LoRa channel resource allocation method based on adaptive virtual MIMO transmission

Abstract

The present invention relates to a LoRa channel resource allocation method based on adaptive virtual MIMO transmission, which introduces virtual MIMO technology into the LoRa uplink system, eliminates the interference problem caused by co-SF through advanced receivers, thereby obtaining diversity gain to improve the performance of the system. A transmission strategy of joint channel resource allocation and adaptive virtual MIMO in the LoRa uplink system is proposed. The optimization problem in this scenario is mainly decomposed into two steps, user allocation and user-channel allocation for research. While reducing the power consumption of the LoRa system by using multiple user allocation methods, the performance of the system is improved by the allocation strategy of secondary allocation combined with SF optimization.

Description

Technical Field

[0001] The present invention belongs to the field of wireless communication transmission, and in particular relates to a LoRa channel resource allocation method based on adaptive virtual MIMO transmission. Background Art

[0002] Since the birth of the Internet of Things in the late 1990s, its related technologies have continued to develop and have had a significant impact on people's daily lives. With the outbreak of the Fourth Industrial Revolution, the Internet of Things has entered a rapid development stage. In recent years, the Internet of Things has been more widely used in various fields such as commerce, industry, consumer electronics, and automobiles.

[0003] LPWAN supports long-distance transmission. Sigfox, LoRa and NB-IoT are the leading emerging technologies in LPWAN. LoRa (Long Range) works in unlicensed frequency bands and can provide secure two-way, low-cost mobile communications for IoT, smart cities, machine-to-machine (M2M) and industrial applications. Due to its unique modulation method, CSS (Chirp Spreading Spectrum) modulation, LoRa has very good robustness and can resist the fading caused by long-distance transmission. There are many key parameters in LoRa modulation, and the spreading factor (SF) is one of the most critical parameters.

[0004] When multiple LoRa nodes transmit on the same time-frequency resource, the system performance is subject to the spreading factor of the terminal node. LoRaWAN theoretically believes that different SFs in LoRa are completely orthogonal, but there are papers showing that different SFs are not completely orthogonal, which will have a certain impact on the performance of the LoRa system. What's worse, interference between the same SF will lead to further deterioration of the performance of the LoRa system. Considering the limited number of SF values ​​in the LoRa system, when more terminal nodes need to access the LoRa network at the same time, some of them will inevitably use the same SF. This will inevitably introduce interference from nodes with the same SF (co-SF) in the system, which will degrade system performance. When multiple LoRa nodes with the same SF are transmitting on the same frequency band at the same time, the current LoRa receiver can only correctly decode the node with the strongest signal when the strongest signal power is at least 6dB greater than the total power of other interfering nodes, and the weaker signal will be drowned by the strong signal. Summary of the invention

[0005] In order to solve the above problems existing in the prior art, the present invention provides a LoRa channel resource allocation method based on adaptive virtual MIMO transmission. The technical problem to be solved by the present invention is achieved by the following technical solutions:

[0006] The present invention provides a LoRa channel resource allocation method based on adaptive virtual MIMO transmission, characterized in that it includes:

[0007] S1: Obtain the initial signal-to-noise ratio of all nodes in the LoRa uplink system on each channel and the minimum data rate of each channel, and assign the node to the channel according to the initial signal-to-noise ratio of the node on each channel and the minimum data rate of the channel, so as to complete the node-channel assignment initialization;

[0008] S2: According to the initialization result of the node-channel allocation and the initial signal-to-noise ratio of all nodes in the LoRa uplink system, the nodes on each channel are paired using a user pairing algorithm to obtain a paired node combination corresponding to the channel;

[0009] S3: adjusting the spreading factors of the nodes in the paired node combination according to the number of nodes corresponding to the spreading factor accommodated by the channel to obtain an adjusted node combination;

[0010] S4: Determine whether the throughput of the adjustment node combination meets the minimum data rate of the channel. If not,

[0011] then reallocating the adjustment node combination to obtain a reallocated node combination;

[0012] S5: Perform power iterative optimization on the adjustment node combination whose throughput meets the minimum data rate of the channel and the reallocation node combination whose throughput meets the minimum data rate of the channel to obtain the transmission mode with maximum energy efficiency of the LoRa uplink system.

[0013] In one embodiment of the present invention, the S1 includes:

[0014] S11: Calculate the initial signal-to-noise ratio of all nodes in the LoRa uplink system on each channel;

[0015] S12: Obtain the minimum data rate of each channel, and sort the priority of the channels according to the minimum data rate of each channel;

[0016] S13: Node allocation is performed according to channel priority sorting, and the node with the largest initial signal-to-noise ratio on the channel is allocated to the channel, completing the node-channel allocation initialization.

[0017] In one embodiment of the present invention, the user pairing algorithm is a greedy maximum capacity user pairing algorithm, a suboptimal maximum energy efficiency user pairing algorithm, or a greedy maximum energy efficiency user pairing algorithm.

[0018] In one embodiment of the present invention, according to the initialization result of the node-channel allocation and the initial signal-to-noise ratio of all nodes in the LoRa uplink system, the nodes on each channel are paired using a greedy maximum capacity user pairing algorithm to obtain a paired node combination corresponding to the channel, including:

[0019] Step 1: Calculate the initial signal-to-noise ratio of all nodes initially allocated on each channel, and obtain the minimum spreading factor supported by the corresponding node according to the initial signal-to-noise ratio of the node;

[0020] Step 2: Sort all nodes initially assigned to the channel in the order of the minimum spreading factor supported by the node from small to large, and the initial signal-to-noise ratio of the node from large to small, and obtain the node arrangement set M sorted ;

[0021] Step 3: Arrange the set M according to the nodes sorted Schedule the nodes one by one and determine whether the throughput of all nodes in the scheduling set Ω satisfies the following formula after adding the current node to the scheduling set Ω:

[0022]

[0023] In the formula, represents the total throughput in the scheduling set Ω at the tth scheduling, represents the total throughput in the scheduling set Ω at the t-1th scheduling, represents the initial throughput of the current node i scheduled;

[0024] If satisfied, the current node is added to the scheduling set Ω;

[0025] If not, determine whether the spreading factor of the current node is equal to 12. If so, give up the scheduling right of the current node. Otherwise, increase the spreading factor of the current node by 1 and repeat step 2 to update the node permutation set M. sorted The scheduling is continued until the number of scheduled nodes reaches the number of nodes that the channel can accommodate or there are no nodes that can continue to be scheduled, and a final scheduling set is obtained as the paired node combination corresponding to the channel.

[0026] In one embodiment of the present invention, according to the initialization result of the node-channel allocation and the initial signal-to-noise ratio of all nodes in the LoRa uplink system, the nodes on each channel are paired using the suboptimal maximum energy efficiency user pairing algorithm to obtain a paired node combination corresponding to the channel, including:

[0027] Step 1: According to the number of nodes accommodated by the channel, all nodes initially allocated on the channel are arranged and combined to obtain several groups of node combinations;

[0028] Step 2: According to the initial signal-to-noise ratios of all nodes initially allocated on the channel, the sum of the initial signal-to-noise ratios of the nodes corresponding to each group of node combinations is calculated;

[0029] Step 3: Select a group of node combinations with the largest sum of initial signal-to-noise ratios of the nodes as the paired node combination corresponding to the channel.

[0030] In one embodiment of the present invention, according to the initialization result of the node-channel allocation and the initial signal-to-noise ratio of all nodes in the LoRa uplink system, the nodes on each channel are paired using the greedy maximum energy efficiency user pairing algorithm to obtain the paired node combination corresponding to the channel, including:

[0031] Step 1: Calculate the initial signal-to-noise ratio of all nodes initially assigned to each channel, and sort all nodes initially assigned to the channel in descending order of their initial signal-to-noise ratio;

[0032] Step 2: According to the number of nodes accommodated by the channel, a corresponding number of nodes are selected from the sorted node set in sequence as the paired node combination corresponding to the channel.

[0033] In one embodiment of the present invention, S3 includes:

[0034] S31: according to the initial signal-to-noise ratios of all nodes in the paired node combination on each channel, obtaining the minimum spreading factor supported by the corresponding node;

[0035] S32: sorting the paired node combinations corresponding to each channel according to the order of the minimum spreading factors supported by the nodes from small to large and the order of the initial signal-to-noise ratios of the nodes from large to small, to obtain the corresponding sorted paired node combinations;

[0036] S33: setting a sliding window according to the number of nodes corresponding to the spreading factor accommodated by the channel;

[0037] S34: sliding the sliding window in the ordered paired node combination, counting the number of nodes corresponding to the spreading factor in the sliding window, and making only nodes corresponding to the spreading factor appear in the sliding window by shrinking the window or increasing the spreading factor of the node;

[0038] If the node's spreading factor exceeds the preset node spreading factor adjustment range after adjustment, the node's scheduling right is abandoned and added to the remaining unscheduled node set M. remain middle;

[0039] S35: Perform rolling spreading factor adjustment on the sorted paired node combination through a sliding window. After all spreading factors are adjusted, an adjusted node combination is obtained. If there are remaining nodes in the sorted paired node combination after removing the adjusted node combination, the scheduling rights of the remaining nodes on the channel are abandoned and added to the remaining unscheduled node set M. remain middle;

[0040] Among them, the remaining unscheduled node set M remain The nodes in the table are sorted in descending order according to the minimum spreading factor supported by the nodes and the initial signal-to-noise ratio of the nodes.

[0041] In one embodiment of the present invention, reallocating the adjustment node combination whose throughput does not meet the minimum data rate of the channel to obtain a reallocated node combination includes:

[0042] Step 1: Calculate the number of nodes that can be filled corresponding to each spreading factor in the adjustment node combination;

[0043] Step 2: Traverse the remaining unscheduled node set M remain all nodes in the adjustment node group, determine the nodes whose spreading factors meet the filling requirements, and insert the nodes that meet the filling requirements into the adjustment node group in order according to the number of nodes that can be filled;

[0044] Step 3: For the node combination after the inserted node, determine whether its throughput meets the minimum data rate of the channel. If so, use the node combination after the inserted node as the reallocated node combination and execute step S5. If not, execute the channel node exchange operation.

[0045] In one embodiment of the present invention, a channel node exchange operation is performed on a node combination after the inserted node whose throughput does not meet the minimum data rate of the channel, including:

[0046] Step 1: Calculate the number of nodes that can be filled corresponding to each spreading factor in the node combination after the node is inserted;

[0047] Step 2: In the order of the minimum data rate of the channel from small to large, search for the adjustment node combination and the reallocation node combination whose throughput meets the minimum data rate of the channel, and fill the nodes that meet the exchange conditions into the node combination after the node is inserted;

[0048] Step 3: For the node combination after filling the nodes, determine whether its throughput meets the minimum data rate of the channel. If so, use the node combination after filling the nodes as the reallocated node combination and execute step S5. If not, abandon the node combination.

[0049] In one embodiment of the present invention, the switching conditions to be satisfied by each switching operation are: the number of fillable nodes of the node combination performing the channel switching operation is non-zero, and the throughput of the node combination providing the switching node meets the minimum data rate of the channel after the switching node is removed.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] The LoRa channel resource allocation method based on adaptive virtual MIMO transmission of the present invention introduces virtual MIMO technology into the LoRa uplink system, eliminates the interference problem caused by co-SF through advanced receivers, thereby obtaining diversity gain to improve the performance of the system. A transmission method of joint channel resource allocation and adaptive virtual MIMO in the LoRa uplink system is proposed, which uses multiple user allocation methods to reduce the power consumption of the LoRa system, and improves the performance of the system through the allocation strategy of secondary allocation combined with SF optimization.

[0052] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the following specifically cites a preferred embodiment and describes it in detail with the accompanying drawings as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a schematic diagram of a LoRa channel resource allocation method based on adaptive virtual MIMO transmission provided by an embodiment of the present invention;

[0054] Figure 2 is a process diagram of node-channel allocation initialization provided by an embodiment of the present invention;

[0055] Figure 3 It is a node pairing flow chart of a greedy maximum capacity user pairing algorithm provided by an embodiment of the present invention;

[0056] Figure 4 is a scatter distribution diagram under a co-SF interference situation provided by an embodiment of the present invention;

[0057] Figure 5 is a scatter distribution diagram under inter-SF interference provided by an embodiment of the present invention;

[0058] Figure 6 This is a case of SF rolling adjustment provided by an embodiment of the present invention;

[0059] Figure 7 This is another case of SF rolling adjustment provided by an embodiment of the present invention;

[0060] Figure 8 This is another case of SF rolling adjustment provided by an embodiment of the present invention;

[0061] Fig. 9 This is a case of node insertion provided by an embodiment of the present invention;

[0062] Fig.10 This is another case of node insertion provided by an embodiment of the present invention;

[0063] Fig.11 This is a case of node exchange provided by an embodiment of the present invention;

[0064] Fig.12 This is another node exchange situation provided by an embodiment of the present invention;

[0065] Fig.13 is a comparison chart of the system average performance of the user pairing algorithm under different numbers of active nodes provided by an embodiment of the present invention;

[0066] Fig.14 is a comparison chart of system average performance of user pairing algorithms under different numbers of user pairings provided by an embodiment of the present invention;

[0067] Fig.15 It is an energy efficiency CDF diagram of adopting the GEE user pairing algorithm and different user-channel allocation schemes provided by an embodiment of the present invention;

[0068] Fig.16 is a performance diagram of different user-channel allocation schemes under different numbers of active nodes provided by an embodiment of the present invention;

[0069] Fig.17 It is a performance curve diagram of a user-channel allocation scheme under different numbers of user pairings provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0070] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, a LoRa channel resource allocation method based on adaptive virtual MIMO transmission proposed in accordance with the present invention is described in detail below in combination with the accompanying drawings and specific implementation methods.

[0071] The above and other technical contents, features and effects of the present invention are clearly presented in the following detailed description of the specific implementation modes in conjunction with the accompanying drawings. Through the description of the specific implementation modes, the technical means and effects adopted by the present invention to achieve the predetermined purpose can be more deeply and specifically understood. However, the attached drawings are only for reference and explanation purposes and are not used to limit the technical solutions of the present invention.

[0072] This paper mainly studies the resource allocation problem of LoRa virtual MIMO system under the condition of multi-channel and multi-SF transmission. Firstly, the system model is modeled, assuming that the gateway has N r There are M active nodes randomly distributed around the gateway, and each node has only one transmitting antenna. VMIMO technology randomly selects certain nodes from M nodes for transmission through node pairing. The receiving end uses MMSE interference elimination to eliminate interference between LoRa nodes and bring receiver gain.

[0073] However, due to the special modulation mode of LoRa signals, when multiple signals with different SFs arrive at the gateway at the same time, these signals are not completely orthogonal. That is to say, although the interference between nodes with different SFs will be very small, interference is still generated. In the present invention, the interference between nodes with the same SF and different SFs is considered, and the single-channel optimization problem is extended to the multi-channel optimization problem.

[0074] In the LoRa virtual MIMO system, the SINR (signal-to-noise ratio) of node m on channel RBn after the linear MMSE receiver can be written as:

[0075]

[0076]

[0077] Among them, p m,n represents the transmission power of node m on channel RBn; g m =A0d m α represents the path loss from node m to the gateway, A0 represents a constant related to the path loss, d m represents the distance from node m to the gateway, α represents the path loss coefficient; h m,n N represents the node m in channel RBn r ×1 small-scale fading channel coefficient of Rayleigh distribution; σ 2 Represents the distribution as The noise power of additive white Gaussian noise, YesN r ×1-dimensional zero vector, YesN r ×N r The identity matrix of . It refers to the mutual correlation coefficient between LoRa waveforms, which is used to measure the degree of interference between LoRa nodes.

[0078] The indication index of node m on channel RBn is:

[0079]

[0080] Assuming that the number of user pairs in the virtual MIMO system is ξ, the index indicating whether the node performs virtual MIMO transmission with ξ is:

[0081]

[0082] Then, the optimization problem considered in the present invention is modeled as:

[0083]

[0084] st

[0085]

[0086]

[0087]

[0088]

[0089]

[0090]

[0091]

[0092] In the optimization problem, constraint (a) means that each node can only select one SF, and the set of SFs is Ψ SF , there are J different SF selection methods; when node m selects SF=7, β m,1 =1 otherwise 0; Constraint (b) means that each node can only choose one pairing mode, including no transmission, single-node SIMO transmission, 2-node VMIMO transmission, 3-node VMIMO transmission, etc. There are a total of Q pairing modes; Condition (c) constrains each node to occupy at most one RB, and if node m is on RB n, it indicates index λ m,n=1 otherwise 0; in constraint (d), l=1∪l=0 indicates that the number of nodes that initially transmit virtual MIMO for each RB and SF combination is ξ, but when the number of nodes for each RB and SF combination does not satisfy the number of paired nodes ξ, virtual MIMO can be transmitted in a manner less than ξ; LoRa has 6 different SFs, and a maximum of 6ξ nodes can be accommodated on one RB. This is because, in addition to the number that the spatial domain can accommodate being less than the number of user pairings, 6 different SF domains are also considered; constraint (e) is a constraint on the transmission power of each node, and this budget is limited by the maximum transmission power of a single node; constraint (f) means that the data rate on each RB is constrained by the minimum data rate on the RB; constraint (g) means that the nodes to which the RB is allocated need to meet the minimum data rate requirement of the node.

[0093] Embodiment 1

[0094] Based on the above optimization problem, this embodiment provides a LoRa channel resource allocation method based on adaptive virtual MIMO transmission, see Figure 1 The schematic diagram of the LoRa channel resource allocation method based on adaptive virtual MIMO transmission is shown in the figure, as shown in the figure, the method includes:

[0095] S1: Obtain the initial signal-to-noise ratio of all nodes in the LoRa uplink system on each channel and the minimum data rate of each channel, assign nodes to channels according to the initial signal-to-noise ratio of nodes on each channel and the minimum data rate of the channel, and complete the node-channel allocation initialization;

[0096] Since throughput has a much greater impact on EE (energy efficiency) performance than power, the transmit power of all nodes is fixed to The ultimate goal of node-channel allocation initialization is to enable each channel to obtain a better data rate. To simplify the complexity, the node-channel allocation initialization in this embodiment adopts a greedy algorithm for matching.

[0097] Specifically, S1 includes:

[0098] S11: Calculate the initial signal-to-noise ratio of all nodes in the LoRa uplink system on each channel;

[0099] In this embodiment, the initial signal-to-noise ratio of the node on each channel is calculated according to formula (1). It should be noted that the initial signal-to-noise ratio of the node does not take into account the interference node. The I in formula (1) SF =0.

[0100] S12: Obtain the minimum data rate of each channel, and sort the priority of the channels according to the minimum data rate of each channel;

[0101] Specifically, according to the minimum data rate of each channel The requirements are different, and the channels are prioritized and the minimum data rate is The larger the channel, the higher the priority.

[0102] S13: Node allocation is performed according to channel priority sorting, and the node with the largest initial signal-to-noise ratio on the channel is allocated to the channel, completing the node-channel allocation initialization.

[0103] See also Figure 2 , Figure 2 This is a process diagram of node-channel allocation initialization provided by an embodiment of the present invention. As shown in the figure, the initial signal-to-noise ratio of all nodes in the LoRa uplink system on the channel is first calculated. Assume that the signal-to-noise ratio matrix is Figure 2 Figure ① in Figure ①; Based on the minimum data rate of each channel The channels are prioritized according to their needs. The larger the channel, the higher the priority. Assume that the priority after sorting is RB4, RB2, RB1, RB3. Select users according to the channel priority, and select the node with the largest initial signal-to-noise ratio on the channel each time. For example, the optimal pairing node for channel 4 in Figure ① is node 2. Each node can only occupy one channel, and the nodes that have been allocated will not participate in the next allocation process. This process is repeated until all active nodes are allocated.

[0104] S2: According to the initialization result of node-channel allocation and the initial signal-to-noise ratio of all nodes in the LoRa uplink system, the nodes on each channel are paired using the user pairing algorithm to obtain the paired node combination corresponding to the channel;

[0105] In this embodiment, the user pairing algorithm is a greedy maximum capacity user pairing algorithm, a suboptimal maximum energy efficiency user pairing algorithm, or a greedy maximum energy efficiency user pairing algorithm.

[0106] In this embodiment, according to the SF special domain of the LoRa system, this embodiment proposes a greedy maximum capacity pairing algorithm for the LoRa system based on greedy scheduling, such as Figure 3 The node pairing flow chart of the greedy maximum capacity user pairing algorithm shown in the figure, the node arrangement set M sorted The nodes in represent the nodes to be paired. The first, second, fourth and fifth schedulings are successful, and the third scheduling is a failed scheduling.

[0107] Specifically, according to the initialization result of node-channel allocation and the initial signal-to-noise ratio of all nodes in the LoRa uplink system, the greedy maximum capacity user pairing algorithm is used to pair the nodes on each channel to obtain the paired node combination corresponding to the channel, including:

[0108] Step 1: Calculate the initial signal-to-noise ratio of all nodes initially allocated on each channel, and obtain the minimum spreading factor supported by the corresponding node according to the initial signal-to-noise ratio of the node;

[0109] In this embodiment, after obtaining the initial signal-to-noise ratios of all nodes initially allocated on each channel, the SF (spreading factor) corresponding SNR (signal-to-noise ratio) threshold table in Table 1 can be queried to obtain the minimum spreading factor supported by the corresponding node.

[0110] Table 1: SF corresponding to SNR threshold table

[0111]

[0112] Step 2: Sort all nodes initially assigned to the channel in the order of the minimum spreading factor supported by the node from small to large, and the initial signal-to-noise ratio of the node from large to small, and obtain the node arrangement set M sorted ;

[0113] Step 3: Arrange the set M according to the nodes sorted Schedule the nodes one by one and determine whether the throughput of all nodes in the scheduling set Ω satisfies the following formula after adding the current node to the scheduling set Ω:

[0114]

[0115] In the formula, represents the total throughput in the scheduling set Ω at the tth scheduling, represents the total throughput in the scheduling set Ω at the t-1th scheduling, represents the initial throughput of the current node i scheduled;

[0116] If satisfied, the current node is added to the scheduling set Ω;

[0117] If not, determine whether the spreading factor of the current node is equal to 12. If so, give up the scheduling right of the current node. Otherwise, increase the spreading factor of the current node by 1 and repeat step 2 to update the node permutation set M. sorted The scheduling is continued until the number of scheduled nodes reaches the number of nodes that the channel can accommodate or there are no nodes that can continue to be scheduled, and the final scheduling set is obtained as the paired node combination corresponding to the channel.

[0118] In this embodiment, the throughput of the node is calculated according to the following formula:

[0119]

[0120] Among them, ψ m represents the spreading factor of the node, Γ m represents the signal-to-noise ratio threshold corresponding to the spreading factor, γ m,n represents the signal-to-noise ratio of node m on channel RBn, and B represents the modulation bandwidth.

[0121] The greedy maximum capacity approach has reduced the complexity of the maximum capacity algorithm, but each time a new node is added, the SF and throughput of all nodes need to be updated, which is also a huge overhead for LoRa. To this end, this embodiment proposes a suboptimal maximum energy efficiency user pairing algorithm with lower complexity by finding certain rules in the exhaustive process.

[0122] Specifically, according to the initialization result of node-channel allocation and the initial signal-to-noise ratio of all nodes in the LoRa uplink system, the suboptimal maximum energy efficiency user pairing algorithm is used to pair the nodes on each channel to obtain the paired node combination corresponding to the channel, including:

[0123] Step 1: According to the number of nodes accommodated by the channel, all nodes initially allocated on the channel are arranged and combined to obtain several groups of node combinations;

[0124] Step 2: According to the initial signal-to-noise ratios of all nodes initially allocated on the channel, the sum of the initial signal-to-noise ratios of the nodes corresponding to each group of node combinations is calculated;

[0125] Step 3: Select a group of node combinations with the largest sum of initial signal-to-noise ratios of the nodes as the paired node combination corresponding to the channel.

[0126] The suboptimal maximum energy efficiency user pairing algorithm uses energy efficiency as a performance indicator. This method attempts to find the relationship between the pairing node combination and the final system energy efficiency. Figure 4 and Figure 5 , Figure 4 is a scatter distribution diagram under co-SF interference provided by an embodiment of the present invention, Figure 5 is a scatter distribution diagram of an inter-SF interference situation provided by an embodiment of the present invention. The horizontal axis in the figure is the sum of the initial signal-to-noise ratios of the paired nodes δ Ω , the vertical axis is the EE of the LoRa system after power optimization.

[0127] Figure 4 The main example in the figure is the scatter plot of selecting 7 nodes from a group of 13 active nodes with the same initial SF. At this time, the number of active nodes is small, and the number of combinations under this combination mode can be listed. By using the interior point algorithm to optimize power, the optimized LoRa system EE can be obtained. Figure 4 Figures (a), (b), (c) and (d) in the figure respectively show the scatter plots for four different cases where SF is 7, 8, 10 and 11. Ω There is no obvious quantitative relationship between EE and , but the scattering trend under different SF is similar, all increasing from the lower left to the upper right. This is beneficial for using the sum of the initial signal-to-noise ratio as the suboptimal selection criterion. Typically, as shown in the distribution diagram when SF = 8 in Figure (c), it can be seen that the optimal EE value does not appear at the maximum δ Ω But through the maximum δ Ω The result selected by this indicator is feasible, and in Figures (a) and (b), it can be seen that the result selected by this indicator is very close to the maximum value.

[0128] Figure 5 The δ for all SFs is given as Ω Specifically, there are 13 active nodes, nodes SF are randomly distributed, and i nodes are selected from the active nodes for pairing. The number of user combinations in this combination mode is Figure 5 Figures (a), (b), and (c) in the figure respectively show the scatter plots of selecting 3, 5, and 7 users from 10 active nodes. It can be seen from the figure that it basically increases from the lower left to the upper right. Figure (d) is a partial enlargement of Figure (c). Through the scatter plot analysis of the co-SF and inter-SF cases, it can be seen that the indicator δ Ω The scatter distribution trend of the system EE increases from the lower left to the upper right. Ω Although the indicator may not reach the optimal solution, it greatly simplifies the calculation steps each time a node is added, greatly reduces the complexity of the algorithm, and can obtain a suboptimal solution close to the optimal one.

[0129] For the suboptimal maximum energy efficiency user pairing algorithm, this method reduces the complexity of calculating throughput and SF compared to the greedy maximum capacity user pairing algorithm, but it still needs to calculate the number of combinations, which is very costly. When the number of active nodes is small and the number of paired nodes is small, all combinations can be calculated, but when the number of nodes is large, this is more costly for the LoRa system. Therefore, based on the suboptimal maximum energy efficiency user pairing algorithm, a greedy maximum energy efficiency user pairing algorithm is proposed.

[0130] Specifically, according to the initialization result of node-channel allocation and the initial signal-to-noise ratio of all nodes in the LoRa uplink system, the nodes on each channel are paired using the greedy maximum energy efficiency user pairing algorithm to obtain the paired node combination corresponding to the channel, including:

[0131] Step 1: Calculate the initial signal-to-noise ratio of all nodes initially assigned to each channel, and sort all nodes initially assigned to the channel in descending order of their initial signal-to-noise ratio;

[0132] Step 2: According to the number of nodes accommodated by the channel, select a corresponding number of nodes from the sorted node set as the pairing node combination corresponding to the channel.

[0133] S3: adjusting the spreading factor of the nodes in the paired node combination according to the number of nodes corresponding to the spreading factor accommodated by the channel to obtain an adjusted node combination;

[0134] Due to the limitation of the number of nodes corresponding to the spreading factor that can be accommodated by the virtual MIMO paired channel, the number of nodes that can be accommodated on each channel is limited. Therefore, after initializing the node-channel allocation and executing the user pairing process, it is necessary to uniformly adjust the SF according to the constraint condition (d). In order to reduce the complexity, in this embodiment, the spreading factor of the node in the paired node combination of each channel is adjusted by rolling adjustment of the SF.

[0135] Specifically, S3 includes:

[0136] S31: according to the initial signal-to-noise ratios of all nodes in the paired node combination on each channel, obtaining the minimum spreading factor supported by the corresponding node;

[0137] S32: sorting the paired node combinations corresponding to each channel according to the order of the minimum spreading factors supported by the nodes from small to large and the order of the initial signal-to-noise ratios of the nodes from large to small, to obtain the corresponding sorted paired node combinations;

[0138] S33: setting a sliding window according to the number of nodes corresponding to the spreading factor accommodated by the channel;

[0139] S34: sliding the sliding window in the sorted paired node combination, counting the number of nodes corresponding to the spreading factor in the sliding window, and making only nodes corresponding to the spreading factor appear in the sliding window by shrinking the window or increasing the spreading factor of the node;

[0140] If the node's spreading factor exceeds the preset node spreading factor adjustment range after adjustment, the node's scheduling right is abandoned and added to the remaining unscheduled node set M. remain middle;

[0141] S35: Perform rolling spread factor adjustment on the sorted paired node combination through a sliding window. After all spread factor adjustments are completed, an adjusted node combination is obtained. If there are remaining nodes in the sorted paired node combination after removing the adjusted node combination, the scheduling rights of the remaining nodes on the channel are abandoned and added to the remaining unscheduled node set M. remain middle;

[0142] Among them, the remaining unscheduled node set M remain The nodes in the table are sorted in descending order according to the minimum spreading factor supported by the nodes and the initial signal-to-noise ratio of the nodes.

[0143] See also Figure 6 , Figure 7 and Figure 8 The three cases of SF rolling adjustment are shown. It should be noted that the size of the sliding window is set according to the number of nodes corresponding to the spreading factor accommodated by the channel. In this embodiment, for the convenience of explanation, it is assumed that the number of nodes corresponding to the spreading factor accommodated by the channel is equal, that is, ξ. Then, the sliding window length is set to ξ.

[0144] like Figure 6 In the example shown in Figure 1, a window with a length of 4 slides to [UE1 UE2 UE3 UE4]. At this time, the SFs in the window are all 7, so there is no need to shrink the window or adjust the SFs. At the same time, when the window continues to slide to [UE5 UE6UE7 UE8], the window has two SFs: 7 and 8. The smaller SF can be adjusted to a larger one, and all SFs of 7 can be adjusted to 8.

[0145] like Figure 7 Another case of SF rolling adjustment is shown. When the window slides to [UE5 UE6 UE7 UE8], SF appears in three cases: 7, 8 and 9. At this time, it is necessary to adjust the SF of 7 to 8 and shrink the window length to [UE5 UE6UE7].

[0146] It is worth noting that the constraint (g) of the optimization problem is to constrain the minimum data rate of each node. The minimum data rate is limited in the hope that nodes with good channel conditions can achieve considerable throughput. The optimization problem proposed in this embodiment is to maximize the system energy efficiency but is limited by the constraint of the minimum data rate of the channel. The minimum data rate of the node given is It is determined by the initial SF of the node. The larger the SF, the smaller the corresponding data rate, and it limits the SF selectable range of each node. At this time, the minimum node data rate is:

[0147]

[0148] Constraint (g) requires that the nodes of paired transmissions satisfy , in order to facilitate the solution, therefore, according to The requirement is to restrict the selection range of SF for all active nodes.

[0149] In this embodiment, each time the SF is adjusted, it is necessary to examine whether the adjusted SF of the node meets the interval of the SF of the node. If it is not satisfied but the SF of the node still needs to be adjusted, the scheduling right of the current node is abandoned.

[0150] like Figure 8 Another case of SF rolling adjustment is shown. After all SFs are adjusted through rolling SF adjustment, if there are remaining nodes, the remaining nodes give up their scheduling rights on the channel.

[0151] S4: determining whether the throughput of the adjusted node combination meets the minimum data rate of the channel, and if not, reallocating the adjusted node combination to obtain a reallocated node combination;

[0152] In this embodiment, the reallocation process is a process of secondary adjustment of resources after the user pairing process on each channel is completed. This process is related to the minimum data rate of the channel. are directly associated. After adjusting SF, the paired nodes satisfy constraint (d), but it is also necessary to consider whether constraint (f) is satisfied. If not, the node-channel needs to be reallocated. The reallocation process mainly includes two types, one is to insert the remaining unscheduled nodes, and the other is to exchange, that is, to exchange the nodes on other channels to another channel.

[0153] Specifically, the adjustment node combination whose throughput does not meet the minimum data rate of the channel is reallocated to obtain a reallocated node combination, including:

[0154] Step 1: Calculate the number of nodes that can be filled corresponding to each spreading factor in the adjustment node combination;

[0155] Step 2: Traverse the remaining unscheduled node set M remain All nodes in the node list are selected to determine nodes whose spreading factors meet the filling requirements, and the nodes that meet the filling requirements are sequentially inserted into the adjustment node combination according to the number of nodes that can be filled;

[0156] Step 3: For the node combination after the inserted node, determine whether its throughput meets the minimum data rate of the channel. If so, use the node combination after the inserted node as the reallocated node combination and execute step S5. If not, execute the channel node exchange operation.

[0157] It should be noted that, in this embodiment, the redistribution is first performed by inserting. If the throughput of the node combination still does not meet the minimum data rate of the channel after the insertion operation, Then continue with the exchange operation.

[0158] Please refer to Fig. 9 and Fig.10 The insertion operation process is described in detail. For the convenience of description, channel RB n All paired nodes, that is, the adjusted node combination, are recorded as Ω n , and record the set of all remaining unscheduled nodes as M remain In channel RB n We need to calculate Ω n Margin τ for each SF ψ , margin τ ψ It refers to the number of nodes that can be filled. Specifically, the pseudo code of the insertion process can be simply described as shown in Algorithm 1:

[0159]

[0160] The main process is M remain According to the margin τ ψ In order of Ω n Execute the insertion process. Fig. 9 Describes the node distribution on the channel RB when the number of nodes corresponding to the channel spreading factor ξ = 4, assuming that the throughput of the channel RB does not meet the minimum data rate of the channel at this time The requirements need to be from M remain Add a node, the node margin of SF 8 is τ8=1, which can be obtained from M remain Insert a node in M remain Found nodes that can be added.

[0161] Continue to look back and find Fig.10 The τ shown 10 = 1, that is, a node can be inserted at the node with SF of 10, according to Fig.10 After all nodes are inserted, the throughput on channel RB is updated to determine whether it is greater than the minimum data rate of the channel. If it is greater, there is no need to continue the swap operation; if it is less, the swap operation is performed.

[0162] The switching operation is to transfer the nodes on the channel with small throughput demand to the channel with large throughput demand, so that the channel with large throughput demand can meet the minimum throughput constraint of the channel.

[0163] Specifically, for a node combination after the inserted node whose throughput does not meet the minimum data rate of the channel, a channel node exchange operation is performed, including:

[0164] Step 1: Calculate the number of nodes that can be filled corresponding to each spreading factor in the node combination after the node is inserted;

[0165] Step 2: In the order of the minimum data rate of the channel from small to large, search for nodes in the adjustment node combination and the reallocation node combination whose throughput meets the minimum data rate of the channel, and fill the nodes that meet the exchange conditions into the node combination after the node is inserted;

[0166] Step 3: For the node combination after filling the nodes, determine whether its throughput meets the minimum data rate of the channel. If so, use the node combination after filling the nodes as the reallocated node combination and execute step S5. If not, abandon the node combination.

[0167] The exchange conditions to be satisfied for each exchange operation are: the number of fillable nodes of the node combination performing the channel exchange operation is non-zero, and the throughput of the node combination providing the exchange node meets the minimum data rate of the channel after the exchange node is removed.

[0168] Please refer to Fig.11 and Fig.12 The switching operation process is described in detail. When the channel RB n There is also a node margin that needs to be exchanged, which can be satisfied by scheduling nodes on other channels to this channel. If RB j →ED m ,ED m →RB n Indicates a swap operation. After the swap is completed, if R j satisfy It is believed that ED m is exchangeable. Specifically, the pseudo code of the exchange process can be simply described as shown in Algorithm 2:

[0169]

[0170]

[0171] During the swap operation, first swap Smaller channels have smaller throughput requirements. The exchange between two channels must meet the exchange conditions to be successful. Fig.11 and Fig.12 , channel RB n With margin τ8=2, search channel RB jIs there a node distribution with SF of 8? If so, add them in order. If not, search for nodes with SF less than 8 and add them in order. ψ =0, At this time, judge Is it true? If so, the exchange is successful, otherwise the optimization problem constraints are not met.

[0172] S5: Perform power iterative optimization on the adjustment node combination whose throughput meets the minimum data rate of the channel and the redistribution node combination whose throughput meets the minimum data rate of the channel to obtain the transmission mode with maximum energy efficiency of the LoRa uplink system.

[0173] After completing the node-channel reallocation, the optimization problem only leaves the transmit power to be optimized. The optimization problem can be simplified to:

[0174]

[0175] In this embodiment, the power iteration optimization is performed using the interior point algorithm. Specifically, the above optimization problem is converted into an unconstrained problem with a barrier function using the interior point algorithm, and then solved by gradient descent. Specifically, the constraint problem is made unconstrained by introducing a barrier function.

[0176]

[0177] Where G(p,r) is the barrier function, f(p) = -η, It is a penalty item D represents the power p∈[0,P max ] is the feasible interval, r is the penalty coefficient of the control penalty term. i The specific expression of (p) is as follows:

[0178]

[0179] The specific details of the interior point algorithm can be divided into the following steps:

[0180] Step 1: Given an initial point p 0 ∈D and cannot take the boundary value of D, the initial penalty coefficient r 0 >0, error precision ζ>0 and number of iterations t=0;

[0181] Step 2: Solve the initial value p t The unconstrained optimization problem minG(p,r t )=f(p)+r t B(p), and get the optimal solution p(r t ) is represented by p t+1 ;

[0182] Step 3: If r t B(p t+1 )≤ζ, there is an optimal solution p t+1 , stop; otherwise, execute Step 4;

[0183] Step 4: Let r t+1 =μr t Where 0<μ<1; t=t+1 and return to Step 2.

[0184] The LoRa channel resource allocation method based on adaptive virtual MIMO transmission in this embodiment introduces virtual MIMO technology into the LoRa uplink system, eliminates the interference problem caused by co-SF through advanced receivers, thereby obtaining diversity gain to improve system performance. A transmission method of joint channel resource allocation and adaptive virtual MIMO in the LoRa uplink system is proposed, which uses multiple user allocation methods to reduce the power consumption of the LoRa system, and improves the system performance through the allocation strategy of secondary allocation combined with SF optimization.

[0185] Embodiment 2

[0186] This embodiment illustrates the effect of the LoRa channel resource allocation method based on adaptive virtual MIMO transmission of Embodiment 1 through simulation experiments.

[0187] Simulation 1.

[0188] The performance of the various user pairing algorithms proposed in the present invention and the common user pairing algorithms in the prior art in the LoRa system are simulated and analyzed to verify the effectiveness of the pairing algorithm proposed in the present invention. Table 2 gives a detailed description of the legend in the simulation results.

[0189] Table 2. Parameters and their descriptions

[0190] parameter describe DPS Determinant User Pairing Algorithm <![CDATA[DPS1]]> Improved serial user pairing algorithm RPS Random user pairing algorithm DPF Double ratio user matching algorithm GTP Greedy Maximum Capacity User Pairing Algorithm GEE Greedy Maximum Energy Efficiency User Pairing Algorithm

[0191] See also Fig.13 The system average performance comparison chart of the user pairing algorithm under different numbers of active nodes, Fig.13 Describes the number of user pairings in all SF scenariosξ all=8, where the average performance of different user pairing algorithms when the number of active nodes is 12, 18, and 24 respectively. The performance of several different algorithms, RPS, DPS1, GTP, and GEE, is compared, and a bar chart of EE performance is given. At this time, EE is not optimized by power. It can be seen that with the increase in the number of active nodes, the performance of the RPS algorithm has basically no significant change, because it adopts a random selection method. However, the performance of the DPS1, GTP, and GEE algorithms increases significantly with the increase in the number of active nodes. This is because the increase in the number of active nodes makes it more likely that the user pairing algorithm will select a superior node. And by comparison, it can be seen that the average SE and EE of the GEE performance are better than those of DPS1, second only to the GTP algorithm, but it can greatly reduce the complexity of the GTP algorithm.

[0192] See also Fig.14 The system average performance comparison chart of the user pairing algorithm under different numbers of user pairings, Fig.14 The number of active nodes is 18, and the number of user pairings in all SF scenarios is ξ all The SE performance of 5, 8, and 12 are shown respectively. The performance of the four different user pairing algorithms, RPS, DPS1, GTP, and GEE, gradually increases with the increase in the number of user pairings. This is because as the number of paired nodes increases, the number of nodes selected by the user pairing algorithm to jointly transmit virtual MIMO increases, which will bring more system SE performance gains. The GEE algorithm performs better than DPS1 and is second only to the performance of the GTP algorithm, which is in line with expectations.

[0193] Simulation 2.

[0194] For the node (user)-channel reallocation scheme proposed in the present invention, the performances of random user-channel allocation, user-channel primary allocation, and user-channel secondary allocation are compared respectively.

[0195] See also Fig.15 The energy efficiency CDF diagram of different user-channel allocation schemes using the GEE user pairing algorithm is shown in the figure. The simulation setting has 270 active nodes, 12 receiving antennas, and a unit data rate of 1.7×10 5[bit / s], the proportion of the four channels is [0.6 0.5 0.5 0.8], that is, the minimum data rate constraints of the four channels are 102000, 85000, 85000, and 136000 respectively. The number of user pairings for each SF is limited to ξ=9, and the maximum transmission power is set to 14dBm. It can be observed that due to the randomness of random allocation, more than half of the simulation results cannot meet the constraints. The greedy user-channel allocation method is adopted in the first allocation, which can make nodes with good channel conditions preferentially allocated to channels with larger throughput requirements, and further improve the percentage of constraints that can be met, and this value can reach 0.1. On this basis, the insertion and exchange algorithm is adopted for secondary allocation, that is, by adjusting individual nodes, all simulation results can meet the constraints.

[0196] See also Fig.16 The performance graphs of different user-channel allocation schemes under different numbers of active nodes are shown, Fig.16 Using the GEE user pairing scheme, the performance curves of different user-channel allocation schemes are given when the number of active nodes is 240, 250, 260, and 270. Fig.16 The simulation parameters and Fig.15 Except for the number of active nodes, they are the same. (a) As can be seen in the figure, as the number of active nodes increases, the average energy efficiency of random allocation, primary allocation and secondary allocation increases. This is because as the number of active nodes increases, the probability of various allocation schemes selecting nodes with better signal-to-noise ratio conditions increases, thereby improving the overall performance of the system. Similarly, (b), (c), and (d) depict the CDF curves of the three allocation schemes as the number of active nodes changes. As can be seen from the figure, as the number of active nodes increases, the number of situations in which the constraints are not met in the CDF curve gradually decreases, and the curve moves downward as a whole. Among them, the performance of the secondary allocation scheme is also much better than that of the random allocation scheme and the primary allocation scheme.

[0197] See also Fig.17 The performance curve of the user-channel allocation scheme under different user pairing numbers is shown in FIG. Fig.17The throughput performance of the user-channel allocation schemes under different numbers of user pairings is plotted. The number of active nodes is set to 260, the unit data rate is 170000, and the proportional allocation on the four channels is [0.6 0.5 0.5 0.8], that is, the minimum data rate constraints of the four channels are 102000, 85000, 85000, and 136000. The initial number of user pairings for each SF is set to 8, 9, and 10, respectively. The maximum transmit power is set to 14dBm. According to the average EE performance curve in Figure (a), when ξ=8, no allocation scheme can meet the data rate requirements of the channel. This is because the number of nodes that can support transmission on the channel is limited, which cannot meet the data rate requirements. As the number of user pairings increases, the average throughput performance of the three user-channel allocation methods increases significantly, and the secondary allocation always remains in the optimal state. Figures (b), (c), and (d) show the CDF trend graphs respectively. It can be seen that under the three user-channel allocation methods, the CDF curve when the number of user pairings is 10 moves significantly downward compared to when the number of user pairings is 9. This shows that an increase in the number of user pairings is conducive to allocating more active nodes to the channel, so that the channel with a higher data rate requirement can meet the constraint conditions.

[0198] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants are intended to cover non-exclusive inclusion, so that an article or device including a series of elements includes not only those elements, but also other elements that are not explicitly listed. In the absence of more restrictions, the elements defined by the statement "including one..." do not exclude the existence of other identical elements in the article or device including the elements.

[0199] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the protection scope of the present invention.

Claims

1. A LoRa channel resource allocation method based on adaptive virtual MIMO transmission, characterized in that: include: S1: Obtain the initial signal-to-noise ratio of all nodes in the LoRa uplink system on each channel and the minimum data rate of each channel, and assign the node to the channel according to the initial signal-to-noise ratio of the node on each channel and the minimum data rate of the channel, so as to complete the node-channel assignment initialization; S2: According to the initialization result of the node-channel allocation and the initial signal-to-noise ratio of all nodes in the LoRa uplink system, the nodes on each channel are paired using a user pairing algorithm to obtain a paired node combination corresponding to the channel; the user pairing algorithm is a greedy maximum capacity user pairing algorithm, a suboptimal maximum energy efficiency user pairing algorithm, or a greedy maximum energy efficiency user pairing algorithm; S3: adjusting the spreading factors of the nodes in the paired node combination according to the number of nodes corresponding to the spreading factor accommodated by the channel to obtain an adjusted node combination; S4: determining whether the throughput of the adjustment node combination meets the minimum data rate of the channel; if not, reallocating the adjustment node combination to obtain a reallocated node combination; S5: Perform power iteration optimization on the adjustment node combination whose throughput meets the minimum data rate of the channel and the redistribution node combination whose throughput meets the minimum data rate of the channel to obtain a transmission mode with maximum energy efficiency for the LoRa uplink system; The S3 includes: S31: according to the initial signal-to-noise ratios of all nodes in the paired node combination on each channel, obtaining the minimum spreading factor supported by the corresponding node; S32: sorting the paired node combinations corresponding to each channel according to the order of the minimum spreading factors supported by the nodes from small to large and the order of the initial signal-to-noise ratios of the nodes from large to small, to obtain the corresponding sorted paired node combinations; S33: setting a sliding window according to the number of nodes corresponding to the spreading factor accommodated by the channel; S34: sliding the sliding window in the ordered paired node combination, counting the number of nodes corresponding to the spreading factor in the sliding window, and making only nodes corresponding to the spreading factor appear in the sliding window by shrinking the window or increasing the spreading factor of the node; If the node's spreading factor exceeds the preset node spreading factor adjustment range after adjustment, the node's scheduling right is abandoned and added to the remaining unscheduled node set M. remain middle; S35: Perform rolling spreading factor adjustment on the sorted paired node combination through a sliding window. After all spreading factors are adjusted, an adjusted node combination is obtained. If there are remaining nodes in the sorted paired node combination after removing the adjusted node combination, the scheduling rights of the remaining nodes on the channel are abandoned and added to the remaining unscheduled node set M. remain middle; Among them, the remaining unscheduled node set M remain The nodes in the table are sorted in descending order according to the minimum spreading factor supported by the nodes and the initial signal-to-noise ratio of the nodes.

2. The LoRa channel resource allocation method based on adaptive virtual MIMO transmission according to claim 1, characterized in that, The S1 includes: S11: Calculate the initial signal-to-noise ratio of all nodes in the LoRa uplink system on each channel; S12: Obtain the minimum data rate of each channel, and sort the priority of the channels according to the minimum data rate of each channel; S13: Node allocation is performed according to channel priority sorting, and the node with the largest initial signal-to-noise ratio on the channel is allocated to the channel, completing the node-channel allocation initialization.

3. The LoRa channel resource allocation method based on adaptive virtual MIMO transmission according to claim 1, characterized in that, According to the initialization result of the node-channel allocation and the initial signal-to-noise ratio of all nodes in the LoRa uplink system, the nodes on each channel are paired using the greedy maximum capacity user pairing algorithm to obtain the paired node combination corresponding to the channel, including: Step 1: Calculate the initial signal-to-noise ratio of all nodes initially allocated on each channel, and obtain the minimum spreading factor supported by the corresponding node according to the initial signal-to-noise ratio of the node; Step 2: Sort all nodes initially allocated on the channel in the order of the minimum spreading factor supported by the node from small to large, and the initial signal-to-noise ratio of the node from large to small, and obtain the node arrangement set M sorted ; Step 3: Arrange the set M according to the nodes sorted Schedule the nodes one by one and determine whether the throughput of all nodes in the scheduling set Ω satisfies the following formula after adding the current node to the scheduling set Ω: In the formula, represents the total throughput in the scheduling set Ω at the tth scheduling, represents the total throughput in the scheduling set Ω at the t-1th scheduling, represents the initial throughput of the current node i scheduled; If satisfied, the current node is added to the scheduling set Ω; If not, determine whether the spreading factor of the current node is equal to 12. If so, give up the scheduling right of the current node. Otherwise, increase the spreading factor of the current node by 1 and repeat step 2 to update the node permutation set M. sorted The scheduling is continued until the number of scheduled nodes reaches the number of nodes that the channel can accommodate or there are no nodes that can continue to be scheduled, and a final scheduling set is obtained as the paired node combination corresponding to the channel.

4. The LoRa channel resource allocation method based on adaptive virtual MIMO transmission according to claim 1, characterized in that, According to the initialization result of the node-channel allocation and the initial signal-to-noise ratio of all nodes in the LoRa uplink system, the nodes on each channel are paired using the suboptimal maximum energy efficiency user pairing algorithm to obtain the paired node combination corresponding to the channel, including: Step 1: According to the number of nodes accommodated by the channel, all nodes initially allocated on the channel are arranged and combined to obtain several groups of node combinations; Step 2: According to the initial signal-to-noise ratios of all nodes initially allocated on the channel, the sum of the initial signal-to-noise ratios of the nodes corresponding to each group of node combinations is calculated; Step 3: Select a group of node combinations with the largest sum of initial signal-to-noise ratios of the nodes as the paired node combination corresponding to the channel.

5. The LoRa channel resource allocation method based on adaptive virtual MIMO transmission according to claim 1, characterized in that, According to the initialization result of the node-channel allocation and the initial signal-to-noise ratio of all nodes in the LoRa uplink system, the nodes on each channel are paired using the greedy maximum energy efficiency user pairing algorithm to obtain the paired node combination corresponding to the channel, including: Step 1: Calculate the initial signal-to-noise ratio of all nodes initially assigned to each channel, and sort all nodes initially assigned to the channel in descending order of their initial signal-to-noise ratio; Step 2: According to the number of nodes accommodated by the channel, a corresponding number of nodes are selected from the sorted node set in sequence as the paired node combination corresponding to the channel.

6. The LoRa channel resource allocation method based on adaptive virtual MIMO transmission according to claim 1, characterized in that, The adjustment node combination whose throughput does not meet the minimum data rate of the channel is reallocated to obtain a reallocated node combination, comprising: Step 1: Calculate the number of nodes that can be filled corresponding to each spreading factor in the adjustment node combination; Step 2: Traverse the remaining unscheduled node set M remain all nodes in the adjustment node group, determine the nodes whose spreading factors meet the filling requirements, and insert the nodes that meet the filling requirements into the adjustment node group in order according to the number of nodes that can be filled; Step 3: For the node combination after the inserted node, determine whether its throughput meets the minimum data rate of the channel. If so, use the node combination after the inserted node as the reallocated node combination and execute step S5. If not, execute the channel node exchange operation.

7. The LoRa channel resource allocation method based on adaptive virtual MIMO transmission according to claim 6, characterized in that, For a node combination after the inserted node whose throughput does not meet the minimum data rate of the channel, a channel node exchange operation is performed, including: Step 1: Calculate the number of nodes that can be filled corresponding to each spreading factor in the node combination after the node is inserted; Step 2: In the order of the minimum data rate of the channel from small to large, search for nodes for the adjustment node combination and the reallocation node combination whose throughput meets the minimum data rate of the channel, and fill the nodes that meet the exchange conditions into the node combination after the node is inserted; Step 3: For the node combination after filling the nodes, determine whether its throughput meets the minimum data rate of the channel. If so, use the node combination after filling the nodes as the reallocated node combination and execute step S5. If not, abandon the node combination.

8. The LoRa channel resource allocation method based on adaptive virtual MIMO transmission according to claim 7, characterized in that, The exchange conditions to be satisfied by each exchange operation are: the number of fillable nodes of the node combination performing the channel exchange operation is non-zero, and the throughput of the node combination providing the exchange node meets the minimum data rate of the channel after the exchange node is removed.

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