KR101269752B1 - Optimization method and optimization apparatus for queue-based cross-layer in wireless ad-hoc network - Google Patents

Abstract

The present invention is a cross-layer optimization method in a wireless ad-hoc network, which determines a link price using the number of packets in a queue and flows using the link price. A channel access rank is determined by determining a flow rate, comparing the link price values of the link price and neighboring nodes, and channel back-off time according to the channel access rank. Determine. Through this, the present invention can provide an optimization method capable of simultaneously seeking fairness and efficiency while determining link capacity adaptively without being affected by interference.

Ad-hoc network, cross layer optimization, congestion control, media access control, queue

Description

CROSS-BASED CROSS-LAYER OPTIMIZATION METHOD AND OPTIMIZATION APPARATUS IN A WIRELESS AD-HOC NETWORK

1 is a diagram showing a link and a flow between nodes;

2 is a flowchart illustrating a congestion control method in a transport layer for optimization according to an embodiment of the present invention;

3 is a flowchart illustrating a method of controlling a media access of a media access control layer for optimization according to an embodiment of the present invention;

4 is a flowchart illustrating a process of determining a channel access rank in media access control according to an embodiment of the present invention;

5 is a flowchart illustrating a cross-layer optimization method according to an embodiment of the present invention;

6 to 9 illustrate simulation results according to an embodiment of the present invention.

The present invention relates to an optimization method and apparatus in an ad-hoc network. In particular, the present invention relates to congestion control in a transport layer and to media access control in a media access control layer. : MAC) relates to an optimization method and apparatus.

Because wired networks and wireless networks differ greatly in two ways, optimization methods must be set differently.

First, wireless and wired networks have different effects of interference. That is, the wired network does not need to consider the influence of interference in the optimization method, but the wireless network cannot transmit simultaneously from two adjacent neighbor nodes due to the influence of the interference. If transmitting at the same time, interference is large and transmission is likely to fail.

Second, wireless networks and wired networks have different link capacity determination methods. In other words, wired networks are easy to determine the capacity of a link, while wireless networks are difficult to determine the capacity of a link. In particular, each node in a wireless ad-hoc network has a property of resource contention according to geographical location. For this reason, the wireless ad-hoc network cannot know the capacity of the link among the parameter values required by the algorithm.

Attempts for optimization methods in wireless networks have been constantly studied. Looking at the conventional clique method for optimization, the click method is modeled by introducing the concept of the maximum click to the graph theory. At this time, each node forms a contention area according to the location, and configures a maximum click on the generated contention graph.

However, the click method is difficult to find clicks in a distributed manner on an ad-hoc network. There is a need for a mechanism to maintain the same click price between nodes belonging to the same click. Moreover, the click method has a problem in that when the network topology does not form a perfect graph, unwanted results are obtained. That is, the click method has an incomplete form to be reflected in reality.

Accordingly, there is a need for an optimization method that considers the effects of interference in a wireless ad-hoc network, solves the problem of determining link capacity, and is practically available and can solve the problem of efficiency and fairness.

An object of the present invention for solving the above problems is to perform congestion control in the transport layer using the number of packets in the queue as a method for cross-layer optimization in an ad-hoc network, and rank according to the congestion price in the media access control layer. To provide an optimization method and apparatus for performing the media access control using.

It is another object of the present invention to provide a congestion control method and apparatus for determining link price, flow price, and flow rate using the number of packets in a queue at the transport layer for optimization in an ad-hoc network.

It is another object of the present invention to provide a medium control access method and apparatus for determining a rank using a price in a medium access control layer for optimization in an ad-hoc network, and determining a backoff time through the rank. It is.

A method according to an embodiment of the present invention for achieving the above object is a cross-layer optimization method in a wireless Ad-hoc network, the link price using the number of packets in the queue (link) price); Determining a flow rate using the link price; Determining a channel access rank by comparing the link price values of the link price and neighboring nodes; And determining a channel access back-off time according to the channel access order.

In addition, the method according to an embodiment of the present invention is a congestion control method of a transport layer for optimization in a wireless Ad-hoc network, the method comprising determining the price of the link using the number of packets in the link queue ; Determining a flow price using the price of the link and the price of a neighboring link; And determining the flow rate using the flow price.

In addition, the method according to an embodiment of the present invention is a media control approach of a media access control layer for optimization in a wireless ad-hoc network, and broadcasts a price value calculated based on a queue to a neighbor node. Making; Determining a channel access rank by comparing a price value of a link broadcast from a neighbor node with its price value; And determining a channel access back-off time using the channel access rank.

In addition, an apparatus according to an embodiment of the present invention is a cross-layer optimization apparatus in a wireless Ad-hoc network, comprising: a memory for storing information including the number of packets in a queue; And a controller for controlling cross-layer optimization using the memory, wherein the controller determines a link price using the number of packets in a queue; Determining a flow rate using the link price; Comparing a link price value of the link price and a neighbor node to determine a channel access rank; And determining a channel access backoff time according to the channel access order.

In addition, the apparatus according to an embodiment of the present invention is an apparatus for controlling congestion in the transport layer for optimization of a wireless Ad-hoc network, the apparatus comprising: a memory for storing information including the number of packets of a queue; And a controller for controlling cross-layer optimization using the memory, wherein the controller periodically monitors the queue size and determines the price of the link using the number of packets in the link queue; Determining a flow price using the price of the link and the price of a neighboring link; And calculating a flow rate using the flow price.

In addition, an apparatus according to an embodiment of the present invention is a media access control apparatus of a media access control layer for optimizing a wireless ad-hoc network, comprising: a memory for storing information including the number of packets in a queue; And a controller for controlling cross-layer optimization using the memory, wherein the controller broadcasts a price value calculated based on a queue to a neighbor node; Receiving a price value from a neighbor node and comparing the price value with its own price value to determine a channel access rank; And determining a channel access back-off time using the channel access rank.

In describing the configuration of the present invention, the same components in the drawings are represented by the same reference numerals. In addition, detailed descriptions of well-known functions and configurations that may unnecessarily obscure the subject matter of the present invention will be omitted.

First, the present invention will be described as a whole.

The present invention relates to an optimization method and apparatus in an ad-hoc network. In particular, the present invention relates to an optimization method and apparatus for controlling congestion in a transport layer and optimizing a medium access control in a media access control layer by optimizing using the number of packets in a queue. It is about.

The present invention uses a cross layer optimization method. In the cross layer optimization method, a transport layer and a media access control layer are used as cross-layers.

An embodiment of the present invention calculates a link price of a link by periodically monitoring the size of a queue as a congestion control method for optimization at the transport layer. In addition, an embodiment of the present invention uses the price of the link price and neighboring nodes to obtain a flow price. The embodiment of the present invention then obtains the flow rate using the flow price. Since the flow rate is obtained using the number of packets in the queue, it can be used as a congestion control method.

An embodiment of the present invention determines the channel access rank of each node using prices using the number of packets in the queue in the media access control layer. In the embodiment of the present invention, the channel access rank is introduced into the backoff time determination to control the medium access. Here, the back off time corresponds to the channel access wait time. Looking at the process of determining the channel access rank, the price of each node is first broadcast. Each node compares its own price with that of its neighbors. Thereafter, each node determines the channel access latency by determining the channel access priority in the longest queue first manner.

The present invention can provide a cross layer optimization method in an ad-hoc network by introducing a price determination method according to the number of packets of a queue available in reality. Accordingly, the present invention allows the bandwidth allocation problem in an ad-hoc network to reach an optimal state in terms of fairness and efficiency. Furthermore, the algorithm presented in the present invention has a relatively low overhead and is easier in actual implementation.

1. Congestion Control Method of Transport Layer for Optimization

Hereinafter, a congestion control method of a transport layer for optimization will be described with reference to FIGS. 1 and 2. An embodiment of the present invention determines the price of a link using the number of packets in the queue of the link in a congestion control method. The embodiment of the present invention determines the flow price using the price of the link and the price of the neighboring link. In addition, an embodiment of the present invention uses the flow price to determine the flow rate. Here, the node updates the flow rate by repeating a series of steps as it receives the next flow.

In an embodiment of the present invention, a link price may be calculated in an ad-hoc network to periodically monitor a queue size of a link to directly predict a price value. The calculation of the price value may be a queue-based pricing algorithm. The queue-based pricing algorithm predicts an average value based on past price values.

Next, the optimization method will be described using a moving average method as a queue-based pricing algorithm.

Looking at the moving average method, the moving average method can be expressed as Equation 1 below. Here, the moving average method calculates the price of the link using the number of packets in the queue, and can be replaced by another equation using a queue-based pricing algorithm.

링크의 큐에서 패킷 수를 나타내며, λ는

링크의 프라이스를 나타내고, _는 실수의 형태에서 소수점 이하는 버리고 정수 부분만을 취하는 것을 나타낸다. Where α represents a small positive number and q is

Represents the number of packets in the link's queue, and λ is

Represents the price of the link, and _ represents a fractional part of the float, taking only integer parts.

The moving average method assumes that the number of packets in the queue is a value corresponding to a congestion price, and calculates a price value. The moving average method predicts an average based on past price values. In this way, the moving average method allows each flow to be predicted based on past values in an asynchronous real network. The moving average method is based on a fluid model.

When each link price is obtained, the flow price and the flow rate using the link price are calculated through a rate control algorithm as shown in Equation 2 below. Here, the speed algorithm is an exemplary equation for calculating the flow price and the flow speed, and may be replaced by the equation of the price of the neighbor node and the price of each link.

링크에 속하는 이웃 노드를 나타내고,

은 흐름 s에 속한다. 그리고, β는 작은 양수를 나타내며, w는

로 표현될 수 있는 비율 조절 인자이다. 이를 상세히 설명하면, 첫 번째 식의 lamda_s 는 특정 s에 대해 이것이 통과한 링크의 주변 링크의 합이고, 두 번째 식에서의 lamda_s(t)는 첫 번째 식을 이용하여 특정 시간 t에 실제 측정된 값이고, bar_lamda_s는 시간 t까지 (이동) 평균한 값을 이용한 것이다. 세 번째 식의 bar_lamda_s는 세츄레이션된 최종 값을 의미한다. Here, n is

Represents a neighbor node belonging to a link,

Belongs to flow s. And β represents a small positive number, w is

It is a ratio control factor that can be expressed as. To explain this in detail, lamda_s in the first equation is the sum of the perimeter links of the link that it passes over for a particular s, and lamda_s (t) in the second equation is the actual measured value at a specific time t using the first equation. , bar_lamda_s is the average value of the (moving) time t. The bar_lamda_s of the third equation is the final value of the segment.

The speed control algorithm increases the efficiency of the network by adding the sum of neighboring links. That is, the speed control algorithm ensures that the overall efficiency of the network does not drop when frequently using links passing through many neighboring nodes. In addition, since the price value is a relative value, the flow rate of using the link can be reduced by adding the price value of the neighboring link to each link. Reducing the flow rate increases efficiency from a network-wide perspective.

Referring to FIG. 1, six nodes, five links, and two flows are shown in FIG. 1. Here, flow 1 consists of links 1, 2, 3, and 4, and flow 2 consists of links 5, 2, 3, and 4.

An embodiment of the present invention determines the link price using the number of packets in the queue at each node to obtain the link price. In addition, the price of the neighboring node is added to the link price to further increase efficiency. By adding the prices of the neighbor links, the ad-hoc network can be more adaptively allocated bandwidth.

In addition, the embodiment of the present invention calculates the flow price by summing the prices of the links belonging to one flow. The flow rate is then used to determine the flow rate. Through this, an embodiment of the present invention can provide a congestion control method of a transport layer.

Hereinafter, a congestion control flowchart of a transport layer for an optimization method in an ad-hoc network will be described with reference to FIG. 2.

In step 210, the node initializes the flow rate. In step 220, the node calculates a link price using the number of packets in the queue. Here, the link price may be used more adaptively by combining the prices of neighboring nodes. In step 230, the node calculates the flow price. In step 240, the node determines the flow rate. In step 250, the node transmits a flow for link price calculation.

2. Media access control of media access control layer for optimization

Hereinafter, a media access method of a media access control layer for optimization according to an embodiment of the present invention will be described with reference to FIGS. 3 and 4.

An embodiment of the present invention may use a longest queue first-maximum independent set (LQF-MIS) algorithm as a medium access method for optimization.

The LQF-MIS algorithm is a heuristic algorithm for giving priority to the node with the highest congestion price. The LQF-MIS algorithm broadcasts a price value calculated based on a queue at each node to a neighbor node. The LQF-MIS algorithm determines a channel access rank by comparing a price value received from a neighbor node at each node with a current price value of each node itself. Thereafter, the LQF-MIS algorithm determines a channel access back-off time based on the determined channel access order.

 An embodiment of the present invention has a feature of using a channel access rank according to a price value as a channel access wait time. In determining the channel access wait time using the channel access order, the channel access wait time may be calculated using Equation 3 below.

Where rank represents the rank of node i, cw represents the contention window (CW) value used in the r-th retry, Rand represents any value between 0 and 1, and SlotTime represents the slot Represents time. Since Equation 3 uses a rank, it is possible to cope with other equations using the rank.

Referring to FIG. 3, a media access control method for optimization in an ad-hoc network according to an embodiment of the present invention will be described.

In step 310, the node calculates a link price. In step 320, the node calculates a rank using the link price. Detailed process in step 320 will be described later in the description of FIG. In step 330, the node determines the backoff time. In step 340, the node transmits a packet.

A method of determining rank in a media access control method according to an embodiment of the present invention will be described with reference to FIG. 4.

In step 410, each node broadcasts the price value calculated based on the queue to the neighbor node. In step 420, each node compares a price value received from neighbor nodes with a current price value. In step 430, each node determines the channel access rank according to the price size.

3. Cross-Tier Optimization Method

Hereinafter, a cross-layer optimization method in an ad-hoc network according to an embodiment of the present invention will be described with reference to FIG. 5.

An embodiment of the present invention uses the transport layer and the media access control layer as a cross-layer optimization method. The transport layer controls congestion by using the number of packets in the queue for optimization. Here the number of packets in the queue is used to calculate the price of the link. The media access control layer then controls media access for optimization. In this case, the medium access control is performed by determining the channel access rank using the price of each link, and the ranking is used to determine the back off time.

First, the operation of the transport layer of the node will be described. In step 510, the node initializes the ratio and transmits a flow required for link price calculation in step 520. In step 530, the node calculates a link price. Here, the link price is calculated by using the number of packets in the queue and using the link price of neighboring nodes. In step 540, the node updates the flow rate using the calculated link price. The updating process may correspond to performing a series of processes of steps 520 to 540 at the node.

Next, the operation of the node's media access control layer will be described. If the link price is calculated in step 530, the node calculates a rank in step 550. The ranking is prioritized using the longest queue priority method. That is, the priority is a rank of the value of the price of each broadcast link. In step 560, the node determines the backoff time using the ranking. In step 570, the node transmits a packet.

The optimization method according to an embodiment of the present invention finds a serviceable near-maximal traffic flow rate. In the conventional method, the actual implementation is less likely due to problems such as NP-complete, but an embodiment of the present invention presents an algorithm that can be actually implemented. Unlike the conventional method, the embodiment of the present invention can provide a relatively fair service to different individual traffics because the flow rate of service can be implemented relatively similarly.

In the related arts, it is difficult to find the maximum traffic flow because the link capacity between nodes in a wireless network is not fixed. However, in the optimization method according to the embodiment of the present invention, when performing congestion control, the serviceable maximum traffic flow may be found in the transport layer. This is because the transport layer determines the flow rate in a new way based on queue information. Furthermore, the embodiment of the present invention can determine the flow rate more adaptively in consideration of the queue situation of the neighboring nodes.

On the other hand, in the recent trend, traffic flow is controlled in a cross-layer manner. An embodiment of the present invention uses the new link price concept based on queue information when scheduling in the media access control layer in the cross-layer scheme. Through this, an embodiment of the present invention can preferentially service a relatively congested link and can reasonably derive the maximum traffic flow.

The optimization method of the present invention may provide a service for an application requiring QoS on a wireless ad-hoc network. The present invention can achieve efficiency from the network-wide point of view and at the same time can use a band that is equal to each other from each flow point of view. Therefore, using the optimization method of the present invention, it is possible to improve the service quality and expand the area of the multimedia application based on the mobile device.

In addition, the optimization method of the present invention can provide a solution for high quality multimedia applications and differentiated services in the wireless ad-hoc network market. Therefore, using the present invention, it is possible to expand the area based on the wireless ad-hoc network and to present various service models.

4. Simulation of optimization methods

6 to 9, a simulation result according to an embodiment of the present invention will be described.

Simulation results according to an embodiment of the present invention uses the matlab simulation results. In simulation, a flow assumes a logarithmic function as its utility function.

4-1. Simple topology

A heuristic algorithm is measured in accordance with an embodiment of the present invention. Simulation results were measured using the topology as shown in FIG.

로 수렴한다. 그리고, 본 발명인 LQF-MIS의 실시예에 따르면

로 수렴하는 것을 볼 수 있다. 여기서 공평성 측면에서는 본 발명의 실시예가 가장 좋은 성능을 보이는 것을 볼 수 있다. 그러나, 자원 이용 효율 면에서는 최적화 알고리즘 보다는 낮은 성능을 보이지만 두 알고리즘 사이의 차이는 매우 작다는 것을 알 수 있다. Referring to Table 1, the optimal algorithm is point

Converge to And, according to the embodiment of the present invention LQF-MIS

You can see the convergence. In terms of fairness, it can be seen that the embodiment of the present invention shows the best performance. However, the resource utilization efficiency is lower than that of the optimization algorithm, but the difference between the two algorithms is very small.

The flow rate of the source and the number of packets in the link queue by the optimization algorithm are shown in FIG. Referring to FIG. 7A, the flow velocity converges to an optimal value with time. 7B, the number of packets in the link's queue also converges accordingly.

Optimization algorithms require a centralized implementation. This is because the computation of finding an independent set in a network requires global network information. Therefore, the optimization method is compared with the LQF-MIS algorithm of FIG. 8, which is more practically applicable. 8 shows the results of an LQF-MIS algorithm. <Table 2> is a table comparing the optimization method and the LQF-MIS algorithm according to an embodiment of the present invention in a more complicated topology. As shown in Table 2, it can be seen that, in the present invention, even though the number of flows increases, a fair result is better than an optimization algorithm.

4-2. Pentagonal topology

와 같은 결과를 보이는데, 실제 이것은 실현 불가능한 흐름 속도 할당이다..Graph-theoretic modeling has problems when the network topology does not form a perfect graph. For example, when having a pentagonal topology as shown in Figure 9, the click-based algorithm

The result is: In practice this is an impractical flow rate assignment.

However, algorithms according to embodiments of the present invention can always produce a possible solution. That is, embodiments of the present invention create a solution even if the topology does not have a perfect graph. Table 3 shows the results of using each algorithm in the pentagonal topology. Among the existing methods, the click-based method shows the maximum network resource utilization efficiency, but impractical resource allocation. The LQF-MIS method proposed by the present invention can be seen to exhibit the same performance in terms of fairness and resource utilization efficiency.

4-3. Simulation result analysis

In detail, the simulation results are analyzed. ALOHA, SINR, and click based algorithms have problems in implementation and scheduling, or are less efficient.

Cross-layer based optimization algorithms can be efficiently used in wireless ad-hoc networks. The optimization algorithm according to the embodiment of the present invention may represent the capacity of the link as a weighted sum of independent sets. Embodiments of the present invention can be heuristically implemented by using a policy that prioritizes the longest queue and the neighbor node's price.

On the other hand, the configuration of the present invention has been described with respect to specific embodiments, the present invention is not limited to the above-described embodiment. The present invention may be modified in various ways without departing from the scope of the present invention. In addition, the accompanying drawings show an example of applying an embodiment of the present invention. Therefore, the scope of the present invention should not be limited to the described embodiments, but should be defined by the following claims and their equivalents.

The present invention has the effect of adaptively determining the capacity of a link without being affected by interference in a cross-layer optimization method in a wireless ad-hoc network.

The present invention has the effect of determining the maximum traffic flow that can be serviced by determining the flow rate in consideration of the queue situation in the transport layer, and preferentially service the congested link by scheduling based on queue information in the media access control layer. It has an effect.

In addition, the present invention provides a service for an application requiring QoS in a wireless ad-hoc network, which is efficient in terms of the entire network and has an effect of providing a fair optimization method in terms of each flow.

Claims (29)

In the cross-layer optimization method in a wireless ad-hoc network, Determining a link price using the number of packets in the queue; Determining a flow rate using the link price; Determining a channel access rank by comparing the link price values of the link price and neighboring nodes; And And determining a channel access back-off time according to the channel access order. The method of claim 1, The link price determining step periodically monitors the size of the link queue, and calculates a price value of the link using a queue-based pricing algorithm. The method of claim 1, The determining of the link price comprises predicting an average value of the link price based on a past link price value. The method of claim 1, And wherein the link price of the link price determination step is calculated using a moving average method as shown in Equation 4 below. &Quot; (4) &quot;

Where α represents a small positive number and q is

Represents the number of packets in the link's queue, and λ is

Represents the price of the link. The method of claim 1, The step of determining the flow rate is determined using the link price of the neighbor node. The method of claim 1, And determining the flow rate using the neighbor node price derived from the information of the packet broadcasted from the neighbor node. The method of claim 1, And said flow rate determining step comprises determining using a flow price, wherein said flow price is determined using past flow price values. The method of claim 1, The flow rate determining step is determined using a flow price, a utility function and a rate regulator. The method of claim 1, The flow rate determining step includes determining using a flow price, wherein the flow price is determined by Equation 5 below. &Quot; (5) &quot;

Where n belongs to flow s

Denotes a neighboring node of the link, β denotes a small positive number, and w denotes a ratio control factor that controls the queue size and flow rate. The method of claim 1, The flow rate determining step is determined by the following Equation (6). &Quot; (6) &quot;

Where x is the flow rate, U is the utility function, λ is the link price, and w is the rate control factor. The method of claim 1, The channel access wait time determining step, Broadcasting a price value calculated based on the queue to a neighbor node; Receiving a price value from a neighbor node and comparing the price value with its own price value to determine a channel access rank; And And determining a channel access wait time using the channel access rank. The method of claim 1, The determining of the channel access waiting time is calculated using Equation (7) below. &Quot; (7) &quot;

Where rank represents the rank of node i, cw represents the contention window (CW) value used in the r-th retry, Rand represents any value between 0 and 1, and SlotTime represents the slot Represents time. The method of claim 1, Updating the link price by receiving the next flow; And And updating the flow rate using the updated link price. In a congestion control method of a transport layer for optimization in a wireless ad-hoc network, Determining a link price using the number of packets in the link's queue; Determining a flow price using the price of the link price and the neighboring link; And Determining a flow rate using the flow price. The method of claim 14, And upon receiving the next flow, repeating determining the link price to update the flow rate. The method of claim 14, The link price determining step may periodically monitor the size of the link queue and calculate a price value of the link using a queue-based pricing algorithm. The method of claim 14, And determining the link price comprises predicting an average value based on a past link price value. The method of claim 14, The flow price determination step is a congestion control method characterized in that using the neighbor node price derived from the broadcasted packet information. The method of claim 14, And determining the flow price by adding the price of each link and the price of neighboring links belonging to each link. The method of claim 14, The determining of the flow price is a congestion control method, characterized in that for determining the average value using the past flow price value. The method of claim 14, The flow rate calculation step is a congestion control method characterized by using a large value by comparing the flow rate and the maximum flow rate using the flow price. The method of claim 14, And the flow rate calculating step uses a ratio adjusting factor obtained by dividing the maximum ratio by the flow price in calculating the flow rate. In the media access control method of the media access control layer for optimization in a wireless Ad-hoc network, Broadcasting a price value calculated based on a queue to a neighbor node; Determining a channel access rank by comparing a price value of a link broadcast from a neighbor node with its own price value; And And determining a channel access back-off time using the channel access rank. 24. The method of claim 23, And the price value is a price value using the number of packets in a queue in a transport layer. In the cross-layer optimization apparatus in a wireless ad-hoc network, A memory for storing information including the number of packets in the queue; And And a control unit coupled to the memory to control cross-layer optimization. The controller determines a link price using the number of packets in the queue, determines a flow rate using the link price, and compares the link price values of the link price and neighboring nodes with each other. And determining a channel access rank and determining a channel access backoff time according to the channel access rank. 26. The method of claim 25, The link price and the flow rate of the control unit is a cross-layer optimization device, characterized in that for determining the average value using the past value. An apparatus for controlling congestion at a transport layer for optimization of a wireless ad-hoc network, A memory for storing information including the number of packets in the queue; And And a control unit coupled to the memory to control cross-layer optimization. The controller periodically monitors the size of the queue, determines a link price using the number of packets in the queue of the link, and determines a flow price using the price of the link price and the neighboring link. And calculating a flow rate using the flow price. 28. The method of claim 27, And the control unit updates the flow rate by repeating the link price determination when receiving the next flow. A medium access control apparatus of a medium access control layer for optimizing a wireless ad-hoc network,  A memory for storing information including the number of packets in the queue; And And a control unit coupled to the memory to control cross-layer optimization. The controller broadcasts a price value calculated based on a queue to a neighbor node, receives a price value from a neighbor node, compares the price value with its own price value, and determines a channel access rank, and uses the channel access rank. And determining a channel access back-off time.

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