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IEEE Global Telecommunications Conference (GLOBECOM 2010)

作者： Chen, Shaolei Zhang, Zhaoyang Chen, Xiaoming Wu, Kedi Zhejiang Univ Inst Informat & Commun Engn Hangzhou 310027 Peoples R China

ISBN: (纸本)9781424456383

In this paper, we investigate a channel selection algorithm for distributed spectrum access in a multichannel multiuser cognitive radio (CR) network employing rateless codes. Each secondary user (SU) uses rateless codes to increase the tolerance of interference either from other SUs or from the primary user (PU). However, due to the unpredictable arrival of PU and the inaccuracy of spectrum sensing, the more the number of channels each SU selects, the more interference to PU will be caused. With the purpose of protecting PU from interference, we derive the optimal number of channels selected by SU to maximize the throughput of SU. Both theoretical analysis and simulation results demonstrate the efficiency of the proposed algorithm.

关键词： channel selection algorithm codes cognitive radio distributed spectrum access interference multichannel multiuser cognitive radio network radiofrequency interference rateless codes spectrum sensing

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Amazon in the White Space: Social Recommendation Aided distributed spectrum access

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IEEE-ACM TRANSACTIONS ON NETWORKING 2017年第1期25卷 536-549页

作者： Chen, Xu Gong, Xiaowen Yang, Lei Zhang, Junshan Arizona State Univ Tempe AZ 85281 USA Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510275 Guangdong Peoples R China Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85287 USA Univ Nevada Dept Comp Sci & Engn Reno NV 89557 USA

distributed spectrum access (DSA) is challenging, since an individual secondary user often has limited sensing capabilities only. One key insight is that channel recommendation among secondary users can help to take advantage of the inherent correlation structure of spectrum availability in both time and space, and enable users to obtain more informed spectrum opportunities. With this insight, we advocate to leverage the wisdom of crowds, and devise social recommendation aided DSA mechanisms to orient secondary users to make more intelligent spectrum access decisions, for both strong and weak network information cases. We start with the strong network information case where secondary users have the statistical information. To mitigate the difficulty due to the curse of dimensionality in the stochastic game approach, we take the one-step Nash approach and cast the social recommendation aided DSA decision making problem at each time slot as a strategic game. We show that it is a potential game, and then devise an algorithm to achieve the Nash equilibrium by exploiting its finite improvement property. For the weak information case where secondary users do not have the statistical information, we develop a distributed reinforcement learning mechanism for social recommendation aided DSA based on the local observations of secondary users only. Appealing to the maximum-norm contraction mapping, we also derive the conditions under which the distributed mechanism converges and characterize the equilibrium therein. Numerical results reveal that the proposed social recommendation aided DSA mechanisms can achieve a superior performance using real social data traces and its performance loss in the weak network information case is insignificant, compared with the strong network information case.

关键词： distributed spectrum access social channel recommendation game theory

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distributed dynamic spectrum access through multi-agent deep recurrent Q-learning in cognitive radio network

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PHYSICAL COMMUNICATION 2023年第1期58卷

作者： Giri, Manish Kumar Majumder, Saikat Natl Inst Technol Raipur Dept Elect & Commun Engn GE Rd Raipur 492010 Chhattisgarh India

This paper addresses the problem of distributed dynamic spectrum access in a cognitive radio (CR) environment utilizing deep recurrent reinforcement learning. Specifically, the network consists of multiple primary users (PU) transmitting intermittently in their respective channels, while the secondary users (SU) attempt to access the channels when PUs are not transmitting. The problem is challenging considering the decentralized nature of CR network where each SU attempts to access a vacant channel, without coordination with other SUs, which result in collision and throughput loss. To address this issue, a multi-agent environment is considered where each of the SUs perform independent reinforcement learning to learn the appropriate policy to transmit opportunistically so as to minimize collisions with other users. In this article, we propose two long short-term memory (LSTM) based deep recurrent Q-network (DRQN) architectures for exploiting the temporal correlation in the transmissions by various nodes in the network. Furthermore, we investigate the effect of the architecture on success rate with varying number of users in the network and partial channel observations. Simulation results are compared with other existing reinforcement learning based techniques to establish the superiority of the proposed method.(c) 2023 Elsevier B.V. All rights reserved.

关键词： distributed spectrum access Reinforcement learning Deep recurrent Q-network Q-learning Resource allocation Long short-term memory

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Multi-Users and Multi-Channels Opportunistic spectrum access in distributed CRN

Multi-Users and Multi-Channels Opportunistic Spectrum Access...

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2010 4th International Conference on Intelligent Information Technology Application (IITA2010)

作者： Member IEEE Institute of Communications Engineering PLA University of Sci.& Tech NanjingChina

Dynamic spectrum access is an important issue in cognitive radio *** can be done by a base station which can collect spectrum information from the secondary users and allocate the spectrum resource to the secondary ***,significant overhead may be incurred to exchange information between the base station and secondary users in a common control *** this paper,we propose a distributed spectrum sensing and access scheme without information exchange between the secondary users in multi-users and multi-channels *** from the traditional spectrum sensing scheme,the scheme proposed allows a secondary users to sense many channels one by one in a *** simulation results show that the proposed scheme can attain a good performance of *** the same time,we analyze three channel-sensing orders of the proposed *** then we propose a adaptive sensing and access scheme under different network parameters according to the simulation results.

关键词： cognitive radio distributed spectrum access multi-users and multi-channels throughput

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Imitation-Based Social spectrum Sharing

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2015年第6期14卷 1189-1202页

作者： Chen, Xu Huang, Jianwei Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85251 USA Chinese Univ Hong Kong Dept Informat Engn Network Commun & Econ Lab Hong Kong Hong Kong Peoples R China

Dynamic spectrum sharing is a promising technology for improving the spectrum utilization. In this paper, we study how secondary users can share the spectrum in a distributed fashion based on social imitations. The imitation-based mechanism leverages the social intelligence of the secondary user crowd and only requires a low computational power for each individual user. We introduce the information sharing graph to model the social information sharing relationship among the secondary users. We propose an imitative spectrum access mechanism on a general information sharing graph such that each secondary user first estimates its expected throughput based on local observations, and then imitates the channel selection of another neighboring user who achieves a higher throughput. We show that the imitative spectrum access mechanism converges to an imitation equilibrium, where no beneficial imitation can be further carried out on the time average. Numerical results show that the imitative spectrum access mechanism can achieve efficient spectrum utilization and meanwhile provide good fairness across secondary users.

关键词： Cognitive radio distributed spectrum access social intelligence imitation imitation equilibrium

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Learning Based Multi-Channel access in Vehicular Networks With Illegal Operators and Users

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第3期72卷 4097-4102页

作者： Li, Li Li, Wei Chen, Xiaonan Wang, Jun Peng, Qihang Univ Elect Sci & Technol China Natl Key Lab Sci & Technol Commun Sichuan 611731 Peoples R China Univ Elect Sci & Technol China Sch Infomat & Commun Engn Sichuan 611731 Peoples R China

This paper investigates the multi-channel access (MCA) problem for vehicular networks in presence of illegal operators and users. Due to the limited spectrum resource, vehicle-to-vehicle (V2V) links need to reuse the frequency spectrum allocated for vehicle-to-infrastructure (V2I) links in networks. For high-mobility environments of vehicular networks, the fast-varying channel conditions will inevitably lead to the significant uncertainty of the acquired channel state information (CSI). Hence, the traditional centralized MCA methods, which rely on the CSI, could not be performed in a timely manner. We formulate the MCA of the vehicle users as a distributed optimization problem, by maximizing the sum capacity of V2I links while guaranteeing the reliability of V2V links as well as reducing the cost due to frequency hopping. A Multi-Agent Deep Deterministic Policy Gradient (MADDPG) framework is introduced to tackle this problem, where each vehicle user, connecting with either a V2I or V2V link, acts as a learning agent to make spectrum access decisions rapidly and locally to meet the latency requirement. In order to adapt to the dynamic environment, we further propose a delayed interaction aided MADDPG algorithm, which enables online training for the distributed MCA problem. Moreover, to guarantee the proposed algorithm to converge stably, we optimize the selecting weights of experience traces and propose a Batch Prioritized Experience Replay (BPER) strategy to ensure that the high priority traces can be learned timely. Compared with baselines, simulation results showcase remarkable performance gain of our proposed algorithm.

关键词： Vehicular networks illegal operator distributed spectrum access multi-agent reinforcement learning

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spectrum Sharing in Vehicular Networks Based on Multi-Agent Reinforcement Learning

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2019年第10期37卷 2282-2292页

作者： Liang, Le Ye, Hao Li, Geoffrey Ye Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30339 USA Intel Labs Hillsboro OR 97124 USA

This paper investigates the spectrum sharing problem in vehicular networks based on multi-agent reinforcement learning, where multiple vehicle-to-vehicle (V2V) links reuse the frequency spectrum preoccupied by vehicleto-infrastructure (V2I) links. Fast channel variations in high mobility vehicular environments preclude the possibility of collecting accurate instantaneous channel state information at the base station for centralized resource management. In response, we model the resource sharing as a multi-agent reinforcement learning problem, which is then solved using a fingerprint-based deep Q-network method that is amenable to a distributed implementation. The V2V links, each acting as an agent, collectively interact with the communication environment, receive distinctive observations yet a common reward, and learn to improve spectrum and power allocation through updating Q-networks using the gained experiences. We demonstrate that with a proper reward design and training mechanism, the multiple V2V agents successfully learn to cooperate in a distributed way to simultaneously improve the sum capacity of V2I links and payload delivery rate of V2V links.

关键词： Vehicular networks distributed spectrum access spectrum and power allocation multi-agent reinforcement learning

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Spatial spectrum access Game

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2015年第3期14卷 646-659页

作者： Chen, Xu Huang, Jianwei Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85281 USA Chinese Univ Hong Kong Dept Informat Engn Network Commun & Econ Lab Hong Kong Hong Kong Peoples R China

A key feature of wireless communications is the spatial reuse. However, the spatial aspect is not yet well understood for the purpose of designing efficient spectrum sharing mechanisms. In this paper, we propose a framework of spatial spectrum access games on directed interference graphs, which can model quite general interference relationship with spatial reuse in wireless networks. We show that a pure Nash equilibrium exists for the two classes of games: (1) any spatial spectrum access games on directed acyclic graphs, and (2) any games satisfying the congestion property on directed trees and directed forests. Under mild technical conditions, the spatial spectrum access games with random backoff and Aloha channel contention mechanisms on undirected graphs also have a pure Nash equilibrium. We also quantify the price of anarchy of the spatial spectrum access game. We then propose a distributed learning algorithm, which only utilizes users' local observations to adaptively adjust the spectrum access strategies. We show that the distributed learning algorithm can converge to an approximate mixed-strategy Nash equilibrium for any spatial spectrum access games. Numerical results demonstrate that the distributed learning algorithm achieves up to 100 percent performance improvement over a random access algorithm.

关键词： distributed spectrum access spatial reuse game theory Nash equilibrium distributed learning

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Evolutionarily Stable spectrum access

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2013年第7期12卷 1281-1293页

作者： Chen, Xu Huang, Jianwei Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85287 USA Chinese Univ Hong Kong Network Commun & Econ Lab Dept Informat Engn Hong Kong Hong Kong Peoples R China

In this paper, we design distributed spectrum access mechanisms with both complete and incomplete network information. We propose an evolutionary spectrum access mechanism with complete network information, and show that the mechanism achieves an equilibrium that is globally evolutionarily stable. With incomplete network information, we propose a distributed learning mechanism, where each user utilizes local observations to estimate the expected throughput and learns to adjust its spectrum access strategy adaptively over time. We show that the learning mechanism converges to the same evolutionary equilibrium on the time average. Numerical results show that the proposed mechanisms achieve up to 35 percent performance improvement over the distributed reinforcement learning mechanism in the literature, and are robust to the perturbations of users' channel selections.

关键词： Cognitive radio distributed spectrum access evolutionary games evolutionarily stable strategy distributed learning

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Database-Assisted distributed and Cloud-Based access Methods for Unlicensed and Radar Bands

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2017年第3期3卷 404-419页

作者： Khan, Zaheer Lehtomaki, Janne J. Aguilar-Gonzalez, Rafael Vuohtoniemi, Risto Hossain, Ekram DaSilva, Luiz A. Marshall, Alan Univ Oulu Oulu 90120 Finland Univ Liverpool Liverpool L69 3GJ Merseyside England Univ Autonoma Metropolitana Mexico City 14387 DF Mexico Univ Manitoba Winnipeg MB R3P 2H2 Canada Trinity Coll Dublin Dublin D02 R590 Ireland

This paper focuses on the design of methods which enable unlicensed devices to use the frequency spectrum already allocated to rotating radar systems without causing interference to those radars. We first present a database-assisted spectrum sharing/co-existence mechanism for radar spectrum bands. This mechanism takes into account the real spectrum usage behavior of different radars, and is also implemented by prototyping a real-time rotating radar beam emulator, a radar spectrum database using MySQL software, and spectrum access algorithm on wireless open access research platform. Then we propose a cloud-based unified channel access (UCA) method and a distributed UCA method for co-existence of multiple competing users in unlicensed and rotating radar spectrum bands. We model the UCA problem as a game and propose iterative methods (i.e., algorithms) to obtain the solution of the game. We study the stability of the proposed methods under several different scenarios. We show that the proposed UCA game is a potential game, and also show that the proposed methods guarantee convergence to a Nash equilibrium. Our results show that the cloud-based method allows fast convergence and can achieve performance that is close to optimal solution.

关键词： spectrum sharing unlicensed channels radar channels game theory potential games cloud-assisted distributed spectrum access MySQL database prototype

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