“Threshold-Deciding” Policy in Distributed Multiuser Opportunistic Spectrum Access

2014 ◽  
Vol 926-930 ◽  
pp. 2867-2870
Author(s):  
Yu Meng Wang ◽  
Liang Shen ◽  
Xiang Gao ◽  
Cheng Long Xu ◽  
Xiao Ya Li ◽  
...  
Keyword(s):  
Opportunistic Spectrum Access ◽  
Spectrum Access ◽  
Secondary Users ◽  
Adaptive Policy ◽  
Markov Decision ◽  
Single User ◽  
Simulation Results ◽  
Partially Observable ◽  
Time And Energy ◽  
The Given

This paper studies the problem of distributed multiuser Opportunistic Spectrum Access based on Partially Observable Markov Decision Process (POMDP). Due to the similarity of spectrum environment, secondary users may choose the same channel adopting their own single user approach, which leads to collision. Referring to the previous works, we propose a more flexible and adaptive policy named “threshold-deciding”. Firstly, the SU gets a channel by adopting the random policy. Secondly, the SU decides whether to sense the channel by comparing the available probability with the given threshold. The policy not only decreases the collisions among SUs but also reduces the consumption of time and energy. The simulation results shows that the upgrade of performance is up to 100% compared with the existing random policy, which demonstrate the advantage of the proposed policy.

Dynamic Spectrum Access Considering Channel Fading in Cognitive Networks

2014 ◽  
Vol 543-547 ◽  
pp. 2013-2016
Author(s):  
Ye Bin Tao ◽  
Shi Ding Zhu
Keyword(s):  
Dynamic Spectrum Access ◽  
Cognitive Networks ◽  
Dynamic Spectrum ◽  
Time Varying ◽  
Channel Fading ◽  
Spectrum Access ◽  
Throughput Performance ◽  
Markov Decision ◽  
Simulation Results ◽  
Partially Observable

This paper investigated the method of Dynamic Spectrum Access (DSA) in cognitive networks, considering the PU channels both time-varying and fading. We used the Partially Observable Markov Decision Process (POMDP) framework to model this problem and designed a greedy strategy. The simulation results shows that the proposed strategy obtained better throughput performance than the existing works.

scholarly journals Optimality of Multichannel Myopic Sensing in the Presence of Sensing Error for Opportunistic Spectrum Access

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaofeng Jiang ◽  
Hongsheng Xi
Keyword(s):  
Optimization Problem ◽  
Infinite Horizon ◽  
Practical Interest ◽  
Opportunistic Spectrum Access ◽  
Spectrum Access ◽  
Opportunistic Access ◽  
Markov Decision ◽  
Myopic Policy ◽  
Limited Sensing ◽  
Partially Observable

The optimization problem for the performance of opportunistic spectrum access is considered in this study. A user, with the limited sensing capacity, has opportunistic access to a communication system with multiple channels. The user can only choose several channels to sense and decides whether to access these channels based on the sensing information in each time slot. Meanwhile, the presence of sensing error is considered. A reward is obtained when the user accesses a channel. The objective is to maximize the expected (discounted or average) reward accrued over an infinite horizon. This problem can be formulated as a partially observable Markov decision process. This study shows the optimality of the simple and robust myopic policy which focuses on maximizing the immediate reward. The results show that the myopic policy is optimal in the case of practical interest.

Multi-User Opportunistic Spectrum Access Using Reinforcement Learning

2014 ◽  
Vol 926-930 ◽  
pp. 2357-2361
Author(s):  
Cheng Long Xu ◽  
Yun Peng Cheng ◽  
Yang Chen ◽  
Cheng Meng Ren ◽  
Heng Yang
Keyword(s):  
Reinforcement Learning ◽  
System Performance ◽  
Opportunistic Spectrum Access ◽  
Spectrum Access ◽  
High Quality ◽  
Secondary Users ◽  
Channel Sensing ◽  
Simulation Results ◽  
Idle Channel ◽  
Channel Sensing Order

This paper studies the channel exploration problem for the distributed opportunistic spectrum access (D-OSA) system, where multiple secondary users (SUs) sequentially sense multiple licensed channels and utilize one of idle channel. However, channel sensing order can affect the system performance seriously. When using a better sensing order, the SU can find faster a free channel with high quality and the less collisions among SUs can happen. In this paper, we propose a mechanism using reinforcement learning to find dynamically out a sensing order for improving the system performance. In the proposed mechanism, the interactions among SUs are considered. Simulation results are provided to show the effectiveness of the proposed mechanism and the significant improvement of the system performance.

scholarly journals A New Decentralized Approach of Multiagent Cooperative Pursuit Based on the Iterated Elimination of Dominated Strategies Model

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Mohammed El Habib Souidi ◽  
Songhao Piao
Keyword(s):  
Game Theory ◽  
Coalition Formation ◽  
Decision Process ◽  
Pursuit Evasion ◽  
Dominated Strategies ◽  
Markov Decision ◽  
Simulation Results ◽  
The Given ◽  
Dynamic Formation ◽  
Motion Strategy

Game Theory is a promising approach to acquire coalition formations in multiagent systems. This paper is focused on the importance of the distributed computation and the dynamic formation and reformation of pursuit groups in pursuit-evasion problems. In order to address this task, we propose a decentralized coalition formation algorithm based on the Iterated Elimination of Dominated Strategies (IEDS). This Game Theory process is common to solve problems requiring the withdrawal of dominated strategies iteratively. Furthermore, we have used the Markov Decision Process (MDP) principles to control the motion strategy of the agents in the environment. The simulation results demonstrate the feasibility and the validity of the given approach in comparison with different decentralized methods.

Game Theoretic Cloud-Assisted Opportunistic Spectrum Access in Cognitive Radio Networks

2016 ◽  
Vol 8 (2) ◽  
pp. 94-110
Author(s):  
Danda B. Rawat ◽  
Sachin Shetty
Keyword(s):  
Cognitive Radio ◽  
Wide Band ◽  
Cognitive Networks ◽  
Opportunistic Spectrum Access ◽  
Packet Error Rate ◽  
Theoretic Approach ◽  
Spectrum Access ◽  
Secondary Users ◽  
Computational Capacity ◽  
Game Theoretic

Opportunistic Spectrum Access (OSA) in a Cognitive Radio Network (CRN) is regarded as emerging technology for utilizing the scarce Radio Frequency (RF) spectrum by allowing unlicensed secondary users (SUs) to access licensed spectrum without creating harmful interference to primary users (PUs). The SUs are considerably constrained by their limited power, memory and computational capacity when they have to make decision about spectrum sensing for wide band regime and OSA. The SUs in CRN have the potential to mitigate these constraints by leveraging the vast storage and computational capacity of cloud computing approaches. In this paper, the authors investigate a game theoretic approach for opportunistic spectrum access in cognitive networks. The proposed algorithm leverages the geo-locations of both SUs and spectrum opportunities to facilitate OSA to SUs. The active SUs using game theory adapt their transmit powers in a distributed manner based on the estimated average packet-error rate while satisfying the Quality-of-Service (QoS) in terms of signal-to-interference-noise-ratio (SINR). Furthermore, to control greedy SUs in distributed power control game, the authors introduce a manager/leader through a Stackelberg power adaptation game. The performance of the proposed approaches is investigated using numerical results obtained from simulations.

Discrete-time Queuing Analysis of Opportunistic Spectrum Access: Single User Case

Frequenz ◽  
2011 ◽  
Vol 65 (11-12) ◽  
Author(s):  
Jin-long Wang ◽  
Yu-hua Xu ◽  
Zhan Gao ◽  
Qi-hui Wu
Keyword(s):  
Discrete Time ◽  
Opportunistic Spectrum Access ◽  
Spectrum Access ◽  
Queuing Analysis ◽  
Single User

scholarly journals Searching and Tracking an Unknown Number of Targets: A Learning-Based Method Enhanced with Maps Merging

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1076
Author(s):  
Peng Yan ◽  
Tao Jia ◽  
Chengchao Bai
Keyword(s):  
Moving Targets ◽  
Search Region ◽  
Area Of Interest ◽  
Markov Decision ◽  
Simulation Results ◽  
Average Observation ◽  
High Flexibility ◽  
Multiple Moving Targets ◽  
Partially Observable ◽  
Multi Uav

Unmanned aerial vehicles (UAVs) have been widely used in search and rescue (SAR) missions due to their high flexibility. A key problem in SAR missions is to search and track moving targets in an area of interest. In this paper, we focus on the problem of Cooperative Multi-UAV Observation of Multiple Moving Targets (CMUOMMT). In contrast to the existing literature, we not only optimize the average observation rate of the discovered targets, but we also emphasize the fairness of the observation of the discovered targets and the continuous exploration of the undiscovered targets, under the assumption that the total number of targets is unknown. To achieve this objective, a deep reinforcement learning (DRL)-based method is proposed under the Partially Observable Markov Decision Process (POMDP) framework, where each UAV maintains four observation history maps, and maps from different UAVs within a communication range can be merged to enhance UAVs’ awareness of the environment. A deep convolutional neural network (CNN) is used to process the merged maps and generate the control commands to UAVs. The simulation results show that our policy can enable UAVs to balance between giving the discovered targets a fair observation and exploring the search region compared with other methods.

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