, and data is cached. The total transmitting power consumed correspondingly in the whole process is calculated, and according to the calculation mode of the total power consumed by each stage, the total service rate corresponding to each stage at the central unit, that is, the total channel capacity can be obtained (that is, according to the power distribution strategy, the service rate at each moment in the whole process can be obtained, and further, the total service rate corresponding to each stage can be obtained). Therefore, the average transmitting power P of the base station in the process of passing through the cell of the train is calculatedaAnd average channel capacity Ca。
According to a power distribution strategy, the transmission power distributed by the base station at each moment in the running process of the train can be predicted, the power distribution in the whole process is divided into five sections, the first section, the third section and the fifth section are equivalent to a water injection algorithm, the second section and the fourth section are equivalent to a channel inversion method, the transmission power consumed in each section of time and an achievable channel capacity value are calculated through an integral method, and then the total transmission power correspondingly consumed in the whole process and the total achievable channel capacity value can be obtained.
And dividing the calculated total transmission power and the reachable channel capacity value in the whole process by the time spent in the whole process to obtain the average transmission power and the average channel capacity of the train in the process of passing through the cell.
Sixth step, power optimization
Determine if there is data loss, i.e. determine Qc(QcFor buffering data amount) is greater than Q (Q is the buffer length, i.e., the maximum amount of data that can be buffered, which is a constant), if Q is greater than QcIf the value is more than Q, data loss is caused, updating the value of kappa by a dichotomy, and returning to the third step; if QcIf the average channel capacity C is less than or equal to Q, no data loss is indicated, and the average channel capacity C of the system is continuously judgedaIf not, if CaIf the average channel capacity is less than the data arrival rate, updating the value of eta by bisection, and returning to the step three; if CaIf the average channel capacity is not less than the data arrival rate, the actual consumed transmission power meets the condition, and whether the actual consumed transmission power meets the condition or not is continuously judged, namely the actual consumed transmission power P is judgedaWhether or not it is greater than the maximum value P of the average transmission powermaxIf P isa＞PmaxIf the result shows that the condition is not met, updating the value of eta by a dichotomy, and returning to the step three; if Pa≤PmaxIt is indicated that the actually consumed transmit power satisfies the condition and the loop is ended and the values of λ, η, and κ are determined.
The simulation setup of this embodiment is as shown in fig. 1, the simulation area is covered by two adjacent remote radio units, and provides service for the train, and the head and tail of the train car are equipped with mobile relays. The base station end integrated unit is provided with a data buffer area, and the base stations communicate with users on the train through mobile relays on the train. The main parameters of the simulation scenario are shown in table 1.
TABLE 1 simulation scenario principal parameters
Perpendicular distance d0 100m
Speed v of train 100m/s
Path loss exponent alpha 2
Distance d between adjacent remote radio unitsn 1000m
Adjacent mobile relay distance dr 400m
Unit band buffer size Q 2.5bits/Hz
Unit data arrival rate u 8bits/s/Hz
Average transmission power Pave 40W
Noise power N 0 1×10-10W/Hz
1) Channel change between radio remote unit and train
FIG. 2 illustrates the change of the radio transmission rate between the RAU and the train, i.e., the train is on the way, when the base station keeps the constant transmission power all the timeThe channel variation between the RAU and it during driving. Since it is considered that starting from the central point, i.e., the train is located at the middle position covered by the two RAUs, it is more and more distant from one of the RAUs and it is more and more close to the other RAU as the train moves at a high speed. As can be seen from fig. 2, the channel condition between the RAU and the train gradually becomes better and then becomes worse. Therefore, at [0, t1]During the time period, the service rate of the RAU is gradually increased, but the service rate is still insufficient to send out all the data streams arriving at the time, and data needs to be buffered at the beginning. [ t ] of1，t2]In the time period, the service rate of the RAU is gradually increased and then gradually decreased, but the service rates are all larger than the data arrival rate at the time, so that not only all the data arriving at the time can be sent out, but also the data buffered before the sending is started. [ t ] of2，T]In the time period, due to the poor channel condition, the service rate of the RAU is lower than the data arrival rate thereof, and a part of data needs to be buffered.
2) Power allocation strategy and corresponding service rate change situation
Fig. 3 depicts the minimum power allocation strategy under the buffer constraint and the corresponding service rate change situation proposed by the present embodiment. The power allocation strategy in fig. 3 can be divided into five sections, the first section, the third section and the fifth section are equivalent to a water injection method, and the second section and the fourth section are equivalent to a channel inversion method. The changing trend of the RAU service rate in the whole process is basically the same as the channel changing trend between the RAU and the train in fig. 2, that is, the service rate is gradually increased as the channel condition becomes better, then is kept unchanged and then is gradually increased, and finally is gradually decreased as the channel condition becomes worse. It can be seen from the dynamic relationship between the RAU service rate and the data arrival rate that the RAU service rate is smaller than the data arrival rate in the first and fifth time periods, the buffer starts to store data, and the total amount of the buffered data in the whole process does not exceed the length of the buffer.
3) Comparison effect between the scheme of the embodiment and other technical schemes
Fig. 4 shows a graph of the power consumed by the RAU versus the data arrival rate and its power versus the amount of buffered data without the buffer constraint. First, as the data arrival rate increases, the power consumed by the RAU gradually increases. As can be seen from fig. 4, although the RAU consumes less power without the buffer constraint, the amount of data that needs to be buffered far exceeds the maximum requirement of the buffer, which may cause the loss of part of the data stream and reduce the communication quality between the base station and the train. Compared with the prior art, although the power consumption of the embodiment is larger, the embodiment does not cause data loss, ensures the communication quality of the two and meets the communication requirement between the two.
Compared with the prior art, the embodiment has the following beneficial effects:
in order to avoid the metal penetration loss of the carriage and improve the communication quality between the base station and the users on the train, the embodiment adopts a scheme combining a mobile relay technology and a multi-antenna technology, and simultaneously considers the matching problem between the data arrival process at the base station and the wireless transmission process, thereby minimizing the power consumption at the base station side. Fig. 4 well shows the beneficial effect of the embodiment, and when there is no buffer limitation in a multi-antenna scenario, although the power consumption is smaller, information is lost when the channel condition is not ideal, and the communication quality of the user is reduced. The embodiment can achieve the limitation of the buffer memory, avoid the loss of data, and simultaneously the consumed power of the embodiment is also in the limited range. Compared with the single-antenna scenario, the multi-antenna can cope with the situation of larger rate requirement of the user within the same power limit.
In short, the invention considers the requirement of data buffer under the distributed antenna system, and then dynamically distributes power according to the relation between the real-time changing wireless transmission rate between the RAU of the base station end and the train and the constant data arrival rate of the base station end, namely, the transmitting power of the base station end is minimized under the condition of meeting the requirements of buffer area and time delay.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can understand that the modifications or substitutions within the technical scope of the present invention are included in the scope of the present invention, and therefore, the scope of the present invention should be subject to the protection scope of the claims.

Claims (3)

1. The power optimization method of the cache constraint of the distributed antenna system in the high-speed rail scene is characterized by comprising the following steps of:
firstly, a power distribution strategy, namely assuming that the transmitting power of the base station end integrated unit is P (t), determining the value range according to the following formula,
Figure FDA0003518224630000011
wherein, lambda, eta and kappa are Lagrange multipliers solving three constraint conditions, t1For the moment of first stop of buffering, t2To begin buffering again, T represents half of the period of use of the train passing through a cell, N0Representing the noise power; c1(t)、C2(t), A (t), B (t) satisfy the following conditions:
Figure FDA0003518224630000012
Figure FDA0003518224630000013
A(t)＝(h11(t)h22(t)-h21(t)h12(t))2，
Figure FDA0003518224630000014
wherein h is11(t)、h12(t)、h21(t)、h22(t) respectively representing large-scale fading gains between the RAU at the base station end and the MR of the mobile relay on the train; pC(t) satisfies the following condition:
Figure FDA0003518224630000015
secondly, setting a Lagrange multiplier range, namely providing a value range of the Lagrange multipliers lambda, eta and kappa, obtaining corresponding initial values of the lambda, eta and kappa according to a dichotomy, judging whether the initial values meet optimized constraint conditions or not, continuously updating the values until the constraint conditions are met, and finally obtaining the optimal lambda, eta and kappa values; the specific operation is as follows: firstly, estimating the ranges of lambda, eta and kappa according to a power distribution strategy and a constraint condition, namely lambdamax、λmin、ηmax、ηmin、κmax、κminThe corresponding initial value can be obtained by searching according to the dichotomy;
then, substituting the initial value into the power optimization allocation strategy of the first step, judging whether kappa meets the optimized constraint condition, namely whether data overflow exists in a buffer area, if the kappa does not meet the condition, continuously updating the kappa value for re-judgment, if the kappa meets the condition, judging whether eta meets the optimized constraint condition, namely judging whether the average channel capacity value is not less than the data arrival rate, if the eta does not meet the condition, continuously updating the eta value for re-judgment, if the eta meets the condition, finally judging whether the lambda meets the optimized constraint condition, namely judging whether the transmission power consumed in the whole process exceeds the maximum transmission power, if the lambda does not meet the condition, continuously updating the lambda value for re-judgment, and if the lambda meets the condition, obtaining the optimal lambda, eta and kappa values;
thirdly, determining the value of the reachable channel capacity, namely calculating the instantaneous channel capacity C (t) of the system according to the formula,
Figure FDA0003518224630000021
wherein, p (t) represents the transmission power of the base station;
fourthly, calculating the cache data quantity-calculating the data cache data quantity Q according to the following formulac，
Figure FDA0003518224630000022
Where T represents half of the time taken for the train to pass through a cell, and T1For the moment of first stop of buffering, t2The time when the buffer is performed again is shown, u is the data arrival rate, C (t) is the instantaneous channel capacity, dt is the time t which is integrated, and t is the time taken by the train to travel from the central positions of two remote radio units at the base station end;
the fifth step, calculate the average transmitting power PaAnd average channel capacity CaFirstly, determining five time periods of a train passing through a cell according to the power distribution strategy in the step one, wherein in the first stage, the service rate of a base station is less than the data arrival rate of the base station, and data caching is started; in the second stage, the service rate of the base station is equal to the data arrival rate; in the third stage, the service rate of the base station is greater than the data arrival rate; the fourth stage is the same as the second stage, and the service rate of the base station is equal to the data arrival rate; the fifth stage is the same as the first stage, the channel condition is not ideal, the service rate of the base station is less than the data arrival rate, and data is cached; respectively calculating the total transmission power and the total reachable channel capacity value correspondingly consumed in the whole process, and further calculating the average transmission power P consumed by the base station in the train running processaAnd average channel capacity Ca；
Sixthly, optimization judgment, namely judging whether data are lost or not, if so, updating the value of kappa by a dichotomy, and returning to the third step; if no data is lost, continuously judging whether the base station can transmit all data to the train in the whole process, if the data infinite delay exists, updating the value of eta through the step two, and returning to the step three; if the data infinite delay does not exist, judging whether the power consumed by the base station is in a specified range when the data is not lost or the data infinite delay does not exist, if not, updating the value of lambda through the step two, returning to the step three, otherwise, ending the cycle; the specific operation is as follows: whether data is lost is judged according to the following formula,
Qc＞Q
wherein Q iscFor buffering data amount, Q is buffer length, if QcIf Q is greater than Q, data loss is indicated, and if Q is greater than QcIf the value is less than or equal to Q, no data loss is indicated;
whether the average channel capacity of the system is not less than the data arrival rate is judged according to the following formula,
Ca＜u
wherein, CaFor the average channel capacity of the system, u is the data arrival rate, if CaIf u, the average channel capacity of the system is less than the data arrival rate, causing infinite delay when the train leaves the cell, if CaIf the average channel capacity is not less than the data arrival rate, infinite delay is not caused;
whether the actually consumed transmission power satisfies the condition is judged according to the following formula,
Pa＞Pmax
wherein, PaFor the actual consumed transmit power, PmaxIs the maximum value of the average transmitted power, if Pa＞PmaxIf P is less than the predetermined range, the base station determines that the transmission power actually consumed does not satisfy the condition and that the power actually consumed by the base station is not within the predetermined rangea≤PmaxThen, it means that the actually consumed transmission power satisfies the condition, and the actually consumed power of the base station is within the specified range.
2. The power optimization method for buffer constraint of the distributed antenna system in the high-speed rail scene according to claim 1, wherein in the first step, the wireless channel gain h between the base station and the train is calculated according to the following formulaij(t)，
Figure FDA0003518224630000041
Wherein d isvRepresents the vertical distance between the base station and the train, dhTable distance between two adjacent base stations, drRepresenting the distance between two moving relays on the train, v representing the trainThe running speed t represents the time taken by the train to run from the central positions of the two remote radio units at the base station end.
3. The power optimization method for buffer constraint of the distributed antenna system in the high-speed rail scene according to claim 2, wherein the λ, η, and κ values obtained in the second step are substituted into the first step, and then the corresponding system channel capacity is calculated by a power optimization allocation strategy.
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