CN107509243A - Bandwidth and power combined control method based on downlink non-orthogonal multiple access system - Google Patents
Bandwidth and power combined control method based on downlink non-orthogonal multiple access system Download PDFInfo
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Abstract
A bandwidth and power combined control method based on a downlink non-orthogonal multiple access system comprises the following steps: (1) the base station transmits data through a non-orthogonal multiple access technology to provide data traffic service for the mobile user; (2) analyzing system characteristics to perform equivalent transformation on the problems; (3) the problem after conversion is proved to be a feasibility checking problem, so that efficient solution can be realized; (4) and designing a feasible and efficient algorithm solution according to the finally converted problem characteristics, and finally substituting the algorithm output result back to the top layer problem to obtain the optimal bandwidth and power distribution value. The invention provides a feasible and efficient optimization method which not only guarantees the data requirements of mobile users, but also minimizes the total resource consumption of the system, so as to improve the utilization rate of system resources and optimize the configuration of the system resources.
Description
Technical Field
The invention relates to a bandwidth and power combined control method based on a downlink non-orthogonal multiple access (NOMA) system in a wireless network.
Background
In order to achieve high spectrum efficiency and large-scale connection in the 5 th generation mobile communication technology, a Non-Orthogonal Multiple Access (NOMA) technology is proposed, and unlike the conventional OMA (OMA) technology, NOMA can serve more users through Non-Orthogonal resource allocation, and can significantly improve spectrum efficiency by enabling a large number of users to simultaneously share the same frequency band channel and eliminating co-channel Interference by using a Successive Interference Cancellation (SIC) mechanism. Therefore, NOMA fits well with the ultimate goal of future 5G cellular networks, providing ultra-high throughput and ultra-dense connections.
Disclosure of Invention
The present invention provides a bandwidth and power joint control method based on a downlink non-orthogonal multiple access system, which is to overcome the disadvantages of the prior art.
The invention applies NOMA technology to transmit data in the wireless cellular network, considers the bandwidth and the power jointly, and realizes the minimization of the resource consumption in the system on the premise of meeting the data flow requirements of all MUs.
The technical scheme adopted by the invention for solving the technical problem is as follows:
a bandwidth and power combined control method based on a downlink non-orthogonal multiple access (NOMA) system in a wireless network comprises the following steps:
(1) There are a total of T Mobile Users (MUs) under the coverage of the mBS, in which case the mBS transmits data using NOMA technology. Taking into account NOMATechnical features, introduction of index setsRepresenting T MUs. First, since the successive interference cancellation mechanism (SIC) orders the channel gains from the mBS to all MUs from large to small, there is the following order:
g B1 >g B2 >…>g Bj >g Bi >…>g BT (1)
wherein g is Bi Represents the channel gain of the mBS to the ith MU,the ith MU (or jth MU) mentioned in the following description is in the index setThe method of (1).
(2) At the mBS end, instantaneous channel gain per MUAre known. Based on NOMA, the mBS will transmit all data to each MU superimposed on the same frequency band. At the MU end, partial co-channel interference between the MUs is eliminated by using the SIC. By MU i 、MU k And MU j To illustrate the working principle of SIC, for MU i Decoding MU first in received data k (k> i, i.e. MU in particular k Arranged at MU i Later) and then deleting the decoded data from the received data (the specific operation order is k = T, T-1,T-2, …, i + 1) while the MU is being operated on j (j< i, i.e. MU in particular j Arranged at MU i The preceding) data signal is treated as noise, MU j Indicates that the MU is arranged at the jth MU; MU (Multi-user) i Indicating that the MU is ranked at the ith MU; MU (Multi-user) k Indicating that the MU is ranked at the kth. From mBS to MU according to the above decoding mechanism i The throughput of (a) is:
wherein the relevant parameters are defined as follows:
p Bi : mBS to MU i The transmit power of (a);
p Bi : mBS to MU i Data throughput of (d);
W B : an amount of bandwidth allocated to service the group of mobile users;
n 0 : power spectral density of background noise.
(3) In this patent, considering the situation of a single mBS, the data requirements of all mobile users are met simultaneously while minimizing the total resource consumption of the system, and the following constraints are set:
whereinRepresents MU i The data requirements of (1).
In a wireless network, a macro cell base station (mBS) transmits data through non-orthogonal multiple access (NOMA), and a Successive Interference Cancellation (SIC) is applied to cancel part of co-channel interference generated by the mBS during transmitting data using the same channel, so as to minimize system resource Consumption (TCM) while ensuring that each MU data requirement is met, and describe the optimization problem as follows:
wherein the relevant parameters are defined as follows:
total power of the mBS;
total bandwidth owned by mBS;
this is a problem of jointly considering bandwidth and power allocation, and the optimal solution of the problem is the minimum consumption of system resources in the case of meeting the data requirements of mobile users.
Note: α and β involved in the TCM represent a price factor of power and a price factor of bandwidth, respectively, that is, the cost per unit power is α and the cost per unit bandwidth is β.
(4) The problem (TCM) is caused by the power p Bi Sum bandwidth W B Jointly determined, the analytical problem property translates equivalently to the bandwidth allocation problem. We introduce beta Bi To represent mBS to MU i Signal to Interference plus Noise Ratio (SINR), i.e.:
it is assumed here thatGiven, mBS to MU can be recursively calculated by the above formula i Is expressed as follows:
the minimum total transmit power that can be obtained from this equation to all mobile users is expressed as follows:
wherein the assumption is g B0 Is a sufficiently large value, so
(5) Will W B Considering as variables, applying the minimum total power expression, the TCM problem can be equivalently converted into a Bandwidth-Allocation (BA) problem as follows:
variables:W B >0.
through the step of equivalence transformation, the problem BA only has one decision variable W compared with the problem TCM B It becomes easier to solve.
Although BA has only one decision variable, it is still difficult to solve the problem directly, so a variable substitution is introduced as follows:
when the substitution formula is available, the problem BA can be equivalently converted by combining the minimum total transmitting power expression, the English letter E is added after the BA to obtain BA-E, and the BA-E is converted to obtain the problem BA-E
The problem BA-E is still non-convex due to the non-convexity of the objective function and the constraints (13), but can be solved effectively through the algorithm steps designed by the invention.
(6) An additional variable v is introduced in this step, where Representing a system resource consumption value. Further transformation of the problem BA-E gives a problem BA-EV which is expressed as follows:
(BA-EV):min v
note that the problem BA-EV is equivalent to the problem BA-E, and the optimal solution v of the problem corresponds to the minimum value of the system resource consumption.
Observing the problem BA-EV finds that if the v value is fixed, the problem BA-EV can be converted into a feasible domain inspection problem with convexity, so that the following optimization problem can be obtained under the given v value, and is marked as BA-EVsub:
(BA-EVsub):
in which inputting a v value can obtain aThe value is obtained. For problem outputA value ifIt indicates that the problem BA-EV is feasible given v and that the v value can be further reduced. If it is usedIt indicates that the problem BA-EV is not feasible and the input v value needs to be increased. When in useWhen the value reaches the set accuracy, the algorithm is ended, and the calculated v is output * And x * 。
(7) In conjunction with the description of the above steps, two conclusions are drawn about the problem BA-EVsub: 71 Given the value of v), the problem BA-EVsub is a convex optimization problem with respect to x; 72 Optimal solution for problem BA-EVsubIs a non-increasing function with respect to v; an algorithm is designed and solved based on the two conclusions, and the algorithm is specifically described as follows, wherein the algorithm is marked as Sol-BA:
the algorithm comprises the following steps of S1: input upper limit value v max Lower limit value v min Ending the threshold tol of the cycle;
an algorithm step S2: according to the condition | v max -v min | ≧ tol, determine whether to enter a loop, that is, | v max -v min If the absolute value is more than or equal to tol, entering circulation, and executing the algorithm steps S3 and v max -v min |&Tol does not enter circulation, and an algorithm step S7 is executed;
an algorithm step S3: setting the value of v toNamely, it is
And an algorithm step S4: solving problem BA-EVsub according to set v value to obtain optimal valueTo correspond to
An algorithm step S5: optimum value obtained according to algorithm step S4To proceed withIt is determined ifThe upper limit value v is updated max = v; otherwise, the lower limit value v is updated min = v, return to algorithm step S2;
an algorithm step S6: ending the circulation;
an algorithm step S7: output optimum valuev * ＝v；
(8) The original problem TCM can be solved by using the output value of the algorithm Sol-BA, and the optimal bandwidth allocation is obtained as follows:
obtaining MU of each mobile user through recursive calculation i The optimal power allocation of (c) is:
the technical conception of the invention is as follows: first, in a wireless network, 1 macro Base Station (mBS) transmits data to T Mobile Users (MUs) through non-orthogonal multiple access (NOMA), and the use of NOMA may improve the spectrum efficiency of the system. Then, a Successive Interference Cancellation (SIC) mechanism is applied to eliminate partial co-channel interference so as to improve the transmission quality of system data. System resource consumption is then minimized while meeting all Mobile User (MU) data traffic requirements. The problem is a non-convex optimization problem, and therefore it is difficult to solve the problem directly. The original problem is subjected to characteristic analysis, the problem is converted into a convex bandwidth allocation problem, and finally an algorithm can be designed for efficient solution.
The invention has the advantages that 1, for the whole system, the introduction of the NOMA technology not only conforms to the development requirement of the fifth generation mobile communication technology (5G) in the future, but also improves the frequency spectrum use efficiency; 2. two different problems of bandwidth allocation and power allocation are considered jointly, and the overall resource consumption of the system is minimized.
Drawings
Fig. 1 is a schematic diagram of a system model including a macro cell base station (mBS) and a plurality of Mobile Users (MUs) in a wireless network to which the method of the present invention is applied.
Detailed Description
The present invention is described in further detail below with reference to the attached drawing figures.
Referring to fig. 1, a bandwidth and power joint control method based on a downlink non-orthogonal multiple access (NOMA) system in a wireless network can minimize the total resource consumption of the system and improve the spectrum utilization efficiency while satisfying the data requirements of all MUs. The invention is applied to a wireless cellular network (as shown in figure 1), the mBS uses NOMA to send data, SIC is introduced to eliminate partial co-channel interference, and simultaneously, the requirement of meeting the data flow of all MUs is considered. The joint control method proposed for the problem has the following steps:
(1) There are a total of T Mobile Users (MUs) under the coverage of the mBS, in which case the mBS transmits data using NOMA technology. In consideration of technical characteristics of NOMA, index set is introducedRepresenting T MUs. First, since the successive interference cancellation mechanism (SIC) orders the channel gains from the mBS to all MUs from large to small, there is the following order:
g B1 >g B2 >…>g Bj >g Bi >…>g BT (1)
wherein g is Bi Representing the channel gain of the mBS to the ith MU,the ith MU (or jth MU) mentioned in the following description is in the index setThe method of (1).
(2) At the mBS end, instantaneous channel gain per MUAre known. Based on NOMA, the mBS will transmit all data to each MU superimposed on the same frequency band. At the MU end, SIC is used for eliminating mutual interference between MUs. By MU i 、MU k And MU j To illustrate the working principle of SIC, for MU i Decoding MU first in received data k (k> i, i.e. MU in particular k Arranged at MU i Later) and then removes the decoded data from the received data (the specific order of operation is k = T, T-1,T-2, …, i + 1), while the MU is operated on j (j< i, i.e. MU in particular j Arranged at MU i The preceding) data signal is treated as noise, MU j Indicates that the MU is arranged at the jth MU; MU (Multi-user) i Indicating that the MU is ranked at the ith MU; MU (Multi-user) k Indicating that the MU is ranked at the kth. From mBS to MU according to the above decoding mechanism i The throughput of (a) is:
wherein the relevant parameters are defined as follows:
p Bi : mBS to MU i The transmit power of (a);
R Bi : mBS to MU i Data throughput of (d);
W B : an amount of bandwidth allocated to serve the group of mobile users;
n 0 : power spectral density of background noise.
(3) In this patent, considering the situation of a single mBS, the data requirements of all mobile users are met simultaneously while minimizing the total resource consumption of the system, and the following constraints are set:
whereinRepresents MU i The data traffic demand of (1).
In a wireless network, a macro cellular base station (mBS) transmits data through non-orthogonal multiple access (NOMA), and applies a Successive Interference Cancellation (SIC) to cancel part of interference generated by the mBS during data transmission using the same channel, so as to minimize system resource Consumption (TCM) while ensuring that data requirements of each MU are met, and describe this optimization problem as follows:
wherein the relevant parameters are defined as follows:
total power of mBS;
total bandwidth possessed by the mBS;
this is a joint consideration of bandwidth and power allocation problems, the optimal solution to the problem is to minimize the system resource consumption value while meeting the mobile user data requirements.
Note: α and β involved in the TCM problem represent a price coefficient of power and a price coefficient of bandwidth, respectively, that is, the cost per unit power is α and the cost per unit bandwidth is β.
(4) Problem TCM is that power p Bi Sum bandwidth W B Jointly determined, the problem characteristics are equivalently converted into a bandwidth allocation problem through analysis. Introduction of beta Bi To represent mBS to MU i Signal to Interference plus Noise Ratio (SINR), i.e.:
it is assumed here thatGiven, mBS to MU can be recursively calculated by the above formula i Is expressed as follows:
observation (8) of MU i With { beta } power distribution Bj } j≤i Is increased, in combination with (6), it is concluded that: when each MU i Is provided withA globally optimal solution for the problem TCM is obtained.
The minimum total transmit power from the resulting mBS to all mobile users is expressed as follows:
wherein the assumption is g B0 Is a sufficiently large value and is therefore
For the above conclusions, the demonstration was carried out by mathematical induction (forward-reduction), and the following demonstration procedure was followed:
step 4.1: at T =1, it can be concluded thatWith mBS to MU i The minimum transmit power expressions of (a) are consistent;
step 4.2: when T >1, it is assumed that all are true for the conclusion;
step 4.3: we further add the i +1 MU while guaranteeing g BT >g BT+1 . When the following formula is proved to be established, the proposed conclusion can be proved to be correct;
step 4.4: proof of step 4.3;
a. for T +1 have
b. Thus, it is possible to obtain
And (5) finishing the certification.
(5) W is to be B Considering as variables, applying the minimum total power expression, the TCM problem can be equivalently converted into a Bandwidth-Allocation (BA) problem as follows:
variables:W B >0.
through the step of equivalence transformation, the problem BA only has one decision variable W compared with the problem TCM B It becomes easier to solve.
Nevertheless, it is difficult to solve the problem directly, and therefore a variable substitution is introduced as follows:
when the substitution formula is available, the problem BA can be equivalently converted by combining the minimum total transmitting power expression, the English letter E is added after the BA to obtain BA-E, and the BA-E is converted to obtain the problem BA-E
The problem BA-E is still non-convex due to the non-convexity of the objective function and the constraint (13), but the algorithm steps designed by the invention can be effectively solved, and the algorithm is specifically explained in (7).
(6) An additional variable v is introduced in this step, where Representing a system resource consumption value. Further transformation of the problem BA-E gives a problem BA-EV which is expressed as follows:
(BA-EV):min v
note that the problem BA-EV is substantially equivalent to the problem BA-E, the optimal solution v to the problem * What corresponds to this is the minimum value of system resource consumption.
Observing the problem BA-EV finds that if the v value is fixed, the problem BA-EV can be transformed into a feasible domain inspection problem with convexity, so that the following optimization problem can be obtained by giving the v value.
(BA-EVsub):
In which inputting a v value can obtain aThe value is obtained. For problem outputValue ifIt indicates that the problem BA-EV is feasible given v and that the v value can be further reduced. If it is notIt indicates that the problem BA-EV is not feasible and the input v value needs to be increased. When in useWhen the value meets the set accuracy condition and is small enough, the algorithm is ended, and the calculated v is output * And x * 。
(7) In conjunction with the description of the above steps, two conclusions are drawn about the problem (BA-EVsub): 71 Given the value of v, the problem (BA-EVsub) is a convex optimization problem with respect to x; 72 Optimal solution of problem (BA-EVsub)Is a non-increasing function with respect to v. An algorithm (Sol-BA) is designed to solve based on the two conclusions, and the specific description is as follows:
algorithm step S1: input upper limit value v max Lower limit value v min Ending the threshold tol of the cycle;
step of algorithmStep S2: according to the condition | v max -v min | ≧ tol, determine whether to enter a loop, that is, | v max -v min If the absolute value is more than or equal to tol, entering circulation, and executing the algorithm steps S3 and v max -v min |&Tol does not enter circulation, and an algorithm step S7 is executed;
algorithm step S3: setting the value of v toNamely, it is
And an algorithm step S4: obtaining an optimal value according to a set v value solution problem (BA-EVsub)To correspond to
An algorithm step S5: optimum value obtained according to algorithm step S4Make a determination ifThe upper limit value v is updated max = v; otherwise, the lower limit value v is updated min = v, return to algorithm step S2;
an algorithm step S6: ending the circulation;
an algorithm step S7: output optimum valuev * ＝v。
(8) The original problem (TCM) can be solved by using the output values of the algorithm (Sol-BA), obtaining an optimal bandwidth allocation as:
obtaining MU of each mobile user through recursive calculation i The optimal power allocation of (c) is:
the problem is thus successfully solved by the algorithm of the present invention.
In this example, fig. 1 is a system model diagram of a macrocell base station (mBS) and T Mobile Users (MUs) in a cellular data network contemplated by the present invention. In this system, the main technical points considered include the following: 1) The mBS sends data through NOMA; 2) Because the mBS transmits data for all the MUs on the same frequency band, SIC is introduced to eliminate partial co-channel interference; 3) And the data flow requirement of each MU is met. According to the technical points, the invention provides an optimization problem of the total resource consumption of the system, but the optimization problem is a non-convex optimization problem. In order to overcome the problem, the invention analyzes the problem characteristics, performs equivalent transformation on the provided optimization problem, the transformed problem is a strict convex optimization problem, and most importantly, the invention provides an efficient algorithm for solving the problem and has good effect.
The embodiment aims to minimize the total resource consumption cost of the system and improve the spectrum efficiency of the system on the premise of simultaneously meeting the data traffic demand of the Mobile User (MU). The invention can make the mobile user in the wireless honeycomb network obtain the service with better quality and lower cost, and further can realize the optimization of the power and frequency spectrum resource allocation of the whole system and higher utilization rate.
Claims (1)
1. The bandwidth and power combined control method based on the downlink non-orthogonal multiple access system comprises the following steps:
(1) There are a total of T mobile users MU under the coverage of the macrocell base station mBS, in which case the mBS transmits data using a non-orthogonal multiple access technique NOMA(ii) a In consideration of technical characteristics of NOMA, index set is introducedRepresents T MU; firstly, because the successive interference cancellation mechanism SIC orders the channel gains from the mBS to all MUs from large to small, there is the following order:
g B1 ＞g B2 ＞…＞g Bj ＞g Bi ＞…＞g BT (1)
wherein g is Bi Represents the channel gain of the mBS to the ith MU,the ith MU or the jth MU mentioned in the following description is in the index setThe method (1) above;
mBS denotes a macro cellular base station; MU represents a mobile user; NOMA denotes a non-orthogonal multiple access technique; SIC denotes a successive interference cancellation mechanism,
(2) At the mBS end, instantaneous channel gain per MUAre all known; based on NOMA, the mBS overlaps all data on the same frequency band and sends the data to each MU; at the MU end, SIC is used for eliminating mutual interference among the MUs; for MU i Decoding MU first in received data k Data of (a), k>, i refers to MU k Arranged at MU i Later, the decoded data is then deleted from the received data, the specific order of operation being k = T, T-1,T-2, …, i +1, while the MU is simultaneously deleted j The data signal of (a) is regarded as noise, j&I refers to MU j Arranged at MU i Front, MU j Indicates that the MU is arranged at the jth MU; MU (Multi-user) i Indicating that the data is arranged at the ith MU; MU (Multi-user) k Indicating that the MU is ranked at the k-th MU, from mBS to MU according to the above decoding scheme i The throughput of (a) is:
wherein the relevant parameters are defined as follows:
p Bi : mBS to MU i The transmit power of (a);
R Bi : mBS to MU i Data throughput of (d);
W B : an amount of bandwidth allocated to service the group of mobile users;
n 0 : power spectral density of background noise;
(3) Considering the case of a single mBS, the following constraints are set to satisfy the data requirements of all mobile users simultaneously while minimizing the total resource consumption of the system:
whereinRepresents MU i The data traffic demand of (1);
in a wireless network, an mBS transmits data through NOMA, and SIC is applied to eliminate partial interference generated by the mBS during transmission of data using the same channel, so as to minimize system resource consumption while ensuring that data requirements of each MU are met, the optimization problem is described as follows, denoted as TCM:
wherein the relevant parameters are defined as follows:
total power of the mBS;
total bandwidth possessed by the mBS;
the problem of bandwidth and power allocation is considered jointly, and the optimal solution of the problem is the minimum value of system resource consumption under the condition of meeting the data requirement of the mobile user;
α and β involved in the TCM problem represent a price coefficient of power and a price coefficient of bandwidth, respectively, that is, the cost per unit power is α and the cost per unit bandwidth is β;
(4) Problem TCM is that power p Bi Sum bandwidth W B Jointly determining, equivalently converting the problem characteristics into a bandwidth allocation problem by analyzing the problem characteristics; introduction of beta Bi To represent mBS to MU i The signal to interference plus noise ratio SINR, i.e.:
it is assumed here thatGiven, mBS to MU can be recursively calculated by the above formula i Is expressed as follows:
observation of mBS to MU i Discovery of MU for minimum transmit power expression i With { beta } power distribution Bj } j≤i Is increased with MU, and i the data traffic demand limiting conditions are combined to draw a conclusion that: when each MU is i Is provided withIs the global optimal solution of the problem TCM;
the minimum total transmit power from the resulting mBS to all mobile users is expressed as follows:
wherein the assumption is g B0 Is a sufficiently large value and therefore
For the above conclusions, as demonstrated by mathematical induction, the procedure was as follows:
step 4.1: at T =1, the conclusion can be drawnWith mBS to MU i The minimum transmit power expressions of (a) are consistent;
step 4.2: when T >1, it is assumed that all are true for the conclusion;
step 4.3: we further add the i +1 MU while guaranteeing g BT ＞g BT+1 (ii) a When the following formula is proved to be immediateThe proposed conclusion can be proved to be correct;
step 4.4: proof of step 4.3;
a. for T +1 have
b. Thus can obtain
Finishing the certification;
(5) W is to be B Considering as a variable, and applying the minimum total power expression, the TCM problem can be equivalently converted into the following bandwidth allocation BA problem, which is denoted as BA:
variables:W B >0.
through the step of equivalence transformation, the problem BA only has one decision variable W compared with the problem TCM B It becomes easier to solve;
nevertheless, it is difficult to solve the problem directly, and an auxiliary variable is then introduced as follows:
when the substitution formula is available, the problem BA can be equivalently converted by combining the minimum total transmitting power expression, the English letter E is added after the BA to obtain BA-E, and the BA-E is converted to obtain the problem BA-E
The problem BA-E is still non-convex due to the non-convexity of the objective function and the limiting conditions, but the algorithm steps designed by the invention can be effectively solved, and the detailed description is provided in the following;
(6) An additional variable v is introduced in this step, whereRepresenting a system resource consumption value; further transformation of the problem BA-E gives a problem BA-EV which is expressed as follows:
BA-EV:min v
note that the problem BA-EV is essentially equivalent to the problem BA-E, the optimal solution v of the problem * The corresponding is the minimum value of system resource consumption;
observing the problem BA-EV, finding that if the v value is fixed, the problem BA-EV can be converted into a feasible domain inspection problem with convexity, and therefore the following optimization problem can be obtained by giving the v value and is marked as BA-EVsub;
in this problem, a value of v is input to obtain a valueA value; for problem outputA value ifIt indicates that the problem BA-EV is feasible given v and that the v value can be further reduced; if it is notIt indicates that the problem BA-EV is not feasible and requires an increase in the input v value; when the temperature is higher than the set temperatureUnder the condition that the value reaches the set accuracy, the algorithm is ended, and the calculated v is output * And x * ；
(7) In conjunction with the description of the above steps, two conclusions are drawn about the problem BA-EVsub: 71 Given the value of v), the problem BA-EVsub is a convex optimization problem with respect to x; 72 Optimal solution for problem BA-EVsubIs a non-increasing function with respect to v; an algorithm is designed and solved based on the two conclusions, and the algorithm is specifically described as follows, wherein the algorithm is marked as Sol-BA:
algorithm step S1: input upper limit value v max Lower limit value v min Ending the threshold tol of the cycle;
an algorithm step S2: according to the condition | v max -v min | ≧ tol, determine whether to enter a loop, that is, | v max -v min If the absolute value is more than or equal to tol, entering circulation, and executing the algorithm steps S3 and v max -v min If the | < tol, no cycle is carried out, and the algorithm step S7 is executed;
an algorithm step S3: setting the value of v toNamely, it is
And an algorithm step S4: obtaining an optimal value according to a set v value to solve the problem BA-EVsubTo correspond to
An algorithm step S5: optimum value obtained according to algorithm step S4Make a determination ifThe upper limit value v is updated max = v; otherwise, the lower limit value v is updated min = v, return to algorithm step S2;
an algorithm step S6: ending the circulation;
an algorithm step S7: output optimum valuev * ＝v；
(8) The original problem TCM can be solved by using the output value of the algorithm Sol-BA, and the optimal bandwidth allocation is obtained as follows:
obtaining MU of each mobile user through recursive calculation i The optimal power allocation of (c) is:
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Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107995639A (en) * | 2017-12-27 | 2018-05-04 | 浙江工业大学 | Energy efficiency optimization double-link data distribution method based on compressed search space and energy acquisition |
| CN108040364A (en) * | 2017-11-07 | 2018-05-15 | 浙江工业大学 | Channel width and the linear search method of power joint distribution in non-orthogonal multiple link |
| CN108260215A (en) * | 2018-01-19 | 2018-07-06 | 北京理工大学 | The resource allocation methods that channel conditions optimize in a kind of NOMA of low-density code |
| CN108770004A (en) * | 2018-05-18 | 2018-11-06 | 浙江工业大学 | A kind of nonopiate access downlink times optimization method based on dichotomous search formula |
| CN108770005A (en) * | 2018-05-18 | 2018-11-06 | 浙江工业大学 | A kind of nonopiate access uplink transmission time optimization method based on particle cluster algorithm |
| CN108770006A (en) * | 2018-05-18 | 2018-11-06 | 浙江工业大学 | A kind of nonopiate access uplink transmission time optimization method based on depth deterministic policy gradient |
| CN108777868A (en) * | 2018-05-18 | 2018-11-09 | 浙江工业大学 | A kind of nonopiate access uplink transmission time optimization method based on dichotomous search formula |
| CN109104768A (en) * | 2018-09-06 | 2018-12-28 | 浙江工业大学 | A kind of non-orthogonal multiple access joint bandwidth and method of rate allocation based on simulated annealing |
| CN109194524A (en) * | 2018-10-11 | 2019-01-11 | 厦门大学 | A kind of distributed traffic allocation algorithm end to end |
| CN109275194A (en) * | 2018-09-06 | 2019-01-25 | 浙江工业大学 | A kind of non-orthogonal multiple access joint bandwidth and method of rate allocation based on particle swarm algorithm |
| CN109388492A (en) * | 2018-10-09 | 2019-02-26 | 浙江工业大学 | A kind of mobile block chain optimization calculation force distribution method under multiple edge calculations server scenes based on simulated annealing |
| CN110139318A (en) * | 2019-05-14 | 2019-08-16 | 北京科技大学 | A kind of NOMA honeycomb heterogeneous network resource allocation methods and system |
| CN111030771A (en) * | 2018-10-09 | 2020-04-17 | 王晋良 | User equipment selection method in non-orthogonal multiple access system and base station thereof |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104836602A (en) * | 2015-03-31 | 2015-08-12 | 重庆大学 | Distributed large-scale MIMO-NOMA high-speed rail mobile communication system |
| CN105554901A (en) * | 2015-12-11 | 2016-05-04 | 清华大学 | Random access method |
| CN105704820A (en) * | 2015-12-31 | 2016-06-22 | 北京邮电大学 | Power distribution method and device in non-orthogonal multiple access |
-
2017
- 2017-08-10 CN CN201710679118.5A patent/CN107509243B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104836602A (en) * | 2015-03-31 | 2015-08-12 | 重庆大学 | Distributed large-scale MIMO-NOMA high-speed rail mobile communication system |
| CN105554901A (en) * | 2015-12-11 | 2016-05-04 | 清华大学 | Random access method |
| CN105704820A (en) * | 2015-12-31 | 2016-06-22 | 北京邮电大学 | Power distribution method and device in non-orthogonal multiple access |
Non-Patent Citations (2)
| Title |
|---|
| 张长青: "面向5G的非正交多址接入技术（NOMA）浅析", 《邮电设计技术》 * |
| 陈佳超: "基于能效优化的设备间协作数据分流机制的研究与实现", 《中国优秀硕士学位论文全文数据库》 * |
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|---|---|---|---|---|
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