Tuan LeAnh

Power control under QoS and interference constraint in Femtocell cognitive networks

Power control is critical for femtocell networks that allow spectrum sharing among Macrocell and Femtocell. In this paper, we derive an optimal power control strategy toward reducing the CO2 emissions and maximize total throughput under both the probability of dropping a packet due to buffer overflow constraints at the Femtocell user equipment (FUE) and the interference constraints to the Macrocell base station (MBS) for uplink transmission. We use linear programming to solve the CO2 emissions minimization problem. For maximizing the total throughput of FUEs, we propose a distributed power control algorithms by employing geometry convex tool. Numerical results are used to validate the analysis and demonstrate a high degree of accuracy for the derived expressions. Results indicate that the performance of the FUEs depends on not only the interference constraint of the MBS but also the delay constraint of the FUEs.

Distributed Power and Channel Allocation for Cognitive Femtocell Network Using a Coalitional Game in Partition-Form Approach

The cognitive femtocell network (CFN) integrated with cognitive-radio-enabled technology has emerged as one of the promising solutions to improve wireless broadband coverage in indoor environments for next-generation mobile networks. In this paper, we study a distributed resource allocation that consists of subchannel- and power-level allocation in the uplink of the two-tier CFN comprised of a conventional macrocell and multiple femtocells using underlay spectrum access. The distributed resource allocation problem is addressed via an optimization problem, in which we maximize the uplink sum rate under constraints of intratier and intertier interference while maintaining the average delay requirement for cognitive femtocell users. Specifically, the aggregated interference from cognitive femtocell users to the macrocell base station (MBS) is also kept under an acceptable level. We show that this optimization problem is NP-hard and propose an autonomous framework, in which the cognitive femtocell users self-organize into disjoint groups (DJGs). Then, instead of maximizing the sum rate in all cognitive femtocells, we only maximize the sum rate of each DJG. After that, we formulate the optimization problem as a coalitional game in partition form, which obtains suboptimal solutions. Moreover, distributed algorithms are also proposed for allocating resources to the CFN. Finally, the proposed framework is tested based on the simulation results and shown to perform efficient resource allocation.

SDN based optimal user association and resource allocation in heterogeneous cognitive networks

The increase in the number of connected smart mobile devices has fueled the exponential growth in mobile data. The next-generation networks must meet the demand for higher capacity. Heterogeneous cognitive networks with multiple base station tiers are a promising approach to achieving the higher data rate target. The user association problem is a major issue in the heterogeneous cognitive networks because of the disparity in transmission powers of the base stations involved. Our objective is to achieve the optimal user association under interference constraints. We formulate the problem into an optimization problem and employ matching theory to propose an algorithm to obtain the optimal user association. The proposed matching algorithm for the optimal user association plays the role of SDN application. We then perform simulations and compare our proposed algorithm with existing ones. The simulations results depict that our proposed algorithm outperforms others.

Matching Theory for Distributed User Association and Resource Allocation in Cognitive Femtocell Networks

In this paper, a novel framework is proposed to jointly optimize user association and resource allocation in the uplink cognitive femtocell network (CFN). In the considered CFN, femtocell base stations (FBSs) are deployed to serve a set of femtocell user equipments (FUEs) by reusing subchannels used in a macrocell base station (MBS). The problem of joint user association, subchannel assignment, and power allocation is formulated as an optimization problem, in which the goal is to maximize the overall uplink throughput while guaranteeing FBSs overloading avoidance, data rate requirements of the served FUEs, and MBS protection. To solve this problem, a distributed framework based on the matching game is proposed to model and analyze the interactions between the FUEs and FBSs. Using this framework, distributed algorithms are developed to enable the CFN to make decisions about user association, subchannel allocation, and transmit power. The algorithms are then shown to converge to a stable matching and exhibit a low computational complexity. Simulation results show that the proposed approach yields a performance improvement in terms of the overall network throughput and outage probability, with a small number of iterations to converge.

Resource Allocation for Virtualized Wireless Networks with Backhaul Constraints

In this letter, we study resource allocation for wireless virtualized networks considering both the backhaul capacity of the infrastructure provider (InP) and the users’ quality-of-service (QoS) requirements. We focus on the profit gained by a mobile virtual network operator (MVNO), which is a middleman who buys physical resource from the InP, bundling them into virtual resources called slides before selling off the service providers. The objective of the MVNO is to maximize its profit while guaranteeing the backhaul constraint and users’ QoS by jointly allocating the slices and the uplink transmit power to the users. To solve the formulated mixed integer non-convex problem, we propose a distributed solution framework based on Lagrangian relaxation to a find suboptimal decision about slice and transmit power allocations. We further propose a low-complexity solution based on the concept of a matching game that does not require any global information. Numerical results are provided to evaluate the performance of the proposed schemes.

Joint pricing and power allocation for uplink macrocell and femtocell cooperation

In this paper, we study cooperation among mobile users for uplink in two-tiers heterogeneous wireless networks. In our cooperative model, a macrocell user equipment can relay its data via a femtocell user equipment when it cannot connect to its macro base station or any femtocell base stations directly. In this scenario, the macrocell user equipment tries to find the best relay user in a set of candidate relay femtocell user equipments to maximize its utility function. Additionally, the candidate relay femtocell user equipments give a pricing-based strategy per each power unit to the macrocell user equipment along with power level at relay femtocell user equipments which would be used for relaying data in order to maximize both the relay femto and macrocell user equipments utility function. In static network environment, this problem is formulated as a Stackelberg game. Moreover, in stochastic network environment we find stochastic optimization in a long-term for both the utility functions by modeling the problem as a restless bandit problem. Simulation results illustrate the efficiency of our proposal.

Distributed power and channel allocation for cognitive femtocell network using a coalitional game approach

The cognitive femtocell network (CFN) integrated with cognitive radio-enabled technology has emerged as one of the promising solutions to improve wireless broadband coverage in indoor environment for next-generation mobile networks. In this paper, we study a distributed resource allocation that consists of subchannel- and power-level allocation in the uplink of the two-tier CFN comprised of a conventional macrocell and multiple femtocells using underlay spectrum access. The distributed resource allocation problem is addressed via an optimization problem, in which we maximize the uplink sum-rate under constraints of intra-tier and inter-tier interferences while maintaining the minimum rate requirement of the served femto users. Specifically, the aggregated interference from cognitive femto users to the macrocell base station is also kept under an acceptable level. We show that this optimization problem is NP-hard and propose a distributed framework to maximize the sum-rate of network based on coalitional game in partition form. The proposed framework is tested based on the simulation results and shown to perform efficient resource allocation.

Data offloading in heterogeneous cellular networks: Stackelberg game based approach

In heterogeneous networks (HetNets), low power smallcells, i.e., Wifi, can be offered an economic incentive in order to offload traffic from high-power macrocell, which is usually overloaded. This becomes important in order to maintain efficient operation of the network and generate benefit of tradeoff between macrocell and smallcells. The benefit to smallcells comes from the economic incentive offered by macrocell and the benefit to macrocell is achieved by reducing the load and saving spectrum. However, two important challenges are faced in this cooperation: 1) How much economic incentive can be offered by macrocell, and 2) How much offloading traffic volumes can be admitted by the smallcells. In this paper, we propose a novel game based approach for data offloading scheme to determine the amount of economic incentive a macrocell should offer to smallcells and to determine how much traffic each smallcell should admit from the macrocell. In our proposal, a two-stage non-cooperative Stackelberg game theory is applied to optimize the strategies of both macrocell and smallcells in order to maximize their utilities.

Optimal network selection coordination in heterogeneous Cognitive Radio Networks

In this paper we propose a new architecture for applications of Cognitive Radio Network (CRN) system based on network selection issue in which secondary users (SU) is able to connect to heterogeneous Cognitive Radio system and selects a network to perform a single transport control protocol (TCP) connection. We use a cross-layer design approach to consider jointly the spectrum sensing, access decision, physical-layer Adaptive Modulation and Coding scheme, and data-link layer frame size in each Cognitive Radio network to maximize the TCP throughput of SUs. Specifically, we formulate the Cognitive Radio Network as a Markov Decision Process, where the finite-state Markov model is used to characterize the time-varying channel states in each network. Then, we maximize Expected end-to-end TCP throughput in long-term by using Iteration method. This is illustrated by simulation results.

Matching-based distributed resource allocation in cognitive femtocell networks

In this paper, a novel framework is proposed for joint subchannel assignment and power allocation in the uplink of cognitive femtocell network (CFN). In the studied model, femtocell base stations (FBSs) are deployed to serve a set of femtocell user equipments (FUEs) by reusing subchannels in a macrocell network. The problem of optimal allocation of subchannels and transmit power is formulated as an optimization problem in which the goal is to maximize the overall uplink throughput while guaranteeing minimum rate requirement of the served FUEs and macrocell base station (MBS) protection. To solve this problem, a novel framework based on matching theory is proposed to model and analyze the competitive behaviors among the FUEs and FBSs. Using this framework, distributed algorithms are implemented to enable the CFN to make decisions on subchannel allocation and power control. The developed algorithms are then shown to converge to stable matchings. Simulation results show that the proposed approach yields a notable performance improvement, in terms of the overall network throughput and outage probability while requiring only a small number of iterations for convergence.