CN111934839B - Interference mitigation and resource allocation method for underwater acoustic soft frequency reuse network - Google Patents

Interference mitigation and resource allocation method for underwater acoustic soft frequency reuse network Download PDF

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CN111934839B
CN111934839B CN202010818450.7A CN202010818450A CN111934839B CN 111934839 B CN111934839 B CN 111934839B CN 202010818450 A CN202010818450 A CN 202010818450A CN 111934839 B CN111934839 B CN 111934839B
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CN111934839A (en
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张育芝
范蕊
王安义
孙彦景
王斌
刘洋
朱静茹
苏越
杨晓苗
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Xian University of Science and Technology
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L5/00—Arrangements affording multiple use of the transmission path
    • H04L5/003—Arrangements for allocating sub-channels of the transmission path
    • H04L5/0053—Allocation of signaling, i.e. of overhead other than pilot signals
    • H04L5/0057—Physical resource allocation for CQI
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L1/00—Arrangements for detecting or preventing errors in the information received
    • H04L1/0001—Systems modifying transmission characteristics according to link quality, e.g. power backoff
    • H04L1/0023—Systems modifying transmission characteristics according to link quality, e.g. power backoff characterised by the signalling
    • H04L1/0026—Transmission of channel quality indication
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
    • H04L25/03006—Arrangements for removing intersymbol interference
    • H04L25/03159—Arrangements for removing intersymbol interference operating in the frequency domain
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L5/00—Arrangements affording multiple use of the transmission path
    • H04L5/003—Arrangements for allocating sub-channels of the transmission path
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L5/00—Arrangements affording multiple use of the transmission path
    • H04L5/003—Arrangements for allocating sub-channels of the transmission path
    • H04L5/0053—Allocation of signaling, i.e. of overhead other than pilot signals
    • H04L5/0055—Physical resource allocation for ACK/NACK
    • 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
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    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Abstract

The invention discloses an interference mitigation and resource allocation method for an underwater acoustic soft frequency reuse network, which comprises the following contents: step 1, in a multi-cell underwater acoustic network, a control node calculates the time delay difference between a received signal i and an interference signal j, and designs the length of a data packet of the control node according to the time delay difference and position information of a plurality of data nodes; step 2, deducing SINR formulas of the central node and the edge nodes; step 3, calculating the average coverage probability, and performing frequency band allocation of the edge area and the central area according to the average coverage probability and the total bandwidth of the underwater acoustic network system; step 4, calculating SINR, constructing a linear finite state Markov chain prediction equation, and predicting CSI with propagation delay; and 5, performing adaptive resource allocation of the data nodes according to the predicted CSI. The method and the device solve the problems of inter-cell interference of a time-varying underwater multi-cell network in the prior art and inaccuracy of long-delay feedback CSI for optimizing resource allocation in cells in the prior art.

Description

Interference mitigation and resource allocation method for underwater acoustic soft frequency reuse network
Technical Field
The invention belongs to the technical field of underwater sound network frequency reuse, and particularly relates to an interference mitigation and resource allocation method of an underwater sound soft frequency reuse network.
Background
In the underwater acoustic network, the single-hop network is easy to realize and control, and can complete the data acquisition task with smaller coverage area and less node number. With the research and application development of the underwater acoustic network, the underwater acoustic network with larger scale is applied to the exploration practice of the ocean. For example, a plurality of buoys are distributed on the sea surface as control nodes (including underwater acoustic communication equipment), the control nodes communicate with each other through wireless links on the buoys, each control node performs underwater acoustic communication with a plurality of data nodes (sensor nodes or aircraft nodes) in the communication range of the control node, and an offshore and undersea integrated communication network is formed, so that data tasks with wide coverage area and complex tasks are completed.
In the underwater acoustic network taking a plurality of control nodes as the center of a plurality of cells, the multiplexing of the plurality of cells can be realized by adopting a frequency multiplexing mode. In the existing frequency reuse scheme, although ffr (fractional frequency reuse) can effectively alleviate the interference degree of the edge node between cells, the system spectrum efficiency is not high; the SFR can not only improve the spectrum efficiency of the system, but also better relieve the influence of interference on cell edge nodes. However, how to better alleviate the interference problem among the cells of the time-varying underwater acoustic network needs to be further researched.
In the multi-cell of the underwater acoustic network, a plurality of data nodes share the allocated frequency band, and the adaptive OFDMA carries out carrier and bit allocation on the data nodes according to Channel State Information (CSI), so that the throughput of the whole system can be further improved. The adaptive OFDMA carries out resource allocation according to the fed-back CSI, but the time-varying characteristic of the underwater acoustic channel enables the transmitter to obtain the CSI from the receiver through a feedback link, so that the channel state at the actual data sending time is predicted according to the fed-back CSI, and effective resource allocation can be carried out according to the CSI more accurately. However, how to overcome the problem of inaccurate CSI with long delay propagation feedback needs further research.
Disclosure of Invention
The invention aims to provide an interference mitigation and resource allocation method for an underwater acoustic soft frequency reuse network, so as to mitigate inter-cell interference of a time-varying underwater multi-cell network in the prior art and optimize the problem of inaccuracy of long-delay feedback CSI used for resource allocation in a cell in the prior art.
The invention adopts the following technical scheme: an interference mitigation and resource allocation method for an underwater acoustic soft frequency reuse network comprises the following contents:
step 1, in a multi-cell underwater acoustic network containing a single control node and a plurality of data nodes, dividing the plurality of data nodes into a central node and an edge node, calculating the time delay difference of a received signal i and an interference signal j by the control node according to the position information of the plurality of data nodes, and designing the length of a data packet of the control node according to the time delay difference and the position information of the plurality of data nodes, wherein the length of the data packet enables the interference factor beta of the central node to be larger than the length of the data packet i 0, the interference factor of the edge node satisfies 0 < beta i ≤1；
Step 2, deducing SINR formulas of the central node and the edge nodes according to an SINR formula of a data node of the underwater acoustic network, the interference factor of the central node and the interference factor of the edge nodes;
step 3, calculating an average coverage probability according to the SINR formula obtained in the step 2 and a preset coverage probability threshold, and performing frequency band allocation of an edge area and a central area according to the average coverage probability and the total bandwidth of the underwater acoustic network system;
step 4, calculating SINR as the CSI fed back according to the result of the frequency band allocation obtained in the step 3, constructing a linear finite state Markov chain prediction equation, and predicting the CSI with propagation delay by adopting a soft mean algorithm;
and 5, carrying out self-adaptive resource allocation on the data node according to the CSI obtained by prediction in the step 4.
Further, in step 1, in a cell including a single control node and a plurality of data nodes, dividing a region smaller than the optimal distance threshold into a central region, otherwise, an edge region, where the data node in the central region is the central node and the data node in the edge region is the edge node;
the receiving messageTime delay difference tau between signal i and interference signal j i Comprises the following steps:
Figure BDA0002633593310000031
in the formula (1), t j,send 、t i,send Time of sending signal for interfering cell control node and target cell control node respectively, (x) i ,y i ,z i )、(x j ,y j ,z j ) Position coordinates, d, of a target cell data node i and an interfering cell control node j, respectively oi Is the distance between the center of the target cell and the data node in the cell, d ij And the distance between the data node of the target cell and the control node of the interference cell is represented as the propagation speed of the acoustic signal in the seawater at the speed of 1500 m/s.
Further, in step 2, the SINR formula of the central node and the edge nodes is:
Figure BDA0002633593310000032
in the formula (2), P i Representing the effective power, P, obtained by the data node i from the target cell control node j Representing the interference power, A, obtained by the data node i from the interfering cell control node i (l,f)，A j (l, f) transmission attenuations of the control node and the interference node at node I, respectively, [ delta ] f represents the frequency bandwidth of each carrier in the underwater sound channel, and I j For received interference from neighboring cells at the same frequency, α is the ratio of the transmission power of the cell edge node to the central node,/ i 、l j Respectively expressed as the distances between the target cell control node and the interfering cell control node and the data node i, k is an expansion factor, and k is 1 when the cylinder surface is transmitted, k is 2 when the sphere surface is transmitted, h i (l i ,f)、h j (l j F) the channel gains obtained by the data node i from the target cell control node and the interfering cell control node, respectively, (l) i ) -k (a(f)) -li 、(l j ) -k (a(f)) -lj The transmission attenuation of the target cell control node and the interference cell control node at the data node i, the power spectral density N (f) of the ocean noise are calculated according to a Wenz model, and the seawater absorption loss coefficient a (f) is calculated according to a Thorp formula:
Figure BDA0002633593310000033
further, in the step 3, according to the SINR and a preset coverage probability threshold T FR Calculating the average coverage probability at different frequencies
Figure BDA0002633593310000045
Instantaneous SINR for data nodes greater than T FR Probability of (c):
Figure BDA0002633593310000041
in the formula (3), T FR Can be reasonably designed according to its influence on the bandwidth of the edge area, or obtained by inverting a complementary cumulative distribution function based on the data node load, Ej [ ·]Expectation of random interference cell j in underwater multi-cell network 1 、j 2 Respectively expressed as interference transmitted to cell centre nodes and edge nodes,
Figure BDA0002633593310000042
denoted as data node i and interference j, respectively 1 、j 2 The distance between them;
according to the total bandwidth B of the system total Average probability of coverage
Figure BDA0002633593310000043
Performing frequency band allocation of the edge region and the central region;
Figure BDA0002633593310000044
in the formula (4), B total Is the total bandwidth of the system, B edge And B int The frequency bandwidth of the edge area and the central area of the cell respectively; for a wideband system, the average coverage probability for band allocation is further averaged over multiple carrier frequencies.
Further, the specific method of step 4 is,
first, CSI gamma is converted into a limited number of channel states C (m), C (m) epsilon [0, S-1]Where m is time and S is the number of channel states; according to v s Threshold value divides CSI into discrete values of finite FSMC states, and equal probability method is adopted to select v s Make the stationary probability of each FSMC state pi s Equal and 1/S;
Figure BDA0002633593310000051
in formula (5), σ 2 Is the rayleigh fading channel gain variance; setting v 0 ＝0，v S+1 Finding each threshold v ∞ s (S-1, 2, …, S) value, partitioning CSI into [0, v 1 ),[v 1 ,v 2 ),…,[v s An infinite); when CSI falls in the interval [ v ] s ,v s+1 ]Definitions c (m) ═ s;
secondly, solving the linear correlation coefficient psi l (ii) a Mapping a training sequence T (m) to different state regions by using a state label (C (m) ═ s) through a quantization method, and calculating psi by using a Yule-Walker equation l (ii) a Update and record psi using equation (6) l ：
ψ l (m,s)＝ψ l (m,C(m+1)＝s|C(m),...,C(m-L+1)) (6)，
In formula (6), phi l (m, s) represents the L-th linear correlation coefficient when the channel status is from C (m-L +1), C (m-L +2), …, C (m) to C (m +1) ═ s; obtaining a plurality of temporary linear coefficients by adopting a plurality of groups of T (m), and then calculating the average value of the linear coefficients;
thirdly, establishing a state transition probability matrix P (m); wherein p is q,w (m) element represents the probability, p, for the CSI to transition from state q to state w q,w (m)＝Pr(C(m)＝q|C(m-1)＝w)，Dimension is S multiplied by S; in a small-scale fading channel, assuming that the FSMC state only occurs between the current state or adjacent states from time m-1 to m, the CSI in the state q can be transferred to the adjacent state (q-1)/(q +1) or be kept in the original q state;
p q,w (m) is approximately expressed as:
Figure BDA0002633593310000052
definition of p 0,0 ，p S-1,S-1 ，
Figure BDA0002633593310000061
In formula (8), T s Is a symbol period, f d Is the maximum doppler shift;
finally, according to the feedback delay CSI, utilizing soft mean algorithm to make psi l (m, s) and P (m) are substituted into equation (10) to predict the delay t m CSI of m +1 after the time slot;
Figure BDA0002633593310000062
in formula (9), P(s) ═ P (C (m +1) ═ s | C (m), C (m-1), C (m-L +1)) represents a conditional probability that H (m +1) holds the C (m +1) state.
Further, the specific method of step 5 is as follows:
firstly, according to the predicted CSI obtained in the step (4), a proportional fair algorithm is adopted to consider the instantaneous transmission rate and the average transmission rate of the data nodes to determine the priority of using the subcarriers by the data nodes at the data scheduling moment, and subcarrier resources are distributed to each data node according to the priority;
secondly, optimizing the number of bits loaded on each data node subcarrier by adopting an adaptive modulation Chow algorithm, and maximizing the channel capacity on each subcarrier under the constraints of a system target bit error rate BER and total power;
finally, the process is carried out in a batch,according to bit distribution result bit n Calculating load power
Figure BDA0002633593310000063
Where n is the number of subcarriers, and Γ ═ ln (5 × BER)/1.6.
The invention has the beneficial effects that: disclosed is a delay difference-based interference mitigation and resource allocation (T-SFR) method for an underwater acoustic soft frequency reuse network, which particularly performs the expansion from the two aspects of inter-cell interference suppression and intra-cell adaptive resource allocation. In a cell, the T-SFR scheme utilizes the time delay difference between different channels to relieve the interference, and performs frequency band allocation based on the coverage probability of an underwater acoustic channel, and compared with the traditional SFR scheme, the T-SFR scheme has higher SINR, can relieve the inter-cell interference and has higher spectral efficiency through verification. In a cell, aiming at the influence of feedback Channel State Information (CSI) delay brought by the time-varying characteristic of an underwater acoustic channel, the invention constructs a Linear Finite State Markov Chain (LFSMC) predictor by combining a linear function and a Markov chain model so as to predict more accurate CSI for self-adaptive resource allocation and further improve the system throughput. The system throughput of the optimized soft underwater frequency multiplexing network is better than the T-SFR system without the LFSMC predictor and the throughput of the SFR system without the LFSMC predictor through the interference mitigation among cells and the self-adaptive resource allocation in the cells.
Drawings
Fig. 1 is a diagram of an application scenario of an underwater acoustic multi-cell network;
fig. 2 is a diagram illustrating a relationship between a location of a cell data node and a system capacity according to different frequency reuse factors in the embodiment of the present invention;
FIG. 3 is a diagram of an underwater acoustic SFR network geometry model of the present invention;
fig. 4 is an analysis diagram of interference situation of data nodes in each area in the cell 1 according to the embodiment of the present invention;
fig. 5 is a graph comparing SINR performances of edge nodes of different multiplexing schemes under frequency variation in the embodiment of the present invention;
fig. 6 is a graph comparing SINR performances of edge nodes at different frequencies according to an embodiment of the present invention;
fig. 7 is a diagram illustrating a relationship between cell radius and system spectrum efficiency under different frequency reuse schemes in the embodiment of the present invention;
FIG. 8 is a diagram illustrating the relationship between the average coverage probability and the coverage probability SINR threshold at different frequencies according to an embodiment of the present invention;
figure 9 is a comparison of the subband widths of the edge nodes under different interference factors in an embodiment of the present invention;
fig. 10 is a diagram of the performance of the bit error rate of the system under the fixed modulation mode in the embodiment of the present invention;
FIG. 11 is a graph comparing throughput performance of four adaptive modes of a fixed adaptive modulation system (AM), a linear adaptive modulation system (LS-AM), an adaptive modulation system based on Markov chain prediction (MC-AM), and an adaptive modulation system based on LFSMC predictor prediction (LFSMC-AM) in an embodiment of the present invention;
fig. 12 is a graph of the overall throughput performance of the entire network system under different frequency reuse schemes in the embodiment of the present invention;
fig. 13 is a flowchart of a method for interference mitigation and resource allocation in an underwater acoustic soft frequency reuse network according to the present invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
The invention provides an interference mitigation and resource allocation method for an underwater acoustic soft frequency reuse network, which comprises the following contents as shown in fig. 13:
step 1, as shown in the schematic diagram of the underwater acoustic multi-cell network in fig. 1, in a cell containing a single control node and a plurality of data nodes, through system simulation, under the condition that the capacities of two schemes of frequency reuse factor 1 and 3 are equal, an optimal distance threshold is obtained, a region smaller than the optimal distance threshold is divided into a central region, otherwise, the region is an edge region, and correspondingly, the data nodes are divided into the central node and the edge node.
A single control node calculates the time delay difference of a received signal i and an interference signal j according to the position information of all data nodes in the cell, and calculates the time delay difference according to the time delay difference and the data nodesThe data packet length is designed according to the position, so that the interference factor beta of the central node i 0, interference factor 0 < beta of edge node i ≤1；
Step 2, deducing SINR formulas of the central node and the edge node according to the SINR formula of the existing underwater acoustic network data node and the interference factors of the central node and the edge node;
step 3, calculating average coverage probability under different frequencies according to the SINR and a preset coverage probability threshold, and performing frequency band allocation of an edge area and a central area according to the obtained average coverage probability and the total bandwidth of the system;
step 4, calculating SINR as feedback CSI according to the result of the frequency band allocation, constructing a linear finite state Markov chain prediction equation under the existing underwater sound fading channel model, and predicting the CSI with propagation delay by adopting a soft mean algorithm;
and step 5, carrying out self-adaptive resource allocation on the data node according to the predicted CSI.
In step 1, in a cell including a single control node and a plurality of data nodes, through system simulation, under the condition that the capacities of two schemes, i.e., a frequency reuse factor of 1 and a frequency reuse factor of 3, are equal, an optimal distance threshold is obtained, an area smaller than the optimal distance threshold is divided into a central area, otherwise, the area is an edge area, and accordingly, the data nodes are divided into the central node and the edge node.
The control node calculates the time delay difference tau of the received signal i and the interference signal j according to the position information of all the data nodes i ：
Figure BDA0002633593310000091
In the formula (1), t j,send 、t i,send Time of sending signal for interfering cell control node and target cell control node respectively, (x) i ,y i ,z i )、(x j ,y j ,z j ) Position coordinates, d, of a target cell data node i and an interfering cell control node j, respectively oi Is small as a targetDistance between the center of the cell and the data node in the cell, d ij And the distance between the data node of the target cell and the control node of the interference cell is represented as the propagation speed of the acoustic signal in the seawater at the speed of 1500 m/s.
Designing a packet length of T p Defining an interference factor beta of the data node i Is the degree of interference to which a packet is affected, expressed as a percentage;
for the central node, let τ i ≥T p The reception of the data packet can be completed before the interference comes, in this case β i 0; for the edge node, the data packet is affected by interference when received for a duration T p -τ i Then, then
Figure BDA0002633593310000092
When tau is i,j ＜T p When 0 < beta i ≤1。
In the step 2, the SINR formulas of the central node and the edge nodes are derived according to the SINR formula of the existing data nodes of the underwater acoustic network and the interference factors of the central node and the edge nodes;
Figure BDA0002633593310000093
in the formula (2), P i Representing the effective power, P, obtained by the data node i from the target cell control node j Representing the interference power, A, obtained by the data node i from the interfering cell control node i (l,f)，A j (l, f) transmission attenuations of the control node and the interference node at node I, respectively, [ delta ] f represents the frequency bandwidth of each carrier in the underwater sound channel, and I j For received interference from neighboring cells at the same frequency, α is the ratio of the transmission power of the cell edge node to the central node,/ i 、l j Respectively expressed as the distance between the target cell control node and the interfering cell control node and the data node i, k is an expansion factor, and k is 1 when cylindrical surface is transmitted, k is 2 when spherical surface is transmitted, h i (l i ,f)、h j (l j ,f) Respectively the channel gains obtained by the data node i from the target cell control node and the interfering cell control node,
Figure BDA0002633593310000101
the transmission attenuation of the target cell control node and the interference cell control node at the data node i, the power spectral density N (f) of the ocean noise are calculated according to a Wenz model, and the seawater absorption loss coefficient a (f) is calculated according to a Thorp formula:
Figure BDA0002633593310000102
in step 3, according to the SINR and a preset coverage probability threshold T FR Calculating the average coverage probability at different frequencies, the coverage probability F (T) FR ) Instantaneous SINR for data nodes greater than T FR Probability of (c):
Figure BDA0002633593310000103
in the formula (3), T FR Can be reasonably designed according to its influence on the bandwidth of the edge area, or obtained by inverting a complementary cumulative distribution function based on the data node load, Ej [ ·]Expectation of random interference cell j in underwater multi-cell network 1 、j 2 Respectively expressed as interference transmitted to cell centre nodes and edge nodes,
Figure BDA0002633593310000111
denoted as data node i and interference j, respectively 1 、j 2 The distance between them.
According to the total bandwidth B of the system total Average probability of coverage
Figure BDA0002633593310000112
Performing frequency band allocation of the edge region and the central region;
Figure BDA0002633593310000113
in the formula (4), B total Is the total bandwidth of the system, B edge And B int The frequency bandwidth of the edge area and the central area of the cell respectively; for a wideband system, the average coverage probability for band allocation is further averaged over multiple carrier frequencies.
In step 4, a linear finite state Markov chain prediction equation under the existing underwater sound fading channel model is constructed, and the linear correlation coefficient psi is converted under the discrete finite channel state number l And the channel transition probability P (m) is substituted into a linear equation to predict the CSI at the data sending moment, so that the predicted CSI is closer to the actual CSI and better in effect. The method specifically comprises the following steps:
first, CSI gamma is converted into a limited number of channel states C (m), C (m) epsilon [0, S-1]Where m is time and S is the number of channel states. According to v s Threshold value divides CSI into discrete values of finite FSMC states, and equal probability method is adopted to select v s Make the stationary probability of each FSMC state pi s Equal and 1/S.
Figure BDA0002633593310000114
In formula (5), σ 2 Is the rayleigh fading channel gain variance. Setting v 0 ＝0，v S+1 Finding each threshold v ∞ s (S-1, 2, …, S). Partitioning CSI into [0, v 1 ),[v 1 ,v 2 ),…,[v s And ∞). When CSI falls in the interval [ v ] s ,v s+1 ]C (m) ═ s is defined.
Secondly, solving the linear correlation coefficient psi l . Mapping a training sequence T (m) to different state regions by using a state label (C (m) ═ s) through a quantization method, and calculating psi by using a Yule-Walker equation l . Update and record psi using equation (6) l ：
ψ l (m,s)＝ψ l (m,C(m+1)＝s|C(m),...,C(m-L+1)) (6)，
In formula (6), phi l (m, s) represents a channel state from C (m-L +1),c (m-L +2), …, C (m) to C (m +1) ═ s, the L-th linear correlation coefficient. Psi obtained because of different T (m) l Different, therefore, a plurality of groups of T (m) are adopted to obtain a plurality of temporary linear coefficients, and then the linear coefficients are averaged.
Again, a state transition probability matrix p (m) is established. Wherein p is q,w (m) element represents the probability, p, for the CSI to transition from state q to state w q,w (m) ═ Pr (C (m) ═ q | C (m-1) ═ w), and the dimension is S × S. In a small-scale fading channel, the CSI in state q may transition to the adjacent state (q-1)/(q +1), or remain in the original q-state, assuming that the FSMC state only occurs between the current or adjacent states from time m-1 to m. In general the Markov process is stationary and the state transition probability is independent of time m. In Rayleigh fading channels, p q,w (m) can be approximately expressed as:
Figure BDA0002633593310000121
definition of p 0,0 ，p S-1,S-1
Figure BDA0002633593310000122
In the formula (8), T s Is a symbol period, f d Is the maximum doppler shift.
Finally, according to the feedback delay CSI, utilizing soft mean algorithm to make psi l Substituting (m, s) and P (m) into equation (10) to predict the delay t m CSI of m +1 after the time slot;
Figure BDA0002633593310000123
in formula (9), P(s) ═ P (C (m +1) ═ s | C (m), C (m-1), C (m-L +1)) represents a conditional probability that H (m +1) holds the C (m +1) state.
In step 5, the following steps are taken for adaptive resource allocation:
firstly, according to the predicted CSI obtained in the step (4), a proportional fair algorithm is adopted to consider the instantaneous transmission rate and the average transmission rate of the data nodes to determine the priority of using the subcarriers by the data nodes at the data scheduling moment, and subcarrier resources are distributed to each data node according to the priority;
secondly, optimizing the number of bits loaded on each data node subcarrier by adopting an adaptive modulation Chow algorithm, and maximizing the channel capacity on each subcarrier under the constraint of a system target bit error rate BER and total power. Finally, according to bit distribution result bit n Calculating load power
Figure BDA0002633593310000131
Where n is the number of subcarriers, Γ ═ ln (5 × BER)/1.6;
and optimizing the number of bits loaded on each data node subcarrier by adopting a Chow algorithm. And transmitting data information according to the optimal bit allocation and power loading so as to obtain the optimal throughput. The CSI during data transmission is predicted to improve the throughput of the system.
Examples
TABLE 1 parameter settings
Figure BDA0002633593310000132
The rationality of the embodiment of the invention was verified from 5 steps as follows, according to the parameters given in table 1 above:
1. in a cell having a single control node and a plurality of data nodes, the abscissa of the graph represents the distance between the data node and the control node, as shown in the graph of the geometric position of the data node of the cell in fig. 2 in relation to the system capacity. Assuming a cell radius of R, a location of 0.72 indicates a distance of 0.72R between the data node and the control node. Simulation results show that the frequency reuse factor of 1 and the frequency reuse factor of 3 in the graph are both that the system capacity is reduced as the distance between the data node and the control node is increased. And it can be obtained that reasonable division of the cell center and the edge area should use the radius ratio of 0.72 as a boundary point, the area with the distance less than 0.72R as a center area, otherwise, the area is an edge area, and correspondingly, the data nodes are divided into center nodes and edge nodes.
According to this method, it can be derived that 6 data nodes in each cell are divided into 4 center nodes and 2 edge nodes. The control node calculates the time delay difference tau of the received signal i and the interference signal j according to the position information of all the data nodes in the cell i And according to the delay difference tau i Designing data packet length T according to data node position p 。
For the central node, let τ i ≥T p The reception of the data packet can be completed before the interference comes, and at this time, the central data node is not interfered by the adjacent cell, beta i 0. Fig. 3 shows a geometric model diagram of an underwater acoustic multi-cell network from a two-dimensional perspective, where data nodes of a cell are divided into a center node and an edge node, and accordingly, the cell is divided into a center region and an edge region; and allocating the corresponding frequency band resources to the data nodes in different areas in each cell according to different power levels in fig. 3- (b), wherein the power level of the cell edge node is higher than that of the central node, so as to reduce the influence of interference on the edge node. As illustrated in connection with fig. 3 and 4: when the central node i is at the five-pointed star position as in FIG. 3- (a), the data node i will not only be at d oi The useful signal received from the serving cell control node is also at d v s ij /v s suffers from interference from neighboring cells; as shown in FIG. 4- (a), when τ 1 is greater than T p Meanwhile, the data packet information of the node is not interfered. When the center node i is located at the circular position as shown in FIG. 3- (a), τ of the data node i is shown in FIG. 4- (b) 2 Is equal to T p At this time, the data packet of the node is just not affected by the interference.
For the edge node, the data packet is affected by interference when received for a duration T p -τ i Then, then
Figure BDA0002633593310000141
Because the edge node is far from the center of the cell, the node may already receive interference from the neighboring cell when not receiving the data packet completely, so that τ is i,j ＜T p So 0 < beta i Less than or equal to 1. As illustrated in connection with fig. 3 and 4: when the edge node i is in the diamond position as in FIG. 3- (a), τ of this node 3 Less than T p Corresponding to FIG. 4- (c), some of the packet information of such edge nodes may be affected by interference, where 0 < β i Less than 1; when the edge node i is located at the triangle position as shown in FIG. 3- (a), τ of the data node is shown in FIG. 4- (d) 4 Less than T p And even tau 4 When beta is equal to 0 i The edge node is severely affected by interference 1.
2. And (3) deducing SINRs of the central node and the edge nodes according to the prior SINR formula of the data nodes of the underwater acoustic network and the interference factors of the central node and the edge nodes by formula (2). Fig. 5 compares the SINR performance of edge nodes for different multiplexing schemes over frequency. The result shows that under theoretical and simulated channels, the SINR of the T-SFR scheme is optimal, the SFR is the second, the FFR is the first, and the SINR changes non-monotonically with the increase of f and has a maximum value at a certain frequency, so that the system can provide the optimal working frequency for the edge node to maximize the SINR. FIG. 6 analyzes edge nodes at different interference factors beta i In the following, the SINR performance of the edge node at varying frequencies, it can be seen that with β i Increasing, and the SINR is reduced in a nonlinear way; and when the working frequency f is equal to the optimal working frequency of 11kHz, the SINR performance of the edge node is optimal. Fig. 7 compares the spectrum efficiencies under different frequency reuse schemes, and it can be seen from simulation results that the performance of the spectrum efficiency at a certain fixed cell radius is optimal, and the spectrum efficiencies of the three schemes have the following relationship: T-SFR>SFR>FFR。
3. Calculating the average coverage probability under different frequencies by using a formula (3) according to the SINR of the edge node and a preset coverage probability threshold, and calculating the total bandwidth B of the system according to the obtained average coverage probability total Lower guide edge area band allocation B in the range of 9kHz to 15kHz edge Further according to the known B edge The frequency band allocation of the central region is derived. FIG. 8 simulates the average coverage probability at different frequencies, and it can be seen that with the interference factor β i And an increase in SINR threshold, the coverage probability decreases. Figure 9 simulates the allocated bandwidth of the edge region at different SINR thresholds. The results show that with the SINR threshold and beta i The larger the frequency bandwidth to which the edge region is assigned. In an underwater acoustic network, by determining beta i And a suitable SINR threshold to meet the rate requirements of the edge nodes.
4. And calculating SINR as feedback CSI required by adaptive resource allocation according to the frequency band allocation result, constructing a linear finite state Markov chain prediction equation according to the existing underwater sound fading channel model, and predicting the CSI at the data sending time by adopting a soft mean algorithm in a formula (9) under the discrete finite channel state number.
5. According to the predicted CSI, 512 subcarrier resources are firstly distributed to data nodes at the data scheduling time by adopting a proportional fair algorithm, then the number of bits loaded on the subcarriers is optimized by utilizing an adaptive modulation Chow algorithm, and finally the loading power is calculated according to the bit distribution result, so that the adaptive resource distribution of the data nodes is completed. Fig. 10 shows the bit error rate performance of the system under four fixed modulation schemes, which can be derived from the graph: under a certain bit error rate, a modulation mode exists in a certain SINR threshold interval to enable the number of loaded bits to be the largest. For example, BER is constrained to 10 at bit error rate -3 Table 2 shows the optimal modulation schemes (corresponding to the number of bits) for different SINR threshold intervals for adaptive resource allocation.
TABLE 2 modulation switching threshold
Figure BDA0002633593310000161
By predicting CSI at the time of actual data transmission, the throughput of the system can be improved. Fig. 11 compares the system throughput performance of several adaptive schemes, specifically the following four schemes: unpredicted Adaptive Modulation (AM), linear predictive adaptive modulation (LS-AM), markov chain predictive adaptive modulation (MC-AM), and proposed LFSMC adaptive modulation (LFSMC-AM). Due to insufficient consideration of channel delay, the unpredicted AM performance based on direct feedback CSI is the worst; the throughput performance of the MC-AM is superior to that of the LS-AM, and the excellent performance of a Markov chain model can be seen; because the CSI prediction method in the LFSMC-AM combines the advantages of a linear function and a Markov chain model, the LFSMC-AM throughput performance is optimal in several ways.
According to the parameters and the method in the above steps, a self-adaptive resource allocation simulation experiment is performed on the time-varying underwater acoustic network, taking a 7-cell underwater acoustic network as an example, the throughput performance of three frequency reuse schemes of FFR, SFR and T-SFR is compared from two angles of CSI prediction and CSI-free prediction, and the performance of each scheme is shown in fig. 12. The T-SFR-LFSMC scheme is superior to other schemes in throughput performance of a central area and an edge area, and is characterized in that the scheme adopts lower transmitting power for a central area node and higher transmitting power for an edge area node to relieve interference on the one hand, and utilizes a delay difference thought of the T-SFR to reduce the influence of partial interference of adjacent cells on the other hand, and utilizes an LFSMC predictor to obtain more accurate CSI on the other hand, thereby improving the self-adaptive resource allocation performance in the cells. The total throughput is the sum of the throughputs of the central area and the edge area, so the total throughput performance under the T-SFR-LFSMC scheme is optimal, and the T-SFR system with the LFSMC predictor is improved by 6.2 percent in throughput and 35 percent in throughput compared with the T-SFR system without the predictor.
The invention specifically performs effect analysis from the following two aspects: in a cell, the T-SFR scheme utilizes the time delay difference between different channels to relieve interference, and carries out frequency band allocation based on the coverage probability of an underwater acoustic channel, the T-SFR scheme can keep better frequency spectrum efficiency while relieving the inter-cell interference, the SINR is improved by 0.9dB compared with the traditional SFR through the T-SFR scheme, and the frequency spectrum efficiency is improved by about 10% on average compared with the traditional SFR. In a cell, aiming at the influence of feedback Channel State Information (CSI) delay brought by the time-varying characteristic of an underwater acoustic channel, the invention constructs a Linear Finite State Markov Chain (LFSMC) predictor by combining a linear function and a Markov chain model so as to predict more accurate CSI for self-adaptive resource allocation and further improve the system throughput. By adopting the implementation scheme provided by the invention, the system throughput of the optimized underwater acoustic soft frequency multiplexing network is obviously improved through the interference mitigation between the cells and the self-adaptive resource allocation in the cells, and the example simulation result shows that the T-SFR system with the LFSMC predictor is improved by 6.2 percent in throughput compared with the T-SFR system without the predictor and is improved by 35 percent in throughput compared with the SFR system without the predictor.

Claims (4)

1. An interference mitigation and resource allocation method for an underwater acoustic soft frequency reuse network is characterized by comprising the following contents:
step 1, in a multi-cell underwater acoustic network containing a single control node and a plurality of data nodes, dividing the plurality of data nodes into a central node and an edge node, calculating the time delay difference of a received signal i and an interference signal j by the control node according to the position information of the plurality of data nodes, and designing the length of a data packet of the control node according to the time delay difference and the position information of the plurality of data nodes, wherein the length of the data packet enables the interference factor beta of the central node to be larger than the length of the data packet i 0, the interference factor of the edge node satisfies 0 < beta i ≤1；
Wherein, for the central node, let τ i ≥T p The reception of the data packet can be completed before the interference comes, in this case beta i 0; for the edge node, the data packet is affected by interference when received for a duration T p -τ i Then, then
Figure FDA0003717809740000011
When tau is i ＜T p When 0 < beta i Less than or equal to 1; the data packet length is T p The control node calculates the time delay difference tau of the received signal i and the interference signal j according to the position information of all the data nodes i ；
Step 2, deducing SINR formulas of the central node and the edge nodes according to an SINR formula of a data node of the underwater acoustic network, the interference factor of the central node and the interference factor of the edge nodes;
step 3, calculating an average coverage probability according to the SINR formula obtained in the step 2 and a preset coverage probability threshold, and performing frequency band allocation of an edge area and a central area according to the average coverage probability and the total bandwidth of the underwater acoustic network system;
in the step 3, according to the SINR and a preset coverage probability threshold T FR Calculating the average coverage probability at different frequencies
Figure FDA0003717809740000012
Instantaneous SINR for data nodes greater than T FR Probability of (c):
Figure FDA0003717809740000021
in the formula (3), T FR Can be reasonably designed according to its influence on the bandwidth of the edge area, or obtained by inverting a complementary cumulative distribution function based on the data node load, Ej [ ·]Expectation of random interference cell j in underwater multi-cell network 1 、j 2 Expressed as interference transmitted to cell centre nodes and edge nodes, respectively,/ j1 、l j2 Denoted as data node i and interference j, respectively 1 、j 2 The distance between them; alpha is the transmission power ratio of the cell edge node to the central node; beta is a i An interference factor for the received signal; p i Representing the effective power obtained by the data node i from the target cell control node; p j Representing the interference power obtained by the data node i from the interference cell control node; a. the i (l, f) controlling the transmission attenuation of the node at the node i for the target cell, and particularly expanding to
Figure FDA0003717809740000022
l i Expressed as the distance between the target cell control node and the data node i; k is a spreading factor; n (f) is the power spectral density of the ocean noise; a (f) is the seawater absorption loss coefficient;
according to the total bandwidth B of the system total Average probability of coverage
Figure FDA0003717809740000023
Performing frequency band allocation of the edge region and the central region;
Figure FDA0003717809740000024
in the formula (4), B total Is the total bandwidth of the system, B edge And B int Respectively the frequency bandwidth of the edge area and the central area of the cell; for a broadband system, the average coverage probability used by frequency band allocation is further taken as the average value of multiple carrier frequencies;
step 4, calculating SINR as the CSI fed back according to the result of the frequency band allocation obtained in the step 3, constructing a linear finite state Markov chain prediction equation, and predicting the CSI with propagation delay by adopting a soft mean algorithm;
the specific method of the step 4 is that,
first, CSI gamma is converted into a limited number of channel states C (m), C (m) epsilon [0, S-1]Where m is time and S is the number of channel states; according to v s Threshold value divides CSI into discrete values of finite FSMC states, and equal probability method is adopted to select v s Make the stationary probability of each FSMC state pi s Equal and 1/S;
Figure FDA0003717809740000031
in the formula (5), σ 2 Is the rayleigh fading channel gain variance; setting v 0 ＝0，v S+1 Finding each threshold v ∞ s (S-1, 2, …, S) value, partitioning CSI into [0, v 1 ),[v 1 ,v 2 ),…,[v s Infinity); when CSI falls in the interval [ v ] s ,v s+1 ]Definitions c (m) ═ s;
secondly, solving the linear correlation coefficient psi l (ii) a Mapping a training sequence T (m) to different state regions by using a state label (C (m) ═ s) through a quantization method, and reusing a Yule-Walker partyProgram calculation psi l (ii) a Update and record psi using equation (6) l ：
ψ l (m,s)＝ψ l (m,C(m+1)＝s|C(m),...,C(m-L+1)) (6)，
In formula (6), phi l (m, s) represents the L-th linear correlation coefficient when the channel status is from C (m-L +1), C (m-L +2), …, C (m) to C (m +1) ═ s; obtaining a plurality of temporary linear coefficients by adopting a plurality of groups of T (m), and then calculating the average value of the linear coefficients;
thirdly, establishing a state transition probability matrix P (m); wherein p is q,w (m) element represents the probability, p, for the CSI to transition from state q to state w q,w (m) ═ Pr (C (m) ═ q | C (m-1) ═ w) with dimensions sxs; in a small-scale fading channel, assuming that the FSMC state only occurs between the current state or adjacent states from time m-1 to m, the CSI in the state q can be transferred to the adjacent state (q-1)/(q +1) or be kept in the original q state;
p q,w (m) is approximately expressed as:
Figure FDA0003717809740000041
definition of p 0,0 ，p S-1,S-1 ，
Figure FDA0003717809740000042
In the formula (8), T s Is a symbol period, f d Is the maximum doppler shift;
finally, according to the feedback delay CSI, utilizing soft mean algorithm to make psi l (m, s) and P (m) are substituted into equation (10) to predict the delay t m CSI of m +1 after the time slot;
Figure FDA0003717809740000043
in formula (9), P(s) ═ P (C (m +1) ═ s | C (m), C (m-1), C (m-L +1)) represents a conditional probability that H (m +1) holds the C (m +1) state;
and 5, performing adaptive resource allocation of the data nodes according to the CSI obtained by prediction in the step 4.
2. The method according to claim 1, wherein in step 1, in a cell containing a single control node and multiple data nodes, a region smaller than the optimal distance threshold is divided into a central region, and vice versa, the data nodes in the central region are central nodes, and the data nodes in the edge region are edge nodes;
the time delay difference tau of the receiving signal i and the interference signal j i Comprises the following steps:
Figure FDA0003717809740000044
in the formula (1), t j,send 、t i,send Time of sending signal for interfering cell control node and target cell control node respectively, (x) i ,y i ,z i )、(x j ,y j ,z j ) Position coordinates, d, of a target cell data node i and an interfering cell control node j, respectively oi Is the distance between the center of the target cell and the data node in the cell, d ij And the distance between the data node of the target cell and the control node of the interference cell is represented as the propagation speed of the acoustic signal in the seawater at the speed of 1500 m/s.
3. The method according to claim 1 or 2, wherein in step 2, the SINR formula of the center node and the edge nodes is:
Figure FDA0003717809740000051
in formula (2), P i Indicating that data node i is from the target cellControlling the effective power, P, obtained by the node j Representing the interference power, A, obtained by the data node i from the interfering cell control node i (l,f)，A j (l, f) transmission attenuations of the control node and the interference node at node I, respectively, [ delta ] f represents the frequency bandwidth of each carrier in the underwater sound channel, and I j For received interference from neighboring cells at the same frequency, α is the ratio of the transmission power of the cell edge node to the central node,/ i 、l j Respectively expressed as the distances between the target cell control node and the interfering cell control node and the data node i, k is an expansion factor, and k is 1 when the cylinder surface is transmitted, k is 2 when the sphere surface is transmitted, h i (l i ,f)、h j (l j F) are the channel gains obtained by the data node i from the target cell control node and the interfering cell control node, respectively,
Figure FDA0003717809740000052
the transmission attenuation of the target cell control node and the interference cell control node at the data node i, the power spectral density N (f) of the ocean noise are calculated according to a Wenz model, and the seawater absorption loss coefficient a (f) is calculated according to a Thorp formula:
Figure FDA0003717809740000053
4. the method for interference mitigation and resource allocation of an underwater acoustic soft frequency reuse network according to claim 1 or 2, wherein the specific method of step 5 is:
firstly, according to the predicted CSI obtained in the step (4), a proportional fair algorithm is adopted to consider the instantaneous transmission rate and the average transmission rate of the data nodes to determine the priority of using the subcarriers by the data nodes at the data scheduling moment, and subcarrier resources are distributed to each data node according to the priority;
secondly, optimizing the number of bits loaded on each data node subcarrier by adopting an adaptive modulation Chow algorithm, and maximizing the channel capacity on each subcarrier under the constraints of a system target bit error rate BER and total power;
finally, according to bit distribution result bit n Calculating load power
Figure FDA0003717809740000061
Where n is the number of subcarriers, and Γ ═ ln (5 × BER)/1.6.
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