CN108183733A - The beam forming optimization method of online NOMA multiaerial systems based on Lyapunov's theory - Google Patents
The beam forming optimization method of online NOMA multiaerial systems based on Lyapunov's theory Download PDFInfo
- Publication number
- CN108183733A CN108183733A CN201810005677.2A CN201810005677A CN108183733A CN 108183733 A CN108183733 A CN 108183733A CN 201810005677 A CN201810005677 A CN 201810005677A CN 108183733 A CN108183733 A CN 108183733A
- Authority
- CN
- China
- Prior art keywords
- user
- cluster
- moment
- weak
- strong
- 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.)
- Granted
Links
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
- H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
- H04B7/0408—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas using two or more beams, i.e. beam diversity
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
- H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
- H04B7/0413—MIMO systems
- H04B7/0426—Power distribution
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
- H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
- H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
- H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
- H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
- H04B7/0617—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal for beam forming
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
- H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
- H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
- H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
- H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
- H04B7/0619—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal using feedback from receiving side
- H04B7/0621—Feedback content
- H04B7/0632—Channel quality parameters, e.g. channel quality indicator [CQI]
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
- H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
- H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
- H04B7/0802—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station using antenna selection
- H04B7/0817—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station using antenna selection with multiple receivers and antenna path selection
-
- 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
Abstract
The present invention considers on-line mode, it uses for reference Lyapunov's theory and solves data causality, the long term time optimization problem that is averaged is melted into the optimization problem at each moment, slack variable is introduced using concave-convex optimization algorithm, carry out multidimensional first order Taylor expansion, it solves the problems, such as that object function and constraint are non-convex, optimal beam forming and power allocation scheme of each moment is obtained by iteration.
Description
Technical field
The present invention relates to wireless communication technology field, more particularly, to a kind of based on the online of Lyapunov's theory
The beam forming optimization method of NOMA multiaerial systems.
Background technology
In recent years with the fast development of mobile communication, frequency spectrum resource becomes more and more in short supply, in face of the shifting being skyrocketed through
Dynamic business demand, the utilization rate for how effectively improving limited frequency spectrum resource urgently solve as the 5th generation (5G) mobile communication system
One of critical issue certainly.Have the characteristics of effectively improving power system capacity, non-orthogonal multiple (Non-Orthogonal
Multiple Access, NOMA) technology is widely regarded as up-and-coming multiple access technology.The basic thought of NOMA is to send out
Sending end uses power sharing technology to be sent with non-orthogonal manner, actively introduces interference information, is disappeared in receiving terminal by serial interference
Except (Successive Interference Cancellation, SIC) receiver realizes correct demodulation, although receiver is answered
Miscellaneous degree is improved to some extent but can be very good to improve the availability of frequency spectrum.
In the non-orthogonal multiple access cooperation communication system of multiple antennas, beam forming optimization and power distribution are also research
One of hot spot.According to the decoding policy of receiving terminal serial interference elimination in non-orthogonal multiple technology, in order to reach system maximum
Handling capacity, transmitting terminal as far as possible can tilt power resource to strong user (user i.e. nearer apart from transmitting terminal).This feelings
Condition can greatly damage the communication performance of weak user (i.e. apart from transmitting terminal user farther out), therefore, it is necessary to take collaboration communication
Corresponding measures is waited to avoid the utilization of resource in a period of time insufficient while improve system performance as far as possible.
Invention content
In order to overcome the above-mentioned deficiencies of the prior art, the present invention provides a kind of online non-based on Lyapunov's theory
The method of orthogonal multiple access access multiaerial system beam forming optimization, the information for forwarding weak user is decoded by strong user, is used for
The weak user of system in a period of time is passed in non-orthogonal multiple access collaboration communication by optimizing beam forming and power distribution
Defeated rate maximizes.
For realization more than goal of the invention, the technical solution adopted is that：
The beam forming optimization method of online NOMA multiaerial systems based on Lyapunov's theory, wherein described more days
Linear system system includes a multi-antenna base station and 2K single antenna reception user, and 2K receives user and form K by cluster algorithm
Cluster, each cluster include 2 users, respectively 1 strong user and 1 weak user；Consider that there be M moment in a period, each
Shi Keyou K+1 time slices, first time slice base station send information with NOMA, are left K time slice and are used by K strong
Family decodes forwarding information to weak user in K cluster；Assuming that base station end is capable of the channel status letter of fully known whole network system
It ceases (Channel State Information, CSI), the channel response of strong user is expressed as h in base station to k-th clusterK, m, arrive
The channel response of weak user is expressed as g in k-th clusterK, m, the channel response of strong user to weak user are expressed as f in clusterK, m, because
This, base station can suitably distribute its transmission power to increase the reachable and rate of whole network.
The beam forming optimization method includes following steps：
S1. structure meet strong user's achievable rate, transimission power, data causality constraint under, maximize the period in
The problem of average and rate of weak user model：
Strong user's transmission rate is R wherein in m-th of moment, k-th of clusterK, m, 1=α log2(1+γK, m, 1,1), at m-th
Carving weak user's transmission rate in k-th of cluster is
Wherein, vK, mIt is the beamforming vectors of m-th of moment strong user of k-th of cluster, wK, mIt is m-th of moment, k-th of cluster
The beamforming vectors of weak user；DK, mFor the data buffer storage queue of m-th of moment strong user of k-th of cluster, CK, m, 2When being m-th
Carve the information content left from buffer queue, DmaxThresholding for buffer queue；rkFor the strong user's transmission rate constraint of k-th of cluster；It is constrained for base station maximum transmission power；PK, mThe biography of weak user is given for user's forwarding information strong in m-th of moment, k-th of cluster
Defeated power,For strong user's max-forwards power constraint；It is shared in expression systemA cluster,Represent that total time is
A moment；α=BT/ (K+1), B are bandwidth, and T is occasion length；γK, m, 1,1For the strong user's decoding of m-th of moment, k-th of cluster certainly
The Signal to Interference plus Noise Ratio of body information；γK, m, 1,2The Signal to Interference plus Noise Ratio of weak user information is decoded for user strong in m-th of moment, k-th of cluster；
γK, m, 2,2The Signal to Interference plus Noise Ratio of self information is decoded for user weak in m-th of moment, k-th of cluster；γK, m, 2,3For m-th of moment kth
Strong user's forwarding information gives the Signal to Interference plus Noise Ratio of weak user in a cluster.
S2. data causality is constrained into conversion and by being averaged in the original optimization period using Lyapunov's theory
Problem is converted into the optimization problem at each moment：
For one group of feasible solution vK, m, wK, m, PK, m, Liapunov, which weights penalty function, the upper bound：
The information content maximum value of wherein m+1 moment into enqueue is
The information content maximum value that the m moment leaves queue isθkFor queue Discontinuous Factors, ρ is in order to control
Parameter.
It is that the upper bound of weighting penalty function is minimized in each moment by model conversation the problem of S1：
S3. in after step S2 conversions the problem of model, object function and constraints are all containing non-convex item, using concave-convex excellent
Change iterative algorithm to be solved, object function is firstly introduced into slack variable dK, m, enable Θk=log2(1+dK, m)-log2(1+|fK, m
|2PK, m/σ2),For about ΘkFirst order Taylor expansion：
WhereinOptimal value for preceding an iteration.
Constraints is existedPoint andPoint carries out multidimensional first order Taylor and expands into：
Wherein,Optimal value for preceding an iteration.
S4. in the l+1 times iteration of concave-convex optimization algorithm, it is known that the optimal solution of the l times iterationStep S41~S45 is performed using interior point method and solves the problem of following：
S41., systematic parameter, initiation parameter m=0, D are setK, m=0；
S42. iteration coefficient l=0 is initialized, gives one group of beamforming vectors and power assignment value for meeting constraints
S43. lower section convex problem is calculated by interior point method and obtains new beamforming vectors and power assignment value
S44. the transmission rate of weak user is calculated, checks whether to restrain, if convergence, terminates this process, current gained power
Allocation result is optimal power allocation scheme；Otherwise l is set：=l+1 and using power distribution result obtained by current iteration under
Secondary iteration initial value, jumps to step S43；
S45. m is updated：=m+1 updates DK, m, step S42-S44 is repeated until m ＞ M.
2. the beam forming of the online NOMA multiaerial systems according to claim 1 based on Lyapunov's theory
Optimization method, it is characterised in that：Signal to Interference plus Noise Ratio of the last k-th user in the 1st time slice decoding of m-th moment be：
Wherein, σ2For channel noise variance, ξK, mIt is interfered between cluster：
Signal to Interference plus Noise Ratio of k-th of weak user in the 1st time slice decoding of m-th moment be：
Wherein ζK, mIt is interfered between cluster：
The Signal to Interference plus Noise Ratio that k-th of weak user receives strong user information is：
Wherein, PK, mIt is strong user's forwarding information to the power of user weak in cluster.
Compared with prior art, the beneficial effects of the invention are as follows：
The present invention considers on-line mode, uses for reference Lyapunov's theory and solves data causality, long term time is averaged excellent
Change problem is melted into the optimization problem at each moment, introduces slack variable using concave-convex optimization algorithm, carries out multidimensional first order Taylor exhibition
It opens, solve the problems, such as object function and constrains non-convex, optimal beam forming of each moment and power distribution are obtained by iteration
Scheme.
Description of the drawings
Fig. 1 is system model figure.
Fig. 2 is the long term time average transmission rate and other conventional methods for the weak user that method provided by the invention is asked for
The comparison diagram of the long term time average transmission rate of the weak user asked for.
Fig. 3 is that the system long term time average transmission rate that the present invention asks for and the system that other conventional methods are asked for are long-term
The comparison diagram of time average transmission rate.
Fig. 4 is the figure of changing of the strong user buffering queue of difference of the present invention whithin a period of time.
Fig. 5 is under the different total transmission power constraint of base station end, and the present invention is based on the algorithms and tradition of Liapunov
Comparison diagram of the even power allocation algorithm in terms of weak user's achievable rate and system and rate.
Fig. 6 is the basic flow chart of emulation experiment of the present invention.
Specific embodiment
The attached figures are only used for illustrative purposes and cannot be understood as limitating the patent；
Below in conjunction with drawings and examples, the present invention is further elaborated.
Embodiment 1
(1) system model
As shown in Figure 1, the model of communication system that the present invention is applicable in is used by a multi-antenna base station and 2K single antenna reception
Family forms, and 2K user forms K cluster, each 2 users of cluster, 1 strong user and 1 weak user, Qiang Yong by cluster algorithm
Family has the information of the weak user of buffer queue decoding storage and forwarding information is to weak user.Consider that there be M moment in a period, often
K+1 time slice is carved with when a, first time slice considers that base station sends information with NOMA, is left K time slice by K
A strong user decodes forwarding information to weak user in K cluster.Assuming that base station end is capable of the channel of fully known whole network system
Status information (Channel State Information, CSI), the channel response of strong user is expressed as in base station to k-th cluster
hK, m, to k-th cluster in the channel response of weak user be expressed as gK, m, the channel response of strong user to weak user are expressed as in cluster
fK, m, therefore, base station can suitably distribute its transmission power to increase the reachable and rate of whole network.
(2) problem describes
(NOMA) technical protocol is accessed according to non-orthogonal multiple, transmitting terminal uses supercomposed coding technology, m-th of moment the 1st
The signal that a time slice base station is sent is：
Wherein sK, m, 1And sK, m, 2The signal of the strong user of k-th of cluster and weak user, v will be sent to by representingK, mIt is that k-th of cluster is strong
The beamforming vectors of user, wK, mIt is the beamforming vectors of the weak user of k-th of cluster.
By successive interference cancellation techniques, the letter when the last k-th user terminal decodes weak user and decoding self information is done
Make an uproar than for：
Wherein, σ2For channel noise variance, ξK, mIt is interfered between cluster：
The Signal to Interference plus Noise Ratio that k-th of weak user decodes self information is：
Wherein ζK, mIt is interfered between cluster：
It is left k time slice, the strong user signal of the weak user of TDMA forwardings, weak user receives by force in k-th of cluster
The signal of user is：
Wherein, PK, mIt is strong user's forwarding information to the power of user weak in cluster, nK, m, 3It is 0 for mean value, variance σ2Letter
Road Gaussian noise.
The Signal to Interference plus Noise Ratio that k-th of weak user receives strong user information is：
The buffer queue of user is by force：
DK, m+1=DK, m+CK, m+1,1-CK, m, 2. (9)
CK, m, 1=α log2(1+γK, m, 1,2), (10)
CK, m, 2=α log2(1+γK, m, 2,2) (11)
Wherein, DK, mFor the information cache queue of m-th of moment strong user of k-th of cluster, CK, m+1,1It it is the m+1 moment into joining the team
The information content of row, CK, m, 2For the information content that the m moment is left from queue, α=BT/ (K+1), B are bandwidth, and T is occasion length.
Multiple antennas downlink non-orthogonal multiple access cooperation communication system in, meet strong user's achievable rate, transimission power,
Under the constraint of data causality, maximize long term time it is average it is weak user's and the problem of rate can be modeled as：
Strong user's transmission rate is R wherein in m-th of moment, k-th of clusterK, m, 1=α log2(1+γK, m, 1,1), at m-th
Carving weak user's transmission rate in k-th of cluster is
Wherein, DK, mFor the data buffer storage queue of m-th of moment strong user of k-th of cluster, CK, m, 2It it is m-th of moment from caching
The information content that queue is left, DmaxThresholding for buffer queue；rkFor the strong user's transmission rate constraint of k-th of cluster；For base station
Maximum transmission power constrains；For strong user's max-forwards power constraint；It is shared in expression systemA cluster,Represent total
Time isA moment；α=BT/ (K+1), B are bandwidth, and T is occasion length.
It is that the upper bound of weighting penalty function is minimized in each moment by model conversation the problem of S1：
S3. in after step S2 conversions the problem of model, object function and constraints are all containing non-convex item, using concave-convex excellent
Change iterative algorithm to be solved, object function is firstly introduced into slack variable dK, m, enable Θk=log2(1+dK, m)-log2(1+|fK, m
|2PK, m/σ2),For about ΘkFirst order Taylor expansion：
WhereinOptimal value for preceding an iteration.
Constraints is existedPoint andPoint carries out multidimensional first order Taylor and expands into：
Wherein,Optimal value for preceding an iteration.
(3) solution
Due to considering on-line mode, the channel at system each moment is uncertain, and the method for salary distribution at previous moment
The latter moment can be had an impact, data causality is constrained conversion first with Lyapunov's theory and will be former by the present invention
The optimization long term time come the problem that is averaged is converted into the optimization problem at each moment.
Lemma 1：For one group of feasible solution vK, m, wk,m, PK, m, Liapunov, which weights penalty function, the upper bound
The information content maximum value of wherein m+1 moment into enqueue isThe m moment
The information content maximum value for leaving queue isθkFor queue Discontinuous Factors, ρ parameters in order to control.
It proves：
Lyapunov's theory is applied in the problem of long term time is average, it is only necessary to know the system shape at current time
Therefore problem, can be converted into the upper bound that weighting penalty function is minimized in each moment by state：
In problem (15), object function and constraints are all containing non-convex item, and the present invention is using concave-convex Optimized Iterative algorithm
It is solved, object function is firstly introduced into slack variable dK, m, enable Θk=log2(1+dK, m)-log2(1+|fK, m|2PK, m/σ2),For about ΘkFirst order Taylor expansion：
WhereinOptimal value for preceding an iteration.
Constraints is existedPoint andPoint carries out multidimensional first order Taylor and expands into：
Wherein,Optimal value for preceding an iteration.
Therefore, in the l+1 times iteration of concave-convex optimization algorithm, it is known that the optimal solution of the l times iterationSolve the problem of following：
Problem (20) is convex problem, can be solved with interior point method.
(4) algorithm is realized
Step 0：Systematic parameter, initiation parameter m=0, D are setK, m=0,；
Step 1：Iteration coefficient l=0 is initialized, gives one group of beamforming vectors and power distribution for meeting constraints
Value
Step 2：Lower section convex problem is calculated by interior point method and obtains new beamforming vectors and power assignment value
Step 3：The weak user's transmission rate of computing system, checks whether to restrain, if convergence, terminates this process, current gained
Power distribution result is optimal power allocation scheme；Otherwise l is set：=l+1 and with power distribution result obtained by current iteration
For next iteration initial value, step 2 is jumped to.
Step 4：Update m：=m+1 updates DK, m, step 1-3 is repeated until m ＞ M.
(5) simulated conditions explanation
Effect of the present invention can further illustrate that the basic procedure of emulation experiment is with reference to figure by following the simulation experiment result
6。
For Fig. 1, Fig. 2 and Fig. 3 and Fig. 4, user's cluster is K=3 in setting system, 2 users in each cluster, antenna for base station
Number is 4, and the channel response of base station to each user are that mean value is respectively for 0, variance Independent same distribution answer
Gaussian random variable, in cluster the channel response of strong user to weak user be mean value be 0, variance isIndependent same distribution answer
Gaussian random variable, and base station total transmission power isThe power of the weak user of user's decoding forwarding is by forceThe rate constraint of the strong users of W is rk=1bps/Hz. Fig. 2, Fig. 3 and Fig. 4 consider system 1000 time slots when
Between Mean Speed, control parameter ρ=5.Fig. 5 considers base station's transmission power 0 to 20dB ranges.
(6) simulation analysis and result
Fig. 2 and Fig. 3 is the long term time average transmission rate of weak user and all users in system, and transmission of the invention is fast
Rate and traditional multiple access method and greedy algorithm compare, from figures 2 and 3, it will be seen that transmission rate is over time
It can tend towards stability, and the present invention has apparent advantage in the weak user's transmission rate of raising and system transfer rate and aspect.
Fig. 4 is the buffering queue situation of change of strong user whithin a period of time, it can be seen that buffering queue can be at one
It is fluctuated in value range, illustrates that Lyapunov's theory can maintain the stability of queue under the premise of data causality is solved, make
System reaches dynamic balance.
Fig. 5 is pair of inventive algorithm and conventional greedy algorithm in rate under the constraint of base station different transmission power
Than.As can be seen that inventive algorithm can effectively improve the on-line normalization rate of weak user and whole system.
In conclusion the performance of the on-line optimization algorithm based on the present invention is better than traditional scheme.
Obviously, the above embodiment of the present invention be only to clearly illustrate example of the present invention, and not be pair
The restriction of embodiments of the present invention.For those of ordinary skill in the art, may be used also on the basis of the above description
To make other variations or changes in different ways.There is no necessity and possibility to exhaust all the enbodiments.It is all this
All any modification, equivalent and improvement made within the spirit and principle of invention etc., should be included in the claims in the present invention
Protection domain within.
Claims (2)
1. the beam forming optimization method of the online NOMA multiaerial systems based on Lyapunov's theory, wherein the multiple antennas
System includes a multi-antenna base station and 2K single antenna reception user, and 2K receives user and form K cluster by cluster algorithm,
Each cluster includes 2 users, respectively 1 strong user and 1 weak user；Consider that there are M moment, each moment in a period
There is K+1 time slice, first time slice base station sends information with NOMA, is left K time slice by K strong user's solution
Code forwarding information is to weak user in K cluster；Assuming that base station end is capable of the channel state information of fully known whole network system, base
The channel response of strong user of standing into k-th cluster is expressed as hK, m, to k-th cluster in the channel response of weak user be expressed as gK, m,
The channel response of strong user to weak user are expressed as f in clusterK, m；
It is characterized in that：The beam forming optimization method includes following steps：
S1. structure meet strong user's achievable rate, transimission power, data causality constraint under, maximize the period in weak use
The problem of average and rate at family model：
s.t.CK, m, 2≤AK, m≤Dmax,
Strong user's transmission rate is R wherein in m-th of moment, k-th of clusterK, m, 1=α log2(1+γK, m, 1,1), m-th of moment kth
Weak user's transmission rate is in a cluster
Wherein, vK, mIt is the beamforming vectors of m-th of moment strong user of k-th of cluster, wK, mIt is m-th of moment weak use of k-th of cluster
The beamforming vectors at family；DK, mFor the data buffer storage queue of m-th of moment strong user of k-th of cluster, CK, m, 2For m-th of moment from
The information content that buffer queue leaves, DmaxThresholding for buffer queue；rkFor the strong user's transmission rate constraint of k-th of cluster；
It is constrained for base station maximum transmission power；PK, mThe transmission work(of weak user is given for user's forwarding information strong in m-th of moment, k-th of cluster
Rate,For strong user's max-forwards power constraint；It is shared in expression systemA cluster,Represent that total time isWhen a
It carves；α=BT/ (K+1), B are bandwidth, and T is occasion length；γK, m, 1,1Itself letter is decoded for m-th of moment strong user of k-th of cluster
The Signal to Interference plus Noise Ratio of breath；γK, m, 1,2The Signal to Interference plus Noise Ratio of weak user information is decoded for user strong in m-th of moment, k-th of cluster；γK, m, 2,2
The Signal to Interference plus Noise Ratio of self information is decoded for user weak in m-th of moment, k-th of cluster；γK, m, 2,3For in m-th of moment, k-th of cluster
Strong user's forwarding information gives the Signal to Interference plus Noise Ratio of weak user；
S2. data causality is constrained into conversion and by the average problem in the original optimization period using Lyapunov's theory
It is converted into the optimization problem at each moment：
For one group of feasible solution vK, m, wK, m, PK, m, Liapunov, which weights penalty function, the upper bound：
The information content maximum value of wherein m+1 moment into enqueue isDuring m
It carves and leaves the information content maximum value of queue and beθkFor queue Discontinuous Factors, ρ parameters in order to control；
It is that the upper bound of weighting penalty function is minimized in each moment by model conversation the problem of S1:
s.t.RK, m, 1≥rk,
S3. in after step S2 conversions the problem of model, object function and constraints all containing non-convex item, are changed using bumps optimization
It is solved for algorithm, object function is firstly introduced into slack variable dK, m, enable Θk=log2(1+dK, m)-log2(1+|fK, m|2PK, m/σ2),For about ΘkFirst order Taylor expansion：
Wherein Optimal value for preceding an iteration；
Constraints is existedPoint andPoint carries out multidimensional first order Taylor and expands into：
Wherein,Optimal value for preceding an iteration；
S4. in the l+1 times iteration of concave-convex optimization algorithm, it is known that the optimal solution of the l times iteration
Step S41~S45 is performed using interior point method and solves the problem of following：
S41., systematic parameter, initiation parameter m=0, D are setK, m=0；
S42. iteration coefficient l=0 is initialized, gives one group of beamforming vectors and power assignment value for meeting constraints
S43. lower section convex problem is calculated by interior point method and obtains new beamforming vectors and power assignment value
S44. the transmission rate of weak user is calculated, checks whether to restrain, if convergence, terminates this process, current gained power distribution
As a result it is optimal power allocation scheme；Otherwise l is set：=l+1：And it is changed using power distribution result obtained by current iteration as next time
For initial value, step S43 is jumped to；
S45. m is updated：=m+1 updates DK, m, step S42-S44 is repeated until m ＞ M.
2. the beam forming optimization of the online NOMA multiaerial systems according to claim 1 based on Lyapunov's theory
Method, it is characterised in that：Signal to Interference plus Noise Ratio of the last k-th user in the 1st time slice decoding of m-th moment be：
Wherein, σ2For channel noise variance, ξK, mIt is interfered between cluster：
Signal to Interference plus Noise Ratio of k-th of weak user in the 1st time slice decoding of m-th moment be：
Wherein ζK, mIt is interfered between cluster：
The Signal to Interference plus Noise Ratio that k-th of weak user receives strong user information is：
Wherein, PK, mIt is strong user's forwarding information to the power of user weak in cluster.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810005677.2A CN108183733B (en) | 2018-01-03 | 2018-01-03 | Beam forming optimization method based on online NOMA multi-antenna system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810005677.2A CN108183733B (en) | 2018-01-03 | 2018-01-03 | Beam forming optimization method based on online NOMA multi-antenna system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108183733A true CN108183733A (en) | 2018-06-19 |
| CN108183733B CN108183733B (en) | 2021-03-16 |
Family
ID=62550000
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810005677.2A Active CN108183733B (en) | 2018-01-03 | 2018-01-03 | Beam forming optimization method based on online NOMA multi-antenna system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108183733B (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108616997A (en) * | 2018-08-03 | 2018-10-02 | 田心记 | Power distribution method in NOMA systems |
| CN110492915A (en) * | 2019-06-03 | 2019-11-22 | 中央民族大学 | A kind of power distribution method based on the short packet transmission of MIMO-NOMA |
| CN110958675A (en) * | 2019-10-29 | 2020-04-03 | 南京邮电大学 | Terminal access method based on 5G fog computing node |
| CN114629535A (en) * | 2022-03-14 | 2022-06-14 | 天津大学 | Intelligent reflector non-cellular large-scale MIMO network capacity optimization method |
| CN114710195A (en) * | 2022-03-24 | 2022-07-05 | 重庆邮电大学 | Low-orbit satellite energy-efficient resource allocation method based on beam hopping technology |
| CN115062494A (en) * | 2022-07-27 | 2022-09-16 | 中国电建集团华东勘测设计研究院有限公司 | Energy optimization method for offshore wind power system based on concave-convex process planning |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102656747A (en) * | 2009-12-03 | 2012-09-05 | 日本电气株式会社 | Antenna beam directivity device and antenna beam directivity method |
| CN106487428A (en) * | 2016-09-29 | 2017-03-08 | 西安电子科技大学 | A kind of method of the optimization wave beam forming based on non-orthogonal multiple technology |
-
2018
- 2018-01-03 CN CN201810005677.2A patent/CN108183733B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102656747A (en) * | 2009-12-03 | 2012-09-05 | 日本电气株式会社 | Antenna beam directivity device and antenna beam directivity method |
| CN106487428A (en) * | 2016-09-29 | 2017-03-08 | 西安电子科技大学 | A kind of method of the optimization wave beam forming based on non-orthogonal multiple technology |
Non-Patent Citations (4)
| Title |
|---|
| AMIRA AKRA 等: "Energy efficient transmit beamforming under queueing stability constraints", 《2016 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE》 * |
| QI ZHANG 等: "Robust Beamforming for Nonorthogonal Multiple-Access Systems in MISO Channels", 《IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY》 * |
| WEI BAO 等: "Joint Rate Control and Power Allocation for Non-Orthogonal Multiple Access Systems", 《IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS》 * |
| YIZHI TAN 等: "Novel Channel Estimation for Non-orthogonal Multiple Access Systems", 《IEEE SIGNAL PROCESSING LETTERS》 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108616997A (en) * | 2018-08-03 | 2018-10-02 | 田心记 | Power distribution method in NOMA systems |
| CN108616997B (en) * | 2018-08-03 | 2022-11-11 | 深圳市创仁水务科技有限公司 | Power distribution method in NOMA system |
| CN110492915A (en) * | 2019-06-03 | 2019-11-22 | 中央民族大学 | A kind of power distribution method based on the short packet transmission of MIMO-NOMA |
| CN110958675A (en) * | 2019-10-29 | 2020-04-03 | 南京邮电大学 | Terminal access method based on 5G fog computing node |
| CN110958675B (en) * | 2019-10-29 | 2021-08-17 | 南京邮电大学 | Terminal access method based on 5G fog computing node |
| CN114629535A (en) * | 2022-03-14 | 2022-06-14 | 天津大学 | Intelligent reflector non-cellular large-scale MIMO network capacity optimization method |
| CN114710195A (en) * | 2022-03-24 | 2022-07-05 | 重庆邮电大学 | Low-orbit satellite energy-efficient resource allocation method based on beam hopping technology |
| CN114710195B (en) * | 2022-03-24 | 2023-07-25 | 重庆邮电大学 | Low-orbit satellite energy-efficient resource allocation method based on beam hopping technology |
| CN115062494A (en) * | 2022-07-27 | 2022-09-16 | 中国电建集团华东勘测设计研究院有限公司 | Energy optimization method for offshore wind power system based on concave-convex process planning |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108183733B (en) | 2021-03-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108183733A (en) | The beam forming optimization method of online NOMA multiaerial systems based on Lyapunov's theory | |
| Li et al. | NOMA-aided cell-free massive MIMO systems | |
| Liu et al. | Developing NOMA to next generation multiple access: Future vision and research opportunities | |
| Zhong et al. | Spatial modulation assisted multi-antenna non-orthogonal multiple access | |
| RU2420880C2 (en) | Unified design and centralised scheduling for dynamic simo, su-mimo and mu-mimo operation for rl transmissions | |
| Liu et al. | Application of NOMA in 6G networks: Future vision and research opportunities for next generation multiple access | |
| CN110190881B (en) | Downlink MIMO-NOMA power distribution method with optimal weight rate | |
| CN107528624B (en) | Design method of steady beam forming based on non-orthogonal multiple access technology | |
| CN110149127A (en) | A kind of D2D communication system precoding vector optimization method based on NOMA technology | |
| CN110062359A (en) | Based on the highly reliable low delay radio resource allocation optimization method of the short coded block transmission of NOMA in MTC | |
| CN111431646A (en) | Dynamic resource allocation method in millimeter wave system | |
| Dursun et al. | Hybrid NOMA based MIMO offloading for mobile edge computing in 6G networks | |
| CN111405596A (en) | Resource optimization method for large-scale antenna wireless energy-carrying communication system under Rice channel | |
| CN114867030A (en) | Double-time-scale intelligent wireless access network slicing method | |
| Choi et al. | Evolutionary game for hybrid uplink NOMA with truncated channel inversion power control | |
| WO2010081412A1 (en) | Method, system, user equipment and base station for pre-coding feedback | |
| Nene et al. | Closest point search in high dimensions | |
| Zhu et al. | Load-aware dynamic mode selection for network-assisted full-duplex cell-free large-scale distributed MIMO systems | |
| CN112770398A (en) | Far-end radio frequency end power control method based on convolutional neural network | |
| CN116321236A (en) | RIS-assisted safe honeycomb-free large-scale MIMO system energy efficiency optimization method | |
| CN115767703A (en) | Long-term power control method for SWIPT-assisted de-cellular large-scale MIMO network | |
| Sun et al. | Multiuser MIMO opportunistic beamforming communications: state-of-the-art and perspectives | |
| Doumit et al. | Resource Allocation in Full-Duplex Uncoordinated Communication Systems with NOMA | |
| Ahsan et al. | Reliable reinforcement learning based NOMA schemes for URLLC | |
| Yang et al. | A low complexity joint user grouping and resource allocation algorithm in massive MIMO systems |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |