Tuğrul Çavdar

Particle Swarm Optimization — Based determination of Ziegler-Nichols parameters for PID controller of brushless DC motors

In this article, Ziegler-Nichols and Particle Swarm Optimization algorithms are combined to calculate optimum values for parameters KP, KI, KD in a Proportional-Integral-Derivative controller. We present a novel method searching two-dimensional space instead of three-dimension, so that the calculations of PID parameters become more accurate due to smaller search space. In this research, the efficiency of the combined method in brushless DC motors has been evaluated and the results are presented and compared with the Genetic Algorithm, standard Particle Swarm Algorithm and Advanced Particle Swarm Algorithm. The simulations show that the proposed PID-PSO-ZN algorithm is candidate to be employed in control systems due to its precision to determine optimum PID parameters which reduce the rising time, the settling and overshoot on system response.

A new sequential decoding algorithm based on branch metric

A new sequential decoding algorithm with an adjustable threshold and a new method of moving through the decoding tree is proposed. Instead of the path metric of the conventional sequential decoding algorithms, the proposed algorithm uses a branch metric based on maximum-likelihood criterion. Two new parameters, the jumping-back distance and going-back distance, are also introduced. The performance of the algorithm for long constraint length convolutional codes is compared to those of the other sequential decoding algorithms and the Viterbi algorithm. The results show that the proposed algorithm is a good candidate for decoding of convolutional codes due to its fast decoding capability and good bit error rate (BER) performance.

A novel SDN-based IoT architecture for big data

Although Internet of Things (IoT) is an evolving paradigm which allows everyday physical objects, devices and appliances to connect to the internet, and to communicate with each other, there are several essential challenges that should be addressed. One of them is big data generated by physical objects. It is quite challenging to manage and control such enormous amount of data. In this paper, we propose an IoT architecture originated from Software Defined Networking (SDN) to overcome big data problem. Instead of evaluating the sensor values in the application layer, we analyze them in lower layers, especially in gateway layer, before being sent to the internet. By the proposed architecture, to the best of our knowledge, big data problem is handled because only useful and helpful packets are sent to the internet.

PSO-optimized Instant Overbooking Framework for cognitive radio networks

Nowadays, the emerging wireless communication technologies cause rapid growth in radio spectrum usage. However, the radio spectrum is a limited resource and has almost been fully assigned by the current network operators (NOs). Although numerous studies have been accomplished on the spectrum management, there are rare evolutionary techniques dealing with booking spectrum on cognitive radio. Instant Overbooking Framework for Cognitive Radio networks (IOFCR) is used to maximize both spectrum utilization and the revenue of NOs under several over-booking and pricing policies. In such a network, when a primary user (PU) activity is sensed, the control of which requesting secondary users (SUs) can be denied and which Active SUs can be ejected is managed by IOFCR. In this paper, the Particle Swarm Optimization (PSO) algorithm is applied into the IOFCR to find optimal values of the over-booking price and the Compensation Cost (CC) ratio to optimize revenue and achieve high performance.

Mobile fleet localization model via RSSI, TOA and TDOA in wireless sensor networks

Wireless sensor networks are created using low cost, low power sensor nodes to collect useful information from a targeted environment in various applications. Location estimation is one of the areas where these networks can be exploited. Main aim of this study is to control a fleet via wireless sensor networks that only leader vehicle has human control. Instead, some of the vehicles can be equipped with GPS receivers and the rest of fleet can find their own position via RSSI (Received Signal Strength Indication), TOA (Time of Arrival) and TDOA (Time Difference of Arrival) methods. Based on our MATLAB based simulations, we analyze the relationship among various factors such as GPS (beacon) node number and distance etc. Simulation results show that location estimation errors can be significantly reduced with right beacon node number selection and correct positioning of nodes.

Application of Sequential Estimation Algorithm Based on Branch Metric to Frequency-Selective Channel Equalization

In this work, a sequential estimation algorithm based on branch metric is used as channel equalizer to combat intersymbol interference in frequency-selective wireless communication channels. The bit error rate (BER) and computational complexity of the algorithm are compared with those of the maximum likelihood sequence estimation (MLSE), the recursive least squares (RLS) algorithm, the Fano sequential algorithm, the stack sequential algorithm, list-type MAP equalizer, soft-output sequential algorithm (SOSA) and maximum-likelihood soft-decision sequential decoding algorithm (MLSDA). The BER results have shown that whilst the sequential estimation algorithm has a close performance to the MLSE using the Viterbi algorithm, its performance is better than the other algorithms. Beside, the sequential estimation algorithm is the best in terms of computational complexity among the algorithms mentioned above, so it performs the channel equalization faster. Especially in M-ary modulated systems, the equalization speed of the algorithm increases exponentially when compared to those of the other algorithms.

Dynamic Resource Sharing in 5G with LSA: Criteria-Based Management Framework

Owing to a steadily increasing demand for efficient spectrum utilization as part of the fifth-generation (5G) cellular concept, it becomes crucial to revise the existing radio spectrum management techniques and provide more flexible solutions for the corresponding challenges. A new wave of spectrum policy reforms can thus be envisaged by producing a paradigm shift from static to dynamic orchestration of shared resources. The emerging Licensed Shared Access (LSA) regulatory framework enables flexible spectrum sharing between a limited number of users that access the same frequency bands, while guaranteeing better interference mitigation. In this work, an advanced user satisfaction-aware spectrum management strategy for dynamic LSA management in 5G networks is proposed to balance both the connected user satisfaction and the Mobile Network Operator (MNO) resource utilization. The approach is based on the MNO decision policy that combines both pricing and rejection rules in the implemented processes. Our study offers a classification built over several types of users, different corresponding attributes, and a number of MNO’s decision scenarios. Our investigations are built on Criteria-Based Resource Management (CBRM) framework, which has been specifically designed to facilitate dynamic LSA management in 5G mobile networks. To verify the proposed model, the results (spectrum utilization, estimated Secondary User price for the future connection, and user selection methodology in case of user rejection process) are validated numerically as we yield important conclusions on the applicability of our approach, which may offer valuable guidelines for efficient radio spectrum management in highly dynamic and heterogeneous 5G environments.

Multi-path route discovery algorithm for cognitive radio ad hoc networks using algebraic connectivity

Cognitive Radio Technology has brought new insights to the problems of spectrum scarcity and inefficiency, in which spectrum holes are opportunistically accessed when the licensed users (primary users) are not using the frequencies that are assigned to those users. In this paper, we propose a multi-path route discovery algorithm for cognitive radio ad hoc networks (CRAHN) using algebraic connectivity. The employed routing mechanism takes into account the impact of the primary users on the non-licensed users (secondary users) in a way that the secondary users choose the paths which are less affected by the non-licensed users. With regard to proposed route discovery algorithm, data traffic congestions are tried to be avoided in the secondary network by reducing the number of all possible paths from source to destination. The results show that the devised method overcomes the technique of the route request (RREQ) message flooding between a source and a destination.

Pre-reservation based spectrum allocation for cognitive radio network

Studies on the current usage of the radio spectrum by several agencies have already revealed that a large fraction of the radio spectrum is inadequately utilized. This basic finding has led to numerous research initiatives. Cognitive radio technology is one of the key candidate technologies to solve the problems of spectrum scarcity and low spectrum utilization. However, random behavior of the primary user (PU) appears to be an enormous challenge. In this paper, a Pre-reservation based spectrum allocation method for cognitive radio network is proposed to apply a PU behavior aware joint spectrum band (SB) selection and allocation scheme. In the first step, the SB is observed in terms of PU usage statistics whereas in the second phase, a network operator (NO) using a spectrum allocation scheme is employed to allocate SBs among secondary users (SUs). We also introduce the concept of reservation and exchange functionality under the priority serving strategy in a time-varying framing process. Simulation results show that the proposed scheme outperforms existing schemes in terms of the spectrum utilization and network revenue. In addition, it helps NO to manage the spectrum on a planned basis with a systematical spectrum reservation management where the NO has the status of time slots. Moreover, SUs have an opportunity to reserve or instantly request a SB that maximizes the SUs satisfaction in terms of quality of experience.

HyMPRo: a hybrid multi-path routing algorithm for cognitive radio ad hoc networks

Routing is one of the major challenges in cognitive radio ad hoc networks. In finding new routes, not only the less impacted paths due the primary user activity, but also bottleneck formations over time in the secondary network and multiple connections initiated by the secondary users are important issues to be investigated. Besides, route search confined to a small fraction of all possible paths can lead to miss the appropriate one. This paper introduces a hybrid method employing three atomic metrics related to route stability, congestion awareness and path diversity based on active connections. Also, the number of alternative paths is kept on a limited scale which avoids the emergence of excessive traffic load in the network. The simulation results demonstrate the effectiveness of the proposed approach in terms of packet delivery ratio, end-to-end delay and throughput. It is also shown that the performance of our routing mechanism outperforms the existing baseline schemes.