Tai Manh Ho

Power Control for Interference Management and QoS Guarantee in Heterogeneous Networks

We consider the sum-rate optimization problem with power control for uplink transmission in a heterogeneous network (HetNet) consisting of a macrocell and multiple femtocells. The considered problem includes the HetNets crucial constraints of both cross-tier interference protection and user QoS in terms of outage probability and average delay. We transform the original nonconvex problem into a convex problem and develop a distributed algorithm that can attain the global optimal transmit power values. This algorithm, however, has heavy network overheads, which may lead to increased energy consumption for femtocell user equipment. We propose a new practical near-optimal distributed algorithm that eliminates these network overheads. Numerical results show that the schemes have nearly identical performance.

Optimized Resource Management in Heterogeneous Wireless Networks

The dense and pervasive deployment of wireless small cells can boost the performance of existing macrocellular networks; however, it poses significant challenges pertaining to the cross-tier interference management. In this letter, the downlink resource allocation problem for an underlay small cell network is studied. In this network, the protection of the macrocell tier is achieved by imposing cross-tier interference constraints in the resource allocation problem. To solve the underlying mixed-integer resource allocation problem, we propose two different algorithms. The first algorithm is developed by applying the duality-based optimization approach for the relaxed problem, which enables distributed implementation. The second distributed algorithm, which enables coordination is devised based on matching theory. Simulation results show that the proposed duality-based algorithm outperforms the greedy approach by 4% in terms of sum-rate whereas the matching-based algorithm with tier-coordination yields performance gains up to 17% compared with the duality-based approach.

Mode Selection and Resource Allocation in Device-to-Device Communications: A Matching Game Approach

Device to device (D2D) communication is considered as an effective technology for enhancing the spectral efficiency and network throughput of existing cellular networks. However, enabling it in an underlay fashion poses a significant challenge pertaining to interference management. In this paper, mode selection and resource allocation for an underlay D2D network is studied while simultaneously providing interference management. The problem is formulated as a combinatorial optimization problem whose objective is to maximize the utility of all D2D pairs. To solve this problem, a learning framework is proposed based on a problem-specific Markov chain. From the local balance equation of the designed Markov chain, the transition probabilities are derived for distributed implementation. Then, a novel two phase algorithm is developed to perform mode selection and resource allocation in the respective phases. This algorithm is then shown to converge to a near optimal solution. Moreover, to reduce the computation in the learning framework, two resource allocation algorithms based on matching theory are proposed to output a specific and deterministic solution. The first algorithm employs the one-to-one matching game approach whereas in the second algorithm, the one-to many matching game with externalities and dynamic quota is employed. Simulation results show that the proposed framework converges to a near optimal solution under all scenarios with probability one. Moreover, our results show that the proposed matching game with externalities achieves a performance gain of up to 35 percent in terms of the average utility compared to a classical matching scheme with no externalities.

Coordinated Resource Partitioning and Data Offloading in Wireless Heterogeneous Networks

In this letter, a game-theoretic framework is proposed for coordinating resource partitioning and data offloading in LTE-based heterogeneous networks (HetNets). The goal of this framework is to determine the amount of radio resources a macrocell should offer to neighboring small cells (SCs) and the amount of traffic each SC should admit from the macrocell. A two-stage Stackelberg game is applied to optimize the strategies of both the macrocell (the leader) and SCs (the followers). The macrocells strategy is shown to be a mixed-boolean nonlinear program, which is NP-hard. To solve this problem efficiently, a branch and bound based method is proposed to obtain the global optimal. We also show that this two-stage game has a unique Stackelberg equilibrium. Numerical results show that the proposed framework outperforms the traditional design by 50% in term of offloaded data. Additionally, reduction of 14% was observed in term of cost paid by MBS.

Network economics approach to data offloading and resource partitioning in two-tier LTE HetNets

In two-tier LTE heterogeneous networks (HetNets), picocells can be offered radio resource in order to mitigate interference to picocell users in downlink transmission from high-power macrocell base station (MBS). This becomes important in order to maintain efficient operation of the network and generate benefit tradeoff between macrocell and picocells. In this paper, we propose a game based approach for joint resource partitioning and data offloading scheme to determine the amount of radio resource a MBS should offer to picocells and to determine how much traffic each picocell access point (AP) should admit from MBS. In our proposal, a two-stage Stackelberg game theory is applied to optimize the strategies of both MBS and APs in order to maximize both of their utilities and this scheme is implemented using the notion of Almost Blank Subframes (ABS) proposed in the LTE standard.

Decentralized spectrum allocation in D2D underlying cellular networks

The proliferation of novel network access devices and demand for high quality of service by the end users are proving to be insufficient and are straining the existing wireless cellular network capacity. An economic and promising alternate to enhance the spectral efficiency and network throughput is device to device (D2D) communication. However, enabling D2D communication poses significant challenges pertaining to the interference management. In this paper, we address the resource allocation problem for underlay D2D pairs. First, we formulate the resource allocation optimization problem with an objective to maximizes the throughput of all D2D pairs by imposing interference constraints for protecting the cellular users. Second, to solve the underlying mixed-integer non linear resource allocation problem, we propose a stable, self-organizing and distributed solution using matching theory. Finally, we simulate our proposition to validate the convergence, cellular user protection, and network throughput gains achieved by the proposal. Simulation results reveal that D2D pairs can achieve significant throughput gains (i.e., up to 45 - 91%) while protecting the cellular users compared to the scenario in which no D2D pairs exist.

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.

Distributed resource allocation for interference management and QoS guarantee in underlay cognitive femtocell networks

Cognitive femotcell networks can opportunistically access the licensed spectrum to enhance spectrum utilization. However, interference management plays a crucial role to effectively utilize the spectrum. In this paper, we consider the joint resource allocation and power control problem for an uplink transmission for a network consisting of a licensed macrocell and multiple cognitive femtocells. Furthermore, our problem imposes crucial constraints of both cross-tier interference for macrocell base station and quality of service for femtocell user. The joint problem is shown to be mix-integer nonlinear nonconvex optimization problem, which is NP-hard. To solve this problem efficiently, we employ a scheme consisting of two distributed algorithms. Numerical results show that the proposed scheme converges to the optimal power and resource allocation with a fast convergence speed. Additionally, our scheme guarantees the interference threshold at MBS and outage QoS for all cognitive femtocell users.

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.

Traffic offloading under outage QoS constraint in heterogeneous cellular networks

Heterogeneous cellular networks offload the mobile data traffic to small cell base stations to reduce the workload on the macro base stations. Our objective is to maximize the sum rate of the down-links for the whole network under outage QoS constraint. To achieve the objective, we have to jointly solve the user association problem and resource allocation problem. We formulate the two problems into a joint optimization problem and convert it into an equivalent game theoretic formulation. We employ payoff based log linear learning and propose an algorithm that converges to one of the existing Nash equilibrium. We then provide extensive simulation results to verify the performance of our proposed algorithm.