CN115865296A - Orthogonal pilot frequency sequence active detection method based on covariance - Google Patents
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Abstract
The invention discloses an orthogonal pilot frequency sequence active detection method based on covariance. The invention solves the problem of active user detection in large-scale authorization-free access in a non-cellular distributed large-scale MIMO communication system, and breaks through the bottleneck problem of being influenced by various interferences in the traditional active user detection algorithm. The present invention firstly provides a covariance interference measurement mode among devices in a communication system, and determines a pilot sequence and a transmission power sent by each access device in the communication system by using a minimum-maximum covariance interference pilot frequency distribution method. The present invention then employs a partial update coordinate descent method to detect device activity in a pilot allocation pattern of min-max covariance interference. The method avoids the problem of overlarge pilot frequency cost in a non-orthogonal pilot frequency sequence detection algorithm, and has very important significance for processing the detection problem of active users in a mobile scene, so the method has certain practical value.
Description
Technical Field
The invention relates to the technical field of active user detection in a non-cellular large-scale MIMO system, in particular to a Covariance-based orthogonal pilot sequence active detection scheme which is used for a method for pilot frequency allocation by utilizing minimum-maximum Covariance Interference (MMCI) and a partial update coordinate descent method in large-scale unlicensed access of the non-cellular large-scale MIMO system.
Background
With the rapid development of the large-scale Internet of Things (iot), the requirements of large-scale machine type Communication and Ultra-Reliable Low-Latency Communication in the 5G standard are incorporated into a new 6G service, which becomes a requirement of large-scale Ultra Reliable Low Latency Communication (mwall). In order to guarantee the low-delay requirement of large-scale access delay sensitive service, a random access without authorization (GFRA) technology is proposed to reduce the access delay and signaling overhead. The first challenge faced by unlicensed random access technologies is active user detection. While there are a large number of potential devices connected to the network in an mliot, these devices are not in service need at all times, and only a small fraction of the devices are active at a given time. For ease of identification, each device is pre-assigned a pilot sequence when accessing the network. However, due to the limited time-frequency resources, it is not possible to assign mutually orthogonal pilot sequences to each device, but the mutual interference between devices using non-orthogonal pilot sequences is not negligible. Therefore, an effective scheme needs to be designed, the limitation of the limited orthogonal pilot sequence length on the device activity detection is overcome, and the pilot interference between devices is reduced, so that the accuracy of the active user detection of the mwrllc device in the mliot network is improved.
Disclosure of Invention
The technical problem is as follows: in view of this, the present invention provides an Orthogonal Pilot sequence based active Detection (OPSAD) scheme based on covariance, which combines the characteristics of a cellular-free distributed large-scale MIMO system and the good cross-correlation characteristics of Orthogonal Pilot Sequences, and can implement high-precision and low-complexity active device Detection. The invention designs a pilot frequency distribution method of minimum-maximum covariance interference, which determines the pilot frequency sequence sent by each access device in the communication system, the power of sending the pilot frequency and the AP cluster for detecting the active state of the device, and then adopts a scheme of detecting the device activity in the pilot frequency distribution mode by adopting a partial update coordinate descent method.
The technical scheme is as follows: the orthogonal pilot frequency sequence active detection method based on the covariance specifically comprises the following steps:
and 3, performing active user detection on the pilot frequency distribution algorithm minimizing the maximum interference among the devices in the covariance-based active user detection algorithm by adopting a partial update coordinate descent method.
Wherein, the step 1 specifically comprises:
step 101: in a large-scale MIMO system without a honeycomb, M Access Points (AP) with N antennae are randomly distributed, and all the APs are connected to a Central Processing Unit (CPU) through a return link; the total number of the devices in the system is K, and each device is assumed to be provided with an antenna; let k be the set [1, K]Is an integer of (1), the active state of the kth device is with an active identifier a k Is shown as a k Has two values of 0 or 1, wherein alpha k =1 indicates that the kth device is in active state, there is a service demand; alpha is alpha k =0 indicates that the kth device is inactive with no service demand; allocable in the systemThe total number of orthogonal pilot sequences is tau p Each pilot sequence has a length of L, and the pilot frequency allocated to the kth user is recorded as
Since there are multiple users sharing limited orthogonal pilots, the total number of orthogonal pilot sequences τ is set p < total equipment number K;
considering a block fading channel model, the length of each coherent block is τ, and m is set as the set [1, M ]]Is given as an integer, the channel matrix between the kth device and the mth AP is represented as:
wherein beta is mk Representing the large-scale fading coefficients of the channel between the kth device and the mth AP, and setting the large-scale fading coefficients beta mk The change is slow and assumed to be known by the CPU; h is mk A small-scale fast fading vector representing the channel between the kth device and the mth AP, subject to a mean of 0 and a correlation matrix of I N Multivariable circularly symmetric complex Gaussian distribution of I N An identity matrix representing N rows and N columns; assuming that the orthogonal pilot sequences assigned to the users are normalized, when l is the set [1, K ]]If the kth device and the l device use the same pilot sequence, the conjugate of the pilot of the kth device is multiplied by the pilot of the l device to be 1, otherwise, the conjugate of the pilot of the kth device is multiplied by the pilot of the l device to be 0;
in each coherent time block, all devices are divided into τ p Any one of the orthogonal pilot sequences, the pilot sequence to which the k-th device is assigned is represented as
In all the devices, only a small part of the devices are in an active state, and the small part of the active devices simultaneously transmit a pilot sequence pre-allocated to the small part of the devices to all the APs, so that a signal received by the mth AP is marked as Y m ：
Wherein, superscript symbol (·) H Which represents the transpose operation of the matrix,
is a pilot matrix which is a complex matrix of L rows and K columns, and>
denotes the pilot to which the kth device is assigned, D a ＝diag(a 1 ,a 2 ,…,a K ) A diagonal matrix being an active state matrix of the device, a k For the active identifier of the kth device, <' > H>
Transmission power matrix, p, for transmitting pilots for a device k For the pilot transmission power of the kth device, G m ＝[g m1 ,g m2 ,…,g mK ]Channel matrix representing all devices to the mth AP, g mk Is a channel matrix between the kth device and the mth AP, W m Representing an additive complex white Gaussian noise matrix, W m Is independent of and obeys->
Symbol->
Representing zero mean and a correlation coefficient of σ 2 Of a multivariate, circularly symmetric complex Gaussian distribution, σ 2 Is the variance value of the channel noise;
step 102: according to formula (1), the mth AP receives signal Y m ＝[y m1 ,…,y mn ,…,y mN ]In which
Representing the signal received by the nth antenna of the mth AP, g mnk Representing the channel between the kth device and the nth antenna of the mth AP, the received channels on the different antennas being independent, so Y m Of (2) covariance matrix Q m Can be defined as:
wherein I L An identity matrix representing L rows and L columns, and y is obtained according to the definition of covariance mn Obey mean of 0 and correlation matrix of Q m Is a multivariate circularly symmetric complex gaussian distribution.
The step 2 specifically comprises:
step 201: suppressing inter-device interference using the same pilot by AP selection and power control;
on a per received signal basis, covariance samples may be obtained
Having sufficient statistical properties for active devices; activity detection in the mth AP for the kth device is based on>
Signal component of
Because of the reuse of orthogonal pilots, the good-signal components transmitted by devices with the same pilot are indistinguishable in covariance samples; detecting active information of different equipment by using different AP sets by using macro diversity gain and sparsity of a power domain of a non-cellular large-scale MIMO system;
denote the set of APs used to detect the k device activity as
With->
Including the increase in the number of APs,the macro diversity gain of the kth device will increase, but the interference using the same pilot will also increase, thus selecting ≦ for each device>
Can inhibit certain interference between the devices and will->
Is indicated as ≥ all APs receiving the pilot signal>
Its covariance is expressed as->
Based on &ifthe interfering signals of other devices using the same pilot are negligibly weak in each device's serving AP cluster>
The active user detection will become more accurate;
because the accuracy improvement of the covariance information collected by the AP far from the kth device on the active detection of the kth device is limited, and the covariance of the pilot signal received by the AP with the optimal channel condition with the kth device can already ensure the accurate active detection of the kth device; therefore, a master AP is selected for each device to detect the activity of the device, and the index of the master AP of the kth device is selected according to formula (3):
wherein
Indicates that order beta is found mk The maximum value of m;
in order to further reduce the interference between the devices, pilot frequency transmission power control is introduced to the devices, and the transmission power of the kth device is selected as follows:
where ρ is ds The expected received channel power of the master AP of the kth device is set to be the same for all devices, and the physical meaning of equation (4) is: if the kth device has an index number of the main AP of the kth device
If the channel condition of the AP is good enough, the kth device does not use too much transmission power; otherwise, a larger transmitting power rho is used k To make up for the lack of channel conditions; if p calculated by equation (4) k Exceeding the maximum transmission power that the kth device can provide, the kth device cannot access the network because the signal transmitted by the kth device cannot be received by any AP;
step 202: defining a new inter-device interference metric to efficiently compute the impact of interference on active user detection;
based on the AP selection and power control in the previous step, the inter-device interference in the covariance detection algorithm is further reduced by using the allocation method, and as can be seen from formula (2), if the kth device and the l-th device are allocated the same pilot frequency, their signal components are indistinguishable at their respective main APs, wherein the interference degree of the l-th device to the kth device can be used
Where p is l And ρ k Indicates the pilot transmission power, < '> or <' > of the i-th device and the k-th device, respectively>
Indicating the lth device and the th>
A large-scale fading coefficient of a channel between the APs, <' > or>
Indicating the kth device and the ^ th>
Large-scale fading coefficients of channels between APs; />
Thus, the interference ζ between the l-th device and the k-th device is defined kl Is defined as follows:
wherein equation (b) is represented by ∑ kl ＝ζ lk Obtaining that the interference of the kth device to the ith device is equal to the interference of the ith device to the kth device;
step 203: minimizing the pilot allocation algorithm based on the maximum interference between devices in the covariance active user detection algorithm,
in a large-scale high-reliability low-delay scenario, the number of APs is much larger than the total number K of devices, and at this time, interference of pilot frequency multiplexing is inevitably generated, but if each device and adjacent devices allocate the middle orthogonal pilot frequency and all devices using the same pilot frequency are located far apart from each other in terms of geographical location, it is ensured that the maximum interference among all devices is minimized, and the pilot frequency allocation algorithm of the kth device is formulated as:
where f represents the maximum inter-device interference value for the kth device among all devices using the same pilot as the kth device, and the objective of the optimization problem is to have the pilot selected by the kth device
F can be minimized;
to solve the problem
An algorithm that minimizes the maximum interference can be used to first create an interference graph with weighted edges between all devices, and if two devices use the same pilot, the weighted value between the devices is ζ in equation (5) kl (ii) a If two devices use different pilots, the weighted value is 0, then a pilot allocation mode for minimizing the maximum inter-device interference is found by iterating the situation that each pilot is adopted on each device, and the iteration is carried out until the pilot matrix phi is not changed any more.
The step 3 specifically includes:
step 301: the activity detection of the kth device can be formulated as a maximum likelihood problem;
the active user detection problem is essentially a maximum likelihood problem when the k-th device is active a k When determined, by
In pilot signal received by all APs>
Calculating to obtain the activity state detection value of the kth equipment
If/or>
Detecting the active user correctly, and if not, detecting the active user incorrectly; the specific calculation process is as follows:
since all large scale fading is assumed to be known, then it is observed
The likelihood function of (d) can be expressed as:
in the formula (I), the compound is shown in the specification,
is denoted by a k When the value of->
In a probability distribution of +>
Representation pair matrix
Summing diagonal elements thereof>
Denotes that the base number is a natural constant e and the index is ^ er>
Is a number, | π Q m I represents the circumferential ratio pi multiplied by Q m Then forming the determinant value of the new matrix;
where the active indications of devices other than the kth device are ignored and are considered to be constant, then the maximum value of equation (10) is equivalent to
So the active user detection problem for the kth device can be expressed as: />
Namely at a k E {0,1} under the constraint of the condition
Minimum value of the number
Φ、a k A value of (d);
step 302: a scheme for detecting the activity of the equipment is simplified by adopting a partial update coordinate descent method,
according to a minimum-maximum covariance interference pilot frequency distribution algorithm, each device only carries out active detection on the device by a main AP of the device, and a pilot frequency matrix phi adopted by each device is also selected; defining its generalized active state identifier gamma for the kth device k ＝a k ρ k And the generalized active state identifier detection value obtained during the calculation of the active user detection algorithm is recorded as
Then->
The active user detection problem can be simplified as follows:
namely at
Within the constraints of this condition, find the ^ or ^ er>
Is minimum value->
A value of (d); the coordinate descent method may be used to solve for mid->
Will->
First initialized to matrix σ 2 I, I denotes the unit matrix, which is first evaluated by the present @ineach iteration>
Solving for>
Get->
Then solved for>
Update>
The iterative process is explained in detail below;
setting the maximum iteration number as T, and taking T as a set [1, T]Any integer of the above, and the calculated generalized active standard state identifier detection value of the kth device in the t-1 iteration
Is recorded as->
Based on the generalized active flag state identifier detection value ≧ calculated during the tth iteration>
Is recorded as->
And that in the t-1 th iteration>
By adding the optimum updated variation value d calculated during the t-th iteration t Get->
I.e. is>
It is therefore necessary to find the optimum updated change value d for each iteration t So that the result of an iteration is->
Closer and closer to the actual value; d t The solution process of (2) is as follows:
firstly, applying a Sherman-Morrison updating algorithm,
can be updated as:
wherein
Is a number which can be calculated such that->
The optimization problem of (a) can be rewritten as:
namely at
Under the constraint of this condition, find c k +loge k -b k Is minimum value->
A value of (d); wherein
Is independent of d t Is constant,. Is greater than or equal to>
Is one and d t The relevant number; />
Thus to loge k -b k By taking the derivative and finding the value that makes it equal to 0, the best updated variance d in the t-th iteration is solved t It is substantially equal to
To ensure
Is not negative, prevents on>
Calculated->
Is a negative number, and needs to be in pair d t Adding a heavy guarantee: />
I.e. if->
Then d t Fetch and hold>
The greater of the number of the first and second sets, and then pass through>
The calculated ^ during the tth iteration is obtained>
So that the t-th iteration ends; then starting the T +1 th iteration until the maximum iteration time T is reached,
according to the best obtained after the end of T iterations
Activity status detection value for a kth device>
Can be obtained by (15):
wherein
Is the threshold value detected by the active user of the kth device when->
Then the active status detection value for the kth device is detected>
Takes a value of 1 when>
When, is greater or less>
The value is 0; if the activation status detection value of the kth device->
Equal to the active identifier a of the kth device k The active user detects correctly, if not, detects an error.
Has the advantages that: the invention provides an orthogonal pilot frequency sequence active detection method based on covariance. Aiming at the problem of active user detection in large-scale authorization-free access in a non-cellular distributed large-scale MIMO communication system, the power domain sparsity of the non-cellular distributed large-scale MIMO system is fully utilized while pilot frequency overhead is reduced, and the bottleneck problem that the traditional active user detection algorithm is influenced by various interferences is broken through. The invention firstly provides a covariance interference measurement mode among devices in a communication system, and determines a pilot sequence sent by each access device, power for sending the pilot and an AP cluster for detecting the active state of the device in the communication system by using a minimum-maximum covariance interference (MMCI) pilot distribution method. The present invention then employs a partial update coordinate descent method to detect device activity in a pilot allocation pattern of minimum-maximum inter-device covariance interference. Compared with the traditional active user detection method based on covariance, the method provided by the invention firstly reduces the interference between devices by using AP clustering, power control and pilot frequency distribution, improves the accuracy of a detection algorithm, and then uses a partial update coordinate descent method to reduce the calculation complexity and increase the calculation speed, thereby having very important significance for processing the detection problem of active users in a mobile scene.
Drawings
FIG. 1 is an overall flow diagram of a covariance-based Orthogonal Pilot Sequence Active Detection (OPSAD) algorithm;
fig. 2 shows a comparison of the performance of 4 different active user detection methods under a given parameter configuration, each point representing the result of one detection threshold, the corresponding false alarm probability and missed detection probability being shown in abscissa and ordinate, respectively. The CAD expresses an active user detection algorithm based on clustering, and the I expresses the number of the APs in each AP cluster; E-OPSAD represents an enhanced OPSAD algorithm where the active state identifier of each device is used to update the covariance matrix for all access points; S-CAD represents a simplified CAD algorithm, where the active state identifier of each device is used to update the covariance matrix of its designated access point;
FIG. 3 shows the trend of performance of the OPSAD and CAD algorithms as a function of the number of antennas per AP, the number of APs, and the SNR variation;
fig. 4 shows the effect of different pilot sequence lengths L and signal-to-noise ratios SNR on the OPSAD and CAD algorithms.
Detailed Description
The present invention will be described in detail below with reference to examples:
suppose a non-cellular distributed massive MIMO scenarioThere are M =800 APs, each AP is equipped with N antennas, and there are K =400 devices in the scene. All APs and equipment are randomly distributed in an area with the radius of r =0.5km, and the large-scale fading coefficient is set to be
Wherein d is mk Denotes the distance between the mth AP and the kth device, F mk Represents a shadow fading component that is subject to a mean value of 0 and a correlation coefficient of->
Multivariable cycle symmetric complex Gaussian distribution->
Representing the shadow fading variance value.
The system has symbol number tau =200 and noise variance sigma in a coherent time block 2 = -109dBm. The orthogonal pilot sequences are generated by a Discrete Fourier Transform (DFT) matrix in the Galois field and the non-orthogonal pilot sequences are generated by a random complex gaussian matrix whose elements satisfy a multivariate circularly symmetric complex gaussian distribution with a mean of the distribution of 0 and a correlation coefficient of 1.
Based on the non-cellular distributed massive MIMO scenario provided above, the covariance-based Orthogonal Pilot Sequence Active Detection (OPSAD) scheme provided in this example specifically includes the following steps:
In this example, step 1 specifically includes:
step 101: considering a block fading channel model, each coherent block has a length τ, and the channel matrix between the kth device and the mth AP is represented as:
wherein beta is mk Large scale fading coefficients representing the channel between the kth device and the mth AP may be combined by +>
Calculating; h is mk A small-scale fast fading vector representing the channel between the kth device and the mth AP, subject to a mean of 0 and a correlation matrix of I N Multivariable circularly symmetric complex gaussian distribution of (I) N An identity matrix representing N rows and N columns; assuming that the orthogonal pilot sequences assigned to the users are normalized, when l is the set [1, K ]]If the kth device and the l device use the same pilot sequence, the conjugate of the pilot of the kth device is multiplied by the pilot of the l device to be 1, otherwise, the conjugate of the pilot of the kth device is multiplied by the pilot of the l device to be 0;
in each coherent time block, all devices are divided into τ p Any one of the orthogonal pilot sequences, the pilot sequence to which the k-th device is assigned is represented as
In all the devices, only a small part of the devices are in an active state, and the small part of the active devices simultaneously transmit a pilot sequence pre-allocated to the small part of the devices to all the APs, so that a signal received by the mth AP is marked as Y m ：
Wherein, superscript symbol (·) H Which represents the transpose operation of the matrix,
is a pilot matrix which is a complex matrix of L rows and K columns>
Denotes the pilot to which the kth device is assigned, D a ＝diag(a 1 ,a 2 ,…,a K ) A diagonal matrix being an active state matrix of the device, a k For active identification of kth deviceSymbol,. Or>
Transmission power matrix, p, for transmitting pilots for a device k For pilot transmission power of kth device, G m ＝[g m1 ,g m2 ,…,g mK ]Channel matrix representing all devices to the mth AP, g mk Is a channel matrix between the kth device and the mth AP, W m Representing an additive complex white Gaussian noise matrix, W m Is independent of and obeys->
Symbol->
Representing zero mean and a correlation coefficient of σ 2 Of a multivariate, circularly symmetric complex Gaussian distribution, σ 2 Is the variance value of the channel noise; />
Step 102: according to formula (1), the mth AP receives signal Y m ＝[y m1 ,…,y mn ,…,y mN ]In which
Representing the signal received by the nth antenna of the mth AP, g mnk Representing the channel between the kth device and the nth antenna of the mth AP, the received channels on the different antennas being independent, so Y m Of the covariance matrix Q m Can be defined as:
in which I L An identity matrix representing L rows and L columns, and y is obtained according to the definition of covariance mn Obey mean value of 0 and correlation matrix of Q m A multivariate, circularly symmetric complex gaussian distribution.
And 2, defining new inter-device interference measurement to calculate the influence of interference on the detection of active users, designing a pilot frequency distribution method of minimum-maximum covariance interference, determining a pilot frequency sequence transmitted by the device which minimizes the interference among various access devices in the communication system, transmitting the power of the pilot frequency, and detecting the AP cluster of each device in an active state.
In this example, step 2 specifically includes:
step 201: suppressing inter-device interference using the same pilot by AP selection and power control;
on a per received signal basis, covariance samples may be obtained
Having sufficient statistical properties for active devices; the detection of activity in the mth AP for the kth device is based on->
Signal component of
Because of the reuse of orthogonal pilots, the good-signal components transmitted by devices with the same pilot are indistinguishable in covariance samples; detecting active information of different equipment by using different AP sets by using macro diversity gain and sparsity of a power domain of a non-cellular large-scale MIMO system;
denote the set of APs used to detect the k device activity as
Is along with->
Involving an increase in the number of APs, the macro diversity gain of the kth device increases, but the interference using the same pilot also increases, thus selecting ≧ for each device>
Can inhibit certain interference between the devices and will->
Is indicated as ≥ all APs receiving the pilot signal>
Its covariance is expressed as->
Based on ≧ if the interfering signal of other devices using the same pilot is negligibly weak in each device's serving AP cluster>
The active user detection will become more accurate;
because the accuracy improvement of the covariance information collected by the AP far from the kth device on the active detection of the kth device is limited, and the covariance of the pilot signal received by the AP with the optimal channel condition with the kth device can already ensure the accurate active detection of the kth device; so a master AP is selected for each device to detect the activity of this device, and the index of the master AP of the kth device is selected by equation (3):
wherein
Indicates that order beta is found mk The maximum value of m;
in order to further reduce the interference between the devices, pilot frequency transmission power control is introduced to the devices, and the transmission power of the kth device is selected as follows:
where ρ is ds The reception channel power expected by the master AP of the kth device is set so that the master AP expects reception for all the devicesThe channel power is the same, and the physical meaning of the formula (4) is: if the kth device has an index number of the main AP of the kth device
If the channel condition of the AP is good enough, the kth device does not use too much transmission power; otherwise, a larger transmitting power rho is used k To make up for the lack of channel conditions; if p calculated by equation (4) k Exceeding the maximum transmission power that the kth device can provide, the kth device cannot access the network because the signal transmitted by the kth device cannot be received by any AP;
step 202: defining a new inter-device interference metric to efficiently compute the impact of interference on active user detection;
based on the AP selection and power control in the previous step, the method of allocation is further used to reduce the inter-device interference in the covariance detection algorithm, and it can be seen from equation (2) that if the kth device and the l-th device are allocated the same pilot, their signal components are indistinguishable at their respective primary APs, wherein the interference level of the l-th device to the kth device can be used
Where p is l And ρ k Indicates the pilot transmission power, < '> or <' > of the i-th device and the k-th device, respectively>
Indicating the lth device and the th>
A large-scale fading coefficient of a channel between the APs, <' > or>
Indicating the kth device and the ^ th>
Large-scale fading coefficients of channels between APs;
thus, the interference ζ between the l-th device and the k-th device is defined kl Is defined as:
wherein equation (b) is represented by ζ kl ＝ζ lk Obtaining that the interference of the kth device to the ith device is equal to the interference of the ith device to the kth device;
step 203: a pilot allocation algorithm that minimizes the maximum inter-device interference in an active covariance-based user detection algorithm,
in a large-scale high-reliability low-delay scenario, the number of APs is much larger than the total number K of devices, and at this time, interference of pilot frequency multiplexing is inevitably generated, but if each device and adjacent devices allocate the middle orthogonal pilot frequency and all devices using the same pilot frequency are located far apart from each other in terms of geographical location, it is ensured that the maximum interference among all devices is minimized, and the pilot frequency allocation algorithm of the kth device is formulated as:
where f represents the maximum inter-device interference value for the kth device among all devices using the same pilot as the kth device, and the objective of the optimization problem is to have the pilot selected by the kth device
F can be minimized;
to solve the problem
An algorithm for minimizing the maximum interference can be used, where an interference graph with weighted edges between all devices is first created, and if two devices use the same pilot, the weighted value between the devices is ζ in equation (5) kl (ii) a If two devices use different pilots, the weight is 0, and then through iterationAnd finding a pilot frequency distribution mode for minimizing the maximum inter-device interference by adopting the condition of each pilot frequency on each device, and iterating until the pilot frequency matrix phi is not changed any more.
And 3, performing active user detection on the pilot frequency distribution algorithm minimizing the maximum interference among the devices in the covariance-based active user detection algorithm by adopting a partial update coordinate descent method.
In this example, step 3 specifically includes:
step 301: the activity detection of the kth device can be formulated as a maximum likelihood problem;
the active user detection problem is essentially a maximum likelihood problem when the k-th device is in active state a k When determined, by
In all APs received pilot signal->
A calculation is carried out which results in an activity status detection value ≥ for the kth device>
If/or>
Detecting the active user correctly, and if not, detecting an error; the specific calculation process is as follows:
since all large scale fading is assumed to be known, then it is observed
The likelihood function of (d) can be expressed as:
in the formula (I), the compound is shown in the specification,
is denoted by a k When the value of->
In a probability distribution of +>
Representation pair matrix
Summing its diagonal elements>
Denotes that the base number is a natural constant e and the index is ^ er>
Is a number, | π Q m I represents the circumferential ratio pi multiplied by Q m Then forming the determinant value of the new matrix;
where the active indications of devices other than the kth device are ignored and are considered to be constant, then the maximum value of equation (10) is equivalent to
So the active user detection problem for the kth device can be expressed as:
namely at a k E {0,1} under the constraint of the condition
Minimum value->
D ρ 、Φ、a k A value of (d);
step 302: the scheme of detecting the activity of the equipment is simplified by adopting a partial update coordinate descent method,
according to a minimum-maximum covariance interference pilot frequency distribution algorithm, each device only carries out active detection on the device by a main AP of the device, and a pilot frequency matrix phi adopted by each device is also selected; defining its generalized active state identifier gamma for the kth device k ＝a k ρ k And the generalized active state identifier detection value obtained during the calculation of the active user detection algorithm is recorded as
Then->
The active user detection problem can be simplified as follows:
namely at
Within the constraints of this condition, find the ^ or ^ er>
Is minimum value->
A value of (d); the coordinate descent method may be used to solve for mid->
Will->
First initialized to matrix sigma 2 I, I denotes the unit matrix, which is first evaluated by the present @ineach iteration>
Solving for>
Get->
Then solved for>
Update>
The iterative process is explained in detail below;
setting the maximum iteration number as T, and taking T as a set [1, T]Any integer of the generalized active flag state identifier detection values of the kth device to be calculated in the process of the t-1 iteration
Is recorded as->
Based on the generalized active flag state identifier detection value ≧ calculated during the tth iteration>
Is recorded as->
And on the t-1 th iteration>
By adding the optimum updated variation value d calculated during the t-th iteration t Get->
I.e. based on>
It is therefore necessary to find the optimum updated change value d for each iteration t To enable iteration out/>
Approaching the actual value more and more; d t The solution process of (2) is as follows: />
Firstly, applying a Sherman-Morrison updating algorithm,
can be updated as:
wherein
Is a number which can be calculated such that->
The optimization problem of (a) can be rewritten as:
namely at
Under the constraint of this condition, find c k +loge k -b k Is minimum value->
A value of (d); wherein
Is independent of d t Is constant,. Is greater than or equal to>
Is one and d t The number of related;
thus to loge k -b k By taking the derivative and finding the value that makes it equal to 0, the best updated variance d in the t-th iteration is solved t It is equal to
To ensure
Is not negative, prevents on>
Calculated>
Is a negative number, and needs to be in pair d t Adding a heavy guarantee: />
I.e. if->
Then d t Fetch and hold>
The greater of the one of (a) and (b), then passes through>
The calculated ^ during the tth iteration is obtained>
So that the t-th iteration ends; then starting the T +1 th iteration until the maximum iteration time T is reached,
according to the best obtained after the end of T iterations
Activity status detection value for a kth device>
Can be obtained by (15):
wherein
Is the threshold value detected by the active user of the kth device when->
Then the active status detection value for the kth device is detected>
Takes a value of 1 when>
When, is greater or less>
The value is 0; if the activation status detection value of the kth device->
Equal to the active identifier a of the kth device k The active user detects correctly, if not, detects an error.
The above demonstrates the overall process of active user detection in a cellular-free distributed massive MIMO system using the method provided by this example.
FIG. 1 is an overall flow chart of the OPSAD algorithm;
fig. 2 shows a comparison of the performance of 4 different active user detection methods under a given parameter configuration. Each point represents the result of a detection threshold, and the corresponding false alarm probability and missed detection probability are displayed in the abscissa and ordinate, respectively. The CAD represents an active user detection algorithm based on clustering, and I represents the number of APs in each AP cluster; E-OPSAD represents an enhanced OPSAD algorithm where the active state identifier of each device is used to update the covariance matrix for all access points; S-CAD represents a simplified CAD algorithm in which the active state identifier of each device is used to update the covariance matrix of its designated access point. It can be seen that the OPSAD algorithm outperforms the CAD algorithm when I =1, but the performance improvement of the CAD algorithm drops sharply with increasing I. This indicates that a reduction in the update frequency of the covariance matrix estimated at the AP results in a significant reduction in the detection performance of CAD algorithms with non-orthogonal pilots, but has little impact on the detection performance with orthogonal pilots, which demonstrates the effectiveness of the proposed OPSAD algorithm in reducing inter-device interference in the covariance information at the AP;
fig. 3 shows the trend of performance of the OPSAD and CAD algorithms as a function of the number of antennas per AP, the number of APs, and the SNR value. As the number of antennas increases, the performance of both algorithms improves significantly. But when the signal-to-noise ratio is low (less than or equal to 10 in the figure), the performance of the CAD algorithm is always worse than that of the OPSAD algorithm under the same antenna configuration even if there are more access points;
fig. 4 shows the effect of different pilot sequence lengths L and signal-to-noise ratios SNR on the OPSAD algorithm and the CAD algorithm. The error probability of both algorithms decreases rapidly with increasing signal-to-noise ratio, but the performance of the OPSAD algorithm is always better than that of the CAD algorithm, and the OPSAD algorithm can reach the upper limit of performance under a shorter pilot sequence, requiring a shorter pilot sequence than the CAD algorithm to achieve the same detection performance.
In conclusion, the invention provides an orthogonal pilot frequency sequence active detection method based on covariance aiming at the problem of active user detection in an mURLLC scene. The method has the advantages that the pilot frequency overhead is reduced, meanwhile, the power domain sparsity of the large-scale MIMO system without the cellular distribution is fully utilized, and the bottleneck problem that the traditional active user detection algorithm is influenced by various interferences is broken through. The method improves the detection precision and simultaneously reduces the complexity of calculation, and has very important significance for processing the detection problem of active users in the mobile scene, so the method has certain practical value.
The invention is not described in detail, but is well known to those skilled in the art.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the prior art according to the concepts of the present invention should be within the scope of protection determined by the claims.
Claims (4)
1. An active detection method of an orthogonal pilot frequency sequence based on covariance is characterized by comprising the following steps:
step 1, establishing a channel model of a non-cellular large-scale MIMO system to obtain an expression of a received signal covariance matrix for active user detection;
step 2, defining new inter-device interference measurement to calculate the influence of interference on the detection of active users, designing a pilot frequency distribution method of minimum-maximum covariance interference, determining a pilot frequency sequence sent by a device which causes the minimum interference among various access devices in a communication system, sending the power of the pilot frequency, and detecting an AP cluster of each device in an active state;
and 3, performing active user detection on the pilot frequency distribution algorithm minimizing the maximum interference among the devices in the covariance-based active user detection algorithm by adopting a partial update coordinate descent method.
2. The method for active detection of orthogonal pilot sequence based on covariance as claimed in claim 1, wherein step 1 specifically comprises:
step 101: in a large-scale MIMO system without a honeycomb, M Access Points (AP) with N antennae are randomly distributed, and all the APs are connected to a Central Processing Unit (CPU) through a return link; the total number of devices in the system is K, and each device is assumed to be provided with an antenna; let k be the set [1, K]Is an integer of (1), the active state of the kth device is with an active identifier a k Is shown as a k With two values of 0 or 1Wherein a is k =1 indicates that the kth device is in active state, there is a service demand; a is k =0 indicates that the kth device is inactive with no service demand; the total number of orthogonal pilot sequences which can be allocated in the system is tau p Each pilot sequence has a length of L, and the pilot frequency allocated to the kth user is recorded as
Since there are multiple users sharing limited orthogonal pilots, the total number of orthogonal pilot sequences τ is set p < total equipment number K;
considering a block fading channel model, the length of each coherent block is τ, and m is set as a set [1, M]Is given as an integer, the channel matrix between the kth device and the mth AP is represented as:
wherein beta is mk A large-scale fading coefficient beta is set to represent the channel between the kth device and the mth AP mk The change is slow and assumed to be known by the CPU; h is mk A small scale fast fading vector representing the channel between the kth device and the mth AP, subject to a mean of 0 and a correlation matrix of I N Multivariable circularly symmetric complex Gaussian distribution of I N An identity matrix representing N rows and N columns; assuming that the orthogonal pilot sequences assigned to the users are normalized, when l is the set [1, K ]]If the kth device and the l device use the same pilot sequence, the conjugate of the pilot of the kth device is multiplied by the pilot of the l device to be 1, otherwise, the conjugate of the pilot of the kth device is multiplied by the pilot of the l device to be 0;
in each coherent time block, all devices are divided into τ p Any one of the orthogonal pilot sequences, the pilot sequence to which the k-th device is assigned is represented as
Of all the devices, only a small part of the devices are inIn active state, the small part of active devices simultaneously transmit pilot sequences pre-allocated to the devices to all APs, and then the signal received by the mth AP is marked as Y m ：
Wherein, superscript symbol (·) H Which represents the transpose operation of the matrix,
is a pilot matrix which is a complex matrix of L rows and K columns, and>
denotes the pilot to which the kth device is assigned, D a ＝diag(a 1 ,a 2 ,…,a K ) A diagonal matrix being an active state matrix of the device, a k For the active identifier of the kth device, <' > H>
Transmission power matrix, p, for transmitting pilots for a device k For pilot transmission power of kth device, G m ＝[g m1 ,g m2 ,…,g mK ]Channel matrix representing all devices to the mth AP, g mk Is a channel matrix between the kth device and the mth AP, W m Representing an additive complex white Gaussian noise matrix, W m Is independent of and obeys->
Symbol->
Representing zero mean and a correlation coefficient of σ 2 Of a multivariate, circularly symmetric complex Gaussian distribution, σ 2 Is the variance value of the channel noise;
step 102: according toEquation (1), signal Y received by mth AP m ＝[y m1 ,…,y mn ,…,y mN ]Wherein
Representing the signal received by the nth antenna of the mth AP, g mnk Representing the channel between the kth device and the nth antenna of the mth AP, the received channels on the different antennas being independent, so Y m Of (2) covariance matrix Q m Can be defined as:
wherein I L An identity matrix representing L rows and L columns, and y is obtained according to the definition of covariance mn Obey mean value of 0 and correlation matrix of Q m A multivariate, circularly symmetric complex gaussian distribution.
3. The method for detecting the active orthogonal pilot sequence based on the covariance as claimed in claim 2, wherein the step 2 specifically comprises:
step 201: suppressing inter-device interference using the same pilot by AP selection and power control;
on a per received signal basis, covariance samples may be obtained
Having sufficient statistical properties for active devices; the detection of activity in the mth AP for the kth device is based on->
Signal component in->
Because of the reuse of orthogonal pilots, the good-signal components transmitted by devices with the same pilot are indistinguishable in covariance samples; detecting active information of different equipment by using different AP sets by using macro diversity gain and sparsity of a power domain of a non-cellular large-scale MIMO system;
denote the set of APs used to detect the k device activity as
Is along with->
Involving an increase in the number of APs, the macro diversity gain of the kth device increases, but the interference using the same pilot also increases, thus selecting ≧ for each device>
Can inhibit certain interference between the devices and will->
Is indicated as ≥ all APs receiving the pilot signal>
Its covariance is expressed as->
Based on &ifthe interfering signals of other devices using the same pilot are negligibly weak in each device's serving AP cluster>
The active user detection will become more accurate;
because the accuracy improvement of the covariance information collected by the AP far from the kth device on the active detection of the kth device is limited, and the covariance of the pilot signal received by the AP with the optimal channel condition with the kth device can already ensure the accurate active detection of the kth device; so a master AP is selected for each device to detect the activity of this device, and the index of the master AP of the kth device is selected by equation (3):
wherein
Indicates that order beta is found mk The maximum value of m;
in order to further reduce the interference between the devices, pilot frequency transmission power control is introduced to the devices, and the transmission power of the kth device is selected as follows:
where ρ is ds The expected received channel power of the master AP of the kth device is set to be the same for all devices, and the physical meaning of equation (4) is: if the kth device has an index number of the main AP of the kth device
If the channel condition of the AP is good enough, the kth device does not use too much transmission power; otherwise, a larger transmitting power rho is used k To make up for the lack of channel conditions; if p calculated by equation (4) k Exceeding the maximum transmission power that the kth device can provide, the kth device cannot access the network because the signal transmitted by the kth device cannot be received by any AP;
step 202: defining a new inter-device interference metric to efficiently compute the impact of interference on active user detection;
further reduction in allocation based on AP selection and power control in previous stepsIn the covariance detection algorithm, as shown in formula (2), if the kth device and the ith device are assigned the same pilot, their signal components are indistinguishable at their respective primary APs, where the interference level of the kth device to the kth device is available
Where p is l And ρ k Indicates the pilot transmission power, < '> or <' > of the i-th device and the k-th device, respectively>
Indicating the lth device and the th>
Large scale fading coefficients for a channel between APs, <' > based on the number of APs>
Denotes the kth device and the
Large-scale fading coefficients of channels between APs;
thus, the interference ζ between the l-th device and the k-th device is defined kl Is defined as:
wherein equation (b) is represented by ζ kl ＝ζ lk Obtaining that the interference of the kth device to the ith device is equal to the interference of the ith device to the kth device;
step 203: minimizing the pilot allocation algorithm based on the maximum interference between devices in the covariance active user detection algorithm,
in a large-scale high-reliability low-delay scenario, the number of APs is much larger than the total number K of devices, and at this time, interference of pilot frequency multiplexing is inevitably generated, but if each device and adjacent devices allocate the middle orthogonal pilot frequency and all devices using the same pilot frequency are located far apart from each other in terms of geographical location, it is ensured that the maximum interference among all devices is minimized, and the pilot frequency allocation algorithm of the kth device is formulated as:
where f represents the maximum inter-device interference value for the kth device among all devices using the same pilot as the kth device, and the objective of the optimization problem is to have the pilot selected by the kth device
F can be minimized;
to solve the problem
An algorithm that minimizes the maximum interference can be used to first create an interference graph with weighted edges between all devices, and if two devices use the same pilot, the weighted value between the devices is ζ in equation (5) kl (ii) a If two devices use different pilots, the weighted value is 0, then a pilot allocation mode for minimizing the interference between the maximum devices is found by iterating the situation of each pilot adopted on each device, and the iteration is carried out until the pilot matrix phi is not changed any more.
4. The method for active detection of orthogonal pilot sequences based on covariance as claimed in claim 1, wherein step 3 specifically comprises:
step 301: the activity detection of the kth device can be formulated as a maximum likelihood problem;
the active user detection problem is essentially a maximum likelihood problem when the k-th device is active a k When determined, by
In all APs received pilot signal->
A calculation is carried out which results in an activity status detection value ≥ for the kth device>
If it is
Detecting the active user correctly, and if not, detecting an error; the specific calculation process is as follows:
since all large scale fading is assumed to be known, then it is observed
The likelihood function of (d) can be expressed as: />
In the formula (I), the compound is shown in the specification,
is denoted by a k When the value of->
Is based on the probability distribution of->
Represents a pair matrix pick>
Summing diagonal elements thereof>
Denotes that the base number is a natural constant e and the index is ^ er>
Is a number, | π Q m I represents the circumferential ratio pi multiplied by Q m Then forming the determinant value of the new matrix;
where the active indications of devices other than the kth device are ignored and are considered to be constant, then the maximum value of equation (10) is equivalent to
So the active user detection problem for the kth device can be expressed as:
namely at a k E {0,1} under the constraint of the condition
Is minimum value->
D ρ 、Φ、a k A value of (d);
step 302: a scheme for detecting the activity of the equipment is simplified by adopting a partial update coordinate descent method,
according to a minimum-maximum covariance interference pilot frequency distribution algorithm, each device only carries out active detection on the device by a main AP of the device, and a pilot frequency matrix phi adopted by each device is also selected; defining its generalized active state identifier gamma for the kth device k ＝a k ρ k And the generalized active state identifier detection value obtained during the calculation of the active user detection algorithm is recorded as
Then->
The active user detection problem can be simplified as follows:
namely at
Within the constraints of this condition, find an on/off ratio>
Is minimum value->
A value of (d); the coordinate descent method may be used to solve for mid->
Will->
First initialized to matrix σ 2 I, I denotes the unit matrix, which is first evaluated by the present @ineach iteration>
Solving for>
Get->
And then solved->
Update>
The iterative process is explained in detail below;
setting the maximum iteration number as T, and taking T as a set [1, T]Any integer of the generalized active flag state identifier detection values of the kth device to be calculated in the process of the t-1 iteration
Is recorded as->
Based on the generalized active flag state identifier detection value calculated during the t-th iteration>
Is recorded as->
And on the t-1 th iteration>
By adding the optimum updated variation value d calculated during the t-th iteration t Get->
I.e. based on>
It is therefore necessary to find the best updated change value d for each iteration t So that the result of an iteration is->
Approaching the actual value more and more; d t The solution process of (2) is as follows:
firstly, applying a Sherman-Morrison updating algorithm,
can be updated as:
wherein
Is a number which can be calculated such that->
The optimization problem of (a) can be rewritten as:
namely at
Under the constraint of this condition, find c k +loge k -b k Is minimum value->
A value of (d); wherein
Is independent of d t Is constant,. Is greater than or equal to>
Is one and d t The number of related;
thus to loge k -b k By taking the derivative and finding the value that makes it equal to 0, the best updated variance d in the t-th iteration is solved t It is substantially equal to
To ensure
Is not negative, prevents on>
Calculated->
Is a negative number, and needs to be in pair d t Adding a heavy guarantee: />
I.e. if->
Then d t Fetch and hold>
Is then passed through
The calculated ^ during the tth iteration is obtained>
So that the t-th iteration ends; then starting the T +1 th iteration until the maximum iteration time T is reached,
according to the best obtained after the end of T iterations
Activity status detection value for a kth device>
Can be used forObtaining by (15):
wherein
Is the threshold value detected by the active user of the kth device when->
Then the active status detection value for the kth device is detected>
Takes a value of 1 when>
When, is greater or less>
The value is 0; if the activation status detection value of the kth device->
Equal to the active identifier a of the kth device k The active user detects correctly, if not, detects an error. />
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