CN109548055B - Autonomous energy management method in ultra-dense wireless network based on energy collection - Google Patents
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Abstract
The invention belongs to the technical field of wireless communication, and discloses an autonomous energy management method in an ultra-dense wireless network based on energy collection; setting that the ultra-dense wireless network comprises a macro base station MBS; constructing a base station management model in the ultra-dense wireless network, a small base station SBS energy consumption model of the ultra-dense wireless network, a macro base station MBS energy consumption model of the ultra-dense wireless network and an energy collection model of the ultra-dense wireless network; establishing a utility function mechanism based on QoS, setting SON as an intelligent agent in an ultra-dense wireless network, and realizing autonomous management in the wireless network; constructing a multi-arm gambling machine model; setting an optimized objective function of a multi-arm gambling machine model; introducing a cost factor C to maximize the utility value of the whole network; and determining the optimal sleep mechanism strategy of the small base station. The invention can improve the network utility value, improve the energy efficiency and reduce the network overhead under the condition of no prior knowledge for energy collection and no global information.
Description
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to an autonomous energy management method in an ultra-dense wireless network based on energy collection.
Background
Currently, the current state of the art commonly used in the industry is such that: in 5G networks, mobile data has been explosively increased due to the rising number of mobile devices and the rise of data-driven emerging services. Therefore, providing higher network capacity and wider network coverage, thereby guaranteeing quality of service (QoS) requirements of users, is a significant challenge facing future 5G wireless networks. An ultra-dense wireless network (SCN) is a new network technology, and can be used as an effective supplement for a macro base station to effectively improve network capacity and coverage. With the intensive deployment of a wireless network, due to the time-space dynamic characteristics of network flow and service load, the operation and maintenance management is more complex, the overhead cost is high, the network load is unbalanced, and the like. With the increasing price of electricity in the power grid, energy consumption becomes a key factor in the management, operation and maintenance of 5G networks. Therefore, in order to effectively improve the network energy efficiency, an effective network energy management mechanism is designed, and network energy consumption is reduced, which is a key challenge in 5G network management. Extensive research has been conducted on how to improve the energy efficiency problem in the wireless network, and mainly includes research on a dynamic deployment strategy mechanism, power control of a base station, a sleep mechanism of the base station, and the like. In addition, the energy collection technology can collect energy from the surrounding wireless environment, provide energy for the SCN network, and supply energy to the SCN network, for example, collect energy for the SCN network by using solar energy, wind energy, and radio frequency energy collection technology. To conserve network energy, traditional energy-saving studies are based on dynamic sleep mechanisms of base stations in the network. In the existing energy efficiency management research based on the energy collection technology, centralized management is performed on the premise that the collected energy and the network global information are known, and a large amount of signaling overhead is needed for acquiring the global network information. Therefore, the network operation and maintenance cost and the network management complexity are increased, however, due to the uncertainty of the wireless environment around the base station and the random arrival characteristic of energy collection, it is difficult to predict the collected energy, and centralized energy efficiency management, especially for ultra-dense wireless networks, makes the network management more complex and the overhead more large, thereby requiring a more complex energy management algorithm.
In summary, the problems of the prior art are: the existing energy efficiency management based on an energy collection technology has centralized management based on the premise that the collected energy and network global information are known, and a large amount of signaling overhead is needed; due to uncertainty of a wireless environment around a base station and random arrival characteristics of energy collection, it is difficult to predict the collected energy amount, and centralized energy efficiency management makes network management more complicated and overhead more huge.
The difficulty and significance for solving the technical problems are as follows:
with the increasing increase of users and the increasing of services, the wireless environment has a highly dynamic characteristic, so that the complexity of the algorithm is high, and a more dynamic energy strategy is required. Furthermore, since energy harvesting has random arrival characteristics, an efficient energy dynamic model needs to be designed. Therefore, by designing the autonomous energy management method in the ultra-dense wireless network based on energy collection, the network signaling overhead and the network management complexity can be effectively reduced, the network operation and maintenance cost is reduced, and the intelligent operation and maintenance capability and the efficient network energy efficiency management of the network are improved.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides an autonomous energy management method in an ultra-dense wireless network based on energy collection.
The invention is realized in such a way that an autonomous energy management method in an ultra-dense wireless network based on energy collection comprises the following steps:
step one, setting that an ultra-dense wireless network comprises a macro base station MBS and a plurality of small base station SBS; constructing a base station management model in the ultra-dense wireless network, a small base station SBS energy consumption model of the ultra-dense wireless network, a macro base station MBS energy consumption model of the ultra-dense wireless network and an energy collection model of the ultra-dense wireless network;
establishing a QoS-based utility function mechanism, setting SON as an intelligent agent in the ultra-dense wireless network, and realizing autonomous management in the wireless network; setting a small base station SBS as a gambling arm in the model, and constructing a multi-arm gambling machine model;
setting an optimized objective function of the multi-arm gambling machine model; introducing an overhead factor C, and continuously interacting SON players in the optimized learning process of the dobby gambling machine model to maximize the utility value of the whole network; adopting a Tomson sampling algorithm TS to obtain the upper limit of the repentance value of the TS strategy; and determining the optimal sleep mechanism strategy of the small base station according to the selected optimal arm.
Further, the base station management model in the ultra-dense wireless network of the first step is as follows: the whole ultra-dense wireless network is provided with M small base stations SBS, expressed as S = { S = 1 ,S 2 ,…S M And the operation and maintenance management model of the small base station is as follows:
wherein if S is m =1, it means that the small cell is in an active state, if S m If =0, it means that the small cell is in a dormant state; in the dormant state, the small base station collects the required energy and then stores the energy in a battery of the small base station, and waits until the active state, the stored energy is collected and used for serving the user.
Further, the SBS energy consumption model of the femtocell of the ultra-dense wireless network of step one is: at time t, the energy consumption model of the small cell is:
wherein the content of the first and second substances,
representing the total energy overhead of the small base station,
represents the fixed energy consumption, ζ, of a small base station SBS Which is the inverse of the efficiency factor of the power amplifier,
representing the small base station radio frequency transmission power.
Further, the macro base station MBS energy consumption model of the ultra-dense wireless network of the step one is as follows:
wherein the content of the first and second substances,
representing the total energy consumption of the macro base station MBS,
representing a fixed energy consumption of the macro base station MBS,
representing the radio frequency transmission power of the macro base station.
Further, the ultra-dense wireless network energy collection model of the step one is as follows: in the j-th transmission round, it is represented as
Due to the random nature of the energy harvesting,
is not known to be present in the form of,
are independent identically distributed random variables.
Further, the utility function of the selected small base station m in the second step is:
wherein the content of the first and second substances,
denotes the maximum number of users, xi, that the small base station m can serve at time t m Representing the overhead of each energy unit;
represents the energy overhead required for initialization of the small base station m; the overall utility function in the entire ultra-dense wireless network is:
further, the optimization objective function of the dobby gambling machine model in the third step is as follows:
wherein Q is π (T) denotes the regret value under the Small cell site-based energy management policy π, i.e., the profit of the arm currently selected by the Player SON
Earning from current best arm
The difference of (c).
Further, the third step adopts a thomson sampling algorithm TS, and the upper limit of the obtained TS policy repentance value is:
the TS algorithm comprises an exploration phase and a utilization phase: in the exploration stage, a polling algorithm is adopted to try different arms to acquire different historical knowledge; in the utilization stage, the SON player selects the arm with the greatest profit margin, and after a time T, the TS algorithm converges to the optimal value.
Another object of the present invention is to provide a mobile communication device to which the autonomous energy management method in the energy harvesting-based ultra-dense wireless network is applied.
Another object of the present invention is to provide a wireless communication system applying the autonomous energy management method in the energy harvesting-based ultra-dense wireless network.
In summary, the advantages and positive effects of the invention are: the invention adopts a dynamic sleep deployment strategy mechanism of a small base station in an ultra-dense wireless network; and energy is collected from the surrounding wireless environment by combining an energy collection technology, so that energy is provided for the ultra-dense wireless network and is used as energy supply of the ultra-dense wireless network. Aiming at an ultra-dense wireless network, a self-organizing network (SON) intelligent agent is introduced to carry out autonomous distributed management, and the SON intelligent agent is combined with an energy collection technology to be used as energy supply of a base station in the network. By adopting a multi-arm gambling machine model, the problem of how to maximize the network utility under the condition of no global information is solved, and the network energy efficiency management efficiency is improved. By comprehensively considering the actual condition of the current network and the actual request of the user, the utility value of the network can be improved, the energy efficiency can be improved and the network overhead can be reduced under the condition of no prior knowledge for energy collection and no whole office information. Compared with a centralized energy management technology based on whole office information, the method can effectively reduce network management complexity and network operation and maintenance cost.
Furthermore, the conventional sleep mechanism of the small cell requires global state information and assumes that the collected energy is known; according to the invention, through the distributed small base station sleep mechanism design, each small base station autonomously makes a decision without global state information or prior knowledge of energy collection; learning through interaction with the surrounding environment; and learning an optimal small base station sleep mechanism through a multi-arm gambling machine model to obtain an energy-efficient base station energy management strategy. According to the invention, by introducing an energy collection technology and a self-organizing network technology, a network energy management model aiming at network energy conservation is constructed, and different network energy management strategies are learned by combining a learning mechanism of a multi-armed gambling machine, so that an ultra-dense wireless network can autonomously decide an optimal energy management strategy.
Drawings
Fig. 1 is a flowchart of an autonomous energy management method in an ultra-dense wireless network based on energy harvesting according to an embodiment of the present invention.
Fig. 2 is a flowchart of an implementation of an autonomous energy management method in an ultra-dense wireless network based on energy harvesting according to an embodiment of the present invention.
FIG. 3 is a schematic diagram of a comparison between the greedy resource allocation strategy and the network utility value aspect provided by the embodiment of the invention.
Fig. 4 is a schematic diagram of a strategy selection for a small cell sleep in the present invention in a small cell sleep strategy according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
A large amount of signaling overhead is needed for the existing energy efficiency management based on the energy collection technology; the amount of energy collected is difficult to predict; more complex network management and more significant overhead. In order to reduce network management overhead, a self-organizing network (SON) is used as a key technology of autonomous network management, an effective way is provided for efficient management of a super-dense wireless network, how to combine a self-organizing network technology and an energy collecting technology, and a high-energy-efficiency network energy management mechanism is provided for a dynamically variable super-dense wireless network, which is a scientific problem of main research; an autonomous energy management method in an ultra-dense wireless network based on energy collection is used for a next generation mobile communication long term evolution system, and solves the problems of energy acquisition in wireless communication and distribution of energy resources facing to actual needs of users.
The application of the principles of the present invention will now be described in detail with reference to the accompanying drawings.
As shown in fig. 1, an autonomous energy management method in an ultra-dense wireless network based on energy harvesting provided by an embodiment of the present invention includes the following steps:
s101: setting that an ultra-dense wireless network comprises a Macro Base Station (MBS); constructing a base station management model in the ultra-dense wireless network, a small base station SBS energy consumption model of the ultra-dense wireless network, a Macro Base Station (MBS) energy consumption model of the ultra-dense wireless network and an energy collection model of the ultra-dense wireless network;
s102: establishing a utility function mechanism based on QoS, setting SON as an intelligent agent in an ultra-dense wireless network, and realizing autonomous management in the wireless network; constructing a multi-arm gambling machine model;
s103: setting an optimized objective function of a multi-arm gambling machine model; introducing an overhead factor C, and continuously interacting SON players in the optimized learning process of the dobby gambling machine model to maximize the utility value of the whole network; adopting a Thomson sampling algorithm (TS) to obtain the upper limit of the repentance value of the TS strategy, and adopting a polling algorithm to try different arms to obtain different historical knowledge in a search stage; in the utilization stage, the SON player selects the arm with the largest profit value, and after a time T, the TS algorithm can converge to the optimal value. And determining the optimal sleep mechanism strategy of the small base station according to the selected optimal arm.
The application of the principles of the present invention will now be described in further detail with reference to the accompanying drawings.
As shown in fig. 2, the autonomous energy management method in the ultra-dense wireless network based on energy harvesting provided in the embodiment of the present invention specifically includes the following steps:
step one, setting that an ultra-dense wireless network comprises a Macro Base Station (MBS) to ensure basic network coverage and M Small Base Stations (SBS) to provide effective support for improving network coverage and capacity, and N users needing to be served to provide conditions for subsequent user management and energy resource allocation. See fig. 3, 4.
Step two, constructing a base station management model in the ultra-dense wireless network: setting M Small Base Stations (SBS) in the whole ultra-dense wireless network, and expressing S = { S = { (S) } 1 ,S 2 ,…S M And the operation and maintenance management model of the small base station is as follows:
wherein if S is m =1, it means that the small cell is in an active state, if S m And =0, it indicates that the small cell is in a sleep state. In the dormant state, the small cell can collect the required energy and then store the energy in the battery of the small cell, and when the small cell is in the active state, the collected and stored energy can be used for serving users.
Step three, a small base station SBS energy consumption model of the ultra-dense wireless network: in an ultra-dense wireless network, a small cell may collect energy in the surrounding wireless environment, both in a dormant state and in an active state. In the dormant state, most components in the small cell will be turned off to reduce power consumption, and in the active state, components of the small cell will be turned on to operate. Therefore, at time t, the energy consumption model of the small cell is:
wherein the content of the first and second substances,
representing the total energy overhead of the small base station,
represents the fixed energy consumption (including the power consumption of the baseband processing circuit and the cooling unit) of the small base station, ζ SBS Which is the inverse of the efficiency factor of the power amplifier,
representing the small base station radio frequency transmission power.
Step four, a Macro Base Station (MBS) energy consumption model of the ultra-dense wireless network:
wherein the content of the first and second substances,
representing the total energy consumption of the macro base station MBS,
representing a fixed energy consumption of the macro base station MBS,
representing the radio frequency transmission power of the macro base station.
Step five, an ultra-dense wireless network energy collection model: in ultra-dense wireless networks, the present invention employs a storage-use energy harvesting strategy. The invention assumes that the energy collection processes between different small base stations are mutually independent, and the transmitted files are transmitted for J times, and the file transmission is completed. Thus, in the j-th transmission round, it is denoted as
Due to the random nature of the energy harvesting,
is unknown, the present invention assumes
Are independent and equally distributed random variables.
Step six, establishing a utility function mechanism based on QoS: in the ultra-dense wireless network, the M belongs to the M and is N m E.n users to serve. User n demand
Energy unit to meet user QoS rate requirement
The utility function of selecting the small base station m is defined as follows:
wherein the content of the first and second substances,
denotes the maximum number of users, xi, that the small base station m can serve at time t m Representing the overhead per energy unit.
Representing the energy overhead required for initialization of the small base station m. Thus, the overall utility function in the entire ultra-dense wireless network is:
step seven, setting the SON as an intelligent agent in the ultra-dense wireless network to realize autonomous management in the wireless network; a dobby gambling machine model is constructed, a small base station SBS is set as the arm of the dobby gambling machine, and an SON agent is set as the player in the dobby gambling machine model. In the dobby gambling machine model, there are mainly two phases: an exploration phase and a utilization phase, players in a dobby model maximize network utility by exploring new wireless environments while utilizing past historical empirical knowledge.
Step eight, setting an optimized objective function of the multi-arm gambling machine model as follows:
wherein Q π (T) denotes the regret value under the Small cell site energy management policy π (i.e., the profit for the arm currently selected by the Player SON)
Earning from current best arm
The difference of (d).
Step nine, because the small base station (arm) in the multi-arm gambling machine model only has local channel state information and does not have global state information, in the optimization learning process of the multi-arm gambling machine model, the SON players are required to continuously interact, so that the overall network utility value is maximized, and therefore the overhead factor C is introduced.
Step ten, in order to solve the problem of maximizing the utility value of the whole network, the invention adopts the Thomson sampling algorithm (TS), therefore, the upper limit of the repentance value of the TS strategy can be obtained as follows:
step eleven, the TS algorithm mainly comprises an exploration stage and a utilization stage: in the exploration stage, a polling algorithm is adopted to try different arms to acquire different historical knowledge; in the utilization stage, the SON player selects the arm with the greatest profit margin and after a time T, the TS algorithm may converge to the optimal value. And determining the optimal sleep mechanism strategy of the small base station according to the selected optimal arm.
According to the invention, prior knowledge of energy collection and global state information are not required, and a high-energy-efficiency network energy management strategy can be designed according to the dynamic change of a wireless environment, so that the network management overhead is reduced.
The application effect of the present invention will be described in detail with reference to the simulation.
As shown in fig. 3, compared with the greedy algorithm, the algorithm provided by the present invention can effectively improve the network utility value and converge more quickly. In addition, the algorithm performance of the algorithm provided by the invention is only inferior to that of the algorithm based on the global information under the condition that only little communication overhead C is required.
As shown in fig. 4, in the design of the small base station sleep mechanism based on the dobby model, the base station performs learning interaction with the wireless environment continuously, and finally selects the optimal arm (small base station) through an effective exploration phase and a utilization phase, so as to obtain the optimal energy management strategy.
Simulation results show that prior knowledge of energy collection and global state information are not needed, a network energy management strategy with high energy efficiency can be designed according to dynamic changes of a wireless environment, and network management overhead is reduced.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (6)
1. An autonomous energy management method in an energy harvesting-based ultra-dense wireless network, the method comprising:
step one, setting that an ultra-dense wireless network comprises a macro base station MBS and a plurality of small base station SBS; constructing a base station management model in the ultra-dense wireless network, a small base station SBS energy consumption model of the ultra-dense wireless network, a macro base station MBS energy consumption model of the ultra-dense wireless network and an energy collection model of the ultra-dense wireless network;
establishing a QoS-based utility function mechanism, setting SON as an intelligent agent in the ultra-dense wireless network, and realizing autonomous management in the wireless network; setting a small base station SBS as a gambling arm in the model, and constructing a multi-arm gambling machine model;
setting an optimized objective function of the multi-arm gambling machine model; introducing an overhead factor C, and continuously interacting SON players in the optimized learning process of the dobby gambling machine model to maximize the utility value of the whole network; adopting a Thomson sampling algorithm TS to obtain the upper limit of the repentance value of the TS strategy; determining an optimal sleep mechanism strategy of the small base station according to the selected optimal arm;
the base station management model in the ultra-dense wireless network of the first step is as follows: there are M small base stations SBS in the whole ultra-dense wireless network, and the expression is S = { S = { (S) } 1 ，S 2 ，…S M And the operation and maintenance management model of the small base station is as follows:
wherein if S is m =1, it means that the small cell is in an active state, if S m =0, then the small cell is in a dormant state; in the dormant state, the small base station collects the required energy and then stores the energy in a battery of the small base station, and when the small base station is in the activated state, the collected and stored energy is used for serving a user;
the SBS energy consumption model of the small base station of the ultra-dense wireless network in the first step is as follows: at time t, the energy consumption model of the small cell is:
wherein the content of the first and second substances,
representing the total energy overhead of the small base station,
represents the fixed energy consumption, ζ, of a small base station SBS Which is the inverse of the efficiency factor of the power amplifier,
representing the radio frequency transmission power of the small base station;
wherein, the macro base station MBS energy consumption model of the ultra-dense wireless network of the step one is as follows:
wherein the content of the first and second substances,
representing the total energy consumption of the macro base station MBS,
representing a fixed energy consumption of the macro base station MBS,
representing the radio frequency transmission power of the macro base station;
the ultra-dense wireless network energy collection model of the first step is as follows: in ultra-dense wireless networks, a storage-use energy harvesting strategy is employed; energy collection processes among different small base stations are mutually independent, and the transmitted files are transmitted for J times and the file transmission is finished; therefore, in the turn of the j-th transmission, it is represented as
Due to the random nature of the energy harvesting,
is not known to be present in the solution,
are independent and equally distributed random variables.
2. The method for autonomous energy management in an ultra-dense wireless network based on energy harvesting as claimed in claim 1, wherein the utility function of selecting the small cell m in the second step is:
wherein, the first and the second end of the pipe are connected with each other,
denotes the maximum number of users, xi, that the small base station m can serve at time t m Represents the overhead of each energy unit;
represents the energy overhead required for initialization of the small base station m; the overall utility function in the entire ultra-dense wireless network is:
3. the method for autonomous energy management in an energy harvesting-based ultra-dense wireless network according to claim 1, wherein the optimization objective function of the dobby gambling machine model of step three is:
wherein Q is π (T) denotes the regret value under the Small cell site-based energy management policy π, i.e., the profit of the arm currently selected by the Player SON
Earning from current best arm
The difference of (a).
4. The method for autonomous energy management in an ultra-dense wireless network based on energy harvesting as claimed in claim 1, wherein said step three employs tomson sampling algorithm TS, and the upper limit of the obtained TS policy regret value is:
the TS algorithm comprises an exploration phase and a utilization phase: in the exploration stage, a polling algorithm is adopted to try different arms to acquire different historical knowledge; in the utilization stage, the SON player selects the arm with the largest profit value, and after a time T, the TS algorithm converges to the optimal value.
5. A mobile communication device applying the autonomous energy management method in the ultra-dense wireless network based on energy harvesting of any one of claims 1 to 4.
6. A wireless communication system applying the autonomous energy management method in an ultra-dense wireless network based on energy harvesting of any one of claims 1 to 4.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2916596A1 (en) * | 2014-03-05 | 2015-09-09 | Fujitsu Limited | Reducing power consumption of a small cell base station |
| CN104954176A (en) * | 2015-05-27 | 2015-09-30 | 北京工业大学 | Context-aware information centralization resource management method in multi-hop cellular network architecture |
| CN105636119A (en) * | 2015-12-29 | 2016-06-01 | 西安交通大学 | Picocell sleep joint optimization method based on energy efficiency and traffic load |
| CN106332203A (en) * | 2016-10-10 | 2017-01-11 | 北京邮电大学 | Green energy-saving algorithm for use in LTE (long-term evolution) hierarchical cell architecture |
| CN106658679A (en) * | 2016-11-11 | 2017-05-10 | 电子科技大学 | Energy efficiency-based base station power control method |
| CN106686707A (en) * | 2017-01-11 | 2017-05-17 | 东南大学 | Tilt-adjusting dynamic sector hibernating method based on centralized self-organizing network architecture |
-
2018
- 2018-11-19 CN CN201811377582.XA patent/CN109548055B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2916596A1 (en) * | 2014-03-05 | 2015-09-09 | Fujitsu Limited | Reducing power consumption of a small cell base station |
| CN104954176A (en) * | 2015-05-27 | 2015-09-30 | 北京工业大学 | Context-aware information centralization resource management method in multi-hop cellular network architecture |
| CN105636119A (en) * | 2015-12-29 | 2016-06-01 | 西安交通大学 | Picocell sleep joint optimization method based on energy efficiency and traffic load |
| CN106332203A (en) * | 2016-10-10 | 2017-01-11 | 北京邮电大学 | Green energy-saving algorithm for use in LTE (long-term evolution) hierarchical cell architecture |
| CN106658679A (en) * | 2016-11-11 | 2017-05-10 | 电子科技大学 | Energy efficiency-based base station power control method |
| CN106686707A (en) * | 2017-01-11 | 2017-05-17 | 东南大学 | Tilt-adjusting dynamic sector hibernating method based on centralized self-organizing network architecture |
Non-Patent Citations (4)
| Title |
|---|
| Cooperative Small Cell HetNets with Sleeping andEnergy Harvesting;Abdullah Alqasir等;《2018 IEEE International Conference on Communications (ICC)》;20180731;正文第II-III部分、第V部分和附图1 * |
| Dynamic clustering and sleep mode strategies for small cell networks;Sumudu Samarakoon 等;《 2014 11th International Symposium on Wireless Communications Systems (ISWCS)》;20141023;全文 * |
| Fuzzy LogicGame-Theoretic Approach for Energy Efficient Operation in HetNets;Karthik Vasudeva等;《2017 IEEE International Conference on Communications Workshops (ICC Workshops)》;20170703;正文第I-II部分 * |
| On Regret-Optimal Learning in Decentralized Multiplayer Multiarmed Bandits;Naumaan Nayyar等;《IEEE Transactions on Control of Network Systems》;20161202;正文第I-IV部分 * |
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