CN116437370A - Network auxiliary full duplex mode optimization method under low-altitude three-dimensional coverage scene - Google Patents
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Abstract
The invention discloses a network auxiliary full duplex mode optimization method under a low-altitude three-dimensional coverage scene, which aims at the practical problem of different uplink and downlink data demands of users in the low-altitude three-dimensional coverage, designs an uplink P-MMSE receiver and downlink P-RZF precoding for scalable joint transmission, determines uplink spectrum efficiency based on a Shannon channel theory and downlink spectrum efficiency based on a limited block length mechanism, and provides an RRU uplink and downlink link selection method based on a DQN reinforcement learning algorithm. The duplex mode optimization method provided by the invention realizes the maximization of the system spectrum efficiency under the constraint of meeting the error probability and the transmission power of the downlink short packet transmission.
Description
Technical Field
The invention relates to a duplex mode optimization method based on a network auxiliary full duplex CF-RAN system, which is applicable to a low-altitude three-dimensional coverage scene, and belongs to the technical field of mobile communication.
Background
As mobile communication is increasingly used in various industries, higher requirements are placed on the spectral efficiency and coverage of the communication system. In the low-altitude three-dimensional coverage scene, unmanned aerial vehicle users and ground users coexist, and compared with ground users with large downlink transmission data volume, unmanned aerial vehicle users have different and quicker change on the uplink and downlink demands. Specifically, the communication of the unmanned aerial vehicle can be classified into two forms of payload communication and CNPC (Control andNon Payload Communications, control and non-payload communication). In uplink communication, the unmanned aerial vehicle transmits payload signals mainly comprising large data volumes such as videos and pictures, the data throughput is large, and the spectrum efficiency requirement is high. In downlink communication, a base station transmits a CNPC signal mainly including control signaling to an unmanned aerial vehicle, so as to realize behavior control on flight altitude, speed and the like of the unmanned aerial vehicle, so that lower time delay and higher reliability are required to be ensured for downlink transmission. In the low-altitude three-dimensional coverage scene, the full duplex communication is adopted to simultaneously transmit and receive data in the same frequency band, and the channel capacity is doubled compared with that of the traditional half duplex communication. However, the duplex mode brings about throughput improvement, and also introduces serious uplink and downlink cross interference, which limits system performance.
A full spectrum non-cellular radio access network (Cell-free RadioAccess Network, CF-RAN) is an implementation architecture of a new non-cellular massive MIMO (Multiple-Input Multiple-Output) system, and is reasonably divided into a remote antenna unit (Remote Radio Unit, RRU), an Edge distribution unit (Edge DistributedUnit, EDU), a User-Centric distributed unit (User-central DistributedUnit, UCDU), and a central control unit (Central Control Unit, CCU) in the physical layer. The RRU receives and transmits radio frequency signals of each frequency band, the EDU realizes distributed precoding and receiving, and the UCDU realizes data distribution and combination with a user as a center. Under the same time-frequency resource block, each RRU can flexibly select uplink receiving or downlink sending, and a specific duplexing mode is determined by UCUD. When the downlink RRU transmission interferes with the uplink RRU reception, the UCDU sends interference information to the associated EDU, and the EDU performs interference elimination by using channel state information between the RRUs. Interference between uplink user transmissions and downlink user receptions may be reduced by user scheduling within the UCDU, a method of co-ordinated duplexing in such CF-RAN is also known as Network-assisted full-duplex (NAFD). Therefore, the network-assisted full duplex CF-RAN architecture is considered to meet the service requirement centering on the user in the low-altitude three-dimensional coverage scene, and the cross link interference is reduced by dynamically adjusting the duplex mode selection of the RRU, so that the problem of asymmetric uplink and downlink transmission is solved, and the expandable flexible duplex wireless communication is realized.
Disclosure of Invention
Technical problems: in view of this, the technical problem to be solved by the present invention is to provide a duplex mode selection optimization method for a network assisted full duplex CF-RAN system in a low-altitude stereoscopic coverage scene, which can maximize spectrum efficiency.
The technical scheme is as follows: in order to achieve the above purpose, the network-assisted full duplex mode optimization method under the low-altitude three-dimensional coverage scene adopts the following scheme:
step S1, in a network auxiliary full duplex CF-RAN system, based on an association strategy taking a user as a center, a P-RZF downlink precoding vector is designed by considering non-ideal channel state information, and the spectrum efficiency of transmission based on a limited block length mechanism under a downlink CNPC link is determined;
step S2, according to the P-RZF downlink pre-coding adopted in the step S1, an uplink P-MMSE receiver is designed in consideration of the situation that residual downlink interference exists, and the spectrum efficiency based on the Shannon channel theory under uplink payload communication is determined;
step S3, designing a joint optimization problem of uplink and downlink spectrum efficiency according to the result in the step S2, and determining a system state function, an action function and a reward function based on the DQN reinforcement learning algorithm principle;
and S4, solving by utilizing an intelligent DQN algorithm according to the joint optimization problem designed in the step S3, and storing a final state set and rewards of the algorithm as an optimal RRU duplex mode and maximized system spectrum efficiency.
The step 1 specifically comprises the following steps:
step S101, consider a CF-RAN system with low-altitude coverage equipped with a CCU, several UCDUs and switches, M EDUs with buffering and computing capabilities, N duplex RRUs with L antennas, assuming N ul Each is an uplink receiving RRU, N dl The RRU is sent for the downlink, K unmanned aerial vehicles and ground users are distributed at random, wherein the positions of the users are K U Each is an uplink sending user, K D The downlink receiving users; each RRU in the system can respectively select uplink receiving or downlink sending according to the user demands, and a proper mode is selected through the EDU and the UCDU, and in the downlink transmission stage, the signal received by the kth user is as follows:
in the formula (1), D k Representing the association vector between the downlink RRU and the kth downlink active user,
representing the channel vector between the downlink RRU and the downlink active user k,/and/or>
For the channel vector, w, between the nth downlink RRU and the kth downlink active user k Is the channel precoding between the downlink RRU and the kth downlink user, s k Signal, w, sent to kth downlink active user for downlink RRU j Is the channel precoding between the downlink RRU and the downlink user j, s j For the signal sent by the downlink RRU to the downlink active user j, p ul,i Representing the transmission power of the ith uplink active user,u k,i cross link interference, x, between the ith uplink transmitting user and the kth downlink receiving user i The signal sent for the ith uplink active user satisfies +.>
z dl,k Is a complex additive white gaussian noise;
the power constraint satisfied by each downlink transmission RRU is:
in the formula (2), W i Precoding matrix of the ith downlink transmission RRU, P is power constraint of each antenna, E i An identity matrix with non-zero column i;
step S102, an edge distribution unit EDU adopts expandable P-RZF linear precoding to eliminate inter-user interference, and the P-RZF precoding vector from a downlink RRU to a kth downlink receiving user is as follows:
in the formula (3) and the formula (4), δ is a normalized coefficient obtained by satisfying the power constraint formula (2),
S k representing the set of downlink users associated to the same RRU as downlink user k,
then represent set S k Channel estimation matrix from all users in (a) to downlink RRU (radio remote Unit), alpha > 0 represents regularization coefficient,/and/or (b)>
Indicating LN dl Order identity matrix>
The P-RZF precoding matrix of the RRU is transmitted for the ith downlink;
step S103, in the downlink CNPC link of the unmanned aerial vehicle, short message control signaling is adopted to meet the transmission requirement of low time delay and high reliability, and based on the P-RZF downlink precoding in the formula (3), the spectral efficiency of the kth downlink user under a finite block length mechanism FBLC (Finite Blocklength Communication) is determined as follows:
in equations (5) and (6), τ is the length of the pilot estimation sequence, T is the coherent slot,
is the P-RZF precoding vector of downlink user k,/and->
Is the P-RZF precoding vector of the downlink user j,/and%>
Is the signal-to-interference-and-noise ratio of the downlink user k, V (γ) =1- (1+γ) -2 Is channel scattering, ε is block error probability, e is natural logarithm, Q -1 (. Cndot.) is the inverse of the complementary cumulative distribution function Q-function of the standard gaussian random variable, μ=bt is the number of bits used per channel, B represents the system bandwidth,/">
Representing downlinkNoise power.
The step 2 specifically comprises the following steps:
step S201, in a low-altitude three-dimensional coverage CF-RAN system, carrying out cooperative processing on an uplink baseband signal and a downlink baseband signal through cooperation among EDUs, and relieving interference of a downlink to an uplink among RRUs; when the P-RZF precoding is adopted in downlink transmission, incomplete channel state information is considered, and channel interference from downlink RRU to uplink RRU cannot be completely eliminated; in the uplink transmission stage, the signal received by the mth EDU is:
in the formula (7) of the present invention,
representing the association vector between the mth EDU and RRU, D k Representing the association vector between uplink active user k and uplink RRU, D i Representing the association vector, g, between the uplink active user i and the uplink RRU ul,k G, the channel vector between the kth uplink active user and the uplink receiving RRU ul,i For the channel vector, p, between the ith uplink active user and the uplink receiving RRU ul,k Representing transmission power, p, of kth uplink active user ul,i Representing transmission power, x of the ith uplink active user k Signal x sent for kth uplink active user i Signals transmitted for the ith uplink active user,/->
For the estimation error of the channel between the downlink RRU and the uplink RRU,/for the channel estimation error between the downlink RRU and the uplink RRU>
Is the P-RZF precoding vector of the jth downlink user, s j Transmission signal for jth downstream active user,/->
As residual interference term, z ul Is a complex additive white gaussian noise;
step S202, an expandable P-MMSE receiver is adopted at a receiving end, and the receiving vector of a kth uplink user is expressed as:
in the formula (8) and the formula (9), definition is given
Σ k Covariance matrix representing residual interference and noise, < >>
For the channel estimation matrix between the uplink active user k and the uplink RRU, S k Representing the set of all uplink users associated to the same RRU as uplink user k, +.>
Is set S k Channel estimation matrix between uplink active user i and uplink RRU, S' k Representing the set of all downlink users associated to the same RRU as uplink user k, +.>
Indicating LN ul Order identity matrix>
Representing uplink noise power;
step S203, uplink transmission of the unmanned aerial vehicle user is mainly payload communication, and requirements on channel capacity and data transmission rate are large, so that the spectrum efficiency is analyzed by adopting the traditional shannon channel theory; and (3) adopting a P-MMSE uplink receiver in a formula (8) to determine the spectrum efficiency of a kth uplink user as follows:
in equations (10) and (11), τ is the length of the pilot estimation sequence, T is the coherent slot,
is the signal-to-interference-plus-noise ratio of the kth upstream user,/->Representing the correlation matrix between the EDU and RRU.
The step 3 specifically comprises the following steps:
step S301, in the network auxiliary full duplex system, a single base station only needs to realize a half duplex function, and uplink and downlink flexible scheduling of RRU is carried out according to real-time requirements of unmanned aerial vehicle and ground users, so that resource expenditure is saved, system performance is improved, and an optimization target of uplink spectrum efficiency is set:
C2:α ul,n +α dl,n ＝1
in formula (12), α ul ＝[α ul,1 ,…,α ul,N ]，α dl ＝[α dl,1 ,…,α dl,N ]N is the total number of RRUs capable of providing uplink and downlink selection, alpha ul,n And alpha dl,n Indicating the uplink and downlink selection of the nth RRU, when alpha ul,n When=1, α dl,n When=0, the nth RRU is responsible for uplink transmission, otherwise, when α ul,n When=0, α dl,n If=1, the nth RRU is responsible for downlink transmission, each RRU can select only one mode, γ ul,k Representing the signal-to-interference-and-noise ratio, gamma, of the uplink user k ul,k,min Representing the minimum signal-to-interference-and-noise ratio, p, that an upstream user needs to achieve ul,k Representing transmission power, P, of kth uplink active user ul The maximum value of the uplink transmission power of the user;
step S302, setting an optimization target of downlink spectrum efficiency based on a finite block length transmission mechanism:
C5:ε≤ε max
C7:p dl,k ≥0
C2-C3
in formula (13), γ dl,k Representing the signal-to-interference-and-noise ratio, gamma, of the downstream user k dl,k,min Representing the minimum signal-to-interference-and-noise ratio that the downstream user needs to reach, epsilon represents the block error probability, epsilon max Represents the maximum error probability that can be tolerated, w k Is the channel precoding between the downlink RRU and the kth downlink user, P is the threshold value of the transmission power of the downlink user, and P dl,k Representing the transmission power of the kth downlink active user;
step S303, in the network auxiliary full duplex CF-RAN system, when the uplink demand is large, the number of uplink service RRUs can be increased to improve the uplink spectrum efficiency, and when the downlink demand is large, the number of downlink service RRUs is increased to improve the downlink spectrum efficiency; since the nth RRU can only select one uplink or downlink mode at a specific moment, the optimization targets of the formula (12) and the formula (13) are contradictory, and the multi-target optimization problem of RRU uplink and downlink mode selection is set:
C1-C7
designing an up-down scheduling scheme of the RRU to obtain a compromise in two problems by multi-objective optimization, and maximizing the gain of the system;
in step S304, the intelligent mode selection algorithm based on DQN is:
the DQN algorithm combines deep learning and reinforcement learning, has high reliability for solving motion selection problems based on discrete variables, and is characterized in that an intelligent agent is based on a state-motion value function Q and an epsilon greedy scheme according to fixed probability epsilon t Selecting an action a (t) with the highest Q value to obtain a reward function r (t), entering a next state s', and continuously training a neural network model of the intelligent agent to obtain an optimal solution by storing the taken action and the obtained reward into a memory;
the minimized square error loss function of the neural network parameters of the intelligent agent is as follows:
in equation (15), r (t) is the reward function of action a (t) at step t, l is the discount factor,
to maximize the Q-value function for the next state s', A is the action set and Q (s (t), a (t)) is in state s (t)Selecting the Q-value function of action a (t) down;
in the DQN-based intelligent upstream-downstream mode selection algorithm, the CCU is considered as an agent, and the state space function s (t) is defined as
A (t) represents an uplink and downlink selection vector of the RRU in the step t; the action space function a (t) is defined as
Representing the change of uplink and downlink selection of the RRU in the step t;
the bonus function r (t) is defined as:
in the formula (16) of the present invention,
representing the total spectral efficiency of the upstream user, +.>
Representing the total spectrum efficiency of the downlink user under the limited block length transmission mechanism, gamma a Is a regularization parameter ensuring network convergence, < >>
Is a constant parameter related to the sum of the spectrum efficiency of the uplink and downlink users.
The step 4 specifically includes:
step S401, initially setting t=0, initializing state S (0) and neural network parameters of the agent;
step S402, according to the probability ε t -greedy policy selection action a (t);
step S403, calculating a Q-value function Q (S (t), a (t)) based on the current state S (t) and the selected action a (t);
step S404, the current state jumps to the next state S' according to the selected action;
step S405, calculating a reward function r (t) according to the formula (16);
step S406, storing the joint transition vector d (t) = [ S (t), a (t), r (t), S' ] in the memory pool, performing neural network training according to formula (15), t=t+1;
step S407, if t=t max Outputting the optimal state, namely the optimal RRU uplink and downlink selection scheme s (t max ) And the maximum prize, i.e., the maximum gain that the system can achieve;
otherwise, the process returns to step S402.
The beneficial effects are that: the invention considers the problem of duplex mode optimization of a network-assisted full duplex CF-RAN system in a low-altitude three-dimensional coverage scene, and determines the spectrum efficiency based on the Shannon channel theory in uplink payload communication of the system and the spectrum efficiency based on limited block length mechanism transmission in a downlink CNPC link by designing an uplink P-MMSE receiver and a downlink P-RZF precoding vector. And the uplink and downlink selection is jointly optimized based on the DQN reinforcement learning algorithm, and the maximization of the system spectrum efficiency is realized under the constraint of meeting the power and service quality requirements.
Drawings
Fig. 1 is a simulation scene diagram of a network assisted full duplex CF-RAN system in a low-altitude stereoscopic coverage scene in embodiment 1;
fig. 2 is a simulation diagram of the relationship between average spectrum efficiency and RRU antenna number based on DQN optimization algorithm in example 1.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, in the present embodiment, it is assumed that in a cylindrical area with a radius of 1km and a height of 100M, the number of EDUs with a height of 3M is m=2, and the number of multi-antenna duplex RRUs with a height of 10M is n=10. K (K) U =10 uplink users and K D The 10 downlink users are randomly distributed in the area, wherein the users can be ground users or unmanned aerial vehicle users, and unmanned aerial vehicle users based on rice channel model are considered here for the sake of convenient simulation universality. The path loss of the unmanned aerial vehicle user is defined as
Wherein d is n,k For the distance between n RRUs to the kth user, a=3.7 is the path loss index, b is the reference distance d n,k Median of average path gain at=1 km. Transmission power p of uplink user ul,k =3w, downlink user transmission power p dl,k Noise power is-174 dBm, coherence slot t=196.
Aiming at the network auxiliary full duplex CF-RAN system in the low-altitude three-dimensional coverage scene, the method of the embodiment specifically comprises the following steps:
step S1, considering non-ideal channel state information, designing a downlink P-RZF precoding vector, and determining the frequency spectrum efficiency of transmission based on a limited block length mechanism under a downlink CNPC link:
in the downlink transmission stage, the signal received by the kth user is:
in the formula (1), D k Representing the association vector between the downlink RRU and the kth downlink active user,
representing the channel vector between the downlink RRU and the downlink active user k,/and/or>
For the channel vector, w, between the nth downlink RRU and the kth downlink active user k Is the channel precoding between the downlink RRU and the kth downlink user, s k Signal, w, sent to kth downlink active user for downlink RRU j Is the channel precoding between the downlink RRU and the downlink user j, s j For the signal sent by the downlink RRU to the downlink active user j, p ul,i Representing the transmission power of the ith uplink active user, u k,i Cross link interference, x, between the ith uplink transmitting user and the kth downlink receiving user i The signal sent for the ith uplink active user satisfies +.>
z dl,k Is a complex additive white gaussian noise;
the power constraint satisfied by each downlink transmission RRU is:
in the formula (2), W i For the precoding matrix of the ith downlink transmission RRU, P is the power constraint of each antenna, and extensible P-RZF linear precoding is adopted on an edge distribution unit EDU to eliminate the interference among users; the P-RZF precoding vector of the channel between the downlink sending RRU and the kth downlink receiving user is as follows:
in the formula (3) and the formula (4), δ is a normalized coefficient obtained by satisfying the power constraint formula (2),
S k representing the set of downlink users associated to the same RRU as downlink user k,
then represent set S k Channel estimation matrix from all users in (a) to downlink RRU (radio remote Unit), alpha > 0 represents regularization coefficient,/and/or (b)>
Indicating LN dl Order identity matrix>
P-RZF precoding matrix E for ith downlink transmission RRU i An identity matrix with non-zero column i;
in the downlink CNPC link of the unmanned plane, short message control signaling is adopted to meet the transmission requirement of low time delay and high reliability, P-RZF downlink precoding in a formula (3) is adopted, and the spectral efficiency of a kth downlink user under a finite block length mechanism (FBLC) is determined as follows:
in equations (5) and (6), τ is the length of the pilot estimation sequence, T is the coherent slot,
is the P-RZF precoding vector of downlink user k,/and->
Is the P-RZF precoding vector of the downlink user j,/and%>
Is the signal-to-interference-and-noise ratio of the downlink user k, V (γ) =1- (1+γ) -2 Is channel scattering, ε is block error probability, e is natural logarithm, Q -1 (-) inverse of the complementary cumulative distribution function Q-function of the standard gaussian random variable, μ=bt is the number of bits used per channel, B represents the system bandwidth, +.>
Representing the downlink noise power;
step S2, according to the P-RZF downlink pre-coding adopted in the step S1, an uplink P-MMSE receiver is designed in consideration of the situation that residual downlink interference exists, and the spectrum efficiency based on the Shannon channel theory under uplink payload communication is determined:
in the uplink transmission stage, the signal received by the mth EDU is:
in the formula (7) of the present invention,
representing the association vector between the mth EDU and RRU, D k Representing the association vector between uplink active user k and uplink RRU, D i Representing the association vector, g, between the uplink active user i and the uplink RRU ul,k G, the channel vector between the kth uplink active user and the uplink receiving RRU ul,i For the channel vector, p, between the ith uplink active user and the uplink receiving RRU ul,k Representing transmission power, p, of kth uplink active user ul,i Representing transmission power, x of the ith uplink active user k Signal x sent for kth uplink active user i Signals transmitted for the ith uplink active user,/->
For the estimation error of the channel between the downlink RRU and the uplink RRU,/for the channel estimation error between the downlink RRU and the uplink RRU>
Is the P-RZF precoding vector of the jth downlink user, s j Transmission signal for jth downstream active user,/->
As residual interference term, z ul Is a complex additive white gaussian noise;
an expandable P-MMSE receiver is adopted at a receiving end, and the receiving vector of a kth uplink user is expressed as:
in the formula (8) and the formula (9), definition is given
Σ k Covariance matrix representing residual interference and noise, < >>
For the channel estimation matrix between the uplink active user k and the uplink RRU, S k Representing the set of all uplink users associated to the same RRU as uplink user k, +.>
Is set S k Channel estimation matrix between uplink active user i and uplink RRU, S' k Representing the set of all downlink users associated to the same RRU as uplink user k, +.>
Indicating LN ul Order identity matrix>
Representing uplink noise power;
the P-MMSE uplink receiver in the formula (8) is adopted, and the spectrum efficiency of the kth uplink user is determined to be:
in equations (10) and (11), τ is the length of the pilot estimation sequence, T is the coherent slot,
is the signal-to-interference-plus-noise ratio of the kth upstream user,/->
Representing an incidence matrix between the EDU and the RRU;
s3, designing the joint optimization problem of the uplink and downlink spectrum efficiency according to formulas (5) and (10), and determining a system state function, an action function and a reward function based on the principle of a DQN reinforcement learning algorithm:
step 301, setting an optimization target of uplink spectrum efficiency:
C2:α ul,n +α dl,n ＝1
in formula (12), α ul ＝[α ul,1 ,…,α ul,N ]，α dl ＝[α dl,1 ,…,α dl,N ]N is the total number of RRUs capable of providing uplink and downlink selection, alpha ul,n And alpha dl,n Indicating the uplink and downlink selection of the nth RRU, when alpha ul,n When=1, α dl,n When=0, the nth RRU is responsible for uplink transmission, otherwise, when α ul,n When=0, α dl,n If=1, the nth RRU is responsible for downlink transmission, each RRU can select only one mode, γ ul,k Representing the signal-to-interference-and-noise ratio, gamma, of the uplink user k ul,k,min Representing the minimum signal-to-interference-and-noise ratio, p, that an upstream user needs to achieve ul,k Representing transmission power, P, of kth uplink active user ul The maximum value of the uplink transmission power of the user;
step S302, setting an optimization target of downlink spectrum efficiency based on a finite block length transmission mechanism:
C5:ε≤ε max
C7:p dl,k ≥0
C2-C3
in formula (13), γ dl,k Representing the signal-to-interference-and-noise ratio, gamma, of the downstream user k dl,k,min Representing the minimum signal-to-interference-and-noise ratio that the downstream user needs to reach, epsilon represents the block error probability, epsilon max Represents the maximum error probability that can be tolerated, w k Is the channel precoding between the downlink RRU and the kth downlink user, P is the threshold value of the transmission power of the downlink user, and P dl,k Representing the kth downlink active userA transmission power;
step S303, setting a multi-objective optimization problem of RRU uplink and downlink mode selection:
C1-C7
considering that the up-down scheduling scheme of RRU is designed by multi-objective optimization to achieve a tradeoff between two problems, maximizing the gain of the system.
Step S304, determining a system state function, an action function and a reward function based on the DQN reinforcement learning algorithm principle:
specifically, the step S304 specifically includes:
the minimized square error loss function of the neural network parameters of the intelligent agent is as follows:
in equation (15), r (t) is the reward function of action a (t) at step t, l is the discount factor,
to maximize the Q-value function for the next state s', a is the action set and Q (s (t), a (t)) is the Q-value function for selecting action a (t) in state s (t).
In the DQN-based intelligent upstream-downstream mode selection algorithm, the CCU is considered as an agent, and the state space function s (t) is defined as
A (t) represents the uplink and downlink selection vector of the RRU in step t. The action space function a (t) is defined as
Indicated at step tThe uplink and downlink selection of the RRU.
The bonus function r (t) is defined as:
in the formula (16) of the present invention,
representing the total upstream spectral efficiency of the user, +.>
Representing the total downlink spectrum efficiency of the user under the finite block length transmission mechanism, gamma a Is a regularization parameter ensuring network convergence, < >>
Is a constant parameter related to the sum of the spectrum efficiency of the uplink and downlink users.
Step S4, solving a multi-objective optimization problem of the formula (14) by adopting an DQN reinforcement learning algorithm, and saving a final state set and rewards of the algorithm as an optimal RRU duplex mode and a maximized system gain:
in this embodiment, it specifically includes:
step S401, initially setting t=0, initializing a state S (0) and neural network parameters of the agent;
step S402, according to the probability epsilon t -greedy policy selection action a (t);
step S403, calculating a Q value function Q (S (t), a (t)) based on the current state S (t) and the selected action a (t);
step S404, the current state jumps to the next state S' according to the selected action;
step S405, calculating a reward function r (t) according to a formula (16);
step S406, storing the joint transition vector d (t) = [ S (t), a (t), r (t), S' ] in the memory pool, and performing neural network training according to formula (15), wherein t=t+1;
step S407, if t=t max Outputting the optimal state, namely the optimal RRU uplink and downlink selection scheme s (t max ) And the maximum prize, i.e., the maximum gain that the system can achieve;
otherwise, the process returns to step S402.
Table 1 shows the relationship between the uplink and downlink selection and the antenna number of RRU based on DQN optimization algorithm in example 1
TABLE 1
Specifically, table 1 and fig. 2 show that, in the network assisted full duplex CF-RAN system under low-altitude stereo coverage, when the P-MMSE receiver is adopted for uplink and the P-RZF precoding is adopted for downlink joint transmission, the RRU uplink and downlink selection and the average spectrum efficiency of the user change along with the number of RRU configuration antennas. As can be seen from table 1, when the number of antennas l=20, the agent selects half of RRUs for uplink service, because uplink data transmission requires a large amount of antenna support, and when the number of antennas on the base station side is low, the system needs to be equipped with more uplink RRUs to serve uplink users. And as the number of antennas increases, the number of selected uplink serving RRUs decreases, and more RRUs are used to support the ultra-reliable low latency requirements of the downlink CNPC link. Thus, in fig. 2, the downlink spectral efficiency increases when the number of antennas l=20 to l=40. When the number of antennas is from l=40 to l=60, the average downlink spectrum efficiency tends to be gentle, and the average uplink spectrum efficiency tends to be increased, because when the system is insufficient to meet the uplink and downlink performance and improve the performance at the same time, the data transmission requirement of the uplink user is preferentially met while ensuring certain reliability of the downlink. The uplink and downlink spectrum efficiency continues to increase after the number of antennas l=60, because the downlink spectrum efficiency continues to be improved after the uplink data transmission requirement is met. Since the P-MMSE receiver is used for the uplink, the spectral efficiency increases logarithmically, and when the number of antennas increases to a certain extent (l=80 to l=100), the average uplink spectral efficiency tends to be flat. The analysis can find that the DQN-based uplink and downlink RRU selection optimization algorithm provided by the invention has rationality and feasibility, effectively improves the spectrum efficiency of uplink and downlink joint transmission in a full duplex CF-RAN system assisted by a low-altitude coverage network, and can be used for flexible scheduling of unmanned aerial vehicles with different uplink concurrency and downlink CNPC requirements.
The present invention is not described in detail in the present application, and is well known to those skilled in the art.
The foregoing describes in detail preferred embodiments of the present invention. It should be understood that numerous modifications and variations can be made in accordance with the concepts of the invention by one of ordinary skill in the art without undue burden. Therefore, all technical solutions which can be obtained by logic analysis, reasoning or limited experiments based on the prior art by the person skilled in the art according to the inventive concept shall be within the scope of protection defined by the claims.
Claims (5)
1. The network auxiliary full duplex mode optimization method under the low-altitude three-dimensional coverage scene is characterized by comprising the following steps of:
step S1, in a network auxiliary full duplex CF-RAN system, based on an association strategy taking a user as a center, a P-RZF downlink precoding vector is designed by considering non-ideal channel state information, and the spectrum efficiency of transmission based on a limited block length mechanism under a downlink CNPC link is determined;
step S2, according to the P-RZF downlink pre-coding adopted in the step S1, an uplink P-MMSE receiver is designed in consideration of the situation that residual downlink interference exists, and the spectrum efficiency based on the Shannon channel theory under uplink payload communication is determined;
step S3, designing a joint optimization problem of uplink and downlink spectrum efficiency according to the result in the step S2, and determining a system state function, an action function and a reward function based on the DQN reinforcement learning algorithm principle;
and S4, solving by utilizing an intelligent DQN algorithm according to the joint optimization problem designed in the step S3, and storing a final state set and rewards of the algorithm as an optimal RRU duplex mode and maximized system spectrum efficiency.
2. The method for optimizing the network-assisted full duplex mode in the low-altitude stereoscopic coverage scene according to claim 1, wherein the step 1 specifically comprises:
step S101, consider a CF-RAN system with low-altitude coverage equipped with a CCU, several UCDUs and switches, M EDUs with buffering and computing capabilities, N duplex RRUs with L antennas, assuming N ul Each is an uplink receiving RRU, N dl The RRU is sent for the downlink, K unmanned aerial vehicles and ground users are distributed at random, wherein the positions of the users are K U Each is an uplink sending user, K D The downlink receiving users; each RRU in the system can respectively select uplink receiving or downlink sending according to the user demands, and a proper mode is selected through the EDU and the UCDU, and in the downlink transmission stage, the signal received by the kth user is as follows:
in the formula (1), D k Representing the association vector between the downlink RRU and the kth downlink active user,
representing the channel vector between the downlink RRU and the downlink active user k,/and/or>
For the channel vector, w, between the nth downlink RRU and the kth downlink active user k Is the channel precoding between the downlink RRU and the kth downlink user, s k Signal, w, sent to kth downlink active user for downlink RRU j Is the channel precoding between the downlink RRU and the downlink user j, s j For the signal sent by the downlink RRU to the downlink active user j, p ul,i Representing the transmission power of the ith uplink active user, u k,i Cross link interference, x, between the ith uplink transmitting user and the kth downlink receiving user i On the ithSignals sent by active users satisfy +.>
z dl,k Is a complex additive white gaussian noise;
the power constraint satisfied by each downlink transmission RRU is:
tr(W i W i H )＝tr(E i W i W i H E i )≤LP (2)
in the formula (2), W i Precoding matrix of the ith downlink transmission RRU, P is power constraint of each antenna, E i An identity matrix with non-zero column i;
step S102, an edge distribution unit EDU adopts expandable P-RZF linear precoding to eliminate inter-user interference, and the P-RZF precoding vector from a downlink RRU to a kth downlink receiving user is as follows:
in the formula (3) and the formula (4), δ is a normalized coefficient obtained by satisfying the power constraint formula (2),
S k representing the set of downlink users associated to the same RRU as downlink user k,
then represent set S k Channel estimation matrix from all users in (a) to downlink RRU (radio remote Unit), alpha > 0 represents regularization coefficient,/and/or (b)>
Indicating LN dl Order identity matrix>
The P-RZF precoding matrix of the RRU is transmitted for the ith downlink;
step S103, in the downlink CNPC link of the unmanned aerial vehicle, short message control signaling is adopted to meet the transmission requirement of low time delay and high reliability, and the spectral efficiency of the kth downlink user under the finite block length mechanism FBLC is determined based on the P-RZF downlink precoding in the formula (3):
in equations (5) and (6), τ is the length of the pilot estimation sequence, T is the coherent slot,
is the P-RZF precoding vector of downlink user k,/and->
Is the P-RZF precoding vector of the downlink user j,/and%>
Is the signal-to-interference-and-noise ratio of the downlink user k, V (γ) =1- (1+γ) -2 Is channel scattering, ε is block error probability, e is natural logarithm, Q -1 (. Cndot.) is the inverse of the complementary cumulative distribution function Q-function of the standard gaussian random variable, μ=bt is the number of bits used per channel, B represents the system bandwidth,
representing the downlink noise power.
3. The method for optimizing the network-assisted full duplex mode in the low-altitude stereoscopic coverage scene according to claim 1, wherein the step 2 specifically comprises:
step S201, in a low-altitude three-dimensional coverage CF-RAN system, carrying out cooperative processing on an uplink baseband signal and a downlink baseband signal through cooperation among EDUs, and relieving interference of a downlink to an uplink among RRUs; when the P-RZF precoding is adopted in downlink transmission, incomplete channel state information is considered, and channel interference from downlink RRU to uplink RRU cannot be completely eliminated; in the uplink transmission stage, the signal received by the mth EDU is:
in the formula (7) of the present invention,
representing the association vector between the mth EDU and RRU, D k Representing the association vector between uplink active user k and uplink RRU, D i Representing the association vector, g, between the uplink active user i and the uplink RRU ul,k G, the channel vector between the kth uplink active user and the uplink receiving RRU ul,i For the channel vector, p, between the ith uplink active user and the uplink receiving RRU ul,k Representing transmission power, p, of kth uplink active user ul,i Representing transmission power, x of the ith uplink active user k Signal x sent for kth uplink active user i Signals transmitted for the ith uplink active user,/->
For the estimation error of the channel between the downlink RRU and the uplink RRU,/for the channel estimation error between the downlink RRU and the uplink RRU>
Is the jth downlink userP-RZF precoding vector s j Transmission signal for jth downstream active user,/->
As residual interference term, z ul Is a complex additive white gaussian noise;
step S202, an expandable P-MMSE receiver is adopted at a receiving end, and the receiving vector of a kth uplink user is expressed as:
in the formula (8) and the formula (9), definition is given
Σ k Covariance matrix representing residual interference and noise, < >>
For the channel estimation matrix between the uplink active user k and the uplink RRU, S k Representing the set of all uplink users associated to the same RRU as uplink user k, +.>
Is set S k Channel estimation matrix between uplink active user i and uplink RRU, S' k Representing the set of all downlink users associated to the same RRU as uplink user k, +.>
Indicating LN ul Order identity matrix>
Representing uplink noise power;
step S203, uplink transmission of the unmanned aerial vehicle user is mainly payload communication, and requirements on channel capacity and data transmission rate are large, so that the spectrum efficiency is analyzed by adopting the traditional shannon channel theory; and (3) adopting a P-MMSE uplink receiver in a formula (8) to determine the spectrum efficiency of a kth uplink user as follows:
in equations (10) and (11), τ is the length of the pilot estimation sequence, T is the coherent slot,
is the signal-to-interference-plus-noise ratio of the kth upstream user,/->
Representing the correlation matrix between the EDU and RRU.
4. The method for optimizing the network-assisted full duplex mode in the low-altitude stereoscopic coverage scene according to claim 1, wherein the step 3 specifically comprises:
step S301, in the network auxiliary full duplex system, a single base station only needs to realize a half duplex function, and uplink and downlink flexible scheduling of RRU is carried out according to real-time requirements of unmanned aerial vehicle and ground users, so that resource expenditure is saved, system performance is improved, and an optimization target of uplink spectrum efficiency is set:
C1:
C2:α ul,n +α dl,n ＝1
C3:
in formula (12), α ul ＝[α ul,1 ,…,α ul,N ]，α dl ＝[α dl,1 ,…,α dl,N ]N is the total number of RRUs capable of providing uplink and downlink selection, alpha ul,n And alpha dl,n Indicating the uplink and downlink selection of the nth RRU, when alpha ul,n When=1, α dl,n When=0, the nth RRU is responsible for uplink transmission, otherwise, when α ul,n When=0, α dl,n If=1, the nth RRU is responsible for downlink transmission, each RRU can select only one mode, γ ul,k Representing the signal-to-interference-and-noise ratio, gamma, of the uplink user k ul,k,min Representing the minimum signal-to-interference-and-noise ratio, p, that an upstream user needs to achieve ul,k Representing transmission power, P, of kth uplink active user ul The maximum value of the uplink transmission power of the user;
step S302, setting an optimization target of downlink spectrum efficiency based on a finite block length transmission mechanism:
C4:
C5:ε≤ε max
C6:
C7:p dl,k ≥0
C2-C3
in formula (13), γ dl,k Representing the signal-to-interference-and-noise ratio, gamma, of the downstream user k dl,k,min Representing the minimum signal-to-interference-and-noise ratio that the downstream user needs to reach, epsilon represents the block error probability, epsilon max Represents the maximum error probability that can be tolerated, w k Is the channel precoding between the downlink RRU and the kth downlink user, P is the threshold value of the transmission power of the downlink user, and P dl,k Representing the transmission power of the kth downlink active user;
step S303, in the network auxiliary full duplex CF-RAN system, when the uplink demand is large, the number of uplink service RRUs can be increased to improve the uplink spectrum efficiency, and when the downlink demand is large, the number of downlink service RRUs is increased to improve the downlink spectrum efficiency; since the nth RRU can only select one uplink or downlink mode at a specific moment, the optimization targets of the formula (12) and the formula (13) are contradictory, and the multi-target optimization problem of RRU uplink and downlink mode selection is set:
C1-C7
designing an up-down scheduling scheme of the RRU to obtain a compromise in two problems by multi-objective optimization, and maximizing the gain of the system;
in step S304, the intelligent mode selection algorithm based on DQN is:
the DQN algorithm combines deep learning and reinforcement learning, has high reliability for solving motion selection problems based on discrete variables, and is characterized in that an intelligent agent is based on a state-motion value function Q and an epsilon greedy scheme according to fixed probability epsilon t Selecting an action a (t) with the highest Q value to obtain a reward function r (t), entering a next state s', and continuously training a neural network model of the intelligent agent to obtain an optimal solution by storing the taken action and the obtained reward into a memory;
the minimized square error loss function of the neural network parameters of the intelligent agent is as follows:
in equation (15), r (t) is the reward function of action a (t) at step t, l is the discount factor,
to maximize the Q-value function for the next state s', a is the action set, Q (s (t), a (t)) is the Q-value function for selecting action a (t) in state s (t);
in the DQN-based intelligent upstream-downstream mode selection algorithm, the CCU is considered as an agent, and the state space function s (t) is defined as
A (t) represents an uplink and downlink selection vector of the RRU in the step t; the action space function a (t) is defined as
Representing the change of uplink and downlink selection of the RRU in the step t;
the bonus function r (t) is defined as:
in the formula (16) of the present invention,
representing the total spectral efficiency of the upstream user, +.>
Representing the total spectrum efficiency of the downlink user under the limited block length transmission mechanism, gamma a Is to ensureA regularization parameter of network convergence, < ->
Is a constant parameter related to the sum of the spectrum efficiency of the uplink and downlink users.
5. The method for optimizing the network-assisted full duplex mode in the low-altitude stereoscopic coverage scene according to claim 1, wherein the step 4 specifically comprises:
step S401, initially setting t=0, initializing state S (0) and neural network parameters of the agent;
step S402, according to the probability ε t -greedy policy selection action a (t);
step S403, calculating a Q-value function Q (S (t), a (t)) based on the current state S (t) and the selected action a (t);
step S404, the current state jumps to the next state S' according to the selected action;
step S405, calculating a reward function r (t) according to the formula (16);
step S406, storing the joint transition vector d (t) = [ S (t), a (t), r (t), S' ] in the memory pool, performing neural network training according to formula (15), t=t+1;
step S407, if t=t max Outputting the optimal state, namely the optimal RRU uplink and downlink selection scheme s (t max ) And the maximum prize, i.e., the maximum gain that the system can achieve;
otherwise, the process returns to step S402.
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