CN114650608A - Multi-central processing unit cooperation method for removing cellular massive MIMO - Google Patents

Multi-central processing unit cooperation method for removing cellular massive MIMO Download PDF

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CN114650608A
CN114650608A CN202210405586.4A CN202210405586A CN114650608A CN 114650608 A CN114650608 A CN 114650608A CN 202210405586 A CN202210405586 A CN 202210405586A CN 114650608 A CN114650608 A CN 114650608A
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CN114650608B (en
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孙强
李飞洋
章嘉懿
张子涵
纪晓迪
陈晓敏
邵蔚
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Nantong University
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    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/12—Wireless traffic scheduling
    • H04W72/1215—Wireless traffic scheduling for collaboration of different radio technologies
    • H—ELECTRICITY
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    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
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    • H04W52/0209—Power saving arrangements in terminal devices
    • H04W52/0261—Power saving arrangements in terminal devices managing power supply demand, e.g. depending on battery level
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    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
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Abstract

The invention discloses a multi-center processing unit cooperation method for removing cellular large-scale MIMO, which comprises the following steps: distributing distributed Access Points (APs) in a large-scale cellular MIMO network, wherein all the APs are divided into a plurality of actual clusters; in the pilot frequency transmission stage, all users send pilot frequency signals to all Access Points (AP), and the central processing unit acquires channel estimation information and statistical information according to the received pilot frequency signals; in the uplink transmission stage, aiming at a certain user, each central processing unit sends the respective statistical information to a certain central processing unit, and the central processing unit calculates the weight for different central processing units based on the generalized Rayleigh entropy theorem according to the statistical information; and by utilizing the calculated weight, the central processing unit performs weighting and combining processing on the data signals received by each central processing unit, so that the central processing units cooperatively serve the user on the same time-frequency resource. The invention has the advantages of low system signal processing complexity and low required signaling overhead.

Description

Multi-central processing unit cooperation method for removing cellular massive MIMO
Technical Field
The invention relates to the technical field of wireless communication, in particular to a multi-center processing unit cooperation method for removing cellular massive MIMO.
Background
With the increasing shortage of spectrum resources and the explosive increase of wireless data traffic, new changes are needed in wireless communication systems beyond 4G. The de-cellular massive MIMO technology can effectively improve spectrum efficiency without increasing power and bandwidth resources, and is currently considered as a key technology of 5G mobile communication. However, the practical deployment of the cellular massive MIMO network still faces many problems, and at present, in order to improve the system expansibility, a multi-center processing unit cooperative transmission technology is adopted, but the current multi-center processing unit adopts instantaneous channel information for cooperative transmission, which puts high requirements on a backhaul link, and is difficult to implement in practice.
Therefore, in order to reduce the target of backhaul link load, it is necessary to optimize the cooperation mode between the central processing units, and the present application further provides a method for cooperation of multiple central processing units for cellular massive MIMO removal.
Disclosure of Invention
The present invention is directed to a method for cooperating multiple central processing units in cellular massive MIMO, so as to solve the above-mentioned problems in the background art.
In order to achieve the purpose, the invention provides the following technical scheme: a multi-center processing unit cooperation method for removing cellular massive MIMO comprises the following steps:
s301, densely distributing distributed Access Points (APs) in a large-scale de-cellular MIMO network, dividing all the APs into a plurality of actual clusters, and controlling all the APs in each actual cluster by a preset central processing unit;
s302, selecting a part of Access Points (AP) by a central processing unit according to a proximity principle to serve a user, wherein a set of the Access Points (AP) is called a virtual cluster;
s303, in a channel estimation stage, a user sends pilot signals to all Access Points (AP), and each Access Point (AP) performs channel estimation by using Minimum Mean Square Error (MMSE) according to the pilot signals;
s304, according to the channel estimation result, the central processing unit calculates corresponding receiver combination, and the user continuously sends a pilot signal to the access point AP for a plurality of times, and the central processing unit calculates by using the obtained pilot signal to obtain preliminary statistical information;
s305, aiming at different users, all central processing units send respective statistical information to a certain central processing unit serving the user, and the central processing unit calculates a weighted item for each central processing unit participating in the service according to the generalized Rayleigh entropy theorem;
s306, in an uplink transmission stage, users send data signals to all Access Points (AP), the Access Points (AP) in a virtual cluster corresponding to each user transmit corresponding data signals to a central processing unit, the central processing unit performs first-step processing, and the central processing unit performs centralized processing on the data signals according to channel estimation of all the Access Points (AP) under control and calculates corresponding received data signals;
and S307, sending the received data signals to the main central processing unit by all the central processing units participating in the service, respectively weighting and combining the received data signals by the main central processing unit according to the weight values calculated in advance to obtain the finally received data signals, and updating the statistical information obtained in advance according to the finally received data signals.
Preferably, in S301, a distributed access point AP is deployed in the cellular massive MIMO system, the access point AP has signal processing capability, and interacts channel estimation information, transceiving pilot signals, and data signals with the central processing unit, all the access point APs are divided into a plurality of actual clusters, and all the access point APs in each actual cluster are controlled by a preset central processing unit.
Preferably, in S302, the central processing unit selects a part of the access points AP for each user according to the proximity principle to form a virtual cluster, the access points AP in the virtual cluster are controlled by the N central processing units, and the same user can be served through the cooperation process among the N central processing units.
Preferably, in S303, in the channel estimation stage, the user sends pilot signals to all access points AP, and each access point AP performs signal processing according to the pilot signals by using the minimum mean square error MMSE formula, so as to obtain corresponding channel estimation.
Preferably, in S304, after the channel estimation is completed, each access point AP transmits the channel estimation result to the corresponding central processing unit, and according to the channel estimation result, the central processing unit calculates the corresponding receiver combination, and then all users will continuously transmit pilot signals to the access points AP, and each access point AP located in the corresponding virtual cluster will transmit the received pilot signals to the corresponding central processing unit, and after receiving the pilot signals, the central processing unit uses the receiver combination item to process and combine the pilot signals, and this process will continue for a plurality of times until the statistical information is obtained.
Preferably, in S305, for different users, all the central processing units send their respective statistics information to a certain central processing unit serving the user through the backhaul link, and the central processing unit is called a main central processing unit, and since different users have different virtual clusters, each virtual cluster is provided with a main central processing unit, and the central processing unit calculates a weighting term for each central processing unit participating in the service according to the generalized rayleigh entropy theorem, which is before the uplink transmission stage, and thus is considered not to affect the actual transmission speed.
Preferably, in S306, in the uplink transmission phase, the user sends a data signal to all the access points AP, the access point AP in the virtual cluster corresponding to each user transmits the corresponding data signal to the central processing unit, the central processing unit performs the first step of processing, performs centralized processing on the data signal according to the channel estimation of all the access points AP under control, and calculates the corresponding received data signal; the access point AP now transmits the received data signal in its entirety to the central processing unit without any processing of the data signal.
Preferably, in S307, all the central processing units participating in the service send the received data signals to the main central processing unit, and the main central processing unit respectively weights and combines the received data signals according to the previously calculated weight values to obtain finally received data signals, and updates the previously obtained statistical information according to the finally received data signals; each central processing unit sends the data signals processed in the first step to the main central processing unit, and the main central processing units respectively perform weighting processing and combination on the data signals of the central processing units, so that the user service is realized in the same time frequency.
By adopting the technical scheme, the invention has the following beneficial effects:
1. the invention can effectively resist large-scale fading through the cooperation between the central processing units, improve the power efficiency and the frequency spectrum efficiency and reduce the calculation pressure on a single central processing unit.
2. According to the invention, only statistical information needs to be exchanged when the central processing units cooperate, so that the complexity of signal processing is reduced, and the pressure on a return link is reduced.
3. The access point AP which is not in the virtual cluster can use the sleep mode, so that the overall energy consumption of the system is further reduced.
4. The present invention is applicable to any receiver combining scheme.
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FIG. 1 is a schematic diagram of a network architecture of a MIMO multi-CPU cooperative system according to the present invention;
FIG. 2 is a flowchart of a method for cooperation of multiple central processing units in large-scale MIMO de-cellular.
Detailed Description
The present invention is further illustrated by the following figures and specific examples, which are to be understood as illustrative only and not as limiting the scope of the invention, which is to be given the full breadth of the appended claims and any and all equivalent modifications thereof which may occur to those skilled in the art upon reading the present specification.
Referring to fig. 1-2, a method for cooperation of multiple central processing units for cellular massive MIMO includes the following steps:
step 301: distributed Access Points (AP) are densely distributed in the large-scale cellular MIMO network, all the AP are divided into a plurality of actual clusters, and all the AP in each actual cluster are controlled by a preset central processing unit.
Fig. 1 shows a large-scale cellular MIMO system network architecture of this embodiment, where an illustrated scenario includes L access points AP, U central processing units, and K users, a set of the access points AP is {1,2, …, L }, and is connected to different central processing units through forwarding links, a set of the users is {1,2, …, K }, and a set of the access points AP serving a kth user is
Figure BDA0003601724730000041
Namely a virtual cluster, wherein the user terminal and the access point AP are both single antennas, and the access point AP set controlled by the u-th central processing unit is
Figure BDA0003601724730000042
I.e., the actual cluster, with adjacent central processing units connected by a return link.
The whole system consists of a module 101, a module 102, a module 103, a module 104 and a module 105, wherein the module 101 is an access point AP and is mainly used for receiving and transmitting data; the module 102 is a central processing unit, and is mainly responsible for baseband signal processing, a user processing unit, an exchange processing unit, an Access Point (AP) scheduling algorithm, and the like; the module 103 is a user terminal, which is a device for receiving and transmitting data by a user; module 104 is a forward link, and is primarily responsible for data transmission from the AP to the central processing unit; the module 105 is a backhaul link, and mainly functions to take charge of data transmission between the central processing units.
Step 302: the central processing unit selects a part of the access points AP to serve the user according to the proximity principle, and the collection of these access points AP is called a virtual cluster.
The invention establishes a virtual cluster according to the proximity criterion, and introduces a dynamic cooperative cluster to assist in definition, by specifying a number dilTo control whether the access point AP participates in the service. If the l access point AP belongs to the virtual cluster serving the i user
Figure BDA0003601724730000043
I.e. the ith access point AP is allowed to pass and decode the data signal from the ith user, then dilIs 1, otherwise, is 0. In fact, the dynamic cooperative cluster does not change the received data signals, since all access points AP receive the data signals of all users physically. However, only a small fraction of the access points AP participate in the data signal detection. According to the above definition, dilCan be expressed as
Figure BDA0003601724730000051
Step 303: in the channel estimation stage, a user sends pilot signals to all access points AP, and each access point AP carries out channel estimation by using minimum mean square error MMSE according to the pilot signals.
In the channel estimation phase, the pilot signal received by the ith access point AP is:
Figure BDA0003601724730000052
wherein p isiFor the transmit power of the ith user,
Figure BDA0003601724730000053
a pilot signal transmitted for the ith user and
Figure BDA0003601724730000054
nladditive White Gaussian Noise (AWGN), obeyed with a mean of 0 and a variance of σ2Normal distribution of (a), hilFor the channel between the ith user and the ith access point AP, it can be expressed as:
Figure BDA0003601724730000055
wherein g isilRepresenting small-scale fading, betailRepresenting large scale fading and related to shadow fading and path loss, hilObeying a mean of 0 and a variance of βilIs normally distributed.
From the received pilot signal, the l-th access point AP places it on the conjugate pilot signal
Figure BDA0003601724730000056
To obtain:
Figure BDA0003601724730000057
thereby obtaining minimum mean square error MMSE estimation
Figure BDA0003601724730000058
Wherein h isklFor channel estimation between the kth user and the l access point AP, pkIs the transmission power of the k-th user, betaklFor large scale fading, Ψ, between the kth user and the l-th access point APklCan be expressed as:
Figure BDA0003601724730000059
the invention sets the channel estimation error
Figure BDA00036017247300000510
Wherein h isklObeying a mean of 0 and a variance of
Figure BDA0003601724730000061
The normal distribution of (c),
Figure BDA0003601724730000062
obeying a mean of 0 and a variance of CklNormal distribution of (a), wherein
Figure BDA0003601724730000063
Step 304: according to the channel estimation result, the central processing unit calculates corresponding receiver combination, the user continuously sends pilot signals to the access point AP for a plurality of times, and the central processing unit calculates by using the obtained pilot signals to obtain preliminary statistical information.
The statistical information acquisition of this example is accomplished by the channel estimation process of the uplink, in which each user in the system continuously sends pilot signals, the pilot signals of all users in the system can be sent on one OFDM symbol of one time slot, and different user pilot signals in the system use different subcarrier resources. The access point AP in the system estimates the channel parameters of each user by using the received pilot signals, calculates the statistical information of each user from the channel parameters and sends the statistical information to the central processing unit. The central processing unit then performs uplink transmission based on the statistical information while continuing to update the statistical information based on the data signals transmitted via the uplink.
Step 305: aiming at different users, all central processing units send respective statistical information to a central processing unit serving the user, and the central processing unit calculates a weighting item for each central processing unit participating in the service according to the generalized Rayleigh entropy theorem.
According to the result of channel estimation, the central processing unit respectively calculates the receiving for the usersReceiver combining term, receiver combining term v of k-th userkuIs composed of
Figure BDA0003601724730000064
Wherein
Figure BDA0003601724730000065
In order to jointly estimate the channel vector for the channel,
Figure BDA0003601724730000066
for the u actual cluster set
Figure BDA0003601724730000067
The potential of (a) is higher than (b),
Figure BDA0003601724730000068
for the dynamic cooperative cluster matrix inside the u-th actual cluster,
Figure BDA0003601724730000069
the channel error matrix inside the u-th actual cluster,
Figure BDA00036017247300000610
is composed of
Figure BDA00036017247300000611
The identity matrix of (2).
The present invention assumes that the central processing units in a virtual cluster are grouped together as
Figure BDA00036017247300000612
According to the calculation results of the central processing units, the main central processing unit receives the statistical information of the central processing units and calculates the weighted term according to the statistical information; order to
Figure BDA00036017247300000613
The main central processing unit receives the total statistical information of
Figure BDA00036017247300000614
Wherein
Figure BDA00036017247300000615
Wherein
Figure BDA00036017247300000616
And (4) combining channels for the Access Points (AP) in the u-th actual cluster. Based on the received statistical information, the main central processing unit calculates corresponding weighted items for each central processing unit
Figure BDA0003601724730000071
Order to
Figure BDA0003601724730000072
The weighted term vector is then:
Figure BDA0003601724730000073
wherein
Figure BDA0003601724730000074
Step 306: in the uplink transmission stage, users send data signals to all Access Points (AP), the access point AP in the virtual cluster corresponding to each user transmits the corresponding data signals to the central processing unit, the central processing unit performs first-step processing, and the central processing unit performs centralized processing on the data signals according to the channel estimation of all the controlled Access Points (AP) and calculates the corresponding received data signals.
In the uplink transmission stage, all users send data signals to the access point AP, and the data signals received by the ith access point AP belonging to the inside of the u-th actual cluster are:
Figure BDA0003601724730000075
wherein s isiThe original data signal transmitted for the ith user, and
Figure BDA0003601724730000076
nu,lthe noise received for the l-th access point AP. The total received data signals of the central processing unit in the u-th actual cluster are as follows:
Figure BDA0003601724730000077
wherein
Figure BDA0003601724730000078
Is the joint noise vector of the access point AP inside the u-th actual cluster.
Step 307: and sending the received data signals to a main central processing unit by all the central processing units participating in the service, respectively weighting and combining the received data signals by the main central processing unit according to the weight values calculated before to obtain the finally received data signals, and updating the statistical information obtained before according to the finally received data signals.
For the k-th user, each central processing unit will send the received data signal to the main central processing unit, which will perform weighting processing to the data signal
Figure BDA0003601724730000079
Namely, it is
Figure BDA00036017247300000710
Wherein
Figure BDA00036017247300000711
The joint noise vectors in each actual cluster serving the kth user.
According to the final data signal, the signal to interference plus noise ratio SINR for the kth user can be obtained as follows:
Figure BDA0003601724730000081
a is tokThe term is simplified to obtain the final signal to interference plus noise ratio SINR as follows:
Figure BDA0003601724730000082
in the examples provided herein, it is to be understood that the disclosed methods may be practiced otherwise than as specifically described without departing from the spirit and scope of the present application. The present embodiment is an exemplary example only, and should not be taken as limiting, and the specific disclosure should not be taken as limiting the purpose of the application. For example, multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented.

Claims (8)

1. A multi-center processing unit cooperation method for removing cellular massive MIMO is characterized by comprising the following steps:
s301, densely distributing distributed Access Points (APs) in the large-scale cellular MIMO network, wherein the APs have signal processing capacity and can interactively count channel information, receive and transmit pilot signals and data signals with a central processing unit; all the access points AP are divided into a plurality of actual clusters, and all the access points AP in each actual cluster are controlled by a preset central processing unit;
s302, selecting a part of Access Points (AP) for each user by a central processing unit according to a proximity principle to serve the users, wherein a set of the AP is called a virtual cluster; the access point AP in the virtual cluster is controlled by the N central processing units, and can serve the same user through the cooperation processing among the N central processing units;
s303, in a channel estimation stage, a user sends pilot signals to all Access Points (AP), and each Access Point (AP) performs channel estimation by using Minimum Mean Square Error (MMSE) according to the pilot signals;
s304, according to the channel estimation result, the central processing unit calculates corresponding receiver combination, and the user continuously sends a pilot signal to the access point AP for a plurality of times, and the central processing unit calculates by using the obtained pilot signal to obtain preliminary statistical information;
s305, aiming at different users, all central processing units send respective statistical information to a certain central processing unit serving the user through a return link, the central processing unit is called as a main central processing unit, and the central processing unit calculates a weighted item for each central processing unit participating in the service according to the generalized Rayleigh entropy theorem;
s306, in an uplink transmission stage, users send data signals to all Access Points (AP), the Access Points (AP) in a virtual cluster corresponding to each user transmit corresponding data signals to a central processing unit, the central processing unit performs first-step processing, and the central processing unit performs centralized processing on the data signals according to channel estimation of all the Access Points (AP) under control and calculates corresponding received data signals;
and S307, sending the received data signals to a main central processing unit by all the central processing units participating in the service, respectively weighting and combining the obtained received data signals by the main central processing unit according to the weight values calculated in advance to obtain the finally received data signals, and updating the statistical information obtained in advance according to the finally received data signals.
2. The method of claim 1, wherein the method comprises: in S301, the architecture of the large-scale cellular MIMO network includes L APs, U central processing units, and K users, where the set of APs is {1,2, …, L }, and is connected to different central processing units via forward links, the set of users is {1,2, …, K }, and the set of APs serving the kth user is
Figure FDA0003601724720000027
Namely a virtual cluster; the user terminal and the access point AP are both single antennas, and the access point AP set controlled by the u-th central processing unit is
Figure FDA0003601724720000028
I.e., the actual cluster, with adjacent central processing units connected by a return link.
3. The method of claim 1, wherein the method comprises: in S302, virtual clusters are established according to the proximity criterion, and dynamic cooperation clusters are introduced to assist definition, wherein a number d is definedilTo control whether the access point AP participates in the service; if the l access point AP belongs to the virtual cluster serving the i user
Figure FDA0003601724720000029
I.e. the ith access point AP is allowed to pass and decode the data signal from the ith user, dilIs 1, otherwise, is 0; according to the above definition, dilCan be expressed as:
Figure FDA0003601724720000021
4. the method of claim 1, wherein the method comprises: in S303, in the channel estimation phase, the pilot signal received by the ith access point AP is:
Figure FDA0003601724720000022
wherein p isiFor the transmit power of the ith user,
Figure FDA0003601724720000023
a pilot signal transmitted for the ith user and
Figure FDA0003601724720000024
nladditive white Gaussian noise AWGN, obeyed with a mean of 0 and a variance of σ2Normal distribution of (a), hilFor the channel between the ith user and the ith access point AP, it can be expressed as:
Figure FDA0003601724720000025
wherein g isilRepresenting small scale fading, betailRepresenting large scale fading and related to shadow fading and path loss, hilObeying a mean of 0 and a variance of βilNormal distribution of (2);
from the received pilot signal, the l-th access point AP places it on the conjugate pilot signal
Figure FDA0003601724720000026
To obtain:
Figure FDA0003601724720000031
thereby yielding a minimum mean square error estimate,
Figure FDA0003601724720000032
wherein h isklFor channel estimation between the kth user and the l access point AP, pkIs the transmission power of the k-th user, betaklFor large scale fading, Ψ, between the kth user and the l-th access point APklCan be expressed as:
Figure FDA0003601724720000033
setting channel estimation error
Figure FDA0003601724720000034
Wherein h isklObeying a mean of 0 and a variance of
Figure FDA0003601724720000035
The normal distribution of (c),
Figure FDA0003601724720000036
obeying a mean of 0 and a variance of CklThe normal distribution of (a), wherein,
Figure FDA0003601724720000037
5. the method of claim 1, wherein the method comprises: in S304, after the channel estimation is completed, each access point AP transmits the channel estimation result to the corresponding central processing unit, and according to the channel estimation result, the central processing unit calculates the corresponding receiver combination, and then all users will continuously transmit pilot signals to the access points AP, and each access point AP located in the corresponding virtual cluster will transmit the received pilot signals to the corresponding central processing unit, and the central processing unit will use the receiver combination item to process and combine the pilot signals after receiving the pilot signals, and this process will continue for a plurality of times until the statistical information is obtained.
6. The method of claim 1, wherein the method comprises: in S305, according to the result of channel estimation, the central processing unit calculates receiver merging items for different users respectively, and the receiver of the k-th user is mergedItem vkuComprises the following steps:
Figure FDA0003601724720000038
wherein
Figure FDA0003601724720000039
In order to jointly estimate the vector of channel estimates,
Figure FDA00036017247200000310
for the u-th actual cluster set
Figure FDA00036017247200000311
Is in a state of being in a neutral state,
Figure FDA00036017247200000312
for the dynamic cooperative cluster matrix inside the u-th actual cluster,
Figure FDA00036017247200000313
the channel error matrix inside the u-th actual cluster,
Figure FDA00036017247200000314
is composed of
Figure FDA00036017247200000315
The identity matrix of (1);
assume that the central processing units in a virtual cluster are grouped together as
Figure FDA0003601724720000041
According to the calculation results of the central processing units, the main central processing unit receives the statistical information of the central processing units and calculates the weighted term according to the statistical information; order to
Figure FDA0003601724720000042
Master central processing unit received total systemThe counting information is
Figure FDA0003601724720000043
Wherein,
Figure FDA0003601724720000044
wherein
Figure FDA0003601724720000045
For the access point AP joint channel in the u-th actual cluster, according to the received statistical information, the main central processing unit calculates corresponding weighted items for each central processing unit
Figure FDA0003601724720000046
Order to
Figure FDA0003601724720000047
The weighted term vector is then:
Figure FDA0003601724720000048
wherein
Figure FDA0003601724720000049
7. The method of claim 1, wherein the method comprises: in S306, in the uplink transmission phase, all users send data signals to the access point AP, and the data signals received by the ith access point AP belonging to the inside of the u-th actual cluster are:
Figure FDA00036017247200000410
wherein s isiThe original data signal transmitted for the ith user, and
Figure FDA00036017247200000411
nu,lfor the noise received by the ith access point AP, the total received data signal of the central processing unit in the u-th actual cluster is:
Figure FDA00036017247200000412
wherein
Figure FDA00036017247200000413
Is the joint noise vector of the access point AP inside the u-th actual cluster.
8. The method of claim 1, wherein the method comprises: in S307, each central processing unit will send the received data signal to the main central processing unit, and the main central processing unit will perform weighting processing on the data signal to obtain the final data signal
Figure FDA0003601724720000051
Namely, it is
Figure FDA0003601724720000052
Wherein
Figure FDA0003601724720000053
The joint noise vector in each actual cluster is obtained;
according to the finally obtained data signal, the signal to interference plus noise ratio SINR for the kth user can be obtained as follows:
Figure FDA0003601724720000054
a is tokThe term is simplified to obtain the final signal to interference plus noise ratio SINR as follows:
Figure FDA0003601724720000055
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