CN116249180A - Satellite Internet of things capacity improving method based on spatial domain and power domain resource joint scheduling - Google Patents
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Abstract
The invention belongs to the technical field of satellite communication, and discloses a satellite Internet of things capacity improving method based on spatial domain and power domain resource joint scheduling. Based on the downlink beamforming of the statistical channel, the power consumption of the satellite is minimized by optimizing the beamforming vector in consideration of the limited power available on the low-orbit satellite, and the cluster head also optimizes the power of the data transmitted to the Internet of things to maximize the total rate and simultaneously meet the transmission requirement in the downlink scene; based on the uplink beam forming of the statistical channel, two uplink total rate maximization models are established, and the optimal transmitting power and satellite beam forming vector of the terminal of the Internet of things are obtained. The method remarkably improves the transmission capacity of the satellite Internet of things by utilizing space and power resources.
Description
Technical Field
The invention belongs to the technical field of satellite communication, and particularly relates to a satellite Internet of things capacity improving method based on spatial domain and power domain resource joint scheduling.
Background
The internet of things can realize the prospect of everything interconnection, so the internet of things becomes a key driving force for 5G and upcoming 6G communication systems. In addition, by 2025, the number of terminals of the internet of things may exceed 270 billions, which shows great development potential. However, such a huge internet of things terminal brings great challenges to the internet of things network, especially in remote areas such as oceans, mountainous areas and deserts. These challenges are due to the challenges and uneconomical deployment of internet of things base stations in remote areas. Compared with the main living areas of the population, the isolated areas have higher dependence on the Internet of things network, such as environment monitoring, intelligent agriculture, ocean monitoring and the like. Furthermore, liu Dewu networked base stations may be subject to natural disasters such as floods, earthquakes, and tsunamis. In contrast, deploying an internet of things base station on a satellite may be considered a complement and extension of a terrestrial internet of things network, as the satellite may overcome the lack of isolated area internet of things base stations described above. In addition, low-orbit satellites may be more suitable for the internet of things than stationary-orbit satellites because the distance between the low-orbit satellites and the internet of things terminal is much shorter.
Satellite-based internet of things has attracted considerable attention. In the satellite internet of things, an internet of things terminal can access a network in a direct connection mode or a random access mode. In the direct connection mode, mass internet of things terminals based on orthogonal multiple access (such as frequency division multiple access and time division multiple access) may not be able to successfully communicate with satellites due to insufficient available frequency and transmission time resources. Thus, non-orthogonal multiple access (NOMA) becomes a viable design. However, random access of a large number of terminals may result in severe Common Frequency Interference (CFI), thereby limiting access capacity. Recently, power domain, time domain and code domain techniques have been designed to assist random access. However, since power control is difficult to achieve and propagation loss differences between different internet of things terminals within the same satellite beam may be small, it is difficult to obtain the required power domain differences in the satellite internet of things. In addition, time domain NOMA techniques such as DSA, CRDSA, IRSA are also somewhat challenging to apply because they rely on global time synchronization within the satellite internet of things, and CRDSA and IRSA still require power differences between internet of things terminals. Furthermore, ACRDA may not be suitable because the power difference it requires is difficult to achieve. In addition, sparse code division multiple access (SCMA) is mainly used for time synchronization systems, and serious CFI may degrade the performance of SCMA. Different from the direct connection mode, the terminal of the Internet of things firstly transmits data to the relay node, and the relay node forwards the received data to the satellite. The mode can improve transmission capacity by reducing CFI caused by simultaneous transmission of a large number of terminals of the Internet of things.
In order to improve transmission capacity, spatial domain resources are widely used in terrestrial networks. By utilizing the spatial domain resources, beams can be generated and terminals in different beams can share the same frequency resources without generating severe CFI. This is because relying on dynamic beamforming can effectively mitigate CFI between beams. As a result, MIMO has been used to assist in uplink transmission of a large number of internet of things terminals in a terrestrial internet of things network, thereby significantly improving capacity. This enhancement relies on the assumption of perfect Channel State Information (CSIs). Although research into terrestrial MIMO has been put into great effort, little research has been done into satellite MIMO, particularly dynamic beamforming MIMO. In conventional satellite communication systems, fixed multi-beam techniques such as multi-beam four-color multiplexing (FCRRM), multi-beam seven-color multiplexing, and multi-beam twelve-color multiplexing are widely employed. These multi-beam techniques rely on allocating different frequency bands or polarizations to adjacent beams to eliminate CFI. In contrast, a full frequency multiplexing multi-beam (FFRM) scheme allows adjacent beams to share the same frequency band, thereby significantly improving spectral efficiency. Recently, FFRM schemes have also been designed for satellite communication systems, in which terminals are assumed to be sparsely deployed. Furthermore, FFRM schemes are further used for satellite internet of things, where it is assumed that the terminals communicate directly with the satellites.
In practice, there are some differences between terrestrial MIMO and satellite MIMO, and the main differences are summarized as follows. First, spatial diversity gain of a terrestrial network is easily obtained using MIMO technology. However, it is difficult for satellites employing MIMO to achieve spatial diversity gain because of the non-rich scattering links between the satellites and the ground terminals. This difference can be used to simplify the design of satellite beamforming; second, terrestrial MIMO may be used to distinguish each terminal. However, as the propagation distance between the satellite and the ground terminal is longer, the angular resolution of satellite MIMO can only distinguish the sparsely distributed terminals of the Internet of things, but cannot distinguish the densely deployed terminals of the Internet of things; third, terrestrial MIMO is highly dependent on perfect CSIs. In contrast, it is challenging to obtain perfect CSIs due to the long propagation delay and motion in orbit of satellites.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a satellite Internet of things capacity improving method based on a capacity enhancing scheme of combining statistical channel state information auxiliary beam forming and power distribution so as to assist uplink and downlink transmission of the satellite Internet of things, and the scheme can also alleviate CFI through dynamic beam forming to realize FFRM.
In order to achieve the above purpose, the invention is realized by the following technical scheme:
the invention relates to a satellite Internet of things capacity improving method based on spatial domain and power domain resource joint scheduling, which comprises two parts of downlink beam forming based on a statistical channel and uplink beam forming based on the statistical channel, and specifically comprises the following steps:
(1) Downlink beamforming based on statistical channels: the method comprises two parts of beam forming vector optimization and power distribution, which are respectively as follows; (1.1) beamforming vector optimization: in the transmission process from the satellite to the cluster head, taking the minimized satellite power consumption as an objective function, setting a signal-to-interference-and-noise ratio constraint for meeting the transmission requirement, and then solving an optimization function to obtain a beam forming vector; (1.2) Power Allocation: in the transmission process from the cluster head to the terminal of the Internet of things, taking the maximized total speed as an objective function, setting a signal-to-interference-and-noise ratio constraint for meeting transmission requirements, assuming that the total power of each cluster head is fixed, distributing less power to the terminal with good channel condition, and then solving an optimization function to obtain a power distribution coefficient of each terminal of the Internet of things;
(2) Uplink beamforming based on statistical channels: the method comprises two parts of power distribution and beam forming vector optimization, which are respectively as follows; (2.1) Power Allocation: in the transmission process from the terminal of the Internet of things to the cluster heads, taking the maximized total speed as an objective function, setting a signal-to-interference-and-noise ratio constraint for meeting transmission requirements, assuming that the total power of each cluster head is fixed, distributing less power to the terminals with good channel conditions, and then solving an optimization function to obtain a power distribution coefficient of each terminal of the Internet of things; (2.2) beamforming vector optimization: in the transmission process from the cluster head to the satellite, setting a beam forming vector module value as 1 by taking the maximized total speed as an objective function, and then solving an optimization function to obtain a beam forming vector;
specifically, in the step (1), the method includes two parts of beamforming vector optimization and power distribution:
(1.1) without loss of generality, consider a cluster head of the mth cluster, at which a received signal can be written as:
wherein M represents the number of cluster heads,
transmitting signals for satellites of the mth beam, +.>
N m Is the number of terminals of the Internet of things in the mth wave beam, alpha m,n Is the power of the nth terminal in the mth cluster, s m,n Is the signal of the nth terminal in the mth cluster, w j Is the beamforming vector of the j-th beam, h m Is the channel model between the satellite and the mth cluster head,
and fc Indicating doppler shift and carrier frequency, respectively, ">
Represents propagation delay, g m Is the chain between the satellite and the mth cluster headChannel gain of the path, n ch,m Is channel noise;
then, the cluster head of the mth cluster further forwards the received data to the terminal served by the cluster head of the mth cluster, and because the transmission distance is long and the transmission loss is large, the signal received at the nth terminal of the mth cluster can be expressed as:
wherein ,nm,n Is the mean value is zero, the variance is
Additive gaussian noise of h m,n The method comprises the steps of representing instantaneous channel state information of a link between an nth terminal in an mth cluster and a cluster head, wherein the average value of the instantaneous channel state information is zero, and the unit variance is obtained;
considering incomplete successive interference cancellation, there are:
wherein ,β′m,n and βm,n Is an imperfect SIC coefficient, and is not less than 0 and not more than beta' m,n ,β m,n ≤1；
Considering that the peak power of a satellite is limited, the satellite power consumption minimization problem is expressed as:
wherein ,γm As the signal-to-interference-and-noise ratio threshold,
is the signal-to-interference-and-noise ratio of the mth cluster head. The rewritten snr formula is:
(1.2) w based on optimizing beamforming vectors m The power distribution coefficient is further optimized by using a downlink optimized power distribution method, and the optimization problem is expressed as follows:
wherein ,
representing the signal-to-interference-and-noise ratio threshold,/->
Representing the sum of the terminal powers in the mth beam, a +.>
Can be expressed as:
wherein ,
specifically, in the step (2), the method includes two parts of power allocation and beamforming vector optimization:
(2.1) without loss of generality, for N in the mth cluster m 1 the terminal sends its data into the cluster, so the signal received by this cluster head can be written as:
then, the cluster head of the mth cluster transmits the received data and its own data to the satellite, and the signal received by the mth cluster on the satellite can be expressed as:
wherein ,
is the signal transmitted by the cluster head, < >>
α m,1 Is the transmit power allocated to the cluster head, assuming +.>
s m,1 Is the signal of the cluster head itself.
In order to improve the transmission capability of the terminal to the cluster head link, an uplink optimized power distribution method is adopted, so that the sum rate of the Internet of things terminal to the cluster head link of each cluster is maximized. Furthermore, the optimization problem is expressed as:
wherein ,
representing the signal-to-interference-and-noise ratio threshold,/->
Representing the sum of the terminal powers in the mth beam, a +.>
Can be expressed as:
(2.2) on the basis of power allocation, beamforming is used to maximize uplink sum rate. Thus, the optimization problem of the design can be expressed as:
wherein ,
can be expressed as:
the beneficial effects of the invention are as follows:
the downlink of the invention mainly considers the limited power available on the low orbit satellite, so the power consumption of the minimum satellite is taken as an objective function to optimize the beam forming vector, the cluster head also optimizes the power of the data transmitted to the Internet of things to maximize the total rate, and simultaneously, the transmission requirement in the downlink scene is met; the uplink of the invention mainly considers channel capacity, establishes two uplink total rate maximization models, and obtains the optimal transmitting power and satellite beam forming vector of the terminal of the Internet of things.
The method for improving the capacity of the cluster-type satellite Internet of things based on the space-power resource joint scheduling provided by the invention has the advantages that the downlink and the uplink are all in full frequency multiplexing aiming at limited frequency resources, perfect channel state information is difficult to obtain, the channel state information based on statistics is adopted, the effect is similar to that of the perfect channel state information, and compared with the traditional four-color multiplexing scheme, the transmission capacity is obviously improved.
In summary, the satellite internet of things transmission capacity is remarkably improved by utilizing the space and the power resources.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention.
Fig. 2 is a graph of the total power of the uplink single beam versus the total rate.
Fig. 3 is a graph of the relationship between the number of uplink antennas and the total rate.
Fig. 4 is a graph of the relationship between downlink signal-to-noise ratio and energy efficiency.
Detailed Description
The invention will be further described with reference to the accompanying drawings.
Fig. 1 shows a satellite internet of things capacity improving method based on spatial domain and power domain resource joint scheduling, which comprises two parts of downlink beam forming based on a statistical channel and uplink beam forming based on the statistical channel.
A first part: downlink beamforming based on statistical channels
The method comprises two parts of beam forming vector optimization and power distribution:
(1.1) without loss of generality, consider a cluster head of the mth cluster, at which a received signal can be written as:
wherein M represents the number of cluster heads,
transmitting signals for satellites of the mth beam, +.>
N m Is the number of terminals of the Internet of things in the mth wave beam, alpha m,n Is the power of the nth terminal in the mth cluster, s m,n Is the signal of the nth terminal in the mth cluster, w j Is the beamforming vector of the j-th beam, h m Is the channel model between the satellite and the mth cluster head,
and fc Indicating doppler shift and carrier frequency, respectively, ">
Represents propagation delay, g m Is the channel gain of the link between the satellite and the mth cluster head, n ch,m Is channel noise;
then, the cluster head of the mth cluster further forwards the received data to the terminal served by the cluster head of the mth cluster, and because the transmission distance is long and the transmission loss is large, the signal received at the nth terminal of the mth cluster can be expressed as:
wherein ,nm,n Is the mean value is zero, the variance is
Additive gaussian noise of h m,n The method comprises the steps of representing instantaneous channel state information of a link between an nth terminal in an mth cluster and a cluster head, wherein the average value of the instantaneous channel state information is zero, and the unit variance is obtained;
considering incomplete successive interference cancellation, there are:
wherein ,β′m,n and βm,n Is an imperfect SIC coefficient, and is not less than 0 and not more than beta' m,n ,β m,n ≤1；
Considering that the peak power of a satellite is limited, the present invention expresses the problem of minimizing satellite power consumption as:
wherein ,γm As the signal-to-interference-and-noise ratio threshold,
is the signal-to-interference-and-noise ratio of the mth cluster head. The rewritten snr formula is:
(1.2) w based on optimizing beamforming vectors m The power distribution coefficient is further optimized by using a downlink optimized power distribution method, and the optimization problem is expressed as follows:
wherein ,
representing the signal-to-interference-and-noise ratio threshold,/->
Representing the sum of the terminal powers in the mth beam, a +.>
Can be expressed as:
wherein ,
a second part: uplink beamforming based on statistical channels
The method comprises two parts of power allocation and beam forming vector optimization:
(2.1) without loss of generality, for N in the mth cluster m 1 the terminal sends its data into the cluster, so the signal received by this cluster head can be written as:
then, the cluster head of the mth cluster transmits the received data and its own data to the satellite, and the signal received by the mth cluster on the satellite can be expressed as:
wherein ,
is the signal transmitted by the cluster head, < >>
α m,1 Is the transmit power allocated to the cluster head, assuming +.>
s m,1 Is the signal of the cluster head itself.
In order to improve the transmission capability of the terminal to the cluster head link, an uplink optimized power distribution method is adopted, so that the sum rate of the Internet of things terminal to the cluster head link of each cluster is maximized. Furthermore, the optimization problem is expressed as:
wherein ,
representing the signal-to-interference-and-noise ratio threshold,/->
Representing the sum of the terminal powers in the mth beam, a +.>
Can be expressed as: />
(2.2) on the basis of power allocation, beamforming is used to maximize uplink sum rate. Thus, the optimization problem of the design can be expressed as:
wherein ,
can be expressed as:
fig. 2 is a simulation based on the method of the present invention, in the uplink, the total rate of five different schemes for a single beam total power of-10 to 20 dB.
Fig. 3 is a simulation based on the method of the present invention, in the uplink, with the number of antennas being 16 to 128, for the total rate of five different schemes.
Fig. 4 is a simulation performed based on the method of the present invention, in the downlink, the total rate of three different schemes is at a signal-to-noise ratio of-10 to 20dB, and it can be seen from the graph that the full frequency scheme is significantly better than the four-color multiplexing scheme and the conventional scheme, and in the high signal-to-noise ratio region, the energy efficiency of the full frequency scheme is improved by nearly 10 times compared with the four-color multiplexing scheme, and is far higher than the conventional scheme.
In summary, the method for improving the capacity of the internet of things of the clustered satellites based on the space-power resource joint scheduling mainly considers that the available power on the low-orbit satellites is limited in downlink, so that the beam forming vector is optimized by taking the minimum satellite power consumption as an objective function, the cluster head also performs power optimization on the data transmitted to the internet of things to maximize the total rate, and simultaneously meets the transmission requirement in a downlink scene; and the uplink mainly considers the channel capacity, two uplink total rate maximization models are established, and the optimal transmitting power and satellite beam forming vector of the terminal of the Internet of things are obtained.
The foregoing is only a preferred embodiment of the invention, it being noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the invention.
Claims (5)
1. A satellite Internet of things capacity improving method based on spatial domain and power domain resource joint scheduling is characterized by comprising the following steps: the satellite Internet of things capacity improving method comprises the following steps:
step 1: downlink beamforming based on statistical channels: the method comprises two parts of beam forming vector optimization and power distribution;
step 2: uplink beamforming based on statistical channels: including two parts, power allocation and beamforming vector optimization.
2. The satellite internet of things capacity improving method based on spatial domain and power domain resource joint scheduling according to claim 1, which is characterized by comprising the following steps: in the step (1) of the process,
beamforming vectors for downlink beamforming based on statistical channels are optimized to: in the transmission process from the satellite to the cluster head, taking the minimized satellite power consumption as an objective function, setting a signal-to-interference-and-noise ratio constraint for meeting the transmission requirement, and then solving an optimization function to obtain a beam forming vector;
the power allocation for downlink beamforming based on statistical channels is: in the transmission process from the cluster head to the terminal of the Internet of things, taking the maximized total speed as an objective function, setting a signal-to-interference-and-noise ratio constraint for meeting transmission requirements, assuming that the total power of each cluster head is fixed, distributing less power to the terminal with good channel condition, and then solving an optimization function to obtain the power distribution coefficient of each terminal of the Internet of things.
3. The satellite internet of things capacity improving method based on spatial domain and power domain resource joint scheduling according to claim 2, which is characterized by comprising the following steps: the step 1 specifically comprises the following steps:
step 1-1: without loss of generality, consider the cluster head of the mth cluster, where the signal received at this cluster head is written as:
wherein M represents the number of cluster heads,
transmitting signals for satellites of the mth beam, +.>
N m Is the number of terminals of the Internet of things in the mth wave beam, alpha m,n Is the power of the nth terminal in the mth cluster, s m,n Is the signal of the nth terminal in the mth cluster, w j Is the beamforming vector of the j-th beam, h m Is the channel model between the satellite and the mth cluster head,
and fc Indicating doppler shift and carrier frequency, respectively, ">
Represents propagation delay, g m Is the channel gain of the link between the satellite and the mth cluster head, n ch,m Is channel noise;
the cluster head of the mth cluster further forwards the received data to the terminal served by the cluster head, and the terminal of the internet of things cannot receive signals sent by other clusters due to long transmission distance and large transmission loss, so that the signal received by the nth terminal of the mth cluster is expressed as:
wherein ,nm,n Is the mean value is zero, the variance is
Additive gaussian noise of h m,n The method comprises the steps of representing instantaneous channel state information of a link between an nth terminal in an mth cluster and a cluster head, wherein the average value of the instantaneous channel state information is zero, and the unit variance is obtained;
considering incomplete successive interference cancellation, there are:
wherein ,β′m,n and βm,n Is an imperfect SIC coefficient, and is not less than 0 and not more than beta' m,n ,β m,n ≤1；
Considering that the peak power of a satellite is limited, the satellite power consumption minimization problem is expressed as:
wherein ,γm As the signal-to-interference-and-noise ratio threshold,
is the signal-to-interference-and-noise ratio of the mth cluster head, and the signal-to-noise ratio formula after rewriting is as follows:
step 1-2: w on the basis of optimizing the beamforming vector m Optimizing power distribution coefficients by using a downlink optimizing power distribution method, the optimizing problem being expressed as：
wherein ,
representing the signal-to-interference-and-noise ratio threshold,/->
Representing the sum of the terminal powers in the mth beam, a +.>
Expressed as:
wherein ,
4. the satellite internet of things capacity improving method based on spatial domain and power domain resource joint scheduling according to claim 3, wherein the method is characterized by comprising the following steps of: step 2 of the method, in which the step 2,
power allocation for uplink beamforming based on statistical channels: in the transmission process from the terminal of the Internet of things to the cluster heads, taking the maximized total speed as an objective function, setting a signal-to-interference-and-noise ratio constraint for meeting transmission requirements, assuming that the total power of each cluster head is fixed, distributing less power to the terminals with good channel conditions, and then solving an optimization function to obtain a power distribution coefficient of each terminal of the Internet of things;
beamforming vector optimization for uplink beamforming based on statistical channels: in the transmission process from the cluster head to the satellite, the maximum total speed is taken as an objective function, the module value of the beam forming vector is set to be 1, and then the optimization function is solved to obtain the beam forming vector.
5. The satellite internet of things capacity improving method based on spatial domain and power domain resource joint scheduling according to claim 4, wherein the method is characterized by comprising the following steps: the step 2 specifically comprises the following steps:
step 2-1: without loss of generality, for N in the mth cluster m -1 the terminal sends its data into the cluster, the signal received by this cluster head being written as:
then, the cluster head of the mth cluster transmits the received data and its own data to the satellite, and the signal received by the mth cluster on the satellite is expressed as:
wherein ,
is the signal transmitted by the cluster head, < >>
α m,1 Is the transmit power allocated to the cluster head, assuming +.>
s m,1 Is the signal of the cluster head itself,
by adopting an uplink optimized power distribution method, the sum rate of the Internet of things terminal to the cluster head link of each cluster is maximized, and the optimization problem is expressed as:
wherein ,
representing the signal-to-interference-and-noise ratio threshold,/->
Represent the firstSum of terminal powers within m beams, < >>
Expressed as:
step 2-2: on the basis of power allocation, beamforming is used to maximize the uplink sum rate, and therefore the optimization problem is expressed as:
wherein ,
can be expressed as:
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