CN112243251A - Cognitive MIMO system energy efficiency optimization method based on SCMA - Google Patents
Cognitive MIMO system energy efficiency optimization method based on SCMA Download PDFInfo
- Publication number
- CN112243251A CN112243251A CN202010576145.1A CN202010576145A CN112243251A CN 112243251 A CN112243251 A CN 112243251A CN 202010576145 A CN202010576145 A CN 202010576145A CN 112243251 A CN112243251 A CN 112243251A
- Authority
- CN
- China
- Prior art keywords
- user
- users
- time
- time slot
- interference
- 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
- 230000001149 cognitive effect Effects 0.000 title claims abstract description 72
- 238000005457 optimization Methods 0.000 title claims abstract description 54
- 238000000034 method Methods 0.000 title claims abstract description 53
- 238000005265 energy consumption Methods 0.000 claims abstract description 43
- 230000005540 biological transmission Effects 0.000 claims abstract description 35
- 238000010276 construction Methods 0.000 claims abstract description 6
- 239000011159 matrix material Substances 0.000 claims description 57
- 238000005516 engineering process Methods 0.000 claims description 17
- 238000004891 communication Methods 0.000 claims description 14
- 238000005562 fading Methods 0.000 claims description 13
- 238000013178 mathematical model Methods 0.000 claims description 9
- 239000000243 solution Substances 0.000 claims description 7
- 230000003247 decreasing effect Effects 0.000 claims description 6
- 239000006185 dispersion Substances 0.000 claims description 6
- 238000002347 injection Methods 0.000 claims description 5
- 239000007924 injection Substances 0.000 claims description 5
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 5
- 230000003595 spectral effect Effects 0.000 claims description 4
- 101001005269 Arabidopsis thaliana Ceramide synthase 1 LOH3 Proteins 0.000 claims description 3
- 101001005312 Arabidopsis thaliana Ceramide synthase LOH1 Proteins 0.000 claims description 3
- 101001089091 Cytisus scoparius 2-acetamido-2-deoxy-D-galactose-binding seed lectin 2 Proteins 0.000 claims description 3
- 239000000654 additive Substances 0.000 claims description 3
- 230000002238 attenuated effect Effects 0.000 claims description 3
- 230000001413 cellular effect Effects 0.000 claims description 3
- NYPJDWWKZLNGGM-RPWUZVMVSA-N esfenvalerate Chemical compound C=1C([C@@H](C#N)OC(=O)[C@@H](C(C)C)C=2C=CC(Cl)=CC=2)=CC=CC=1OC1=CC=CC=C1 NYPJDWWKZLNGGM-RPWUZVMVSA-N 0.000 claims description 3
- 230000002452 interceptive effect Effects 0.000 claims description 3
- 230000001360 synchronised effect Effects 0.000 claims description 3
- 230000000996 additive effect Effects 0.000 claims description 2
- 230000003334 potential effect Effects 0.000 claims description 2
- 238000001228 spectrum Methods 0.000 description 7
- 238000004088 simulation Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 5
- 230000002829 reductive effect Effects 0.000 description 5
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- 241000985610 Forpus Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 230000000052 comparative effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 230000003116 impacting effect Effects 0.000 description 1
- 230000000670 limiting effect Effects 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
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
-
- 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
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W72/00—Local resource management
- H04W72/04—Wireless resource allocation
- H04W72/044—Wireless resource allocation based on the type of the allocated resource
- H04W72/0446—Resources in time domain, e.g. slots or frames
-
- 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 a cognitive MIMO system energy efficiency optimization method based on SCMA, which comprises the following steps: establishing a system model in a cognitive MIMO network; problem construction and optimization: converting a non-convex problem solved by the energy consumption of a plurality of secondary user systems into a convex problem optimized by time distribution; solving an objective function: two methods are adopted to carry out allocation optimization on the time slots, which are respectively as follows: TDMA-based orthogonal time slot allocation optimization; non-orthogonal slot allocation optimization based on SCMA. The invention adopts two methods to optimize the time slot distribution: orthogonal time slot allocation optimization and non-orthogonal time slot allocation optimization based on SCMA, and the minimum time requirement of each secondary user for data transmission can be obtained by solving the maximum transmission rate of each secondary user; under the condition of meeting the requirement of the minimum time slot of the secondary users, the total energy consumption of the secondary system of a plurality of secondary users can be obtained by optimizing the orthogonal time slot allocation of the TDMA.
Description
Technical Field
The invention relates to the technical field of wireless communication, in particular to a cognitive MIMO system energy efficiency optimization method based on SCMA.
Background
The future wireless communication system has the application characteristics of continuous wide area coverage, high capacity of hot spot areas, low energy consumption, massive connection and the like. To meet these demands, the problems of increasing energy consumption and limited spectrum supply and demand are becoming more and more prominent. Low power consumption is key to reducing carbon emissions, extending battery life of wireless devices, and green communications. In order to fully utilize spectrum resources, improve spectrum utilization, reduce Energy consumption, and improve Energy Efficiency (EE), this document aims at time slot share, Energy efficiency and user access amount of a multi-antenna cognitive radio network.
Cognitive radio is an intelligent wireless communication system with dynamic spectrum access, and can opportunistically utilize spectrum opportunities without affecting authorized users, improve the utilization rate of allocated spectrum, increase the number of user accesses of the system, and is more and more concerned by a plurality of researchers. The MIMO (Multi-input Multi-output) technology ensures that the secondary user (unauthorized user) and the primary user (authorized user) coexist in the spatial domain, and the secondary user does not cause serious interference to the primary user link pair. Sparse Code Multiple Access (SCMA) technology for 5 th generation (5G) mobile communication provides strong support for time slot sharing. Therefore, under the same time resource, the cognitive wireless network can serve more users at the same time.
In the present stage, the orthogonal time slot allocation based on the TDMA avoids the interference between secondary users, but also limits the number of users accessed by the system. Non-orthogonal multiple access (NOMA) technology oriented to next-generation wireless communication can realize the sharing of time-frequency resources, so that the access of users does not need to be strictly orthogonal, and the users do not have serious interference. Among the plurality of NOMA technologies, the sparse Code Multiple access technology scma (sparse Code Multiple access) is a very potential air interface technology, and under the condition of the same time frequency resource, 150%, 200%, or even more device connection numbers can be realized. In order to further optimize time slot allocation, the invention provides a cognitive MIMO system energy efficiency optimization method based on SCMA.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a cognitive MIMO system energy efficiency optimization method based on SCMA (sparse code multiple Access). the method analyzes the energy consumption of secondary users and the system user capacity in a cognitive MIMO system under an underlay model; the space division multiplexing of the MIMO technology can simultaneously send multiple data streams to the base station, so that better energy efficiency than that of transmit diversity can be obtained, and the average interference between primary and secondary users is reduced; TDMA divides continuous time into discrete orthogonal time slots, thereby avoiding interference among users under multi-user access, which limits the user access amount of the cognitive system; in order to avoid the interference between the secondary users, the non-orthogonal time slot access scheme based on the SCMA is adopted, so that the secondary users are not seriously interfered, the energy efficiency is better than that under the condition that the secondary system accesses more users, and the average interference of the secondary users to the primary user is reduced; and under the condition of meeting the requirement of the rate required by the secondary user, the quality of service (QoS) of the secondary user is ensured, and the user capacity of the secondary system is also greatly improved. The method of the invention can realize the sharing of the TDMA time slot, can realize the overload rate of 150 percent, 200 percent, 250 percent or even higher under the condition of the same time slot resource, and can not cause serious interference among secondary users. Simulation results show that when the number of secondary users is large, the energy consumption can be reduced and the average interference power between the secondary users and the primary users is also reduced based on the non-orthogonal time slot allocation of the SCMA; under the condition of meeting the requirement of the minimum rate of the secondary user, the user capacity of the system is greatly improved.
In order to achieve the purpose, the invention adopts the following technical scheme:
a cognitive MIMO system energy efficiency optimization method based on SCMA is characterized by comprising the following steps:
(1) establishing a system model in a cognitive MIMO network;
(2) problem construction and optimization: converting a non-convex problem solved by the energy consumption of a plurality of secondary user systems into a convex problem optimized by time distribution;
(3) solving an objective function: two methods are adopted to carry out allocation optimization on the time slots, which are respectively as follows:
(3.1) TDMA-based orthogonal time slot allocation optimization;
and (3.2) non-orthogonal time slot allocation optimization based on SCMA.
Further, the SCMA-based cognitive MIMO system energy efficiency optimization method is characterized in that the specific process of establishing a system model in the cognitive MIMO network in step (1) is as follows:
suppose there are K sub-users SUs and J primary user PUs in a cognitive radio network, with SkDenotes the kth secondary user, PjRepresents the j-th pair of primary user transceivers; under the underlay model, a main user link is in potential activity and is always protected; the master user communication network is composed of J pairs of user transceivers, the secondary user system is a single cellular network, uplink of all secondary users is sent to the same cognitive base station through TDMA, the cognitive base station is in the central position of the cognitive network, and the cognitive base station can evaluate the secondary user SkChannel matrix to cognitive base station
Feeding back to corresponding secondary users through an independent control channel; thus the secondary subscriber SkThe channel matrix of the cognitive base station is known by the cognitive base station, and the uplink transmission of the secondary user is synchronous with the cognitive base station, so that the secondary user is distributed to transmit in mutually non-interfering orthogonal time slots; as the primary user and the secondary user coexist in the cognitive network, the methodMutual interference exists between the two; for the convenience of analysis, assuming that the channel is a frequency flat fading channel, it is specified that the channel matrix within the bandwidth is invariant; further, if the channel is a block fading channel, the channel matrix in one frame will not change, and the channel matrix between frames is not related;
in cognitive MIMO networks, both primary and secondary users can transmit multiple data streams via MIMO, assuming
Denotes SkNumber of transmitting antennas of, NBSIndicating the number of receiving antennas of the cognitive base station;
represents PjThe number of the transmitting antennas of (1),
represents PjThe number of receive antennas of (1);
denotes SkThe data stream of (a) is transmitted,
represents PjThe data stream of (2); skThe data stream matrix transmitted by the antenna is
PjThe data stream matrix transmitted by the antenna is
Then
Has a covariance matrix of
Has a covariance matrix of
And is
And
all are semi-positive definite Hermitian matrixes;
secondary subscriber SkThe cognitive base station of the secondary user system receives S when the S reaches a receiving end through a channelkThe data of (a) are:
wherein n isBSIs noise power spectral density of N0(ii) additive white gaussian noise,/2, w being the bandwidth;
assuming that the secondary BS cannot continuously cancel interference and the interference from the primary user is regarded as noise, S is performedkWhen transmitting data, the interference noise covariance matrix of the secondary BS is
Wherein
Is NBS×NBSA semi-positive definite Hermitian matrix of (1);
according to the capacity formula of Shannon' S theorem in MIMO link, SkAn achievable rate of
Wherein the transmission rate
Is the secondary subscriber SkActive instantaneous transmission rate, unit: nats/second;
secondary subscriber SkAt the placeWith total transmission power over the antenna of
Secondary subscriber SkTo master user PjResulting in a total interference power of
Further, the SCMA-based cognitive MIMO system energy efficiency optimization method is characterized in that the specific process of problem construction and optimization in the step (2) is as follows:
the system aims to allocate proper transmission time for each secondary user, so that the total energy consumption of all secondary users is minimized, the interference of a primary user from the secondary users is ensured to be as small as possible, and the QoS (quality of service) of each secondary user is also ensured; in a statistical CSI model, in order to ensure the interference of a secondary user to a primary user, an interference threshold value aiming at the primary user must be set for each secondary user so as to protect the QoS of the primary user to the greatest extent; to guarantee QoS per secondary user, the rate requirement per secondary user must be met
Unit: nats/frame, i.e. the amount of data to be transmitted within a frame time; the length of TDMA frame in the system of specified secondary users is 1, each secondary user occupies
For transmitting data, and each secondary subscriber SkIs limited to its maximum power
Thus, the target can be converted to the following mathematical formula:
wherein the objective function is the total energy consumption of all secondary users in the secondary system;
constraint (5a) is to guarantee a minimum rate requirement per secondary user; (5b) the total time allocated by all the secondary users does not exceed the frame length of a TDMA, and the secondary users cannot transmit simultaneously because the secondary system is a TDMA access network, so that the interference between the secondary users is avoided; (5c) representing that the interference suffered by each primary user receiver is lower than an interference threshold, and representing the interference suffered by the primary user receiver by using the interference power received on an antenna of the primary user receiver; (5d) meaning that the maximum power transmitted by each secondary user does not exceed
(5e) Indicating that the time allocated by each secondary user is non-negative; (5f) indicating a secondary user SkCovariance matrix of
Is a semi-positive definite matrix;
for this mathematical model, where both the objective function and (5a) are non-convex problems; channel matrix in Rayleigh fading channel and rich dispersion environment under statistical CSI model
Each item in the (1) is a Gaussian random variable with a mean value of 0 and covariance obeys independent same distribution; considering practical conditions, allowing the interference of the signals transmitted by the cognitive users to the main users to exceed the interference threshold of the main users
And adopts a water injection method to solve the problem of the secondary user SkThe transmit power limits of (5a) - (5f) may be converted to a convex problem for time allocation optimization; thus, each secondary subscriber SkOver time
The energy consumption formula of (a) is:
wherein,
is the optimal number of data streams, is time
The piecewise function of (d):
as can be seen from (9), the non-convex problem of energy consumption solution of the subsystem including a plurality of sub-users is solved by a subsection to be able to transform the convex problem of time allocation optimization.
Further, the SCMA-based cognitive MIMO system energy efficiency optimization method is characterized in that the specific process of the TDMA-based orthogonal time slot allocation optimization in step (3) is as follows:
channel matrix in Rayleigh fading channel and rich dispersion environments with statistical CSI
Is a mean of 0 and a covariance of
And the Gaussian random variables are independently and identically distributed; wherein
Defined as secondary subscriber SkTo the master user PjIs attenuated, and
for the secondary subscriber SkAre known; if the Rayleigh distribution of the channel is given, the requirement of a primary user P can be metjExponential distribution of interference constraints, i.e.
Wherein the parameter of the exponential distribution is
The wireless application can accept the interruption of the segment station under the condition of not influencing the QoS of the user; in practical situation, the interference of the secondary user to the primary user is allowed to exceed the interference threshold of the primary user
And setting its outage probability to
In case of statistical CSI, each secondary user SkMaximum instantaneous rate of
Unit: and nats/s, depending on the maximum transmission power and the interference constraint of the secondary users to the primary user, solving the maximum instantaneous rate expression of each secondary user as follows:
wherein:
(10) the solution can be realized through a standard water injection algorithm;
each secondary userSkMinimum time resource to meet rate requirements
In the secondary system, the range of feasible region of the rate requirement of each secondary user in one frame time resource is as follows by an objective function (9)
Then the time resource satisfies the constraint of
In a TDMA radio system, a frame is divided into a plurality of time slots, one time slot representing the smallest unit in a time allocation; in the actual allocation each secondary subscriber SkThe allocated time resource is integral multiple of the time slot, not the actual time; assume a normalized time period of T slots, and therefore, without loss of generality (9) time variance
Integer constraints are added, and the mathematical model is as follows:
wherein
Is each sub-subscriber SkA minimum number of slots to meet a rate requirement;
(11) an integer convex optimization problem is satisfied if and only if the following conditions are satisfied:
(11) can be solved by a simple greedy algorithm, assuming that the number of time slots allocated by the secondary users is
To
Has an energy consumption difference of
Namely, it is
In a normalized time period, the time is divided into T time slots, and initialization is the minimum time slot number required for meeting the speed requirements of all secondary users, namely
Then the rest time slots are sequentially according to the minimum time slot
To the corresponding secondary user.
Further, the SCMA-based cognitive MIMO system energy efficiency optimization method is characterized in that, in the step (3), the SCMA-based non-orthogonal time slot allocation optimization specifically includes:
(3.2.1) implementation of timeslot sharing
Assuming an uplink multi-user SCMA communication system, J users share N orthogonal time frequency resources (J is more than N) and transmit data to the same base station, wherein an overload factor is defined as lambda as J/N; bit data streams of 1 to J users directly obtain code words mapped on N time frequency resources through an SCMA encoder, and reach a receiving end through a channel, and the receiving end recovers transmitted bit data of each user through an MPA multi-user detector with low complexity;
factor map matrix for encoding in SCMA (F ═ F)1,f2,…fJ) Is expressed if and only if FnjWhen 1, variable node VjTo functional node FnConnecting; when the variable node represents a secondary user and the functional node represents a time slot, accessing the time slot orthogonally divided by the TDMA into more secondary users through grouping and sharing; under the condition of accessing the same number of users and the completion of time slot allocation in one frame, the time slot allocation strategy based on the SCMA non-orthogonal time slot sharing increases the time finally allocated to each secondary user;
(3.2.2) timeslot Allocation
The time slot sharing is realized by taking N time slots as units, which are called as shared time slot units; grouping the total time slots in a frame, wherein each continuous N time slots are divided into one group, the groups are not overlapped, namely the groups are in an orthogonal relationship, the N time slots contained in each group are non-orthogonal, and the total number T is obtainedN:
TNI.e. the total number of shared slots after the grouping, which is the minimum time allocation unit T for slot sharingτThe sum of (a);
after grouping, starting to access a secondary user in each group, wherein the secondary user needs to access by using an SCMA (sparse code multiple access) technology and meets the minimum time requirement that the secondary user needs to transmit data; by
Found skMinimum number of slots to be satisfied
Need to be extended to shared slots, i.e. mapped to TτIf, if
Directly extend it to 1TτThe transmission is carried out; but when
This needs to be extended to multiple TsτThe data transmission is carried out by adopting a method of virtually increasing users, namely according to skMinimum number of slots to be satisfied
Handle skIs regarded as
An
A secondary user of, i.e.
The total number of users L of the secondary system access at this time:
the minimum time slots which need to be met by the L secondary users are all 1; due to virtualized secondary subscriber Sk,iThe required minimum time slots are all 1, so that the data need to be mapped to several shared time slot units is not considered; assuming that a shared slot unit can access J secondary users, then L secondary users need to be grouped, the grouping principle is as follows：
fix (.) is the integer part of the division of L and J, mod (.) is the remainder of the division of L and J; wherein L isτ,minIs the minimum time to meet the rate requirements of L users; the formula shows that when the number of the remaining users is less than J when the users are allocated to the last group, another shared time slot unit needs to be allocated to the remaining users;
when L isτ,min＜TNThen, the extra shared time slot units need to be distributed to users in a certain group according to a certain criterion;
n time slots can be shared by J secondary users, the consumption of transmission energy of the J users needs to be considered comprehensively so that the extra shared time slots can be accurately distributed to all members in a certain group, and redundant shared time slot units are distributed by adopting a greedy algorithm and combining the average energy difference of all the members in the shared time slots;
suppose Lτ,minThe shared slot unit contained in the time period is Lt,1～Lt,nEach sharing a slot unit Lt,iThe number of the secondary users accessed in (1 is more than or equal to i and less than or equal to n) is Jt,iThen the average energy difference of the members in the shared slot group is:
in a greedy algorithm, the allocation of shared slots is from a minimum time
Starting allocation, and accessing 1-J users in each time slot; the redundant shared time slots are distributed in sequence, when the shared time slot L ist,iIs/are as follows
When the number of the shared time slot units is the minimum in all the shared time slot groups, 1 shared time slot unit needing to be allocated at the moment is allocated to all the members of the group.
Compared with the prior art, the invention has the beneficial effects that:
the invention adopts two methods to optimize the time slot distribution: orthogonal time slot allocation optimization and non-orthogonal time slot allocation optimization based on SCMA, and the minimum time requirement of each secondary user for data transmission can be obtained by solving the maximum transmission rate of each secondary user; under the condition of meeting the minimum time slot requirement of a secondary user, the total energy consumption of a secondary system of a plurality of secondary users can be obtained by optimizing the orthogonal time slot allocation of the TDMA;
the invention adopts a method for virtually increasing secondary users to simplify the non-orthogonal time slot access adopting the SCMA technology, and determines the distribution optimization criterion of the time slots by the average energy consumption difference of a plurality of secondary users accessing to the same shared time slot;
simulation results show that when the number of secondary users is large, the non-orthogonal time slot distribution based on the SCMA is more superior to the orthogonal time slot distribution in energy consumption and interference noise ratio of a primary user link to a receiving end; and, as the SCMA overload rate increases, this advantage becomes more pronounced; under the condition of meeting the requirement of the secondary user rate, the non-orthogonal time slot access of the SCMA is adopted, and the number of users accessed by the secondary system is greatly improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.
Fig. 1 is a diagram of an example of a random CR network of 35 Secondary Users (SUs) and 2-to-Primary User (PUs) links in the present invention;
fig. 2 is an uplink SCMA communication system model with J6 and N4 according to the present invention, where N in fig. 2 is white gaussian noise and N-cN (0, σ) is set as N2I)；
FIG. 3 is a diagram showing the correspondence between a matrix F and a factor graph according to the present invention;
FIG. 4 is a process diagram of virtualizing K secondary users into L secondary users in the present invention;
FIG. 5 is a diagram of the time slot division and SUs access in the present invention;
FIG. 6 is a graph of the average energy consumption per bit of SUs in the statistical CSI model of the present invention;
FIG. 7 is a graph of the number of secondary users versus the average interference power at the receiving end of the primary link in the present invention;
FIG. 8 is a graph showing the variation of the subscriber capacity of the subsystem with the number of slots when the SUs minimum slot condition is satisfied according to the present invention;
FIG. 9 is a diagram of interference power of SUs to the receiving end of the PUS link in the case of time slot allocation satisfying the requirement of the rate of the secondary user in the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
In the description of the present invention, it is to be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention.
Example 1
A cognitive MIMO system energy efficiency optimization method based on SCMA comprises the following steps:
(1) establishing a system model in a cognitive MIMO network, wherein the specific process comprises the following steps:
suppose there are K sub-users SUs and J primary user PUs in a cognitive radio network, with SkDenotes the kth secondary user, PjRepresents the j-th pair of primary user transceivers; under the underlay model, the main user link is potentially active and always protected. The main user network consists of J pairs of user transceivers, the secondary user system is a single cellular network, uplink of all secondary users is sent to the same cognitive base station through TDMA, the cognitive base station is in the central position of the cognitive network, and the cognitive base station can evaluate the secondary users SkChannel matrix to cognitive base station
Feeding back to corresponding secondary users through an independent control channel; thus the secondary subscriber SkThe channel matrix of the cognitive base station is known by the cognitive base station, and the uplink transmission of the secondary user is synchronous with the cognitive base station, so that the secondary user is distributed to transmit in mutually non-interfering orthogonal time slots; because the primary user and the secondary user coexist in the cognitive network, mutual interference exists between the primary user and the secondary user; for the convenience of analysis, assuming that the channel is a frequency flat fading channel, it is specified that the channel matrix within the bandwidth is invariant; further, if the channel is a block fading channel, the channel matrix in one frame will not change, and the channel matrix between frames is not related; for example, two main link pairs, the distance between the link pairs is 10 meters, and the two main link pairs respectively represent a transmitting end and a receiving end; the secondary users are uniformly distributed in an area of 200m by 200m, and the distance between the secondary users and the receiving end of the main link pair is not less than 35 m; an example of a random CR network of 35 Secondary Users (SUs) and 2-to-Primary User (PUs) links is shown in fig. 1.
In cognitive MIMO networks, both primary and secondary users can transmit multiple data streams via MIMO, assuming
Denotes SkNumber of transmitting antennas of, NBSIndicating the number of receiving antennas of the cognitive base station;
represents PjThe number of the transmitting antennas of (1),
represents PjThe number of receive antennas of (1);
denotes SkThe data stream of (a) is transmitted,
represents PjThe data stream of (2); skThe data stream matrix transmitted by the antenna is
PjThe data stream matrix transmitted by the antenna is
Then
Has a covariance matrix of
Has a covariance matrix of
And is
And
all are semi-positive definite Hermitian matrixes;
secondary subscriber SkThe cognitive base station of the secondary user system receives S when the S reaches a receiving end through a channelkThe data of (a) are:
wherein n isBSIs noise power spectral density of N0Additive white gaussian noise,/2, w is the bandwidth.
Assuming that the secondary BS cannot continuously cancel interference and the interference from the primary user is regarded as noise, S is performedkWhen transmitting data, the interference noise covariance matrix of the secondary BS is
Wherein
Is NBS×NBSA semi-positive definite Hermitian matrix of (1);
according to the capacity formula of Shannon' S theorem in MIMO link, SkAn achievable rate of
Wherein the transmission rate
Is the secondary subscriber SkActive instantaneous transmission rate, unit: nats/second; secondary subscriber SkThe total transmission power on all antennas is
Secondary subscriber SkTo master user PjResulting in a total interference power of
(2) Problem construction and optimization: converting a non-convex problem solved by the energy consumption of a plurality of secondary user systems into a convex problem optimized by time distribution; the specific process is as follows:
the goal of the system is to allocate an appropriate transmission time for each secondary user, thereby minimizing the total energy consumption of all secondary users, which not only ensures that the primary user experiences as little interference as possible from the secondary users, but also ensures the QoS of each secondary user. In a statistical CSI model, in order to ensure the interference of a secondary user to a primary user, an interference threshold value aiming at the primary user must be set for each secondary user so as to protect the QoS of the primary user to the greatest extent; to guarantee QoS per secondary user, the rate requirement per secondary user must be met
The unit is nat/frame, which is the amount of data to be transmitted in a frame time. To avoid loss of generality, the length of a TDMA frame in a secondary user system is specified to be 1, and each secondary user occupies
For transmitting data, and each secondary subscriber SkIs limited to its maximum power
Thus, the target can be converted to the following mathematical formula:
where the objective function is the total energy consumption of all secondary users in the secondary system. The constraint (5a) is to guarantee a minimum rate requirement per secondary user. (5b) Meaning that the total time allocated by all secondary users does not exceed the frame length of one TDMA. Since the secondary system is a TDMA access network, the secondary users cannot transmit simultaneously, avoiding interference between secondary users. (5c) The interference suffered by each primary user receiver is lower than the interference threshold, and the interference power received on the antenna of the primary user receiver is used for representing the interference suffered by the primary user receiver. (5d) Meaning that the maximum power transmitted by each secondary user does not exceed
(5e) Indicating that the time allocated per secondary user is non-negative. (5f) Indicating a secondary user SkCovariance matrix of
Is a semi-positive definite matrix.
For this mathematical model, where both the objective function and (5a) are non-convex problems, this model is an N-P puzzle. It is generally difficult to directly solve the N-P problem. Channel matrix in Rayleigh fading channel and rich dispersion environment under statistical CSI model
Each term in (a) is a gaussian random variable with a mean of 0 and covariance obeys independent co-distribution. Allowing the cognitive user to send out by considering the actual situationThe interference of the transmitted signal to the main user exceeds the interference threshold of the main user
And adopts 'water injection method' to solve the problem of secondary user SkThe transmit power limits of (5a) - (5f) may be translated into a convex problem for time allocation optimization. Thus, each secondary subscriber SkOver time
The energy consumption formula of (a) is:
wherein,
is the optimal number of data streams, is time
The piecewise function of (d):
as can be seen from (9), the non-convex problem of energy consumption solution of the subsystem including a plurality of sub-users is solved by a subsection to be able to transform the convex problem of time allocation optimization.
(3) Solving an objective function: two methods are adopted to carry out allocation optimization on the time slots, which are respectively as follows: orthogonal time slot allocation optimization based on TDMA, non-orthogonal time slot allocation optimization based on SCMA; the specific process is as follows:
(3.1) TDMA-based orthogonal time slot allocation optimization
Channel matrix in Rayleigh fading channel and rich dispersion environments with statistical CSI
Is a mean of 0 and a covariance of
And independent and identically distributed gaussian random variables. Wherein
Defined as secondary subscriber SkTo the master user PjIs attenuated, and
for the secondary subscriber SkAre known. If the Rayleigh distribution of the channel is given, the requirement of a primary user P can be metjExponential distribution of interference constraints, i.e.
Wherein the parameters of the exponential distributionIs composed of
It is known that many wireless applications (e.g., streaming video, voice over IP) can accept segment station interruptions without impacting user QoS. In practical situation, the interference of the secondary user to the primary user is allowed to exceed the interference threshold of the primary user
And setting its outage probability to
But this probability should be small to ensure that a higher probability meets the interference constraints of the primary users.
In case of statistical CSI, each secondary user SkMaximum instantaneous rate of
The (unit: nats/s) depends on the maximum transmission power and the interference constraint of the secondary users to the primary users. Solving the maximum instantaneous rate for each secondary user is expressed as follows:
wherein:
(10) the solution can be done by standard water-filling algorithms.
Then each secondary subscriber SkMinimum time resource to meet rate requirements
In the secondary system, the range of feasible region of the rate requirement of each secondary user in one frame time resource is as follows by an objective function (9)
Then the time resource satisfies the constraint of
In a TDMA radio system, a frame is divided into a number of time slots, one time slot representing the smallest unit in the time allocation. In the actual allocation each secondary subscriber SkThe allocated time resources are integer multiples of the time slot, not the actual time. Assume a normalized time period of T slots, and therefore, without loss of generality (9) time variance
Integer constraints are added, and the mathematical model is as follows:
wherein
Is each sub-subscriber SkThe minimum number of slots to meet the rate requirement.
(11) An integer convex optimization problem is satisfied if and only if the following conditions are satisfied:
(11) can be solved using a simple greedy algorithm. Suppose that the number of time slots allocated to the secondary user is
To
Has an energy consumption difference of
Namely, it is
In a normalized time period, the time is divided into T time slots, and initialization is the minimum time slot number required for meeting the speed requirements of all secondary users, namely
Then the rest time slots are sequentially according to the minimum time slot
To the corresponding secondary user.
(3.2) SCMA-based non-orthogonal slot allocation optimization
From the formula of energy consumption
The current subscriber s can be known by monotonicity ofkThe more time it takes to dispense, the less energy it consumes. TDMA based orthogonal timeThe slot allocation avoids the interference between the secondary users, but also limits the number of users accessed by the system. Non-orthogonal multiple access (NOMA) technology oriented to next-generation wireless communication can realize the sharing of time-frequency resources, so that the access of users does not need to be strictly orthogonal, and the users do not have serious interference. Among the plurality of NOMA technologies, the sparse Code Multiple access technology scma (sparse Code Multiple access) is a very potential air interface technology, and under the condition of the same time frequency resource, 150%, 200%, or even more device connection numbers can be realized. In order to further optimize the time slot allocation, the present embodiment proposes a non-orthogonal time slot allocation strategy based on SCMA. The allocation method can realize the sharing of the TDMA time slot, can realize the overload rate of 150 percent, 200 percent, 250 percent or even higher under the condition of the same time slot resource, and can not cause serious interference among secondary users. The implementation will be described in detail below.
(3.2.1) implementation of timeslot sharing
This embodiment takes the overload rate λ of 150% in the SCMA technique as an example, and describes the implementation of non-orthogonal slots based on SCMA.
The SCMA based on sparse spread spectrum opens new chapters for sharing time-frequency resources. Assuming an uplink multi-user SCMA communication system, J users share N orthogonal time frequency resources (J is more than N) and transmit data to the same base station, and the overload factor is defined as lambda as J/N. The uplink SCMA system model with J6 and N4 is shown in fig. 2. The bit data stream of 1 to J users directly obtains the code words mapped on N time frequency resources through an SCMA encoder, and reaches a receiving end through a channel, and the receiving end can recover the bit data transmitted by each user through an MPA multi-user detector with low complexity. In FIG. 2, n is white Gaussian noise and n-cN (0, σ)2I)。
The SCMA may be encoded using a factor graph matrix F ═ F (F)1,f2,…fJ) To indicate. If and only if FnjWhen 1, variable node VjTo functional node FnAnd (4) connecting. When variable node represents secondary user and functional node represents time slot, SCMA factor graph and its matrix FThe corresponding relationship of (a) is shown in fig. 3. As can be seen from fig. 2 and 3, the principle of SCMA technology is utilized to access the TDMA orthogonal time slots to more secondary users through packet sharing. It is known that TDMA is implemented by dividing a time frame into time slots of a fixed time length, and by means of orthogonalized time slots, it is possible to implement access of multiple users, avoiding data fusion between users, and thus solving the problem of interference between users. Therefore, in comparison, under the condition of accessing the same number of users and the completion of the time slot allocation in one frame, the time slot allocation strategy based on the non-orthogonal time slot sharing of the SCMA will result in more time finally allocated to each secondary user.
(3.2.2) timeslot Allocation
From the corresponding relationship between the factor graph matrix F and the factor graph, it can be seen that N time slots can carry more secondary user data through sharing, such as overload rates of 150% and 200%. Due to time slot sharing, non-orthogonal time slot allocation based on SCMA requires different time slot allocation strategies than TDMA. The slot sharing is realized by taking N slots as units, and is called as a shared slot unit. The total time slots within a frame need to be grouped. Every continuous N time slots are divided into one group, and the groups do not overlap, namely, the groups are in an orthogonal relationship, and each group comprises N time slots which are not orthogonalized. The total number T obtainedN:
TNI.e. the total number of shared slots after the grouping, which is the minimum time allocation unit T for slot sharingτThe sum of (a) and (b).
After grouping, the secondary users are accessed in each group, and the secondary users need to be accessed by using the SCMA technology and meet the minimum time requirement that the secondary users need to transmit data. By
Found skMinimum number of slots to be satisfied
Need to be extended to shared slots, i.e. mapped to TτIf, if
Directly extend it to 1TτAnd the transmission is performed. But when
This needs to be extended to multiple TsτAnd carrying out data transmission. Thus at a TτIt is troublesome to allocate J users to share the time slot, and for simplification, a method of virtually adding users is adopted, namely according to skMinimum number of slots to be satisfied
Handle skIs regarded as
An
A secondary user of, i.e.
The implementation process is shown in fig. 4. The total number of users L of the secondary system access at this time:
the minimum time slots which need to be met by the L secondary users are all 1;
secondary subscriber SkThe virtualization process of the method can not influence users, and the method can conveniently realize the access of secondary users to the shared time slot unit. Due to virtualized secondary subscriber Sk,iThe minimum required slot is 1, so there is no need to consider that its data needs to be mapped onto several shared slot units. Suppose a shared slotThe unit can access J secondary users, and then needs to group L secondary users, the grouping principle is as follows:
fix (. lamda.) is the integer part of the division of L and J, and mod (. lamda.) is the remainder of the division of L and J. Wherein L isτ,minIs the minimum time to meet the rate requirements of L users. This equation illustrates that when the number of remaining users is less than J when allocated to the last group, another shared slot unit needs to be allocated to the remaining users.
When L isτ,min＜TNIt is then necessary to assign the excess shared slot units to users in a group according to some criteria.
Since J secondary users can share N time slots (1 shared time slot unit), the consumption of transmission energy of J users needs to be considered comprehensively, so that the extra shared time slots can be accurately distributed to all members in a certain group. The part still adopts a greedy algorithm and combines the average energy difference of all members in the shared time slot to distribute redundant shared time slot units.
Suppose Lτ,minThe shared slot unit contained in the time period is Lt,1～Lt,nEach sharing a slot unit Lt,iThe number of the secondary users accessed in (1 is more than or equal to i and less than or equal to n) is Jt,iThen the average energy difference of the members in the shared slot group is:
in a greedy algorithm, the allocation of shared slots is from a minimum time
And starting allocation, and accessing 1-J users in each time slot. The redundant shared time slots are distributed in sequence, when the shared time slot L ist,iIs/are as follows
When the number of the shared time slot units is the minimum in all the shared time slot groups, 1 shared time slot unit needing to be allocated at the moment is allocated to all the members of the group. The implementation process is as shown in fig. 5.
Simulation verification:
in order to verify the energy consumption, the interference to a main system and the user capacity of the algorithm in the MIMO cognitive system, the simulation system is established as follows: the distance between two main link pairs is 10 meters, and the two main link pairs respectively represent a transmitting end and a receiving end; the secondary users are uniformly distributed in an area of 200m by 200m, and the distance between the secondary users and the receiving end of the primary link pair is not less than 35 m. And assume that there are 4 antennas per node in the cognitive network. W is the bandwidth, and the band frame adopted in this embodiment is 20 MHz.
The length of a time frame in the TDMA is 20ms, and each frame comprises 200 time slots; the rate requirement for each SU is 32 kbps. The carrier frequency is 1GHz and the bandwidth is w-20 MHz. The channel model is rayleigh fading channel, maximum transmit power of PUs is 20dBm, maximum transmit power of SUs is 27.5dBm, and noise power spectral density is-178 dBm/Hz at N0. The ratio of the interference power threshold to N0 w is 25 dB. In the statistical CSI model, the outage probability.
Simulation experiments were performed on the matlab platform.
Energy consumption
The average energy consumption per bit of SUs under the statistical CSI model is shown in fig. 6. This example performs comparative simulations for maximum rate, MIMO & optimum time, and 150%, 200%, 250% of overload rate SCMA & optimum time. The maximum rate is an energy consumption curve graph obtained according to the minimum time obtained by a formula in the space division multiplexing mode of the MIMO technology. The other 4 plots are based on TDMA time slots and the best time derived by the greedy algorithm for energy consumption. As can be seen from fig. 6, the average energy consumption per bit based on the optimal time for TDMA slot allocation is lower than the energy consumption for maximum rate transmission. The non-orthogonal time allocation based on SCMA proposed herein has higher average energy consumption per bit than under orthogonal slot allocation when there is less SUS for secondary system access, because the data rate of SUS is increased by sparse spreading of SCMA. However, when there are many SUs, the non-orthogonal time slot allocation using SCMA has certain advantages in energy efficiency, and as the overload rate of SCMA increases, the energy efficiency advantage becomes more obvious, because the time slot allocation is completed, and a non-orthogonal time slot sharing allocation strategy is adopted, each SU gets more time than an orthogonal time slot allocation strategy, which makes up for the regret of the data size of SUs increased by the spreading sequence. With the future trend of wireless communication, the secondary system will access more SUs. Therefore, the non-orthogonal time slot sharing allocation strategy adopting the SCMA has certain advantages in the aspects of reducing energy consumption and improving energy efficiency.
Interference to the receiving end of the main link
FIG. 7 shows: under the statistical CSI model, the SUs is used for Pus link to receiving end average interference power, and the average interference power takes the ratio of interference to noise as evaluation standard. As can be seen from fig. 7, the average interference-to-noise ratio in the ' maximum rate transmission ' mode is less than 25dB required because the secondary user's transmit power needs to meet its interference limit for the primary user. And the redundant time slots are reasonably distributed, and the interference-to-noise ratios of the redundant time slots are all smaller than the maximum rate transmission', so that the interference of the secondary users to the primary users can be relieved. The average interference-to-noise ratio based on the best time for TDMA orthogonal time slot allocation is lower than the interference-to-noise ratio for 'maximum rate transmission'. The non-orthogonal time allocation based on SCMA proposed herein has a higher average noise to interference ratio than the average noise to interference ratio for orthogonal slot allocation when there are fewer SUs for secondary system access, since the data rate of SUs is increased by sparse spreading with SCMA. However, when there are many SUs, the non-orthogonal time slot allocation using SCMA has certain advantages in reducing interference, and as the overload rate of SCMA increases, the advantages become more obvious, because the time slot allocation is completed, and the non-orthogonal time slot sharing allocation strategy is adopted, each SU gets more time than the orthogonal time slot allocation strategy, which reduces the average energy consumption per bit, and thus the interference is also reduced.
User capacity and interference to the receiving end of the main link
Fig. 8 is a graph of the change of the user capacity of the secondary system with the number of slots when the conditions of the SUs minimum slots are satisfied, that is, when the conditions of the SUs minimum slots are satisfied, no time allocation and registration allocation is performed on the extra slots, but more secondary users are accessed, and the obtained user capacity graph of the secondary system is obtained, and the number of slots in one frame is set to be 100. As can be seen from fig. 8, as the number of time slots increases, the number of users accessed by the secondary system also increases. In contrast, non-orthogonal slot access with SCMA is more advantageous in terms of user volume than orthogonal slot access. For example, when T is 100 under the condition of overload rate 150%, the capacity of the user accessed by the non-orthogonal time slot is about 1.5 times that of the user accessed by the orthogonal time slot. As the overload rate increases, the user capacity of the secondary users also increases. It can be seen that non-orthogonal slot access is an effective means for increasing the user capacity of the system.
Fig. 9 is a graph showing the variation of the interference-to-noise ratio with the number of sub-users, when the number of slots in one frame is 100, and the condition of satisfying the SUs minimum slot, without considering the problem of allocating extra slots. As can be seen from fig. 9, when the allocation is not performed on the redundant time slots, no matter the time slots are accessed orthogonally, or the time slots are accessed non-orthogonally, the interference signal-to-noise ratio of the redundant time slots and the interference signal-to-noise ratio of the redundant time slots are less than 25dB, because each secondary user needs to satisfy the interference threshold limit for the primary user no matter which access method is used. However, the interference to noise ratio of SCMA-based non-orthogonal slot access is higher than the interference to noise ratio of orthogonal slot access. The difference between the two is about 2 dB. This is because the essence of SCMA is that non-orthogonal access is achieved through sparse spreading sequences. The average interference-to-noise ratio of the SCMA technique using overload rate of 150% and 200% is similar. Therefore, the overload rate has little influence on the average interference power of the secondary users to the primary users.
Claims (5)
1. A cognitive MIMO system energy efficiency optimization method based on SCMA is characterized by comprising the following steps:
(1) establishing a system model in a cognitive MIMO network;
(2) problem construction and optimization: converting a non-convex problem solved by the energy consumption of a plurality of secondary user systems into a convex problem optimized by time distribution;
(3) solving an objective function: two methods are adopted to carry out allocation optimization on the time slots, which are respectively as follows:
(3.1) TDMA-based orthogonal time slot allocation optimization;
and (3.2) non-orthogonal time slot allocation optimization based on SCMA.
2. The SCMA-based cognitive MIMO system energy efficiency optimization method according to claim 1, wherein the specific process of establishing the system model in the cognitive MIMO network in the step (1) is as follows:
suppose there are K sub-users SUs and J primary user PUs in a cognitive radio network, with SkDenotes the kth secondary user, PjRepresents the j-th pair of primary user transceivers; under the underlay model, a main user link is in potential activity and is always protected; the master user communication network consists of J pairs of user transceivers, the secondary user system is a single cellular network, uplink of all secondary users is sent to the same cognitive base station through TDMA, the cognitive base station is in the central position of the cognitive network, and the cognitive base station can evaluate the secondary user SkChannel matrix to cognitive base station
Feeding back to corresponding secondary users through an independent control channel; thus the secondary subscriber SkThe channel matrix of the cognitive base station is known by the cognitive base station, and the uplink transmission of the secondary user is synchronous with the cognitive base station, so that the secondary user is distributed to transmit in mutually non-interfering orthogonal time slots; because the primary user and the secondary user coexist in the cognitive network, mutual interference exists between the primary user and the secondary user; for the purpose of analysis, the channel is assumed to be a frequency flat fading channel, and is defined within a bandwidthThe channel matrix is invariant; further, if the channel is a block fading channel, the channel matrix in one frame will not change, and the channel matrix between frames is not related;
in cognitive MIMO networks, both primary and secondary users can transmit multiple data streams via MIMO, assuming
Denotes SkNumber of transmitting antennas of, NBSIndicating the number of receiving antennas of the cognitive base station;
represents PjThe number of the transmitting antennas of (1),
represents PjThe number of receive antennas of (1);
denotes SkThe data stream of (a) is transmitted,
represents PjThe data stream of (2); skThe data stream matrix transmitted by the antenna is
PjThe data stream matrix transmitted by the antenna is
Then
Has a covariance matrix of
Has a covariance matrix of
And is
And
all are semi-positive definite Hermitian matrixes;
secondary subscriber SkThe cognitive base station of the secondary user system receives S when the S reaches a receiving end through a channelkThe data of (a) are:
wherein n isBSIs noise power spectral density of N0(ii) additive white gaussian noise,/2, w being the bandwidth;
assuming that the secondary BS cannot continuously cancel interference and the interference from the primary user is regarded as noise, S is performedkWhen transmitting data, the interference noise covariance matrix of the secondary BS is
Wherein
Is NBS×NBSA semi-positive definite Hermitian matrix of (1);
according to the capacity formula of Shannon' S theorem in MIMO link, SkAn achievable rate of
Wherein the transmission rate
Is the secondary subscriber SkActive instantaneous transmission rate, unit: nats/second;
secondary subscriber SkThe total transmission power on all antennas is
Secondary subscriber SkTo master user PjResulting in a total interference power of
3. The SCMA-based cognitive MIMO system energy efficiency optimization method according to claim 2, wherein the specific process of problem construction and optimization in step (2) is as follows:
the system aims to allocate proper transmission time for each secondary user, so that the total energy consumption of all secondary users is minimized, the interference of a primary user from the secondary users is ensured to be as small as possible, and the QoS (quality of service) of each secondary user is also ensured; in a statistical CSI model, in order to ensure the interference of a secondary user to a primary user, an interference threshold value aiming at the primary user must be set for each secondary user so as to protect the QoS of the primary user to the greatest extent; to guarantee QoS per secondary user, the rate requirement per secondary user must be met
Unit: nats/frame, i.e. the amount of data to be transmitted within a frame time; the length of TDMA frame in the system of specified secondary users is 1, each secondary user occupies
For transmitting data, and each secondary subscriber SkIs limited to its maximum power
Thus, the target can be converted to the following mathematical formula:
wherein the objective function is the total energy consumption of all secondary users in the secondary system;
constraint (5a) is to guarantee a minimum rate requirement per secondary user; (5b) indicating that the total time allocated by all secondary users does not exceed the frame length of a TDMAThe secondary system is a TDMA access network, so that secondary users cannot transmit simultaneously, and the interference between the secondary users is avoided; (5c) the interference suffered by each primary user receiver is lower than an interference threshold, and the interference suffered by the primary user receiver is represented by the interference power received on the antenna of the primary user receiver; (5d) meaning that the maximum power transmitted by each secondary user does not exceed
(5e) Indicating that the time allocated by each secondary user is non-negative; (5f) indicating a secondary user SkCovariance matrix of
Is a semi-positive definite matrix;
for this mathematical model, where both the objective function and (5a) are non-convex problems; channel matrix in Rayleigh fading channel and rich dispersion environment under statistical CSI model
Each item in the (1) is a Gaussian random variable with a mean value of 0 and covariance obeys independent same distribution; considering practical conditions, allowing the interference of the signals transmitted by the cognitive users to the main users to exceed the interference threshold of the main users
And adopts a water injection method to solve the problem of the secondary user SkThe transmit power limits of (5a) - (5f) may be converted to a convex problem for time allocation optimization; thus, each secondary subscriber SkOver time
The energy consumption formula of (a) is:
wherein,
is the optimal number of data streams, is time
The piecewise function of (d):
as can be seen from (9), the non-convex problem of energy consumption solution of the subsystem including a plurality of sub-users is solved by a subsection to be able to transform the convex problem of time allocation optimization.
4. The SCMA-based cognitive MIMO system energy efficiency optimization method according to claim 3, wherein the specific process of the TDMA-based orthogonal time slot allocation optimization in the step (3) is as follows:
channel matrix in Rayleigh fading channel and rich dispersion environments with statistical CSI
Is a mean of 0 and a covariance of
And the Gaussian random variables are independently and identically distributed; wherein
Defined as secondary subscriber SkTo the master user PjIs attenuated, and
for the secondary subscriber SkAre known; if the Rayleigh distribution of the channel is given, the requirement of a primary user P can be metjExponential distribution of interference constraints, i.e.
Wherein the parameter of the exponential distribution is
The wireless application can accept the interruption of the segment station under the condition of not influencing the QoS of the user; in practical situation, the interference of the secondary user to the primary user is allowed to exceed the interference threshold of the primary user
And setting its outage probability to
In case of statistical CSI, each secondary user SkMaximum instantaneous rate of
Unit: and nats/s, depending on the maximum transmission power and the interference constraint of the secondary users to the primary user, solving the maximum instantaneous rate expression of each secondary user as follows:
wherein:
(10) the solution can be realized through a standard water injection algorithm;
then each secondary subscriber SkMinimum time resource to meet rate requirements
In the secondary system, the range of feasible region of the rate requirement of each secondary user in one frame time resource is as follows by an objective function (9)
Then the time resource satisfies the constraint of
In a TDMA radio system, a frame is divided into a plurality of time slots, one time slot representing the smallest unit in a time allocation; in the actual allocation each secondary subscriber SkThe allocated time resource is integral multiple of the time slot, not the actual time; assume a normalized time period of T slots, and therefore, without loss of generality (9) time variance
Integer constraints are added, and the mathematical model is as follows:
wherein
Is each sub-subscriber SkA minimum number of slots to meet a rate requirement;
(11) an integer convex optimization problem is satisfied if and only if the following conditions are satisfied:
(11) can be solved by a simple greedy algorithm, assuming that the number of time slots allocated by the secondary users is
To
Has an energy consumption difference of
Namely, it is
In a normalized time period, the time is divided into T time slots, and initialization is the minimum time slot number required for meeting the speed requirements of all secondary users, namely
Then the rest time slots are sequentially according to the minimum time slot
To the corresponding secondary user.
5. The method for optimizing the energy efficiency of the cognitive MIMO system based on the SCMA as claimed in claim 4, wherein the specific process of the SCMA-based non-orthogonal time slot allocation optimization in the step (3) is as follows:
(3.2.1) implementation of timeslot sharing
Assuming an uplink multi-user SCMA communication system, J users share N orthogonal time frequency resources (J is more than N) and transmit data to the same base station, wherein an overload factor is defined as lambda as J/N; bit data streams of 1 to J users directly obtain code words mapped on N time frequency resources through an SCMA encoder, and reach a receiving end through a channel, and the receiving end recovers transmitted bit data of each user through an MPA multi-user detector with low complexity;
factor map matrix for encoding in SCMA (F ═ F)1，f2，…fJ) Is expressed if and only if FnjWhen 1, variable node VjTo functional node FnConnecting; when the variable node represents a secondary user and the functional node represents a time slot, accessing the time slot orthogonally divided by the TDMA into more secondary users through grouping and sharing; under the condition of accessing the same number of users and the completion of time slot allocation in one frame, the time slot allocation strategy based on the SCMA non-orthogonal time slot sharing increases the time finally allocated to each secondary user;
(3.2.2) timeslot Allocation
The time slot sharing is realized by taking N time slots as units, which are called as shared time slot units; grouping the total time slots in a frame, wherein each continuous N time slots are divided into one group, the groups are not overlapped, namely the groups are in an orthogonal relationship, the N time slots contained in each group are non-orthogonal, and the total number T is obtainedN：
TNI.e. the total number of shared slots after the grouping, which is the minimum time allocation unit T for slot sharingτThe sum of (a);
after grouping, starting to access a secondary user in each group, wherein the secondary user needs to access by using an SCMA (sparse code multiple access) technology and meets the minimum time requirement that the secondary user needs to transmit data; by
Found skMinimum number of slots to be satisfied
Need to be extended to shared slots, i.e. mapped to TτIf, if
Directly extend it to 1TτThe transmission is carried out; but when
This needs to be extended to multiple TsτThe data transmission is carried out by adopting a method of virtually increasing users, namely according to skMinimum number of slots to be satisfied
Handle skIs regarded as
An
A secondary user of, i.e.
The total number of users L of the secondary system access at this time:
the minimum time slots which need to be met by the L secondary users are all 1; due to virtualized secondary subscriber Sk，iThe required minimum time slots are all 1, so that the data need to be mapped to several shared time slot units is not considered; assuming that a shared slot unit can access J secondary users, then L secondary users need to be grouped, the grouping principle is as follows:
fix (.) is the integer part of the division of L and J, mod (.) is the remainder of the division of L and J; wherein L isτ，minIs the minimum time to meet the rate requirements of L users; the formula shows that when the number of the remaining users is less than J when the users are allocated to the last group, another shared time slot unit needs to be allocated to the remaining users;
when L isτ，min＜TNThen, the extra shared time slot units need to be distributed to users in a certain group according to a certain criterion;
n time slots can be shared by J secondary users, the consumption of transmission energy of the J users needs to be considered comprehensively so that the extra shared time slots can be accurately distributed to all members in a certain group, and redundant shared time slot units are distributed by adopting a greedy algorithm and combining the average energy difference of all the members in the shared time slots;
suppose Lτ，minThe shared slot unit contained in the time period is Lt，1～Lt，nEach sharing a slot unit Lt，iThe number of the secondary users accessed in (1 is more than or equal to i and less than or equal to n) is Jt，iThen the average energy difference of the members in the shared slot group is:
in a greedy algorithm, the allocation of shared slots is from a minimum time
Starting allocation, and accessing 1-J users in each time slot; the redundant shared time slots are distributed in sequence, when the shared time slot L ist，iIs/are as follows
When the number of the shared time slot units is the minimum in all the shared time slot groups, 1 shared time slot unit needing to be allocated at the moment is allocated to all the members of the group.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2019113822399 | 2019-12-27 | ||
| CN201911382239 | 2019-12-27 |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112243251A true CN112243251A (en) | 2021-01-19 |
| CN112243251B CN112243251B (en) | 2022-08-02 |
Family
ID=74170675
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010576145.1A Active CN112243251B (en) | 2019-12-27 | 2020-06-22 | Cognitive MIMO system energy efficiency optimization method based on SCMA |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112243251B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114143887A (en) * | 2021-12-10 | 2022-03-04 | 东南大学 | Grouping-based downlink MIMO-SCMA low-complexity receiving method |
Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103384174A (en) * | 2013-05-10 | 2013-11-06 | 江苏科技大学 | Method based on cooperation of multiple users and multiple antennas for optimizing spectrum sensing detection probability |
| CN104955161A (en) * | 2015-06-19 | 2015-09-30 | 西安电子科技大学 | Femtocell clustering and macro user scheduling-based interference coordination method in heterogeneous network |
| CN105846945A (en) * | 2010-12-02 | 2016-08-10 | 交互数字专利控股公司 | Wireless device and method executed by the wireless devcie |
| CN106507487A (en) * | 2016-09-19 | 2017-03-15 | 南京邮电大学 | A kind of Power Ratio Bi-Objective Optimization Method in multiuser MIMO ofdm system |
| CN106793111A (en) * | 2016-11-28 | 2017-05-31 | 西安电子科技大学 | Joint virtual MIMO resource allocation methods are matched based on dynamic multiple cell user |
| US20170353947A1 (en) * | 2016-06-06 | 2017-12-07 | Qualcomm Incorporated | Downlink slot structure, channel placement, and processing timeline options |
| CN107733490A (en) * | 2017-10-16 | 2018-02-23 | 西北工业大学 | United beam shaping and optimal power allocation method in two-way insincere junction network |
| CN107994933A (en) * | 2017-11-30 | 2018-05-04 | 重庆邮电大学 | Recognize the optimization method of time custom system capacity in MIMO networks |
| CN108462662A (en) * | 2017-02-17 | 2018-08-28 | 索尼公司 | Electronic equipment and communication means for the multiple access access based on non orthogonal resources |
| WO2018200748A1 (en) * | 2017-04-25 | 2018-11-01 | Qualcomm Incorporated | Flexible scheduling in new radio (nr) networks |
| CN108886792A (en) * | 2016-04-01 | 2018-11-23 | 索尼公司 | Electronic equipment and communication means in wireless communication system |
| CN109039504A (en) * | 2018-09-14 | 2018-12-18 | 重庆邮电大学 | Cognitive radio efficiency power distribution method based on non-orthogonal multiple access |
| US20190208387A1 (en) * | 2017-12-28 | 2019-07-04 | Qualcomm Incorporated | System and method for ranging-assisted vehicle positioning |
| CN110299937A (en) * | 2019-07-24 | 2019-10-01 | 南京邮电大学 | A kind of Uplink MIMO-NOMA wireless communication system beam-forming method |
| 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 |
-
2020
- 2020-06-22 CN CN202010576145.1A patent/CN112243251B/en active Active
Patent Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105846945A (en) * | 2010-12-02 | 2016-08-10 | 交互数字专利控股公司 | Wireless device and method executed by the wireless devcie |
| CN103384174A (en) * | 2013-05-10 | 2013-11-06 | 江苏科技大学 | Method based on cooperation of multiple users and multiple antennas for optimizing spectrum sensing detection probability |
| CN104955161A (en) * | 2015-06-19 | 2015-09-30 | 西安电子科技大学 | Femtocell clustering and macro user scheduling-based interference coordination method in heterogeneous network |
| CN108886792A (en) * | 2016-04-01 | 2018-11-23 | 索尼公司 | Electronic equipment and communication means in wireless communication system |
| US20170353947A1 (en) * | 2016-06-06 | 2017-12-07 | Qualcomm Incorporated | Downlink slot structure, channel placement, and processing timeline options |
| WO2017214054A1 (en) * | 2016-06-06 | 2017-12-14 | Qualcomm Incorporated | Downlink slot structure, channel placement, and processing timeline options |
| CN106507487A (en) * | 2016-09-19 | 2017-03-15 | 南京邮电大学 | A kind of Power Ratio Bi-Objective Optimization Method in multiuser MIMO ofdm system |
| CN106793111A (en) * | 2016-11-28 | 2017-05-31 | 西安电子科技大学 | Joint virtual MIMO resource allocation methods are matched based on dynamic multiple cell user |
| CN108462662A (en) * | 2017-02-17 | 2018-08-28 | 索尼公司 | Electronic equipment and communication means for the multiple access access based on non orthogonal resources |
| WO2018200748A1 (en) * | 2017-04-25 | 2018-11-01 | Qualcomm Incorporated | Flexible scheduling in new radio (nr) networks |
| CN107733490A (en) * | 2017-10-16 | 2018-02-23 | 西北工业大学 | United beam shaping and optimal power allocation method in two-way insincere junction network |
| CN107994933A (en) * | 2017-11-30 | 2018-05-04 | 重庆邮电大学 | Recognize the optimization method of time custom system capacity in MIMO networks |
| US20190208387A1 (en) * | 2017-12-28 | 2019-07-04 | Qualcomm Incorporated | System and method for ranging-assisted vehicle positioning |
| CN109039504A (en) * | 2018-09-14 | 2018-12-18 | 重庆邮电大学 | Cognitive radio efficiency power distribution method based on non-orthogonal multiple access |
| 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 |
| CN110299937A (en) * | 2019-07-24 | 2019-10-01 | 南京邮电大学 | A kind of Uplink MIMO-NOMA wireless communication system beam-forming method |
Non-Patent Citations (3)
| Title |
|---|
| ""Draft_Minutes_report_RAN1#92_v010"", 《3GPP TSG_RAN\WG1_RL1》 * |
| ZHENYU XIAO: "User_Fairness_Non-Orthogonal_Multiple_Access_NOMA_for_Millimeter-Wave_Communications_With_Analog_Beamforming", 《IEEE XPLORE》 * |
| 韩略: "5G非正交多址技术研究", 《中国优秀硕士学位论文全文数据库（电子期刊）信息科技辑》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114143887A (en) * | 2021-12-10 | 2022-03-04 | 东南大学 | Grouping-based downlink MIMO-SCMA low-complexity receiving method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112243251B (en) | 2022-08-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| AU2018203567B2 (en) | Systems and methods to coordinate transmissions in distributed wireless systems via user clustering | |
| Hoang et al. | Power control and channel allocation in cognitive radio networks with primary users' cooperation | |
| TWI699984B (en) | Systems and methods to exploit areas of coherence in wireless systems | |
| KR20160015200A (en) | Method for managing wireless resource and apparatus therefor | |
| CN111586646B (en) | Resource allocation method for D2D communication combining uplink and downlink channels in cellular network | |
| KR102667404B1 (en) | Systems and methods for mitigating interference within actively used spectrum | |
| CN104918257A (en) | D2D communication resource allocation method in relay cooperative heterogeneous cellular network | |
| CN109327895B (en) | NOMA and CR network-based power distribution method | |
| KR20050032796A (en) | Method for controlling the loading to increase system throughput in wireless packet cellular network | |
| CN110139281A (en) | A method of the full duplex D2D sub-clustering resource allocation based on K-means | |
| CN112243251B (en) | Cognitive MIMO system energy efficiency optimization method based on SCMA | |
| Liang et al. | Joint downlink beamforming, power control, and data rate allocation for DS-CDMA mobile radio with multimedia services | |
| CN111314938A (en) | Optimization method for time-frequency domain resource allocation of cellular network of single cell | |
| Radaydeh | On the performance of imperfect simultaneous d2d association under co-channel interference | |
| Ellenbeck et al. | Autonomous beam coordination for the downlink of an IMT-Advanced cellular system | |
| Gemici et al. | User scheduling and power allocation for nonfull buffer traffic in NOMA downlink systems | |
| Wang et al. | Adaptive joint subchannel and power allocation for multi-user MIMO-OFDM systems | |
| Moretti et al. | Distributed radio resource allocation for the downlink of multi-cell OFDMA radio systems | |
| Ghimire et al. | OFDMA-TDD networks with busy burst enabled grid-of-beam selection | |
| Hsu et al. | Continuous and discrete sum-rate maximization for multiuser MIMO-OFDM systems with CoMP | |
| Ke | Resource-Allocation Strategy for VLC/RF Systems | |
| Yan et al. | Design of Batch Cooperative Allocation System of Wireless Communication Spectrum Resources in Forest Based on Big Data | |
| Abu Al-Atta et al. | A Comprehensive Overview of Power Domain NOMA in 5G | |
| Stolyar et al. | Self-organizing Dynamic Fractional Frequency Reuse Through Distributed Inter-cell Coordination: The Case of Best-Effort Traffic | |
| Zhu et al. | Capacity of hybrid spectrum sharing system and analysis model in cognitive radio |
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 |