Efficient Cognitive Radio Networks Research Articles

Carrier Frequency Offset (CFO) Synchronization and Peak Average Power Ratio (PAPR) Minimization for Energy Efficient Cognitive Radio Network (CRN) for 5G Wireless Communication

Carrier Frequency Offset (CFO) Synchronization and Peak Average Power Ratio (PAPR) Minimization for Energy Efficient Cognitive Radio Network (CRN) for 5G Wireless Communication

Hierarchical Cooperation Improves Delay in Cognitive Radio Networks with Heterogeneous Mobile Secondary Nodes

This paper characterizes the throughput and delay performance of Cognitive Radio Networks (CRNs), where both primary and secondary networks coexist in a unit torus. Specifically, the primary network consists of static primary nodes (PNs) of density $n$ n , which have a higher priority to access the spectrum. In contrast, the secondary network consists of mobile secondary nodes (SNs) of density $m=n^{\beta }$ m = n β with $\beta \geq 1$ β ≥ 1 , which move according to a hybrid random walk mobility model and have opportunistic access to the spectrum without affecting primary packet transmissions. Motivated by the fact that cooperation between primary and secondary nodes leads to possible improvement on the performance of CRNs, as well as the fact that the heterogeneous moving regions of secondary nodes will bring about further improvement, we propose a novel hierarchical cooperative scheduling mechanism, where secondary nodes serve as relays for primary packet transmissions by exploiting their mobility heterogeneity and geographic information. Our findings include: (i) For the primary network, stronger mobility heterogeneity of secondary nodes leads to better delay performance of the primary network, and meanwhile the delay scaling can be significantly reduced to $\Theta (n^{\sqrt{\beta /{(4\;\log n)}}}\log ^{3/2}n)$ Θ ( n β / ( 4 log n ) log 3 / 2 n ) when a near-optimal per-node throughput of $\Theta ({1}/{\log n})$ Θ ( 1 / log n ) is obtained. (ii) For the secondary network, we also adopt a similar hierarchical cooperative scheduling mechanism, and obtain a near-optimal per-node throughput of $\Theta {(1/{\log m})}$ Θ ( 1 / log m ) with the delay scaling of $\Theta ({m^{1-(1/\sqrt{\log m})}})$ Θ ( m 1 - ( 1 / log m ) ) . (iii) The delay of secondary source-destination pairs is determined by the moving region of destinations and has no relation with sources. Our work provides deeper understandings of the cooperation, heterogeneous mobility, and geographic information on the performance of CRNs, and sheds light on designing more efficient CRNs.

Optimal Sensing and Transmission of Energy Efficient Cognitive Radio Networks

The issue of spectrum scarcity can be alleviated by the cognitive radio technology with efficient spectrum sensing and allocation of free spectrum bands. In Cognitive Radio Networks energy efficiency improvement is the state of art now days. This paper considers the case of primary user protection from cognitive user transmission to optimize the energy efficiency. The parameters of optimal design problem are sensing, transmission time and transmission power. A Sub Optimal Iterative Search Algorithm is proposed to maximize efficiency by optimizing sensing time and transmitting time. Simulation results exhibits substantial improvement in energy efficiency compared to the recent algorithms.

Spectrum sharing via hybrid cognitive players evaluated by an M/D/1 queuing model

We consider a cognitive wireless network in which users adopt a spectrum sharing strategy based on cooperation constraints. The majority of cognitive radio schemes bifurcate the role of players as either cooperative or non-cooperative. In this work, however, we modify this strategy to one in which players are hybrid, i.e., both cooperative and non-cooperative. Using a Stackelberg game strategy, we evaluate the improvement in performance of a cognitive radio network with these hybrid cognitive players using an M/D/1 queuing model. We use a novel game strategy (which we call altruism) to “police” a wireless network by monitoring the network and finding the non-cooperative players. Upon introduction of this new player, we present and test a series of predictive algorithms that shows improvements in wireless channel utilization over traditional collision-detection algorithms. Our results demonstrate the viability of using this strategy to inform and create more efficient cognitive radio networks. Next, we study a Stackelberg competition with the primary license holder as the leader and investigate the impact of multiple leaders by modeling the wireless channel as an M/D/1 queue. We find that in the Stackelberg game, the leader can improve its utility by influencing followers’ decisions using its advertised cost function and the number of followers accepted in the network. The gain in utility monotonically increases until the network is saturated. The Stackelberg game formulation shows the existence of a unique Nash equilibrium using an appropriate cost function. The equilibrium maximizes the total utility of the network and allows spectrum sharing between primary and secondary cognitive users.

Sensing throughput trade‐off for an energy efficient cognitive radio network under faded sensing and reporting channel

SummaryThe trade‐off between sensing time and throughput is investigated in the context of an energy‐efficient cognitive radio network considering both the sensing and reporting channels are Rayleigh faded, while the existing literature considered the fading in sensing channel only. In this paper, such a trade‐off is re‐examined under Rayleigh faded sensing as well as reporting channel. Novel analytical expressions for overall detection probability and false alarm probability are developed under such scenario. The performance is investigated in terms of detection probability, false alarm probability, throughput and energy efficiency of the network for different sensing parameters such as sensing time, number of samples, sensing channel signal‐to‐noise ratio and reporting channel signal‐to‐noise ratio. Our analysis shows that the quality of the reporting channel significantly affects the trade‐off performance of the network. Copyright © 2015 John Wiley & Sons, Ltd.

Data-Throughput Enhancement Using Data Mining-Informed Cognitive Radio

We propose the data mining-informed cognitive radio, which uses non-traditional data sources and data-mining techniques for decision making and improving the performance of a wireless network. To date, the application of information other than wireless channel data in cognitive radios has not been significantly studied. We use a novel dataset (Twitter traffic) as an indicator of network load in a wireless channel. Using this dataset, we present and test a series of predictive algorithms that show an improvement in wireless channel utilization over traditional collision-detection algorithms. Our results demonstrate the viability of using these novel datasets to inform and create more efficient cognitive radio networks.

Energy efficient cognitive radio network based on multiband sensing and spectrum sharing

The authors consider the problem of energy consumption in a multiband cognitive radio network, in which a secondary user can sense multiple frequency bands authorised to the primary user (PU) before it decides to access some frequency bands and keep the interference introduced to the PU under a certain threshold. The sensing time and power allocation need to be designed carefully to reduce the energy consumption. Therefore they identify the optimal sensing time and accessing power to minimise the energy consumption of the cognitive radio that simultaneously senses and accesses multiple bands under the multiband sensing-based spectrum sharing scheme. To obtain the optimal solution, they transform the original problem formulated as the ratio of two functions into another parametric formulation. The interference power is considered as one of constraints in the author's scheme in order to protect the PU. The algorithm of optimal sensing time and power allocation is proposed for the multiband cognitive radio system. Furthermore, they discuss the effect of channel number, interference power and gain estimation of channels on energy consumption and optimal sensing time and validate their schemes and analysis by simulation results.

Improving Spectrum Efficiency via In-Network Computations in Cognitive Radio Sensor Networks

To alleviate the spectrum shortage for sensor networks with tremendous sensors, cognitive radio technology enabling multi-hop opportunistic networking and concurrent transmissions overlaying the primary system suggests an attractive facilitation of large-scale wireless sensor networks (WSNs) and machine-to-machine communications. However, subsequent significant end-to-end delay in large WSN can prohibit practical applications. Leveraging the nature of traffic in sensor networks, we develop in-network computation to reduce requisite transmissions and to accommodate more concurrent transmissions under a given spectrum. Specifically, distributed source coding and broadcasting in wireless communication are exploited to build the computational framework and the achievable network capacity is examined. Furthermore, a greedy networking algorithm is adopted to justify significant improvement on end-to-end delay and further statistical QoS guarantee, while yielding considerable system throughput gain for practical deployment of WSNs. Performance evaluations confirm that we successfully demonstrate communication efficiency from in-network computations and facilitate a new paradigm for spectrum efficient cognitive radio networks, which shall be applicable in general multi-hop wireless networks and spectrum-sharing WSNs.

Reinforcement learning based sensing policy optimization for energy efficient cognitive radio networks

This paper introduces a machine learning based collaborative multi-band spectrum sensing policy for cognitive radios. The proposed sensing policy guides secondary users to focus the search of unused radio spectrum to those frequencies that persistently provide them high data rate. The proposed policy is based on machine learning, which makes it adaptive with the temporally and spatially varying radio spectrum. Furthermore, there is no need for dynamic modeling of the primary activity since it is implicitly learned over time. Energy efficiency is achieved by minimizing the number of assigned sensors per each subband under a constraint on miss detection probability. It is important to control the missed detections because they cause collisions with primary transmissions and lead to retransmissions at both the primary and secondary user. Simulations show that the proposed machine learning based sensing policy improves the overall throughput of the secondary network and improves the energy efficiency while controlling the miss detection probability.