Implementation of relay based collaborative spectrum sensing using coalitional games in wireless cognitive radio networks

Manna, Tanumay; Misra, Iti Saha

doi:10.1016/j.compeleceng.2015.03.006

Cited by 10 publications

(5 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In cooperation based on the notion of coalitional game theory [27], subgroups of nodes combine their forces and decide to act together, so that this formed coalition gives the optimum payoff. Therefore the formed coalitions should keep their stability.…”

Section: Related Workmentioning

confidence: 99%

A Coalition-Formation Game Model for Energy-Efficient Routing in Mobile Ad-hoc Network

Sahnoun¹,

Habbani²,

Abbadi³

2018

IJECE

View full text Add to dashboard Cite

One of the most routing problems in Mobile Ad-hoc Network is the node’s selfishness. Nodes are generally selfish and try to maximize their own benefit; hence these nodes refuse to forward packet on behalf of others to preserve their limited energy resources. This selfishness may lead to a low efficiency of routing. Therefore, it is important to study mechanisms which can be used encourage cooperation among nodes, to maintain the network efficiency. In this paper, we propose a cooperative game theoretic model to support more energy-aware and available bandwidth routing in MANET. We introduce a novel framework from coalitional-formation game theory, called hedonic coalition-formation game. We integrate this model to OLSR protocol that is an optimization over the classical link state protocol for the MANETs. Within each coalition, a coalition coordinator acts as a special MPR node to improve the energy efficient and the packet success rate of the transmission. Simulation results show how the proposed algorithm improve the performance in terms of the percentage of selected MPR nodes in the network, the percentage of alive nodes by time, and the Packet Delivery Ratio. Which prove that our proposed model leads, to better results compared to the classical OLSR.

show abstract

Section: Related Workmentioning

confidence: 99%

A Coalition-Formation Game Model for Energy-Efficient Routing in Mobile Ad-hoc Network

Sahnoun¹,

Habbani²,

Abbadi³

2018

IJECE

View full text Add to dashboard Cite

show abstract

“…In this scenario, the optimal detector is an energy detector, 45 which is also one of the most commonly used detection technique in CRNs. 46 The detection threshold ( th ) of the CBS, in the presence of white Gaussian noise at the receiver, can be represented as Manna and Misra, 46 The PU signal is detected by comparing the received signal energy with a threshold computed using an estimated noise power as described in Manna and Misra.…”

Section: Sensingmentioning

confidence: 99%

“…46 It is worth noting that all communications have been setup in a slotted manner, with each slot duration being equal to the 0.384 ms, which is the time taken by WARP board to transmit or read the complete buffer of 2 14 samples excluding 1000 delay samples. The PU transmit power is set such that the detected PU SNR at the CBS is −20 dB, ie, PU = −20 dB.…”

Section: Figure 18mentioning

confidence: 99%

“…The PU signal is detected by comparing the received signal energy with a threshold computed using an estimated noise power as described in Manna and Misra. 46 The detection threshold ( th ) of the CBS, in the presence of white Gaussian noise at the receiver, can be represented as Manna and Misra, 46…”

Section: Sensingmentioning

confidence: 99%

“…The baseband frequency spectrum for the coexistence of primary signal and control channel is shown in our paper. 46 It is worth noting that all communications have been setup in a slotted manner, with each slot duration being equal to the 0.384 ms, which is the time taken by WARP board to transmit or read the complete buffer of 2 14 samples excluding 1000 delay samples. Following which, all the timing parameters, viz, sensing time, transmission time, ON and OFF times of PU signals, have been mapped into equivalent number of slots.…”

Section: Figure 18mentioning

confidence: 99%

See 2 more Smart Citations

A prediction and scheduling framework in centralized cognitive radio network for energy efficient non‐real time communication

Manna

Misra

2018

Int J Communication

Self Cite

View full text Add to dashboard Cite

Advent of Internet of Things led to an exponential rise in battery-operated sensors transmitting small non-real time (NRT) data regularly. To this end, this work proposes a framework for centralized cognitive radio network (CRN) that facilitates better spectrum utilization and low-cost opportunistic NRT data transfer with high energy efficiency. The novelty of this framework is to incorporate Hidden Markov Model-based prediction within the traditional cognitive radio sensing-transmission cycle. To minimize the prediction time, we design a Hardware-based Hidden Markov Model engine (H2M2) to be used by the cognitive base station (CBS). CBS exploits the H2M2 engine over high primary user (PU) activity channels to minimize the collisions between PUs and NRT secondary users, thereby reducing the SU energy consumption. However, this is at the cost of reduced throughput. Taking this into account, we propose an Intersensing-Prediction Time Optimization algorithm that identifies the predictable PU activity channels and maximizes the throughput within a PU interference threshold. Furthermore, to minimize the total battery consumption of all the SUs within CRN, a Battery Consumption Minimizing Scheduler is designed at the CBS that efficiently allocates the predictable PU channels to the NRT SUs. By exploiting the unutilized high PU activity channels, the proposed Centralized Scheduling, Sensing and Prediction (CSSP) framework improves the spectral efficiency of the CRN. Exhaustive performance studies show that CSSP outperforms traditional nonpredictive sensing techniques in terms of energy efficiency and interference management. Finally, through a proof of concept, we validate the ability of CSSP framework in enabling NRT communication.

show abstract

Principles and Challenges of Cooperative Spectrum Sensing in Cognitive Radio Networks

Khalid¹,

Anpalagan²

2017

Handbook of Cognitive Radio

View full text Add to dashboard Cite

Implementation of relay based collaborative spectrum sensing using coalitional games in wireless cognitive radio networks

Cited by 10 publications

References 18 publications

A Coalition-Formation Game Model for Energy-Efficient Routing in Mobile Ad-hoc Network

A Coalition-Formation Game Model for Energy-Efficient Routing in Mobile Ad-hoc Network

A prediction and scheduling framework in centralized cognitive radio network for energy efficient non‐real time communication

Principles and Challenges of Cooperative Spectrum Sensing in Cognitive Radio Networks

Contact Info

Product

Resources

About