Resource Allocation In Heterogeneous Networks Research Articles

An enhanced spectrum resource allocation algorithm in Hetnets

Efficient spectrum resource allocation in wireless heterogeneous networks is important for improving the system throughput and guaranteeing the user's Quality-of-Service (QoS). In this paper, we propose an enhanced algorithm for spectrum resource allocation in heterogeneous networks. First, the bandwidth of each user is determined by the user's rate demand and the channel state. Second, graph theory is enhanced and used to improve the spectrum efficiency. Third, spectrum resource is dynamically split between macrocell and femtocells with the changes of users' conditions. Our simulation results show that the proposed algorithm improves the system throughput significantly and also guarantees the fairness for the users.

Energy‐efficient resource allocation in heterogeneous networks with cell range expansion

Cell range expansion (CRE) has emerged as a promising technique to improve spectral and energy efficiency (EE) in heterogeneous networks (HetNets). In this study, a resource allocation (RA) problem for maximising the EE is formulated with the considerations on CRE and cross-tier interference coordination (CTIC). Owing to the forms of the objective function and the constraints, the optimisation problem is non-convex and intractable. To solve this problem efficiently, we convert the original problem into an equivalent problem. Then, an efficient iterative algorithm is proposed, where the closed-form expressions of power and resource block allocations are derived by Lagrange dual decomposition method in each iteration. It has been revealed that the proposed iterative algorithm converges to the energy-efficient RA solutions. Compared with the algorithm without CTIC, the simulation results present that the proposed algorithm can achieve the EE gains by 70 percents when the number of the expanded-range users is large and the quality of service requirement is high.

Game Theoretic Max-logit Learning Approaches for Joint Base Station Selection and Resource Allocation in Heterogeneous Networks

This paper investigates the problem of joint base station selection and resource allocation in an orthogonal frequency division multiple access (OFDMA) heterogeneous cellular network. The original throughput maximization problem is NP-hard and we propose solving it by using game theoretic stochastic learning approaches. To this end, we first transform the original problem into a tractable form, which has a weighted utility function. Then we prove that an exact potential game applies and it exists the best Nash equilibria which is a near optimal solution of the original problem when an efficient solution method of the weights is employed. To obtain the optimal solution, we redesign the utility function by leveraging a state space to formulate the original problem into an ordinal state based potential game, which is proved that it exists a recurrent state equilibrium point that maximizes system throughput. Furthermore, we propose two different variants of Max-logit learning algorithm based on these two games respectively: one is a simultaneous learning algorithm with less information exchange, which achieves the best Nash equilibria point of the exact potential game and the other is an efficient learning algorithm for the ordinal state based potential game, which can converge to the global optimization solution. Finally, numerical results are given to validate that theoretical findings.

Low-Complexity Stackelberg Game Approach for Energy-Efficient Resource Allocation in Heterogeneous Networks

In this letter, we propose a low-complexity game-theoretic approach for energy-efficient resource allocation in a two-tier heterogeneous network. We measure the energy efficiency (EE) by “Revenue per Cost” in the utility domain considering both power allocation and interference coordination. Since global optimization for maximizing the overall EE is computationally expensive, we transform this nonconvex problem to a two-stage Stackelberg game inspired by fractional programming. We then employ the backward induction method and the Lagrange dual decomposition method to solve this game. An efficient iterative algorithm is designed to achieve Stackelberg equilibrium. Simulation results validate the effectiveness of the proposed approach.

Energy-Per-Bit Minimized Radio Resource Allocation in Heterogeneous Networks

In this paper, we present an energy-per-bit minimized radio resource allocation scheme in heterogeneous networks equipped with multi-homing capability, simultaneously connecting to different wireless interfaces. Specifically, we formulate an optimization problem related to minimization of energy-per-bit which takes a form of nonlinear fractional programming. Then we derive a parametric optimization problem out of that fractional programming and solve the original problem by using a double-loop iteration method. In each iteration, we derive the optimal resource allocation policy by applying Lagrangian duality and an efficient dual update method. In addition, we present suboptimal resource allocation algorithms using the properties of the optimal resource allocation policy. Numerical results reveal that the optimal allocation algorithm improves energy efficiency significantly over the existing resource allocation algorithms designed for homogeneous networks and its performance is superior to suboptimal algorithms in reducing energy consumption as well as in enhancing network energy efficiency.

Intracell Cooperation and Resource Allocation in a Heterogeneous Network With Relays

With macro and micro base stations (BSs) of different transmission and processing capabilities jointly deployed, heterogeneous networks effectively extend the coverage and capacity of wireless networks and have been adopted as one of the key technologies in Fourth-Generation-Long-Term Evolution (4G-LTE). To fully exploit the benefits of heterogeneous networks, proper mobile association, interference management, and radio resource management schemes need to be developed. In this paper, we investigate radio resource allocation for heterogeneous networks with cooperative relays, where the relay nodes (RNs) with in-band backhaul act as micro BSs and are able to serve user equipment (UE) either independently or cooperatively with the BSs. In such a network, radio resource-allocation schemes need to decide for each resource block: 1) whether a UE is served by a BS or an RN, and 2) whether a UE should be cooperatively served or not. In making these decisions, radio resource consumption on RN's in-band backhaul links and fairness among the UEs should be taken into consideration, which further complicates the resource-allocation problem. In this paper, we propose a radio resource-allocation framework and derive a resource-allocation strategy that is asymptotically optimal on the proportional fairness metric. The derived resource-allocation scheme gives insights on the optimal radio resource allocation for the heterogeneous networks with cooperative RNs using in-band backhauls.

Optimal routing policy

We investigate the problem of resource allocation in heterogeneous networks of computational resources. We provide an explicit analytical solution for a situation where the computational environment can be described by M/M/l queueing theory. We illustrate the quality of our solution by comparing results with those obtained via a simple ad hoc resource allocation in large heterogeneous networks consisting of N = 104 nodes with computational resources distributed either uniformly in a given interval, or exponentially in R+.