CN113098641B - Opportunistic spectrum access method under energy limitation condition - Google Patents
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Abstract
The invention discloses an opportunistic spectrum access method under the condition of energy limitation. The method comprises the following steps: establishing a perception access energy consumption channel model; checking the residual energy of the cognitive user and determining a channel set to be sensed; sensing channels to obtain a sensing idle channel set; the secondary user accesses all channels perceived as idle; and the secondary user updates the channel statistical information and the energy information. The perception access energy consumption channel model established by the invention is closer to reality, the problem is abstracted into the energy-limited multi-arm gambling machine model based on sequential decision and statistical learning theory, and a channel perception access strategy is provided by utilizing the knapsack problem in the optimization problem. In addition, the algorithm designed by the invention is in a nonlinear relation with the energy of the user in simulation and theory, and shows that the larger the energy of the user is, the more the sensing time is, the better the user can sense the access to a better channel, and the better the benefit is obtained.
Description
Technical Field
The invention relates to the technical field of wireless networks, in particular to an opportunistic spectrum access method under the condition of energy limitation.
Background
Cognitive radio originated from the foundational work of doctor Joseph Mitola 1999, with the core idea that network users have learning capabilities to interact with the surrounding environment to sense and utilize the available spectrum in the space and reduce the occurrence of collisions with other users. More specifically, cognitive radios discover portions of the radio spectrum that are not occupied at particular locations at particular times and move operation to these portions known as "spectrum holes" (white space) for opportunistic access. Thus, cognitive radio has two main operations: spectrum sensing and spectrum access (information transfer). Spectrum sensing refers to the cognitive user obtaining the spectrum use information in the wireless network through various signal detection and processing means. From the view of the function layering of the wireless network, the spectrum sensing technology mainly relates to a physical layer and a link layer, wherein the physical layer mainly focuses on various specific local detection algorithms, and the link layer mainly focuses on cooperation among users and optimization of 3 aspects of local sensing, cooperative sensing and sensing mechanisms. When the spectrum holes are found, the cognitive users perform spectrum access according to a specific protocol, namely, information transmission is performed. Therefore, spectrum sensing has an extremely important position in many cases, and how to design an effective spectrum sensing strategy is a process continuously explored and considered by the industry.
On the other hand, with the wide application of the internet of things, the energy limitation condition of the equipment of the internet of things is widely researched. Generally speaking, a large number of internet of things devices have limited energy and stop operating when the energy is exhausted. When the device transmits information, it needs to search for a free spectrum in the space, so that both the spectrum sensing and the access process need to consume a certain amount of energy. Therefore, the limited energy will affect the process of sensing access. How to design an efficient strategy under the condition of limited energy so that a user obtains the maximum information transmission amount in channel sensing access is a problem to be considered urgently in the industry.
At present, most research works study the multi-channel perception access problem of users under the condition that the secondary users are supposed to know the main user channel statistical information or related information, an optimal method for multi-channel perception access is provided, and the effect that the secondary users expect the maximum benefit is achieved. Among them, YunxiaChen et al (Y.X.Chen, Q.ZHao, A.Swami (2009), Distributed Spectrum Sensing and Access in Cognitive Radio Networks with Energy Constraint, IEEE Transactions on Signal Processing,57,2,783 and 797) have studied the perceptual Access strategy under Energy Constraint based on the partially observable Markov decision process, assuming that the channel transfer probability is known. However, in an actual cognitive network, the primary user statistical information is usually unknown to the secondary users and needs to be obtained through a large amount of time measurement. In addition, the statistical information of the main user can change along with the time, so that the measurement result has larger deviation. However, the existing research is difficult to solve the problems well. In order to solve the channel sensing and access problem under the condition that the statistical information of the primary user is unknown, part of work models the channel sensing and access problem of the secondary user into a classical statistical problem, namely a Multi-arm slot machine problem (MABP). LifengLai et al (Lai, L.F., El Gamal, H., Jiang, H.and Poor, H.V. (2011) Cognitive medium access: expansion, and compatibility, IEEE Transactions on Mobile Computing,10, 239-. However, the above-mentioned earlier methods do not take into account the energy limitation.
Therefore, currently, effective method design research is still lacked for channel sensing access under the conditions that channel statistical information is unknown and secondary user energy is limited.
Disclosure of Invention
The invention aims to provide an opportunistic spectrum access method under the conditions of unknown channel statistical information and limited energy.
The technical solution for realizing the purpose of the invention is as follows: an opportunistic spectrum access method under the condition of energy limitation comprises the following steps:
and 5, updating the channel statistical information and the energy information by the secondary user.
Further, the step 1 of establishing the cognitive access energy consumption channel model specifically includes:
assuming that N channels exist in the network, adopting a time slot model, namely, a cognitive user performs channel perception access in each equal-length time slot; in each time slot, a cognitive user can simultaneously sense and access M channels and record the M channels in a set phi (t), meanwhile, the cognitive user has limited energy which is set as epsilon, and the residual energy of the cognitive user at the time t is epsilon (t); for each channel i e {1, 2.., N }, the probability that the channel state is idle is μ i The energy required for channel sensing on channel i is
Energy consumption for channel i access is
Energy expectations for energy-aware access to each channel
According to μ i /c i Setting goodness, mu, for each channel i /c i The greater the channel goodness, i.e. the greater the amount of information that can be transmitted when consuming unit energy, the better the channel;
when the cognitive user perceives the channel, the time slot number of the cognitive user perception channel i before the time slot T is recorded as T i (t) the number of free slots is noted as Y i (t), then the estimate of the channel idle probability is expressed as
The perception of a free channel set at time slot t is noted as
When the cognitive user successfully accesses the channel i, the cognitive user is assumed to obtain some benefits, which are recorded as r i (t) 1, total profit before time slot t is recorded as U π (t), where π represents a strategy.
Further, step 2, checking the residual energy of the cognitive user, and determining a channel set to be perceived specifically as follows:
firstly, judging whether the residual energy epsilon (t) of the cognitive user is greater than 0 or not in the time slot t, and if the residual energy epsilon (t) is greater than 0, carrying out channel selection on N channels according to the formula
Sorting the sizes, taking the first M channels and recording as phi (t); wherein
Is an estimation of the channel goodness order;
an estimate representing a channel idle probability;
represents the energy that the sensing channel i needs to consume;
represents the energy consumed to access channel i; alpha represents a hyper parameter and is set before the algorithm starts; t is i (t) represents the number of times channel i is perceived by the time t; when ε (t) < 0, the algorithmic process is stopped.
Further, the secondary user in step 5 updates the channel statistical information and the energy information, specifically as follows:
and updating the income:
wherein U is π (epsilon) represents the gain for the case where the energy is epsilon;
updating the energy:
updating the time: t is t + 1.
Compared with the prior art, the invention has the following remarkable advantages: (1) when the user energy is limited and the channel perception access needs to consume energy, the established perception access energy consumption channel model is closer to the reality; (2) abstracting the problem into a multi-arm gambling machine model with limited energy based on sequential decision and a statistical learning theory, and providing a channel perception access strategy by utilizing a knapsack problem in an optimization problem; (3) the designed algorithm is in a nonlinear relation with the energy of the user in simulation and theory, the larger the energy of the user is, the more the sensing time is, the better the user can sense the channel accessed, and the better the benefit is obtained.
Drawings
Fig. 1 is a schematic diagram of an opportunistic spectrum access method in an energy limited situation.
Fig. 2 is a schematic diagram of channel related information when N is 8.
Fig. 3 is a normalized remorse simulation diagram when N-8.
Fig. 4 is a schematic diagram of channel related information when N is 20.
Fig. 5 is a simulation diagram of channel-related information when N is 20.
Fig. 6 is a normalized remorse simulation diagram when N-20.
Detailed Description
First, for ease of understanding, the technical terms used herein are explained as follows:
opportunistic spectrum access: if the secondary user does not interfere with the primary user, the secondary user may identify the opportunistic spectrum and use it into the system.
A main user: users authorized to access the spectrum.
And (4) the secondary user: and accessing the users of the frequency spectrum under the condition of not influencing the master user.
Channel idle probability: the master user occupies the channel according to a certain probability, namely, the cognitive user perceives the access channel according to a certain probability.
Monte carlo method: Monte-Carlo, also known as statistical simulation method. Refers to a method that uses random numbers (or more commonly pseudo-random numbers) to solve many computational problems.
The invention is described in further detail below with reference to the figures and the embodiments.
With reference to fig. 1, the present invention provides a spectrum sensing access method for the situations of unknown channel statistical information and limited energy. The method comprises the following specific steps:
and 5, updating the channel statistical information and the energy information by the secondary user.
Further, the step 1 of establishing the cognitive access energy consumption channel model specifically includes the following steps:
assuming that N channels exist in the network, a time slot model is adopted, namely, a cognitive user performs channel perception access in each equal-length time slot. In each time slot, the cognitive user can simultaneously sense and access M channels and record the channels at phi (t). At the same time, the cognitive user has limited energy, set as ε. For each channel i e {1, 2.., N }, the probability that its channel state is idle is μ i The energy required for channel sensing is
When the energy consumption of accessing it is
The energy required for energy-aware access to each channel is therefore expected to be
For convenience of presentation, the invention is based on i /c i Setting goodness, mu, for each channel i /c i The greater the channel goodness (the greater the amount of information that can be transmitted when consuming a unit of energy, the better the channel).
When the cognitive user perceives the channel, the number of the time slots for perceiving the channel i before the time slot T is recorded as T i (t) the number of free slots is noted as Y i (t); sensing a set of idle channels at time slot t is denoted as
When the cognitive user successfully accesses the channel, it is assumed that the cognitive user obtains some benefit, denoted as r i (t) 1, the total profit before the time slot t is recorded as U π (t), where π represents a strategy.
Further, step 2, checking the residual energy of the cognitive user, and determining a channel set to be perceived specifically as follows:
firstly, judging whether the residual energy epsilon (t) of the cognitive user is more than 0 or not in the time slot t, and if epsilon (t) is more than 0, carrying out the channel selection on N channels according to the formula
Sorting the sizes, taking the first M channels and recording as phi (t); wherein
Is an estimate of the channel goodness order;
an estimate representing a channel idle probability;
represents the energy that the sensing channel i needs to consume;
represents the energy consumed to access channel i; alpha represents a hyper parameter and is set before the algorithm starts; t is i (t) represents the number of times channel i is perceived by the deadline t; when ε (t) < 0, the algorithmic process is stopped.
Further, the sensing of the channels in steps 3 and 4 to obtain a sensing idle channel set, and accessing the idle channel specifically include:
set of perceived idleness is noted as
And accessing all idle channels;
further, in step 5, the secondary user updates the channel statistical information and the energy information, which are specifically as follows:
updating the earnings:
wherein U is π (epsilon) represents the gain for the case where the energy is epsilon;
updating the energy:
updating the time: t is t + 1.
The algorithm pseudo code is as follows:
assuming that the secondary user knows the statistical information of all channels, a method for obtaining the optimal benefit under the condition of sensing access by selecting the optimal channel every time is named as a pre-known auxiliary method;
according to the first-known-aided method, the expectation of the cognitive users with energy epsilon to obtain the benefits is
E[·]Expressed as desired; definition of regret
The smaller the regret value is, the closer the designed algorithm is to the optimal algorithm is.
The invention is described in further detail below with reference to the figures and the embodiments.
Examples
The invention uses Monte-Carlo simulation to verify the above analysis conclusion, and assumes that the number of channels in the wireless cognitive network is N-8, and the related information is shown in fig. 2.
The results obtained after the simulation are shown in fig. 3: shown in the figure is the relationship between the normalized remorse and the total amount of energy epsilon. It can be seen in the figure that the regret curve normalized by ln t is kept at the upper bound, i.e. when epsilon is large enough, the loss of revenue will remain unchanged, i.e. the proposed method will perceive the access to the optimal channel according to the theoretical optimal method.
Further, consider multiple channels and situations in which an error may be perceived in an actual scenario. Assuming that the number of channels is N-20, an experiment is performed with respect to the number of channels M to be perceived by the cognitive user per time slot as 1, 2, 3. The channel information is shown in fig. 4: wherein, mu i Indicating the channel idle probability, c i To sense the desire to access the channel for the energy consumed,
representing the energy required to sense the channel,
indicating the energy consumed for accessing the idle channel, "orderof ratio" indicating mu i /c i 。
Through simulation experiments, the number of perceived accesses of each channel can be obtained as shown in fig. 5, which shows that the number of perceived accesses of the optimal channel '6' is the largest. The better the channel (mu) i /c i The larger the channel) the more times it is perceived to be accessed. The curve between the regret and the energy, considering that in practice there is some error in the channel perception (assuming a probability of perception of correctness of 0.9), is shown in fig. 6: as can be seen from the results in the figure, there is still an upper bound on the normalized loss of revenue function R (π, ε). Indicating the effectiveness of the algorithm. When a certain error exists in channel sensing, the required sensing frequency is increased, and therefore the regret value is increased. According with the practical situation.
The perception access energy consumption channel model established by the invention is closer to reality, the problem is abstracted into the energy-limited multi-arm gambling machine model based on sequential decision and statistical learning theory, and a channel perception access strategy is provided by utilizing the knapsack problem in the optimization problem. In addition, the algorithm designed by the invention is in a nonlinear relation with the energy of the user in simulation and theory, and shows that the larger the energy of the user is, the more the sensing time is, the better the user can sense the access to a better channel, and the better the benefit is obtained.
Claims (2)
1. An opportunistic spectrum access method under the condition of energy limitation is characterized by comprising the following steps:
step 1, establishing a perception access energy consumption channel model;
step 2, checking the residual energy of the cognitive user and determining a channel set to be sensed;
step 3, sensing channels to obtain a sensing idle channel set;
step 4, the secondary user accesses all channels which are perceived as idle;
step 5, the secondary user updates the channel statistical information and the energy information;
step 1, establishing a perception access energy consumption channel model, specifically as follows:
assuming that N channels exist in the network, adopting a time slot model, namely, a cognitive user carries out channel perception access in each equal-length time slot; in each time slot, a cognitive user can simultaneously sense and access M channels and record the M channels in a set phi (t), meanwhile, the cognitive user has limited energy which is set as epsilon, and the residual energy of the cognitive user at the time t is epsilon (t); for each channel i e {1, 2.., N }, the probability that the channel state is idle is μ i The energy required for channel sensing on channel i is
The energy consumption for access to channel i is
Energy expectations required for energy-aware access to each channel
According to μ i /c i Setting goodness, mu, for each channel i /c i The greater the goodness of the channel, i.e., the greater the amount of information that can be transmitted while consuming a unit of energyIf the channel is large, the channel is more optimal;
when the cognitive user perceives the channel, the time slot number of the cognitive user perception channel i before the time slot T is recorded as T i (t) the number of free slots is noted as Y i (t), then the estimate of the channel idle probability is expressed as
The perception of a free channel set at time slot t is noted as
When the cognitive user successfully accesses the channel i, the cognitive user is assumed to obtain some benefits, which are recorded as r i (t) 1, total profit before time slot t is marked as U π (t), wherein π represents a strategy;
step 2, checking the residual energy of the cognitive user, and determining a channel set to be sensed, wherein the method specifically comprises the following steps:
firstly, judging whether the residual energy epsilon (t) of the cognitive user is more than 0 or not in the time slot t, and if epsilon (t) is more than 0, carrying out the channel selection on N channels according to the formula
Sorting the sizes, taking the first M channels and recording as phi (t); wherein
Is an estimation of the channel goodness order;
an estimate representing a channel idle probability;
representing the energy that the sensing channel i needs to consume;
represents the energy consumed to access channel i; alpha represents a hyper-parameter and is set before the algorithm starts; t is i (t) denotes the cut-off timethe number of times channel i is sensed at t; when ε (t) < 0, the algorithmic process is stopped.
2. The opportunistic spectrum access method under the energy-limited condition as claimed in claim 1, wherein the secondary user updates channel statistics information and energy information in step 5, specifically as follows:
and updating the income:
wherein U is π (epsilon) represents the gain for the case where the energy is epsilon;
updating the energy:
and (3) updating the time: t is t + 1.
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