Ahsan Adeel

Critical Analysis of Learning Algorithms in Random Neural Network Based Cognitive Engine for LTE Systems

In this paper, we critically analyze the performance of an intelligent Long-Term Evolution-Uplink (LTE-UL) system having a cognitive engine (CE) embedded in e-NodeB. Performance characterization, optimal radio parameters prediction, and inter-cell-interference coordination (ICIC) are studied. The embedded CE allocates the optimal radio parameters to serving users and suggests the acceptable transmit power to users served by adjacent cells for ICIC. The desired cognition has been achieved with a novel random neural network (RNN) based CE architecture. To achieve the best learning performance, we critically analyzed three learning algorithms, gradient descent (GD), adaptive inertia weight particle swarm optimization (AIW-PSO) and differential evolution (DE). The analysis showed that AIW-PSO was 10.57% better than GD and 8.012% better than DE in terms of learning accuracy (based on MSE), but with considerable compromise on computational time as compared to GD. Moreover, in terms of convergence time (to achieve the MSE less than 1.04E-03), AIW-PSO took 60% less iterations than GD and 50% less than DE.

Efficient use of random neural networks for cognitive radio system in LTE-UL

Cognitive radio networks (CRNs) or self-organizing mobile cellular networks are a promising technology for 5G that manages the spectrum frequency domain more efficiently. At the heart of CRNs is the cognitive engine (CE), which is responsible for decision making on the optimal configuration settings for the CRN in real time if possible. In this paper a novel paradigm for decision making in the CE will be presented called hierarchical random neural networks (HRNNs). The proposed HRNN model decomposes a large complex neural network into a network of loosely interconnected localized subnets, which allow the simplified understanding of network behaviour and also allows the addition of more nodes for long-term memory (LTM). The model can also accurately capture the dynamic nature of the system. Simulation results of the proposed HRNN structure has shown improvements in learning efficiency (based on required execution time for convergent result) in the range of 33% to 35% with reduced computations.

Performance analysis of random neural networks in LTE-UL of a cognitive radio system

In cognitive radio networks (CRNs), the cognitive Engine (CE) is responsible for decision making. This is quite a challenging task as it requires finding the balance between prediction accuracy and efficient learning for optimal configuration settings for the CRN. Artificial neural networks (ANNs) have been widely used as predictive tools in cognitive radio. In this paper, random neural networks (RNNs) have been proposed to achieve better generalization and to speed up the cognition process in LTE (Long Term Evolution) cognitive-eNodeB. The developed CE is characterizing the achievable communication performance (throughput) of available configuration settings and suggesting the optimal radio parameters for specific service demand. Furthermore, the RNN-CE is coordinating the inter-cell-interference by suggesting the acceptable transmit power of adjacent channel users. Performance evaluation has revealed 42.85% better prediction accuracy (based on MSE) and 68% better learning efficiency (based on epochs required for convergent result) of RNN as compared to ANN.

Improved Efficient RFID Tag Estimation Scheme

Dynamic Frame Slotted Aloha) based anti-collision algorithms resolve the collision among the RFID (Radio frequency-ID) tags by adjusting the frame size for the incoming frame according to the number of un-identified tags. The system efficiency directly depends upon the estimated number of tags that contribute towards the frame size adjustment. In this paper, we propose an improved scheme for DFSA which accurately estimates the tags that have to participate in the incoming frame. The scheme also adjusts the incoming frame size according to the estimated tags and keeps the system efficiency very close to the optimal. The simulation results show that for the proposed scheme the tag estimation time and the estimation error rate is far less compared to conventional methods and system efficiency is close to optimal. KeywordsUHF RFID, Tag estimation scheme, EPC global class 1 Gen 2, maximum system efficiency

Persian Named Entity Recognition

Named Entity Recognition (NER) is an important natural language processing (NLP) tool for information extraction and retrieval from unstructured texts such as newspapers, blogs and emails. NER involves processing unstructured text for classification of words or expressions into relevant categories. In literature, NER has been developed for various languages but limited work has been conducted to develop NER for Persian text. This is due to limited resources (such as corpus, lexicons etc.) and tools for Persian named entities. In this paper, a novel scalable system for Persian Named Entity Recognition (PNER) is presented. The proposed PNER can recognize and extract three most important named entities in Persian script: the person name, location and date. The proposed PNER has been developed by combining a grammatical rule-based approach with machine learning. The proposed framework has integrated dictionaries of Persian named entities, Persian grammar rules and a Support Vector Machine (SVM). The performance evaluation of PNER in terms of precision, recall and f-measure has achieved comparable results with the state-of-the-art NER frameworks in other languages.

Performance Analysis of Artificial Neural Network-Based Learning Schemes for Cognitive Radio Systems in LTE-UL

Cognitive radio is widely accepted as a promising technology to intelligently manage the scarce radio resources and correspondingly select the optimal radio configurations. The process of cognition is challenging because of the trade-offs among response time, accuracy, available training samples, and NN structure complexity, which is a limiting factor for cognitive radio (CR) to achieve optimal configuration settings in real time. In this paper, a complex model of LTE uplink is analysed and a cognitive engine(CE) is introduced with ANN as an artificial intelligence technique. The CE is characterizing the achievable communication performance of all available secondary and primary users configurations. Furthermore, Suggesting the optimal radio configurations, taking into account the user requirements as well as the electromagnetic environment. Performance evaluation of the proposed ANN has revealed 60% improvement in accuracy and efficiency as compared to existing ANN models for the same parameters configurations.

A Comparative Study of Persian Sentiment Analysis Based on Different Feature Combinations

In recent years, the use of internet and correspondingly the number of online reviews, comments and opinions have increased significantly. It is indeed very difficult for humans to read these opinions and classify them accurately. Consequently, there is a need for an automated system to process this big data. In this paper, a novel sentiment analysis framework for Persian language has been proposed. The proposed framework comprises three basic steps: pre-processing, feature extraction, and support vector machine (SVM) based classification. The performance of the proposed framework has been evaluated taking into account different features combinations. The simulation results have revealed that the best performance could be achieved by integrating unigram, bigram, and trigram features.

Random neural network based novel decision making framework for optimized and autonomous power control in LTE uplink system

This paper presents a novel decision making framework for cognitive radio networks. The traditional continuous process of sensing, analysis, reasoning, and adaptation in a cognitive cycle has been divided into two levels. In the first level, the process of sensing and adaptation runs over the radio transmission hardware during run-time. In the second level, the process of analysis and reasoning runs in the background in offline mode. This arrangement offloads the convergence time and complexity problem of reasoning process during run-time. For implementation of the first level, a random neural network (RNN) based controller trained on an open loop case based database on the cloud has been designed. For the second level, a genetic algorithm (GA) based reasoning and an RNN based learning has been developed. The proposed framework is used to address the uplink power control problem of long-term evolution (LTE) system. The performance of RNN is compared with artificial neural network (ANN) and state-of-the-art fractional power control (FPC) scheme in terms of essential cognitive engine (CE) design requirements, capacity, and coverage optimization (CCO). The simulation results have shown that RNN based CE can achieve comparable results with faster adaptation, even subject to severe environment changes without the need of retraining.

Random Neural Network Based Cognitive-eNodeB Deployment in LTE Uplink

Artificial intelligence (AI)/machine learning (ML) based cognitive solutions have widely been applied to deal with downlink inter-cell interference coordination (ICIC) in long-term evolution (LTE) systems. This paper presents a random neural network (RNN) based novel framework to improve ICIC and radio resource management (RRM) in LTE-Uplink system. The RNN based cognitive engine (CE) is embedded within the eNodeB which allocates optimal radio parameters to attached users and also suggests acceptable transmit power to users served by adjacent cells, in order to reduce inter-cell interference (ICI). The proposed CE concurrently achieves long-term learning, fast decision making, and less computational complexity. These three CE design features are essential for the development and practical deployment of any real-time cognitive communication system and most of the existing AI/ML based cognitive solutions in literature lack them. The performance of RNN based optimization framework is compared with artificial neural network (ANN) and state-of-the-art fractional power control (FPC) scheme. In six different test-cases, simulation results have shown an improvement of 53.88%-87.53% in decision making accuracy and a decrease of 44.22% in scheduling delay as compared to ANN. In addition, throughput gain of 16.13% and 18.62% has been achieved as compared to ANN and FPC schemes respectively.

A Survey on the Role of Wireless Sensor Networks and IoT in Disaster Management

Extreme events and disasters resulting from climate change or other ecological factors are difficult to predict and manage. Current limitations of state-of-the-art approaches to disaster prediction and management could be addressed by adopting new unorthodox risk assessment and management strategies. The next generation Internet of Things (IoT), Wireless Sensor Networks (WSNs), 5G wireless communication, and big data analytics technologies are the key enablers for future effective disaster management infrastructures. In this chapter, we commissioned a survey on emerging wireless communication technologies with potential for enhancing disaster prediction, monitoring, and management systems. Challenges, opportunities, and future research trends are highlighted to provide some insight on the potential future work for researchers in this field.