• Proceedings of the IEEK Conference
• /
• 1998.06a
• /
• pp.909-912
• /
• 1998

In this paper, a new connection admission controller using neural network is presented. The controller measures traffic flow, cell loss rate, and cell delay periodically. Using those measured information, it learns the distributions of traffics of each traffic. Also the proposed controller is able to measure and manage the delays that source traffics experience through the network by using DWRR multiplexer with buffers dedicated to each traffic source. Experimental result show that the heterogeneous traffic sources with various QoS requirement.

• Journal of Korea Multimedia Society
• /
• v.15 no.12
• /
• pp.1456-1463
• /
• 2012

In this paper, we study the ON/OFF control policy of base stations in two-tier heterogeneous cellular networks to minimize the total power consumption of the system. Using heterogeneous cellular networks is a potential approach of providing higher throughput and coverage compared to conventional networks with only macrocell deployment, but in fact heterogeneous cellular networks often operates regardless of total power consumption, which is a very important issue of modern cellular networks. We propose a policy that controls the activation/deactivation of base stations in heterogeneous cellular networks to minimize total power consumption. Under this policy, the total power consumed can be significantly reduced when the traffic is low while the QoS requirement is satisfied.

• Proceedings of the IEEK Conference
• /
• 1999.06a
• /
• pp.94-97
• /
• 1999

In this paper, a new real-time neural network connection admission controller is proposed. The proposed controller measures traffic flows, cell loss rate and cell delay periodically each classes. The Neural network learns the relation between those measured information and service quality by real-time. Also the proposed controller uses the DWRR multiplexer with buffer dedicated to every traffic source in order to measure the delay that cells experience in buffer. Experimental result shows that the proposed method can control effectively heterogeneous traffic sources with diverse QoS requirement.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.33 no.7A
• /
• pp.757-767
• /
• 2008

Start Ongoing next generation networks are expected to be deployed over current existing networks, in the form of overlayed heterogeneous networks, in particular, in hot spot areas. Therefore, it will be necessary to develop an interworking technique such as load balancing, to achieve increased overall resource utilization in the various heterogeneous networks. In this paper, we present a new load balancing mechanism termed 'soft' load balancing where the IP(Internet Protocol) traffic of a user is divided into sub-traffic, each of which flows into a different access network. The terminology of soft load balancing involves the use of both load sharing and handover techniques. Through a numerical analysis, we obtain an optimal LBR (Load Balancing Ratio) for determining the volume of traffic delivered to each network over an overlayed multi-cell environment. Using the optimal LBR, a more reliable channel transmission can be achieved by reducing the outage probability efficiently for a given user traffic.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.37 no.7A
• /
• pp.560-568
• /
• 2012

Spectrum sensing and spectrum decision are the main functions that cognitive radios (CRs) have to perform in order to get the best available spectrum band for the establishment of a wireless communication. These problems are worsened in the presence of users with different demands and spectrum channels with different properties in a heterogeneous network. The primary objective in this work is to design and simulate a new spectrum decision algorithm for heterogeneous cognitive radio system. To this end, first, we consider all cognitive users are separated into different traffic classes according to their Quality of Service (QoS). The cognitive users within one traffic class perform spectrum sensing in centralized group-based cooperative spectrum sensing system and the users of different traffic classes share the sensing results. Second, we propose a novel use of the Analytic Hierarchy Process (AHP) to optimally select available bands according to user requirements and detected spectrum channel characteristics (SCC). In this paper, utility function is used as spectrum decision algorithm. Simulation results demonstrate that the proposed method shows can effectively select the best available spectrum channels with a low complexity.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.5
• /
• pp.2243-2257
• /
• 2019

A heterogeneous cellular network (HCN) is useful to increase the spectral and energy efficiency of wireless networks and to reduce the traffic load from the macro cell. The performance of the secondary user equipment (SUE) is affected by interference from the eNodeB (eNB) in a macro cell. To decrease the interference between the macro cell and the small cell, allocating resources properly is essential to an HCN. This study considers the scenario of a software-defined heterogeneous cellular network and performs the resource allocation process. First, we show the system model of HCN and formulate the optimization problem. The optimization problem is a complex process including power and frequency resource allocation, which imposes an extremely high complexity to the HCN. Therefore, a hierarchical resource allocation scheme is proposed, which including subchannel selection and a particle swarm optimization (PSO)-based power allocation algorithm. Simulation results show that the proposed hierarchical scheme is effective in improving the system capacity and energy efficiency.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.32 no.10A
• /
• pp.941-949
• /
• 2007

By the newly emerging Network access technology, we face the new heterogeneous network environment. The required level of service quality and diversity are now multiplied by the increment of wireless service subscribers. Focusing on the co-existence of multiple access network technology and the complex service needs of users, the wireless service operators should present the stable service quality for every user. The service operators should build the new operation framework which combines the pre-established networks and newly adopted ones. Our problem is finding the optimal heterogeneous network operation framework. We suggest a market-based marginal cost function for evaluating the relative value of resource of each network and develop the whole new heterogeneous network operation framework.

• Journal of Communications and Networks
• /
• v.17 no.1
• /
• pp.47-57
• /
• 2015

With the ever growing demand of data applications, the joint macro-relay networks are emerging as a promising heterogeneous deployment to provide coverage extension and throughput enhancement. However, the current cellular networks are usually designed to be performance-oriented without enough considerations on the traffic variation, causing substantial energy waste. In this paper, we consider a joint macro-relay network with densely deployed relay stations (RSs), where the traffic load varies in both time and spatial domains. An energy-efficient scheme is proposed to dynamically adjust the RS working modes (active or sleeping) according to the traffic variations, which is called traffic-aware relay sleep control (TRSC). To evaluate the performance of TRSC,we establish an analytical model using stochastic geometry theory and derive explicit expressions of coverage probability, mean achievable rate and network energy efficiency (NEE). Simulation results demonstrate that the derived analytic results are reasonable and the proposed TRSC can significantly improve the NEE when the network traffic varies dynamically.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.8
• /
• pp.2774-2796
• /
• 2015

Quality-of-service (QoS) provisioning for a cognitive mesh network (CMN) with heterogeneous services has become a challenging area of research in recent days. Considering both real-time (RT) and non-real-time (NRT) traffic in a multihop CMN, [1] studied cross-layer resource management, including joint access control, route selection, and resource allocation. Due to the complexity of the formulated resource allocation problems, which are mixed-integer non-linear programming, a low-complexity yet efficient algorithm was proposed there to approximately solve the formulated optimization problems. In contrast, in this work, we present an application of genetic algorithm (GA) to re-address the hard resource allocation problems studied in [1]. Novel initialization, selection, crossover, and mutation operations are designed such that solutions with enough randomness can be generated and converge with as less number of attempts as possible, thus improving the efficiency of the algorithm effectively. Simulation results show the effectiveness of the newly proposed GA-based algorithm. Furthermore, by comparing the performance of the newly proposed algorithm with the one proposed in [1], more insights have been obtained in terms of the tradeoff among QoS provisioning for RT traffic, throughput maximization for NRT traffic, and time complexity of an algorithm for resource allocation in a multihop network such as CMN.

• Journal of KIISE:Information Networking
• /
• v.30 no.3
• /
• pp.416-423
• /
• 2003

This paper proposes a continuous-time queuing model of a shared-buffer packet switch and an approximate algorithm. N arrival processes have heterogeneous busty traffic characteristics. The arrival processes are modeled by Coxian distribution with order 2 that is equivalent to Interruped Poisson Process. The service time is modeled by Erlang distribution with r stages. First the approximate algorithm performs the aggregation of N arrival processes as a single state variable. Next the algorithm discompose the queuing system into N subsystems which are represented by aggregated state variables. And the balance equations based on these aggregated state variables are solved for by iterative method. Finally the algorithm is validated by comparing the results with those of simulation.