CN115278728B - On-line joint control method for transmitting power, modulation order and coding rate in energy collection wireless communication system - Google Patents
On-line joint control method for transmitting power, modulation order and coding rate in energy collection wireless communication system Download PDFInfo
- Publication number
- CN115278728B CN115278728B CN202210725370.6A CN202210725370A CN115278728B CN 115278728 B CN115278728 B CN 115278728B CN 202210725370 A CN202210725370 A CN 202210725370A CN 115278728 B CN115278728 B CN 115278728B
- Authority
- CN
- China
- Prior art keywords
- power
- energy
- battery
- transmission rate
- drift
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000004891 communication Methods 0.000 title claims abstract description 30
- 230000005540 biological transmission Effects 0.000 claims abstract description 119
- 238000005457 optimization Methods 0.000 claims abstract description 85
- 230000007774 longterm Effects 0.000 claims abstract description 30
- 230000008859 change Effects 0.000 claims abstract description 11
- 230000008054 signal transmission Effects 0.000 claims description 7
- 238000003860 storage Methods 0.000 claims description 7
- 238000012937 correction Methods 0.000 claims description 5
- 238000012545 processing Methods 0.000 claims description 3
- 230000011664 signaling Effects 0.000 claims 1
- 238000004364 calculation method Methods 0.000 abstract description 2
- 238000004422 calculation algorithm Methods 0.000 description 74
- 230000006870 function Effects 0.000 description 36
- 238000004088 simulation Methods 0.000 description 20
- 230000008569 process Effects 0.000 description 11
- 239000000243 solution Substances 0.000 description 11
- 238000003306 harvesting Methods 0.000 description 10
- 238000005562 fading Methods 0.000 description 9
- 238000009826 distribution Methods 0.000 description 7
- 230000003044 adaptive effect Effects 0.000 description 6
- 230000000694 effects Effects 0.000 description 6
- 238000002347 injection Methods 0.000 description 6
- 239000007924 injection Substances 0.000 description 6
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 6
- 238000010248 power generation Methods 0.000 description 5
- 230000002829 reductive effect Effects 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 4
- 238000009827 uniform distribution Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 239000002131 composite material Substances 0.000 description 3
- 230000003247 decreasing effect Effects 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 239000000654 additive Substances 0.000 description 2
- 230000000996 additive effect Effects 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 230000007423 decrease Effects 0.000 description 2
- 238000003064 k means clustering Methods 0.000 description 2
- 230000036961 partial effect Effects 0.000 description 2
- 230000003595 spectral effect Effects 0.000 description 2
- 238000012546 transfer Methods 0.000 description 2
- 230000007704 transition Effects 0.000 description 2
- 230000002411 adverse Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000001174 ascending effect Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000007599 discharging Methods 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000002787 reinforcement Effects 0.000 description 1
- 230000002040 relaxant effect Effects 0.000 description 1
- 238000010845 search algorithm Methods 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
- 230000006641 stabilisation Effects 0.000 description 1
- 238000011105 stabilization Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/02—Arrangements for optimising operational condition
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- 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
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The invention discloses an online joint control method for transmitting power, modulation order and coding code rate in an energy collection wireless communication system, which aims at a point-to-point energy collection wireless communication system with an energy collection device at a transmitting end, and maximizes long-term average transmission rate under the condition of random change of energy arrival and channel state. The method utilizes a Lyapunov optimization framework to convert the optimal control problem method of maximizing the long-term average transmission rate under the constraints of battery operation and available energy into a multi-variable joint control method which aims at minimizing virtual queue drift and punishment per time slot, and provides an efficient numerical solution method. The invention also provides a self-adaptive adjustment method for the two parameters of the weight and the virtual queue offset in the drift and punishment of the K-means cluster based on the sliding window. The method provided by the invention only depends on the current battery state and channel state information to make a control decision, and has low calculation complexity and strong practicability.
Description
Technical Field
The invention relates to the field of information communication, in particular to an online joint control method for transmitting power, a modulation mode and a coding rate of a communication system powered by collected energy.
Background
Energy harvesting (ENERGY HARVESTING, EH) technology can convert different types of green energy into electrical energy, and EH technology is adopted in a communication system, so that environmental protection and node deployment are facilitated. Because the energy sources in the environment are uncontrollable, the energy collection has random fluctuation, so for a communication system running by means of the collected energy, the energy use strategy is an important factor affecting the system performance and service quality, and how to dynamically control the transmission power and the transmission rate according to the fading of a wireless channel and the condition of the collected energy, so that the efficient utilization of the energy and the channel is an important subject.
The power control algorithms in EH communication systems include both off-line and on-line power control. Before information transmission starts, the information such as channel fading is known, and the transmission power is distributed by adopting an off-line power control algorithm, wherein the off-line water injection algorithm is a common off-line power control algorithm. In practice, the energy arrival and channel fading are randomly varying and online power control algorithms that do not rely on future system state information are more practical. The online algorithm makes decisions based on statistics of energy arrival, channel fading, etc., as well as current and past system conditions. Modeling the power control process as a markov decision process (Markov Decision Process, MDP) and solving using dynamic programming is a common online power control algorithm. The MDP plus dynamic programming method relies on statistical information such as state transition probability and the like, and the solving complexity is high when the system state space is large.
The Lyapunov optimization technique only needs to make decisions based on the current and past states of the system to optimize long term average performance. Specifically, an optimization target is used as a punishment item, the sum of the queue length drift and the punishment item is used as an objective function, the objective function is minimized to meet the stability of the queue under long-term time average and the optimization of the performance target, and a stable virtual queue is constructed to meet the constraint condition in the optimization problem, so that the complexity of solving the optimization problem can be reduced. In recent years, there are several literature studies on solving the optimization problem of power control in EH communication systems using Lyapunov optimization framework. The literature [AMIMAVAEI F,DONG M.Online power control optimization for wireless transmission with energy harvesting and storage[J].IEEE Transactions on Wireless Communications,2016,15(7):4888-4901.] and the literature [DONG M,Li W,AMIRNAVAEI F.Online joint power control for two-hop wireless relay networks with energy harvesting[J].IEEE Transactions on Signal Processing,2018,66(2):463-478.] address the problem of maximizing the average rate in a point-to-point communication system and a relay transmission system in which a transmitting end and a relay node are powered by an EH device, and utilize a Lyapunov optimization framework to change constraint into virtual queue requirement, and convert the problem of long-term average optimization into a single-slot online optimization which only depends on the current battery state and the channel state. Document [LEI Weijia,LI Qin.Online power control based on Lyapunov optimization framework for decode-and-forward relay systems with energy harvesting[J].IEEE Access,2019,7:71335-71349.] considers a two-hop decoding forwarding relay system, aims at the joint optimization problem of the transmitting end and the transmitting power of the relay node aiming at the minimization of the average energy consumption of the transmitting end and the maximization of the relay transmission rate, converts the optimization problem into two sub-problems under the virtual queue stability condition by using a Lyapunov optimization framework, and solves the sub-problems.
The literature on power control in EH systems is mostly aimed at optimizing the theoretical maximum transmission rate or throughput of the system. In order to adapt to the random time variation of the channel fading and the energy collection amount, an adaptive coding modulation technology is needed to adjust the code rate and the modulation order of the channel coding according to the channel state so as to improve the transmission rate. In recent years, there are few documents that have studied the control problems of transmission power and modulation scheme in EH communication systems. Literature [MAR,ZHANG W.Adaptive MQAM for energy harvesting wireless communications with 1-Bit channel feedback[J].IEEE Transactions on Wireless Communications,2015,14(11):6459-6470.] proposes a backward iterative algorithm based on 1-bit channel state information fed back by a receiving end to obtain an optimal modulation mode and transmission power combination for a point-to-point EH wireless communication system, so as to maximize throughput. Literature [LI Mingyu,ZHAO Xiaohui,Liang H,et al.Deep reinforcement learning optimal transmission policy for communication systems with energy harvesting and adaptive MQAM[J].IEEE Transactions on Vehicular Technology,2019,68(6):5782-5793.] models the control process of the transmission power and modulation order under the system error performance constraint as an MDP with unknown state transition probability for a point-to-point EH wireless communication system, and solves in a continuous state space using a deep Q network algorithm. Literature [QIU Chengrun,HU Yang,CHEN Yan,et al.Lyapunov optimization for energy harvesting wireless sensor communications[J].IEEE Internet of Things Journal,2018,5(3):1947-1956.] researches the problem of maximizing the transmission rate in an EH wireless communication system, and proposes a combined optimization strategy of the transmission power and the modulation mode based on the battery power and the channel state under the constraint of the bit error rate and the limited modulation modes. In documents [ Lei Weijia, sun Jialin, xie Xian, etc. ] an online joint optimization strategy of power and modulation mode in an energy collection communication system [ J ]. Electronic and information report 2022,44 (03): 1024-1033.] consider a point-to-point EH wireless communication system, a joint optimization problem of transmission power, modulation mode and frame length is constructed with the objective of maximizing the long-term average practically achievable transmission rate of the system, and then the optimization problem is converted by using a Lyapunov optimization framework, and a solution of the problem is given.
Disclosure of Invention
The invention researches the scheduling optimization problem of power and transmission rate aiming at maximizing the long-term average throughput of the system in the point-to-point energy collection wireless communication system. The transmitting end is equipped with energy harvesting means and a rechargeable battery, the energy of the transmitted signal per time slot being derived from the energy harvested from the surrounding environment. Under the condition of limited battery capacity, the Lyapunov framework is utilized to solve the joint optimization problem of the transmission power, the modulation mode and the coded information bit length, and the average throughput in long term time is maximized.
In order to achieve the above purpose, the invention constructs an optimization problem aiming at maximizing the transmission rate of the system, converts the optimization problem into an optimization problem of long-term time average value, then solves the optimization problem by using a Lyapunov framework, constructs a drift plus penalty term, converts a minimum drift plus penalty term into an upper bound of the minimum drift plus penalty term to achieve the optimization purpose, and solves a three-parameter optimization problem comprising transmission power, modulation mode and information bit length to obtain an optimal solution.
The specific technical scheme adopted by the invention is that the energy of a signal transmitted by a transmitting end comes from energy collecting equipment, the energy arrives randomly in the process, a channel is a time-varying fading channel, and the battery capacity is limited, the transmitting power, the modulation mode and the information bit length of the transmitting end are jointly controlled only depending on the current battery state and the channel state, and the average transmission rate in long time is maximized; the method specifically comprises the following steps:
(1) Modeling a communication system, collecting energy from the surrounding environment by a transmitting end in each time slot for transmitting information to a receiving end, and constructing an optimization problem by taking maximum transmission power limit, limit of battery storage electric quantity on transmission power and battery electric quantity change constraint conditions and taking maximum average transmission rate in long term as targets;
(2) The method comprises the steps of obtaining a virtual queue after adding offset to battery electric quantity of a sending end by utilizing a Lyapunov optimization framework, constructing a drift and penalty item by taking a negative value of a transmission rate as a penalty item, and converting a constrained optimization problem of maximizing a long-term average transmission rate into a minimized drift and penalty item;
(3) Converting the minimized "drift plus penalty" term into an upper bound of the minimized "drift plus penalty" term;
(4) And making a decision according to the energy arrival and the channel state, and searching an optimal combination of the transmission power, the modulation mode and the information bit length, namely solving an optimal solution of the optimization objective function.
Specifically, in the step (2), the Lyapunov optimization framework is used to solve the optimization problem, that is, from the stability of the queue, the battery power of the transmitting end is added with an offset to be used as an energy virtual queue, the queue is kept stable to meet constraint conditions, the long-term average transmission rate of the optimization target is used as a penalty term, and the drift and penalty term is constructed and minimized to achieve the purpose of optimization
Specifically, in the step (3), the constraint condition is satisfied by minimizing the upper bound of the "drift plus penalty", and an upper bound expression of the "drift plus penalty" term is obtained by using a Lyapunov function and a Lyapunov drift formula;
Specifically, in the step (4), the transmission power, the modulation mode and the information bit length should be appropriately adjusted according to the energy arrival and the channel state. Since these 3 variables cannot be directly jointly optimized, but the modulation order M and the information bit length k are discrete values of a limited number, the transmission power P T (t) under each combination can be optimized by traversing all possible combinations of modulation orders and information bit lengths, and M, k and the corresponding P T (t) that maximize the objective function are selected as optimal solutions. When X (t) is equal to or greater than 0, the objective function is a monotonically increasing function of the signal transmission power P T (t). At this time, in order to maximize the objective function, the signal transmission power P T (t) should take the maximum value under the support of the battery discharge power and the stored energy When X (t) < 0, equally dividing the transmission power range P T,min,PT,max](PT,min into a plurality of cells with delta 1, judging whether each cell contains a maximum point, if so, finding the maximum point by adopting a golden section method. After obtaining the optimal transmit power for all available channel coding, modulation scheme combinations, their objective function values F (P T (t), M, k) are compared, and the modulation order and information bit length combination that maximizes the objective function is selected, and the corresponding transmit power is the solution to the optimization problem.
Aiming at the problem of maximizing the information transmission rate practically achieved by the system in the energy collection wireless communication system, the invention researches the joint optimization strategy of the transmission power, the modulation mode and the channel coding parameters. The transmitting end of the system is equipped with energy harvesting means and a rechargeable battery of limited capacity, the energy of the transmitted signal per time slot being derived from the energy harvested from the surrounding environment. Firstly, constructing a long-term optimization problem under the constraints of available energy and battery use, then converting the optimization problem by using a Lyapunov optimization framework, and then providing a solving method. Unlike the related documents, the optimization target of the invention is not the limit of the theoretical transmission rate, namely the channel capacity, but the transmission rate which can be realized by the actual system, and the joint optimization is carried out on the transmission power, the modulation mode and the channel coding parameters. The simulation result shows that the practical information transmission rate of the algorithm provided by the invention is higher than that of the algorithm taking the theoretical limit transmission rate as the optimization target.
Drawings
FIG. 1 is a communication system model of the present invention;
Fig. 2 is a diagram showing an optimal transmit power search algorithm according to the present invention;
FIG. 3 shows the information transmission rates of the algorithm according to the present invention under different weights and offsets;
FIG. 4 shows the information transmission rate variation of the algorithm according to the present invention under different weights and offsets;
FIG. 5 is a graph showing the average information transmission rate of the algorithm and the comparison algorithm according to the present invention;
FIG. 6 is a graph showing the change of the battery power of the transmitting end with time according to the algorithm and the comparison algorithm of the present invention;
fig. 7 shows the effect of energy arrival rate λ on the information transfer rate;
FIG. 8 shows the effect of the blade radius R on the information transmission rate in a wind power generation model;
Fig. 9 shows the effect of the energy collection probability p on the information transfer rate when the arrival energy obeys the bernoulli distribution.
Detailed Description
The system model of the present invention is shown in fig. 1. The transmitting end is provided with an energy collecting device and a chargeable battery with limited capacity, wherein the energy collecting device collects energy from the surrounding environment and converts the energy into electric energy to be stored in the battery, so as to provide energy for the transmitting end. After the information generated by the information source is subjected to channel coding and modulation, a symbol with a symbol period of T s is generated and sent to a channel for transmission. The length of the slot is recorded as T ts,Tts＞＞Ts. The transmitting end dynamically adjusts the transmitting power, the modulation mode and the coding parameters according to the instantaneous channel state and the energy collection condition, and maximizes the long-term time average information transmission rate.
The information sequence generated by the information source takes k bits as a group, and n-bit code words are obtained after binary channel coding, and the code elements after coding are modulated to obtain the transmitting symbols. Let the channel bandwidth be B Hz and the symbol rate R s = B Baud. Assuming that the modulation order is M, the transmission rate of the information is
And x (t) is a unit power signal obtained after modulation at a transmitting end. Assuming that the channel is a flat fading channel, the channel coefficient is h (t), which remains unchanged during one slot. The sending signal is transmitted through a channel, and the signal received by the receiving end is that
Wherein P T (t) is signal transmission power, N (t) is additive Gaussian white noise with average value of 0 and power spectrum density of N 0/2.
Assume that the set of alternative modulation schemes is Ω= { BPSK, QPSK,16QAM,64QAM,256QAM }. When the modulation is adopted, the Bit Error Rate (BER) after demodulation of the receiving end is as follows
Where N 0 is the single-sided power spectral density of additive white gaussian noise,E bav is the average bit energy of the received signal, expressed as a Gaussian Q function
T s is the symbol period, and h (T) is the channel coefficient. The demodulated binary symbol sequence is sent to a decoder for channel coding for decoding by taking the code word as a unit. For block codes with error correction capability of r, when the error occurring in one code word does not exceed r, the error can be corrected, and the probability of the decoder to decode correctly is
For the calculation of the number of combinations, n is the code length.
Codewords that are decoded in error will be discarded. The invention takes the rate of correctly transmitting information on unit bandwidth as an index for measuring the system performance, and the expression is that
K represents the information bit length, M represents the modulation order, R represents the error correction capability, R bt represents the number of information bits transmitted per time slot by the transmitting end, and P eb represents the bit error rate.
The power consumption P (t) of the transmitting end includes the power consumption P cc (t) of the circuit in addition to the power consumption P T (t) of the transmitting signal. The circuit power consumption can be divided into coding circuit power consumption P c (t), modulation circuit power consumption P m (t) and other circuit power consumption P A, namely
P(t)＝Pc(t)+Pm(t)+PA+PT(t)＝Pcc(t)+PT(t)
The power consumption of a channel encoder is related to the information rate and can be expressed as
Pc(t)＝σ1·Rbt
Wherein σ 1 is a constant coefficient, and is related to a specific adopted coding mode, a coding circuit, a circuit parameter and the like. The power consumption of the modulator is proportional to the number of binary symbols modulated per second and can be expressed as
Pm(t)＝σ2·Blog2M
Where σ 2 is a constant, a specific value is related to the circuit or signal processing device.
The total power P (t) consumed by the transmitting end in the t time slot is limited by the battery storage capacity E b (t) and the maximum discharge power P d,max:
let the maximum capacity of the battery be E B and the maximum charging power of the rechargeable battery be P c,max. The energy collected by the time slot t is E a (t), and the energy E H (t) stored in the battery by the time slot t can be expressed as
0≤EH(t)≤min(Ea(t),Tts·Pc,max,EB-(Eb(t)-Tts·P(t)))
Wherein the last term of the minimum function is the remaining storage capacity of the battery. After charging and discharging in one time slot, the electric quantity in the battery is updated to
Eb(t+1)＝Eb(t)-Tts·P(t)+EH(t)
E H (t) represents the energy of the time slot t to the battery.
It is known that P eb is related to the transmission power P T and the modulation order M, and the error correction capability r of channel coding is related to the code rate. To avoid the change of the length of the code word after coding, the code rate of the coding is generally adjusted in the communication system by changing the information length k in the code word. It is obvious that the larger the transmission power is, the higher the information transmission rate of the current time slot is, but the available energy of the transmitting end is limited, and energy needs to be reasonably allocated and used among the time slots according to the channel state under the constraint of the available energy. The larger M, k is, the higher the information rate sent by the sending end is, but the higher the error probability is, and M, k also affects the power consumption of the coding and modulation circuit, so that the appropriate M, k needs to be selected in combination with the sending power and the channel state, so that the actual information transmission rate is maximized. By combining the analysis, the optimization problem of the invention is to perform joint optimization on the transmission power, the modulation order M and the information bit length k of each time slot under the constraint of energy collection, and maximize the long-term average information transmission rate of the system:
Eb(t+1)＝Eb(t)-Tts·P(t)+EH(t)
M∈Ω
k∈K
where E [. Cndot. ] denotes the desired operation, K is a set of selectable information bit lengths in one codeword, and T denotes the total duration of the simulation in relation to the selected channel coding.
Rewriting constraint E b(t+1)＝Eb(t)-Tts·P(t)+EH (t) to be
Eb(t+1)-Eb(t)＝EH(t)-Tts·P(t)
Time slot T has from 0 to T-1
Superposing the two ends and obtaining the expected availability
Dividing the left and right ends by T, and obtaining the limit of T-infinity
Wherein,Representing the average energy collected per slot and the average power consumption at the transmitting end, respectively. The meaning of the above formula is that the energy collected by the system should be totally consumed in the long term to keep balance between energy collection and use. Relaxing single-slot battery power constraint in optimization problem into long-term time power constraint, and converting the optimization problem into
M∈Ω
k∈K
Delta (X (t)) represents Lyapunov drift, X (t) represents a battery energy virtual queue, V represents a weight between drift and penalty terms, and R b (t) represents a rate of correctly transmitting information per unit bandwidth.
The objective function and constraint condition of the optimization problem relate to long-term average, and the joint optimization of the whole transmission process is required to be carried out under the condition of having future energy collection and channel fading change conditions or statistical information thereof, so that the solving complexity is high. The invention adopts Lyapunov optimization framework to convert long-term average constraint into maintaining virtual queue stability, and approximates the random optimization problem by single time slot optimization. Firstly, constructing a battery energy virtual queue of a transmitting end:
X(t)＝Eb(t)-A
Where A is an offset for controlling the charge level of the battery. The update formula of the easily available energy virtual queue according to the battery power update formula E b(t+1)＝Eb(t)-Tts·P(t)+EH (t) is as follows
X(t+1)＝X(t)-Tts·P(t)+EH(t)
Defining a quadratic Lyapunov function:
lyapunov drift is defined as
The smaller the drift, the smaller the change in queue length between two slots, the closer the battery charge is to a, and the more stable the battery charge. If the energy deficiency line can be kept stable, i.e. the electric quantity of the battery fluctuates within a limited range, the collected energy and the consumed energy are equal in the long term. Taking the negative value of the information transmission rate R b to be maximized as a penalty term, constructing a drift plus penalty together with Lyapunov drift:
Δ(X(t))-VE[Rb(t)|X(t)]
Where V is the weight between the drift and penalty terms. If "drift plus penalty" can be minimized, the transmission rate of information is maximized while maintaining the stability of the virtual queue (i.e., battery power), i.e., satisfying E b(t+1)＝Eb(t)-Tts·P(t)+EH (t). The effect of the weight V in the "drift plus penalty" is to trade off the stability of the queue and the maximization of the transmission rate. The drift plus penalty has an upper bound, and the optimization is changed into the minimization of the upper bound, so that the complexity of solving the optimization problem can be further reduced, and the method can be obtained
In the formula (E b(t)-Tts·P(t))2 is a non-negative finite value, so that there is a non-negative constant C to satisfy
Thus there is
ΔX(t)≤C+X(t)E[EH(t)-Tts·P(t)|X(t)]
The upper bound of "drift plus penalty" can be obtained as
ΔX(t)-VE[Rb(t)|X(t)]≤C+X(t)E[EH(t)-Tts·P(t)|X(t)]-VE[Rb(t)|X(t)]
The optimization problem may be approximated with a minimization of the "drift plus penalty" upper bound that removes constraint E b(t+1)＝Eb(t)-Tts·P(t)+EH (t). Further removing the mean value operation in the upper bound and substituting the expression of the rate, the optimization problem can be converted into a single-slot optimization problem:
M∈Ω
k∈K
The above method adds a negative sign to the optimization objective function, changes the minimization into the maximization, and the constraint condition Has been satisfied by virtual queue stabilization and therefore no longer appears in the optimization problem.
Recording the optimization objective function in the optimization problem as F (P T (t), M, k), and expanding the expression of the total power P (t) to obtain
The objective function contains three optimization variables, wherein the modulation order M and the information bit length k are discrete values with a limited number, and the transmission power P T (t) under each combination can be optimized by traversing all possible combinations of the modulation order and the information bit length, and M, k and the corresponding P T (t) which maximize the objective function are selected as optimal solutions. The solution of optimal P T (t) given M, k is discussed first below.
The transmission rate of the current slot increases as P T (t) increases, so does the value of item 2 in the objective function. It is apparent that when X (t) > 0, the objective function is a monotonically increasing function of the signal transmission power P T (t). At this time, in order to maximize the objective function, the signal transmission power P T (t) should take the maximum value under the support of the battery discharge power and the stored energy:
In the practical physical sense, X (t) is equal to or greater than 0, which indicates that enough electric quantity in the battery supports information transmission. When X (t) <0, item 1 in the objective function decreases with an increase in P T (t), while item 2 increases with an increase in P T (t), so that it is not possible to directly judge the monotonicity of the optimized objective function, requiring further analysis.
Obtaining the partial derivative of the objective function to the transmission power P T (t) to obtain
If it isWhen the target function is monotonically decreasing, the sending power should be 0; otherwise, the maximum point of the objective function in the range of [0, P T,max (t) ] needs to be found, namely, the maximum point of the objective function meets the requirementIs a solution to (a). Observing the partial derivative expression and the BER expression, it is easy to know that the equation is a high-order nonlinear equation containing integral and exponential functions and related to P T (t), and an analytical solution of the equation cannot be obtained, so that the invention adopts a numerical method to solve. Since there may be more than one extreme point, the transmission power range P T,min,PT,max](PT,min is first divided into a plurality of cells of delta 1, and if the maximum point is included in each cell, the golden section method is then used to find the maximum point. For the ith cell, if there is a maximum point in that region, the objective function is incremented in the right neighborhood of P i and decremented in the left neighborhood of P i+δ1. Thus, if F (P i)＜F(Pi+δ2) and F (P i+δ1-δ2)＞F(Pi+δ1), there is a maximum point in this interval, where δ 2＜δ1 is a small constant. After finding all the extreme points, comparing the objective function values at each extreme point and the boundary point P T,min、PT,max, and selecting the point with the maximum objective function as the optimal power point. The flow of the search is shown in fig. 2.
Any channel coding has a characteristic that when the sequence error probability of an incoming decoder exceeds a certain threshold, the decoding error probability is high, and only when the input sequence error probability is lower than the threshold, the error correction capability of the coding can be exerted, and the error probability of an output sequence can be rapidly reduced along with the reduction of the input error probability. According to the limit of the decoder input error probability threshold BER P eb,max, there is a corresponding minimum received signal power requirement for the modulation mode, corresponding to the minimum transmit power P T,min in the current channel state. If the current channel condition and P T,min＞PT,max under the modulation scheme indicate that the BER using the modulation scheme exceeds the threshold required by the channel coding, the BER cannot be used.
After obtaining the optimal transmit power for all available channel coding, modulation scheme combinations, their objective function values F (P T (t), M, k) are compared, and the modulation order and information bit length combination that maximizes the objective function is selected, and the corresponding transmit power is the solution to the optimization problem.
Summarizing the adaptive optimization solution algorithm as shown in algorithm 2:
There are two parameters in the optimization problem, namely the weight V in the "drift plus penalty" and the offset a in the virtual battery level queue. The weight reflects the importance of the system to the optimization target, and the greater the weight value, the system tends to consume more electric quantity so that the current time slot obtains a larger transmission rate, but at the same time, the fluctuation of the electric quantity is increased, and the average electric quantity of the battery is reduced; the smaller the weight value, the lower the transmission rate optimization strength of a single time slot, but the average electric quantity of the battery is increased, and a larger energy scheduling space exists among the time slots. The offset is used for controlling the electric quantity level of the battery, the larger the offset is, the smaller the value of the virtual queue is under the same electric quantity of the battery, the smaller the transmission power is selected in the optimization, and the higher the average electric quantity of the battery is; the smaller the offset, the lower the average power of the battery, and the more preferable the transmission power is selected in the optimization. Both parameters being too large or too small can adversely affect the long-term performance of the system, with optimum values being related to the random nature of the channel and energy, etc. However, even in the case of statistical distribution information of channel fading and energy arrival, it is difficult to obtain optimal values of the two parameters by a mathematical analysis method, and in the case of unknown statistical distribution, it is impossible to determine in advance. One of the features of the Lyapunov method is that the system is adaptively controlled independent of statistical information and future state information of the system, so that both parameters should be adaptively adjusted.
Both the weight V and the virtual queue offset a have a direct effect on the battery charge fluctuation and the average charge level, so that they can be adjusted appropriately according to the charge state of the battery over time. On the premise of not knowing prior information, the data can be divided into different classes by clustering, so that elements in the same class are similar as much as possible, and element differences in different classes are as large as possible. The K-means algorithm is simple, the time complexity and the data integration are in linear relation, and the execution efficiency of the algorithm is high when the data set is small. According to the invention, battery electric quantity points in a period of time are divided into different clusters by using a K-means clustering method based on a sliding window, and fluctuation and average characteristics of the battery electric quantity are reflected by using the relation between the electric quantity in the clusters and the electric quantity among the clusters. The sliding window is recorded as l in size, and the window at the current moment is the past l time slots. The whole thought of the self-adaptive adjustment algorithm is as follows: k-means clustering is carried out on the battery electric quantity of the current sliding window, and the average electric quantity after clusteringAnd weighted average powerAnd (5) analyzing to further determine whether the electric quantity of the battery is stable. Updating the values of the weights and offsets once every d slots toWhether the electric quantity is in the allowable fluctuation range is judged according to whether the electric quantity is stable, and if the electric quantity is not stable, the weight is adjusted; to be used forAnd adjusting the offset according to whether the upper threshold value and the lower threshold value are exceeded.
And taking the time and the electric quantity as coordinates in a two-dimensional Euclidean space, determining a point in the Euclidean space by the time and the battery electric quantity of the time, calculating Euclidean distance between the points, dividing the points in the sliding window into m clusters according to the Euclidean distance, and expressing the electric quantity at the center of the cluster as E c,i, wherein i=1. The average electric quantity and the weighted average electric quantity in the sliding window are respectively
Wherein w i is the weight of the ith cluster, which satisfiesThe closer in time to the current slot the greater the weight of the cluster center, i.e., w 1＜w2＜…＜wi. Taking the ratio of the weighted average electric quantity to the average electric quantity as the electric quantity ascending and descending trend ratio in the sliding window of the time slot t:
the electric quantity up-down trend ratio represents the electric quantity change trend in a sliding window and can be used for measuring the stability of the electric quantity of the battery. q 1 (t) >1 shows a tendency of increasing the amount of electricity in the current sliding window, and q 1 (t) <1 shows a tendency of decreasing. A range [ gamma min,γmax ] is set, and if q 1 (t) is within the range, the electric quantity can be considered to be stable, otherwise the electric quantity fluctuation is considered to be excessive, and the weight should be adjusted. If q 1(t)＞γmax shows that the battery power increases rapidly with time, the weight should be increased appropriately to increase the transmission power. If q 1(t)＜γmin indicates that the battery charge decreases too rapidly with time, the weight should be reduced appropriately. The step length of the weight adjustment is in direct proportion to the difference value of the weighted average electric quantity and the average electric quantity in the sliding window, and the difference value reflects the increasing or decreasing degree of the electric quantity under the current sliding window. The formula of weight adjustment is
Wherein b 1 is a constant, and V (t-d) is the value of the weight of the previous parameter adjustment time.
The offset a directly affects the average charge of the battery. Defining the relative average electric quantity in the sliding window as the ratio of the average electric quantity to the battery capacity:
For the average power in the sliding window, E B is the battery capacity, when it is between the upper and lower thresholds η max and η min, η min≤q2(t)≤ηmax, indicating that the battery power is maintained in the proper range, the offset does not need to be adjusted, otherwise the offset needs to be adjusted. The formula of the adjustment is
Wherein b 2 is a fixed constant.
The present invention will be described in further detail with reference to the accompanying drawings. Unless otherwise specified, the parameters in the simulation were set as follows: the channel coefficient h (t) obeys a complex gaussian distribution with a mean of 0 and a variance of 2x 10 -9, i.e. the channel attenuation is 87dB; the energy arrival process of the transmitting end is subjected to a poisson process with composite uniform distribution, the arrival rate is lambda=0.5 units/time slot, and the energy of each unit is subjected to uniform distribution between [0,0.2] J; battery capacity E B =50j, initial battery charge of 15J, maximum discharge and charge power of P c,max =0.6w and P d,max =1.2w, respectively; the slot length T ts =1 s, the simulation duration t=1× 5 s; the maximum BER limited by the channel codec is P eb,max＝2.5×10-2; noise power spectral density N 0＝1×10-17 W/Hz; bandwidth b=1×10 6 Hz, symbol rate R s＝1×106 Baud. In algorithm 1The sliding window length in parameter adjustment, i=100 s, the number of clusters, m=4, the weights of the central power points after clustering, w i, i=1,., the set of m is {0.1,0.2,0.3,0.4}, the adjustment interval d=10s for the weights and offsets, the adjustment thresholds are γ min＝0.6,γmax＝1.4,ηmax＝0.8,ηmin＝0.1;b1 and b 2 are 0.025 and 0.01, respectively. If not specifically stated, the initial value of the offset a of the battery virtual queue is 42, and the initial value of the penalty weight V is 2.5.
The (n, K, r) BCH code is selected as channel coding, the code length is n=255, and the value set of the information bit length K is K= {247,239,231,223,215,207,199,191,187,179,171,163,155,147,139,131}. The encoding circuit employs a feedback shift register circuit comprising k shift registers and an average of about k/2 exclusive OR (adder). Assuming CMOS circuitry, ignoring static power consumption of the circuitry, the power consumption of a shift register or an exclusive OR is at each clock cycle
Eco＝α·CL·Vdd 2
Where α=1 is an activity factor, C L＝1.5×10-11 f=15pf is a load capacitance, and V dd =1.0v is an operating voltage. The coding circuit performs a shift operation in one clock cycle, the clock frequency is equal to the rate R s＝B·log2 M of the output code element of the coder, so the power consumption of the coder is
T ts is a unit time slot, and modulation is accomplished by using a digital signal processor, for example, a C55x series processor of TI, which operates at a power as low as 0.15mW/MHz. When an M-order modulation mode is adopted, log 2 M operations are needed to be carried out on each symbol obtained by modulation, and Blog 2 M operations are needed to be carried out each second. Assuming that the operation is completed once every two clocks, the power consumption of the modulation part is
Pm(t)＝3×10-10·Blog2M
B is the channel bandwidth, M is the modulation order, and the other circuit power consumption is set to P A =0.011W.
The effectiveness of the adaptive adjustment algorithm of the weight V and the offset A is verified. Firstly, the optimal values of the weight and the offset of the highest long-term average information transmission rate can be obtained under the set simulation conditions by a two-dimensional searching method through a computer. And fixing the weight and the offset in the Lyapunov optimization algorithm to the optimal value, and simulating to obtain the long-term average rate serving as a reference for the performance evaluation of the self-adaptive adjustment of the weight and the offset. Fig. 3 shows a three-dimensional plot of average information transmission rate over 5000 slots as a function of offset and weight. It can be seen that the average information transmission rate of the system is highest at the weight v=2.5 and the offset a=42.
Fig. 4 shows the trajectory of the average information transmission rate over time from the start of the simulation to the current slot when the weights V and the offsets a are set to different initial values and adaptively adjusted, these two parameters respectively fixing the average rates of 2.5, 42 set to be optimal as a reference. It can be seen that in the algorithm for adaptively adjusting the weight and the offset, when the initial values are different, the transmission rate values of the initial simulation segment are different, but as the simulation proceeds, the average information transmission rates under different initial values gradually tend to be consistent, and are slightly lower than the average transmission rate with the weight and the offset fixed as the optimal values. This is because the adaptive adjustment process of the weights and offsets is a gradual convergence process, and there is a performance penalty due to the fact that the parameters deviate more from the optimal values at the beginning of the simulation. As the simulation proceeds, the parameters gradually converge to an optimal value, and the performance gradually increases and approaches the optimal value. Simulation results show that the parameter self-adaptive adjustment algorithm provided by the invention is effective.
To verify the performance of the algorithm of the present invention, it is compared with the offline water-filling algorithm, the half-power algorithm, and the fixed ratio algorithm. A specific description of several comparison algorithms follows. (1) offline water-filling algorithm: the transmitting end obtains the change condition of the channel and the energy collection condition in the whole transmission process before transmission, and obtains the total power consumption of the system according to the total energy collected in the transmission process, and the causality of the electric quantity use and the overflow of the battery electric quantity are not considered. And then under the constraint of the total power consumption, adopting an alternate iterative water injection algorithm to obtain the optimal transmission power with the maximized average channel capacity, and combining the optimal modulation order and the information bit length under the transmission power. (2) half-power algorithm (Half Power Algorithm, HPA): the total energy consumed per time slot is half of the current charge in the battery, i.e
(3) Fixed ratio algorithm (Fixed Fraction Policies, FFP): the total energy consumed per time slot is a fixed proportion of the available energy of the battery, the proportion being the ratio of the multiple of the average energy reached over a long period to the battery capacity, the specific multiple value being set in relation to the overflow of the battery charge, expressed as
When the average information transmission rates of the half-power algorithm and the fixed-ratio algorithm are simulated, the total power is obtained according to the algorithm, then the circuit power consumption and the corresponding transmission power under the combination of each modulation order M and the information bit length k are calculated, then the respective information transmission rate R b (t) is calculated, and the maximum value is selected as the information transmission rate reached by the algorithm.
Fig. 5 shows the variation trace of the average information transmission rate of different algorithms with time, and the average information transmission rate per time slot is the average value of the information transmission rates from the simulation start to the current time slot. Simulation results show that the theoretical and actual transmission rates realized by the algorithm of the invention are higher than those of the half-power algorithm and the fixed algorithm. The half power algorithm and the fixed proportion algorithm are used for calculating the transmitting power according to the battery power only, and the influence caused by the change of the channel state is not considered, so that the performance is obviously lower than that of the algorithm optimized according to the channel state and the energy constraint. The optimization objective of the offline water-filling algorithm is to maximize the theoretical long-term average information transmission rate, so that the algorithm is superior to the algorithm in the invention in average channel capacity, and has 1.6% of advantages compared with the algorithm in the invention. However, the off-line water injection algorithm does not have the three variables of modulation order, coding information bit length and transmitting power, and the algorithm of the invention performs the joint optimization of the three variables with the aim of maximizing the actual reachable transmission rate, so that the actual reachable information transmission rate is higher than the algorithm although the channel capacity is lower than that of the off-line water injection algorithm, and the method has the advantage of 9.4 percent.
Fig. 6 shows the variation trace of the battery power of the 3 on-line algorithms with time, and the off-line water injection algorithm directly distributes the power of the total collected energy, without considering the overflow condition of the battery power, so the variation trace of the battery power is not shown. Simulation results show that the battery electric quantity of the algorithm provided by the invention can fluctuate at a certain level, and enough electric quantity is provided for information transmission in each time slot battery, and enough storage space is reserved for the collected energy. The half-power algorithm consumes the pre-stored electric quantity in a short time, and then the electric quantity is stabilized at a very low level; the fixed ratio algorithm is too conservative in power consumption, only uses the fixed ratio part of the stored power in the existing battery, and is not adjusted according to the channel state, so that the performance is lower, and meanwhile, the possibility of overflow of the collected energy is increased due to the smaller residual space of the battery.
In order to verify the applicability of the algorithm, the average transmission rate is simulated under a random wind power generation model and an energy random model with the arrival energy obeying Bernoulli distribution, besides the simulation is performed when the energy arrival process is the composite Poisson uniform distribution. (1) wind power generation: the output electric power P a of the wind power generation module is
Pa＝0.5ρSCpvT 3η1η2
Wherein ρ is air density, the simulation takes density of 1.22kg/m 3;vT at 20 ℃ as wind speed which changes randomly, and the simulation is carried out by adopting a four-component random model; the wind energy utilization coefficient C p is 0.593; s is the swept area of the fan blade, the relation between the s=pi R 2;η1 =0.94 and the radius of the fan blade is the energy conversion efficiency of the gear box; η 2 =0.52 is the energy conversion efficiency of the motor.
(2) Bernoulli distribution: the energy reached per time slot is an independent and equidistributed bernoulli random variable:
Where the probability p is the probability of energy arrival.
Fig. 7 shows simulation results of the long-term average information transmission rate as a function of the energy arrival rate λ when the energy obeys the poisson process of composite uniform distribution. As the energy arrival rate increases, the average transmit power of the signal increases, and thus the average information transmission rate of all algorithms increases. Compared with the comparison algorithm, the algorithm of the invention can obtain higher information transmission rate because the transmission power, the modulation mode and the coding parameters are jointly optimized at the practically reachable transmission rate maximization. Since the fixed ratio algorithm and the half power algorithm do not consider the channel condition, the achieved information transmission rate differs significantly from the present invention. The water-filling algorithm is optimized by maximizing the theoretical channel capacity, and the information transmission rate which can be achieved under the practically available channel coding and modulation is not the maximum.
Fig. 8 shows simulation results of the long-term average transmission rate of different fan blade radii R in the wind power generation model. As can be seen from the figure, as the radius of the fan blade increases, the swept area of the wind power generator increases, and the output electric power increases, so that a higher information transmission rate can be obtained. The information transmission rate which can be achieved by the algorithm of the invention is still highest, and then the off-line water injection algorithm and the half-power algorithm are lowest.
Fig. 9 shows simulation results of the long-term average transmission rate of different energy arrival probabilities p when the energy obeys the bernoulli distribution. As can be seen from the figure, as the probability of energy arrival increases, the average transmit power increases, and the information transmission rate increases accordingly. Similar to fig. 8 and 9, the transmission rate obtainable by the algorithm of the present invention is still the highest, followed by the off-line water-filling algorithm and the half-power algorithm is the lowest.
Claims (1)
1. The online joint control method for the transmitting power, the modulation order and the coding rate in the energy collection wireless communication system is characterized by comprising the following steps:
(1) Modeling a communication system, collecting energy from the surrounding environment by a transmitting end in each time slot for transmitting information to a receiving end, and constructing an optimization problem by taking maximum transmission power limit, limit of battery storage electric quantity on transmission power and battery electric quantity change constraint conditions and taking maximum average transmission rate in long term as targets;
Modeling the communication system comprises modeling the actual reachable transmission rate of the system and a battery electric quantity queue of a transmitting end;
The actual achievable transmission rate of the system is
B represents channel bandwidth, M represents modulation order, k represents information bit length, n represents coding code length, R represents error correction capability, P eb represents bit error rate, R bt represents information bit number transmitted by a transmitting end in each time slot, and P cf represents correctly decoded codeword probability;
The battery power queue of the transmitting end is P (t) =p c(t)+Pm(t)+PA+PT(t)＝Pcc(t)+PT (t), P (t) represents the power consumption of the transmitting end, P T (t) represents the power consumption of a transmitting signal, P cc (t) represents the power consumption of a circuit, and the circuit power consumption is divided into coding circuit power consumption P c (t), modulation circuit power consumption P m (t) and other circuit power consumption P A;
Pc(t)＝σ1·Rbt
σ 1 is a constant coefficient, and is related to a specific adopted coding mode, a coding circuit and a circuit parameter;
Pm(t)＝σ2·Blog2M
σ 2 is a constant, and a specific value is related to a circuit or a signal processing device;
The optimization problem is that
M∈Ω
k∈K
Wherein E [. Cndot. ] represents the desired operation, T ts is the unit time slot, K is the set of selectable information bit lengths in one codeword, and is related to the selected channel coding; omega is a set of optional modulation schemes; p d,max is the maximum transmit power constraint; e b (t) is a battery storage capacity constraint; For long-term power constraints, i.e. all collected energy is used for signaling, wherein, P is the average electric energy stored in the battery and the total power consumption in each time slot;
(2) Obtaining a virtual queue by utilizing a Lyapunov optimization framework after adding offset to the battery electric quantity of a transmitting end, constructing a drift and punishment item by taking a negative value of a transmission rate as a punishment item, and converting a constrained optimization problem of maximizing a long-term average transmission rate into a minimized drift and punishment item;
Adding an offset to the battery power of the transmitting end to be used as an energy virtual queue:
X(t)＝Eb(t)-A
Wherein E b (t) is the battery power, A is the offset and is a positive constant;
defining a quadratic Lyapunov function
Lyapunov drift is defined as
Drift plus penalty term is
Δ(X(t))-VE[Rb(t)|X(t)]
Where E [ R b (t) ] is the target of optimization, the average transmission rate, its negative value is the penalty term, V is the weight between drift and penalty term, is a normal quantity used to trade-off between queue stability and maximization of system transmission rate, the optimization problem translates into minimizing drift plus penalty term, i.e
M∈Ω
k∈K
(3) Converting the minimized "drift plus penalty" term into an upper bound of the minimized "drift plus penalty" term;
the energy update formula and Lyapunov function are defined by
In the formula (E b(t)-Tts·P(t))2 is a non-negative finite value, so that there is a non-negative constant C to satisfy
Thus there is
ΔX(t)≤C+X(t)E[EH(t)-Tts·P(t)|X(t)]
The upper bound of "drift plus penalty" can be obtained as
ΔX(t)-VE[Rb(t)|X(t)]≤C+X(t)E[EH(t)-Tts·P(t)|X(t)]-VE[Rb(t)|X(t)]
The optimization problem can be approximated by minimizing the upper bound of "drift plus penalty", further removing the mean operation in the upper bound, and converting the expression of the rate into a single-slot optimization problem:
M∈Ω
k∈K
(4) Making a decision according to the energy arrival and the channel state, searching an optimal combination of the transmission power, the modulation mode and the information bit length, namely solving an optimal solution of an optimization objective function, wherein the solving of the optimal solution is as follows: when X (t) is equal to or greater than 0, the objective function is a monotonically increasing function of the signal transmission power P T (t), and in order to maximize the objective function, the signal transmission power P T (t) should take the maximum value under the support of the battery discharge power and the stored energy When X (t) < 0, equally dividing the transmission power range [ P T,min,PT,max ] into a plurality of cells with delta 1, judging whether each cell contains a maximum point, if so, finding the maximum point by adopting a golden section method, comparing the objective function values F (P T (t), M and k) of all the available channel coding and modulation mode combinations after obtaining the optimal transmission power, and selecting the modulation order and information bit length combination which make the objective function maximum, and the corresponding transmission power as a solution of the optimization problem.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210725370.6A CN115278728B (en) | 2022-06-23 | 2022-06-23 | On-line joint control method for transmitting power, modulation order and coding rate in energy collection wireless communication system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210725370.6A CN115278728B (en) | 2022-06-23 | 2022-06-23 | On-line joint control method for transmitting power, modulation order and coding rate in energy collection wireless communication system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115278728A CN115278728A (en) | 2022-11-01 |
| CN115278728B true CN115278728B (en) | 2024-07-09 |
Family
ID=83762497
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210725370.6A Active CN115278728B (en) | 2022-06-23 | 2022-06-23 | On-line joint control method for transmitting power, modulation order and coding rate in energy collection wireless communication system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115278728B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113329419A (en) * | 2021-05-11 | 2021-08-31 | 重庆邮电大学 | Raptor code-based online joint control method for power and rate of energy collection communication system |
| CN114629535A (en) * | 2022-03-14 | 2022-06-14 | 天津大学 | Intelligent reflector non-cellular large-scale MIMO network capacity optimization method |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9755711B2 (en) * | 2013-09-09 | 2017-09-05 | Taiwan Semiconductor Manufacturing Company, Ltd. | Large deviation delay analysis of queue-aware multi-user MIMO systems with multi-timescale mobile-driven feedback |
| CN109829332B (en) * | 2019-01-03 | 2021-05-04 | 武汉理工大学 | Joint calculation unloading method and device based on energy collection technology |
| WO2020208607A1 (en) * | 2019-04-12 | 2020-10-15 | Telefonaktiebolaget Lm Ericsson (Publ) | Online mimo wireless network virtualization with unknown channel information |
| CN110213766B (en) * | 2019-05-14 | 2022-05-03 | 重庆邮电大学 | Online power control method based on Lyapunov framework in energy collection safety transmission system |
| CN112788629B (en) * | 2020-12-31 | 2022-05-03 | 重庆邮电大学 | Online combined control method for power and modulation mode of energy collection communication system |
| CN113141646B (en) * | 2021-04-21 | 2022-10-18 | 重庆邮电大学 | Power control method under actual charge-discharge characteristic of battery in energy collection wireless communication system |
-
2022
- 2022-06-23 CN CN202210725370.6A patent/CN115278728B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113329419A (en) * | 2021-05-11 | 2021-08-31 | 重庆邮电大学 | Raptor code-based online joint control method for power and rate of energy collection communication system |
| CN114629535A (en) * | 2022-03-14 | 2022-06-14 | 天津大学 | Intelligent reflector non-cellular large-scale MIMO network capacity optimization method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115278728A (en) | 2022-11-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112788629B (en) | Online combined control method for power and modulation mode of energy collection communication system | |
| Berry et al. | Communication over fading channels with delay constraints | |
| Rajesh et al. | Capacity of fading Gaussian channel with an energy harvesting sensor node | |
| CN110213766B (en) | Online power control method based on Lyapunov framework in energy collection safety transmission system | |
| Tutuncuoglu et al. | Optimal power policy for energy harvesting transmitters with inefficient energy storage | |
| CN113329419B (en) | Online combined control method for power and rate of energy collection communication system | |
| Tutuncuoglu et al. | Communicating using an energy harvesting transmitter: Optimum policies under energy storage losses | |
| CN112738849B (en) | Load balancing regulation and control method applied to multi-hop environment backscatter wireless network | |
| EP1579581A4 (en) | Method and device for adaptive quantization of soft bits | |
| Reddy et al. | Profile-based load scheduling in wireless energy harvesting sensors for data rate maximization | |
| CN114513855B (en) | Edge computing unloading decision and resource allocation method based on wireless energy-carrying communication | |
| CN106506113B (en) | A kind of implementation method of the adaptive modeling fountain codes towards Ka band communication | |
| CN112333809B (en) | Energy collection relay safe transmission online power control method based on Lyapunov framework | |
| Bacinoglu et al. | Finite horizon online lazy scheduling with energy harvesting transmitters over fading channels | |
| Liu et al. | Power and rate control in wireless communication systems with energy harvesting and rateless codes | |
| CN115278728B (en) | On-line joint control method for transmitting power, modulation order and coding rate in energy collection wireless communication system | |
| CN110190875B (en) | Wireless data and energy integrated coding method based on battery state of receiver | |
| Pielli et al. | Minimizing data distortion of periodically reporting iot devices with energy harvesting | |
| CN113141646B (en) | Power control method under actual charge-discharge characteristic of battery in energy collection wireless communication system | |
| Bhat et al. | Distortion minimization in energy harvesting sensor nodes with compression power constraints | |
| CN115460654A (en) | Real-time adjustment method for packet length aiming at information age minimization in Internet of things | |
| Zibaeenejad et al. | Power management policies for slowly varying Bernoulli energy harvesting channels | |
| Panem et al. | PCA data reduction for MIMO based channel, capacity enhancement using 16-QAM modulation | |
| Zibaeenejad et al. | Optimal universal power management policies for channels with slow-varying harvested energy | |
| CN112218378A (en) | Wireless resource allocation method in imperfect channel state information fading channel |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20240517 Address after: 518000 1104, Building A, Zhiyun Industrial Park, No. 13, Huaxing Road, Henglang Community, Longhua District, Shenzhen, Guangdong Province Applicant after: Shenzhen Hongyue Enterprise Management Consulting Co.,Ltd. Country or region after: China Address before: 400065 Chongwen Road, Nanshan Street, Nanan District, Chongqing Applicant before: CHONGQING University OF POSTS AND TELECOMMUNICATIONS Country or region before: China |
|
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20240613 Address after: Room 1122, Building 4, Artificial Intelligence Industrial Park, No. 266 Chuangyan Road, Qilin Technology Innovation Park, Nanjing, Jiangsu Province, 211100 Applicant after: Zhongke Xinchuang (Nanjing) Technology Co.,Ltd. Country or region after: China Address before: 518000 1104, Building A, Zhiyun Industrial Park, No. 13, Huaxing Road, Henglang Community, Longhua District, Shenzhen, Guangdong Province Applicant before: Shenzhen Hongyue Enterprise Management Consulting Co.,Ltd. Country or region before: China |
|
| TA01 | Transfer of patent application right | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |