An adaptive modulation method based on k-means clustering algorithm

Abstract

The present invention discloses an adaptive modulation method based on a k-means clustering algorithm, comprising: step S1, clustering all nodes using the k-means clustering algorithm according to the geographical locations of all nodes and the number of clusters K, and obtaining the center coordinates of all clusters; step S2, selecting a single node in the cluster as a cluster head, and determining the modulation mode used for uplink wireless transmission from the cluster head node to the base station; step S3, judging whether the modulation modes used by all cluster heads meet the transmission rate requirement after data aggregation of member nodes in the cluster, if so, executing step S5, otherwise executing step S4; step S4, increasing the number of clusters K=K+1 and returning to step S1; step S5, transmitting data between the cluster member nodes and the cluster head node according to the determined clustering and modulation mode. The present invention can improve the topology of a wireless sensor network, overcome the disadvantage that the transmission rate of the cluster head node cannot meet the concurrent aggregation and forwarding of member nodes in the cluster, and effectively improve the network throughput of the wireless sensor network.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless communications, and in particular to an adaptive modulation method based on a K-means clustering algorithm. Background Art

[0002] Adaptive modulation is an effective way to improve spectrum efficiency while ensuring the data transmission quality (i.e., bit error rate) of the wireless channel. Its basic concept is to adaptively select different modulation schemes based on real-time channel state information (i.e., signal-to-noise ratio). Specifically, the receiver estimates the current channel state and transmits this information to the transmitter via a feedback link. The transmitter then dynamically adjusts transmission parameters, such as modulation mode, transmit power, and coding scheme, based on this current channel state information.

[0003] Different modulation schemes offer different transmission rates and qualities. Generally speaking, high-order modulation (such as 64QAM) offers higher transmission rates but relatively poor noise immunity, resulting in higher bit error rates (BERs) when the signal-to-noise ratio is low. Low-order modulation (such as QPSK) offers lower transmission rates and stronger noise immunity. Therefore, adaptive modulation selects a modulation scheme based on the following principles: when the signal-to-noise ratio is high, a relatively high-order modulation is selected to increase the transmission rate; when the signal-to-noise ratio is low, a relatively low-order modulation is selected to meet the required BER.

[0004] Wireless channel quality is dynamic and time-varying. Dynamically selecting the physical channel coding and modulation scheme based on this dynamic and time-varying quality is key to adaptive modulation and coding. When channel conditions are poor, a lower modulation scheme and coding rate are selected; when channel conditions are good, a higher modulation scheme is selected, maximizing the transmission rate. When coding techniques and modulation schemes are adjusted, channel quality is correspondingly improved.

[0005] Wireless sensor networks typically consist of a large number of sensor nodes (referred to as nodes) used for environmental monitoring, human health monitoring, smart homes, smart factories, disaster management, and other applications. Each node can send data to other nodes via wireless links or act as a relay or data fusion node to forward data to a wireless base station. After receiving the node data, the wireless base station can process it locally or act as a gateway to forward the data to a remote data server.

[0006] Wireless sensor networks are typically dynamic, multi-hop (ad-hoc) networks, and topology management is a method for ensuring a stable, reliable, and efficient network structure. Clustering is currently a popular technique for topology management in wireless sensor networks. Clustering divides all nodes into different groups based on various criteria, such as power consumption optimization and network load balancing. Each group is called a cluster. Each cluster has a cluster head, which aggregates data from other member nodes in the cluster and forwards it to the base station. Clustering eliminates the need for cluster member nodes to send data directly to the wireless base station. This would result in unnecessary node energy loss, access collisions, and co-channel interference.

[0007] The inventors have discovered that existing technical solutions all focus on improving the performance of adaptive modulation (coding) and do not involve combining with the clustering algorithm (k-means algorithm) of wireless sensor networks. Summary of the Invention

[0008] The technical problem to be solved by the present invention is to provide an adaptive modulation method based on the k-means clustering algorithm to overcome the problem that the data transmission rate of the cluster head node in the wireless sensor network is limited and cannot meet the forwarding problem after the concurrent data of the member nodes in the cluster are aggregated.

[0009] To solve the above technical problems, the present invention provides an adaptive modulation method based on a k-means clustering algorithm, comprising:

[0010] Step S1: Based on the geographical locations of all nodes and the number of clusters K, all nodes are clustered using the k-means clustering algorithm, and the center coordinates of all clusters are obtained;

[0011] Step S2: Select a single node in the cluster as a cluster head, and determine the modulation mode used for uplink wireless transmission from the cluster head node to the base station;

[0012] Step S3, determining whether the modulation mode adopted by all cluster heads meets the transmission rate requirement after the data of the member nodes in the cluster are aggregated, if so, executing step S5, otherwise executing step S4;

[0013] Step S4, increase the number of clusters K=K+1, and go to step S1;

[0014] Step S5: According to the determined clustering and modulation mode, the cluster member nodes and the cluster head node perform data transmission.

[0015] Furthermore, in step S1, the input of the k-means clustering algorithm is the geographical coordinates of all nodes {x1, x2, ..., x N} and the total number of clusters K, the output is cluster Θ1, Θ2, ...Θ KThe composition and corresponding cluster center coordinates u1,u2,…,u K , N is the number of nodes.

[0016] Furthermore, the step S1 specifically includes:

[0017] Step S11: randomly select K coordinates within the coverage area of ​​the wireless base station as the two-dimensional coordinates u1, u2, ..., u of the cluster center. K The initial value of

[0018] Step S12, traverse N nodes, for node i = 1, 2, ..., N, find the cluster center coordinate u closest to the node k , assign the node to cluster Θ k , node i satisfies ||x i -u k || 2 ≤||x i -u n || 2 ,

[0019] Step S13, traverse K clusters, for cluster k = 1, 2, ..., K, the cluster center coordinate u k Update to the average value of all node coordinates in the cluster;

[0020] After updating all cluster center coordinates, go to step S14;

[0021] Step S14, determine whether the algorithm has converged. If it has converged, exit the algorithm and go to step S15; if the algorithm has not converged, continue iterating and go to step S12;

[0022] Step S15, output cluster set clusters Θ1, Θ2, ... Θ K And the corresponding cluster center coordinates u1,u2,…,u K .

[0023] Furthermore, step S14 determines whether the algorithm has converged based on the deviation range between the cluster center coordinates calculated in the previous iteration and the cluster center coordinates calculated in the current iteration. If the deviation range is lower than a set threshold, the algorithm is determined to have converged.

[0024] Furthermore, in step S13, the cluster center coordinate u k Updated to:

[0025]

[0026] Among them, |Θ k | represents the total number of nodes in the cluster, including cluster heads and cluster member nodes.

[0027] Further, after obtaining the cluster set Θ1, Θ2, ...Θ K and the corresponding cluster center coordinates u1,u2,…,u K Then, step S2 selects one of the member nodes of each cluster as the cluster head node; the member node selected as the cluster head node meets the following two conditions:

[0028] The first condition: the distance between the member node and the cluster center coordinate is less than the threshold r max , that is, ||x i -u k || <r k ,x i ∈Θ k ;

[0029] The second condition: Based on the channel status from the member node to the base station, the adaptive modulation mode is selected, and the transmission rate satisfies the concurrent converged data transmission of the member nodes in the cluster.

[0030] Furthermore, the link from cluster member node to cluster head adopts a fixed modulation mode and the transmission rate is γ FM ; The adaptive modulation mode is used from cluster head to base station. The modulation mode is updated according to the channel state. The transmission rate is γ AM ; then the second condition needs to be met:

[0031] γ AM ≥α k ·|Θ k |·γ FM

[0032] Among them, |Θ k | represents the total number of nodes in the cluster, α k ≤1 is the concurrent transmission rate of member nodes in the cluster.

[0033] Furthermore, the specific process of cluster head selection includes:

[0034] Step S21, initialize k=1, and go to step S22;

[0035] Step S22: Initialize the radius r of the cluster head candidate area of ​​cluster k k =r min , go to step S23;

[0036] Step S23, based on the cluster center coordinate u k , set the radius to r kThe circular area is the candidate area, and the nodes falling within the candidate area are selected as candidate nodes, that is, the candidate nodes meet the first condition. If there is no candidate node, go to step S24; determine the adaptive modulation mode based on the wireless link status from the candidate node to the base station, select the candidate node that meets the concurrent transmission requirements of the member nodes in the cluster, and go to step S24;

[0037] Step S24: If there is a candidate node that meets the second condition, set the candidate node as the cluster head node, and then go to step S25; if there is no candidate node that meets the second condition, go to step S26;

[0038] Step S25: All clusters have completed cluster head selection, that is, k is equal to the number of clusters K, and go to step S27; otherwise, traverse the next cluster, k = k + 1, and go to step S22;

[0039] Step S26, expand the candidate area, r k =r k +Δr, if r k >r max Then go to step S28, otherwise go to step S23; Δr is the increment of the radius of the candidate area;

[0040] Step S27, successful completion;

[0041] Step S28, ends in failure.

[0042] Furthermore, the cluster center coordinates are calculated using the average value of all terminal coordinates within the cluster.

[0043] Furthermore, r max Choose 10%-20% of the cluster radius.

[0044] The implementation of the present invention has the following beneficial effects: the wireless sensor cluster head node adopts adaptive modulation coding and performs classification based on the k-means clustering algorithm, which can improve the topology of the wireless sensor network and overcome the disadvantage that the transmission rate of the cluster head node cannot meet the concurrent convergence and forwarding of the member nodes in the cluster, thereby effectively improving the network throughput of the wireless sensor network. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0046] Figure 1 Schematic diagram of wireless sensor network clustering in an embodiment of the present invention.

[0047] Figure 2 Schematic diagram of the wireless transmission system architecture from cluster members to cluster head nodes in an embodiment of the present invention.

[0048] Figure 3 Schematic diagram of the wireless transmission system architecture from cluster head nodes to base stations in an embodiment of the present invention.

[0049] Figure 4 A schematic flow chart of an adaptive modulation method based on a k-means clustering algorithm according to an embodiment of the present invention.

[0050] Figure 5 The figure is a schematic diagram of the clustering process of the k-means clustering algorithm in an embodiment of the present invention.

[0051] Figure 6 Schematic diagram of candidate cluster head nodes and candidate areas in an embodiment of the present invention.

[0052] Figure 7 The figure is a schematic diagram of the cluster head selection process in an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The following descriptions of the embodiments refer to the accompanying drawings to illustrate specific embodiments in which the present invention may be implemented.

[0054] The present invention considers a typical wireless sensor application scenario, where a wireless base station collects sensor data distributed in different geographical locations through a wireless link.

[0055] Within the coverage area of ​​a single wireless base station, N wireless sensor nodes are distributed, and the geographical locations of all nodes are {x1, x2, ..., x N} is known, the geographic coordinates of the i-th (1≤i≤N) node are x i .

[0056] For these N wireless sensor nodes, they are divided into K clusters Θ1, Θ2, ...Θ K , where Θ1, Θ2, … Θ K is the set {x1,x2,…,x N}. If node i belongs to cluster k, then x i ∈Θ k .

[0057] There are no overlapping nodes between different clusters, and the total number of nodes in all clusters is N, that is, (where Φ is an empty set):

[0058]

[0059] like Figure 1As shown, each cluster has a single cluster head node. The member nodes in the cluster send data to the corresponding cluster head node through a wireless link. The cluster head node aggregates the data of other member nodes in the cluster and forwards it to the base station through a wireless link.

[0060] The wireless link between the cluster member nodes and the cluster head node adopts a fixed modulation method, such as QPSK, PSK, C4FM, etc., to meet the rate and reliability requirements of single node data transmission. Figure 2 As shown, the fixed modulation scheme is known at both the cluster member node transmitter side and the cluster head node receiver side. According to this modulation scheme, the transmitter modulates and transmits the signal, and the receiver receives the signal, performs channel estimation based on the reference signal, and then performs corresponding demodulation to obtain a bit data stream.

[0061] For example Figure 3 As shown, the wireless link from the cluster head node to the wireless base station uses adaptive modulation, for example, selecting from multiple modulation schemes based on cluster member information and current wireless channel information. Generally, the available modulation schemes include BPSK, QPSK, 16QAM, 64QAM, and 256QAM.

[0062] Please refer to Figure 4 As shown, an embodiment of the present invention provides an adaptive modulation method based on a k-means clustering algorithm, comprising:

[0063] Step S1: Based on the geographical locations of all nodes and the number of clusters K, all nodes are clustered using the k-means clustering algorithm, and the center coordinates of all clusters are obtained;

[0064] Step S2: Select a single node in the cluster as a cluster head, and determine the modulation mode used for uplink wireless transmission from the cluster head node to the base station;

[0065] Step S3, determining whether the modulation mode adopted by all cluster heads meets the transmission rate requirement after the data of the member nodes in the cluster are aggregated, if so, executing step S5, otherwise executing step S4;

[0066] Step S4, increase the number of clusters K=K+1, and go to step S1;

[0067] Step S5: According to the determined clustering and modulation mode, the cluster member nodes and the cluster head node perform data transmission.

[0068] Specifically, in step S1, all nodes are clustered using the k-means clustering algorithm. Specifically, the input of the k-means clustering algorithm is the geographical coordinates of all nodes {x1, x2, ..., x N} and the total number of clusters K, the output is cluster Θ1, Θ2, ...Θ KThe composition and corresponding cluster center coordinates u1,u2,…,u K , N is the number of nodes. It should be noted that the cluster center coordinates do not necessarily coincide with the coordinates of a terminal, that is, the cluster center coordinates and the coordinates of a single terminal are independent of each other, but the cluster center coordinates are calculated using the average value of the coordinates of all terminals in the cluster.

[0069] The calculation steps of the K-means clustering algorithm are as follows:

[0070] Step S11: randomly select K coordinates within the coverage area of ​​the wireless base station as the two-dimensional coordinates u1, u2, ..., u of the cluster center. K The initial value of

[0071] Step S12, traverse N nodes, for node i = 1, 2, ..., N, find the cluster center coordinate u closest to the node k , assign the node to cluster Θ k , that is, for node i, ||x i -u k || 2 ≤||x i -u n || 2 ,

[0072] Step S13, traverse K clusters, for cluster k = 1, 2, ..., K, the cluster center coordinate u k Updated to the average value of all node coordinates in the cluster, that is,

[0073]

[0074] where |Θ k | represents the total number of nodes in the cluster, including cluster heads and cluster member nodes;

[0075] After updating all cluster center coordinates, go to step S14;

[0076] Step S14, determine whether the algorithm has converged. If the algorithm has converged, exit the algorithm and go to step S15 to output the cluster set clusters Θ1, Θ2, ...Θ K And the corresponding cluster center coordinates u1,u2,…,u K If the algorithm has not converged, the iteration continues and the process goes to step S12. In step S14, the algorithm can be judged whether it has converged by the deviation range between the cluster center coordinates calculated in the previous iteration and the cluster center coordinates calculated in the current iteration. If the deviation range is lower than the set threshold value, the algorithm is judged to have converged.

[0077] Step S15, output cluster set clusters Θ1, Θ2, ... Θ KAnd the corresponding cluster center coordinates u1,u2,…,u K .

[0078] In step S2, the selection of the cluster head node and the corresponding modulation method are described in detail as follows:

[0079] like Figure 5 As shown, we get the cluster set Θ1, Θ2,…Θ K and the corresponding cluster center coordinates u1,u2,…,u K Afterwards, one of the member nodes of each cluster is selected as the cluster head node. The selection of a member node as the cluster head node must meet two conditions:

[0080] The first condition: the distance between the member node and the cluster center coordinate is less than the threshold r max , that is, ||x i -u k || <r k ,x i ∈Θ k .

[0081] r max The selection of can be determined based on the size of the cluster, and is usually 10%-20% of the cluster (approximate) radius. max If the value is too large, the cluster head node will not cover the member nodes in the cluster sufficiently, which will affect the data transmission quality from the cluster member nodes to the cluster head.

[0082] The second condition: Based on the channel state from the member node to the base station, the adaptive modulation mode selected has a transmission rate that can meet the concurrent convergence data transmission of the member nodes in the cluster. Let the link from the cluster member node to the cluster head adopt a fixed modulation mode with a transmission rate of γ FM ; The adaptive modulation mode is used from cluster head to base station. The modulation mode is updated according to the channel state. The transmission rate is γ AM . Then the second condition needs to be met:

[0083] γ AM ≥α k ·|Θ k |·γ FM

[0084] Among them, |Θ k | represents the total number of nodes in the cluster, α k ≤1 is the concurrent transmission rate of member nodes in the cluster.

[0085] For example Figure 6 As shown in FIG, in steps S2 and S3, the specific process of cluster head selection is as follows:

[0086] Step S21, initialize k=1, and go to step S22;

[0087] Step S22: Initialize the radius r of the cluster head candidate area of ​​cluster k k =r min , go to step S23;

[0088] Step S23: Based on the cluster center coordinates, set the radius to r k The circular area is the candidate area, and the nodes falling within the candidate area are selected as candidate nodes, that is, the candidate nodes meet the first condition. If there is no candidate node, go to step S24; determine the adaptive modulation mode based on the wireless link status (signal-to-noise ratio) from the candidate node to the base station, select the candidate node that meets the concurrent transmission requirements of the member nodes in the cluster, and go to step S24;

[0089] Step S24: If there is a candidate node that meets the second condition, set the candidate node as the cluster head node, and then go to step S25; if there is no candidate node that meets the second condition, go to step S26;

[0090] Step S25: All clusters have completed cluster head selection, that is, k is equal to the number of clusters K, and go to step S27; otherwise, traverse the next cluster, k = k + 1, and go to step S22;

[0091] Step S26, expand the candidate area, r k =r k +Δr, if r k >r max Then go to step S28, otherwise go to step S23;

[0092] Step S27, successful completion;

[0093] Step S28: Failure ends. You may consider increasing the number of clusters and re-clustering, as in the above step S4.

[0094] Wherein, Δr in step S26 is the increment of the radius of the candidate area.

[0095] Step S23 determines the adaptive modulation mode based on the wireless link status (signal-to-noise ratio) from the candidate node to the base station. Adaptive modulation technology is to select different modulation technologies according to the state of the channel. Generally speaking, a high-order modulation mode is selected when the signal-to-noise ratio is high. Modulation is the process of mapping binary bits into the amplitude and phase of a specific symbol. The higher the modulation order, the more bits a single symbol transmits. When the bandwidth is constant, the total number of bits transmitted is greater. Based on this characteristic, due to the large number of nodes in the cluster, the wireless link from the cluster head to the base station requires a much higher transmission rate than the link from the cluster member node to the cluster head. In the screening process of the cluster head, candidate nodes with a high signal-to-noise ratio should be given priority to meet the second condition.

[0096] From the above description, it can be seen that compared with the existing technology, the beneficial effect of the present invention is that: the wireless sensor cluster head node adopts adaptive modulation coding and is classified based on the k-means clustering algorithm, which can improve the topology of the wireless sensor network and overcome the disadvantage that the transmission rate of the cluster head node cannot meet the concurrent convergence and forwarding of the member nodes in the cluster, thereby effectively improving the network throughput of the wireless sensor network.

[0097] The above disclosure is merely a preferred embodiment of the present invention and is not intended to limit the scope of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope of the present invention.

Claims

1. An adaptive modulation method based on a k-means clustering algorithm, characterized in that: include: Step S1: Based on the geographical locations of all nodes and the number of clusters K, all nodes are clustered using the k-means clustering algorithm, and the center coordinates of all clusters are obtained; Step S2: Select a single node in the cluster as a cluster head, and determine the modulation mode used for uplink wireless transmission from the cluster head node to the base station; Step S3, determining whether the modulation mode adopted by all cluster heads meets the transmission rate requirement after the data of the member nodes in the cluster are aggregated, if so, executing step S5, otherwise executing step S4; Step S4, increase the number of clusters K=K+1, and go to step S1; Step S5: The cluster member nodes and the cluster head node perform data transmission according to the determined clustering and modulation mode; In step S1, the input of the k-means clustering algorithm is the geographical coordinates of all nodes {x1, x2, ..., x N } and the total number of clusters K, the output is cluster Θ1, Θ2, ...Θ K The composition and corresponding cluster center coordinates u1,u2,…,u K , N is the number of nodes; The step S1 specifically includes: Step S11: randomly select K coordinates within the coverage area of ​​the wireless base station as the two-dimensional coordinates u1, u2, ..., u of the cluster center. K The initial value of Step S12, traverse N nodes, for node i = 1, 2, ..., N, find the cluster center coordinate u closest to the node k , assign the node to cluster Θ k , node i satisfies ||x i -u k || 2 ≤||x i -u n || 2 , Step S13, traverse K clusters, for cluster k = 1, 2, ..., K, the cluster center coordinates u k Update to the average value of all node coordinates in the cluster; After updating all cluster center coordinates, go to step S14; Step S14, determine whether the algorithm has converged. If it has converged, exit the algorithm and go to step S15; if the algorithm has not converged, continue iterating and go to step S12; Step S15, output cluster set clusters Θ1, Θ2, ... Θ K And the corresponding cluster center coordinates u1,u2,…,u K ; After obtaining the cluster set Θ1, Θ2,…Θ K and the corresponding cluster center coordinates u1,u2,…,u K Then, step S2 selects one of the member nodes of each cluster as the cluster head node; the member node selected as the cluster head node meets the following two conditions: The first condition: the distance between the member node and the cluster center coordinate is less than the threshold r max , that is, ||x i -u k || <r k ,x i ∈Θ k ; The second condition is that the adaptive modulation mode selected based on the channel state from the member node to the base station should have a transmission rate that satisfies the concurrent converged data transmission of the member nodes in the cluster. The link from cluster member node to cluster head adopts a fixed modulation mode and the transmission rate is γ FM ; The adaptive modulation mode is used from cluster head to base station. The modulation mode is updated according to the channel state. The transmission rate is γ AM ; then the second condition needs to be met: c AM ≥a k ·|I k |·c FM Among them, |Θ k | represents the total number of nodes in the cluster, α k ≤1 is the concurrent transmission rate of member nodes in the cluster.

2. The method according to claim 1, characterized in that The step S14 determines whether the algorithm has converged based on the deviation range between the cluster center coordinates calculated in the previous iteration and the cluster center coordinates calculated in the current iteration. If the deviation range is lower than a set threshold, it is determined that the algorithm has converged.

3. The method according to claim 1, characterized in that In step S13, the cluster center coordinates u k Updated to: Among them, |Θ k | represents the total number of nodes in the cluster, including cluster heads and cluster member nodes.

4. The method according to claim 1, wherein The specific process of cluster head selection includes: Step S21, initialize k=1, and go to step S22; Step S22: Initialize the radius r of the cluster head candidate area of ​​cluster k k =r min , go to step S23; Step S23, based on the cluster center coordinate u k , set the radius to r k The circular area is the candidate area, and the nodes falling within the candidate area are selected as candidate nodes, that is, the candidate nodes meet the first condition. If there is no candidate node, go to step S24; determine the adaptive modulation mode based on the wireless link status from the candidate node to the base station, select the candidate node that meets the concurrent transmission requirements of the member nodes in the cluster, and go to step S24; Step S24: If there is a candidate node that meets the second condition, set the candidate node as the cluster head node, and then go to step S25; if there is no candidate node that meets the second condition, go to step S26; Step S25: All clusters have completed cluster head selection, that is, k is equal to the number of clusters K, and go to step S27; otherwise, traverse the next cluster, k = k + 1, and go to step S22; Step S26, expand the candidate area, r k =r k +△r, if r k >r max Then go to step S28, otherwise go to step S23; △r is the increment of the radius of the candidate area; Step S27, successful completion; Step S28, ends in failure.

5. The method according to claim 1, wherein The cluster center coordinates are calculated using the average of all terminal coordinates within the cluster.

6. The method according to claim 1, characterized in that r max Choose 10%-20% of the cluster radius.

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