CN114614864A - 3D beam forming and intelligent reflecting surface phase shift optimization method for multi-user scene - Google Patents
3D beam forming and intelligent reflecting surface phase shift optimization method for multi-user scene Download PDFInfo
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Abstract
The invention discloses a 3D beam forming and intelligent reflecting surface phase shift optimization method for a multi-user scene, which adopts an alternative iteration optimization method of firstly jointly optimizing a base station antenna downward inclination angle and a base station beam forming matrix and then optimizing an intelligent reflecting surface phase shift matrix, and the method is sequentially and circularly carried out until an iteration stopping condition is met; and finally, performing combined beam forming processing according to the globally optimal base station antenna downward inclination angle, the base station beam forming matrix and the intelligent reflecting surface phase shift matrix. The invention has obvious performance gain and higher effectiveness, and is beneficial to improving the system performance of the wireless communication system in a complex electromagnetic environment.
Description
Technical Field
The invention belongs to the technical field of signal and information processing, and particularly relates to a 3D beam forming and intelligent reflecting surface phase shift optimization method for a multi-user scene.
Background
In recent years, with the continuous progress of society, wireless communication technology is rapidly developed, various advanced technologies are continuously innovated, and the level of intelligence is gradually improved, but higher requirements on the performance of a wireless communication network are also provided, particularly on the aspects of transmission rate, reliability, time delay, energy consumption and the like. Although some advanced communication technology designs have advanced the performance indicators mentioned above, it is still difficult to meet the high performance requirements of future wireless communication systems. One of the key factors that limit the performance of wireless communication systems is channel randomness, which can significantly affect the proper reception of the desired signal. In order to reduce the influence of channel randomness, numerous transceiving techniques and transmission protocols with excellent performance have been proposed in the academia and the industry. However, the prior art has the disadvantages of insufficient performance gain, high resource overhead and the like, and is difficult to meet the application requirements of future wireless communication. Therefore, in order to better adapt to the development of future wireless communication systems, the need for researching efficient and reliable advanced communication technologies is increasingly urgent.
In recent years, the intelligent reflector is proposed as a low-cost wireless communication technical solution, and is receiving wide attention from both academic and industrial fields. The wireless channel dynamic response device is composed of a large number of passive reflection elements, and functions of reflection, refraction, focusing and the like can be realized by properly adjusting the amplitude and the phase of an incident signal, so that expected wireless channel response is dynamically established, and an innovative thought is provided for solving the random influence of a wireless channel. The technology can control the wireless propagation environment in a near-passive signal processing mode, so that the energy efficiency of the wireless communication system can be greatly improved. It can be seen that the intelligent reflector technology is an innovative approach to address the challenges of future wireless communication networks.
Furthermore, 3D beamforming is another advanced wireless communication technology. The large-scale antenna array deployed by the base station is benefited, 3D beam forming can dynamically adjust the radiation mode of the base station antenna in real time, the quality of received signals can be improved, and interference can be effectively reduced. More importantly, since a higher communication frequency band is adopted in a future wireless communication system, the application of the 3D beamforming technology is facilitated. Since the base station antenna radiation pattern is usually defined by using the downtilt angle, how to reasonably set the downtilt angle is the most important problem in 3D beamforming.
Based on the requirements of future wireless communication systems, the challenges of channel randomness and the like are solved, and innovative wireless communication technical schemes must be researched. Because the intelligent reflector technology and the 3D beam forming technology both use beam forming as a technical core to improve the performance of a wireless communication system, the joint design of the 3D beam forming and the intelligent reflector reflection optimization is a feasible innovative idea, and is beneficial to solving the wireless communication challenge. In a multi-user scenario, the base station and the intelligent reflecting surface serve multiple users at the same time, and how to reasonably design the base station antenna downward inclination angle, the base station beam forming matrix and the intelligent reflecting surface phase shift matrix is very important for improving the performance of the wireless communication system.
Disclosure of Invention
In order to solve the problems in the background art, the invention provides a 3D beam forming and intelligent reflecting surface phase shift optimization method for a multi-user scene.
The invention discloses a 3D beam forming and intelligent reflecting surface phase shift optimization method for a multi-user scene, which comprises the following steps:
step 1: random initialization of base station antenna downtilt at time 0
And intelligent reflector phase shift matrix
Then, a base station beam forming matrix is initialized according to a zero-forcing beam forming algorithm
Finally initializing the relative increment gamma of the weighted sum rate to be infinity, and the iteration index t to be 0; in addition, the base station transmission power is denoted by P,
for the channel from the base station to user k,
for the channel from the intelligent reflecting surface to user k, G is from the base station to the intelligenceThe channels between the surfaces can be reflected and,
representing the noise power, theta, of the k-th userd，kIs the tilt angle of user k relative to the base station antenna, thetarFor the angle of inclination of the intelligent reflecting surface relative to the base station antenna, theta3dBRepresents the 3dB beam width, K is the total number of users, (. DEG)HRepresenting conjugate transpose, | · non-calculation2The modulo square value of the calculated complex variable is indicated.
Step 2: if gamma is more than or equal to epsilon and T is less than or equal to T, executing the step 3, otherwise executing the step 6; wherein T is the maximum iteration number, and epsilon is a preset iteration stop threshold.
And step 3: locally optimal intelligent reflecting surface phase shift matrix obtained according to the t iteration
Local optimal base station antenna downward inclination angle in t +1 th iteration
And base station beam forming matrix
And 4, step 4: obtaining the local optimal base station antenna downward inclination angle according to the t +1 iteration
And base station beam forming matrix
Solving local optimal intelligent reflecting surface phase shift matrix in t +1 iteration
And 5: locally optimal from the t +1 th iteration
And
computing weighted sum rates for K users
Wherein etakIs the weighting factor for the k-th user,
the signal-to-noise ratio of the signal received by the kth user in the t +1 th iteration; further, the relative increment γ ═ O of the weighted sum rate is calculated(t+1)-O(t))/O(t+1)And simultaneously, the step 2 is executed by returning t to f + 1.
Step 6: obtaining the globally optimal intelligent reflecting surface phase shift matrix according to the step 2-5
The intelligent reflective surface phase shift is configured,
the phase of each diagonal element in the intelligent reflecting surface is the phase value corresponding to each reflecting unit of the intelligent reflecting surface; further, obtaining the global optimal base station antenna downward inclination angle according to the step 2-5
Adjusting the beam direction of the antenna according to an electronic downtilt mode; finally, obtaining the global optimal base station beam forming matrix according to the step 2-5
And carrying out beam forming processing on the transmission signals.
Further, in step 3, the method for optimizing the base station antenna downtilt angle and the base station beamforming matrix in the t +1 th iteration specifically includes:
s31: initializing the vector at time 0
The relative increment of the weighted sum rate is gamma1Infinity, the iteration index a is 0; wherein, the vector
The first K elements of (a) represent initial transmit powers allocated by the base station for K users.
S32: if gamma is1And ≧ ε, execute step S33, otherwise execute step S37.
S33: solving the beam forming direction in the a +1 iteration according to the zero-forcing beam forming algorithm
S34: is calculated by the formula (1)
Further calculating a locally optimal vector at the a +1 th iteration according to a gradient descent method
Wherein,
xkis the kth element of the vector x, xK+1Is the K +1 th element of the vector x,
is a matrix
The vector of the k-th column of (c),
representing the noise power of the k-th user,
s35: solving for the optimal vector according to the optimization method shown in equation (2)
Wherein,
to optimize the variables, a ═ 1, 1, …, 1, 0]，
Representing vectors
The (k) th element of (a),
representing vectors
The K +1 th element of (1).
S36: computing weighted sum rates
Wherein etakIs the weighting factor for the k-th user,
the signal-to-noise ratio of the signal received by the kth user in the (a + 1) th iteration; further, a relative increment gamma of the weighted sum rate is calculated1＝(O(a+1)-O(a))/O(a+1)At the same time, let a be a +1, the process returns to step S32.
S37: obtaining a locally optimal vector according to the steps S32-S36
Further, vectors
The K +1 th element value is the local optimal base station antenna downward inclination angle obtained by the a +1 th iteration
Vector
The first K element values are the sending power distributed to K users by the base station, and a base station beam forming matrix is further obtained according to a zero forcing beam forming algorithm
Further, in step 4, the method for optimizing the intelligent reflecting surface phase shift matrix in the t +1 th iteration specifically includes:
s41: initializing the relative increment of the weighted sum rate at time 0 to γ2Infinity, the iteration index c is 0.
S42: if gamma is equal to2And ≧ ε, execute step S43, otherwise execute step S47.
S43: calculating a locally optimal decoding factor corresponding to the user k in the c +1 th iteration according to the formula (3)
Wherein,
and the equivalent channel corresponding to the kth user. Wherein,
the method is the intelligent reflecting surface phase shift matrix which is locally optimal in the c iteration.
S44: calculating the auxiliary parameter of the user k corresponding to the local optimum in the c +1 th iteration
Wherein,
wherein
Representation taking complex real part processing, (.)HThe conjugate transpose process is shown.
S45: calculating the local optimal vector at the c +1 th iteration by adopting an alternative direction multiplier algorithm according to the formula (4)
Further, the intelligent reflecting surface phase shift matrix is expressed as
Wherein diag {. denotes generating a diagonalized matrix.
Wherein phi is(c+1)To optimize the variables, (#)(c+1))HIs a vector phi(c+1)The conjugate of the transposed vector of (a),
is a vector phi(c+1)N is the number of intelligent reflecting surface units, and U and v are determined according to a weighting and mean square error equivalent method.
S46: computing weighted sum rates
Wherein etakWeighting factor for the k-th userIn the case of a hybrid vehicle,
the signal-to-noise ratio of the signal received by the kth user in the c +1 th iteration; further, a relative increment gamma of the weighted sum rate is calculated2＝(O(c+1)-O(c))/O(c+1)At the same time, c is made c +1, and the process returns to step S42.
S47: obtaining the locally optimal intelligent reflecting surface phase shift matrix according to the steps S42-S46
The beneficial technical effects of the invention are as follows:
the method adopts an alternative optimization method of firstly jointly optimizing the base station antenna downward inclination angle theta and the base station beam forming matrix W and then optimizing the intelligent reflecting surface phase shift matrix phi, circularly carries out the optimization until the condition of stopping iteration is met, and finally obtains the optimal base station antenna downward inclination angle theta and the optimal base station beam forming matrix W according to the result
And carrying out combined beam forming processing. In the method, the original optimization problem is equivalent to two sub-optimization problems to be solved, so that the signal processing flow is simplified, and the optimization difficulty is reduced. Compared with a base station antenna downward inclination angle pointing intelligent reflecting surface, an optimized phase shift method, a base station antenna downward inclination angle pointing intelligent reflecting surface, a random phase shift method and a non-intelligent reflecting surface method, the method provided by the invention has better output performance. Therefore, the method provided by the invention is beneficial to improving the performance of the wireless communication network.
Drawings
FIG. 1 is a schematic process flow diagram of the method of the present invention.
Fig. 2 shows that the position coordinates of the base station antenna are (0, 0, 30) m, the position coordinates of the intelligent reflection surface are (4, 200, 10) m, the number of users K is 2, the coordinates are (2, 160, 1.5) m, (2, 180, 1.5) m, and the channel fading index is α in the method of the present inventionBU＝3.8、αBI＝2.2、αIU2.8, the rice factor is betaBU＝1、βBI＝∞、β IU0, 3dB beamwidth θ3dBThe number of base station antennas is equal to 36, the number of intelligent reflecting surface reflecting units is equal to 100, the maximum iteration time T is equal to 20, and a preset iteration stop threshold epsilon is equal to 10-4In the meantime, the method of the invention and the weighting and rate performance comparison curves of the base station antenna downward inclination angle pointing to the intelligent reflecting surface, the optimized phase shift method, the base station antenna downward inclination angle pointing to the intelligent reflecting surface, the random phase shift method and the non-intelligent reflecting surface method are used at different sending powers. The abscissa of the graph is the base station transmission power (unit: dB) and the ordinate is the weight sum rate (unit: bit/s/Hz). In the figure, the symbol "o" represents the method of the present invention, "diamond" represents "base station antenna downward inclination is directed to the intelligent reflection surface, the optimal phase shift method," □ "represents" base station antenna downward inclination is directed to the intelligent reflection surface, the random phase shift method, "and" no intelligent reflection surface method.
Fig. 3 shows that in the method of the present invention, the position coordinate of the base station antenna is (0, 0, 30) m, the position coordinate of the intelligent reflection surface is (4, 200, 10) m, the number of users K is 2, the coordinates are (2, 160, 1.5) m, (2, 180, 1.5) m, and the channel path fading index is αBU＝3.8、αBI＝2.2、αIU2.8, the rice factor is betaBU＝1、βBI＝∞、β IU0, the base station transmission power is 5dB and 3dB beam width theta3dBThe number of base station antennas is 10 DEG, M is 36, the maximum iteration time T is 20, and the preset iteration stop threshold epsilon is 10-4In the time, when the number of the reflecting units of the intelligent reflecting surface is different, the method of the invention and the weighting and speed performance comparison curve of the base station antenna downward inclination angle pointing to the intelligent reflecting surface, the optimized phase shift method, the base station antenna downward inclination angle pointing to the intelligent reflecting surface, the random phase shift method and the intelligent reflecting surface-free method are adopted. The abscissa in the figure is the number of the reflection units of the intelligent reflection surface, and the ordinate is the weighting sum rate (unit: bit/s/Hz). In the figure, the symbol "o" represents the method of the present invention, "diamond" represents "the base station antenna downward inclination is directed to the intelligent reflection surface, the optimal phase shift method," □ "represents" the base station antenna downward inclination is directed to the intelligent reflection surface, the random phase shift method, "" diamond "represents" the intelligent reflection surfaceThe table "no intelligent reflector method".
FIG. 4 shows the location coordinates of the base station antenna as (0, 0, 30) m, the location coordinates of the intelligent reflection surface as (4, 200, 10) m, and the channel fading index as αBU＝3.8、αBI＝2.2、αIU2.8, the rice factor is betaBU＝1、βBI＝∞、β IU0, the base station transmission power is 5dB and 3dB beam width theta3dBThe number of base station antennas is 10 DEG, M is 36, the maximum iteration time T is 20, and the preset iteration stop threshold epsilon is 10-4In time, the method of the invention and the weighting and rate performance comparison curves of the base station antenna downward inclination angle pointing to the intelligent reflecting surface, the optimized phase shift method, the base station antenna downward inclination angle pointing to the intelligent reflecting surface, the random phase shift method and the non-intelligent reflecting surface method are carried out at different users. In the figure, the abscissa is the number of users and the ordinate is the weight sum rate (unit: bit/s/Hz). In the figure, the symbol "o" represents the method of the present invention, "diamond" represents "base station antenna downward inclination is directed to the intelligent reflection surface, the optimal phase shift method," □ "represents" base station antenna downward inclination is directed to the intelligent reflection surface, the random phase shift method, "and" no intelligent reflection surface method.
Detailed Description
The invention is further described in detail below with reference to the drawings and the detailed description.
The 3D beam forming and intelligent reflecting surface phase shift optimization method for the multi-user scene adopts an alternative optimization method of firstly jointly optimizing a base station antenna downward inclination angle and a base station beam forming matrix and then optimizing an intelligent reflecting surface phase shift matrix, and the optimization method is circularly carried out until the condition of stopping iteration is met. The processing flow of the method is shown in figure 1, and specifically comprises the following steps:
step 1: random initialization of base station antenna downtilt at time 0
And intelligent reflector phase shift matrix
Then, a base station beam forming matrix is initialized according to a zero-forcing beam forming algorithm
Finally initializing the relative increment gamma of the weighted sum rate to be infinity, and the iteration index t to be 0; in addition, the base station transmission power is denoted by P,
for the channel from the base station to user k,
the channel from the intelligent reflecting surface to the user k, G is the channel from the base station to the intelligent reflecting surface,
representing the noise power, theta, of the k-th userd，kIs the tilt angle of user k relative to the base station antenna, thetarFor the angle of inclination of the intelligent reflecting surface relative to the base station antenna, theta3dBRepresents the 3dB beam width, K is the total number of users, (. DEG)HRepresenting a conjugate transpose, | · non-conducting phosphor2The modulo square value of the calculated complex variable is indicated.
Step 2: if gamma is more than or equal to epsilon and T is less than or equal to T, executing the step 3, otherwise executing the step 6; wherein T is the maximum iteration number, and epsilon is a preset iteration stop threshold.
And step 3: locally optimal intelligent reflecting surface phase shift matrix obtained according to the t iteration
Local optimal base station antenna downward inclination angle in t +1 th iteration
And base station beam forming matrix
S31: initializing a vector at time 0
The relative increment of the weighted sum rate is gamma1Infinity, the iteration index a is 0; wherein the vector
The first K elements of (a) represent initial transmit powers allocated by the base station for K users.
S32: if gamma is equal to1And ≧ ε, execute step S33, otherwise execute step S37.
S33: solving the beam forming direction in the a +1 iteration according to the zero-forcing beam forming algorithm
S34: is calculated by the formula (1)
Further calculating a locally optimal vector at the a +1 th iteration according to a gradient descent method
Wherein,
xkis the kth element of the vector x, xK+1Is the K +1 th element of the vector x,
is a matrix
The vector of the k-th column of (c),
representing the noise power of the k-th user,
s35: solving for the optimal vector according to the optimization method shown in equation (2)
Wherein,
to optimize the variables, a ═ 1, 1, …, 1, 0]，
Representing vectors
The (k) th element of (a),
representing vectors
The K +1 th element of (1).
S36: computing weighted sum rates
Wherein etakIs the weighting factor for the k-th user,
the signal-to-noise ratio of the signal received by the kth user in the (a + 1) th iteration; further, a relative increment gamma of the weighted sum rate is calculated1＝(O(a+1)-O(a))/O(a+1)At the same time, let a be a +1, the process returns to step S32.
S37: obtaining a locally optimal vector according to the steps S32-S36
Further, vectors
The K +1 th element value is the local optimal base station antenna downward inclination angle obtained by the a +1 th iteration
Vector
The first K element values are the sending power distributed to K users by the base station, and a base station beam forming matrix is further obtained according to a zero forcing beam forming algorithm
And 4, step 4: obtaining the local optimal base station antenna downward inclination angle according to the t +1 iteration
And base station beam forming matrix
Solving local optimal intelligent reflecting surface phase shift matrix in t +1 iteration
S41: initializing the relative increment of the weighted sum rate at time 0 to γ2And f, the iteration index c is 0.
S42: if gamma is2≧ ε, step S43 is executed, otherwise step S47 is executed.
S43: calculating a locally optimal decoding factor corresponding to the user k in the c +1 th iteration according to the formula (3)
Wherein,
and the equivalent channel corresponding to the kth user. Wherein,
the method is the intelligent reflecting surface phase shift matrix which is locally optimal in the c iteration.
S44: calculating the auxiliary parameter of the user k corresponding to the local optimum in the c +1 th iteration
Wherein,
wherein
Representation taking complex real part processing, (.)HThe conjugate transpose process is shown.
S45: calculating the local optimal vector at the c +1 th iteration by adopting an alternative direction multiplier algorithm according to the formula (4)
Further, the intelligent reflecting surface phase shift matrix is expressed as
Wherein diag {. denotes generating a diagonalized matrix.
Wherein phi is(c+1)To optimize the variables, (. phi.) (phi.)(c+1))HIs a vector phi(c+1)Conjugation of (2)The vector is transposed with respect to the vector,
is a vector phi(c+1)N is the number of units of the intelligent reflecting surface, and U and upsilon are determined according to a weighting and mean square error equivalent method.
S46: computing weighted sum rates
Wherein etakIs the weighting factor for the k-th user,
the signal-to-noise ratio of the signal received by the kth user in the c +1 th iteration; further, a relative increment gamma of the weighted sum rate is calculated2＝(O(c+1)-O(c))/O(c+1)At the same time, c is made c +1, and the process returns to step S42.
S47: obtaining the locally optimal intelligent reflecting surface phase shift matrix according to the steps S42-S46
And 5: locally optimal from the t +1 th iteration
And
computing weighted sum rates for K users
Wherein etakIs the weighting factor for the k-th user,
the signal-to-noise ratio of the signal received by the kth user in the t +1 th iteration; further, the relative increment γ ═ O of the weighted sum rate is calculated(t+1)-O(t))/O(t+1)And simultaneously, when t is equal to t +1, returning to the step of executing2。
Step 6: obtaining the globally optimal intelligent reflecting surface phase shift matrix according to the step 2-5
The intelligent reflective surface phase shift is configured such that,
the phase of each diagonal element in the intelligent reflecting surface is the phase value corresponding to each reflecting unit of the intelligent reflecting surface; further, obtaining the global optimal base station antenna downward inclination angle according to the step 2-5
Adjusting the beam direction of the antenna according to an electronic downtilt mode; finally, obtaining the global optimal base station beam forming matrix according to the step 2-5
And carrying out beam forming processing on the transmission signals.
Simulation experiment
The specific conditions of the simulation experiment are as follows: the position coordinate of the base station is (0, 0, 30) m, the position coordinate of the intelligent reflecting surface is (4, 200, 10) m, and the channel path fading index is alphaBU＝3.8、αBI＝2.2、αIU2.8, channel rice factor βBU＝1、βBI＝∞、β IU0, 20 is the maximum iteration time T, 10 is the preset iteration stop threshold epsilon-4。
Fig. 2 is a weighting and rate performance comparison curve of the method of the present invention and "base station antenna downward inclination points to the intelligent reflecting surface, optimized phase shifting method", "base station antenna downward inclination points to the intelligent reflecting surface, random phase shifting method" and "no intelligent reflecting surface method" under the above specific simulation conditions at different transmission powers. The abscissa of the graph is the base station transmission power (unit: dB) and the ordinate is the weight sum rate (unit: bit/s/Hz). In the figure, the symbol "o" represents the method of the present invention, "diamond" represents "base station antenna downward inclination is directed to the intelligent reflection surface, the optimal phase shift method," □ "represents" base station antenna downward inclination is directed to the intelligent reflection surface, the random phase shift method, "and" no intelligent reflection surface method.
As can be seen from fig. 2, the weighting and rate performance of each method is rapidly improved by increasing the base station transmission power. The method has the most excellent performance all the time, and shows the effectiveness of joint design on the base station antenna downward inclination angle, the intelligent reflecting surface phase shift matrix and the base station beam forming matrix by adopting an alternative iteration mode. In addition, when the base station antenna downward inclination angle points to the intelligent reflecting surface, the optimized phase shift and the random phase shift method are compared, so that the system performance can be greatly improved after the intelligent reflecting surface phase shift matrix is optimized. Finally, the weighting and rate performance of the "base station antenna downtilt angle points to the intelligent reflector, random phase shift method" is lower than that of the "no intelligent reflector method" because the base station antenna downtilt angle and the intelligent reflector phase shift matrix are not reasonably configured.
FIG. 3 is a comparison curve of the weighting and rate performance between the method of the present invention and the "base station antenna downtilt angle pointing to the intelligent reflecting surface, the optimized phase shift method", "base station antenna downtilt angle pointing to the intelligent reflecting surface, the random phase shift method" and the "no intelligent reflecting surface method" when the number of reflecting units of the intelligent reflecting surface is different under the above specific simulation conditions. The abscissa in the figure is the number of the reflection units of the intelligent reflection surface, and the ordinate is the weighting sum rate (unit: bit/s/Hz). In the figure, the symbol "o" represents the method of the present invention, "diamond" represents "base station antenna downward inclination is directed to the intelligent reflection surface, the optimal phase shift method," □ "represents" base station antenna downward inclination is directed to the intelligent reflection surface, the random phase shift method, "and" no intelligent reflection surface method.
As can be seen from FIG. 3, as the number of the reflection units of the intelligent reflection surface increases, the weighting and speed performance of the method of the present invention is rapidly improved, which shows the superiority of the method of the present invention. Secondly, the weighting and speed performance of the 'base station antenna downward inclination points to the intelligent reflecting surface and the optimized phase shift method' is improved along with the increase of the number of reflecting units of the intelligent reflecting surface, but is far lower than that of the method. In addition, the weighting and rate performance of the "base station antenna downward inclination angle points to the intelligent reflection surface, and the random phase shift method" is almost kept unchanged, which indicates that even if the number of reflection units of the intelligent reflection surface is increased during random phase shift configuration, the system performance is difficult to be effectively improved. Finally, the "dumb reflector approach" does not have additional reflective links to assist communication, so the weighting and rate performance remains unchanged.
Fig. 4 is a comparison curve of the weighting and rate performance of the method of the present invention and "base station antenna downward inclination angle points to the intelligent reflection surface, optimized phase shift method", "base station antenna downward inclination angle points to the intelligent reflection surface, random phase shift method" and "no intelligent reflection surface method" in different numbers of users under the above specific simulation conditions. In the figure, the abscissa is the number of users and the ordinate is the weight sum rate (unit: bit/s/Hz). The symbol "o" in the figure represents the method of the present invention, "o" represents "base station antenna downward inclination is directed to the intelligent reflection surface, optimized phase shift method", "□" represents "base station antenna downward inclination is directed to the intelligent reflection surface, random phase shift method", and "o" represents "non-intelligent reflection surface method".
As can be seen from fig. 4, the weighting and rate performance of each method decreases as the number of users increases, but the method of the present invention always has the most excellent performance. In addition, as the number of users increases, the performance of the "base station antenna downtilt angle points to the intelligent reflecting surface, the optimized phase shift method" and the "base station antenna downtilt angle points to the intelligent reflecting surface, and the random phase shift method" is rapidly reduced, and when the number of users is large, the performance is even lower than that of the "method without the intelligent reflecting surface", which indicates that it is unreasonable to point the base station antenna downtilt angle to the intelligent reflecting surface in a large-scale user scene.
Claims (3)
1. A3D beam forming and intelligent reflecting surface phase shift optimization method for a multi-user scene is characterized by comprising the following steps:
step 1: random initialization of base station antenna downtilt at time 0
And intelligent reflector phase shift matrix
Then, a base station beam forming matrix is initialized according to a zero-forcing beam forming algorithm
Finally initializing the relative increment of the weighted sum rate, y ═ infinity, and the iteration index t ═ 0; in addition, the base station transmission power is denoted by P,
for the channel from the base station to user k,
the channel from the intelligent reflecting surface to the user k, G is the channel from the base station to the intelligent reflecting surface,
representing the noise power, theta, of the k-th userd，kIs the tilt angle of user k relative to the base station antenna, thetarFor the angle of inclination of the intelligent reflecting surface relative to the base station antenna, theta3dBRepresents the 3dB beam width, K is the total number of users, (. DEG)HRepresenting a conjugate transpose, | · non-conducting phosphor2A modulo square value representing the calculated complex variable;
step 2: if γ is more than or equal to ε and T is less than or equal to T, executing step 3, otherwise executing step 6; wherein T is the maximum iteration number, and epsilon is a preset iteration stop threshold;
and step 3: locally optimal intelligent reflecting surface phase shift matrix obtained according to the t iteration
Local optimal base station antenna downward inclination angle in t +1 th iteration
And base station beam forming matrix
And 4, step 4: obtaining the local optimal base station antenna downward inclination angle according to the t +1 iteration
And base station beam forming matrix
Solving local optimal intelligent reflecting surface phase shift matrix in t +1 iteration
And 5: locally optimal from the t +1 th iteration
And
computing weighted sum rates for K users
Wherein etakIs the weighting factor for the k-th user,
the signal-to-noise ratio of the signal received by the kth user in the t +1 th iteration; further, the relative increment y (O) of the weighted sum rate was calculated(t+1)-O(t))/O(t+1)And simultaneously, making t equal to t +1, and returning to execute the step 2;
step 6: obtaining the globally optimal intelligent reflecting surface phase shift matrix according to the step 2-5
The intelligent reflective surface phase shift is configured,
the phase of each diagonal element in the intelligent reflecting surface is the phase value corresponding to each reflecting unit of the intelligent reflecting surface; further, obtaining the global optimal base station antenna downward inclination angle according to the step 2-5
Adjusting the beam direction of the antenna according to an electronic downtilt mode; finally, the globally optimal base station beam forming matrix obtained according to the step 2-5
And carrying out beam forming processing on the transmission signal.
2. The method for optimizing 3D beamforming and intelligent reflecting surface phase shift in a multi-user scenario according to claim 1, wherein in step 3, the method for optimizing the base station antenna downtilt and the base station beamforming matrix in the t +1 th iteration specifically comprises:
s31: initializing a vector at time 0
The relative increment of the weighted sum rate was y1Infinity, iteration index a is 0; wherein, the vector
The first K elements of (A) represent the initial transmit power allocated by the base station for K users;
s32: if y1At least epsilon, executing step S33, otherwise executing step S37;
s33: solving the beam forming direction in the a +1 iteration according to the zero-forcing beam forming algorithm
S34: is calculated by the formula (1)
Further calculating a locally optimal vector at the a +1 th iteration according to a gradient descent method
Wherein,
xkis the kth element of the vector x, xK+1Is the K +1 th element of the vector x,
is a matrix
The vector of the k-th column of (c),
representing the noise power of the k-th user,
s35: solving for the optimal vector according to the optimization method shown in equation (2)
Wherein,
to optimize variables, a ═ 1, 1, …, 1, 0]，
Representing vectors
The (k) th element of (a),
representing vectors
The K +1 th element of (1);
s36: computing weighted sum rates
Wherein etakIs the weighting factor for the k-th user,
the signal-to-noise ratio of the signal received by the kth user in the (a + 1) th iteration; further, the relative increment of the weight sum rate was calculated1＝(O(a+1)-O(a))/O(a+1)While making a +1, the process returns to step S32;
s37: obtaining a locally optimal vector according to the steps S32-S36
Further, vectors
The K +1 th element value is the local optimal base station antenna downward inclination angle obtained by the a +1 th iteration
Vector
The first K element values are the sending power distributed to K users by the base station, and a base station beam forming matrix is further obtained according to a zero forcing beam forming algorithm
3. The method for 3D beamforming and intelligent reflection surface phase shift optimization in a multi-user scenario according to claim 2, wherein in step 4, the method for optimizing the intelligent reflection surface phase shift matrix in the t +1 th iteration specifically comprises:
s41: the relative increment of the weight sum rate at initialization time 0 was y2Infinity, the iteration index c is 0;
s42: if y2If not, executing step S43, otherwise executing step S47;
s43: calculating a locally optimal decoding factor corresponding to the user k in the c +1 th iteration according to the formula (3)
Wherein,
and the equivalent channel corresponding to the kth user. Wherein,
the intelligent reflecting surface phase shift matrix is locally optimal in the c iteration;
s44: calculate the c +1 st iterationAuxiliary parameter corresponding to local optimum of user k
Wherein,
wherein
Representation taking complex real part processing, (.)HRepresenting a conjugate transpose process;
s45: calculating the local optimal vector at the c +1 th iteration by adopting an alternative direction multiplier algorithm according to the formula (4)
Further, the intelligent reflecting surface phase shift matrix is expressed as
Wherein, diag {. denotes generating a diagonalized matrix;
wherein phi is(c+1)To optimize the variables, (. phi.) (phi.)(c+1))HIs a vector phi(c+1)The conjugate of (a) the transposed vector (v),
is a vector phi(c+1)N is the number of intelligent reflecting surface units, and U and v are determined according to a weighting and mean square error equivalent method;
s46: computing weighted sum rates
Wherein etakIs the weighting factor for the k-th user,
the signal-to-noise ratio of the signal received by the kth user in the c +1 th iteration; further, the relative increment of the weight sum rate was calculated2＝(O(c+1)-O(c))/O(c+1)Meanwhile, when c is equal to c +1, the process returns to step S42;
s47: obtaining the locally optimal intelligent reflecting surface phase shift matrix according to the steps S42-S46
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