Opportunistic Channel Scheduling for Ad Hoc Networks with Queue Stability

Frequenz ◽  
2015 ◽  
Vol 69 (3-4) ◽  
Author(s):  
Lei Dong ◽  
Yongchao Wang
Keyword(s):  
Ad Hoc ◽  
Real Data ◽  
Channel Access ◽  
Multiple Sources ◽  
Current Channel ◽  
Access Problem ◽  
Channel Scheduling ◽  
Opportunistic Channel Access ◽  
Hoc Networks ◽  
The Stability

AbstractIn this paper, a distributed opportunistic channel access strategy in ad hoc network is proposed. We consider the multiple sources contend for the transmission opportunity, the winner source decides to transmit or restart contention based on the current channel condition. Owing to real data assumption at all links, the decision still needs to consider the stability of the queues. We formulate the channel opportunistic scheduling as a constrained optimization problem which maximizes the system average throughput with the constraints that the queues of all links are stable. The proposed optimization model is solved by Lyapunov stability in queueing theory. The successive channel access problem is decoupled into single optimal stopping problem at every frame and solved with

Channel access-based self-organized clustering in ad hoc networks

2003 ◽  
Vol 2 (2) ◽  
pp. 102-113 ◽  
Author(s):  
Zhijun Cai ◽  
Mi Lu ◽  
Xiaodong Wang
Keyword(s):  
Ad Hoc Networks ◽  
Ad Hoc ◽  
Channel Access ◽  
Self Organized ◽  
Hoc Networks

scholarly journals Minimizing The Number of Channel Switches of Mobile Users in Cognitive Radio Ad-Hoc Networks

2020 ◽  
Vol 9 (2) ◽  
pp. 23 ◽  
Author(s):  
Rajorshi Biswas ◽  
Jie Wu
Keyword(s):  
Cognitive Radio ◽  
Ad Hoc Networks ◽  
Ad Hoc ◽  
Path Loss ◽  
Access Point ◽  
Primary User ◽  
Mobile Users ◽  
Mobility Patterns ◽  
Current Channel ◽  
Hoc Networks

Cognitive radio (CR) technology is envisioned to use wireless spectrum opportunistically when the primary user (PU) is not using it. In cognitive radio ad-hoc networks (CRAHNs), the mobile users form a distributed multi-hop network using the unused spectrum. The qualities of the channels are different in different locations. When a user moves from one place to another, it needs to switch the channel to maintain the quality-of-service (QoS) required by different applications. The QoS of a channel depends on the amount of usage. A user can select the channels that meet the QoS requirement during its movement. In this paper, we study the mobility patterns of users, predict their next locations and probabilities to move there based on its history. We extract the mobility patterns from each user’s location history and match the recent trajectory with the patterns to find future locations. We construct a spectrum database using Wi-Fi access point location data and the free space path loss formula. We propose a machine learning-based mechanism to predict spectrum status of some missing locations in the spectrum database. We formulate a problem to select the current channel in order to minimize the total number of channel switches during a certain number of next moves of a user. We conduct an extensive simulation combining real and synthetic datasets to support our model.

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