End-to-end utility-based resource allocation for multi-RAT nodes in heterogeneous cognitive wireless networks

Wireless communication encapsulates the earth into a global village with its reach to every nook and corner of the globe. The fourth generation (4G systems) of wireless communication was expected to be up for grabs from 2010s. However, due to the complicated hardware required the evolution of technology into 4G has been sluggish. Since 4G services require the integration of all the existing communication services into a global standard, new advancements are required in technology to solve the issues in the path to progress. The main purpose of this paper is to find an effective solution for the network selection issue, which is one of the the primary obstacles in 4G communication systems. This paper discusses the Quality of Service (QoS) based network selection technique, which utilizes multiple performance parameters, with the proposal for introducing a preplanner module for emergency services. And also the paper tries to find out the effectiveness of the proposal from the results obtained through the simulation executed.

A QoS based network selection scheme for 4G systems with preplanner for emergency services

The high dynamics of mobile and wireless networks calls for intelligent mechanisms to select access networks and corresponding points of access for the clients and their active applications. However, one needs to be careful not to increase the number of handovers substantially as it may cause large communication overhead to the network. In this paper, we consider mechanisms located at the client-side where the greedy selfish behavior should be regulated by using algorithms which simultaneously improve the quality of experience (QoE) but do not disturb much or, in the best case, even improve the overall network performance. Specifically, we introduce a Q-learning based QoE-aware access selection algorithm which enables the clients to learn from past experiences in order to find the optimal actions. The statuses of the available points of access are described by a cascade fuzzy classifier. The Q-learning based solution is compared to the default mechanism and an opportunistic fuzzy inference algorithm by simulation. The results indicate that a Q-learning approach is able to keep the number of handovers reasonably low while still achieving a good QoE, thus providing a better approach both from the user and the network operator perspective.

Cognitive wireless access selection at client side: Performance study of a Q-learning approach

4G communication systems provide a global communication standard for each and every communication facilities and internet and telecommunication service providers of the globe. The major attraction of 4G is the network selection policy which enables users to select services offered by the best of the various service providers. The main purpose of this paper is to discuss network selection in 4G systems. This paper discusses the method of network selection, need for special schemes and its benefits and shortcomings. In addition to that a proposal is made for a new collaborative method to select networks based on their Quality of Service (QoS) parameters and their Quality of Experience (QoE) in functioning in a heterogeneous environment of networks

A QoS and QoE collaborative scheme for effective network selection in 4G communication systems

In state-of-the-art mobile devices (MDs), non-radio contexts (i.e., non-real-time data) provided by various sensors have been excluded in determining the modem configuration because of the problems in applying them in real-time modem operations. This paper presents a reconfiguration procedure and hardware implementation of a European Telecommunications Standards Institute (ETSI)-standard reconfigurable MD with a multi-antenna system. Borrowing the ETSI-standard architecture, the proposed MD can exploit a high-level abstraction of configuration-related operations, which consequently provides its application processor operating in non-real-time domain a uniform way of managing the multi-antenna-related hardware platform operating in real time. The configuration of the proposed MD is determined in accordance with non-radio and radio context information for opportunistically utilizing multiple antenna resources. From an implemented multi-antenna MD system operating in an indoor layout and computer simulations of the multi-antenna MD operating in a modeled outdoor layout, we verify that the MD configuration can be optimized in accordance with radio and non-radio contexts not only for fixed indoor but also for mobile outdoor environments. This enables the multi-antenna MD to efficiently utilize its antenna resources, such that the throughput maximization and/or power consumption minimization can be achieved.

/pdf/etsi-standard-reconfigurable-mobile-device-with-an-1hsu1bzpxx.pdf

ETSI-Standard Reconfigurable Mobile Device With an Opportunistic Multi-Antenna System Based on Radio and Non-Radio Contexts

In a B3G/4G wireless communication system, the users will connect to the network using one of several available radio access technologies. In this paper, we proposed a Q-learning based algorithm for terminals' independent access network selection with the aim of improving the resource utilization and providing the best quality of service with respect to the wireless environment status, network performance and user' requirement. In particular, for the first time we introduced the concept of low-carbon as one of the evaluation indicators of wireless communication performance, in order to reduce the power consumption and achieve a balance between quality and consumption. The proposed scheme is based on the concept of cognitive network, which has been proposed recently by the motivation of complexity, heterogeneity and reliability requirements of tomorrow's network and the cognitive pilot channel used in it. The performance of the access network selection algorithm is shown in the simulation and it can be seen that this algorithm significantly reduced the blockrate and power consumption as well as increased the throughput compared with random accessing approach. In future work, we will continue to research on the effective access network selection algorithm and try to introduce the low-carbon indicator to other aspects of the wireless communication system.

/pdf/a-novel-access-network-selection-scheme-using-q-learning-4ayzaf8k4l.pdf

A novel access network selection scheme using Q-learning algorithm for cognitive terminal

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A novel access network selection scheme using Q-learning algorithm for cognitive terminal