Alia Asheralieva

Performance analysis of VoIP services on the LTE network

With the arrival of LTE standard it is expected that the mobile voice services paradigm will shift from the circuit switched to fully packet switched mode supporting the VoIP services. VoIP services took quite a bit of time before they were accepted as the main stream telephony service in the fixed networks. To provided VoIP services over the LTE networks with appropriate QoS it is necessary to analyze the performance of such services on the LTE network and optimize the network parameters. This paper analyses the performance of VoIP services on the LTE network using the FD and the SMP packet scheduling techniques. This work identifies and analyzes the features of above LTE packet scheduling techniques to enhance the QoS of VoIP services. An OPNET based simulation model is used to analyse the performance of VoIP services on the LTE network by incorporating G.711 and G.723 speech coders. The work also studied the performance of VoIP services in variable transmission channel conditions.

Resource allocation for LTE-based cognitive radio network with queue stability and interference constraints

Abstract We consider the problem of interference management and resource allocation in a cognitive radio network (CRNs) where the licensed spectrum holders (primary users) share their spare capacity with the non-licensed spectrum holders (secondary users). Under such shared spectrum usage the transmissions of the secondary users should have a minimal impact on the quality of service (QoS) and the operating conditions of the primary users. Therefore, it is important to distinguish the two types of users, and formulate the problem of resource allocation considering hard restrictions on the user-perceived QoS (such as packet end-to-end delay and loss) and physical-layer channel characteristics (such as noise and interference) of the primary users. To achieve this goal, we propose to assign the bandwidth and transmission power to minimize the total buffer occupancy in the system subject to capacity constraints, queue stability constraints, and interference requirements of the primary users. We apply this approach for resource allocation in a CRN built upon a Third Generation Partnership Project (3GPP) long-term evolution (LTE) standard platform. Performance of the algorithm is evaluated using simulations in OPNET environment. The algorithm shows consistent performance improvement when compared with other relevant resource allocation techniques.

A two-step resource allocation procedure for LTE-based cognitive radio network

We consider the problem of resource allocation for the Third Generation Partnership Project (3GPP) long-term evolution (LTE) cognitive radio network (CRN). The CRN is made up of the licensed (primary) service stations which can share their spectrum resources with the unlicensed (secondary) stations. The objective is to provide wireless access to the secondary stations on a best effort basis without compromising the quality of service (QoS) for the primary stations. To accomplish this we employ a simple two-step procedure. In the first step the spectrum resources are allocated to primary stations to maximize the QoS for the primary users. In the second step the spare service capacity of the primary channels is distributed among the secondary stations to maximize the QoS of the secondary users. The proposed theoretical framework adheres closely with the LTE specification. The corresponding resource allocation algorithm does not involve additional network signaling over the wireless medium, and improves the overall QoS in the network. These advantages of the proposed approach for resource allocation make it ready for implementation in a real network.

Traffic prediction based packet transmission priority technique in an infrastructure wireless network

Priority based packet transmission techniques are commonly used in communication networks to support multimedia services. In wireless networks mainly type of service or queue measurement based packet transmission priority techniques are used. This paper introduces a novel two stage traffic prediction and type of service based priority technique for an infrastructure based Wireless Local Area Network. The developed algorithm alters the priority of transmission queues and services in a radio access network based on the predicted traffic volume and the conventional type of service priority technique for multimedia packet transmissions. Simulation results show that our proposed algorithm improves the QoS of multimedia traffic significantly. An OPNET based simulation model has been developed to obtain the performance results for a multiple access points based wireless infrastructure network.

Dynamic resource allocation in a LTE/WLAN heterogeneous network

Resource allocation in a heterogeneous network is a complex task due to diversified requirements of its member networks. In a heterogeneous network different subnetworks cooperate with each other to offer services to various applications through smart terminals. In this paper we present a dynamic radio resource allocation technique which allocates transmission bandwidth to subnetworks in a heterogeneous network in a cooperative manner to maximize the network capacity, application QoS and bandwidth utilization. We investigate a LTE/WLAN based infrastructure based heterogeneous network where transmission resources are allocated to its member network in a spectrally efficient manner to maximize the throughput of the combined network. The proposed radio resource allocation algorithm uses a traffic prediction technique to estimate the expected load of member networks and then allocate the transmission resources to those networks in a cooperative manner. The performance of the proposed algorithm was analyzed using an OPNET simulation model. Performance results show that the proposed cooperative resource allocation technique could significantly improve the application QoS of LTE and WLAN users.

A predictive network resource allocation technique for cognitive wireless networks

Future wireless networks are evolving towards a heterogeneous cooperative and cognitive architecture to support broadband communication needs of different types of traffic. Future wireless networks will allocate network resources based on cooperative techniques. Resource controllers will apply the cognitive principle to find out status of various networks. We propose a predictive resource allocation strategy where we employ adaptive algorithms to predict the network loading. These algorithms can detect changes in the traffic characteristics, and adapt automatically. To validate our claim, we present the results of applying these adaptive algorithms on real-world network traffic traces.

Joint power and bandwidth allocation in IEEE802.22 based cognitive LTE network

We investigate the problem of energy-efficient dynamic spectrum access (DSA) in a cognitive Third Generation Partnership Project (3GPP) long-term evolution (LTE) network based on IEEE802.22 architecture. In this architecture, the network resources are allocated to the end-users and the evolved NodeBs (eNBs) by the spectrum manager (SM) using some optimal resource allocation strategy. In particular, we propose to assign the bandwidth and transmission power to the uplink and downlink of LTE system so that the total transmission power is minimized subject to capacity constraints, queue stability constraints, and some integer restrictions on the bandwidth. To find the buffer occupancy in the system, we use modified Shannon expression which depends on signal-to-noise ratio (SNR) and modulation and coding scheme (MCS). Unlike previous works, the proposed resource allocation algorithm is derived based on realistic assumptions, such as discrete spectrum resources, relation between transmission rate, MCS and the channel quality. Performance of the algorithm is evaluated using simulations in OPNET environment. The algorithm shows consistent performance improvement when compared with other relevant resource allocation techniques.

Delay Aware Resource Allocation Scheme for a Cognitive LTE Based Radio Network

We explore the problem of resource allocation in a Third Generation Partnership Project (3GPP) long-term evolution (LTE) based cognitive radio network (CRN). The network model consists of a number of service providers (SPs) with fixed licensed spectrum bands. The network offers the wireless services to two types of users: primary and secondary. The primary users (PUs) get prioritized access to the licensed spectrum bands. The secondary users (SUs) are served on the best-effort (non-prioritized) basis. In this paper we consider the specific design features of LTE radio interface associated with the uplink spectrum access, scheduling process, and limited control channel capacity of the LTE system. We establish the relation between the number of users in the system, and the scheduling delay (which is the largest contributor to the packet end-to-end delay in LTE network). Using these results, we propose a simple algorithm to assign the spectrum for the SUs without violating the quality of service (QoS) requirements of the PU, and implement it in an LTE-based CRN. Consistent performance of the algorithm is verified using OPNET-based simulations.

Resource allocation algorithm for cognitive radio network with heterogeneous user traffic

In this paper we present a novel approach for resource allocation in cognitive radio network (CRN) with heterogeneous user traffic. In this approach we deploy some form of reinforcement learning, and make a short-term resource allocation based on the long-term traffic prediction. The corresponding resource allocation algorithm derived in the paper is implemented in cognitive 3rd Generation Partnership Project Long Term Evolution (3GPP LTE) network. Performance analysis of the algorithm has shown that the proposed approach for resource allocation achieves better performance than other schemes designed to deal with the problem of heterogeneous user applications.

Prediction based bandwidth allocation for cognitive LTE network

In this paper we present a novel dynamic bandwidth allocation technique in which different base stations share the total available spectrum to maximize the quality of service (QoS) in the network, and show the implementation of this technique in a cognitive 3rd Generation Partnership Project Long Term Evolution (3GPP LTE) network. Assuming, that each base station is characterized by a concave increasing utility and a positive weight, we conduct a weighted utility maximization framework, and develop a simple prediction-based bandwidth allocation algorithm. To deal with heterogeneous network applications we propose to deploy the approach used in optimal flow and congestion control (OFC) where the resources are assigned based on speed of load increase. Using the appropriate load indictors, the algorithm first identifies the base stations with increasing (decreasing) load, and then decrease (increase) the channel utilization of base stations with increased (decreased) load using weighted proportional fairness criterion.