In this paper, we present a novel approach for robust optimal resource allocation with joint carrier aggregation to allocate multiple carriers resources optimally among users with elastic and inelastic traffic in cellular networks. We use utility proportional fairness allocation policy, where the fairness among users is in utility percentage of the application running on the user equipment (UE). Each UE is assigned an application utility function based on the type of its application. Our objective is to allocate multiple carriers resources optimally among users subscribing for mobile services. In addition, each user is guaranteed a minimum quality of service (QoS) that varies based on the user's application type. We present a robust algorithm that solves the drawback in the algorithm presented in [1] by preventing the fluctuations in the resource allocation process, in the case of scarce resources, and allocates optimal rates for both high-traffic and low-traffic situations. Our distributed resource allocation algorithm allocates an optimal rate to each user from all carriers in its range while providing the minimum price for the allocated rate. In addition, we analyze the convergence of the algorithm with different network traffic densities and show that our algorithm provides traffic dependent pricing for network providers. Finally, we present simulation results for the performance of our resource allocation algorithm.
On the other hand, resource allocation for single cell multi-carrier systems have been given extensive attention in recent years @cite_21 @cite_37 @cite_13 . In @cite_10 @cite_17 @cite_14 @cite_6 , the authors have represented this challenge in optimization problems. Their objective is to maximize the overall cell throughput with some constraints such as fairness and transmission power. However, transforming the problem into a utility maximization framework can achieve better users satisfaction rather than better system-centric throughput. Also, in practical systems, the challenge is to perform multi-carrier radio resource allocation for multiple cells. The authors in @cite_12 @cite_8 suggested using a distributed resource allocation rather than a centralized one to reduce the implementation complexity. In @cite_30 , the authors propose a collaborative scheme in a multiple base stations (BSs) environment, where each user is served by the BS that has the best channel gain with that user. The authors in @cite_31 have addressed the problem of spectrum resource allocation in carrier aggregation based LTE Advanced systems, with the consideration of UEs’ MIMO capability and the modulation and coding schemes (MCSs) selection.
@cite_13 @cite_30 @cite_37 @cite_14 @cite_8 @cite_21 @cite_6 @cite_31 @cite_10 @cite_12 @cite_17
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We consider a scheduled orthogonal frequency division multiplexed (OFDM) wireless cellular network where the channels from the base-station to the n mobile users undergo flat fading. Spectral resources are to be divided among the users in order to maximize total user utility. We show that this problem can be cast as a nonlinear convex optimization problem, and describe an O(n) algorithm to solve it. Computational experiments show that the algorithm typically converges in around 25 iterations, where each iteration has a cost that is O(n), with a modest constant. When the algorithm starts from an initial resource allocation that is close to optimal, convergence typically takes even fewer iterations. Thus, the algorithm can efficiently track the optimal resource allocation as the channel conditions change due to fading. We also show how our techniques can be extended to solve resource allocation problems that arise in wideband networks with frequency selective fading and when the utility of a user is also a function of the resource allocations in the past. This paper studies a joint optimization problem of sub-carrier assignment and power allocation in orthogonal frequency division multiple access (OFDMA) wireless networks. A major challenge in solving the optimization problem is non-convexity caused by the combinatorial nature of sub-carrier assignment problem and-or non-convex objective functions. To address the combinatorial complexity, we formulate the resource allocation problem as an optimization problem with continuous variables. We propose a novel approach based on a penalty function method and an interior point method (PM-IPM) to solve the problem. In specific, using a two-step implementation, the penalty method is applied first to convert the non-convex feasible region to a convex one. Then, the interior point method is deployed to solve the problem which is non-convex only in the objective function. To evaluate the performance of PM-IPM, we apply a genetic algorithm (GA) that achieves near optimal solutions of the problem by iterative searching. Copyright © 2009 John Wiley & Sons, Ltd. Non-convexity of utility-based resource allocation in OFDMA wireless networks is treated in this paper. A penalty function method and an interior point method (PM-IPM) are proposed to solve the resource allocation optimization problem. The performance of PM-IPM is compared by a genetic algorithm one. Given the availability of multiple orthogonal channels and multimedia transmission rate requirements from multiple wireless OFDMA users, we are interested in a joint channel, power and rate assignment scheme that satisfies the given requirements with the minimum total transmit power. Algorithms for finding suboptimal and optimal solutions to sum power minimization resource allocation problems in OFDMA-based networks haven been proposed. But the complexity of finding the optimal solution is prohibitively high. We present two efficient algorithms with which each channel (subcarrier) is assigned to at most one user. The first approach, which gives near-optimal solutions, employs a dynamic programming (DP) based tree search and adopts a fair initial condition that offers every user all available channels and removes a channel from all but one user at each stage. Each removal is based on the criterion of the least total power increase. Using the DP-based solution as the initial upper bound and the partial path cost used in the DP approach as the lower bound for each visited node, we develop an efficient branch-and-bound based algorithm that guaranteed to lead to the optimal solution. The average complexities of both algorithms are evaluated and effective schemes to further reduce the required complexity are proposed. We also provide performance and complexity comparisons with other suboptimal algorithms that are modified from the existing ones. Long Term Evolution-Advanced (LTE-A) networks exploit the Carrier Aggregation (CA) technique to achieve a higher data rate by allowing user equipments (UEs) to simultaneously aggregate multiple component carriers (CCs). Moreover, MIMO technologies have become increasingly mature and been adopted as a default choice of the 4G standards. However, most of existing studies on resource allocation with carrier aggregation do not consider the MIMO capability of UEs. In this paper, we address the spectrum resource allocation problem with consideration of UEs' MIMO capability as well as modulation and coding schemes (MCSs) selection in carrier aggregation based LTE-A systems. We formulate the problem under both backlogged and finite queue traffic models as an optimization model, and prove its NP-hardness. As a result, a 1 2-approximation algorithm is proposed to find a suboptimal solution of resource allocation. Simulation results show that the proposed algorithm outperforms the existing schemes, and performs fairly close to the optimal solution under the small-scale scenarios. This paper presents an OFDMA radio resource control (RRC) scheme where dynamic resource allocation is realized at both a radio network controller (RNC) and base stations (BTSs). The scheme is semi-distributed in the sense that the RNC coordinates the mutual interference (inter-cell) at a super-frame level, whereas each BTS makes faster frame-level channel assignment decision based on resources' utility value to users. Another contribution of the paper is a set of computationally efficient algorithms that perform dynamic channel allocation at RNC and BTSs. Simulations show that the algorithm yields excellent performance for both real-time and non real-time services, even under very fast fading.