Math and Computer Science

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A novel genetic algorithm application is proposed for adaptive power and subcarrier allocation in multi-user OFDM systems. The proposed method is compared with Eshan's [2] power and subcarrier allocation algorithm and Wang's modified genetic algorithm [23]. Our method has fast convergence and can handle large allocations of subcarriers to users without performance degradation. The simulation results show that our approach is a viable alternative to existing methods for optimum resource allocation.

In this research we introduce the game theory model for efficient spectrum allocation. The proposed game theory model uses the genetic algorithm application to analyze a radio frequency and optimize the utility function (each player's preferences). The simulations were done using MATLAB 'gatool'. The results show that genetic algorithms have positive effect on game theory models in spectrum allocation.

Wireless sensor networks has grown a lot in recent years and offers excellent opportunities in defense related applications such as monitoring environment and collecting data related to antisocial activities. Currently, the needs of wireless sensors have become inevitable in daily life. With the continuous growth of Wireless sensor networks in daily life, business, and defense applications, the security of transferring data from sensors to their destination has become an important research area. Due to the limitations of power, storage, and processing capabilities, current security mechanisms of wireless networks or wired networks can not apply directly to wireless sensor networks. So there is a need to develop new techniques or modify the current security mechanisms to transfer data from source (from the field) to base station (destination). In this paper, we discuss currently available intrusion detection techniques, attack models using game theory, and then propose a new framework to detect malicious nodes using zero sum game approach for nodes in the forward data path. The first part of research provides the game model with probability of energy required for transferring the data packets. The second part derives the model to detect the malicious nodes using probability of acknowledgement at source.

With increasing demand for data transfer requirements on wireless networks, spectrum became a scarce resource due to inefficient allocation and management. Recent research efforts diverted the problem towards dynamic spectrum access models for effective utilization of the unused or idled spectrum. The models include overlay/underlay techniques by designing the framework to improve the spectrum efficiently by using business and game theory models. Surveying of these models concludes that the key component for efficient utilization of the unused spectrum is the detection of the unused spectrum at any given time. Further, we found that by allocating the unused spectrum using appropriate techniques (business, game, and hybrid models) will produce better results. Among these models, the game models were identified as one of the powerful mathematical tools to detect and allocate the unused spectrum. In this paper, we first discussed the role of game models in wireless communications, player"s strategy selection for better utility, and then proposed a correlated equilibrium algorithm for efficient allocation of spectrum. The simulations conclude that the mixed strategies are better than the pure strategies in resource allocation.

Abstract: Trust is very important in wireless sensor networks to transfer the data from source to destination. The Dynamic Source Protocol calculates the alternate path, if any node fails to transfer the data. The Dynamic Source Protocol does not have any built-in functionality ...

Abstract: Spectrum is a scarce resource. The cognitive radio environments utilize the spectrum efficiently through the dynamic spectrum access approach. Game theory, genetic algorithms, neural network, marketing, and economic models became the catalysts to ...

Dynamic spectrum access is a way of gaining access to individual frequencies on a temporary basis. This makes use of the frequency assigned to a specific user (primary user) by using specific devices and/or spectrum management techniques. The spectrum management techniques can be done by allocating the spectrum (a) through auctions (market based), (b) using management techniques (c) spectrum sharing (detects and utilizes the unutilized part of the spectrum) (d) command and control, and (e) through opportunistic spectrum access. In opportunistic spectrum access, the secondary or unlicensed user transfers the data with high speed and at short distances with tolerable interference (without disturbing) to the primary signal. Efficient spectrum allocation techniques were discussed using stochastic models, economic models, genetic algorithms, and optimization techniques. The existing models need to be tuned for better performance with optimum utilization of the power. In this paper, we proposed a model that provides access with tolerable interference from secondary users to the primary users while maximizing the spectrum utilization. Furthermore, we designed a congestion game model for efficient utilization of the spectrum by secondary users with minimum interference to primary users. The simulation results show that the congestion game model helps to utilize the spectrum efficiently.

The objective of this research is to provide appropriate cross-layer architecture for wireless cognitive networks for efficient resource allocation and improved quality of service. We proposed the blackboard model, which is known for coordinating multiple agents (cognitive nodes) in a real-time manner, receiving the current state of information from these nodes, providing conclusions basing on information received from these nodes, updating these nodes with current conclusions, and suggesting needed actions for these nodes. Each cognitive node is assumed as an agent to the blackboard. The parameter values abstracted from these cognitive nodes to blackboard are structured messages that are optimized with respective to an objective function. This paper introduces the crosslayer design of a cognitive network, the role of the blackboard architecture, and possible applications.

Cognitive radio environments utilize the scarce resource like spectrum efficiently through the dynamic spectrum access approach. Various models, including economic, game theory, genetic algorithms, and neural network models will become the catalysts to boost the dynamic spectrum access for efficient utilization of the spectrum. Combinations of these models may sometimes help better in allocation of unused spectrum (spectrum holes) for cognitive users (secondary users). In this paper, we introduced the case-base reasoning to identify a channel preferred by the secondary user. We then introduced automatic collaborative filtering for preference between two users which assigns the particular channel to the highest prioritized user. These two approaches were used along with the cooperative game concept to select the preferred channel for a cognitive user at any given time slot. The framework was designed using the CBR-ACF Algorithm and discussed using examples generated through simulations.

In the wireless network, the node is visible but is not available for communication. Similarly, the primary signal is present and difficult to detect. The primary signal problem is similar to hidden node problem, but currently interpreted as false alarms. In this research, two models (probability based and Drake's equation) were designed and tested through simulations. The results show that Drake's equation with probability-based model performs better than basic probability-based model to detect the hidden signal.

Cognitive radio is a recent innovative technique to exploit the current limitations on spectrum scarcity. Defined from software defined radio, the cognitive users use the unused spectrum (spectrum holes) using various techniques to detect and efficiently allocate the unused spectrum. The process requires complex computations and many times software packages to be downloaded. This drains the battery power and panics the mobile users and other users with limited battery power. To meet the consequences, a new model called cognitive radio networks with cloud (CRNC) is introduced in this research. The proposed model eliminates the current problems in transmission of information and helps in the real-time process. Simulations were conducted with the proposed CRNC model and discussed the possible packet transfer.

Today, the industry old adage of sequential processing is certainly no longer sufficient. The need for high performance computation is ever growing, even though certain problem sets remain within the realm of super high performance computing with applications such as weather forecasting, quantum physics and climate research to name a few. Within the commercial realm of computation, NVIDIA has proposed an architectural framework (NVIDIA CUDA) to harness the power of GPUs which before was only been utilized for Graphics Application like 3D games, but now recently, been used for certain types of high performance computation. In this paper, we will take a critical look at different performance techniques such as tiling, memory coalescing, perfecting, and loop unrolling, in trying to evaluate which method is the most efficient approach for our problem set (matrix operation of n size matrices).

In this paper, a novel genetic algorithm application is proposed for adaptive power and subcarrier allocation in multiuser Orthogonal Frequency Division Multiplexing (OFDM) systems. To test the application, a simple genetic algorithm was implemented in MATLAB language. With the goal of minimizing the overall transmit power while ensuring the fulfillment of each user's rate and bit error rate (BER) requirements, the proposed algorithm acquires the needed allocation through genetic search. The simulations were tested for BER 0.1 to 0.00001, data rate of 256 bit per OFDM block and chromosome length of 128. The results show that genetic algorithm outperforms the results in [3] in subcarrier allocation. The convergence of GA model with 8 users and 128 subcarriers performs better in power requirement compared to that in [4] but converges more slowly.