Energy efficient resource allocation in two-tier OFDMA networks with QoS guarantees (original) (raw)
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In this paper, we consider the joint subchannel and power allocation problem in both the uplink and the downlink for two-tier networks comprising spectrum-sharing macrocells and femtocells. A joint subchannel and power allocation scheme for co-channel femtocells is proposed, aiming to maximize the capacity for delay-tolerant users subject to delay-sensitive users' quality of service and interference constraints imposed by macrocells. The joint subchannel and power allocation problem is modeled as an mixed integer programming problem, then transformed into a convex optimization problem by relaxing subchannel sharing, and finally solved by a dual decomposition approach. The effectiveness of the proposed approach is verified by simulations and compared with existing scheme.
Game Theory Based Energy-Aware Uplink Resource Allocation in OFDMA Femtocell Networks
International Journal of Distributed Sensor Networks, 2014
Femtocell is a promising technique not only in operator networks but also in having potential applications for industrial wireless sensor networks. In this paper, we investigate energy efficient uplink power control and subchannel allocation in two-tier femtocell networks. Taking transmit power and circuit power into account, we model the power control and subchannel allocation problem as a supermodular game to maximize energy efficiency of femtocell users. To reduce the cochannel interference from femtousers to neighboring femtocells and macrocells, we introduce a convex pricing scheme to curb their selfish behavior. We decompose the resource allocation problem into two subproblems, that is, a distributed subchannel allocation scheme and a distributed power control scheme to reduce costs and complexity. Simulation results show that the proposed algorithm can improve user utilities significantly, compared with existing power control and subchannel allocation algorithms.
Capacity Enhancement with Joint Subchannel and Power Allocation Scheme for OFDMA Femtocell Networks
Efficient power and subchannel allocation methods are required for orthogonal frequency division multiple access (OFDMA) based femtocell networks to improve the capacity of the system. This paper considers a joint subchannel and power allocation algorithm with capacity maximization for downlink of an OFDMA based femtocell networks. In the proposed algorithm subchannel allocation is first performed based on signal to interference plus noise ratio (SINR) of the channel with equal power distribution. Then for enhancing capacity with optimal power allocation, successive convex approximation (SCA) based power optimization is adopted. The effect of Arithmetic geometric mean (AGM) approximation with SCA on power optimization is also investigated. The optimal power is subsequently distributed by water-filling algorithm.
2014 IEEE 22nd International Symposium of Quality of Service (IWQoS), 2014
Recently, LTE-based femtocell system has received significant attention as a promising solution offering high-speed services, enhanced indoor coverage and increased system capacity. Intelligently allocate resources in multiuser OFDMAbased network is the substantial aim towards interference mitigation and enhancing power and spectral efficiencies. In this paper, we propose a downlink joint resource allocation with Adaptive Modulation and Coding (AMC) technique for such system, namely AMC-QRAP. The proposal core is adjusting the transmission link to the channel status and users demand through the power control and suitable selection of the modulation/coding scheme. Clustered network is adopted and users differentiation is considered providing Quality of Service (QoS) in the network. Our resolution model is solved as an optimization problem using the linear programming. We show through extensive simulations the outperformance of our method compare to different state-ofthe-art methods using different evaluation metrics.
QoS-based power control and resource allocation in OFDMA femtocell networks
2012 IEEE Global Communications Conference (GLOBECOM), 2012
This paper proposes a new joint power control and resource allocation algorithm in OFDMA femtocell networks. We consider both QoS constrained high-priority (HP) and best-effort (BE) users having different types of application and bandwidth requirements. Our objective is to minimize the transmit power of each femtocell, while satisfying a maximum number of HP users and serving BE users as well as possible. This optimization problem is multi-objective NP-hard. Hence, we propose a new scheme based on clustering and taking into account QoS requirements of users. We show by extensive network simulation results that our proposal outperforms three state of the art schemes (Centralized-Dynamic Frequency Planning, C-DFP, Distributed Random Access, DRA and Distributed Resource Allocation with Power Minimization, DRAPM as well as our previous proposal, FCRA, in both low and high density networks. The results concern the rate of rejected users, the throughput satisfaction rate, the spectrum spatial reuse, fairness, as well as computation time.
Resource allocation in spectrum-sharing ofdma femtocells with heterogeneous services
Femtocells are being considered a promising technique to improve the capacity and coverage for indoor wireless users. However, the cross-tier interference in the spectrum-sharing deployment of femtocells can degrade the system performance seriously. The resource allocation problem in both the uplink and the downlink for two-tier networks comprising spectrum-sharing femtocells and macrocells is investigated. A resource allocation scheme for cochannel femtocells is proposed, aiming to maximize the capacity for both delay-sensitive users and delay-tolerant users subject to the delay-sensitive users' quality-of-service constraint and an interference constraint imposed by the macrocell. The subchannel and power allocation problem is modeled as a mixed-integer programming problem, and then, it is transformed into a convex optimization problem by relaxing subchannel sharing; finally, it is solved by the dual decomposition method. Subsequently, an iterative subchannel and power allocation algorithm considering heterogeneous services and cross-tier interference is proposed for the problem using the subgradient update. A practical low-complexity distributed subchannel and power allocation algorithm is developed to reduce the computational cost. The complexity of the proposed algorithms is analyzed, and the effectiveness of the proposed algorithms is verified by simulations.
Joint Subchannel Assignment and Power Allocation for OFDMA Femtocell Networks
IEEE Transactions on Wireless Communications, 2014
In this paper, we propose a joint subchannel and power allocation algorithm for the downlink of an orthogonal frequency-division multiple access (OFDMA) mixed femtocell/macrocell network deployment. Specifically, the total throughput of all femtocell user equipments (FUEs) is maximized while the network capacity of an existing macrocell is always protected. Towards this end, we employ an iterative approach in which OFDM subchannels and transmit powers of base stations (BS) are alternatively assigned and optimized at every step. For a fixed power allocation, we prove that the optimal policy in each cell is to give each subchannel to the user with the highest signal-to-interference-plus-noise ratio (SINR) on that subchannel. For a given subchannel assignment, we adopt the successive convex approximation (SCA) approach and transform the highly nonconvex power allocation problem into a sequence of convex subproblems. In the arithmetic-geometric mean (AGM) approximation, we apply geometric programming to find optimal solutions after condensing a posynomial into a monomial. On the other hand, logarithmic and difference-of-two-concave-functions (D.C.) approximations lead us to solving a series of convex relaxation programs. With the three proposed SCA-based power optimization solutions, we show that the overall joint subchannel and power allocation algorithm converges to some local maximum of the original design problem. While a central processing unit is required to implement the AGM approximation-based solution, each BS locally computes the optimal subchannel and power allocation for its own servicing cell in the logarithmic and D.C. approximation-based solutions. Numerical examples confirm the merits of the proposed algorithm.
Power Minimization Based Resource Allocation for Interference Mitigation in OFDMA Femtocell Networks
IEEE Journal on Selected Areas in Communications, 2000
With the introduction of femtocells, cellular networks are moving from the conventional centralized network architecture to a distributed one, where each network cell should make its own radio resource allocation decisions, while providing inter-cell interference mitigation. However, realizing such distributed network architecture is not a trivial task. In this paper, we first introduce a simple self-organization rule, based on minimizing cell transmit power, following which a distributed cellular network is able to converge into an efficient resource reuse pattern. Based on such self-organization rule and taking realistic resource allocation constraints into account, we also propose two novel resource allocation algorithms, being autonomous and coordinated, respectively. Performance of the proposed self-organization rule and resource allocation algorithms are evaluated using system-level simulations, and show that power efficiency is not necessarily in conflict with capacity improvements at the network level. The proposed resource allocation algorithms provide significant performance improvements in terms of user outages and network capacity over cutting-edge resource allocation algorithms proposed in the literature.
International Journal of Communication Systems, 2015
Energy efficiency (EE) has currently turn into one of the major issues in heterogeneous networks (HetNet) paradigm of today's wireless communication industry. In this paper, we optimize EE for downlink OFDMA system in HetNet, taking into account realistic network power consumption model, that is, considering circuit power. This paper investigates the EE maximization using convex optimization theory where primary optimization criterion is data rate in a downlink multiuser HetNet. Given QoS (data rate) requirement, for maximizing EE, a constrained based optimization problem is devised. Because the optimization problem is non-convex in nature, we reconstruct the optimization problem as a convex one and devise a pragmatically efficient novel resource assignment algorithm for maximizing achievable EE, with quick convergence. The considered optimization problem is transformed into a convex optimization problem by redefining the constraint using cubic inequality, which results in an efficient iterative resource allocation algorithm. In each iteration, the transformed problem is solved by using dual decomposition with a projected gradient method. Analytical insights and numerical results exhibit the potency of the devised scheme for the targeted complex wireless systems.
QoS-aware energy-efficient resource allocation in OFDM-based heterogenous cellular networks
International Journal of Communication Systems, 2015
Recently, in order to satisfy the heavy demands of network capacity brought about by the proliferation of wireless devices, service providers are increasingly deploying heterogeneous cellular networks (HetNets) for boosting the network coverage and capacity. In this paper, we present an iterative energy-efficient scheduling scheme (IEESS) for downlink OFDM-based HetNets with quality-of-service (QoS) consideration. We formulate the problem as a nonlinear fractional programming problem aiming to maximize the QoS-aware energy efficiency (QEE) in HetNets. In order to solve this problem, we first transform it into a parametric programming problem, which takes QEE as an evolved parameter in the iterative procedure of IEESS. In each iteration, for the given value of QEE, subchannel and power assignment sub-problem is a nonlinear NP-hard problem. And hence we adopt dual decomposition method for obtaining the optimal assignment of subchannels and power of the sub-problem for the given value of QEE. Simulation results depict that both outer QEE parameter search and inner subgradient search can converge in a few iterations and the resultant solutions outperform the equal power allocation scheme (EPAS) [1] and capacity maximization scheme (CMS) [2] in terms of QEE.