CN108063642B - Channel capacity optimization method of multi-user molecular communication model based on diffusion - Google Patents
Channel capacity optimization method of multi-user molecular communication model based on diffusion Download PDFInfo
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Abstract
A channel capacity optimization method based on a diffusion multi-user molecular communication model comprises the following steps: firstly, obtaining the number of received molecules of the RN at the current time slot by utilizing normal distribution to approach binomial distribution; secondly, establishing a hypothetical detection channel model based on a diffusion molecular communication model; thirdly, obtaining a mathematical expression of the optimal decision threshold by using a minimum error probability judgment criterion so as to obtain an optimal decision threshold eta; and fourthly, obtaining the optimal value of the channel capacity on the basis of the optimal decision threshold eta. The invention provides a channel capacity optimization method based on a diffusion multi-user molecular communication model, which can effectively improve the channel capacity.
Description
Technical Field
The invention relates to biotechnology, nanotechnology and communication technology, in particular to a channel capacity optimization method based on a diffusion multi-user molecular communication model.
Background
In recent years, nanotechnology has been developed rapidly, which paves the way for the manufacture of nanomachines (nanomachines) as basic units for nanoscale communication. Nanoscale communication networks capable of interconnecting and cooperating these nanomachines to accomplish more complex tasks due to the rather limited functionality and communication capabilities of the individual nanomachines[Are being proposed. On the micro-nano scale, due to the limitation of factors such as the wavelength of electromagnetic signals and the size ratio of antennas, electromagnetic-based nano Communication is not applicable, and a Molecular Communication (Molecular Communication) model using molecules as information transfer carriers is widely considered as one of the most promising solutions.
In molecular communication, molecules can follow a particular path or be directed by a fluid medium to a destination, the mode of propagation of which is generally limited to diffusion, e.g., communication between insects through pheromones diffused in the air or calcium signaling between living cells. In a multi-user molecular communication system based on diffusion, a plurality of Transmitter Nanomachines (TN) release modulated and encoded information molecules into a shared fluid medium, the movement of the molecules follows the brownian motion rule, and the information molecules enter a receiving range of a Receiver Nanomachine (RN) through free diffusion, are absorbed and no longer exist in the medium, and are decoded in a specific manner.
In a multi-user molecular communication model based on diffusion, because molecules follow the Brownian motion rule, the intersymbol interference of all preceding time slots to a receiving nano machine at the current time slot is inevitable; because of the simultaneous operation of multiple sending-side nanomachines, shared channels and indistinguishable nature of the same information molecules, the receiving-side nanomachines are simultaneously interfered by users. Therefore, the research of the multi-user molecular communication model based on the diffusion also faces more challenges, and one of the challenges is how to improve the channel capacity of the multi-user molecular communication model based on the multi-slot intersymbol interference and the multi-user interference.
Disclosure of Invention
In order to overcome the defect that the existing diffusion multi-user molecular communication model has lower channel capacity, the invention provides a diffusion-based molecular communication model channel capacity optimization method for effectively improving the channel capacity.
In order to solve the technical problems, the invention adopts the following technical scheme:
a channel capacity optimization method based on a spread multi-user molecular communication model comprises the following steps:
firstly, obtaining the number of molecules received by a nanometer machine of a current time slot receiver by utilizing normal distribution to approach binomial distribution;
secondly, establishing a hypothetical detection channel model of a multi-user molecular communication model based on diffusion;
thirdly, obtaining a mathematical expression of the optimal decision threshold by utilizing normal distribution, thereby obtaining an optimal decision threshold eta;
and fourthly, obtaining the optimal value of the channel capacity on the basis of the optimal decision threshold eta.
Further, in the first step, in a binary diffusion-based multi-user partitionIn the sub-communication model, the input and output are binary information bits 1 or 0, and OOK is used as a modulation technology, namely, a nano machine TN at a sending party expresses the sending bit 1 by releasing a certain number of molecules, and does not release any molecules to express the sending bit 0, once the molecules are released into a biological environment, the molecules can diffuse freely, and can be absorbed immediately after moving to the detection range of the nano machine RN at a receiving party, and do not exist in the biological environment any more, after the nano machine at the sending party releases the molecules, the motion form of the molecules in a medium follows the Brownian motion law, and one molecule is transmitted from the nano machine TN at the sending partyiTo a distance diThe probability density distribution function f (t) of the time t required by the receiver nano machine is that i is more than or equal to 1 and less than or equal to M:
wherein D is the distance from the nanometer machine of the sender to the center of the nanometer machine of the receiver, D is the diffusion coefficient of the biological environment, the cumulative distribution function corresponding to the probability density distribution function is the probability that a molecule is received by RN with the detection radius R in t time, and F is usedi(t,di) Is represented as follows:
considering the slotted diffusion molecular communication model, assuming that events in which all the molecules are received occur at discrete time points, the information transmission time is divided into equal slot intervals, denoted as T-ntsWhere T is the time of information transmission, TsFor each time slot duration, n is the number of divided time slots;
starting at the n-k time slot, k is more than or equal to 1 and less than or equal to n-1, TNiReleasing QiA numerator represents a transmitted bit 1 and a non-transmitted numerator represents a transmitted bit 0, Pi(k) Representing the probability that the released numerator at the nth-k time slot is received at the nth time slot, the calculation formula is as follows:
Pi(k)＝Fi((k+1)ts)－Fi(kts)
suppose TNiThe number of the released numerator in the N-k time slot received by the RN in the current nth time slot is Ni(k) Is represented by Ni(k) Obey the following two distribution:
Ni(k)～B(Qi,Pi(k))
due to Pi(k) Is about 0.1, and a random variable Ni(k) The obeyed binomial distribution is approximated by a normal distribution, the approximated distribution being as follows:
assuming that the optimal decision threshold of the current time slot is η, if N isi(k) If the RN is more than or equal to eta, the RN outputs 1; if N is presenti(k)<η, RN outputs 0.
Due to the randomness of the brownian motion, the remaining numerator that the RN did not receive in the previous slot will cause intersymbol interference for the subsequent bit reception, and thus, for the current slot n, TNiThe number of interference molecules generated by the first (N-1) time slots is Ni ISIIs represented by Ni ISIThe obeyed normal distribution is expressed as follows:
for a multi-user molecular communication system based on diffusion, a plurality of sending-side nanometer machines share a channel, RN can receive molecules from other TN to generate inter-user interference, and therefore, for the current time slot n, TNjThe numerator sum of all interference generated by the current slot and the previous (n-1) slot is recorded as
J is more than or equal to 1 and less than or equal to M, j is not equal to i, and the obeyed normal distribution is represented as follows:
considering that some unavoidable errors occur in the statistics of RN, introducing an external noise which is assumed to obey
Normal distribution of (2), whereinCAnd
mean and variance of the noise, respectively;
TNithe total number of received numerators y in the current time slot ni[n]The device consists of four parts: 1) TN (twisted nematic)iThe numerator transmitted in the nth time slot, using Ni[n]Represents; 2) molecules produced by ISI, using
Represents; 3) IUI-produced molecules, use
Represents; 4) external noise, use
And (4) showing. Suppose TNiTotal number of molecules disturbed
Indicates that there is
Thus:
still further, in the second step, let
And YnRespectively represent the current timeThe input and the output of the slot are,
indicating false alarm rate, i.e. TNiThe probability that the input is 0 and the RN output is 1;
indicating the detection rate, i.e. TNiThe input is 1, and the RN output is 1, which are respectively defined as follows:
by using
Indicating that the RN currently receives TN in the nth slotiNumber of transmitted molecules, H0And H1Respectively, representing the case where 0 and 1 are transmitted in the current slot. With continuous type random variables
Binary hypothesis test problem for observations:
thus, this binary hypothesis testing model can be modeled as:
wherein, the parameters of the normal distribution are as follows:
μ0＝μi,I
μ1＝QiPi(0)+μ0
further, in the third step, the minimum error probability decision criterion is:
wherein, P (H)0) And P (H)1) The probability of sending a 0 and a 1 for the current time slot, respectively, is P (H)1)＝pi，P(H0)＝1-pi，p(z|H0) And p (z | H)1) Respectively representing the probability that the RN receives z molecules under the condition that the current time slot transmits 0 and 1; the likelihood ratio is expressed by Λ (z), and the likelihood ratio calculation formula obtained by the minimum error probability decision criterion is as follows:
wherein,
and
are respectively H0And H1And obtaining the likelihood ratio to satisfy the following probability density function of the obeyed normal distribution:
taking the natural logarithm on both sides of the equation:
and further solving z by combining the equation obtained according to the minimum error probability judgment criterion:
where η is the optimal threshold, and the parameter A, B, C is:
in the fourth step, useCumulative distribution function calculation false alarm rate of normal distribution
And detection rate
The calculation formula is as follows:
wherein,
through the above calculation formula, the channel capacity of the diffusion molecular communication model can be optimized, and the calculation formula of the channel capacity is as follows:
C＝max I(X；Y)
wherein
The technical conception of the invention is as follows: the invention fully combines the characteristic of the random behavior of the movement of molecules in the biological environment in the diffused multi-user molecular communication model to research the channel capacity optimization scheme of the diffused multi-user molecular communication model. In a diffused multi-user molecular communication model, a plurality of sending party nano machines work simultaneously, and by releasing a certain number of same molecules into a biological environment, the molecules are freely diffused in a transmission channel according to the brownian motion law and finally randomly reach a receiving party nano machine. Therefore, intersymbol interference of molecules released by all previous time slots of the sending-side nanomachines to the current time slot and mutual interference among different sending-side nanomachines are inevitable. In consideration of intersymbol interference, it is important to study how to improve the channel capacity of the dispersive molecular communication model. The invention mainly develops a communication technology which can be used for a nano network and takes molecular communication as the basis for optimizing the channel capacity. The probability of 1 or 0 transmitted by the nano machine of the transmitting party in each time slot is controlled, and meanwhile, the mathematical expression of the optimal decision threshold value is obtained by utilizing the minimum error probability judgment criterion, so that the channel capacity is optimized.
The invention has the following beneficial effects: 1. under the condition of considering the intersymbol interference of all the time slots to the current time slot and the user to user, meanwhile, the probability that different nanometer machines of a sending party send 1 or 0 in each time slot is considered, and the number of molecules received by the RN of the current time slot is obtained by utilizing normal distribution to approach binomial distribution. On the basis, a mathematical expression of the optimal decision threshold is obtained by utilizing a minimum error probability judgment criterion. 2. On the basis of the optimal decision threshold, the optimal value of the mutual information is obtained, and different parameters including the distance from the TN to the RN center, the number of molecules released by the nanometer machine of the sender in each time slot, the number of time slots and the influence of external noise on the mutual information are shown. 3. Compared with the existing research on the multi-user molecular communication system based on diffusion, the invention considers the influence of the interference among multi-slot users, multi-users and external noise on the system.
Drawings
Fig. 1 is a topological structure diagram of a diffusion-based multi-user molecular communication model. The information source consists of a plurality of TNs with the same physical property, the TNs work simultaneously to release the same information molecules, and the receiver is an RN with a detection radius R.
FIG. 2 shows TNiProbability P of the molecule released in the first k time slots being received in the current time sloti(k) Graph of the relation with the number of previous time slots.
FIG. 3 shows a signal at TN1And distance d between RN1Mutual information that can be reached under the condition of taking different values
And TN1A priori probability p of1The relationship (2) of (c).
FIG. 4 shows an interfering nanomachine TNj(j is more than or equal to 2 and less than or equal to 4) the number of transmitted molecules Q in each time slotj(2 ≦ j ≦ 4) the mutual information that can be achieved when different values are taken
And TN1Number of transmitted molecules per time slot Q1The relationship (2) of (c).
FIG. 5 shows the mutual information that can be achieved when the number M of TNs takes different values
Variance with external noise
The relationship (2) of (c).
FIG. 6 shows the mutual information that can be achieved when the TN number M takes different values and 3 different numbers of timeslots are set for systems with different values of M
And TN1A priori probability p of1The relationship (2) of (c).
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Referring to fig. 1 to 6, a channel capacity optimization method based on a diffusion molecular communication model includes the following steps:
firstly, obtaining the number of received molecules of the RN at the current time slot by approximating binomial distribution by normal distribution, wherein the process is as follows:
in a binary diffusion-based multi-user molecular communication model, input and output are binary information bits 1 or 0, OOK is used as a modulation technology, namely a transmitting-side nanometer machine TN expresses the transmitting bit 1 by releasing a certain number of molecules and expresses the transmitting bit 0 by not releasing any molecules, the molecules are free to diffuse once released into a biological environment, and when in operation, the molecules are spread freelyAfter moving to the detection range of the receiver nano machine RN, the molecules are immediately absorbed and do not exist in the biological environment any more, after the sender nano machine releases the molecules, the motion form of the molecules in the medium follows the Brownian motion law, and one molecule is TN from the sender nano machineiTo a distance diThe probability density distribution function f (t) of the time t required by the receiver nano machine is that i is more than or equal to 1 and less than or equal to M:
wherein D is the distance from the nanometer machine of the sender to the center of the nanometer machine of the receiver, D is the diffusion coefficient of the biological environment, the cumulative distribution function corresponding to the probability density distribution function is the probability that a molecule is received by RN with the detection radius R in t time, and F is usedi(t,di) Is represented as follows:
considering the slotted diffusion molecular communication model, assuming that events in which all the molecules are received occur at discrete time points, the information transmission time is divided into equal slot intervals, denoted as T-ntsWhere T is the time of information transmission, TsFor each time slot duration, n is the number of divided time slots;
starting at the n-k time slot, k is more than or equal to 1 and less than or equal to n-1, TNiReleasing QiA numerator represents a transmitted bit 1 and a non-transmitted numerator represents a transmitted bit 0, Pi(k) Representing the probability that the released numerator at the nth-k time slot is received at the nth time slot, the calculation formula is as follows:
Pi(k)＝Fi((k+1)ts)－Fi(kts)
suppose TNiThe number of the released numerator in the N-k time slot received by the RN in the current nth time slot is Ni(k) Is represented by Ni(k) Obey the following two distribution:
Ni(k)～B(Qi,Pi(k))
due to Pi(k) Is about 0.1, and a random variable Ni(k) The obeyed binomial distribution can be approximated by a normal distribution, the approximated distribution being formulated as follows:
assuming that the optimal decision threshold of the current time slot is η, if N isi(k) If the RN is more than or equal to eta, the RN outputs 1; if N is presenti(k)<η, RN outputs 0.
Due to the randomness of the brownian motion, the remaining numerator that the RN did not receive in the previous slot will cause intersymbol interference for the subsequent bit reception, and thus, for the current slot n, TNiThe number of interference molecules generated by the first (n-1) time slots is used
It is shown that,
the obeyed normal distribution is expressed as follows:
for a multi-user molecular communication system based on diffusion, a plurality of sending-side nanometer machines share a channel, RN can receive molecules from other TN to generate inter-user interference, and therefore, for the current time slot n, TNj(1 ≦ j ≦ M, j ≠ i) the numerator sum of all the interference generated by the current slot and the previous (n-1) slot is recorded as
The obeyed normal distribution is expressed as follows:
considering that some unavoidable errors occur in the statistics of the RN, an external noise is introduced, and the noise is generally assumed to be obeyed
Normal distribution of (2), whereinCAnd
respectively, the mean and variance of the noise.
To sum up, TNiThe total number of received numerators y in the current time slot ni[n]The device consists of four parts: 1) TN (twisted nematic)iThe numerator transmitted in the nth time slot, using Ni[n]Represents; 2) molecules produced by ISI, using
Represents; 3) IUI-produced molecules, use
Represents; 4) external noise, use
And (4) showing. Suppose TNiTotal number of molecules disturbed
Indicates that there is
Thus:
secondly, establishing a hypothetical detection channel model of a multi-user molecular communication model based on diffusion;
order to
And YnRespectively representing the input and output of the current time slot,
indicating false alarm rate, i.e. TNiThe probability that the input is 0 and the RN output is 1;
indicating the detection rate, i.e. TNiInput is 1, RN output is 1. They are defined as follows:
by using
Indicating that the RN currently receives TN in the nth slotiThe number of transmitted molecules. H0And H1Respectively, representing the case where 0 and 1 are transmitted in the current slot. With continuous type random variables
Binary hypothesis test problem for observations:
thus, this binary hypothesis testing model can be modeled as:
wherein, the parameters of the normal distribution are as follows:
μ0＝μi,I
μ1＝QiPi(0)+μ0
thirdly, realizing an optimal detection scheme by using a minimum error probability criterion so as to obtain an optimal decision threshold eta;
the minimum error probability decision criterion is:
wherein, P (H)0) And P (H)1) The probability of sending a 0 and a 1 for the current time slot, respectively, is P (H)1)＝pi，P(H0)＝1-pi。p(z|H0) And p (z | H)1) Respectively representing the probability that the RN receives z molecules under the condition that the current time slot transmits 0 and 1; the likelihood ratio is expressed by Λ (z), and the likelihood ratio calculation formula obtained by the minimum error probability decision criterion is as follows:
wherein,
and
are respectively H0And H1The probability density function of the normal distribution to which it is subjected. The available likelihood ratio satisfies:
taking the natural logarithm on both sides of the equation, one can obtain:
further solving for z in relation to the above equation obtained from the minimum error probability decision criterion yields:
where η is the optimal threshold, and the parameter A, B, C is:
and fourthly, obtaining the optimal value of the channel capacity on the basis of the optimal decision threshold eta. Calculating false alarm rate by using cumulative distribution function of normal distribution
And detection rate
The calculation formula is as follows:
wherein,
through the above calculation formula, the channel capacity of the diffusion molecular communication model can be optimized, and the calculation formula of the channel capacity is as follows:
C＝max I(X；Y)
wherein
And fifthly, the influence of different parameters including the distance from the TN to the RN center, the number of molecules released by the nano machine of the sender in each time slot, the number of time slots and the external noise on mutual information is shown through experimental simulation. The method can obtain a better mutual information value, and the number of molecules used in each time slot is far less than that of the existing molecular communication model.
FIG. 3 shows a signal at TN1Distance d to RN center1Mutual information that can be reached under the condition of taking different values
And TN1A priori probability p of1The relationship (2) of (c). It can be seen that TN1Distance d to RN center1The smaller the value of the mutual information.
FIG. 4 shows an interfering nanomachine TNj(j is more than or equal to 2 and less than or equal to 4) the number of transmitted molecules Q in each time slotj(2 ≦ j ≦ 4) the mutual information that can be achieved when different values are taken
And TN1Number of transmitted molecules per time slot Q1The relationship (2) of (c). When Q is1The larger, QjThe smaller (j is more than or equal to 2 and less than or equal to 4), the larger the value of mutual information is.
FIG. 5 shows the mutual information that can be achieved when the number M of TNs takes different values
Variance with external noise
The relationship (2) of (c). It can be seen that the larger the external noise is, the smaller the mutual information is; the smaller the number of TNs, the smaller the interference between users, and the greater the proportion of external noise to the total interference, the greater the influence on mutual information.
FIG. 6 shows the mutual information that can be achieved when the TN number M takes different values and 3 different numbers of timeslots are set for systems with different values of M
And TN1A priori probability p of1The relationship (2) of (c). In a system with M being the same, an increase in n will cause ISI and IUI interference to become larger, and mutual information will therefore decrease with a small magnitude, but this magnitude will become smaller with an increasing number of system users.
Claims (1)
1. A channel capacity optimization method based on a diffusion multi-user molecular communication model is characterized by comprising the following steps: the optimization method comprises the following steps:
firstly, obtaining the number of molecules received by a nanometer machine of a current time slot receiver by utilizing normal distribution to approach binomial distribution;
secondly, establishing a hypothetical detection channel model of a multi-user molecular communication model based on diffusion;
thirdly, obtaining a mathematical expression of the optimal decision threshold by utilizing normal distribution, thereby obtaining an optimal decision threshold eta;
fourthly, obtaining an optimal channel capacity value on the basis of the optimal decision threshold eta;
in the first step, in a binary diffusion-based multi-user molecular communication model, the input and output are binary information bits 1 or 0, OOK is used as a modulation technology, namely, a transmitting side nanometer machine TN expresses the transmission bits 1 by releasing a certain number of molecules and does not release any molecules to express the transmission bits 0, the molecules once released into a biological environment can be diffused freely, and can be absorbed immediately after moving to the detection range of a receiving side nanometer machine RN and do not exist in the biological environment any more, after the transmitting side nanometer machine releases the molecules, the motion form of the molecules in a medium follows the Brownian motion law, and one molecule is transmitted from the transmitting side nanometer machine TNiTo a distance diThe probability density distribution function f (t) of the time t required by the receiver nano machine is that i is more than or equal to 1 and less than or equal to M:
wherein D is the distance from the nanometer machine of the sender to the center of the nanometer machine of the receiver, D is the diffusion coefficient of the biological environment, the cumulative distribution function corresponding to the probability density distribution function is the probability that a molecule is received by RN with the detection radius R in t time, and F is usedi(t,di) Is represented as follows:
considering the slotted diffusion molecular communication model, assuming that events in which all the molecules are received occur at discrete time points, the information transmission time is divided into equal slot intervals, denoted as T-ntsWhere T is the time of information transmission, TsFor each time slot duration, n is the number of divided time slots;
starting at the n-k time slot, k is more than or equal to 1 and less than or equal to n-1, TNiReleasing QiA numerator represents a transmitted bit 1 and a non-transmitted numerator represents a transmitted bit 0, Pi(k) Representing the probability that the released numerator at the nth-k time slot is received at the nth time slot, the calculation formula is as follows:
Pi(k)＝Fi((k+1)ts)－Fi(kts)
suppose TNiThe number of the released numerator in the N-k time slot received by the RN in the current nth time slot is Ni(k) Is represented by Ni(k) Obey the following two distribution:
Ni(k)～B(Qi,Pi(k))
due to Pi(k) Is about 0.1, and a random variable Ni(k) The obeyed binomial distribution can be approximated by a normal distribution, the approximated distribution being formulated as follows:
assuming that the optimal decision threshold of the current time slot is η, if N isi(k) If the RN is more than or equal to eta, the RN outputs 1; if N is presenti(k)<Eta, RN outputs 0;
due to the randomness of the brownian motion, the remaining numerator that the RN did not receive in the previous slot will cause intersymbol interference for the subsequent bit reception, and thus, for the current slot n, TNiThe number of interference molecules generated by the first (n-1) time slots is used
It is shown that,
the obeyed normal distribution is expressed as follows:
for a multi-user molecular communication system based on diffusion, a plurality of sending-side nanometer machines share a channel, RN can receive molecules from other TN to generate inter-user interference, and therefore, for the current time slot n, TNjThe numerator sum of all interference generated by the current slot and the previous (n-1) slot is recorded as
J is more than or equal to 1 and less than or equal to M, j is not equal to i, and the obeyed normal distribution is represented as follows:
considering that some unavoidable errors occur in the statistics of the RN, an external noise is introduced, and the noise is generally assumed to be obeyed
Normal distribution of (2), whereinCAnd
mean and variance of the noise, respectively;
TNithe total number of received numerators y in the current time slot ni[n]The device consists of four parts: 1) TN (twisted nematic)iThe numerator transmitted in the nth time slot, using Ni[n]Represents; 2) molecules produced by ISI, using
Represents; 3) IUI-produced molecules, use
Represents; 4) external noise, use
To show, suppose TNiTotal number of molecules disturbed
Indicates that there is
Thus:
in the second step, let
And YnRespectively representing the input and output of the current time slot,
indicating false alarm rate, i.e. TNiThe probability that the input is 0 and the RN output is 1;
indicating the detection rate, i.e. TNiThe probability that the input is 1 and the RN output is 1; they are defined as follows:
by using
Indicating that the RN currently receives TN in the nth slotiNumber of transmitted molecules, H0And H1Respectively representing the situation of transmitting 0 and 1 in the current time slot, and using continuous random variables
Binary hypothesis test problem for observations:
thus, this binary hypothesis testing model can be modeled as:
the parameters of the above normal distribution are as follows:
in the third step, the best detection scheme is realized by using a minimum error probability criterion, wherein the minimum error probability criterion is as follows:
wherein, P (H)0) And P (H)1) The probability of sending a 0 and a 1 for the current time slot, respectively, is P (H)1)＝pi，P(H0)＝1-pi，p(z|H0) And p (z | H)1) Respectively representing the probability that the RN receives z molecules under the condition that the current time slot transmits 0 and 1; the likelihood ratio is expressed by Λ (z), and the likelihood ratio calculation formula obtained by the minimum error probability decision criterion is as follows:
wherein,
and
are respectively H0And H1And obtaining the likelihood ratio to satisfy the following probability density function of the obeyed normal distribution:
taking the natural logarithm on both sides of the equation:
further solving for z yields:
where η is the optimal threshold, and the parameter A, B, C is:
in the fourth step, the false alarm rate is calculated by using the cumulative distribution function of normal distribution
And detection rate
The calculation formula is as follows:
wherein
Through the above calculation formula, the channel capacity of the diffusion molecular communication model can be optimized, and the calculation formula of the channel capacity is as follows:
C＝maxI(X；Y)
wherein
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| CN110730094B (en) * | 2019-09-20 | 2022-06-17 | 浙江工业大学 | Energy efficiency optimization method of two-hop mobile molecular communication system |
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